SIENTIAPDE-1646

Update README, requirements, and E2E tests for improved configuration and functionality

- Enhanced the README with updated model configuration examples, including the addition of an alias for production.
- Removed the `requirements-light.txt` file and updated `requirements-local.txt` and `requirements.txt` to replace `asyncua` with `opcua`.
- Refactored E2E test scenarios to utilize scenario input files for better maintainability and clarity.
- Improved test coverage for MinIO offload functionality and added new helper functions for loading scenario inputs.
- Updated `values.yaml` to reflect new global configurations and environment variables for the laborious worker.

README.md

@@ -463,7 +463,7 @@ flowchart LR
   "source_table_name": "laborious_data",
   "target_table_name": "drift_metrics",
   "interval": 60,
-  "model_config": { "target": "temperature" },
+  "model_config": { "target": "temperature", "alias": "production" },
   "drift_metrics": ["kolmogorov_smirnov", "jensen_shannon", "wasserstein"],
   "chunk_period": "min"
 }
@@ -498,7 +498,7 @@ flowchart LR
   "data_table_name": "laborious_data",
   "target_table_name": "simple_metrics",
   "interval_minutes": 60,
-  "model_config": { "target": "temperature" },
+  "model_config": { "target": "temperature", "alias": "production" },
   "metrics": ["rmse", "mse", "mae", "r2"]
 }
 ```
@@ -953,7 +953,7 @@ For single OPC server, use individual environment variables:
 
 ### PI Web API Configuration
 
-PI Web API configuration is built from environment variables using the `build_api_config` function from `sientia_do.connectors_config`. The configuration includes:
+PI Web API configuration is built from environment variables using the `build_api_config` function from `sientia_do.utils.connectors_config`. The configuration includes:
 
 - `PI_WEB_API_BASE_URL`: Base URL of the PI Web API server
 - `PI_WEB_API_AUTH_TYPE`: Authentication type ('basic' or 'bearer')
@@ -986,9 +986,30 @@ Where:
 
 MongoDB pipeline configuration:
 
-#### Predictions Batch Workflow configuration sample
+#### MongoDB input samples (updated)
 
-This is the configuration for the Predictions Batch Workflow, to be inserted into the MongoDB pipeline collection.
+Updated examples are available in `input_sample.json` at the repository root.
+The sample already reflects the runtime-aware and alias-based flow:
+
+- `model_config` uses `target`, `retention_minutes`, and `alias`.
+- `transform_flavor` / `predict_flavor` are not used anymore.
+
+Example model document:
+
+```json
+{
+  "id": "4",
+  "name": "vcm-nox",
+  "active": false,
+  "model_config": {
+    "alias": "production",
+    "retention_minutes": 60,
+    "target": "CI-W3W01A3"
+  }
+}
+```
+
+Example predictions_batch schedule document:
 
 ```json
 {
@@ -998,41 +1019,67 @@ This is the configuration for the Predictions Batch Workflow, to be inserted int
   "frequency": "30s",
   "max_retry_policy": 1,
   "query": "select * from sientia_data.laborious_data where model_id = 1 and \"timestamp\" > NOW() - INTERVAL '5 minutes' order by \"timestamp\" desc limit 30;",
+  "retention_time": 60,
   "write_tags": [
     {
-      "server_id": "server1",
+      "server_id": "1",
       "type": "prediction",
       "addr": "ns=2;i=5",
       "data_type": "double"
     },
     {
-      "server_id": "server1",
+      "server_id": "1",
       "type": "confidence",
-      "addr": "ns=2;i=6",
+      "addr": "ns=2;i=5",
       "data_type": "double"
     }
   ],
-  "input_filters": {
-    "EMPTY_DATA": {"POLICY": "STOP"},
-    "SPECIFIC_VARIABLES_NULL_VALUES": {
-      "POLICY": "CONTINUE",
-      "config": {"variables": ["Counter"]}
+  "input_filters": [
+    {
+      "filter_name": "EMPTY_DATA",
+      "policy": "STOP"
+    },
+    {
+      "filter_name": "SPECIFIC_VARIABLES_NULL_VALUES",
+      "policy": "CONTINUE",
+      "config": {
+        "variables": ["Counter"]
+      }
     }
-  },
-  "mlflow_transform_filters": {
-    "API_ERROR": {"POLICY": "REPEAT"},
-    "NAN_VALUES": {"POLICY": "STOP"}
-  },
-  "mlflow_predict_filters": {
-    "API_ERROR": {"POLICY": "CONTINUE"}
-  },
+  ],
+  "mlflow_transform_filters": [
+    {
+      "filter_name": "API_ERROR",
+      "policy": "REPEAT"
+    },
+    {
+      "filter_name": "NAN_VALUES",
+      "policy": "STOP"
+    }
+  ],
+  "mlflow_predict_filters": [
+    {
+      "filter_name": "API_ERROR",
+      "policy": "CONTINUE"
+    }
+  ],
   "path_priority": ["STOP", "CONTINUE", "REPEAT"],
   "active": true,
   "updated_at": {
-    "$date": "2025-09-16T10:00:00.000Z"
+    "$date": "2026-01-27T17:35:01.600Z"
   },
-  "datetime_columns": ["timestamp", "created_at"],
-  "predictions_storage_policy": "lts:1"
+  "save_transform": false,
+  "pi_web_api_output_config": {
+    "endpoint": "/streamsets/value",
+    "prediction_tags": {},
+    "confidence_tags": {}
+  },
+  "model_config": {
+    "alias": "production",
+    "retention_minutes": 60,
+    "target": "CI-W3W01A3"
+  },
+  "datetime_columns": ["timestamp", "created_at"]
 }
 ```
 
@@ -1125,7 +1172,7 @@ laborious/
 2. **MLFlow Connection Issues**
    - Verify MLFlow server is running and accessible
    - Check authentication credentials and permissions
-   - Ensure model names and versions exist
+   - Ensure model names exist and the expected alias (for example `production`) is registered
 
 3. **Database Connection Issues**
    - Verify PostgreSQL service is running

e2e/helpers.py

@@ -3,13 +3,60 @@ Shared helpers for E2E tests (Temporal workflows + PostgreSQL).
 """
 
 import asyncio
+import json
 from datetime import datetime
 from decimal import Decimal
+from pathlib import Path
 from typing import Any
 
+import pandas as pd
 from sqlalchemy import text
 from sqlalchemy.engine import Engine
 
+SCENARIO_INPUTS_DIR = Path(__file__).parent / 'scenario_inputs'
+
+
+def _replace_template_values(payload: Any, model_id: int) -> Any:
+    """
+    Replace string placeholders in scenario payloads with the concrete model id.
+
+    Args:
+        payload: JSON-like structure loaded from scenario input file.
+        model_id: Model id used to render template placeholders.
+
+    Return:
+        Any: Payload with ``{{MODEL_ID}}`` replaced where applicable.
+    """
+    if isinstance(payload, dict):
+        return {key: _replace_template_values(value, model_id) for key, value in payload.items()}
+    if isinstance(payload, list):
+        return [_replace_template_values(item, model_id) for item in payload]
+    if isinstance(payload, str):
+        if payload == '{{MODEL_ID}}':
+            return model_id
+        return payload.replace('{{MODEL_ID}}', str(model_id))
+    return payload
+
+
+def load_scenario_input(file_name: str, model_id: int | None = None) -> dict[str, Any]:
+    """
+    Load a scenario input JSON from ``e2e/scenario_inputs``.
+
+    Args:
+        file_name: JSON file name inside ``e2e/scenario_inputs``.
+        model_id: Optional model id used to render ``{{MODEL_ID}}`` placeholders.
+
+    Return:
+        dict[str, Any]: Input payload ready to be passed to workflow/activity calls.
+    """
+    file_path = SCENARIO_INPUTS_DIR / file_name
+    with file_path.open('r', encoding='utf-8') as f:
+        payload = json.load(f)
+
+    if model_id is not None:
+        return _replace_template_values(payload, model_id)
+    return payload
+
 
 async def start_and_await_workflow(client, workflow_run, input_data: dict, workflow_id: str, timeout: float = 60.0):
     """
@@ -172,3 +219,133 @@ def assert_repeat(postgres_engine: Engine, model_id: int, last_prediction: tuple
 def make_workflow_id(prefix: str) -> str:
     """Build a unique workflow id using a prefix and current timestamp."""
     return f'{prefix}-{datetime.now().timestamp()}'
+
+
+def insert_target_data_for_drift(
+    postgres_engine: Engine,
+    model_id: int,
+    timestamps: list[str],
+    variables_values: dict[str, list[float]],
+) -> None:
+    """
+    Insert one row per (timestamp, variable) pair into ``laborious_data``.
+
+    Used by drift scenarios that need wide-format input where the pivot keeps a
+    full row for every timestamp.
+
+    Args:
+     - postgres_engine: SQLAlchemy engine bound to the test container.
+     - model_id: Model id stamped on every row.
+     - timestamps: ISO-8601 strings used both as ``timestamp`` and ``created_at``.
+     - variables_values: Mapping of variable name to a list of values; each list
+       must be the same length as ``timestamps``.
+    """
+    for var_name, values in variables_values.items():
+        if len(values) != len(timestamps):
+            raise ValueError(
+                f"Variable '{var_name}' has {len(values)} values but {len(timestamps)} timestamps"
+            )
+
+    rows_sql = []
+    for index, ts in enumerate(timestamps):
+        for var_name, values in variables_values.items():
+            rows_sql.append(
+                f"({model_id}, '{var_name}', {values[index]}, '{ts}', '{ts}')"
+            )
+
+    with postgres_engine.begin() as conn:
+        conn.execute(
+            text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}')
+        )
+        if rows_sql:
+            conn.execute(
+                text(
+                    'INSERT INTO predictions_schema.laborious_data '
+                    '(model_id, variable, value, "timestamp", created_at) VALUES '
+                    + ', '.join(rows_sql)
+                )
+            )
+
+
+def build_drift_dataframe(
+    timestamps: list[str],
+    features: list[str],
+    methods: list[str],
+    statistic: float = 1.0,
+    drift_flags: dict[tuple[str, str], bool] | None = None,
+    include_multivariate: bool = True,
+    multivariate_value: float = 16.0,
+    multivariate_drift: bool = True,
+    extra_rows: list[dict[str, Any]] | None = None,
+) -> Any:
+    """
+    Build a deterministic dataframe matching the schema returned by
+    ``sientia_model.analytics.model_analysis.ModelAnalysis.get_drift_metrics_dataframe``
+    so drift E2E tests can pin the exact rows persisted to PostgreSQL.
+
+    The output mirrors the analyzer's canonical schema:
+    ``timestamp, feature, metric, statistic, p_value, alert, chunk_index,
+    chunk_start_date, chunk_end_date``. ``calculate_drift`` then renames
+    ``alert -> drift``, ``chunk_index -> chunk``, ``chunk_end_date ->
+    timestamp_end`` and drops ``p_value`` / ``chunk_start_date``.
+
+    Args:
+     - timestamps: Truncated chunk start timestamps (``'2024-01-01 12:00'`` for ``min``).
+     - features: Univariate feature names (one row per feature/method/timestamp).
+     - methods: Univariate methods such as ``kolmogorov_smirnov``.
+     - statistic: Default univariate statistic value.
+     - drift_flags: Optional override of the ``alert`` flag per ``(feature, method)`` pair.
+     - include_multivariate: Whether to add a final multivariate row block.
+     - multivariate_value: Value placed on multivariate rows.
+     - multivariate_drift: Drift flag placed on multivariate rows.
+     - extra_rows: Additional pre-built rows to append (used for dedup/p_value tests).
+
+    Return:
+     pandas.DataFrame with columns: timestamp, feature, metric, statistic,
+     p_value, alert, chunk_index, chunk_start_date, chunk_end_date.
+    """
+    rows: list[dict[str, Any]] = []
+    drift_flags = drift_flags or {}
+    # Synthetic chunk-end offset that mirrors the high-precision boundary
+    # (``...:59.999999999``) emitted by ``ModelAnalysis`` for minute chunks.
+    # Computing via ``Timedelta`` instead of string concatenation keeps the
+    # helper safe for both minute- and second-precision timestamps.
+    chunk_span = pd.Timedelta(seconds=59, nanoseconds=999999999)
+
+    for chunk_index, ts in enumerate(timestamps):
+        chunk_start = pd.Timestamp(ts)
+        chunk_end = chunk_start + chunk_span
+        for feature in features:
+            for method in methods:
+                rows.append(
+                    {
+                        'timestamp': chunk_start,
+                        'feature': feature,
+                        'metric': method,
+                        'statistic': statistic,
+                        'p_value': 0.5,
+                        'alert': drift_flags.get((feature, method), False),
+                        'chunk_index': chunk_index,
+                        'chunk_start_date': chunk_start,
+                        'chunk_end_date': chunk_end,
+                    }
+                )
+        if include_multivariate:
+            rows.append(
+                {
+                    'timestamp': chunk_start,
+                    'feature': 'multivariate',
+                    'metric': 'multivariate',
+                    'statistic': multivariate_value,
+                    'p_value': 0.0,
+                    'alert': multivariate_drift,
+                    'chunk_index': chunk_index,
+                    'chunk_start_date': chunk_start,
+                    'chunk_end_date': chunk_end,
+                }
+            )
+
+    if extra_rows:
+        rows.extend(extra_rows)
+
+    return pd.DataFrame(rows)

e2e/scenario_inputs/drift_base.json

@@ -0,0 +1,14 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "source_table_name": "laborious_data",
+  "target_table_name": "drift",
+  "interval": 60,
+  "drift_metrics": ["kolmogorov_smirnov", "jensen_shannon", "wasserstein"],
+  "chunk_period": "min",
+  "model_config": {
+    "target": "sensor_1"
+  }
+}

e2e/scenario_inputs/format_export_base.json

@@ -0,0 +1,37 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "EMPTY_DATA": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": true,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "retention_minutes": 0,
+    "target": "sensor_1"
+  },
+  "datetime_columns": ["timestamp", "created_at"]
+}

e2e/scenario_inputs/main_happy_path.json

@@ -0,0 +1,45 @@
+{
+  "metadata": {
+    "metadata": {
+      "model_id": "{{MODEL_ID}}",
+      "model_name": "test_model",
+      "schedule_name": "test-schedule",
+      "workflow_name": "predictions_batch"
+    }
+  },
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "EMPTY_DATA": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": true,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "target": "sensor_1",
+    "retention_minutes": 0
+  },
+  "datetime_columns": ["timestamp", "created_at"]
+}

e2e/scenario_inputs/main_invalid_datetime.json

@@ -0,0 +1,45 @@
+{
+  "metadata": {
+    "metadata": {
+      "model_id": "{{MODEL_ID}}",
+      "model_name": "test_model",
+      "schedule_name": "test-schedule",
+      "workflow_name": "predictions_batch"
+    }
+  },
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "EMPTY_DATA": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": true,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "target": "sensor_1",
+    "retention_minutes": 0
+  },
+  "datetime_columns": ["nonexistent_column"]
+}

e2e/scenario_inputs/main_missing_required.json

@@ -0,0 +1,16 @@
+{
+  "metadata": {
+    "metadata": {
+      "model_id": "{{MODEL_ID}}",
+      "model_name": "test_model",
+      "schedule_name": "test-schedule",
+      "workflow_name": "predictions_batch"
+    }
+  },
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data"
+}

e2e/scenario_inputs/main_sql_error.json

@@ -0,0 +1,44 @@
+{
+  "metadata": {
+    "metadata": {
+      "model_id": "{{MODEL_ID}}",
+      "model_name": "test_model",
+      "schedule_name": "test-schedule",
+      "workflow_name": "predictions_batch"
+    }
+  },
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT * FROM nonexistent_table WHERE invalid_syntax =",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "EMPTY_DATA": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": true,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "target": "sensor_1",
+    "retention_minutes": 0
+  }
+}

e2e/scenario_inputs/minimal_retrain_base.json

@@ -0,0 +1,12 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "retrain_reports",
+  "datetime_columns": ["timestamp", "created_at"],
+  "model_config": {
+    "target": "sensor_1"
+  }
+}

e2e/scenario_inputs/minio_offload_load_query.json

@@ -0,0 +1,11 @@
+{
+  "metadata": {
+    "schedule_name": "test-schedule",
+    "model_name": "test_model",
+    "model_id": "{{MODEL_ID}}",
+    "workflow_name": "predictions_batch"
+  },
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "model_name": "test_model",
+  "datetime_columns": ["timestamp", "created_at"]
+}

e2e/scenario_inputs/minio_offload_workflow.json

@@ -0,0 +1,37 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "EMPTY_DATA": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": false,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "retention_minutes": 0,
+    "target": "sensor_1"
+  },
+  "datetime_columns": ["timestamp", "created_at"]
+}

e2e/scenario_inputs/prediction_process_base.json

@@ -0,0 +1,39 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "query": "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "table_name": "predictions",
+  "transform_table_name": "transformed_data",
+  "input_filters": {
+    "SPECIFIC_VARIABLES_NULL_VALUES": {
+      "POLICY": "CONTINUE",
+      "CONFIG": {
+        "variables": ["sensor_1"]
+      }
+    }
+  },
+  "mlflow_transform_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "mlflow_predict_filters": {
+    "API_ERROR": {
+      "POLICY": "STOP",
+      "CONFIG": {}
+    }
+  },
+  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
+  "opc_output_config": {},
+  "pi_web_api_output_config": {},
+  "save_transform": true,
+  "prediction_store_policy": "lts:1",
+  "model_config": {
+    "retention_minutes": 0,
+    "target": "sensor_1"
+  },
+  "datetime_columns": ["timestamp", "created_at"]
+}

e2e/scenario_inputs/simple_metrics_base.json

@@ -0,0 +1,14 @@
+{
+  "schedule_name": "test-schedule",
+  "model_name": "test_model",
+  "model_id": "{{MODEL_ID}}",
+  "schema": "predictions_schema",
+  "predictions_table_name": "predictions",
+  "data_table_name": "laborious_data",
+  "target_table_name": "simple_metrics_data",
+  "interval_minutes": 60,
+  "metrics": ["rmse", "mse", "mae", "r2"],
+  "model_config": {
+    "target": "sensor_target"
+  }
+}

e2e/scenarios.md

@@ -1,520 +1,402 @@
-# Test Scenarios for Predictions Batch Workflow
+# E2E Scenario Documentation - Predictions Batch
 
-This document describes all possible test scenarios for the `predictions_batch` workflow and its child workflows `prediction_process` and `format_and_export_prediction`.
+This document describes the end-to-end scenarios for `predictions_batch` and its child workflows:
+`prediction_process` and `format_and_export_prediction`.
 
-## Running automated E2E tests (`e2e/`)
+It is a functional reference of scenario behavior, inputs, and expected outcomes.
 
-- **Runtime**: Docker (or a Docker-compatible daemon) must be available so [testcontainers](https://testcontainers.com/) can start **PostgreSQL** and **MinIO** containers.
-- **Dependencies**: install dev requirements (includes `testcontainers[postgres,minio]`).
-- **Invocation**: run only integration-marked tests, for example: `pytest e2e/ -m integration`.
-- **MinIO tests**: `e2e/test_minio_offload.py` exercises real S3 uploads; other E2E modules continue to mock MinIO on the worker used by most scenarios.
+## Execution Context
 
-## Workflow Overview
-
-The `predictions_batch` workflow:
-1. Loads data using a custom SQL query
-2. Prepares prediction configuration
-3. Delegates to `prediction_process` child workflow which:
-   - Retrieves last timestamp for incremental processing
-   - Applies input data quality gates
-   - Executes MLFlow transform operation
-   - Validates transform response
-   - Executes MLFlow predict operation
-   - Validates predict response
-   - Delegates to `format_and_export_prediction` child workflow
-4. The `format_and_export_prediction` workflow:
-   - Formats prediction data (normal or default)
-   - Exports to PI Web API (optional)
-   - Exports to OPC server (optional)
-   - Exports to PostgreSQL
-   - Writes metrics
+- Tests run under `e2e/` and are marked with `@pytest.mark.integration`.
+- PostgreSQL and MinIO are provisioned with testcontainers.
+- `test_minio_offload.py` uses real MinIO I/O; other scenario suites may use stubs/mocks for optional outputs.
 
 ---
 
-## 1. Predictions Batch - Main Workflow Scenarios
+## 1. Main Workflow Scenarios
+Source: `e2e/test_predictions_batch_main_workflow.py`
 
-### 1.1 Success Scenarios
+### 1.1.1 Happy Path - Complete Success
+**Summary**: Full workflow succeeds with valid query and default gate behavior.
 
-#### Scenario 1.1.1: Happy Path - Complete Success
-**Description**: Workflow completes successfully with valid SQL query and all activities succeed
+**Description**:
+- Query returns rows for a model.
+- `prediction_process` runs transform and predict paths.
+- Final prediction and transformed data are persisted.
 
-**Input**:
-- Valid `schedule_name`, `model_name`, `model_id`
-- Valid `query` returning non-empty DataFrame
-- Valid `schema`, `table_name`, `transform_table_name`
-- Optional `datetime_columns` for timestamp parsing
-- Optional `input_filters`, `mlflow_transform_filters`, `mlflow_predict_filters`
-- Optional `path_priority`, `opc_output_config`, `pi_web_api_output_config`
+**Expected Outcome**:
+- Exactly one prediction row is created.
+- Transform rows are created.
+- Confidence/status/comments are success values.
 
-**Expected Behavior**:
-- `load_custom_query` returns DataFrame with data
-- Workflow prepares prediction input with all configurations
-- `prediction_process` child workflow executes successfully
-- All gates pass with no issues
-- Transform and predict operations succeed
-- Data exported to PostgreSQL
-- Metrics written
+### 1.2.1 SQL Query Execution Error
+**Summary**: Invalid SQL leads to no persisted prediction.
 
-**Assertions**:
-- SQL query executed once
-- `prediction_process` workflow called with correct parameters
-- Data exists in PostgreSQL (predictions table)
-- Metrics recorded
-- No errors raised
+**Description**:
+- Input query is invalid.
+- Load step fails and workflow follows error/short-circuit path.
+
+**Expected Outcome**:
+- No prediction rows for the model.
+- Workflow does not require retry-loop assumptions in assertions.
+
+### 1.2.2 Missing Required Parameters
+**Summary**: Missing required fields prevent workflow completion path.
+
+**Description**:
+- Required input key (e.g. `query`) is omitted.
+- Workflow fails to produce actionable input for child flow.
+
+**Expected Outcome**:
+- No prediction rows are persisted.
+- Workflow handle may require explicit terminate in E2E harness.
+
+### 1.2.3 Invalid Datetime Column Specification (de-prioritized)
+**Summary**: Legacy invalid datetime-column case is retained only as low-priority legacy coverage.
+
+**Description**:
+- `datetime_columns` references non-existing columns.
+- Behavior may vary by query shape and parser fallback.
+
+**Expected Outcome**:
+- No predictions persisted in the covered legacy assertion path.
+- Scenario is not considered primary behavior coverage.
 
 ---
 
-### 1.2 Error Scenarios
+## 2. Prediction Process Scenarios
+Source: `e2e/test_predictions_batch_prediction_process.py`
 
-#### Scenario 1.2.1: SQL Query Execution Error
-**Description**: SQL query fails due to syntax error or connection issue
+### 2.1 Input Gate Path Decisions
 
-**Input**:
-- Invalid SQL query (syntax error)
-- Or database connection unavailable
+#### 2.1.1 CONTINUE
+**Summary**: Input filter flags quality issue but allows continuation via default path.
 
-**Expected Behavior**:
-- `load_custom_query` raises exception (caught by Temporal retry policy)
-- Notification sent with SQL error details
-- After retries, activity may return empty data or workflow may fail
-- If empty data returned, workflow completes with early exit via input gate
+**Description**:
+- Input gate returns `CONTINUE`.
+- MLFlow transform/predict are skipped.
+- Export path persists default-style prediction with warning context.
 
-**Assertions**:
-- Error notification sent
-- Workflow completes (either fails or exits early)
-- No data in predictions table
+#### 2.1.2 STOP
+**Summary**: Input filter blocks processing.
+
+**Description**:
+- Input gate returns `STOP`.
+- Workflow exits without export.
+
+#### 2.1.3 REPEAT with history
+**Summary**: Prior prediction is reused.
+
+**Description**:
+- Input gate returns `REPEAT`.
+- `repeat_last_prediction` path is executed using existing historical row.
+
+#### 2.1.4 REPEAT without history
+**Summary**: Repeat requested but no previous prediction exists.
+
+**Description**:
+- Input gate returns `REPEAT`.
+- No prior row is available to duplicate.
+
+**Expected Outcome**:
+- No new prediction rows are created for the model.
+
+### 2.2 Transform Gate Decisions
+
+#### 2.2.1 CONTINUE on transform response error
+**Summary**: Transform response is degraded, but workflow continues.
+
+#### 2.2.2 STOP on transform response error
+**Summary**: Transform response error blocks downstream processing.
+
+#### 2.2.3 REPEAT on transform response error
+**Summary**: Transform response error triggers repeat-last-prediction path.
+
+#### 2.2.4 STOP on transform content NaN
+**Summary**: Content gate (`NAN_VALUES`) blocks on all-NaN transform payload.
+
+### 2.3 Predict Gate Decisions
+
+#### 2.3.1 CONTINUE on predict response error
+**Summary**: Predict response degraded; workflow exports with degraded metadata.
+
+#### 2.3.2 STOP on predict response error
+**Summary**: Predict response error blocks export.
+
+#### 2.3.3 REPEAT on predict response error
+**Summary**: Predict response error routes to repeat-last-prediction.
+
+### 2.4.1 Priority Conflict Resolution
+**Summary**: Deterministic selection when multiple filters produce different flags.
+
+**Description**:
+- Multiple filters may produce `STOP`, `CONTINUE`, and/or `REPEAT`.
+- `path_priority` defines precedence.
+
+**Expected Outcome**:
+- Highest-priority flag is applied consistently.
+- Executed branch matches configured priority ordering.
 
 ---
 
-#### Scenario 1.2.2: Missing Required Parameters
-**Description**: Essential parameters missing from input
+## 3. Format and Export Scenarios
+Source: `e2e/test_predictions_batch_format_export.py`
 
-**Input**:
-- Missing `query` or `model_id` or `schema` or `table_name`
+### 3.1 Output Combination Scenarios
 
-**Expected Behavior**:
-- Workflow or activity raises KeyError or validation error
-- Workflow fails immediately
+#### 3.1.1 Default prediction export
+**Summary**: Non-`None` path flag uses `format_default_prediction`.
 
-**Assertions**:
-- Workflow fails with parameter error
-- Error notification sent
-- No child workflow called
+**Description**:
+- Default prediction is generated.
+- Transform export is skipped.
+- Optional outputs (PI/OPC) still execute when configured.
+
+#### 3.1.2 OPC only
+**Summary**: Postgres + OPC writes, PI Web API disabled.
+
+#### 3.1.3 PI Web API only
+**Summary**: Postgres + PI writes, OPC disabled.
+
+#### 3.1.4 Postgres only
+**Summary**: Both optional outputs disabled; only Postgres persistence and metrics.
+
+#### 3.1.5 No transformed data export
+**Summary**: Prediction is persisted; transformed table is not written.
+
+### 3.2 Degraded-but-successful Completion
+
+#### 3.2.1 PI Web API write error
+**Summary**: PI write failure does not fail workflow.
+
+**Expected Outcome**:
+- Workflow completes.
+- Prediction persisted with degraded confidence/comments (PI error semantics).
+
+#### 3.2.2 OPC write error
+**Summary**: OPC write failure does not fail workflow.
+
+**Expected Outcome**:
+- Workflow completes.
+- Prediction persisted with OPC degraded confidence/comments.
+
+#### 3.2.3 PI Web API partial write error
+**Summary**: Partial PI acknowledgement is treated as degraded success.
+
+**Expected Outcome**:
+- Workflow completes.
+- Prediction persisted with PI error confidence and descriptive comment.
+
+### 3.3.1 Combined Optional Outputs (PI + OPC)
+**Summary**: Both external output channels are enabled together.
+
+**Description**:
+- PI Web API and OPC configs are both present.
+- Output mutation order matters for final persisted payload.
+
+**Expected Outcome**:
+- PI write executes before OPC write in workflow sequence.
+- Final Postgres payload reflects any confidence/comment updates.
+- OPC metrics are emitted when tag writes return response times.
 
 ---
 
-#### Scenario 1.2.3: Invalid Datetime Column Specification
-**Description**: Datetime column specified doesn't exist in query results
+## 4. MinIO Offload Scenarios
+Source: `e2e/test_minio_offload.py`
 
-**Input**:
-- `datetime_columns: ['nonexistent_column']`
-- Query results don't have this column
+### 4.1.1 Forced offload to MinIO
+**Summary**: Very low threshold forces parquet upload.
 
-**Expected Behavior**:
-- `load_custom_query` may raise KeyError or warning
-- Depending on implementation, workflow may fail or continue
-- Error notification sent
+**Description**:
+- Payload is offloaded (`object_key` present, inline data absent/empty).
+- Object is present in MinIO under `prediction_datasets/...`.
+- Retrieval reconstructs the dataframe.
 
-**Assertions**:
-- Error raised or warning logged
-- Workflow behavior depends on error handling policy
+### 4.1.2 Full workflow with offloaded load payload
+**Summary**: Offload path works during full `predictions_batch` execution.
+
+**Expected Outcome**:
+- Workflow completes.
+- Prediction row is persisted.
+
+### 4.2.1 Inline payload below threshold
+**Summary**: Data remains inline when threshold is not exceeded.
+
+**Expected Outcome**:
+- Payload stores inline `data`.
+- `object_key` is `None`.
+- Downstream persistence behavior matches offload scenario semantics.
 
 ---
 
-## 2. Prediction Process - Child Workflow Scenarios
+## 5. Drift Workflow Scenarios
+Source: `e2e/test_drift.py`
 
-### 2.1 Input gate Early Exit Scenarios
+The drift suite mocks `sientia.ModelAnalysis.ModelAnalysis` (not installed; see
+`CODE_ISSUES.md` issue #1) through the controllable `_FakeModelAnalysis` stub
+exposed by the `model_analysis_stub` fixture. The `mlflow_repository_stub`
+provides the reference-data CSV via `download_artifacts`. Every scenario asserts
+postgres rows in `predictions_schema.drift` against this canonical schema:
 
-#### Scenario 2.1.1: Input Gate Triggers CONTINUE
-**Description**: Input gate determines data should use previous prediction
+`id, model_id, feature, method, value, drift, chunk, timestamp, timestamp_end, accurate, created_at, updated_at`.
 
-**Input**:
-- Data that should continue with input data as prediction
-- `input_filters` configured with `POLICY: 'CONTINUE'`
-- `path_priority` includes CONTINUE
+### 5.1 Happy paths
 
-**Expected Behavior**:
-- `input_gate` returns `path_flag='CONTINUE'`
-- `path_flag_handler` calls export workflow with input data directly
-- MLFlow transform and predict skipped
-- Data exported as-is
+#### D.1.1 Full pipeline persists all columns with reference data
+**Summary**: ModelAnalysis returns a deterministic drift dataframe; the
+reference CSV is downloaded from the MLflow stub.
 
-**Assertions**:
-- `input_gate` called
-- MLFlow operations NOT called
-- Export workflow called with original data
-- Workflow completes
+**Expected Outcome**:
+- One row per `(chunk, feature, method)` plus a `multivariate` block per chunk.
+- Every drift column is populated and `accurate=True`.
+- `timestamp_end` preserves the high-precision string (`HH:MM:59.999999999`).
+- `p_value` is dropped before persistence.
+- `drift` flags propagate per `(feature, method)` configuration.
 
+#### D.1.2 30% fallback when reference data is unavailable
+**Summary**: `get_reference_data` fails alias resolution and returns `None`;
+`calculate_drift` uses the first 30% of target rows as reference.
 
-#### Scenario 2.1.2: Input Gate Triggers STOP
-**Description**: Input data quality gate fails with STOP policy
+**Expected Outcome**:
+- Persisted rows carry `accurate=False`.
+- A `MODEL_METRICS_REFERENCE_DATA_WARNING` notification is emitted to MongoDB.
 
-**Input**:
-- Data with EMPTY_DATA or other critical issues
-- `input_filters` configured with `POLICY: 'STOP'`
+### 5.2 Filtering / dedup invariants
 
-**Expected Behavior**:
-- `input_gate` returns `path_flag='STOP'`
-- `path_flag_handler` detects STOP
-- Workflow returns early without calling MLFlow
-- No prediction exported
+#### D.2.1 Deduplication and `p_value` removal
+**Summary**: ModelAnalysis returns duplicate `(timestamp, method, feature)` rows
+plus a `p_value` column.
 
-**Assertions**:
-- `input_gate` called
-- `path_flag_handler` returns True (early exit)
-- MLFlow transform NOT called
-- Export workflow NOT called
-- Workflow completes without error
+**Expected Outcome**:
+- Duplicates are collapsed keeping the first occurrence.
+- `p_value` is absent from the persisted rows.
 
+#### D.2.2 Out-of-range timestamps filtered
+**Summary**: Drift rows whose timestamps are not present in the target window
+must be discarded before persistence.
 
-#### Scenario 2.1.3: Input Gate Triggers REPEAT
-**Description**: Input gate determines data should repeat last prediction
+### 5.3 Failure paths
 
-**Input**:
-- Data with quality issues that require using previous prediction
-- `input_filters` configured with `POLICY: 'REPEAT'`
-- `path_priority` includes REPEAT
+#### D.3.1 Empty target data short-circuits the workflow
+**Summary**: `load_custom_query` returns no rows; ModelAnalysis is never
+instantiated and no drift rows are written.
 
-**Expected Behavior**:
-- `input_gate` returns `path_flag='REPEAT'`
-- `path_flag_handler` calls `repeat_last_prediction` activity
-- MLFlow transform and predict skipped
-- Last prediction repeated and exported
+#### D.3.2 ModelAnalysis raises during dataframe assembly
+**Summary**: `get_drift_metrics_dataframe` raises. The activity catches the
+error, sends a `MODEL_METRICS_GET_DRIFT_METRICS_ERROR` notification, and the
+workflow completes without persisting drift rows.
 
-**Assertions**:
-- `input_gate` called
-- MLFlow operations NOT called
-- `repeat_last_prediction` activity called
-- Workflow completes
+### 5.4 Configuration paths
+
+#### D.4.1 Default drift metrics propagated to analyzer
+**Summary**: Omitting `drift_metrics` defaults to
+`['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']` and forwards the
+exact list to `detect_univariate_drift`.
+
+#### D.4.2 Invalid `chunk_period` raises ValueError
+**Summary**: Anything other than `min` / `s` is rejected by `calculate_drift`.
+
+#### D.4.3 `chunk_period='s'` keeps seconds in timestamp filtering
+**Summary**: Truncated `YYYY-MM-DD HH:MM` rows are filtered out when chunking
+runs at second granularity.
 
 ---
 
-### 2.2 Transform gate Early Exit Scenarios
+## 6. Simple Metrics Workflow Scenarios
+Source: `e2e/test_simple_metrics.py`
 
-#### Scenario 2.2.1: Transform Gate Triggers CONTINUE
-**Description**: Transform response gate determines data should continue despite issues
+Validates `predictions_schema.simple_metrics_data` columns:
+`id, model_id, metric, value, timestamp, data_size, interval_minutes, created_at`.
 
-**Input**:
-- Valid input data
-- Transform response has quality issues but policy is CONTINUE
-- `mlflow_transform_filters` configured with `POLICY: 'CONTINUE'`
-- `path_priority` includes CONTINUE
+### 6.1 Happy paths
 
-**Expected Behavior**:
-- `request_transform` succeeds
-- `mlflow_response_gate` for transform returns `path_flag='CONTINUE'`
-- `path_flag_handler` calls export workflow with transform data
-- MLFlow predict skipped
-- Transform data exported as-is
+#### S.1.1 rmse/mse/mae/r2 happy path
+**Summary**: Prediction/target pairs are inserted; the activity computes all
+four metrics with closed-form expected values.
 
-**Assertions**:
-- Transform completed
-- `mlflow_response_gate` called for transform
-- MLFlow predict NOT called
-- Export workflow called with transform data
-- Workflow completes
+**Expected Outcome**:
+- One row per metric is persisted; all columns populated.
+- `data_size` matches the joined row count and `interval_minutes=60`.
+
+#### S.1.2 Subset metrics
+**Summary**: Requesting `metrics=['rmse']` writes only the rmse row.
+
+### 6.2 Edge cases
+
+#### S.2.1 Zero-variance target returns r2=0
+**Summary**: When all targets are equal, `ss_tot=0`; the activity must guard
+against division by zero and return `r2=0`.
+
+### 6.3 Failure paths
+
+#### S.3.1 No overlapping data short-circuits persistence
+**Summary**: With no `laborious_data` rows for the configured target variable
+the workflow exits before `calculate_simple_metrics` and writes nothing.
 
 ---
 
-#### Scenario 2.2.2: Transform Gate Triggers STOP
-**Description**: Transform response validation fails with STOP policy
+## 7. Minimal Retrain Workflow Scenarios
+Source: `e2e/test_minimal_retrain.py`
 
-**Input**:
-- Valid input data
-- Transform response has critical errors
-- `mlflow_transform_filters` configured with `POLICY: 'STOP'`
+The MLflow registry is fully mocked (no real artifacts in test container).
+Validates `predictions_schema.retrain_reports` columns:
+`id, model_id, model_name, timestamp, status, version, mlflow_run_id, mlflow_experiment_id, created_at`.
 
-**Expected Behavior**:
-- `request_transform` succeeds but response invalid
-- `mlflow_response_gate` for transform returns `path_flag='STOP'`
-- Workflow exits without calling predict or export
+### 7.1 Happy path
 
-**Assertions**:
-- Transform completed but validation failed
-- `mlflow_response_gate` called for transform
-- MLFlow predict NOT called
-- Export workflow NOT called
-- Workflow completes without error
+#### MR.1.1 Successful retrain + promotion
+**Summary**: Training data loads via MinIO offload, `wrapper.retrain` succeeds,
+the new version is promoted to the `production` alias.
+
+**Expected Outcome**:
+- Report row has success status, `version='7'`, `mlflow_run_id='retrain-run-id'`,
+  `mlflow_experiment_id='experiment-id'`.
+- `mlflow.log_artifact` is called with the input CSV.
+- `promote_to_alias` is called once with the resolved version and alias.
+
+### 7.2 Failure paths
+
+#### MR.2.1 Wrapper retrain raises
+**Summary**: `wrapper.retrain` raises `RuntimeError`. The activity returns
+`success=False`, `update_production_model` is NOT invoked.
+
+**Expected Outcome**:
+- Report row carries the error message and `version`/`mlflow_*` columns are NULL.
+
+#### MR.2.2 Missing `model_config.target`
+**Summary**: Empty model config short-circuits before any MLflow call.
+
+**Expected Outcome**:
+- Report row carries the explicit guard message.
+- `get_cached_model` is never invoked.
+
+#### MR.3.1 No training data
+**Summary**: The training query returns no rows; the workflow does not
+persist any report row. The current code raises plain `ValueError` from the
+workflow function, which Temporal treats as a workflow-task failure (see
+`CODE_ISSUES.md` issue MR-1).
 
 ---
 
-#### Scenario 2.2.3: Transform Gate Triggers REPEAT
-**Description**: Transform response gate determines data should repeat last prediction
+## Input Contract Reference
 
-**Input**:
-- Valid input data
-- Transform response has quality issues that require using previous prediction
-- `mlflow_transform_filters` configured with `POLICY: 'REPEAT'`
-- `path_priority` includes REPEAT
+Common scenario input fields:
+- `schedule_name`
+- `model_name`
+- `model_id`
+- `query`
+- `schema`
+- `table_name`
+- `transform_table_name`
+- `input_filters`
+- `mlflow_transform_filters`
+- `mlflow_predict_filters`
+- `path_priority` (default order: `STOP`, `CONTINUE`, `REPEAT`)
+- `save_transform`
+- `prediction_store_policy`
+- `model_config.target`
+- `datetime_columns` (when query returns temporal fields)
 
-**Expected Behavior**:
-- `request_transform` succeeds but response has issues
-- `mlflow_response_gate` for transform returns `path_flag='REPEAT'`
-- `path_flag_handler` calls `repeat_last_prediction` activity
-- MLFlow predict skipped
-- Last prediction repeated and exported
-
-**Assertions**:
-- Transform completed but validation triggered REPEAT
-- `mlflow_response_gate` called for transform
-- MLFlow predict NOT called
-- `repeat_last_prediction` activity called
-- Workflow completes
-
----
-
-### 2.3 Predict gate Early Exit Scenarios
-
-#### Scenario 2.3.1: Predict Gate Triggers CONTINUE
-**Description**: Predict response gate determines data should continue despite issues
-
-**Input**:
-- Valid input and transform data
-- Predict response has quality issues but policy is CONTINUE
-- `mlflow_predict_filters` configured with `POLICY: 'CONTINUE'`
-- `path_priority` includes CONTINUE
-
-**Expected Behavior**:
-- `request_predict` succeeds
-- `mlflow_response_gate` for predict returns `path_flag='CONTINUE'`
-- `path_flag_handler` calls export workflow with predict data
-- Prediction exported despite quality issues
-
-**Assertions**:
-- Transform and predict completed
-- `mlflow_response_gate` called for predict
-- Export workflow called with predict data
-- Workflow completes
-
----
-
-#### Scenario 2.3.2: Predict Gate Triggers STOP
-**Description**: Prediction validation fails with STOP policy
-
-**Input**:
-- Valid input and transform
-- Predict response has critical errors
-- `mlflow_predict_filters` configured with `POLICY: 'STOP'`
-
-**Expected Behavior**:
-- `request_predict` succeeds but response invalid
-- `mlflow_response_gate` for predict returns `path_flag='STOP'`
-- Workflow exits without export
-
-**Assertions**:
-- Transform completed
-- Predict completed but validation failed
-- Export workflow NOT called
-- Workflow completes without error
-
----
-
-#### Scenario 2.3.3: Predict Gate Triggers REPEAT
-**Description**: Predict response gate determines data should repeat last prediction
-
-**Input**:
-- Valid input and transform data
-- Predict response has quality issues that require using previous prediction
-- `mlflow_predict_filters` configured with `POLICY: 'REPEAT'`
-- `path_priority` includes REPEAT
-
-**Expected Behavior**:
-- `request_predict` succeeds but response has issues
-- `mlflow_response_gate` for predict returns `path_flag='REPEAT'`
-- `path_flag_handler` calls `repeat_last_prediction` activity
-- Last prediction repeated and exported
-
-**Assertions**:
-- Transform and predict completed but validation triggered REPEAT
-- `mlflow_response_gate` called for predict
-- `repeat_last_prediction` activity called
-- Export workflow NOT called with current prediction
-- Workflow completes
-
----
-
-## 3. Format and Export Prediction - Child Workflow Scenarios
-
-### 3.1 Success Scenarios
-
-#### Scenario 3.1.1: Default Prediction Export
-**Description**: Error prediction path creates default prediction
-
-**Input**:
-- `path_flag: 'ERROR'` or other non-None value (not STOP/CONTINUE/REPEAT)
-- `comment` provided with error details
-
-**Expected Behavior**:
-- `format_default_prediction` called instead of `format_prediction`
-- Default prediction created with error metadata
-- Exported to PostgreSQL only
-- Transformed data NOT processed
-- Metrics written
-
-**Assertions**:
-- `format_default_prediction` called
-- `format_prediction` NOT called
-- `format_transformed_data` NOT called
-- One PostgreSQL export only
-- Default values in prediction data
-- Comment included
-
----
-
-#### Scenario 3.1.2: Export with OPC only
-**Description**: Export to PostgreSQL and OPC server only (no PI Web API)
-
-**Input**:
-- `path_flag: None`
-- `opc_output_config` configured with valid OPC settings
-- `pi_web_api_output_config: None` or `{}`
-
-**Expected Behavior**:
-- Normal formatting
-- PostgreSQL export executed
-- OPC export executed
-- PI Web API activity skipped
-- Metrics written with OPC metrics
-
-**Assertions**:
-- PI Web API activity NOT called
-- OPC activity called
-- PostgreSQL export called
-- Metrics written with `opc_metrics` populated
-
----
-
-#### Scenario 3.1.3: Export with PI Web API only
-**Description**: Export to PostgreSQL and PI Web API only (no OPC)
-
-**Input**:
-- `path_flag: None`
-- `pi_web_api_output_config` configured with valid PI Web API settings
-- `opc_output_config: None` or `{}`
-
-**Expected Behavior**:
-- Normal formatting
-- PostgreSQL export executed
-- PI Web API export executed
-- OPC activity skipped
-- Metrics written without OPC metrics
-
-**Assertions**:
-- OPC activity NOT called
-- PI Web API activity called
-- PostgreSQL export called
-- Metrics written with empty `opc_metrics`
-
----
-
-#### Scenario 3.1.4: Export Without Optional Outputs
-**Description**: Export only to PostgreSQL (no OPC or PI Web API)
-
-**Input**:
-- `path_flag: None`
-- `opc_output_config: None` or `{}`
-- `pi_web_api_output_config: None` or `{}`
-
-**Expected Behavior**:
-- Normal formatting
-- Only PostgreSQL export executed
-- OPC and PI Web API activities skipped
-- Metrics written without OPC metrics
-
-**Assertions**:
-- PI Web API activity NOT called
-- OPC activity NOT called
-- PostgreSQL export called
-- Metrics written with empty `opc_metrics`
-
----
-
-#### Scenario 3.1.5: Export Without Transformed Data
-**Description**: Only prediction exported, no transform table
-
-**Input**:
-- `path_flag: None`
-- `transformed_data: None` or `save_transform: False`
-- `opc_output_config: None` or `{}`
-- `pi_web_api_output_config: None` or `{}`
-
-**Expected Behavior**:
-- Only prediction formatted and exported
-- Transform export skipped
-- Single PostgreSQL write
-
-**Assertions**:
-- `format_transformed_data` NOT called
-- One PostgreSQL export
-- Transform table remains empty
-
----
-
-### 3.2 Error Scenarios
-
-These paths do **not** rely on Temporal activity retries for export failures: the write activities run once, errors are handled inside the activity, and the **workflow completes successfully** with degraded metadata on the persisted prediction (`prediction_confidence` and `comments`).
-
-#### Scenario 3.2.1: PI Web API Write Error
-**Description**: PI Web API export fails
-
-**Input**:
-- Valid prediction
-- PI Web API service unavailable or invalid config
-
-**Expected Behavior**:
-- `write_pi_web_api_data` surfaces the failure (exception handled in the activity layer)
-- Notification may be sent
-- Workflow **completes** (does not fail)
-- Prediction row is still written to PostgreSQL with error confidence **13** and a comment describing the PI error
-- Subsequent steps (e.g. OPC, Postgres) still run per workflow order with the updated prediction payload
-
-**Assertions**:
-- PI Web API error notification sent (when applicable)
-- Workflow completes
-- PostgreSQL contains the prediction with `prediction_confidence` 13 and expected `comments`
-
----
-
-#### Scenario 3.2.2: OPC Write Error
-**Description**: OPC server write fails
-
-**Input**:
-- Valid prediction
-- OPC server unavailable or invalid configuration
-
-**Expected Behavior**:
-- `write_opc_data` reports failure without aborting the workflow
-- Notification may be sent
-- Workflow **completes** (does not fail)
-- Prediction row is written to PostgreSQL with OPC error confidence **12** and a comment indicating OPC write issues
-
-**Assertions**:
-- OPC error notification sent (when applicable)
-- Workflow completes
-- PostgreSQL contains the prediction with `prediction_confidence` 12 and expected `comments`
-
----
-
-#### Scenario 3.2.3: PI Web API Partial Write Error
-**Description**: Two prediction tags attempt to be written to PI Web API, but only one succeeds
-
-**Input**:
-- Valid prediction
-- Two prediction tags configured
-- PI Web API returns partial success (one tag succeeds, one fails)
-
-**Expected Behavior**:
-- `write_pi_web_api_data` processes response
-- `process_pi_web_api_response` detects partial failure
-- Error confidence set (13)
-- Notification sent for failed tag
-- Workflow completes with error confidence (single activity attempt; no retry loop)
-
-**Assertions**:
-- One tag written successfully
-- One tag failed
-- Error confidence set in prediction
-- Error notification sent
-- Workflow completes
-
----
+Optional outputs:
+- `opc_output_config`
+- `pi_web_api_output_config`

e2e/test_drift.py

@@ -0,0 +1,786 @@
+"""
+End-to-end tests for the Drift workflow.
+
+Coverage focus:
+
+- Full pipeline persists drift rows with **all** columns expected by the
+  ``predictions_schema.drift`` table (model_id, feature, method, value, drift,
+  chunk, timestamp, timestamp_end, accurate, plus DB-managed id/created_at/updated_at).
+- ``get_reference_data`` happy path (CSV downloaded from MLflow stub) and
+  fallback path (30% of target data when reference is unavailable).
+- ``calculate_drift`` invariants: ``p_value`` dropped, duplicates removed, rows
+  outside target timestamps filtered, default ``drift_metrics`` propagated.
+- Failure paths: ``ModelAnalysis.get_drift_metrics_dataframe`` raises ⇒
+  workflow completes without writes; empty target data ⇒ workflow short-circuits;
+  invalid ``chunk_period`` ⇒ activity raises and workflow surfaces the error.
+"""
+
+from datetime import datetime, timedelta, timezone
+from decimal import Decimal
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+from sqlalchemy import text
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import (
+    build_drift_dataframe,
+    insert_target_data_for_drift,
+    load_scenario_input,
+    make_workflow_id,
+    start_and_await_workflow,
+)
+from laborious.activities.activities import Activities
+from laborious.workflows.drift import Drift
+
+# Drift columns that must be present (and non-null where required) on every row.
+EXPECTED_DRIFT_COLUMNS = [
+    'id',
+    'model_id',
+    'feature',
+    'method',
+    'value',
+    'drift',
+    'chunk',
+    'timestamp',
+    'timestamp_end',
+    'accurate',
+    'created_at',
+    'updated_at',
+]
+
+
+def _drift_input(model_id: int, **overrides) -> dict:
+    """Load the base drift scenario JSON and apply ad-hoc overrides."""
+    input_data = load_scenario_input('drift_base.json', model_id=model_id)
+    input_data.update(overrides)
+    return input_data
+
+
+def _five_minute_window(offset_minutes: int = 6) -> tuple[list[str], list[str]]:
+    """
+    Build five consecutive UTC minute timestamps positioned in the recent past.
+
+    The Drift workflow filters target rows with ``timestamp > NOW() - INTERVAL``,
+    so timestamps must be recent for tests to retrieve any data. We snap to
+    minute precision and back off ``offset_minutes`` minutes so all chunks land
+    well inside the default 60-minute interval defined in ``drift_base.json``.
+
+    Args:
+     - offset_minutes: How many minutes ago the most recent chunk should be.
+
+    Return:
+     Tuple ``(target_timestamps, chunk_timestamps)``:
+
+     - ``target_timestamps``: ISO strings with ``+0000`` used as ``timestamp``
+       and ``created_at`` columns when inserting target rows.
+     - ``chunk_timestamps``: ``YYYY-MM-DD HH:MM`` truncations matching what
+       ``calculate_drift`` filters on for ``chunk_period='min'``.
+    """
+    base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(
+        minutes=offset_minutes
+    )
+    target_timestamps = [
+        (base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M:%S%z') for i in range(5)
+    ]
+    chunk_timestamps = [
+        (base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M') for i in range(5)
+    ]
+    return target_timestamps, chunk_timestamps
+
+
+def _configure_reference_csv(mlflow_repository_stub, reference_rows: pd.DataFrame) -> None:
+    """
+    Wire ``mlflow_repository_stub`` so ``get_reference_data`` returns
+    ``reference_rows`` by writing them to ``dst_path/evaluation_data.csv``.
+    """
+
+    def _download(run_id: str, artifact_path: str, dst_path: str, metadata=None):
+        target = Path(dst_path) / 'evaluation_data.csv'
+        reference_rows.to_csv(target, index=False)
+
+    mlflow_repository_stub._client.get_model_version_by_alias.return_value = MagicMock(
+        run_id='fake-reference-run'
+    )
+    mlflow_repository_stub.download_artifacts.side_effect = _download
+
+
+def _force_reference_unavailable(mlflow_repository_stub) -> None:
+    """Make ``get_reference_data`` return ``None`` by failing alias resolution."""
+    mlflow_repository_stub._client.get_model_version_by_alias.side_effect = Exception(
+        'no production alias registered'
+    )
+
+
+def _assert_drift_columns_complete(rows, *, expected_count: int) -> None:
+    """Validate row count and that the canonical drift columns are populated."""
+    assert len(rows) == expected_count, (
+        f'Expected {expected_count} drift rows persisted, got {len(rows)}'
+    )
+    seen_columns = set(rows[0]._mapping.keys()) if rows else set()
+    for column in EXPECTED_DRIFT_COLUMNS:
+        assert column in seen_columns, f'Missing drift column in postgres: {column}'
+
+    for row in rows:
+        mapping = dict(row._mapping)
+        for column in EXPECTED_DRIFT_COLUMNS:
+            if column == 'value':
+                # ``value`` is nullable in the table; skip null check, only ensure key exists.
+                continue
+            assert mapping[column] is not None, f"Column '{column}' is NULL in {mapping}"
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_happy_path_persists_all_columns_with_reference_data(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.1.1: Happy path with reference data downloaded from MLflow.
+
+    Validates the full drift pipeline produces one row per (chunk, feature, method)
+    combination plus the multivariate row block, with **all** columns required by
+    ``predictions_schema.drift`` populated. Uses a deterministic mocked drift
+    dataframe so the postgres assertions remain stable across runs.
+    """
+    client = temporal_test_env.client
+    model_id = 411
+
+    target_timestamps, chunk_timestamps = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [10.0, 11.0, 12.0, 13.0, 14.0],
+            'sensor_2': [20.0, 21.0, 22.0, 23.0, 24.0],
+        },
+    )
+
+    reference_df = pd.DataFrame(
+        {
+            'timestamp': ['2023-12-31 11:00:00+00:00', '2023-12-31 11:01:00+00:00'],
+            'sensor_1': [9.5, 9.6],
+            'sensor_2': [19.5, 19.6],
+        }
+    )
+    _configure_reference_csv(mlflow_repository_stub, reference_df)
+
+    features = ['sensor_1', 'sensor_2']
+    methods = ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
+    drift_flags = {
+        ('sensor_1', 'wasserstein'): True,
+        ('sensor_2', 'wasserstein'): True,
+        ('sensor_2', 'jensen_shannon'): True,
+    }
+
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps,
+            features=features,
+            methods=methods,
+            statistic=0.42,
+            drift_flags=drift_flags,
+            multivariate_value=15.5,
+            multivariate_drift=True,
+        )
+    )
+
+    input_data = _drift_input(model_id)
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-happy-path')
+    )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT * FROM predictions_schema.drift '
+                    'WHERE model_id = :model_id ORDER BY chunk, feature, method'
+                ),
+                {'model_id': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    expected_rows = len(chunk_timestamps) * (len(features) * len(methods) + 1)  # univariate + multivariate
+    assert len(rows) == expected_rows
+    for column in EXPECTED_DRIFT_COLUMNS:
+        assert column in rows[0], f'Missing drift column in postgres: {column}'
+
+    for row in rows:
+        for column in EXPECTED_DRIFT_COLUMNS:
+            if column == 'value':
+                continue
+            assert row[column] is not None, f"Column '{column}' is NULL in {dict(row)}"
+
+    multivariate_rows = [r for r in rows if r['feature'] == 'multivariate']
+    univariate_rows = [r for r in rows if r['feature'] != 'multivariate']
+
+    assert len(multivariate_rows) == len(chunk_timestamps)
+    assert all(r['method'] == 'multivariate' for r in multivariate_rows)
+    assert all(r['drift'] is True for r in multivariate_rows)
+    assert all(Decimal(str(r['value'])) == Decimal('15.5') for r in multivariate_rows)
+
+    assert len(univariate_rows) == len(features) * len(methods) * len(chunk_timestamps)
+    assert {r['method'] for r in univariate_rows} == set(methods)
+    assert {r['feature'] for r in univariate_rows} == set(features)
+
+    drift_pairs = {(r['feature'], r['method']): r['drift'] for r in univariate_rows}
+    for (feature, method), expected_drift in drift_flags.items():
+        assert drift_pairs[(feature, method)] is expected_drift
+
+    assert all(r['accurate'] is True for r in rows), 'reference path should mark rows as accurate'
+
+    assert all(
+        r['timestamp_end'].endswith(':59.999999999') and 'T' in r['timestamp_end']
+        for r in rows
+    ), 'timestamp_end should preserve the high-precision ISO string from ModelAnalysis'
+
+    assert all('p_value' not in r for r in rows), 'p_value must be dropped before postgres'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_uses_30pct_fallback_when_reference_unavailable(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+    notification_inserts,
+):
+    """
+    Scenario D.1.2: ``get_reference_data`` returns ``None`` (production alias missing),
+    so ``calculate_drift`` falls back to using the first 30% of target rows as
+    reference. Persisted rows must report ``accurate=False`` and a warning
+    notification must be emitted to mongo.
+    """
+    client = temporal_test_env.client
+    model_id = 412
+
+    target_timestamps, chunk_timestamps = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [10.0, 10.1, 10.2, 10.3, 10.4],
+            'sensor_2': [20.0, 20.1, 20.2, 20.3, 20.4],
+        },
+    )
+
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps,
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            statistic=0.7,
+            include_multivariate=False,
+        )
+    )
+
+    input_data = _drift_input(model_id)
+
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-fallback')
+    )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text('SELECT * FROM predictions_schema.drift WHERE model_id = :m'),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert len(rows) == len(chunk_timestamps)
+    assert all(r['accurate'] is False for r in rows), 'fallback path must mark rows as inaccurate'
+    assert all(r['feature'] == 'sensor_1' for r in rows)
+
+    fallback_warnings = [
+        call
+        for call in notification_inserts.call_args_list
+        if call.args
+        and isinstance(call.args[0], dict)
+        and call.args[0].get('notification_id') == 'MODEL_METRICS_REFERENCE_DATA_WARNING'
+    ]
+    assert len(fallback_warnings) >= 1, 'expected reference fallback warning notification'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_drops_p_value_and_dedupes_by_timestamp_method_feature(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.2.1: ModelAnalysis returns duplicate (timestamp, method, feature)
+    rows and a populated ``p_value`` column. ``calculate_drift`` must deduplicate
+    keeping the first occurrence and the persisted rows must not contain
+    ``p_value``.
+    """
+    client = temporal_test_env.client
+    model_id = 421
+
+    target_timestamps, chunk_timestamps = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        },
+    )
+
+    duplicates = [
+        # Re-emit the first chunk for sensor_1/kolmogorov_smirnov with a different value
+        # so we can prove the dedup keeps the FIRST row.
+        {
+            'timestamp': pd.Timestamp(chunk_timestamps[0]),
+            'feature': 'sensor_1',
+            'metric': 'kolmogorov_smirnov',
+            'statistic': 0.99,
+            'p_value': 0.02,
+            'alert': True,
+            'chunk_index': 0,
+            'chunk_start_date': pd.Timestamp(chunk_timestamps[0]),
+            'chunk_end_date': pd.Timestamp(f'{chunk_timestamps[0]}:59.999999999'),
+        }
+    ]
+
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps,
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            statistic=0.5,
+            include_multivariate=False,
+            extra_rows=duplicates,
+        )
+    )
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    input_data = _drift_input(model_id)
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-dedupe')
+    )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT chunk, feature, method, value FROM predictions_schema.drift '
+                    'WHERE model_id = :m ORDER BY chunk'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert len(rows) == len(chunk_timestamps), 'duplicates must be removed before persistence'
+
+    first_chunk_rows = [r for r in rows if r['chunk'] == 0]
+    assert len(first_chunk_rows) == 1
+    assert Decimal(str(first_chunk_rows[0]['value'])) == Decimal('0.5'), (
+        'dedup must keep the FIRST occurrence (statistic=0.5), not the duplicate (statistic=0.99)'
+    )
+
+    columns = set(rows[0].keys())
+    assert 'p_value' not in columns
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_drops_rows_outside_target_timestamps(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.2.2: Drift rows whose timestamps are not present in the target
+    data (e.g. came from the reference distribution) must be discarded so the
+    saved drift only reflects analysis chunks.
+    """
+    client = temporal_test_env.client
+    model_id = 422
+
+    target_timestamps, chunk_timestamps = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        },
+    )
+
+    out_of_range_timestamp = pd.Timestamp('2099-12-31 23:59')
+    extra = [
+        {
+            'timestamp': out_of_range_timestamp,
+            'feature': 'sensor_1',
+            'metric': 'kolmogorov_smirnov',
+            'statistic': 0.5,
+            'p_value': 0.0,
+            'alert': True,
+            'chunk_index': 99,
+            'chunk_start_date': out_of_range_timestamp,
+            'chunk_end_date': pd.Timestamp('2099-12-31 23:59:59.999999999'),
+        }
+    ]
+
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps,
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            include_multivariate=False,
+            extra_rows=extra,
+        )
+    )
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    input_data = _drift_input(model_id)
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-tts-filter')
+    )
+
+    with postgres_engine.connect() as conn:
+        chunks = [
+            r[0]
+            for r in conn.execute(
+                text(
+                    'SELECT chunk FROM predictions_schema.drift WHERE model_id = :m ORDER BY chunk'
+                ),
+                {'m': model_id},
+            ).all()
+        ]
+
+    assert chunks == [0, 1, 2, 3, 4], 'out-of-range timestamps must be filtered out'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_empty_target_data_short_circuits_workflow(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.3.1: When ``load_custom_query`` returns no rows the workflow must
+    return early without invoking ModelAnalysis or writing any drift rows.
+    """
+    client = temporal_test_env.client
+    model_id = 431
+
+    with postgres_engine.begin() as conn:
+        conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
+
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    drift_calls = {'count': 0}
+
+    def _factory(univariate, multivariate):
+        drift_calls['count'] += 1
+        return build_drift_dataframe(
+            timestamps=['2024-01-01 12:00'],
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            include_multivariate=False,
+        )
+
+    model_analysis_stub.set_drift_dataframe(_factory)
+
+    input_data = _drift_input(model_id)
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-empty-target')
+    )
+
+    with postgres_engine.connect() as conn:
+        count = conn.execute(
+            text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
+            {'m': model_id},
+        ).scalar()
+    assert count == 0
+    assert drift_calls['count'] == 0, 'ModelAnalysis must not be invoked when target data is empty'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_model_analysis_failure_keeps_workflow_alive_no_writes(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+    notification_inserts,
+):
+    """
+    Scenario D.3.2: ``ModelAnalysis.get_drift_metrics_dataframe`` raises. The
+    activity must catch the error, send an error notification and return ``[]``
+    so the workflow completes without persisting drift rows.
+    """
+    client = temporal_test_env.client
+    model_id = 432
+
+    target_timestamps, _ = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        },
+    )
+
+    _force_reference_unavailable(mlflow_repository_stub)
+    model_analysis_stub.raise_on_get_drift_metrics_dataframe(
+        RuntimeError('drift analyzer crashed')
+    )
+
+    input_data = _drift_input(model_id)
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-analyzer-error')
+    )
+
+    with postgres_engine.connect() as conn:
+        count = conn.execute(
+            text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
+            {'m': model_id},
+        ).scalar()
+    assert count == 0
+
+    error_notifications = [
+        call
+        for call in notification_inserts.call_args_list
+        if call.args
+        and isinstance(call.args[0], dict)
+        and call.args[0].get('notification_id') == 'MODEL_METRICS_GET_DRIFT_METRICS_ERROR'
+    ]
+    assert len(error_notifications) >= 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_default_drift_metrics_propagated_to_analyzer(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.4.1: When ``drift_metrics`` is omitted from input the workflow
+    must default to ``['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']``
+    and forward exactly that list to ``ModelAnalysis.detect_univariate_drift``.
+    """
+    client = temporal_test_env.client
+    model_id = 441
+
+    target_timestamps, chunk_timestamps = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        },
+    )
+
+    _force_reference_unavailable(mlflow_repository_stub)
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps,
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            include_multivariate=False,
+        )
+    )
+
+    input_data = _drift_input(model_id)
+    input_data.pop('drift_metrics', None)
+
+    await start_and_await_workflow(
+        client, Drift.run, input_data, make_workflow_id('test-drift-default-methods')
+    )
+
+    instance = model_analysis_stub.last_instance
+    assert instance is not None, 'ModelAnalysis must have been instantiated'
+    assert len(instance.detect_univariate_drift_calls) == 1
+    forwarded_methods = instance.detect_univariate_drift_calls[0]['methods']
+    assert forwarded_methods == ['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein']
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_invalid_chunk_period_raises_value_error(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+):
+    """
+    Scenario D.4.2: ``calculate_drift`` validates ``chunk_period`` and rejects
+    anything other than ``min`` / ``s``. The workflow must surface the
+    ``ValueError`` to the caller and persist nothing.
+    """
+    client = temporal_test_env.client
+    model_id = 442
+
+    target_timestamps, _ = _five_minute_window()
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'sensor_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        },
+    )
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    input_data = _drift_input(model_id, chunk_period='hour')
+
+    with pytest.raises(Exception) as excinfo:
+        await start_and_await_workflow(
+            client,
+            Drift.run,
+            input_data,
+            make_workflow_id('test-drift-bad-chunk-period'),
+        )
+
+    # Temporal wraps the activity ValueError in WorkflowFailureError; the message
+    # may live on ``.message`` or ``str(exc)`` depending on the SDK error class.
+    cause_descriptions = []
+    current: BaseException | None = excinfo.value
+    while current is not None:
+        cause_descriptions.append(
+            getattr(current, 'message', None) or str(current) or repr(current)
+        )
+        current = current.__cause__
+    assert any('Invalid chunk period' in msg for msg in cause_descriptions), (
+        f'Expected ValueError about chunk period in chain, got: {cause_descriptions}'
+    )
+
+    with postgres_engine.connect() as conn:
+        count = conn.execute(
+            text('SELECT COUNT(*) FROM predictions_schema.drift WHERE model_id = :m'),
+            {'m': model_id},
+        ).scalar()
+    assert count == 0
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_drift_chunk_period_seconds_preserves_seconds_in_timestamp_column(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_drift: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+    model_analysis_stub,
+):
+    """
+    Scenario D.4.3: With ``chunk_period='s'`` the persisted ``timestamp``
+    column must preserve second-level precision instead of being flattened to
+    the start of the minute, and ``chunk_period`` must be propagated to the
+    analyzer so it actually chunks at second granularity.
+    """
+    client = temporal_test_env.client
+    model_id = 443
+
+    base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=2)
+    target_timestamps = [
+        base.strftime('%Y-%m-%d %H:%M:%S%z'),
+        (base + timedelta(seconds=30)).strftime('%Y-%m-%d %H:%M:%S%z'),
+        (base + timedelta(minutes=1)).strftime('%Y-%m-%d %H:%M:%S%z'),
+    ]
+    chunk_timestamps_full = [
+        base.strftime('%Y-%m-%d %H:%M:%S'),
+        (base + timedelta(seconds=30)).strftime('%Y-%m-%d %H:%M:%S'),
+        (base + timedelta(minutes=1)).strftime('%Y-%m-%d %H:%M:%S'),
+    ]
+
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [1.0, 2.0, 3.0],
+            'sensor_2': [10.0, 20.0, 30.0],
+        },
+    )
+    _force_reference_unavailable(mlflow_repository_stub)
+
+    model_analysis_stub.set_drift_dataframe(
+        lambda univariate, multivariate: build_drift_dataframe(
+            timestamps=chunk_timestamps_full,
+            features=['sensor_1'],
+            methods=['kolmogorov_smirnov'],
+            include_multivariate=False,
+        )
+    )
+
+    input_data = _drift_input(model_id, chunk_period='s')
+    await start_and_await_workflow(
+        client,
+        Drift.run,
+        input_data,
+        make_workflow_id('test-drift-chunk-seconds'),
+    )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT chunk, timestamp FROM predictions_schema.drift '
+                    'WHERE model_id = :m ORDER BY chunk'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert [r['chunk'] for r in rows] == [0, 1, 2]
+    seconds_present = {r['timestamp'].second for r in rows}
+    assert seconds_present == {0, 30}, (
+        f'expected seconds 0 and 30 to be preserved, got {seconds_present}'
+    )
+
+    instance = model_analysis_stub.last_instance
+    assert instance is not None
+    assert instance.detect_univariate_drift_calls[0]['chunk_period'] == 's'

e2e/test_minimal_retrain.py

@@ -0,0 +1,335 @@
+"""
+End-to-end tests for the MinimalRetrain workflow.
+
+The MLflow registry is fully stubbed because no real artifacts exist in a
+test container; we only validate that the workflow:
+
+- Loads training data via ``load_query_with_minio_offload``.
+- Calls ``retrain_model`` with a payload pointing at MinIO.
+- Calls ``update_production_model`` only when retrain succeeds.
+- Persists ``retrain_reports`` rows with all required columns; success rows
+  carry the new ``version`` / ``mlflow_run_id`` / ``mlflow_experiment_id``
+  while failure rows leave them ``NULL``.
+"""
+
+from contextlib import contextmanager
+from datetime import datetime, timedelta, timezone
+from unittest.mock import MagicMock, patch
+
+import pytest
+from sqlalchemy import text
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import (
+    insert_target_data_for_drift,
+    load_scenario_input,
+    make_workflow_id,
+    start_and_await_workflow,
+)
+from laborious.activities.activities import Activities
+from laborious.workflows.minimal_retrain import MinimalRetrain
+
+EXPECTED_RETRAIN_REPORT_COLUMNS = [
+    'id',
+    'model_id',
+    'model_name',
+    'timestamp',
+    'status',
+    'version',
+    'mlflow_run_id',
+    'mlflow_experiment_id',
+    'created_at',
+]
+
+
+def _retrain_input(model_id: int, **overrides) -> dict:
+    """Load and override the minimal-retrain base scenario."""
+    payload = load_scenario_input('minimal_retrain_base.json', model_id=model_id)
+    payload.update(overrides)
+    return payload
+
+
+def _seed_retrain_training_rows(postgres_engine, model_id: int) -> None:
+    """
+    Insert training rows in long format that pivot cleanly into
+    ``index=timestamp`` / ``columns={sensor_1, sensor_2}`` for ``retrain_model``.
+    """
+    target_timestamps = [
+        (datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(minutes=10 - i))
+        .strftime('%Y-%m-%d %H:%M:%S%z')
+        for i in range(5)
+    ]
+    insert_target_data_for_drift(
+        postgres_engine,
+        model_id=model_id,
+        timestamps=target_timestamps,
+        variables_values={
+            'sensor_1': [10.0, 11.0, 12.0, 13.0, 14.0],
+            'sensor_2': [20.0, 21.0, 22.0, 23.0, 24.0],
+        },
+    )
+
+
+def _configure_retrain_happy_path(mlflow_repository_stub) -> None:
+    """
+    Wire ``mlflow_repository_stub`` so retrain + update_production succeed.
+
+    Mocks (in order of consumption):
+
+    - ``_client.get_model_version_by_alias``: returns ``mv`` with a stable
+      ``run_id`` (used as ``source_run_id``).
+    - ``get_cached_model``: returns a wrapper exposing inert ``retrain`` and
+      ``store_model`` methods.
+    - ``start_run``: returns a context manager yielding a ``run_info`` with
+      run/experiment ids.
+    - ``log_params``: inert.
+    - ``_client.search_model_versions``: returns one registry entry whose
+      ``version`` is promoted by ``update_production_model``.
+    - ``promote_to_alias``: inert success.
+    """
+    mv_src = MagicMock()
+    mv_src.run_id = 'source-run-id'
+
+    new_version = MagicMock()
+    new_version.version = '7'
+    new_version.run_id = 'retrain-run-id'
+
+    mlflow_repository_stub._client.get_model_version_by_alias.return_value = mv_src
+
+    cached_wrapper = MagicMock()
+    cached_wrapper.retrain = MagicMock(return_value=None)
+    cached_wrapper.store_model = MagicMock(return_value=None)
+    mlflow_repository_stub.get_cached_model.return_value = cached_wrapper
+
+    @contextmanager
+    def fake_start_run(**kwargs):
+        run_info = MagicMock()
+        run_info.run_id = 'retrain-run-id'
+        run_info.experiment_id = 'experiment-id'
+        yield run_info
+
+    mlflow_repository_stub.start_run.side_effect = fake_start_run
+    mlflow_repository_stub.log_params = MagicMock(return_value=None)
+    mlflow_repository_stub._client.search_model_versions.return_value = [new_version]
+    mlflow_repository_stub.promote_to_alias = MagicMock(return_value=None)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_minimal_retrain_happy_path_writes_success_report(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_minimal_retrain: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+):
+    """
+    Scenario MR.1.1: Retrain succeeds. ``retrain_reports`` must contain a
+    success row with version/mlflow_run_id/mlflow_experiment_id populated and
+    the registry must have been told to promote the new version to the
+    configured alias.
+    """
+    client = temporal_test_env.client
+    model_id = 711
+
+    _seed_retrain_training_rows(postgres_engine, model_id)
+    _configure_retrain_happy_path(mlflow_repository_stub)
+
+    input_data = _retrain_input(model_id)
+
+    with patch('laborious.activities.mlflow.mlflow.log_artifact') as log_artifact_mock:
+        await start_and_await_workflow(
+            client,
+            MinimalRetrain.run,
+            input_data,
+            make_workflow_id('test-retrain-happy'),
+        )
+
+    assert log_artifact_mock.called, 'retrain_model should log the input CSV artifact'
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT * FROM predictions_schema.retrain_reports '
+                    'WHERE model_id = :m'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert len(rows) == 1
+    for column in EXPECTED_RETRAIN_REPORT_COLUMNS:
+        assert column in rows[0], f'Missing retrain report column: {column}'
+
+    row = rows[0]
+    assert row['model_id'] == model_id
+    assert row['model_name'] == 'test_model'
+    assert row['status'] == 'Model retrained successfully.'
+    assert row['version'] == '7'
+    assert row['mlflow_run_id'] == 'retrain-run-id'
+    assert row['mlflow_experiment_id'] == 'experiment-id'
+    assert row['timestamp'] is not None
+    assert row['created_at'] is not None
+
+    mlflow_repository_stub.promote_to_alias.assert_called_once()
+    promote_kwargs = mlflow_repository_stub.promote_to_alias.call_args.kwargs
+    assert promote_kwargs['model_name'] == 'test_model'
+    assert promote_kwargs['version'] == '7'
+    assert promote_kwargs['alias'] == 'production'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_minimal_retrain_failure_writes_report_without_version_columns(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_minimal_retrain: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+):
+    """
+    Scenario MR.2.1: ``wrapper.retrain`` raises. The activity must catch the
+    error, return ``success=False`` so ``update_production_model`` is skipped,
+    and ``format_retrain_report`` must produce a row with the error message
+    and NULL version columns.
+    """
+    client = temporal_test_env.client
+    model_id = 721
+
+    _seed_retrain_training_rows(postgres_engine, model_id)
+    _configure_retrain_happy_path(mlflow_repository_stub)
+    mlflow_repository_stub.get_cached_model.return_value.retrain.side_effect = RuntimeError(
+        'training did not converge'
+    )
+
+    input_data = _retrain_input(model_id)
+
+    with patch('laborious.activities.mlflow.mlflow.log_artifact'):
+        await start_and_await_workflow(
+            client,
+            MinimalRetrain.run,
+            input_data,
+            make_workflow_id('test-retrain-failure'),
+        )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT * FROM predictions_schema.retrain_reports '
+                    'WHERE model_id = :m'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert len(rows) == 1
+    row = rows[0]
+    assert row['model_id'] == model_id
+    assert row['model_name'] == 'test_model'
+    assert 'training did not converge' in row['status']
+    assert row['version'] is None
+    assert row['mlflow_run_id'] is None
+    assert row['mlflow_experiment_id'] is None
+
+    mlflow_repository_stub.promote_to_alias.assert_not_called()
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_minimal_retrain_missing_target_writes_failure_report(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_minimal_retrain: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+):
+    """
+    Scenario MR.2.2: ``model_config`` does not declare ``target``. The retrain
+    activity must short-circuit before any MLflow call and the report row must
+    carry the explicit guard message.
+    """
+    client = temporal_test_env.client
+    model_id = 722
+
+    _seed_retrain_training_rows(postgres_engine, model_id)
+    _configure_retrain_happy_path(mlflow_repository_stub)
+
+    input_data = _retrain_input(model_id, model_config={})
+
+    with patch('laborious.activities.mlflow.mlflow.log_artifact'):
+        await start_and_await_workflow(
+            client,
+            MinimalRetrain.run,
+            input_data,
+            make_workflow_id('test-retrain-missing-target'),
+        )
+
+    with postgres_engine.connect() as conn:
+        row = (
+            conn.execute(
+                text(
+                    'SELECT * FROM predictions_schema.retrain_reports '
+                    'WHERE model_id = :m'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .first()
+        )
+
+    assert row is not None
+    assert 'target' in row['status'].lower(), (
+        f"expected target-missing message, got status={row['status']!r}"
+    )
+    assert row['version'] is None
+    mlflow_repository_stub.get_cached_model.assert_not_called()
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_minimal_retrain_no_training_data_does_not_persist_report(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_minimal_retrain: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    mlflow_repository_stub,
+):
+    """
+    Scenario MR.3.1: When the training query returns no rows the workflow must
+    not persist any report row. The workflow currently raises plain
+    ``ValueError`` which Temporal treats as a workflow-task failure (causing
+    indefinite retries until the test environment times out), so the assertion
+    here is constrained to the persistence side-effect. See ``e2e/CODE_ISSUES.md``
+    issue MR-1 for the recommended ``ApplicationError`` fix.
+    """
+    client = temporal_test_env.client
+    model_id = 731
+
+    with postgres_engine.begin() as conn:
+        conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
+
+    _configure_retrain_happy_path(mlflow_repository_stub)
+
+    input_data = _retrain_input(model_id)
+
+    with pytest.raises(Exception), patch('laborious.activities.mlflow.mlflow.log_artifact'):
+        await start_and_await_workflow(
+            client,
+            MinimalRetrain.run,
+            input_data,
+            make_workflow_id('test-retrain-no-data'),
+        )
+
+    with postgres_engine.connect() as conn:
+        count = conn.execute(
+            text('SELECT COUNT(*) FROM predictions_schema.retrain_reports WHERE model_id = :m'),
+            {'m': model_id},
+        ).scalar()
+    assert count == 0

e2e/test_minio_offload.py

@@ -9,7 +9,12 @@ from sqlalchemy import text
 from temporalio.testing import WorkflowEnvironment
 from temporalio.worker import Worker
 
-from e2e.helpers import insert_sample_data, make_workflow_id, start_and_await_workflow
+from e2e.helpers import (
+    insert_sample_data,
+    load_scenario_input,
+    make_workflow_id,
+    start_and_await_workflow,
+)
 from laborious.activities.activities import Activities
 from laborious.utils.models import minio_dataframe_payload as mdp
 from laborious.workflows.predictions_batch import PredictionsBatch
@@ -32,30 +37,14 @@ async def test_load_query_with_minio_offload_writes_object_to_bucket(
         conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
     insert_sample_data(postgres_engine, model_id, [1.0, 2.0])
 
-    metadata = {
-        'metadata': {
-            'schedule_name': 'test-schedule',
-            'model_name': 'test_model',
-            'model_id': model_id,
-            'workflow_name': 'predictions_batch',
-        }
-    }
+    scenario_input = load_scenario_input('minio_offload_load_query.json', model_id=model_id)
+    metadata = {'metadata': scenario_input['metadata']}
     with patch.object(mdp, 'OFFLOAD_THRESHOLD_BYTES', 1):
-        payload = await test_activities_real_minio.load_query_with_minio_offload(
-            {
-                **metadata,
-                'query': (
-                    'SELECT timestamp, variable, value, created_at '
-                    f'FROM predictions_schema.laborious_data WHERE model_id = {model_id}'
-                ),
-                'model_name': 'test_model',
-                'datetime_columns': ['timestamp', 'created_at'],
-            }
-        )
+        payload = test_activities_real_minio.load_query_with_minio_offload(scenario_input)
     assert payload.object_key, 'offloaded payload must reference a MinIO object'
     assert payload.data is None or payload.data == {}, 'large payloads should not inline tabular dict'
 
-    df = await payload.retrieve(test_activities_real_minio.minio_repository, metadata['metadata'])
+    df = payload.retrieve(test_activities_real_minio.minio_repository, metadata['metadata'])
     assert len(df) >= 1
 
     client = minio_container.get_client()
@@ -82,31 +71,7 @@ async def test_predictions_batch_with_minio_offload_path(
         conn.execute(text(f'DELETE FROM predictions_schema.transformed_data WHERE model_id = {model_id}'))
     insert_sample_data(postgres_engine, model_id, [10.0, 20.0, 30.0])
 
-    input_data = {
-        'schedule_name': 'test-schedule',
-        'model_name': 'test_model',
-        'model_id': model_id,
-        'query': (
-            'SELECT timestamp, variable, value, created_at '
-            f'FROM predictions_schema.laborious_data WHERE model_id = {model_id}'
-        ),
-        'schema': 'predictions_schema',
-        'table_name': 'predictions',
-        'transform_table_name': 'transformed_data',
-        'input_filters': {'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}}},
-        'mlflow_transform_filters': {'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}}},
-        'mlflow_predict_filters': {'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}}},
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-        'opc_output_config': {},
-        'pi_web_api_output_config': {},
-        'save_transform': True,
-        'prediction_store_policy': 'lts:1',
-        'model_config': {
-            'retention_minutes': 0,
-            'target': 'sensor_1',
-        },
-        'datetime_columns': ['timestamp', 'created_at'],
-    }
+    input_data = load_scenario_input('minio_offload_workflow.json', model_id=model_id)
 
     with patch.object(mdp, 'OFFLOAD_THRESHOLD_BYTES', 1):
         await start_and_await_workflow(
@@ -121,3 +86,23 @@ async def test_predictions_batch_with_minio_offload_path(
             text(f'SELECT COUNT(*) FROM predictions_schema.predictions WHERE model_id = {model_id}')
         ).scalar()
     assert count == 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_load_query_with_inline_payload_when_below_threshold(
+    postgres_engine,
+    test_activities_real_minio: Activities,
+):
+    """Scenario 4.2.1: payload stays inline when threshold is high enough."""
+    model_id = 503
+    with postgres_engine.begin() as conn:
+        conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
+    insert_sample_data(postgres_engine, model_id, [1.0, 2.0])
+
+    scenario_input = load_scenario_input('minio_offload_load_query.json', model_id=model_id)
+    with patch.object(mdp, 'OFFLOAD_THRESHOLD_BYTES', 10**9):
+        payload = test_activities_real_minio.load_query_with_minio_offload(scenario_input)
+
+    assert payload.object_key is None
+    assert payload.data is not None

e2e/test_predictions_batch_format_export.py

@@ -4,7 +4,7 @@ End-to-end tests for PredictionsBatch workflow - Format and Export scenarios.
 
 from decimal import Decimal
 from typing import Any, cast
-from unittest.mock import ANY, AsyncMock, call
+from unittest.mock import ANY, call
 
 import pytest
 from sientia_do.notifications.models import NotificationLevel
@@ -12,47 +12,18 @@ from sqlalchemy import text
 from temporalio.testing import WorkflowEnvironment
 from temporalio.worker import Worker
 
-from e2e.helpers import assert_prediction, insert_sample_data, make_workflow_id, start_and_await_workflow
+from e2e.helpers import (
+    assert_prediction,
+    insert_sample_data,
+    load_scenario_input,
+    make_workflow_id,
+    start_and_await_workflow,
+)
 from laborious.activities.activities import Activities
 from laborious.workflows.predictions_batch import PredictionsBatch
 
-base_input_data = {
-    'schedule_name': 'test-schedule',
-    'model_name': 'test_model',
-    'model_id': 301,
-    'query': 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = 301',
-    'schema': 'predictions_schema',
-    'table_name': 'predictions',
-    'transform_table_name': 'transformed_data',
-    'input_filters': {
-        'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}},
-    },
-    'mlflow_transform_filters': {
-        'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-    },
-    'mlflow_predict_filters': {
-        'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-    },
-    'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-    'opc_output_config': {},
-    'pi_web_api_output_config': {},
-    'save_transform': True,
-    'prediction_store_policy': 'lts:1',
-    'model_config': {
-        'retention_minutes': 0,
-        'target': 'sensor_1',
-    },
-    'datetime_columns': ['timestamp', 'created_at'],
-}
-
-base_query = "SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {model_id}"
-
 def get_base_input_data(model_id):
-    return {
-        **base_input_data,
-        'model_id': model_id,
-        'query': base_query.format(model_id=model_id),
-    }
+    return load_scenario_input('format_export_base.json', model_id=model_id)
 
 
 @pytest.mark.asyncio
@@ -668,8 +639,8 @@ async def test_scenario_3_2_3_pi_web_api_partial_write_error(
 
     insert_sample_data(postgres_engine, model_id, [23.5, 78.2])
 
-    test_activities.pi_web_api_client.write_value = AsyncMock(
-        side_effect=[
+    test_activities.pi_web_api_client.set_side_effect(
+        [
             # Prediction batch: two web_ids requested, only one acknowledged.
             [{'WebId': 'web_id_1', 'Errors': []}],
             # Confidence write succeeds.
@@ -707,4 +678,42 @@ async def test_scenario_3_2_3_pi_web_api_partial_write_error(
         prediction_confidence=13,
         comments="The number of written tags does not match the number of tag names: Expected ['tag_1', 'tag_3'] tags, but ['tag_1'] tags were written.",
     )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_3_3_1_combined_pi_and_opc_outputs(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    test_activities: Activities,
+    postgres_engine,
+):
+    """
+    Scenario 3.3.1: PI and OPC enabled together.
+    """
+    client = temporal_test_env.client
+    model_id = 333
+    insert_sample_data(postgres_engine, model_id, [23.5, 78.2])
+
+    input_data = get_base_input_data(model_id)
+    input_data['pi_web_api_output_config'] = {
+        'endpoint': 'test_endpoint',
+        'prediction_tags': {'tag_1': 'web_id_1'},
+        'confidence_tags': {'tag_2': 'web_id_2'},
+    }
+    input_data['opc_output_config'] = {
+        '1': {
+            'prediction_tags': {'addr_1': {'data_type': 'float'}},
+            'confidence_tags': {'addr_2': {'data_type': 'float'}},
+        }
+    }
+
+    await start_and_await_workflow(
+        client, PredictionsBatch.run, input_data, make_workflow_id('test-pi-opc-combined')
+    )
+
+    assert test_activities.pi_web_api_client.write_value.call_count == 2
+    opc_write_data = cast(Any, test_activities.opc_repository['1'].write_data)
+    assert opc_write_data.call_count == 2
+    assert_prediction(postgres_engine, model_id)
     

e2e/test_predictions_batch_main_workflow.py

@@ -10,7 +10,7 @@ from temporalio.worker import Worker
 
 import pytest
 
-from e2e.helpers import make_workflow_id, start_and_await_workflow
+from e2e.helpers import load_scenario_input, make_workflow_id, start_and_await_workflow
 from laborious.activities.activities import Activities
 from laborious.workflows.predictions_batch import PredictionsBatch
 
@@ -37,42 +37,7 @@ async def test_scenario_1_1_1_happy_path_complete_success(
         """
         conn.execute(text(insert_sql))
 
-    input_data = {
-        'metadata': {
-            'metadata': {
-                'model_id': 123,
-                'model_name': 'test_model',
-                'schedule_name': 'test-schedule',
-                'workflow_name': 'predictions_batch',
-            }
-        },
-        'schedule_name': 'test-schedule',
-        'model_name': 'test_model',
-        'model_id': 123,
-        'query': 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = 123',
-        'schema': 'predictions_schema',
-        'table_name': 'predictions',
-        'transform_table_name': 'transformed_data',
-        'input_filters': {
-            'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_transform_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_predict_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-        'opc_output_config': {},
-        'pi_web_api_output_config': {},
-        'save_transform': True,
-        'prediction_store_policy': 'lts:1',
-        'model_config': {
-            'target': 'sensor_1',
-            'retention_minutes': 0,
-        },
-        'datetime_columns': ['timestamp', 'created_at'],
-    }
+    input_data = load_scenario_input('main_happy_path.json', model_id=123)
 
     await start_and_await_workflow(
         client,
@@ -125,41 +90,7 @@ async def test_scenario_1_2_1_sql_query_execution_error(
     """Invalid SQL: workflow may complete with early exit; no prediction rows."""
     client = temporal_test_env.client
 
-    input_data = {
-        'metadata': {
-            'metadata': {
-                'model_id': 128,
-                'model_name': 'test_model',
-                'schedule_name': 'test-schedule',
-                'workflow_name': 'predictions_batch',
-            }
-        },
-        'schedule_name': 'test-schedule',
-        'model_name': 'test_model',
-        'model_id': 128,
-        'query': 'SELECT * FROM nonexistent_table WHERE invalid_syntax =',
-        'schema': 'predictions_schema',
-        'table_name': 'predictions',
-        'transform_table_name': 'transformed_data',
-        'input_filters': {
-            'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_transform_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_predict_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-        'opc_output_config': {},
-        'pi_web_api_output_config': {},
-        'save_transform': True,
-        'prediction_store_policy': 'lts:1',
-        'model_config': {
-            'target': 'sensor_1',
-            'retention_minutes': 0,
-        },
-    }
+    input_data = load_scenario_input('main_sql_error.json', model_id=128)
 
     await start_and_await_workflow(
         client,
@@ -186,22 +117,7 @@ async def test_scenario_1_2_2_missing_required_parameters(
     """Missing query: workflow does not produce predictions and is terminated explicitly."""
     client = temporal_test_env.client
 
-    input_data = {
-        'metadata': {
-            'metadata': {
-                'model_id': 129,
-                'model_name': 'test_model',
-                'schedule_name': 'test-schedule',
-                'workflow_name': 'predictions_batch',
-            }
-        },
-        'schedule_name': 'test-schedule',
-        'model_name': 'test_model',
-        'model_id': 129,
-        'schema': 'predictions_schema',
-        'table_name': 'predictions',
-        'transform_table_name': 'transformed_data',
-    }
+    input_data = load_scenario_input('main_missing_required.json', model_id=129)
 
     handle = await client.start_workflow(
         PredictionsBatch.run,
@@ -244,42 +160,7 @@ async def test_scenario_1_2_3_invalid_datetime_column_specification(
             )
         )
 
-    input_data = {
-        'metadata': {
-            'metadata': {
-                'model_id': 130,
-                'model_name': 'test_model',
-                'schedule_name': 'test-schedule',
-                'workflow_name': 'predictions_batch',
-            }
-        },
-        'schedule_name': 'test-schedule',
-        'model_name': 'test_model',
-        'model_id': 130,
-        'query': 'SELECT timestamp, variable, value FROM predictions_schema.laborious_data WHERE model_id = 130',
-        'schema': 'predictions_schema',
-        'table_name': 'predictions',
-        'transform_table_name': 'transformed_data',
-        'input_filters': {
-            'EMPTY_DATA': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_transform_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'mlflow_predict_filters': {
-            'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-        },
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-        'opc_output_config': {},
-        'pi_web_api_output_config': {},
-        'save_transform': True,
-        'prediction_store_policy': 'lts:1',
-        'model_config': {
-            'target': 'sensor_1',
-            'retention_minutes': 0,
-        },
-        'datetime_columns': ['nonexistent_column'],
-    }
+    input_data = load_scenario_input('main_invalid_datetime.json', model_id=130)
 
     handle = await client.start_workflow(
         PredictionsBatch.run,

e2e/test_predictions_batch_prediction_process.py

@@ -14,6 +14,7 @@ from temporalio.worker import Worker
 
 from e2e.helpers import (
     assert_continue,
+    load_scenario_input,
     assert_repeat,
     assert_stop,
     insert_sample_data,
@@ -23,47 +24,8 @@ from e2e.helpers import (
 from laborious.activities.activities import Activities
 from laborious.workflows.predictions_batch import PredictionsBatch
 
-base_input_data = {
-    'schedule_name': 'test-schedule',
-    'model_name': 'test_model',
-    'model_id': 201,
-    'query': 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = 201',
-    'schema': 'predictions_schema',
-    'table_name': 'predictions',
-    'transform_table_name': 'transformed_data',
-    'input_filters': {
-        'SPECIFIC_VARIABLES_NULL_VALUES': {
-            'POLICY': 'CONTINUE',
-            'CONFIG': {'variables': ['sensor_1']},
-        },
-    },
-    'mlflow_transform_filters': {
-        'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-    },
-    'mlflow_predict_filters': {
-        'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}},
-    },
-    'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
-    'opc_output_config': {},
-    'pi_web_api_output_config': {},
-    'save_transform': True,
-    'prediction_store_policy': 'lts:1',
-    'model_config': {
-        'retention_minutes': 0,
-        'target': 'sensor_1',
-    },
-    'datetime_columns': ['timestamp', 'created_at'],
-}
-
-base_query = 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {model_id}'
-
-
 def get_base_input_data(model_id):
-    return {
-        **base_input_data,
-        'model_id': model_id,
-        'query': base_query.format(model_id=model_id),
-    }
+    return load_scenario_input('prediction_process_base.json', model_id=model_id)
 
 
 def insert_sample_prediction(postgres_engine, model_id):
@@ -383,3 +345,32 @@ async def test_scenario_2_4_1_input_empty_data_stop(
         client, PredictionsBatch.run, input_data, make_workflow_id('test-empty-data-stop')
     )
     assert_stop(postgres_engine, model_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_4_1_priority_conflict_resolution(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    test_activities: Activities,
+    postgres_engine,
+    bad_data_model,
+):
+    """Conflicting filter outputs must honor configured path_priority order."""
+    client = temporal_test_env.client
+    model_id = 242
+    insert_sample_data(postgres_engine, model_id, [60.0, 78.2])
+    input_data = get_base_input_data(model_id)
+    input_data['mlflow_transform_filters'] = {'API_ERROR': {'POLICY': 'CONTINUE', 'CONFIG': {}}}
+    input_data['mlflow_predict_filters'] = {'API_ERROR': {'POLICY': 'STOP', 'CONFIG': {}}}
+    input_data['path_priority'] = ['STOP', 'CONTINUE', 'REPEAT']
+
+    await start_and_await_workflow(
+        client, PredictionsBatch.run, input_data, make_workflow_id('test-priority-conflict')
+    )
+    assert_continue(
+        postgres_engine=postgres_engine,
+        model_id=model_id,
+        prediction_confidence=Decimal(10),
+        comments='Unknown MLFlow API error',
+    )

e2e/test_simple_metrics.py

@@ -0,0 +1,322 @@
+"""
+End-to-end tests for the SimpleMetrics workflow.
+
+Coverage focus:
+
+- Happy path computes rmse/mse/mae/r2 from predictions joined against ``laborious_data``
+  and persists rows to ``predictions_schema.simple_metrics_data`` with all required columns.
+- Subset metric selection (only rmse) writes exactly the requested rows.
+- Zero-variance target produces ``r2=0`` per division-by-zero guard.
+- Empty join (no overlapping data) short-circuits without persisting anything.
+"""
+
+from datetime import datetime, timedelta, timezone
+from decimal import Decimal
+
+import math
+import pytest
+from sqlalchemy import text
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import load_scenario_input, make_workflow_id, start_and_await_workflow
+from laborious.activities.activities import Activities
+from laborious.workflows.simple_metrics import SimpleMetrics
+
+EXPECTED_SIMPLE_METRICS_COLUMNS = [
+    'id',
+    'model_id',
+    'metric',
+    'value',
+    'timestamp',
+    'data_size',
+    'interval_minutes',
+    'created_at',
+]
+
+
+def _simple_metrics_input(model_id: int, **overrides) -> dict:
+    """Load and override the simple-metrics base scenario."""
+    payload = load_scenario_input('simple_metrics_base.json', model_id=model_id)
+    payload.update(overrides)
+    return payload
+
+
+def _seed_predictions_and_targets(
+    postgres_engine,
+    model_id: int,
+    pairs: list[tuple[float, float]],
+    target_name: str = 'sensor_target',
+    offset_minutes: int = 6,
+) -> list[str]:
+    """
+    Insert matching prediction/target rows used by the SimpleMetrics SQL JOIN.
+
+    For each ``(prediction, target)`` pair we write a row in ``predictions`` and
+    a matching row in ``laborious_data`` with ``variable=target_name`` so the
+    inner join in the workflow query yields one row per pair.
+
+    Args:
+     - postgres_engine: SQLAlchemy engine bound to the test container.
+     - model_id: Model id stamped on every row.
+     - pairs: ``(prediction, target)`` pairs, one per minute.
+     - target_name: Variable name in ``laborious_data`` representing the target.
+     - offset_minutes: Earliest row sits this many minutes ago so timestamps fall
+       inside the workflow's recent-data window.
+
+    Return:
+     List of timestamp strings written for the inserted rows.
+    """
+    base = datetime.now(timezone.utc).replace(second=0, microsecond=0) - timedelta(
+        minutes=offset_minutes
+    )
+    timestamps = [
+        (base + timedelta(minutes=i)).strftime('%Y-%m-%d %H:%M:%S%z')
+        for i in range(len(pairs))
+    ]
+
+    prediction_rows = []
+    target_rows = []
+    for index, (prediction, target_value) in enumerate(pairs):
+        ts = timestamps[index]
+        prediction_rows.append(
+            f"({model_id}, {prediction}, 0, 0, 'Good', '{ts}', '{ts}')"
+        )
+        target_rows.append(
+            f"({model_id}, '{target_name}', {target_value}, '{ts}', '{ts}')"
+        )
+
+    with postgres_engine.begin() as conn:
+        conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
+        conn.execute(text(f'DELETE FROM predictions_schema.laborious_data WHERE model_id = {model_id}'))
+        # The SimpleMetrics SQL JOIN only filters ``predictions.model_id``; it does
+        # NOT filter ``laborious_data.model_id`` (see ``e2e/CODE_ISSUES.md`` issue
+        # SM-1). Without this cross-model cleanup, a previous test's target rows
+        # under the same variable name would join into this test's predictions
+        # whenever timestamps happened to overlap.
+        conn.execute(
+            text(
+                "DELETE FROM predictions_schema.laborious_data "
+                "WHERE variable IN (:sensor_default, :target_name) "
+                "AND timestamp >= NOW() - INTERVAL '120 minutes'"
+            ),
+            {'sensor_default': 'sensor_target', 'target_name': target_name},
+        )
+        if prediction_rows:
+            conn.execute(
+                text(
+                    'INSERT INTO predictions_schema.predictions '
+                    '(model_id, prediction, prediction_confidence, response_time, '
+                    'prediction_status, "timestamp", created_at) VALUES '
+                    + ', '.join(prediction_rows)
+                )
+            )
+            conn.execute(
+                text(
+                    'INSERT INTO predictions_schema.laborious_data '
+                    '(model_id, variable, value, "timestamp", created_at) VALUES '
+                    + ', '.join(target_rows)
+                )
+            )
+    return timestamps
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_simple_metrics_happy_path_persists_all_metrics_and_columns(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_simple_metrics: Worker,
+    test_activities: Activities,
+    postgres_engine,
+):
+    """
+    Scenario S.1.1: rmse/mse/mae/r2 are calculated from a deterministic
+    prediction/target pair set and written one row per metric. Every column
+    expected by ``predictions_schema.simple_metrics_data`` must be populated and
+    the numerical values must match closed-form expectations.
+    """
+    client = temporal_test_env.client
+    model_id = 511
+
+    pairs = [
+        (1.0, 2.0),
+        (2.0, 4.0),
+        (3.0, 5.0),
+        (4.0, 9.0),
+        (5.0, 12.0),
+    ]
+    diffs = [target - prediction for prediction, target in pairs]
+    n = len(diffs)
+    expected_rmse = math.sqrt(sum(d * d for d in diffs) / n)
+    expected_mse = sum(d * d for d in diffs) / n
+    expected_mae = sum(abs(d) for d in diffs) / n
+    target_mean = sum(t for _, t in pairs) / n
+    ss_res = sum((target - prediction) ** 2 for prediction, target in pairs)
+    ss_tot = sum((t - target_mean) ** 2 for _, t in pairs)
+    expected_r2 = 1.0 - (ss_res / ss_tot)
+
+    _seed_predictions_and_targets(postgres_engine, model_id=model_id, pairs=pairs)
+
+    input_data = _simple_metrics_input(model_id)
+    await start_and_await_workflow(
+        client,
+        SimpleMetrics.run,
+        input_data,
+        make_workflow_id('test-simple-metrics-happy'),
+    )
+
+    with postgres_engine.connect() as conn:
+        rows = (
+            conn.execute(
+                text(
+                    'SELECT * FROM predictions_schema.simple_metrics_data '
+                    'WHERE model_id = :m ORDER BY metric'
+                ),
+                {'m': model_id},
+            )
+            .mappings()
+            .all()
+        )
+
+    assert len(rows) == 4, f'Expected 4 metric rows, got {len(rows)}'
+    for column in EXPECTED_SIMPLE_METRICS_COLUMNS:
+        assert column in rows[0], f'Missing simple_metrics column: {column}'
+    for row in rows:
+        for column in EXPECTED_SIMPLE_METRICS_COLUMNS:
+            assert row[column] is not None, f"Column '{column}' is NULL in {dict(row)}"
+
+    by_metric = {row['metric']: row for row in rows}
+    assert set(by_metric) == {'rmse', 'mse', 'mae', 'r2'}
+
+    def _decimal_close(actual, expected, places: int = 6) -> bool:
+        return abs(float(actual) - expected) < 10 ** (-places)
+
+    assert _decimal_close(by_metric['rmse']['value'], expected_rmse)
+    assert _decimal_close(by_metric['mse']['value'], expected_mse)
+    assert _decimal_close(by_metric['mae']['value'], expected_mae)
+    assert _decimal_close(by_metric['r2']['value'], expected_r2)
+
+    assert all(row['data_size'] == n for row in rows), 'data_size must equal target row count'
+    assert all(row['interval_minutes'] == 60 for row in rows)
+    assert all(row['model_id'] == model_id for row in rows)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_simple_metrics_subset_metrics_writes_only_requested_rows(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_simple_metrics: Worker,
+    test_activities: Activities,
+    postgres_engine,
+):
+    """
+    Scenario S.1.2: Requesting ``metrics=['rmse']`` must persist exactly one row
+    with metric ``rmse`` and skip mse/mae/r2.
+    """
+    client = temporal_test_env.client
+    model_id = 512
+
+    pairs = [(1.0, 2.0), (2.0, 4.0), (3.0, 6.0)]
+    _seed_predictions_and_targets(postgres_engine, model_id=model_id, pairs=pairs)
+
+    input_data = _simple_metrics_input(model_id, metrics=['rmse'])
+    await start_and_await_workflow(
+        client,
+        SimpleMetrics.run,
+        input_data,
+        make_workflow_id('test-simple-metrics-subset'),
+    )
+
+    with postgres_engine.connect() as conn:
+        metrics = [
+            r[0]
+            for r in conn.execute(
+                text(
+                    'SELECT metric FROM predictions_schema.simple_metrics_data '
+                    'WHERE model_id = :m'
+                ),
+                {'m': model_id},
+            ).all()
+        ]
+    assert metrics == ['rmse']
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_simple_metrics_zero_variance_target_returns_zero_r2(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_simple_metrics: Worker,
+    test_activities: Activities,
+    postgres_engine,
+):
+    """
+    Scenario S.2.1: When the target column has zero variance the activity must
+    return ``r2 = 0`` (division-by-zero guard) and still persist all four metrics.
+    """
+    client = temporal_test_env.client
+    model_id = 521
+
+    pairs = [(0.0, 5.0), (1.0, 5.0), (2.0, 5.0), (3.0, 5.0)]
+    _seed_predictions_and_targets(postgres_engine, model_id=model_id, pairs=pairs)
+
+    input_data = _simple_metrics_input(model_id)
+    await start_and_await_workflow(
+        client,
+        SimpleMetrics.run,
+        input_data,
+        make_workflow_id('test-simple-metrics-zero-variance'),
+    )
+
+    with postgres_engine.connect() as conn:
+        r2_value = conn.execute(
+            text(
+                "SELECT value FROM predictions_schema.simple_metrics_data "
+                "WHERE model_id = :m AND metric = 'r2'"
+            ),
+            {'m': model_id},
+        ).scalar()
+    assert r2_value is not None
+    assert Decimal(str(r2_value)) == Decimal('0'), f'expected r2=0, got {r2_value!r}'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_simple_metrics_no_overlapping_data_short_circuits(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker_simple_metrics: Worker,
+    test_activities: Activities,
+    postgres_engine,
+):
+    """
+    Scenario S.3.1: When the join produces no rows (no matching laborious_data
+    row for the configured ``target``), the workflow returns early without
+    invoking ``calculate_simple_metrics`` and writes nothing.
+    """
+    client = temporal_test_env.client
+    model_id = 531
+
+    # Insert predictions but no matching target rows for the configured variable.
+    _seed_predictions_and_targets(
+        postgres_engine,
+        model_id=model_id,
+        pairs=[(1.0, 1.0)],
+        target_name='wrong_variable_name',
+    )
+
+    input_data = _simple_metrics_input(model_id)
+    await start_and_await_workflow(
+        client,
+        SimpleMetrics.run,
+        input_data,
+        make_workflow_id('test-simple-metrics-empty-join'),
+    )
+
+    with postgres_engine.connect() as conn:
+        count = conn.execute(
+            text(
+                'SELECT COUNT(*) FROM predictions_schema.simple_metrics_data '
+                'WHERE model_id = :m'
+            ),
+            {'m': model_id},
+        ).scalar()
+    assert count == 0, 'Empty target data must short-circuit and skip persistence'

input_sample.json

@@ -0,0 +1,72 @@
+{
+  "models": [
+    {
+      "id": "1001",
+      "name": "test-runtime",
+      "active": false,
+      "model_config": {
+        "alias": "production",
+        "retention_minutes": 60,
+        "target": "Square"
+      }
+    }
+  ],
+  "pipelines": [
+    {
+      "schedule_name": "laborious-test-runtime",
+      "model_id": "1001",
+      "workflow_type": "predictions_batch",
+      "frequency": "60s",
+      "max_retry_policy": 1,
+      "query": "select * from sientia_data.laborious_data where model_id = 1 and \"timestamp\" > NOW() - INTERVAL '5 minutes' order by \"timestamp\" desc limit 30;",
+      "retention_time": 60,
+      "write_tags": [],
+      "input_filters": [
+        {
+          "filter_name": "EMPTY_DATA",
+          "policy": "STOP"
+        },
+        {
+          "filter_name": "SPECIFIC_VARIABLES_NULL_VALUES",
+          "policy": "CONTINUE",
+          "config": {
+            "variables": [
+              "Counter"
+            ]
+          }
+        }
+      ],
+      "mlflow_transform_filters": [
+        {
+          "filter_name": "API_ERROR",
+          "policy": "REPEAT"
+        },
+        {
+          "filter_name": "NAN_VALUES",
+          "policy": "STOP"
+        }
+      ],
+      "mlflow_predict_filters": [
+        {
+          "filter_name": "API_ERROR",
+          "policy": "CONTINUE"
+        }
+      ],
+      "path_priority": [
+        "STOP",
+        "CONTINUE",
+        "REPEAT"
+      ],
+      "active": true,
+      "updated_at": {
+        "$date": "2026-05-07T23:35:01.600Z"
+      },
+      "save_transform": false,
+      "pi_web_api_output_config": {},
+      "datetime_columns": [
+        "timestamp",
+        "created_at"
+      ]
+    }
+  ]
+}

laborious/activities/activities.py

@@ -6,7 +6,7 @@ with workflow.unsafe.imports_passed_through():
 
     from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
     from sientia_do.observability.logger import Logger
-    from sientia_do.repository.minio_repository import MinioRepository
+    from sientia_do.repository.minio_repository_sync import MinioRepository
     from sientia_model.model_repository.mlflow_repository import SientiaMLflowRepository
     from sientia_model.model_repository.plugin_store import PluginStore
 
@@ -159,7 +159,7 @@ class Activities(Storage, MLFlow, Gates, OPC, ModelMetrics, API):
             metrics_controller=mc,
         )
 
-    async def shutdown(self):
+    def shutdown(self) -> None:
         """
         Close database pools, sync clients, and OPC sessions in a defined order.
 
@@ -172,6 +172,6 @@ class Activities(Storage, MLFlow, Gates, OPC, ModelMetrics, API):
         Storage.close(self)
         MLFlow.close(self)
         Gates.close(self)
-        await OPC.close(self)
+        OPC.close(self)
         ModelMetrics.close(self)
         API.close(self)

laborious/activities/api.py

@@ -11,7 +11,7 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
     from sientia_do.observability.sientia_monitoring import SientiaMonitoring
-    from sientia_do.repository.pi_web_api_client import PIWebAPIClient
+    from sientia_do.repository.pi_web_api_client_sync import PIWebAPIClient
 
     from laborious import metrics
 
@@ -105,7 +105,7 @@ class API(SientiaMonitoring):
         self.pi_web_api_client.close()
         SientiaMonitoring.shutdown(self)
 
-    async def process_pi_web_api_response(
+    def process_pi_web_api_response(
         self,
         response_data: list[dict[str, Any]],
         tags: dict[str, str],
@@ -156,7 +156,7 @@ class API(SientiaMonitoring):
                 self.error(
                     f'Error writing tag {tag_name}:{web_id} to PI Web API: {errors}', metadata
                 )
-                await self.emit_metric(
+                self.emit_metric_sync(
                     metric_object=metrics.PI_WEB_API_PREDICTION_WRITTEN_ERROR_COUNT,
                     tags={
                         **core_labels,
@@ -165,7 +165,7 @@ class API(SientiaMonitoring):
                 )
                 confidence = PI_WEB_API_PREDICTION_ERROR_CONFIDENCE
             else:
-                await self.emit_metric(
+                self.emit_metric_sync(
                     metric_object=metrics.PI_WEB_API_PREDICTION_WRITTEN_COUNT,
                     tags={
                         **core_labels,
@@ -182,7 +182,7 @@ class API(SientiaMonitoring):
                 metadata,
             )
 
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='WRITE_PI_WEB_API_PREDICTION_ERROR',
                 message=f'The number of written tags does not match the number of tag names: Expected {tag_names} tags, but {written_tags} tags were written.\nResponse:\n {json.dumps(response_data, indent=4)}\nTags:\n {json.dumps(tags, indent=4)}',
@@ -194,7 +194,7 @@ class API(SientiaMonitoring):
         return confidence, message
 
     @activity.defn(name='write_pi_web_api_data')
-    async def write_pi_web_api_data(self, input_data: dict[str, Any]) -> dict[Any, Any]:
+    def write_pi_web_api_data(self, input_data: dict[str, Any]) -> dict[Any, Any]:
         """
         Write prediction and confidence data to PI Web API.
 
@@ -232,7 +232,7 @@ class API(SientiaMonitoring):
         confidence_value = data.head(1)['prediction_confidence'].values[0]
 
         try:
-            prediction_response = await self.pi_web_api_client.write_value(
+            prediction_response = self.pi_web_api_client.write_value(
                 web_ids=prediction_tags,
                 value={
                     'Timestamp': data.head(1)['timestamp'].values[0],
@@ -241,7 +241,7 @@ class API(SientiaMonitoring):
                 metadata=metadata,
             )
 
-            confidence, message = await self.process_pi_web_api_response(
+            confidence, message = self.process_pi_web_api_response(
                 response_data=prediction_response,
                 tags=raw_prediction_tags,
                 core_labels=core_labels,
@@ -258,7 +258,7 @@ class API(SientiaMonitoring):
 
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='WRITE_PI_WEB_API_PREDICTION_ERROR',
                 message=f'Error writing prediction data to PI Web API: {e}\n Tags: {raw_prediction_tags}',
@@ -275,7 +275,7 @@ class API(SientiaMonitoring):
             return data.to_dict()
 
         try:
-            confidence_response = await self.pi_web_api_client.write_value(
+            confidence_response = self.pi_web_api_client.write_value(
                 web_ids=confidence_tags,
                 value={
                     'Timestamp': data.head(1)['timestamp'].values[0],
@@ -284,7 +284,7 @@ class API(SientiaMonitoring):
                 metadata=metadata,
             )
 
-            await self.process_pi_web_api_response(
+            self.process_pi_web_api_response(
                 response_data=confidence_response,
                 tags=raw_confidence_tags,
                 core_labels=core_labels,
@@ -293,7 +293,7 @@ class API(SientiaMonitoring):
 
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='WRITE_PI_WEB_API_CONFIDENCE_ERROR',
                 message=f'Error writing confidence data to PI Web API: {e}\n Tags: {raw_confidence_tags}',

laborious/activities/gates.py

@@ -1,8 +1,5 @@
-from sientia_do.repository.minio_repository import MinioRepository
 from temporalio import activity, workflow
 
-from laborious.utils.repository.minio_manager import MinioManager
-
 with workflow.unsafe.imports_passed_through():
     import traceback
     from collections.abc import Callable, Mapping
@@ -13,6 +10,8 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.minio_repository_sync import MinioRepository
     from sientia_do.utils.formatters import create_sample_dict
 
     from laborious import metrics
@@ -66,7 +65,7 @@ mlflow_content_path_confidence: Mapping[str, int] = {
 }
 
 
-class Gates(MinioManager):
+class Gates(SientiaMonitoring):
     """
     Data quality gates and filtering activities for the Laborious system.
 
@@ -106,8 +105,12 @@ class Gates(MinioManager):
         Raises:
             Exception: If BaseActivity initialization fails
         """
-        MinioManager.__init__(
-            self, minio_repository, logger, notification_handler, metrics_controller
+        self.minio_repository = minio_repository
+        SientiaMonitoring.__init__(
+            self,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
         )
 
     def close(self) -> None:
@@ -115,7 +118,12 @@ class Gates(MinioManager):
         Close the gates activity and clean up resources.
         """
 
-        MinioManager.close(self)
+        if self.minio_repository is not None:
+            try:
+                self.minio_repository.close()
+            finally:
+                self.minio_repository = None
+        SientiaMonitoring.shutdown(self)
 
     def __del__(self):
         self.close()
@@ -155,7 +163,7 @@ class Gates(MinioManager):
         return policy, filter_config
 
     @activity.defn(name='input_gate')
-    async def input_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+    def input_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
         """
         Apply input data quality filters and validation.
 
@@ -194,7 +202,7 @@ class Gates(MinioManager):
 
         filters = input_data['filters']
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
         path_priority = input_data['path_priority']
 
         filter_output = []
@@ -214,7 +222,7 @@ class Gates(MinioManager):
                     filter_output.append(policy)
             except Exception as e:
                 trace = traceback.format_exc()
-                await self.send_notification_async(
+                self.send_notification(
                     metadata=metadata,
                     notification_id=f'INTPUT_GATE_ERROR__{fil}',
                     message=f'Error in filter {fil}:{config}: \n {e}',
@@ -235,7 +243,7 @@ class Gates(MinioManager):
         return None, 0, ''
 
     @activity.defn(name='mlflow_response_gate')
-    async def mlflow_response_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+    def mlflow_response_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
         """
         Validate MLFlow API response quality and integrity.
 
@@ -279,7 +287,7 @@ class Gates(MinioManager):
         self.debug(f'Filters: {filters}', metadata)
 
         payload = MinioDataFramePayload.from_dict(raw_data)
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         gate_type = input_data['type']
         path_priority = input_data['path_priority']
@@ -298,7 +306,7 @@ class Gates(MinioManager):
                 if mlflow_response_filter_functions[fil](status, filter_config):
                     filter_output.append(policy)
                     comments.append(status.get('message', 'Unknown MLFlow API error'))
-                    await self.send_notification_async(
+                    self.send_notification(
                         metadata=metadata,
                         notification_id=f'{gate_type.upper()}_GATE_RESPONSE_FILTER__{fil}',
                         message=status.get('message', 'Unknown MLFlow API error'),
@@ -308,7 +316,7 @@ class Gates(MinioManager):
                     )
             except Exception as e:
                 trace = traceback.format_exc()
-                await self.send_notification_async(
+                self.send_notification(
                     metadata=metadata,
                     notification_id=f'MLFLOW_GATE_RESPONSE_FILTER__{fil}',
                     message=f'Error in filter {fil}:{config}: \n {e}',
@@ -329,7 +337,7 @@ class Gates(MinioManager):
         return None, 0, ''
 
     @activity.defn(name='mlflow_content_gate')
-    async def mlflow_content_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+    def mlflow_content_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
         """
         Validate MLFlow prediction content quality and integrity.
 
@@ -368,7 +376,7 @@ class Gates(MinioManager):
         filters = input_data['filters']
 
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         gate_type = input_data['type']
         path_priority = input_data['path_priority']
@@ -385,7 +393,7 @@ class Gates(MinioManager):
             try:
                 if mlflow_content_filter_functions[fil](data, filter_config):
                     filter_output.append(policy)
-                    await self.send_notification_async(
+                    self.send_notification(
                         metadata=metadata,
                         notification_id=f'{gate_type.upper()}_GATE_CONTENT_FILTER__{fil}',
                         message=f'Data not passed the content filter {fil}:{config}',
@@ -395,7 +403,7 @@ class Gates(MinioManager):
                     )
             except Exception as e:
                 trace = traceback.format_exc()
-                await self.send_notification_async(
+                self.send_notification(
                     metadata=metadata,
                     notification_id=f'MLFLOW_GATE_CONTENT_FILTER__{fil}',
                     message=f'Error in filter {fil}:{config}: \n {e}',
@@ -471,7 +479,7 @@ class Gates(MinioManager):
         return policy_type, int(policy_value)
 
     @activity.defn(name='format_transformed_data')
-    async def format_transformed_data(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+    def format_transformed_data(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
         """
         Format transformed data for storage and export operations.
 
@@ -507,7 +515,7 @@ class Gates(MinioManager):
         self.info('Formatting transformed data...', metadata)
 
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         data['timestamp'] = data.index
         data = data.reset_index(drop=True)
@@ -515,7 +523,7 @@ class Gates(MinioManager):
         data = data.melt(id_vars='timestamp', var_name='variable', value_name='value')
         data['model_id'] = model_id
 
-        return await MinioDataFramePayload.from_dataframe(
+        return MinioDataFramePayload.from_dataframe(
             dataframe=data,
             minio_repo=self.minio_repository,
             model_name=input_data['model_name'],
@@ -526,7 +534,7 @@ class Gates(MinioManager):
         )
 
     @activity.defn(name='format_prediction')
-    async def format_prediction(self, input_data: dict[str, Any]) -> dict:
+    def format_prediction(self, input_data: dict[str, Any]) -> dict:
         """
         Format prediction data according to configured storage policies.
 
@@ -558,7 +566,7 @@ class Gates(MinioManager):
         self.info('Formatting prediction...', metadata)
 
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         # Create timestamp column from index and reset index
         data['timestamp'] = data.index
@@ -607,7 +615,7 @@ class Gates(MinioManager):
         return data.to_dict()
 
     @activity.defn(name='format_default_prediction')
-    async def format_default_prediction(self, input_data: dict[str, Any]) -> dict:
+    def format_default_prediction(self, input_data: dict[str, Any]) -> dict:
         """
         Create and format default prediction data for error conditions.
 
@@ -652,7 +660,7 @@ class Gates(MinioManager):
         return data.to_dict()
 
     @activity.defn(name='format_retrain_report')
-    async def format_retrain_report(self, input_data: dict[str, Any]) -> dict:
+    def format_retrain_report(self, input_data: dict[str, Any]) -> dict:
         """
         Format retrain report data for storage and audit trail maintenance.
 
@@ -722,7 +730,7 @@ class Gates(MinioManager):
         return report.to_dict()
 
     @activity.defn(name='write_metrics')
-    async def write_metrics(self, input_data: dict[str, Any]):
+    def write_metrics(self, input_data: dict[str, Any]):
         """
         Write prediction performance metrics to Prometheus monitoring system.
 
@@ -759,19 +767,19 @@ class Gates(MinioManager):
             'model_name': metadata['model_name'],
             'workflow_name': metadata['workflow_name'],
         }
-        await self.emit_metric(
+        self.emit_metric_sync(
             metric_object=metrics.PREDICTIONS_WRITTEN_COUNT,
             tags=core_tags,
         )
 
-        await self.emit_metric(
+        self.emit_metric_sync(
             metric_object=metrics.PREDICTION_CONFIDENCE_MONITOR,
             method='set',
             tags=core_tags,
             value=prediction_confidence,
         )
 
-        await self.emit_metric(
+        self.emit_metric_sync(
             metric_object=metrics.PREDICTION_RESPONSE_TIME_MONITOR,
             method='observe',
             tags=core_tags,
@@ -781,7 +789,7 @@ class Gates(MinioManager):
         for server_id, tags in opc_metrics.items():
             for tag, response_time in tags.items():
                 if response_time is not None:
-                    await self.emit_metric(
+                    self.emit_metric_sync(
                         metric_object=metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
                         method='observe',
                         tags={
@@ -792,7 +800,7 @@ class Gates(MinioManager):
                         value=response_time,
                     )
 
-                    await self.emit_metric(
+                    self.emit_metric_sync(
                         metric_object=metrics.PREDICTION_OPC_WRITING_COUNT,
                         tags={
                             **core_tags,

laborious/activities/mlflow.py

@@ -16,7 +16,8 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
-    from sientia_do.repository.minio_repository import MinioRepository
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.minio_repository_sync import MinioRepository
     from sientia_do.temporal.constants import (
         DATETIME_FORMAT,
         DATETIME_FORMAT_MS_WITH_TZ,
@@ -29,10 +30,9 @@ with workflow.unsafe.imports_passed_through():
 
     from laborious.utils.dataframe_debug import build_dataframe_debug_message
     from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
-    from laborious.utils.repository.minio_manager import MinioManager
 
 
-class MLFlow(MinioManager):
+class MLFlow(SientiaMonitoring):
     """
     Temporal activities that talk to MLflow through ``SientiaMLflowRepository`` and ``SientiaModel`` wrappers.
 
@@ -78,8 +78,12 @@ class MLFlow(MinioManager):
             None
         """
 
-        MinioManager.__init__(
-            self, minio_repository, logger, notification_handler, metrics_controller
+        self.minio_repository = minio_repository
+        SientiaMonitoring.__init__(
+            self,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
         )
         self.mlflow_repository = mlflow_repository
         self.plugin_store = plugin_store
@@ -91,7 +95,12 @@ class MLFlow(MinioManager):
         Return:
             None
         """
-        MinioManager.close(self)
+        if self.minio_repository is not None:
+            try:
+                self.minio_repository.close()
+            finally:
+                self.minio_repository = None
+        SientiaMonitoring.shutdown(self)
 
     def __del__(self):
         self.close()
@@ -207,7 +216,7 @@ class MLFlow(MinioManager):
         return alias or self._DEFAULT_MODEL_ALIAS
 
     @activity.defn(name='request_transform')
-    async def request_transform(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+    def request_transform(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
         """
         Pivot long-format sensor rows, load the production wrapper, and run ``wrapper.transform``.
 
@@ -228,7 +237,7 @@ class MLFlow(MinioManager):
         self.info('Transforming data...', metadata)
 
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         model_name = input_data['model_name']
         model_config = input_data.get('model_config', {})
@@ -284,7 +293,7 @@ class MLFlow(MinioManager):
         self.info('Data transformed successfully', metadata)
 
         if not response_data.get('success', False):
-            return await MinioDataFramePayload.from_dataframe(
+            return MinioDataFramePayload.from_dataframe(
                 dataframe=None,
                 minio_repo=self.minio_repository,
                 model_name=model_name,
@@ -295,7 +304,7 @@ class MLFlow(MinioManager):
                 logger=self.logger,
             )
 
-        return await MinioDataFramePayload.from_dataframe(
+        return MinioDataFramePayload.from_dataframe(
             dataframe=response_data['content'],
             minio_repo=self.minio_repository,
             model_name=model_name,
@@ -309,7 +318,7 @@ class MLFlow(MinioManager):
         )
 
     @activity.defn(name='request_predict')
-    async def request_predict(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+    def request_predict(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
         """
         Load the production wrapper and call ``wrapper.predict`` on the prepared feature frame.
 
@@ -329,7 +338,7 @@ class MLFlow(MinioManager):
         self.info('Predicting data...', metadata)
 
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
         model_name = input_data['model_name']
         model_config = input_data.get('model_config', {})
@@ -390,7 +399,7 @@ class MLFlow(MinioManager):
         self.info('Data predicted successfully', metadata)
 
         if not response_data.get('success', False):
-            return await MinioDataFramePayload.from_dataframe(
+            return MinioDataFramePayload.from_dataframe(
                 dataframe=None,
                 minio_repo=self.minio_repository,
                 model_name=model_name,
@@ -401,7 +410,7 @@ class MLFlow(MinioManager):
                 logger=self.logger,
             )
 
-        return await MinioDataFramePayload.from_dataframe(
+        return MinioDataFramePayload.from_dataframe(
             dataframe=response_data['content'],
             minio_repo=self.minio_repository,
             model_name=model_name,
@@ -415,7 +424,7 @@ class MLFlow(MinioManager):
         )
 
     @activity.defn(name='retrain_model')
-    async def retrain_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
+    def retrain_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
         """
         Fit an updated wrapper from historical data, then log and register in MLflow.
 
@@ -441,11 +450,11 @@ class MLFlow(MinioManager):
 
         try:
             payload = MinioDataFramePayload.from_dict(input_data['data'])
-            data = await payload.retrieve(self.minio_repository, metadata)
+            data = payload.retrieve(self.minio_repository, metadata)
 
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='ERROR_LOADING_RETRAIN_DATA',
                 message=f'Error loading retrain data: {e}',
@@ -576,7 +585,7 @@ class MLFlow(MinioManager):
             }
 
     @activity.defn(name='update_production_model')
-    async def update_production_model(self, input_data: dict[str, Any]) -> dict[Any, Any]:
+    def update_production_model(self, input_data: dict[str, Any]) -> dict[Any, Any]:
         """
         Point the ``production`` alias at the model version registered for the retrain run.
 
@@ -622,7 +631,7 @@ class MLFlow(MinioManager):
 
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='UPDATE_PRODUCTION_MODEL_ERROR',
                 message=f'Error updating production model {model_name}: {e}',
@@ -634,7 +643,7 @@ class MLFlow(MinioManager):
             raise e
 
     @activity.defn(name='get_reference_data')
-    async def get_reference_data(self, input_data: dict[str, Any]) -> list[dict] | None:
+    def get_reference_data(self, input_data: dict[str, Any]) -> list[dict] | None:
         """
         Download ``evaluation_data.csv`` from the MLflow run linked to ``production`` and parse it.
 

laborious/activities/model_metrics.py

@@ -7,14 +7,15 @@ with workflow.unsafe.imports_passed_through():
     from typing import Any
 
     import numpy as np
-    from pandas import DataFrame, Index, to_datetime
-    from sientia.ModelAnalysis import ModelAnalysis
+    import pandas as pd
+    from pandas import DataFrame, Index, Series, to_datetime
     from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
     from sientia_do.observability.sientia_monitoring import SientiaMonitoring
-    from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
+    from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
+    from sientia_model.analytics.model_analysis import ModelAnalysis
 
     from laborious import metrics
     from laborious.utils.dataframe_debug import build_dataframe_debug_message
@@ -27,9 +28,12 @@ warnings.filterwarnings(
 
 class ModelMetrics(SientiaMonitoring):
     """
-    Metrics activities for the Laborious system.
+    Metrics and statistical analysis activities for the Laborious pipeline.
 
-    This class provides activities for writing metrics to the Prometheus monitoring system.
+    This class centralizes drift/statistical computations and model-quality
+    aggregates used by scheduled workflows. Besides producing tabular outputs
+    for persistence, it also emits operational metrics (count, lag, error)
+    through ``SientiaMonitoring`` so execution health is observable in runtime.
     """
 
     _MAX_DEBUG_DATAFRAME_ROWS = 100
@@ -44,7 +48,10 @@ class ModelMetrics(SientiaMonitoring):
 
     def close(self) -> None:
         """
-        Close the model metrics activity and clean up resources.
+        Shutdown monitoring resources associated with model metrics activities.
+
+        This is invoked during worker teardown to flush/close metric controller
+        internals and prevent dangling telemetry tasks.
         """
         SientiaMonitoring.shutdown(self)
 
@@ -69,7 +76,7 @@ class ModelMetrics(SientiaMonitoring):
             metadata,
         )
 
-    async def get_drift_metrics(
+    def get_drift_metrics(
         self,
         reference_data: DataFrame,
         target_data: DataFrame,
@@ -80,14 +87,23 @@ class ModelMetrics(SientiaMonitoring):
         metadata: dict[str, Any],
     ) -> DataFrame:
         """
-        Calculate univariate drift metrics for a model.
+        Compute univariate and multivariate drift outputs and merge them into one dataframe.
+
+        The method orchestrates three analysis stages (univariate drift,
+        multivariate drift, and dataframe projection), emitting lag/count/error
+        metrics for each stage independently so failures are attributable.
+
         Args:
-            model_analysis (ModelAnalysis): Model analysis object
-            reference_data (DataFrame): Reference data
-            target_data (DataFrame): Target data
-            reference_columns (list[str]): Reference columns
-            drift_metrics (list[str]): Drift metrics
-            metadata (dict[str, Any]): Workflow execution metadata
+         - reference_data (DataFrame): Baseline dataset representing expected behavior.
+         - target_data (DataFrame): Current analysis dataset to compare against reference.
+         - target_name (str): Target column name used by ``ModelAnalysis`` config.
+         - reference_columns (Index): Feature columns evaluated for drift.
+         - drift_metrics (list[str]): Enabled univariate methods.
+         - chunk_period (str): Time bucket granularity used by analysis methods.
+         - metadata (dict[str, Any]): Workflow metadata for logs and notifications.
+
+        Return:
+         DataFrame: Consolidated drift dataframe ready for downstream formatting/persistence.
         """
 
         config = {
@@ -118,12 +134,10 @@ class ModelMetrics(SientiaMonitoring):
             )
         except Exception as e:
             self.error(f'Error detecting univariate drift: {e}', metadata)
-            await self.emit_metric(
-                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
-            )
+            self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels)
             raise e
-        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
-        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+        self.observe_lag_sync(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
 
         core_labels = self.get_core_labels(metadata, operation_type='detect_multivariate_drift')
         start_time = time.time()
@@ -137,12 +151,10 @@ class ModelMetrics(SientiaMonitoring):
             )
         except Exception as e:
             self.error(f'Error detecting multivariate drift: {e}', metadata)
-            await self.emit_metric(
-                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
-            )
+            self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels)
             raise e
-        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
-        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+        self.observe_lag_sync(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
 
         start_time = time.time()
         core_labels = self.get_core_labels(metadata, operation_type='get_drift_metrics_dataframe')
@@ -153,19 +165,40 @@ class ModelMetrics(SientiaMonitoring):
             )
         except Exception as e:
             self.error(f'Error getting drift metrics: {e}', metadata)
-            await self.emit_metric(
-                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
-            )
+            self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels)
             raise e
-        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
-        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+        self.observe_lag_sync(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        self.emit_metric_sync(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
 
         self._debug_dataframe(f'Drift dataframe: Size {drift_df.shape}', drift_df, metadata)
 
         return drift_df
 
+    @staticmethod
+    def _to_naive_utc(series: Series) -> Series:
+        """
+        Parse ``series`` as datetime and return a TZ-naive UTC copy.
+
+        ``sientia_model.analytics.model_analysis.ModelAnalysis`` preserves the
+        timezone of the input dataframe in its outputs, while target rows
+        loaded from PostgreSQL come in with ``+00:00``. Forcing both sides of
+        a comparison to TZ-naive UTC keeps ``isin`` / ``floor`` operations
+        deterministic regardless of how the analyzer (or a test double)
+        constructs its timestamps.
+
+        Args:
+         - series (Series): Input series containing datetime-parseable values.
+
+        Return:
+         Series: Datetime64 series with ``tz=None`` representing UTC instants.
+        """
+        parsed = to_datetime(series)
+        if getattr(parsed.dt, 'tz', None) is not None:
+            parsed = parsed.dt.tz_convert('UTC').dt.tz_localize(None)
+        return parsed
+
     @activity.defn(name='calculate_drift')
-    async def calculate_drift(self, input_data: dict[str, Any]) -> list[dict]:
+    def calculate_drift(self, input_data: dict[str, Any]) -> list[dict]:
         """
         Calculate drift metrics for a model.
 
@@ -195,14 +228,17 @@ class ModelMetrics(SientiaMonitoring):
 
         target_data = target_data.pivot(index='timestamp', columns='variable', values='value')
         target_data['timestamp'] = target_data.index
+        # Keep timestamps as datetime: ModelAnalysis._chunk_dataframe relies on
+        # ``pd.Grouper(freq=...)`` which rejects string timestamp columns.
         target_data['timestamp'] = to_datetime(target_data['timestamp'])
-        target_data['timestamp'] = target_data['timestamp'].dt.strftime(DATETIME_FORMAT)
         target_data = target_data.reset_index(drop=True)
         target_data.dropna(inplace=True)
 
         if reference_raw_data is not None:
             self.info('Using reference data', metadata)
             reference_data = DataFrame(reference_raw_data)
+            if 'timestamp' in reference_data.columns:
+                reference_data['timestamp'] = to_datetime(reference_data['timestamp'])
             accurate = True
         else:
             # Get 30% first rows of target_data
@@ -211,7 +247,7 @@ class ModelMetrics(SientiaMonitoring):
             reference_data = target_data.head(int(len(target_data) * 0.3))
             accurate = False
 
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='MODEL_METRICS_REFERENCE_DATA_WARNING',
                 message='Using 30% first rows of target data as reference data',
@@ -225,7 +261,7 @@ class ModelMetrics(SientiaMonitoring):
         ).columns
 
         try:
-            drift_df = await self.get_drift_metrics(
+            drift_df = self.get_drift_metrics(
                 reference_data=reference_data,
                 target_data=target_data,
                 target_name=target_name,
@@ -236,7 +272,7 @@ class ModelMetrics(SientiaMonitoring):
             )
         except Exception as e:
             self.error(f'Error getting drift metrics: {e}', metadata)
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='MODEL_METRICS_GET_DRIFT_METRICS_ERROR',
                 message=f'Error getting drift metrics: {e}',
@@ -250,17 +286,13 @@ class ModelMetrics(SientiaMonitoring):
             self.warning('No drift metrics found', metadata)
             return []
 
-        # Drop unnecessary columns
-        drift_df.drop(columns=['p_value'], inplace=True)
-
-        # Extract timestamps only until minutes
-        if chunk_period == 'min':
-            target_timestamps = target_data['timestamp'].apply(lambda x: x[:16])
-        else:
-            target_timestamps = target_data['timestamp']
-
-        # Drop rows where timestamp is not in target data, to avoid save drift from reference
-        drift_df = drift_df[drift_df['timestamp'].isin(target_timestamps)]
+        # Defense-in-depth: drop chunks whose floored timestamp does not appear
+        # in the analysis window. ``ModelAnalysis`` already chunks only over
+        # ``analysis_df`` so this only excludes rows injected by upstream
+        # callers that pre-merge reference data into the result.
+        target_floor = self._to_naive_utc(target_data['timestamp']).dt.floor(chunk_period)
+        drift_floor = self._to_naive_utc(drift_df['timestamp']).dt.floor(chunk_period)
+        drift_df = drift_df[drift_floor.isin(target_floor)]
 
         if drift_df.empty:
             self.warning(
@@ -269,14 +301,21 @@ class ModelMetrics(SientiaMonitoring):
             )
             return []
 
-        # Rename columns to match database columns
-        drift_df.rename(
+        # Map ``sientia_model.analytics.model_analysis`` schema onto the drift
+        # table columns: ``metric -> method``, ``statistic -> value``,
+        # ``alert -> drift``, ``chunk_index -> chunk``,
+        # ``chunk_end_date -> timestamp_end``. ``p_value`` and
+        # ``chunk_start_date`` are not persisted.
+        drift_df = drift_df.rename(
             columns={
                 'metric': 'method',
                 'statistic': 'value',
-            },
-            inplace=True,
+                'alert': 'drift',
+                'chunk_index': 'chunk',
+                'chunk_end_date': 'timestamp_end',
+            }
         )
+        drift_df.drop(columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore')
 
         # Drop duplicates
         drift_df.drop_duplicates(
@@ -286,16 +325,23 @@ class ModelMetrics(SientiaMonitoring):
         drift_df['model_id'] = model_id
         drift_df['accurate'] = accurate
 
-        drift_df['timestamp'] = to_datetime(drift_df['timestamp'])
+        drift_df['timestamp'] = self._to_naive_utc(drift_df['timestamp'])
         drift_df['timestamp'] = drift_df['timestamp'].dt.tz_localize('UTC')
         drift_df['timestamp'] = drift_df['timestamp'].dt.strftime(DATETIME_FORMAT_WITH_TZ)
 
+        # ``timestamp_end`` may carry nanosecond precision (beyond
+        # ``timestamptz`` microseconds), so serialize as ISO text for the
+        # ``text`` Postgres column.
+        drift_df['timestamp_end'] = drift_df['timestamp_end'].apply(
+            lambda value: pd.Timestamp(value).isoformat() if pd.notna(value) else None
+        )
+
         self._debug_dataframe(f'Drift dataframe: Size {drift_df.shape}', drift_df, metadata)
 
         return drift_df.to_dict(orient='records')
 
     @activity.defn(name='calculate_simple_metrics')
-    async def calculate_simple_metrics(self, input_data: dict[str, Any]) -> list[dict]:
+    def calculate_simple_metrics(self, input_data: dict[str, Any]) -> list[dict]:
         """
         Calculate simple metrics for a model. Metrics available are:
             - rmse

laborious/activities/opc.py

@@ -51,7 +51,7 @@ class OPC(SientiaMonitoring):
 
         self.opc_repository: dict[str, OpcRepository] = {}
 
-    async def init_opc(self):
+    def init_opc(self) -> None:
         """
         Initialize OPC server connections and establish communication channels.
 
@@ -59,58 +59,43 @@ class OPC(SientiaMonitoring):
         establish secure connections using certificate-based authentication.
         Each server connection is managed independently, and connection failures
         are reported through the notification system.
-
-        The method performs the following operations:
-        1. Creates OpcRepository instances for each configured server
-        2. Establishes secure connections with certificate validation
-        3. Reports connection success/failure through notifications
-        4. Logs connection status for operational visibility
-
-        Raises:
-            Exception: If OPC repository initialization fails or connection
-                establishment encounters critical errors
-
-        Note:
-            Connection failures are logged and reported but do not prevent
-            the initialization of other OPC servers. Each server is handled
-            independently to ensure maximum availability.
         """
+
         self.logger.info('Initializing OPC servers...')
         for opc_id, server in self.opc_servers.items():
             self.opc_repository[opc_id] = OpcRepository(
-                opc_id=server['id'],
-                server_name=server['server_name'],
+                opc_id=opc_id,
                 url=server['url'],
+                server_name=server['server_name'],
                 logger=self.logger,
+                notification_handler=self.notification_handler,
+                metrics_controller=self.metrics_controller,
                 server_uri=server['server_uri'],
                 cert_path=server['cert_path'],
                 private_key_path=server['private_key_path'],
                 server_cert_path=server['server_cert_path'],
-                notification_handler=self.notification_handler,
-                reconnection_interval=server['reconnection_interval'],
-                metrics_controller=self.metrics_controller,
             )
-            is_connected, error_data = await self.opc_repository[opc_id].connect()
-            if not is_connected:
-                await self.send_notification_async(
+            ok, err = self.opc_repository[opc_id].connect()
+            if not ok:
+                self.send_notification(
                     metadata={
                         'model_id': '-',
                         'model_name': '-',
                         'workflow_name': '-',
                         'schedule_name': 'INITIALIZATION',
                     },
-                    notification_id=error_data['notification_id'],
-                    message=error_data['message'],
-                    block=error_data['block'],
-                    level=error_data.get('level', NotificationLevel.ERROR),
-                    attachment_content=error_data.get('attachment_content', None),
+                    notification_id=f'OPC_CONNECTION_ERROR_{server.get("id", opc_id)}',
+                    message=err.get('message', 'Failed to connect to OPC server'),
+                    block='opc_repository',
+                    level=NotificationLevel.ERROR,
+                    attachment_content=err.get('attachment_content', traceback.format_exc()),
                 )
             else:
                 self.logger.info(
                     f'OPC server {opc_id}:{server["server_name"]} connected successfully.'
                 )
 
-    async def write_data(
+    def write_data(
         self,
         server_id: str,
         tag: str,
@@ -122,11 +107,6 @@ class OPC(SientiaMonitoring):
         """
         Write data to a specific OPC server tag with comprehensive error handling.
 
-        This method provides a secure and reliable way to write data to OPC servers
-        with automatic error handling, notification integration, and detailed logging.
-        It validates server availability before attempting write operations and
-        provides comprehensive error reporting for operational monitoring.
-
         Args:
             - server_id (str): The id of the OPC server.
             - tag (str): The tag to write to.
@@ -135,15 +115,15 @@ class OPC(SientiaMonitoring):
             - tag_type (str): The tag type.
 
         Returns:
-            - bool: True if the data was written successfully, False otherwise.
+            - float | None: Response time in seconds if successful, None otherwise.
         """
 
         try:
-            is_success, info_data = await self.opc_repository[server_id].write_data(
+            is_success, info_data = self.opc_repository[server_id].write_data(
                 tag, data, data_type, self.logger, metadata
             )
             if not is_success:
-                await self.send_notification_async(
+                self.send_notification(
                     metadata=metadata,
                     notification_id=info_data['notification_id'],
                     message=info_data['message'],
@@ -155,7 +135,7 @@ class OPC(SientiaMonitoring):
             return info_data['response_time']
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id=f'WRITE_OPC_{tag_type.upper()}_ERROR',
                 message=f'Error writing data to OPC server: {e}',
@@ -165,30 +145,25 @@ class OPC(SientiaMonitoring):
             )
             raise e
 
-    async def validate_server(self, server_id: str, metadata: dict[str, Any]) -> bool:
+    def validate_server(self, server_id: str, metadata: dict[str, Any]) -> bool:
         """
-        Validate that an OPC server is available and configured for write operations.
+        Validate that an OPC repository exists for the requested server identifier.
 
-        This method checks if the specified OPC server exists in the active
-        repository and is available for data writing operations. It provides
-        immediate feedback for server availability and logs validation failures
-        for operational monitoring.
+        This guard prevents write attempts against unknown/uninitialized servers.
+        When the server is missing, it emits an error notification with the list
+        of available repositories to help operators diagnose configuration drift.
 
         Args:
-            server_id (str): Unique identifier for the OPC server to validate
-            metadata (dict[str, Any]): Context metadata for logging and notifications
+         - server_id (str): OPC server identifier from workflow output config.
+         - metadata (dict[str, Any]): Workflow metadata used for logs/alerts.
 
-        Returns:
-            bool: True if server is available, False otherwise
-
-        Note:
-            Server validation failures are automatically reported through the
-            notification system with detailed information about available servers.
-            This helps operators quickly identify configuration issues.
+        Return:
+         bool: ``True`` when the server repository is available; ``False`` otherwise.
         """
+
         if self.opc_repository.get(server_id) is None:
             message = f'OPC server {server_id} not found to perform write operation.'
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='OPC_SERVER_NOT_FOUND',
                 message=message,
@@ -199,7 +174,7 @@ class OPC(SientiaMonitoring):
             return False
         return True
 
-    async def manage_output_tags(
+    def manage_output_tags(
         self,
         server_id: str,
         config: dict[str, Any],
@@ -207,37 +182,30 @@ class OPC(SientiaMonitoring):
         metadata: dict[str, Any],
     ) -> tuple[bool, dict[str, float | None]]:
         """
-        Manage the writing of prediction and confidence data to OPC server tags.
+        Write prediction and confidence values for one OPC server configuration.
 
-        This method orchestrates the writing of multiple data types to OPC servers
-        based on configuration. It handles both prediction data and confidence
-        values independently, allowing for flexible tag configuration and
-        comprehensive error handling.
-
-        The method supports two main tag types:
-        1. Prediction tags: Write actual prediction values to configured OPC tags
-        2. Confidence tags: Write confidence scores to separate OPC tags
+        The method iterates through optional ``prediction_tags`` and
+        ``confidence_tags``, performs synchronous writes for each tag, collects
+        per-tag response times, and returns an aggregate success flag
+        (all tags successful) with a metrics-friendly response map.
 
         Args:
-            server_id (str): Unique identifier for the target OPC server
-            config (dict[str, Any]): OPC tag configuration containing:
-                - prediction_tags (dict, optional): Prediction tag configurations
-                - confidence_tags (dict, optional): Confidence tag configurations
-            data (DataFrame): DataFrame containing prediction and confidence data
-            metadata (dict[str, Any]): Context metadata for logging and notifications
-            success (bool): Current success status to maintain across operations
+         - server_id (str): Target OPC server id.
+         - config (dict[str, Any]): Server output configuration containing optional
+           ``prediction_tags`` and ``confidence_tags`` sections.
+         - data (DataFrame): Prediction dataframe used as source values.
+         - metadata (dict[str, Any]): Workflow metadata for logging/notifications.
 
-        Returns:
-            tuple[bool, int]: (overall_success, total_tags_written)
-                - overall_success: True if all configured tags were written successfully
-                - total_tags_written: Count of successfully written tags
+        Return:
+         tuple[bool, dict[str, float | None]]: Global success flag and response-time
+         map per tag (``None`` for failed writes).
         """
 
         response_times: dict[str, float | None] = {}
 
         if 'prediction_tags' in config:
             for tag, tag_config in config['prediction_tags'].items():
-                response_time = await self.write_data(
+                response_time = self.write_data(
                     server_id=server_id,
                     tag=tag,
                     data=data.head(1)['prediction'].values[0],
@@ -254,7 +222,7 @@ class OPC(SientiaMonitoring):
 
         if 'confidence_tags' in config:
             for tag, tag_config in config['confidence_tags'].items():
-                response_time = await self.write_data(
+                response_time = self.write_data(
                     server_id=server_id,
                     tag=tag,
                     data=data.head(1)['prediction_confidence'].values[0],
@@ -274,25 +242,25 @@ class OPC(SientiaMonitoring):
         return success, response_times
 
     @activity.defn(name='write_opc_data')
-    async def write_opc_data(
+    def write_opc_data(
         self, input_data: dict[str, Any]
     ) -> tuple[dict[Hashable, Any], dict[str, dict[str, float | None]]]:
         """
-        Write prediction and confidence data to OPC servers. The two writing
-        operations are optional and independent of each other.
+        Execute OPC writes across all configured servers and collect per-tag metrics.
+
+        For each server in ``opc_output_config``, this activity validates server
+        availability, writes enabled prediction/confidence tags, accumulates
+        response-time metrics, and then normalizes confidence/comments in the
+        returned prediction payload when at least one write fails.
 
         Args:
-            - input_data(dict[str, Any]): The input data. Contains the following keys:
-                - data(dict[str, Any]): The dataframe that contains the data to write
-                to the OPC servers.
-                - opc_output_config(dict[str, Any]): The OPC writing configuration.
-                The keys are the OPC server names and the values contain:
-                    - prediction_tags(dict[str, Any]): The tags to write to the OPC servers.
-                    - confidence_tags(dict[str, Any]): The tags to write to the OPC servers.
-
-        Returns:
-            - dict[Any, Any]: The data that was written to the OPC servers.
+         - input_data (dict[str, Any]): Payload containing workflow metadata, data
+           to write, and ``opc_output_config`` server/tag definitions.
 
+        Return:
+         tuple[dict[Hashable, Any], dict[str, dict[str, float | None]]]: Updated
+         prediction payload dict and nested metrics
+         ``{server_id: {tag_name: response_time_or_none}}``.
         """
         metadata = input_data['metadata']
         self.info('Writing data to OPC servers...', metadata)
@@ -305,19 +273,21 @@ class OPC(SientiaMonitoring):
         metrics: dict[str, dict[str, float | None]] = {}
 
         for server_id, config in opc_output_config.items():
-            if not await self.validate_server(server_id, metadata):
+            if not self.validate_server(server_id, metadata):
                 success = False
                 continue
 
-            local_success, local_response_times = await self.manage_output_tags(
+            local_success, local_response_times = self.manage_output_tags(
                 server_id, config, data, metadata
             )
             metrics[server_id] = local_response_times
             local_count = len(local_response_times)
             success = success and local_success
 
+            n_pred = len(config.get('prediction_tags') or {})
+            n_conf = len(config.get('confidence_tags') or {})
             self.info(
-                f'Process completed for OPC server {server_id}: {local_count} of {len(config.get("prediction_tags", []))} prediction tags and {len(config.get("confidence_tags", []))} confidence tags',
+                f'Process completed for OPC server {server_id}: {local_count} of {n_pred} prediction tags and {n_conf} confidence tags',
                 metadata,
             )
 
@@ -327,29 +297,16 @@ class OPC(SientiaMonitoring):
         self, data: DataFrame, success: bool, metadata: dict[str, Any]
     ) -> dict[Hashable, Any]:
         """
-        Process prediction confidence based on OPC write operation success.
-
-        This method updates the prediction confidence values in the DataFrame
-        based on the success status of OPC server write operations. If any
-        write operations failed, it sets the confidence to a predefined error
-        value to indicate data quality issues.
-
-        The method implements a confidence degradation strategy:
-        - Success: Maintains original confidence values
-        - Failure: Sets confidence to error value for operational awareness
+        Apply fallback confidence/comment values when OPC writes are not fully successful.
 
         Args:
-            data (DataFrame): DataFrame containing prediction and confidence data
-            success (bool): Overall success status of OPC write operations
-            metadata (dict[str, Any]): Context metadata for logging and notifications
+         - data (DataFrame): Prediction dataframe to be returned to downstream steps.
+         - success (bool): Aggregate write status across all attempted OPC tags.
+         - metadata (dict[str, Any]): Workflow metadata used for debug logs.
 
-        Returns:
-            dict[Any, Any]: Processed data as a dictionary with updated confidence values
-
-        Note:
-            The error confidence value (OPC_WRITTING_ERROR_CONFIDENCE = 12) is
-            used to indicate that data was not successfully exported to OPC servers.
-            This allows downstream systems to handle data quality appropriately.
+        Return:
+         dict[Hashable, Any]: Serialized dataframe dict with original values on success,
+         or downgraded confidence/comment fields on failure.
         """
 
         message = 'Some data could not be written to OPC servers'
@@ -367,25 +324,14 @@ class OPC(SientiaMonitoring):
 
         return data.to_dict()
 
-    async def close(self):
+    def close(self) -> None:
         """
-        Gracefully shutdown all OPC server connections and cleanup resources.
+        Disconnect all tracked OPC repositories and clear in-memory references.
 
-        This method ensures proper cleanup of all active OPC server connections
-        by calling the disconnect method on each repository instance. It's
-        designed to be called during application shutdown to prevent resource
-        leaks and ensure clean termination.
-
-        The method performs the following cleanup operations:
-        1. Iterates through all active OPC repository connections
-        2. Calls disconnect() on each repository instance
-        3. Allows for graceful connection termination
-        4. Prevents resource leaks and connection hanging
-
-        Note:
-            This method should be called during application shutdown to ensure
-            proper cleanup. It handles all active connections regardless of
-            their current state and provides a clean shutdown experience.
+        This method should be called during worker shutdown to ensure every
+        synchronous OPC session is explicitly closed before process exit.
         """
+
         for opc in self.opc_repository.values():
-            await opc.disconnect()
+            opc.disconnect()
+        self.opc_repository.clear()

laborious/activities/storage.py

@@ -1,7 +1,5 @@
 from temporalio import activity, workflow
 
-from laborious.utils.repository.minio_manager import MinioManager
-
 with workflow.unsafe.imports_passed_through():
     # Extend the Temporal Postgres activities for convenient query -> MinIO export
     import traceback
@@ -13,8 +11,9 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
-    from sientia_do.repository.minio_repository import MinioRepository
-    from sientia_do.temporal.activities.postgres import Postgres
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.minio_repository_sync import MinioRepository
+    from sientia_do.temporal.activities.postgres_sync import Postgres
     from sientia_do.temporal.constants import now
 
     from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
@@ -22,7 +21,7 @@ with workflow.unsafe.imports_passed_through():
 _LOAD_QUERY_OFFLOAD_SKIP_KEYS = frozenset({'model_name', 'key_prefix', 'size_threshold_bytes'})
 
 
-class Storage(Postgres, MinioManager):
+class Storage(Postgres, SientiaMonitoring):
     """
     Extensions for Postgres activities with a helper to export query results
     directly to MinIO as Parquet and return the object name.
@@ -60,14 +59,16 @@ class Storage(Postgres, MinioManager):
             metrics_controller=metrics_controller,
         )
 
-        MinioManager.__init__(
-            self, minio_repository, logger, notification_handler, metrics_controller
+        self.minio_repository = minio_repository
+        SientiaMonitoring.__init__(
+            self,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
         )
 
     @activity.defn(name='load_query_with_minio_offload')
-    async def load_query_with_minio_offload(
-        self, input_data: dict[str, Any]
-    ) -> MinioDataFramePayload:
+    def load_query_with_minio_offload(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
         """
         Run the custom SQL load, then return a MinIO-aware dataframe wire dict.
 
@@ -88,7 +89,7 @@ class Storage(Postgres, MinioManager):
         metadata: dict = input_data.get('metadata', {})
         model_name = input_data['model_name']
 
-        rows = await self.load_custom_query(
+        rows = self.load_custom_query(
             input_data,
         )
         if not rows:
@@ -99,7 +100,7 @@ class Storage(Postgres, MinioManager):
         else:
             dataframe = pd.DataFrame(rows)
 
-        return await MinioDataFramePayload.from_dataframe(
+        return MinioDataFramePayload.from_dataframe(
             dataframe,
             minio_repo=self.minio_repository,
             workflow_metadata=metadata,
@@ -109,15 +110,29 @@ class Storage(Postgres, MinioManager):
         )
 
     @activity.defn(name='export_payload_to_postgres')
-    async def export_payload_to_postgres(self, input_data: dict[str, Any]) -> dict:
+    def export_payload_to_postgres(self, input_data: dict[str, Any]) -> dict:
         """
-        Export a payload to PostgreSQL.
+        Resolve a MinIO-aware payload into a DataFrame and persist it into PostgreSQL.
+
+        This activity accepts the serialized payload produced by previous steps
+        (inline dict or MinIO object reference), reconstructs the tabular data,
+        and delegates the final write to ``export_data_to_postgres`` using the
+        same input contract expected by the Postgres activity mixin.
+
+        Args:
+         - input_data (dict[str, Any]): Activity input containing ``data`` as a
+           ``MinioDataFramePayload``-compatible dict plus database write options
+           (schema/table/on_conflict/metadata and related fields).
+
+        Return:
+         dict: Result dictionary returned by ``export_data_to_postgres``, including
+         success status and optional write diagnostics.
         """
         metadata = input_data.get('metadata')
         payload = MinioDataFramePayload.from_dict(input_data['data'])
-        data = await payload.retrieve(self.minio_repository, metadata)
+        data = payload.retrieve(self.minio_repository, metadata)
 
-        return await self.export_data_to_postgres(
+        return self.export_data_to_postgres(
             {
                 **input_data,
                 'data': data,
@@ -125,7 +140,7 @@ class Storage(Postgres, MinioManager):
         )
 
     @activity.defn(name='cleanup_minio_objects_expired')
-    async def cleanup_minio_objects_expired(self, input_data: dict[str, Any]) -> dict[str, Any]:
+    def cleanup_minio_objects_expired(self, input_data: dict[str, Any]) -> dict[str, Any]:
         """
         Delete objects under the given prefixes that are older than the retention window.
 
@@ -154,7 +169,7 @@ class Storage(Postgres, MinioManager):
             'deleted_count': 0,
         }
         try:
-            keys = await self.minio_repository.list_objects(
+            keys = self.minio_repository.list_objects(
                 prefix=prefix,
                 recursive=True,
                 metadata=metadata,
@@ -166,7 +181,7 @@ class Storage(Postgres, MinioManager):
                         continue
                     if ts >= cutoff:
                         continue
-                    await self.minio_repository.delete_file(
+                    self.minio_repository.delete_file(
                         object_name=key,
                         metadata=metadata,
                     )
@@ -184,7 +199,7 @@ class Storage(Postgres, MinioManager):
                 report['deleted_count'] += 1
         except Exception as e:
             trace = traceback.format_exc()
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=metadata,
                 notification_id='ERROR_CLEANUP_MINIO_OBJECTS_EXPIRED',
                 message=f'Error cleaning up MinIO objects: {e}',
@@ -201,9 +216,16 @@ class Storage(Postgres, MinioManager):
         return report
 
     def close(self) -> None:
-        """Close Storage resources (MinIO client and Postgres engine)."""
-        Postgres.close(self)
-        MinioManager.close(self)
+        """
+        Shutdown Storage resources in deterministic order.
 
-    def __del__(self):
-        self.close()
+        The method first closes Postgres resources via ``Postgres.close`` (engine,
+        sessions, and monitoring hooks), then closes the optional MinIO repository
+        and clears the local reference to avoid accidental reuse after shutdown.
+        """
+        Postgres.close(self)
+        if self.minio_repository is not None:
+            try:
+                self.minio_repository.close()
+            finally:
+                self.minio_repository = None

laborious/utils/models/minio_dataframe_payload.py

@@ -21,7 +21,7 @@ from typing import Any, Literal
 
 from pandas import DataFrame, read_parquet
 from sientia_do.observability.logger import Logger
-from sientia_do.repository.minio_repository import MinioRepository
+from sientia_do.repository.minio_repository_sync import MinioRepository
 from sientia_do.temporal.constants import DATETIME_FORMAT_FILENAME, DATETIME_FORMAT_WITH_TZ, now
 
 # Keys that are part of the serialized wire format (not arbitrary metadata).
@@ -89,7 +89,7 @@ class MinioDataFramePayload:
         metadata: dict[str, Any] | None = None,
     ) -> None:
         """
-        Emit debug logs only when logger is provided
+        Emit a debug message only when a logger instance is available.
 
         Args:
          - logger (Logger | None): Logger instance used for debug messages
@@ -182,7 +182,14 @@ class MinioDataFramePayload:
 
     def cleanup_prefix(self) -> str | None:
         """
-        Return True if cleanup is enabled for this payload.
+        Return the MinIO prefix eligible for retention cleanup.
+
+        Cleanup is only applicable when payload data was offloaded to MinIO
+        (``object_key`` present and inline ``data`` absent). Inline-only payloads
+        return ``None`` because there is no object tree to prune.
+
+        Return:
+         str | None: Prefix used by cleanup listing, or ``None`` when cleanup does not apply.
         """
         if self.object_key is not None and self.data is None:
             return self.object_prefix
@@ -190,12 +197,19 @@ class MinioDataFramePayload:
 
     def has_data(self) -> bool:
         """
-        Return True if the payload has some data internally or in MinIO.
+        Indicate whether the payload contains retrievable tabular content.
+
+        A payload is considered non-empty when either inline ``data`` exists
+        (and is not an empty dict) or an ``object_key`` is available for MinIO
+        download.
+
+        Return:
+         bool: ``True`` when data can be retrieved, ``False`` otherwise.
         """
         return (self.data is not None and self.data != {}) or self.object_key is not None
 
     @classmethod
-    async def from_dataframe(
+    def from_dataframe(
         cls,
         dataframe: DataFrame | None,
         minio_repo: MinioRepository,
@@ -274,7 +288,7 @@ class MinioDataFramePayload:
         dataframe.to_parquet(parquet_buffer, engine='pyarrow', index=True)
         file_bytes = parquet_buffer.getvalue()
 
-        upload_result = await minio_repo.upload_file(
+        upload_result = minio_repo.upload_file(
             file_bytes=file_bytes,
             relative_key=object_key,
             metadata=workflow_metadata,
@@ -299,7 +313,7 @@ class MinioDataFramePayload:
             status=status,
         )
 
-    async def retrieve(
+    def retrieve(
         self,
         minio_repo: MinioRepository,
         workflow_metadata: dict[str, Any] | None = None,
@@ -336,7 +350,7 @@ class MinioDataFramePayload:
             f'MinioDataFramePayload.retrieve downloading object from MinIO: {self.object_key}',
             workflow_metadata,
         )
-        file_bytes = await minio_repo.download_file(
+        file_bytes = minio_repo.download_file(
             object_name=self.object_key, metadata=workflow_metadata
         )
         df = read_parquet(BytesIO(file_bytes))

laborious/utils/repository/minio_manager.py

@@ -1,32 +0,0 @@
-from sientia_do.notifications.handlers import NotificationHandler
-from sientia_do.observability.logger import Logger
-from sientia_do.observability.metrics_controller import MetricsController
-from sientia_do.observability.sientia_monitoring import SientiaMonitoring
-from sientia_do.repository.minio_repository import MinioRepository
-
-
-class MinioManager(SientiaMonitoring):
-    minio_repository: MinioRepository | None = None
-
-    def __init__(
-        self,
-        minio_repository: MinioRepository | None = None,
-        logger: Logger | None = None,
-        notification_handler: NotificationHandler | None = None,
-        metrics_controller: MetricsController | None = None,
-    ):
-        if self.minio_repository is None:
-            self.minio_repository = minio_repository
-        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
-
-    def close(self) -> None:
-        """
-        Close the MinioManager and clean up resources.
-        """
-        if self.minio_repository is not None:
-            try:
-                self.minio_repository.close()
-            finally:
-                self.minio_repository = None
-
-        SientiaMonitoring.shutdown(self)

laborious/utils/repository/opc_repository.py

@@ -1,4 +1,10 @@
-import asyncio
+"""
+Synchronous OPC UA client repository using python-opcua (opcua package).
+
+Connects to OPC UA servers, optionally configures Basic256 security, validates sessions,
+and writes node values with typed variants and Prometheus-compatible metrics.
+"""
+
 import json
 import time
 import traceback
@@ -6,9 +12,8 @@ from datetime import datetime
 from pathlib import Path
 from typing import Any
 
-from asyncua import Client
-from asyncua.crypto.security_policies import SecurityPolicyBasic256
-from asyncua.ua import DataValue, Variant, VariantType
+from opcua import Client, ua
+from opcua.crypto import security_policies
 from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
 from sientia_do.notifications.models import NotificationLevel
 from sientia_do.observability.logger import Logger
@@ -20,28 +25,38 @@ from laborious import metrics
 data_type_map = {
     'float': {
         'converter': float,
-        'opc_type': VariantType.Float,
+        'opc_type': ua.VariantType.Float,
     },
     'double': {
         'converter': float,
-        'opc_type': VariantType.Double,
+        'opc_type': ua.VariantType.Double,
     },
     'int': {
         'converter': int,
-        'opc_type': VariantType.Int32,
+        'opc_type': ua.VariantType.Int32,
     },
     'bool': {
         'converter': bool,
-        'opc_type': VariantType.Boolean,
+        'opc_type': ua.VariantType.Boolean,
     },
     'str': {
         'converter': str,
-        'opc_type': VariantType.String,
+        'opc_type': ua.VariantType.String,
     },
 }
 
 
 class OpcRepository(SientiaMonitoring):
+    """
+    Synchronous OPC UA repository for connect/disconnect and typed writes.
+
+    Attributes:
+        url: OPC UA endpoint URL.
+        id: Server identifier used in metrics and notifications.
+        server_name: Human-readable server name for labels.
+        client: Active opcua.Client instance while connected.
+    """
+
     def __init__(
         self,
         opc_id: str,
@@ -66,10 +81,10 @@ class OpcRepository(SientiaMonitoring):
         self.logger = logger
         self.error_count = 0
         self.reconnection_interval = reconnection_interval
-        self.last_reconnection_time: None | datetime = None
+        self.last_reconnection_time: datetime | None = None
         self.disconnection_interval = 10.0
         self.notification_handler = notification_handler
-        self.client: None | Client = None
+        self.client: Client | None = None
 
         SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
 
@@ -80,24 +95,12 @@ class OpcRepository(SientiaMonitoring):
             'schedule_name': '-',
         }
 
-    async def set_security(self):
+    def set_security(self) -> None:
         """
-        Configures the security settings for the OPC UA client.
-        This method sets up the security policy, certificates, and timeouts
-        required for establishing a secure connection with the OPC UA server.
+        Configure Basic256 security policy, certificates, and long channel/session timeouts.
+
         Raises:
-            ValueError: If either the certificate path or private key path is not provided.
-        Attributes:
-            - cert_path (str): Path to the client's certificate file.
-            - private_key_path (str): Path to the client's private key file.
-            - server_cert_path (str, optional): Path to the server's certificate file.
-            - server_uri (str): The URI of the server to be used as the application URI.
-            - client (opcua.Client): The OPC UA client instance.
-            - logger (logging.Logger): Logger instance for logging information.
-        Security Settings:
-            - Security Policy: Basic256
-            - Secure Channel Timeout: 10,000,000 ms
-            - Session Timeout: 10,000,000 ms
+            ValueError: If certificate paths are missing or client is not initialized.
         """
 
         if self.cert_path is None or self.private_key_path is None:
@@ -114,26 +117,24 @@ class OpcRepository(SientiaMonitoring):
 
         self.client.application_uri = self.server_uri
         self.logger.custom_info('Setting security...', self.metadata)
-        await self.client.set_security(
-            SecurityPolicyBasic256,
-            certificate=str(cert),
-            private_key=str(private_key),
-            server_certificate=str(server_cert) if server_cert else None,
+        self.client.set_security(
+            security_policies.SecurityPolicyBasic256,
+            str(cert),
+            str(private_key),
+            str(server_cert) if server_cert else None,
         )
         self.client.secure_channel_timeout = 10000000
         self.client.session_timeout = 10000000
 
-    async def connect(self) -> tuple[bool, dict[str, Any]]:
+    def connect(self) -> tuple[bool, dict[str, Any]]:
         """
-        Establishes a connection to the OPC server.
-        This method initializes the OPC client using the provided URL and
-        sets up security if a certificate path is specified. It then
-        attempts to connect to the server and logs the connection status.
-        Raises:
-            Exception: If the connection to the OPC server fails.
+        Create the synchronous client, optionally apply security, and connect to the server.
+
+        Return:
+            tuple[bool, dict[str, Any]]: Success flag and error payload when False.
         """
 
-        self.client = Client(self.url, timeout=10, watchdog_intervall=3600000)  # type: ignore[attr-defined]
+        self.client = Client(self.url, timeout=10)
 
         self.client.name = self.pod_id
         self.client.application_name = self.pod_id
@@ -142,32 +143,25 @@ class OpcRepository(SientiaMonitoring):
         self.client.product_uri = pod_uri
 
         if self.cert_path:
-            await self.set_security()
+            self.set_security()
         self.logger.custom_info(
             f'Starting connection to OPC server {self.id}:{self.server_name}...', self.metadata
         )
-        return await self.try_connect()
+        return self.try_connect()
 
-    async def try_connect(self) -> tuple[bool, dict[str, Any]]:
+    def try_connect(self) -> tuple[bool, dict[str, Any]]:
         """
-        Attempt to establish connection to the OPC server.
+        Perform the TCP/session handshake and emit connection metrics.
 
-        This method performs the actual connection attempt to the OPC server
-        and handles connection failures with comprehensive error reporting.
-        It updates reconnection timing and provides detailed error information
-        for operational monitoring and debugging.
-
-        Returns:
-            tuple[bool, dict[str, Any]]: Connection result
-                - bool: True if connection successful, False otherwise
-                - dict: Error information if connection failed
+        Return:
+            tuple[bool, dict[str, Any]]: Success flag and structured error when False.
         """
 
         tags = {
             'pod_id': self.pod_id,
             'server_name': self.server_name,
         }
-        await self.emit_metric(metrics.OPC_CONNECTIONS_TOTAL, tags)
+        self.emit_metric_sync(metrics.OPC_CONNECTIONS_TOTAL, tags)
         try:
             self.last_reconnection_time = datetime.now()
             if self.client is None:
@@ -177,9 +171,9 @@ class OpcRepository(SientiaMonitoring):
                     'block': 'opc_repository',
                     'level': NotificationLevel.ERROR,
                 }
-            await self.client.connect()
+            self.client.connect()
 
-            await self.emit_metric(
+            self.emit_metric_sync(
                 metric_object=metrics.OPC_CONNECTION_STATUS,
                 method='set',
                 tags={
@@ -191,12 +185,12 @@ class OpcRepository(SientiaMonitoring):
 
             return True, {}
         except Exception as e:
-            await self.disconnect()
+            self.disconnect()
 
             trace = traceback.format_exc()
             self.logger.custom_error(trace, self.metadata)
 
-            await self.emit_metric(metrics.OPC_CONNECTIONS_FAILED, tags)
+            self.emit_metric_sync(metrics.OPC_CONNECTIONS_FAILED, tags)
 
             return False, {
                 'notification_id': f'OPC_CONNECTION_ERROR_{self.id}',
@@ -206,21 +200,27 @@ class OpcRepository(SientiaMonitoring):
                 'attachment_content': trace,
             }
 
-    async def disconnection_fallback(self) -> list:
+    def disconnection_fallback(self) -> list[dict[str, Any]]:
         """
-        Tries 5 times to disconnect from the OPC UA server, with a delay of 100ms x try.
+        Retry disconnect up to five times with linear backoff.
+
+        Return:
+            list[dict[str, Any]]: Empty on success, otherwise error records per attempt.
         """
 
         assert self.client is not None
         error_stack = []
         for i in range(5):
             try:
-                self.logger.info(f'Disconnecting from OPC UA server, attempt {i + 1} of 5')
-                await self.client.disconnect()
+                self.logger.custom_info(
+                    f'Disconnecting from OPC UA server, attempt {i + 1} of 5', self.metadata
+                )
+                self.client.disconnect()
                 return []
             except Exception as e:
-                self.logger.error(
-                    f'Failed to disconnect from OPC UA server in attempt {i + 1} of 5: {e}'
+                self.logger.custom_error(
+                    f'Failed to disconnect from OPC UA server in attempt {i + 1} of 5: {e}',
+                    self.metadata,
                 )
                 error_stack.append(
                     {
@@ -229,23 +229,20 @@ class OpcRepository(SientiaMonitoring):
                         'traceback': traceback.format_exc(),
                     }
                 )
-                await asyncio.sleep(self.disconnection_interval * i)
+                time.sleep(self.disconnection_interval * i)
         return error_stack
 
-    async def disconnect(self):
+    def disconnect(self) -> None:
+        """
+        Tear down the UA session and reset connection metrics.
         """
-        Gracefully disconnect from the OPC server.
 
-        This method safely terminates the connection to the OPC server
-        and cleans up client resources. It handles disconnection errors
-        gracefully and ensures proper resource cleanup.
-        """
         if self.client is None:
             return
 
-        errors = await self.disconnection_fallback()
+        errors = self.disconnection_fallback()
         if errors:
-            await self.send_notification_async(
+            self.send_notification(
                 metadata=self.metadata,
                 notification_id=f'OPC_DISCONNECTION_ERROR_{self.id}',
                 message='Failed to disconnect from OPC server in 5 attempts.',
@@ -254,8 +251,10 @@ class OpcRepository(SientiaMonitoring):
                 attachment_content=json.dumps(errors, indent=4),
             )
         else:
-            self.logger.warning(f'Disconnected from OPC server {self.id} successfully')
-        await self.emit_metric(
+            self.logger.custom_warning(
+                f'Disconnected from OPC server {self.id} successfully', self.metadata
+            )
+        self.emit_metric_sync(
             metric_object=metrics.OPC_CONNECTION_STATUS,
             method='set',
             tags={
@@ -268,77 +267,52 @@ class OpcRepository(SientiaMonitoring):
 
         self.client = None
 
-    async def validate_connection(self) -> tuple[bool, dict[str, Any]]:
+    def _session_alive(self) -> bool:
         """
-        Validate and maintain OPC server connection health.
+        Best-effort check that the synchronous client still has a working session.
 
-        This method performs comprehensive connection validation and
-        implements automatic reconnection logic for production reliability.
-        It handles various connection states and implements intelligent
-        reconnection strategies with error counting and timing controls.
-
-        Connection Validation:
-            1. Checks client existence and connection state
-            2. Implements error counting with automatic disconnection
-            3. Enforces reconnection timing windows
-            4. Provides detailed error reporting and notifications
-
-        Reconnection Strategy:
-            - Error Count Threshold: Disconnects after 5 consecutive errors
-            - Reconnection Window: Enforces minimum intervals between attempts
-            - Automatic Recovery: Attempts reconnection when conditions allow
-            - State Monitoring: Continuously monitors connection health
-
-        Args:
-            None
-
-        Returns:
-            tuple[bool, dict[str, Any]]: Connection validation result
-                - bool: True if connection is healthy, False otherwise
-                - dict: Error information if validation fails
+        Return:
+            bool: True if a root browse succeeds, False otherwise.
         """
+
         if self.client is None:
-            return await self.connect()
-
-        # if self.error_count > 5: # NOSONAR
-        #     self.logger.custom_warning(
-        #         f'OPC server {self.id} will be disconnected due to multiple errors', self.metadata
-        #     )
-        #     try:
-        #         await self.disconnect()
-        #     except Exception as e:
-        #         trace = traceback.format_exc()
-        #         self.logger.custom_error(
-        #             f'Failed to disconnect from OPC server: {e}', self.metadata
-        #         )
-        #         self.logger.custom_error(trace, self.metadata)
-        #     self.logger.custom_info(
-        #         f'Attempting to reconnect to OPC server {self.id}...', self.metadata
-        #     )
-        #     return await self.connect()
-
-        # Check if client is connected using asyncua's connection state
+            return False
         try:
-            if (
-                self.client.uaclient.protocol is None
-                or self.client.uaclient.protocol.state == 'closed'
-            ):
-                # OPC server is not connected
+            self.client.get_root_node()
+            return True
+        except Exception:
+            return False
+
+    def validate_connection(self) -> tuple[bool, dict[str, Any]]:
+        """
+        Ensure the UA session is usable; reconnect when outside the backoff window.
+
+        Return:
+            tuple[bool, dict[str, Any]]: Whether the session is ready and optional error payload.
+        """
+
+        if self.client is None:
+            return self.connect()
+
+        try:
+            if not self._session_alive():
                 self.logger.custom_error(f'OPC server {self.id} is not connected', self.metadata)
                 if (
                     self.last_reconnection_time is None
                     or (datetime.now() - self.last_reconnection_time).total_seconds()
                     > self.reconnection_interval
                 ):
-                    await self.disconnect()
+                    self.disconnect()
                     self.logger.custom_info(
                         f'Trying to reconnect to OPC server {self.id}...', self.metadata
                     )
-                    return await self.connect()
+                    return self.connect()
 
                 return False, {
                     'notification_id': f'OPC_CONNECTION_AWAITING_RECONNECTION_WINDOW_{self.id}',
-                    'message': f'OPC server {self.id} is not connected, waiting for next reconnection window...',
+                    'message': (
+                        f'OPC server {self.id} is not connected, waiting for next reconnection window...'
+                    ),
                     'block': 'opc_repository',
                     'level': NotificationLevel.WARNING,
                 }
@@ -355,39 +329,24 @@ class OpcRepository(SientiaMonitoring):
                 'attachment_content': trace,
             }
 
-    async def write_data(
+    def write_data(
         self, node: str, value: Any, data_type: str, logger: Logger, metadata: dict[str, Any]
     ) -> tuple[bool, dict[str, Any]]:
         """
-        Write data to OPC server with comprehensive validation and monitoring.
-
-        This method provides secure and reliable data writing to OPC servers
-        with automatic connection validation, data type conversion, and
-        comprehensive error handling. It implements performance monitoring
-        and metrics collection for operational visibility.
-
-        Data Writing Process:
-            1. Connection validation and automatic reconnection
-            2. Node validation and error handling
-            3. Data type conversion and validation
-            4. OPC data writing with timestamp
-            5. Performance metrics collection
-            6. Error handling and notification
+        Write a typed value to an OPC UA node after validating connectivity.
 
         Args:
-            node (str): OPC node identifier to write data to
-            value (Any): Data value to write to the OPC node
-            data_type (str): Data type for OPC conversion
-            logger (Logger): Logger instance for operation logging
-            metadata (dict[str, Any]): Context metadata for logging and metrics
+            node: Node id string accepted by opcua Client.get_node.
+            value: Scalar value to encode.
+            data_type: Key into ``data_type_map`` (e.g. float, str).
+            logger: Caller logger for per-write traces.
+            metadata: Workflow metadata for error context.
 
-        Returns:
-            tuple[bool, dict[str, Any]]: Write operation result
-                - bool: True if write successful, False otherwise
-                - dict: Error information if write failed
+        Return:
+            tuple[bool, dict[str, Any]]: Success flag and either ``response_time`` or error fields.
         """
 
-        is_connected, error = await self.validate_connection()
+        is_connected, error = self.validate_connection()
 
         if not is_connected:
             return False, error
@@ -395,8 +354,8 @@ class OpcRepository(SientiaMonitoring):
         start_time = time.time()
 
         try:
-            # ignored because self.validate_connection is called before, so we know self.client is not None
-            node_obj = self.client.get_node(node)  # type: ignore[union-attr]
+            assert self.client is not None
+            node_obj = self.client.get_node(node)
         except Exception as e:
             trace = traceback.format_exc()
             logger.custom_error(trace, metadata.get('schedule_name', 'N/A'))
@@ -419,16 +378,10 @@ class OpcRepository(SientiaMonitoring):
 
         data = data_type_map[data_type]['converter'](value)
         logger.custom_info(f'Writing {data} - {type(data)} to {node}', metadata)
-        # now = datetime.now() # NOSONAR
-        ua_data = DataValue(
-            Variant(data, data_type_map[data_type]['opc_type']),
-            # SourceTimestamp=DateTime( # NOSONAR
-            #     now.year, now.month, now.day, now.hour, now.minute, now.second, now.microsecond # NOSONAR
-            # ), # NOSONAR
-        )
+        variant_type = data_type_map[data_type]['opc_type']
 
         try:
-            await node_obj.write_value(ua_data)
+            node_obj.set_value(data, variant_type)
 
             end_time = time.time()
             response_time = end_time - start_time

laborious/worker/worker.py

@@ -162,7 +162,7 @@ async def main():
     )
 
     logger.custom_info('Initializing OPC...', metadata)
-    await activities.init_opc()
+    activities.init_opc()
 
     logger.custom_info(f'Starting SDK Metrics Server on port {SDK_METRICS_PORT}...', metadata)
 
@@ -265,7 +265,7 @@ async def main():
         exit_code = 1
     finally:
         notification_handler.shutdown()
-        await activities.shutdown()
+        activities.shutdown()
         metrics.APP_UP.labels(pod_id=POD_ID).set(0)  # Mark app as DOWN
         sys.exit(exit_code)
 

laborious/workflows/sub_workflows/format_and_export_prediction.py

@@ -161,7 +161,7 @@ class FormatAndExportPrediction:
 
             write_transformed_handler = None
 
-        opc_metrics = {}
+        opc_metrics: dict[str, dict[str, float | None]] = {}
 
         # write to pi web api
         if pi_web_api_output_config:

laborious/workflows/sub_workflows/prediction_process.py

@@ -250,7 +250,7 @@ class PredictionProcess:
     async def path_flag_handler(
         self,
         data: dict[str, Any],
-        path_flag: str,
+        path_flag: str | None,
         input_data: dict,
         confidence: int,
         last_timestamp: str,

requirements-light.txt

@@ -1,12 +0,0 @@
-temporalio
-psycopg2-binary
-sqlalchemy
-asyncua
-redis
-git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.0
-prometheus-client
-botocore
-boto3
-s3fs
-pyarrow
-mlflow

requirements-local.txt

@@ -1,12 +1,13 @@
 temporalio
 psycopg2-binary
 sqlalchemy
-asyncua
+opcua
 redis
-git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.0
-#git+ssh://git@github.com/Aignosi/sientia-model-library.git@0.8.2
+#git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.0
+/home/grezewave/Documents/projects/sientia/sientia-dataops-library
+#git+ssh://git@github.com/Aignosi/sientia-model-library.git@0.8.3
 /home/grezewave/Documents/projects/sientia/sientia-model-library
-prometheus-client
+prometheus-client   
 botocore
 boto3
 s3fs

requirements.txt

@@ -1,7 +1,7 @@
 temporalio
 psycopg2-binary
 sqlalchemy
-asyncua
+opcua
 redis
 sientia_do==1.12.0
 sientia_model==0.8.2

tests/conftest.py

@@ -2,6 +2,19 @@ import os
 import sys
 from unittest.mock import MagicMock
 
+from sientia_do.temporal.activities.postgres_sync import Postgres
+
+
+def _noop_postgres_del(_self):
+    """
+    Unit tests use MagicMock metrics controllers; postgres_sync.Postgres.__del__ calls
+    close() during GC and triggers async shutdown. Explicit ``close()`` is covered in tests.
+    """
+    return None
+
+
+Postgres.__del__ = _noop_postgres_del  # type: ignore[method-assign]
+
 # The production code converts SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES to int at import-time.
 # Tests must set it to a valid integer string to avoid import errors.
 os.environ.setdefault('SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES', '1')

tests/laborious/activities/test_activities.py

@@ -1,6 +1,4 @@
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
-
-from pytest import mark
+from unittest.mock import ANY, MagicMock, patch
 
 from laborious.activities.activities import Activities
 from laborious.activities.api import API
@@ -156,7 +154,6 @@ def test___init__(
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.activities.Storage')
 @patch('laborious.activities.activities.MLFlow')
 @patch('laborious.activities.activities.OPC')
@@ -164,7 +161,7 @@ def test___init__(
 @patch('laborious.activities.activities.ModelMetrics')
 @patch('laborious.activities.activities.API')
 @patch('laborious.activities.activities.MinioRepository')
-async def test_shutdown(
+def test_shutdown(
     _mock_minio_repository,
     mock_api_init,
     mock_model_metrics_init,
@@ -173,7 +170,7 @@ async def test_shutdown(
     mock_mlflow_init,
     mock_storage_init,
 ):
-    mock_opc_init.close = AsyncMock()
+    mock_opc_init.close = MagicMock()
     postgres_config = {
         'host': 'localhost',
         'port': 5432,
@@ -222,7 +219,7 @@ async def test_shutdown(
         mlflow_repository=mlflow_repository,
     )
 
-    await activities.shutdown()
+    activities.shutdown()
     mock_opc_init.close.assert_called_once()
     mock_storage_init.close.assert_called_once()
     mock_mlflow_init.close.assert_called_once()

tests/laborious/activities/test_api.py

@@ -1,7 +1,6 @@
-from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
+from unittest.mock import ANY, MagicMock, call, patch
 
-import pytest_asyncio
-from pytest import fixture, mark
+from pytest import fixture
 from sientia_do.notifications.models import NotificationLevel
 
 from laborious.activities.api import API, PI_WEB_API_PREDICTION_ERROR_CONFIDENCE
@@ -71,7 +70,7 @@ def test_get_pi_web_api_core_labels_without_operation_type(mock_pi_web_api_clien
         auth_token='test_token',
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     with patch.object(
         SientiaMonitoring,
@@ -105,7 +104,7 @@ def test_get_pi_web_api_core_labels_with_operation_type(mock_pi_web_api_client):
         auth_token='test_token',
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     with patch.object(
         SientiaMonitoring,
@@ -132,17 +131,17 @@ def test__init__():
         auth_token='test_token',
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
 
     assert api.pi_web_api_client is not None
 
 
-@pytest_asyncio.fixture
+@fixture
 @patch('laborious.activities.api.PIWebAPIClient')
 def api(mock_pi_web_api_client):
     mock_client = MagicMock()
-    mock_client.write_value = AsyncMock()
+    mock_client.write_value = MagicMock()
     mock_client.close = MagicMock()
     mock_client.base_url = 'https://test-pi-server.com'
     mock_pi_web_api_client.return_value = mock_client
@@ -153,12 +152,12 @@ def api(mock_pi_web_api_client):
         auth_token='test_token',
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
-    api_instance.send_notification_async = AsyncMock()
+    api_instance.send_notification = MagicMock()
     api_instance.info = MagicMock()
     api_instance.error = MagicMock()
-    api_instance.emit_metric = AsyncMock()
+    api_instance.emit_metric_sync = MagicMock()
     api_instance.get_core_labels = MagicMock(
         return_value={
             'pod_id': 'test_pod',
@@ -170,9 +169,8 @@ def api(mock_pi_web_api_client):
     return api_instance
 
 
-@mark.asyncio
 @patch('laborious.activities.api.DataFrame')
-async def test_write_pi_web_api_data_success(mock_dataframe, api, base_input_data):
+def test_write_pi_web_api_data_success(mock_dataframe, api, base_input_data):
     input_data = {
         **base_input_data,
         'pi_web_api_output_config': {
@@ -190,7 +188,7 @@ async def test_write_pi_web_api_data_success(mock_dataframe, api, base_input_dat
         [{'WebId': 'web_id_3', 'Errors': []}, {'WebId': 'web_id_4', 'Errors': []}],
     ]
 
-    result = await api.write_pi_web_api_data(input_data)
+    result = api.write_pi_web_api_data(input_data)
 
     api.pi_web_api_client.write_value.assert_has_calls(
         [
@@ -220,9 +218,8 @@ async def test_write_pi_web_api_data_success(mock_dataframe, api, base_input_dat
     }
 
 
-@mark.asyncio
 @patch('laborious.activities.api.DataFrame')
-async def test_write_pi_web_api_data_prediction_error(mock_dataframe, api, base_input_data):
+def test_write_pi_web_api_data_prediction_error(mock_dataframe, api, base_input_data):
     mock_dataframe.return_value = _create_mock_dataframe(
         {
             'prediction': [0.75],
@@ -233,9 +230,9 @@ async def test_write_pi_web_api_data_prediction_error(mock_dataframe, api, base_
 
     api.pi_web_api_client.write_value.side_effect = Exception('Prediction write failed')
 
-    result = await api.write_pi_web_api_data(base_input_data)
+    result = api.write_pi_web_api_data(base_input_data)
 
-    api.send_notification_async.assert_called_once_with(
+    api.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='WRITE_PI_WEB_API_PREDICTION_ERROR',
         message="Error writing prediction data to PI Web API: Prediction write failed\n Tags: {'tag1': 'web_id_1'}",
@@ -248,9 +245,8 @@ async def test_write_pi_web_api_data_prediction_error(mock_dataframe, api, base_
     assert api.pi_web_api_client.write_value.call_count == 1
 
 
-@mark.asyncio
 @patch('laborious.activities.api.DataFrame')
-async def test_write_pi_web_api_data_confidence_error(mock_dataframe, api, base_input_data):
+def test_write_pi_web_api_data_confidence_error(mock_dataframe, api, base_input_data):
     mock_dataframe.return_value = _create_mock_dataframe()
 
     # First call succeeds, second fails
@@ -259,9 +255,9 @@ async def test_write_pi_web_api_data_confidence_error(mock_dataframe, api, base_
         Exception('Confidence write failed'),
     ]
 
-    result = await api.write_pi_web_api_data(base_input_data)
+    result = api.write_pi_web_api_data(base_input_data)
 
-    api.send_notification_async.assert_called_once_with(
+    api.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='WRITE_PI_WEB_API_CONFIDENCE_ERROR',
         message="Error writing confidence data to PI Web API: Confidence write failed\n Tags: {'tag2': 'web_id_2'}",
@@ -278,9 +274,8 @@ async def test_write_pi_web_api_data_confidence_error(mock_dataframe, api, base_
     assert api.pi_web_api_client.write_value.call_count == 2
 
 
-@mark.asyncio
 @patch('laborious.activities.api.DataFrame')
-async def test_write_pi_web_api_data_empty_tags(mock_dataframe, api, base_input_data):
+def test_write_pi_web_api_data_empty_tags(mock_dataframe, api, base_input_data):
     input_data = {
         **base_input_data,
         'pi_web_api_output_config': {
@@ -298,7 +293,7 @@ async def test_write_pi_web_api_data_empty_tags(mock_dataframe, api, base_input_
         [],
     ]
 
-    result = await api.write_pi_web_api_data(input_data)
+    result = api.write_pi_web_api_data(input_data)
 
     api.pi_web_api_client.write_value.assert_has_calls(
         [
@@ -328,9 +323,8 @@ async def test_write_pi_web_api_data_empty_tags(mock_dataframe, api, base_input_
     }
 
 
-@mark.asyncio
 @patch('laborious.activities.api.DataFrame')
-async def test_write_pi_web_api_data_updates_confidence_and_comments(
+def test_write_pi_web_api_data_updates_confidence_and_comments(
     mock_dataframe, api, base_input_data
 ):
     mock_dataframe.return_value = _create_mock_dataframe()
@@ -341,23 +335,23 @@ async def test_write_pi_web_api_data_updates_confidence_and_comments(
     with patch.object(
         api,
         'process_pi_web_api_response',
-        new=AsyncMock(side_effect=[(0.33, 'PI warning'), (0, '')]),
+        new=MagicMock(side_effect=[(0.33, 'PI warning'), (0, '')]),
     ) as process_mock:
-        result = await api.write_pi_web_api_data(base_input_data)
+        result = api.write_pi_web_api_data(base_input_data)
 
-    assert process_mock.await_count == 2
+    assert process_mock.call_count == 2
     assert result is not None
 
 
-@mark.asyncio
-async def test_close(api):
+@patch('laborious.activities.api.SientiaMonitoring.shutdown')
+def test_close(mock_shutdown, api):
     api.close()
 
     api.pi_web_api_client.close.assert_called_once()
+    mock_shutdown.assert_called_once_with(api)
 
 
-@mark.asyncio
-async def test_process_pi_web_api_response_success(api):
+def test_process_pi_web_api_response_success(api):
     """Test successful processing of PI Web API response with all tags written."""
     response_data = [
         {'WebId': 'web_id_1', 'Errors': []},
@@ -371,7 +365,7 @@ async def test_process_pi_web_api_response_success(api):
         'workflow_name': 'test_workflow',
     }
 
-    confidence, message = await api.process_pi_web_api_response(
+    confidence, message = api.process_pi_web_api_response(
         response_data=response_data,
         tags=tags,
         core_labels=core_labels,
@@ -380,9 +374,9 @@ async def test_process_pi_web_api_response_success(api):
 
     assert confidence == 0
     assert message == ''
-    assert api.emit_metric.call_count == 2
-    # Verify that emit_metric was called with correct tags structure
-    call_args_list = api.emit_metric.call_args_list
+    assert api.emit_metric_sync.call_count == 2
+    # Verify that emit_metric_sync was called with correct tags structure
+    call_args_list = api.emit_metric_sync.call_args_list
     assert len(call_args_list) == 2
     # Check that all calls include core_labels and tag_name
     for call_args in call_args_list:
@@ -390,8 +384,7 @@ async def test_process_pi_web_api_response_success(api):
         assert call_args.kwargs['tags']['tag_name'] in ['tag1', 'tag2']
 
 
-@mark.asyncio
-async def test_process_pi_web_api_response_with_errors(api):
+def test_process_pi_web_api_response_with_errors(api):
     """Test processing response with errors in some tags."""
     response_data = [
         {'WebId': 'web_id_1', 'Errors': ['Error writing tag']},
@@ -405,7 +398,7 @@ async def test_process_pi_web_api_response_with_errors(api):
         'workflow_name': 'test_workflow',
     }
 
-    confidence, message = await api.process_pi_web_api_response(
+    confidence, message = api.process_pi_web_api_response(
         response_data=response_data,
         tags=tags,
         core_labels=core_labels,
@@ -417,11 +410,10 @@ async def test_process_pi_web_api_response_with_errors(api):
         message
         == "The number of written tags does not match the number of tag names: Expected ['tag1', 'tag2'] tags, but ['tag2'] tags were written."
     )
-    assert api.emit_metric.call_count == 2
+    assert api.emit_metric_sync.call_count == 2
 
 
-@mark.asyncio
-async def test_process_pi_web_api_response_missing_tags(api):
+def test_process_pi_web_api_response_missing_tags(api):
     """Test processing response when number of written tags doesn't match expected."""
     response_data = [
         {'WebId': 'web_id_1', 'Errors': []},
@@ -434,7 +426,7 @@ async def test_process_pi_web_api_response_missing_tags(api):
         'workflow_name': 'test_workflow',
     }
 
-    confidence, message = await api.process_pi_web_api_response(
+    confidence, message = api.process_pi_web_api_response(
         response_data=response_data,
         tags=tags,
         core_labels=core_labels,
@@ -446,14 +438,13 @@ async def test_process_pi_web_api_response_missing_tags(api):
         message
         == "The number of written tags does not match the number of tag names: Expected ['tag1', 'tag2'] tags, but ['tag1'] tags were written."
     )
-    api.send_notification_async.assert_called_once()
-    call_args = api.send_notification_async.call_args
+    api.send_notification.assert_called_once()
+    call_args = api.send_notification.call_args
     assert call_args.kwargs['notification_id'] == 'WRITE_PI_WEB_API_PREDICTION_ERROR'
     assert call_args.kwargs['level'] == NotificationLevel.ERROR
 
 
-@mark.asyncio
-async def test_process_pi_web_api_response_missing_webid(api):
+def test_process_pi_web_api_response_missing_webid(api):
     """Test processing response when WebId is missing in response item."""
     response_data = [
         {'Errors': []},
@@ -467,7 +458,7 @@ async def test_process_pi_web_api_response_missing_webid(api):
         'workflow_name': 'test_workflow',
     }
 
-    confidence, message = await api.process_pi_web_api_response(
+    confidence, message = api.process_pi_web_api_response(
         response_data=response_data,
         tags=tags,
         core_labels=core_labels,
@@ -482,8 +473,7 @@ async def test_process_pi_web_api_response_missing_webid(api):
     api.error.assert_any_call('The response did not contain some WebIds', metadata['metadata'])
 
 
-@mark.asyncio
-async def test_process_pi_web_api_response_missing_tag_name(api):
+def test_process_pi_web_api_response_missing_tag_name(api):
     """Test processing response when tag name is not found for WebId."""
     response_data = [
         {'WebId': 'unknown_web_id', 'Errors': []},
@@ -496,7 +486,7 @@ async def test_process_pi_web_api_response_missing_tag_name(api):
         'workflow_name': 'test_workflow',
     }
 
-    confidence, message = await api.process_pi_web_api_response(
+    confidence, message = api.process_pi_web_api_response(
         response_data=response_data,
         tags=tags,
         core_labels=core_labels,

tests/laborious/activities/test_gates.py

@@ -1,8 +1,9 @@
-from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
+from unittest.mock import ANY, MagicMock, call, patch
 
 from pandas import DataFrame
-from pytest import fixture, mark
+from pytest import fixture
 from sientia_do.notifications.models import NotificationLevel
+from sientia_do.observability.sientia_monitoring import SientiaMonitoring
 
 from laborious.activities.gates import Gates
 
@@ -17,17 +18,17 @@ def _passthrough_from_dict():
 
 def _minio_payload(retrieve_return, status=None):
     """
-    Build a MinioDataFramePayload-like test double with async retrieve.
+    Build a MinioDataFramePayload-like test double with retrieve.
 
     Args:
-        retrieve_return: Value returned from await retrieve(minio_repo, metadata).
+        retrieve_return: Value returned from retrieve(minio_repo, metadata).
         status: Optional status dict for MLflow response gate (payload.status).
 
     Return:
         MagicMock: Object with async retrieve and optional status.
     """
     p = MagicMock()
-    p.retrieve = AsyncMock(return_value=retrieve_return)
+    p.retrieve = MagicMock(return_value=retrieve_return)
     p.status = status
     return p
 
@@ -37,7 +38,7 @@ def gates_activity():
     gates = Gates(
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     gates.error = MagicMock()
     gates.debug = MagicMock()
@@ -45,8 +46,7 @@ def gates_activity():
     gates.warning = MagicMock()
     gates.critical = MagicMock()
     gates.send_notification = MagicMock()
-    gates.send_notification_async = AsyncMock()
-    gates.emit_metric = AsyncMock()
+    gates.emit_metric_sync = MagicMock()
     return gates
 
 
@@ -60,8 +60,7 @@ metadata = {
 }
 
 
-@mark.asyncio
-async def test_input_gate_invalid_filter(gates_activity):
+def test_input_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -71,7 +70,7 @@ async def test_input_gate_invalid_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
@@ -80,9 +79,8 @@ async def test_input_gate_invalid_filter(gates_activity):
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.gates.input_filter_functions')
-async def test_input_gate_filter_exception(mock_input_filter_functions, gates_activity):
+def test_input_gate_filter_exception(mock_input_filter_functions, gates_activity):
     # Arrange
     mock_input_filter_functions.__contains__.return_value = True
     mock_input_filter_functions.__getitem__.return_value = MagicMock(
@@ -96,11 +94,11 @@ async def test_input_gate_filter_exception(mock_input_filter_functions, gates_ac
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
-    gates_activity.send_notification_async.assert_called_once_with(
+    gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='INTPUT_GATE_ERROR__EMPTY_DATA',
         message="Error in filter EMPTY_DATA:{'POLICY': 'STOP', 'CONFIG': {}}: \n Test error",
@@ -110,8 +108,7 @@ async def test_input_gate_filter_exception(mock_input_filter_functions, gates_ac
     )
 
 
-@mark.asyncio
-async def test_input_gate_no_filters(gates_activity):
+def test_input_gate_no_filters(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -121,15 +118,14 @@ async def test_input_gate_no_filters(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_input_gate_with_filter(gates_activity):
+def test_input_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -139,15 +135,14 @@ async def test_input_gate_with_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'Input data with bad quality')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_input_gate_with_filter_lowercase_keys(gates_activity):
+def test_input_gate_with_filter_lowercase_keys(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -157,14 +152,13 @@ async def test_input_gate_with_filter_lowercase_keys(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'Input data with bad quality')
 
 
-@mark.asyncio
-async def test_input_gate_with_filter_capitalized_keys(gates_activity):
+def test_input_gate_with_filter_capitalized_keys(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -174,14 +168,13 @@ async def test_input_gate_with_filter_capitalized_keys(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'Input data with bad quality')
 
 
-@mark.asyncio
-async def test_input_gate_with_filter_not_caught(gates_activity):
+def test_input_gate_with_filter_not_caught(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -191,15 +184,14 @@ async def test_input_gate_with_filter_not_caught(gates_activity):
     }
 
     # Act
-    result = await gates_activity.input_gate(input_data)
+    result = gates_activity.input_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_response_gate_invalid_filter(gates_activity):
+def test_mlflow_response_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -213,15 +205,14 @@ async def test_mlflow_response_gate_invalid_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
 
 
-@mark.asyncio
 @patch('laborious.activities.gates.mlflow_response_filter_functions')
-async def test_mlflow_response_gate_filter_exception(
+def test_mlflow_response_gate_filter_exception(
     mock_mlflow_response_filter_functions, gates_activity
 ):
     # Arrange
@@ -241,11 +232,11 @@ async def test_mlflow_response_gate_filter_exception(
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
-    gates_activity.send_notification_async.assert_called_once_with(
+    gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER',
         message="Error in filter INVALID_FILTER:{'POLICY': 'STOP'}: \n Test error",
@@ -255,8 +246,7 @@ async def test_mlflow_response_gate_filter_exception(
     )
 
 
-@mark.asyncio
-async def test_mlflow_response_gate_no_filters(gates_activity):
+def test_mlflow_response_gate_no_filters(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -270,15 +260,14 @@ async def test_mlflow_response_gate_no_filters(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_response_gate_with_filter(gates_activity):
+def test_mlflow_response_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -292,16 +281,15 @@ async def test_mlflow_response_gate_with_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'API error occurred')
     gates_activity.debug.assert_called()
-    gates_activity.send_notification_async.assert_called()
+    gates_activity.send_notification.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
+def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -315,14 +303,13 @@ async def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity)
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'API error occurred')
 
 
-@mark.asyncio
-async def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
+def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -336,15 +323,14 @@ async def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_response_gate(input_data)
+    result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_content_gate_invalid_filter(gates_activity):
+def test_mlflow_content_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -355,17 +341,14 @@ async def test_mlflow_content_gate_invalid_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
 
 
-@mark.asyncio
 @patch('laborious.activities.gates.mlflow_content_filter_functions')
-async def test_mlflow_content_gate_filter_exception(
-    mock_mlflow_content_filter_functions, gates_activity
-):
+def test_mlflow_content_gate_filter_exception(mock_mlflow_content_filter_functions, gates_activity):
     # Arrange
     mock_mlflow_content_filter_functions.__contains__.return_value = True
     mock_mlflow_content_filter_functions.__getitem__.return_value = MagicMock(
@@ -380,12 +363,12 @@ async def test_mlflow_content_gate_filter_exception(
     }
 
     # Act
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
-    gates_activity.send_notification_async.assert_called_once_with(
+    gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='MLFLOW_GATE_CONTENT_FILTER__API_ERROR',
         message="Error in filter API_ERROR:{'POLICY': 'STOP'}: \n Test error",
@@ -395,8 +378,7 @@ async def test_mlflow_content_gate_filter_exception(
     )
 
 
-@mark.asyncio
-async def test_mlflow_content_gate_no_filters(gates_activity):
+def test_mlflow_content_gate_no_filters(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -407,15 +389,14 @@ async def test_mlflow_content_gate_no_filters(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_content_gate_with_filter(gates_activity):
+def test_mlflow_content_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -426,16 +407,15 @@ async def test_mlflow_content_gate_with_filter(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     # Assert
     assert result == ('STOP', -1, 'Transformed data not passed the content filter')
     gates_activity.debug.assert_called()
-    gates_activity.send_notification_async.assert_called()
+    gates_activity.send_notification.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_content_gate_with_filter_not_caught(gates_activity):
+def test_mlflow_content_gate_with_filter_not_caught(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -446,15 +426,14 @@ async def test_mlflow_content_gate_with_filter_not_caught(gates_activity):
     }
 
     # Act
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     # Assert
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_mlflow_content_gate_filter_returns_false(gates_activity):
+def test_mlflow_content_gate_filter_returns_false(gates_activity):
     input_data = {
         **metadata,
         'filters': {'NAN_VALUES': {'POLICY': 'STOP', 'CONFIG': {}}},
@@ -463,7 +442,7 @@ async def test_mlflow_content_gate_filter_returns_false(gates_activity):
         'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
-    result = await gates_activity.mlflow_content_gate(input_data)
+    result = gates_activity.mlflow_content_gate(input_data)
 
     assert result == (None, 0, '')
     gates_activity.debug.assert_called()
@@ -525,8 +504,7 @@ def test_get_prediction_store_policy_valid_policy(gates_activity):
     assert policy_value == 1
 
 
-@mark.asyncio
-async def test_format_prediction_no_timestamp(gates_activity):
+def test_format_prediction_no_timestamp(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -545,7 +523,7 @@ async def test_format_prediction_no_timestamp(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_prediction(input_data)
+    result = gates_activity.format_prediction(input_data)
 
     # Assert
     assert result['prediction'] == {0: 1}
@@ -557,8 +535,7 @@ async def test_format_prediction_no_timestamp(gates_activity):
     assert result['comments'] == {0: ''}
 
 
-@mark.asyncio
-async def test_format_prediction_with_timestamp_erl(gates_activity):
+def test_format_prediction_with_timestamp_erl(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -585,7 +562,7 @@ async def test_format_prediction_with_timestamp_erl(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_prediction(input_data)
+    result = gates_activity.format_prediction(input_data)
 
     # Assert
     assert result['prediction'] == {0: 2, 1: 1}
@@ -597,8 +574,7 @@ async def test_format_prediction_with_timestamp_erl(gates_activity):
     assert result['comments'] == {0: '', 1: ''}
 
 
-@mark.asyncio
-async def test_format_prediction_with_timestamp_lts(gates_activity):
+def test_format_prediction_with_timestamp_lts(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -625,7 +601,7 @@ async def test_format_prediction_with_timestamp_lts(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_prediction(input_data)
+    result = gates_activity.format_prediction(input_data)
 
     # Assert
     assert result['prediction'] == {0: 3, 1: 2}
@@ -637,8 +613,7 @@ async def test_format_prediction_with_timestamp_lts(gates_activity):
     assert result['comments'] == {0: '', 1: ''}
 
 
-@mark.asyncio
-async def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
+def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -663,16 +638,15 @@ async def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
     gates_activity.get_prediction_store_policy = MagicMock(return_value=('invalid', 1))
 
     try:
-        await gates_activity.format_prediction(input_data)
+        gates_activity.format_prediction(input_data)
     except ValueError as e:
         assert str(e) == 'Invalid policy type: invalid'
     else:
         raise AssertionError('Expected ValueError')
 
 
-@mark.asyncio
-@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=AsyncMock)
-async def test_format_transformed_data_single_row(mock_from_dataframe, gates_activity):
+@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
+def test_format_transformed_data_single_row(mock_from_dataframe, gates_activity):
     # Arrange
     payload_result = MagicMock()
     mock_from_dataframe.return_value = payload_result
@@ -691,7 +665,7 @@ async def test_format_transformed_data_single_row(mock_from_dataframe, gates_act
     }
 
     # Act
-    result = await gates_activity.format_transformed_data(input_data)
+    result = gates_activity.format_transformed_data(input_data)
 
     # Assert
     assert result is payload_result
@@ -705,9 +679,8 @@ async def test_format_transformed_data_single_row(mock_from_dataframe, gates_act
     gates_activity.info.assert_called()
 
 
-@mark.asyncio
-@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=AsyncMock)
-async def test_format_transformed_data_multiple_rows(mock_from_dataframe, gates_activity):
+@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
+def test_format_transformed_data_multiple_rows(mock_from_dataframe, gates_activity):
     # Arrange
     payload_result = MagicMock()
     mock_from_dataframe.return_value = payload_result
@@ -732,7 +705,7 @@ async def test_format_transformed_data_multiple_rows(mock_from_dataframe, gates_
     }
 
     # Act
-    result = await gates_activity.format_transformed_data(input_data)
+    result = gates_activity.format_transformed_data(input_data)
 
     # Assert
     assert result is payload_result
@@ -746,9 +719,8 @@ async def test_format_transformed_data_multiple_rows(mock_from_dataframe, gates_
     gates_activity.info.assert_called()
 
 
-@mark.asyncio
-@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=AsyncMock)
-async def test_format_transformed_data_empty_data(mock_from_dataframe, gates_activity):
+@patch('laborious.activities.gates.MinioDataFramePayload.from_dataframe', new_callable=MagicMock)
+def test_format_transformed_data_empty_data(mock_from_dataframe, gates_activity):
     # Arrange
     payload_result = MagicMock()
     mock_from_dataframe.return_value = payload_result
@@ -760,7 +732,7 @@ async def test_format_transformed_data_empty_data(mock_from_dataframe, gates_act
     }
 
     # Act
-    result = await gates_activity.format_transformed_data(input_data)
+    result = gates_activity.format_transformed_data(input_data)
 
     # Assert
     assert result is payload_result
@@ -774,8 +746,7 @@ async def test_format_transformed_data_empty_data(mock_from_dataframe, gates_act
     gates_activity.info.assert_called()
 
 
-@mark.asyncio
-async def test_format_default_prediction(gates_activity):
+def test_format_default_prediction(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -786,7 +757,7 @@ async def test_format_default_prediction(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_default_prediction(input_data)
+    result = gates_activity.format_default_prediction(input_data)
 
     # Assert
     assert result['prediction'] == {0: 0}
@@ -799,8 +770,7 @@ async def test_format_default_prediction(gates_activity):
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
-async def test_format_retrain_report(gates_activity):
+def test_format_retrain_report(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -819,7 +789,7 @@ async def test_format_retrain_report(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_retrain_report(input_data)
+    result = gates_activity.format_retrain_report(input_data)
 
     # Assert
     assert result['model_id'] == {0: 'test_model'}
@@ -831,8 +801,7 @@ async def test_format_retrain_report(gates_activity):
     assert result['mlflow_experiment_id'] == {0: 'test_mlflow_experiment_id'}
 
 
-@mark.asyncio
-async def test_format_retrain_report_failure(gates_activity):
+def test_format_retrain_report_failure(gates_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -851,7 +820,7 @@ async def test_format_retrain_report_failure(gates_activity):
     }
 
     # Act
-    result = await gates_activity.format_retrain_report(input_data)
+    result = gates_activity.format_retrain_report(input_data)
 
     # Assert
     assert result['model_id'] == {0: 'test_model'}
@@ -865,9 +834,8 @@ async def test_format_retrain_report_failure(gates_activity):
     gates_activity.debug.assert_called()
 
 
-@mark.asyncio
 @patch('laborious.activities.gates.metrics')
-async def test_write_metrics(mock_metrics, gates_activity):
+def test_write_metrics(mock_metrics, gates_activity):
     """Test write_metrics method."""
     input_data = {
         **metadata,
@@ -878,7 +846,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
         },
         'opc_metrics': {'server1': {'tag1': 0.1, 'tag2': 0.2}},
     }
-    await gates_activity.write_metrics(input_data)
+    gates_activity.write_metrics(input_data)
     core_tags = {
         'pod_id': gates_activity.pod_id,
         'runtime': gates_activity.runtime,
@@ -886,7 +854,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
         'model_name': metadata['metadata']['model_name'],
         'workflow_name': metadata['metadata']['workflow_name'],
     }
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTIONS_WRITTEN_COUNT,
@@ -894,7 +862,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_CONFIDENCE_MONITOR,
@@ -904,7 +872,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR,
@@ -914,7 +882,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_OPC_WRITING_COUNT,
@@ -926,7 +894,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
@@ -940,7 +908,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_OPC_WRITING_COUNT,
@@ -952,7 +920,7 @@ async def test_write_metrics(mock_metrics, gates_activity):
             ),
         ]
     )
-    gates_activity.emit_metric.assert_has_calls(
+    gates_activity.emit_metric_sync.assert_has_calls(
         [
             call(
                 metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
@@ -968,9 +936,8 @@ async def test_write_metrics(mock_metrics, gates_activity):
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.gates.metrics')
-async def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_activity):
+def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_activity):
     """Test write_metrics method with None response_time in opc_metrics."""
     input_data = {
         **metadata,
@@ -981,10 +948,10 @@ async def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_act
         },
         'opc_metrics': {'server1': {'tag1': 0.1, 'tag2': None}},
     }
-    await gates_activity.write_metrics(input_data)
+    gates_activity.write_metrics(input_data)
 
     # Verify that metrics for tag1 are emitted
-    gates_activity.emit_metric.assert_any_call(
+    gates_activity.emit_metric_sync.assert_any_call(
         metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
         method='observe',
         tags={
@@ -1002,7 +969,38 @@ async def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_act
     # Verify that metrics for tag2 (with None response_time) are NOT emitted
     calls = [
         c
-        for c in gates_activity.emit_metric.call_args_list
+        for c in gates_activity.emit_metric_sync.call_args_list
         if len(c[1].get('tags', {})) > 0 and c[1]['tags'].get('tag') == 'tag2'
     ]
     assert len(calls) == 0, 'Metrics should not be emitted for None response_time'
+
+
+@patch.object(SientiaMonitoring, 'shutdown')
+def test_close_disposes_minio_repository(mock_shutdown):
+    """
+    ``Gates.close`` should close the optional MinIO client and clear the repository reference.
+    """
+    minio = MagicMock()
+    gates = Gates(
+        minio_repository=minio,
+        logger=MagicMock(),
+        notification_handler=MagicMock(),
+        metrics_controller=MagicMock(),
+    )
+    gates.close()
+    minio.close.assert_called_once()
+    assert gates.minio_repository is None
+    mock_shutdown.assert_called_once_with(gates)
+
+
+@patch.object(SientiaMonitoring, 'shutdown')
+def test_close_without_minio_repository(mock_shutdown):
+    """When no MinIO repository is configured, ``close`` only shuts down monitoring."""
+    gates = Gates(
+        minio_repository=None,
+        logger=MagicMock(),
+        notification_handler=MagicMock(),
+        metrics_controller=MagicMock(),
+    )
+    gates.close()
+    mock_shutdown.assert_called_once_with(gates)

tests/laborious/activities/test_mlflow.py

@@ -1,9 +1,9 @@
 from datetime import datetime
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
+from unittest.mock import ANY, MagicMock, patch
 
 import numpy as np
 import pandas as pd
-from pytest import fixture, mark, raises
+from pytest import fixture, raises
 from sientia_do.notifications.models import NotificationLevel
 from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
 
@@ -22,7 +22,7 @@ def _passthrough_from_dict():
 def test___init__(mock_minio_repository):
     logger = MagicMock()
     notification_handler = MagicMock()
-    metrics_controller = AsyncMock()
+    metrics_controller = MagicMock()
     mlflow_repo = MagicMock()
     plugin_store = MagicMock()
 
@@ -63,7 +63,7 @@ def test___init__(mock_minio_repository):
 def mlflow(mock_minio_repository):
     logger = MagicMock()
     notification_handler = MagicMock()
-    metrics_controller = AsyncMock()
+    metrics_controller = MagicMock()
     mlflow_repo = MagicMock()
     plugin_store = MagicMock()
 
@@ -85,11 +85,10 @@ def mlflow(mock_minio_repository):
         metrics_controller=metrics_controller,
     )
 
-    mlflow.minio_repository = AsyncMock()
+    mlflow.minio_repository = MagicMock()
 
     mlflow.send_notification = MagicMock()
-    mlflow.emit_metric = AsyncMock()
-    mlflow.send_notification_async = AsyncMock()
+    mlflow.emit_metric = MagicMock()
     mlflow.error = MagicMock()
     mlflow.debug = MagicMock()
     mlflow.info = MagicMock()
@@ -150,12 +149,11 @@ def test_detect_and_parse_datetime_index_timestamp_with_tz_success(mlflow):
     assert out.index[0].endswith('+0000')
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
-async def test_request_transform_success(mock_from_dataframe, mlflow):
+def test_request_transform_success(mock_from_dataframe, mlflow):
     ts = pd.Timestamp('2020-01-01', tz='UTC')
     raw = pd.DataFrame(
         {
@@ -180,8 +178,8 @@ async def test_request_transform_success(mock_from_dataframe, mlflow):
     wrapper.transform.return_value = (out_df, {'meta': True})
     mlflow.mlflow_repository.get_cached_model.return_value = wrapper
 
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw)
 
     input_data = {
         **metadata,
@@ -190,7 +188,7 @@ async def test_request_transform_success(mock_from_dataframe, mlflow):
         'model_config': {},
     }
 
-    response_data = await mlflow.request_transform(input_data)
+    response_data = mlflow.request_transform(input_data)
 
     mlflow.mlflow_repository.get_cached_model.assert_called_once_with(
         model_name='test_model',
@@ -202,12 +200,11 @@ async def test_request_transform_success(mock_from_dataframe, mlflow):
     assert response_data == mock_from_dataframe.return_value
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
-async def test_request_transform_success_without_transform_meta(mock_from_dataframe, mlflow):
+def test_request_transform_success_without_transform_meta(mock_from_dataframe, mlflow):
     ts = pd.Timestamp('2020-01-01', tz='UTC')
     raw = pd.DataFrame(
         {
@@ -223,8 +220,8 @@ async def test_request_transform_success_without_transform_meta(mock_from_datafr
     wrapper.transform.return_value = (out_df, {})
     mlflow.mlflow_repository.get_cached_model.return_value = wrapper
 
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw)
 
     input_data = {
         **metadata,
@@ -233,21 +230,20 @@ async def test_request_transform_success_without_transform_meta(mock_from_datafr
         'model_config': {},
     }
 
-    await mlflow.request_transform(input_data)
+    mlflow.request_transform(input_data)
     mock_from_dataframe.assert_called_once()
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
-async def test_request_transform_failure(mock_from_dataframe, mlflow):
+def test_request_transform_failure(mock_from_dataframe, mlflow):
     mlflow.mlflow_repository.get_cached_model.side_effect = RuntimeError('boom')
 
     data_mock = MagicMock()
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=data_mock)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=data_mock)
 
     input_data = {
         **metadata,
@@ -260,7 +256,7 @@ async def test_request_transform_failure(mock_from_dataframe, mlflow):
     data_mock.drop_duplicates.return_value = data_mock
     data_mock.pivot.return_value = data_mock
 
-    await mlflow.request_transform(input_data)
+    mlflow.request_transform(input_data)
 
     mock_from_dataframe.assert_called_once_with(
         dataframe=None,
@@ -274,13 +270,12 @@ async def test_request_transform_failure(mock_from_dataframe, mlflow):
     )
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
+def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
     wrapper = MagicMock()
     pred_df = MagicMock()
     wrapper.predict.return_value = (pred_df, {})
@@ -288,8 +283,8 @@ async def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
 
     data_mock = MagicMock()
     data_mock.index = pd.DatetimeIndex([pd.Timestamp('2020-01-01', tz='UTC')])
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=data_mock)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=data_mock)
 
     input_data = {
         **metadata,
@@ -301,7 +296,7 @@ async def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
     pred_df.columns = MagicMock()
     pred_df.__setitem__ = MagicMock()
 
-    response_data = await mlflow.request_predict(input_data)
+    response_data = mlflow.request_predict(input_data)
 
     data_mock.replace.assert_called_once_with(np.nan, None, inplace=True)
     mock_to_datetime.assert_called()
@@ -310,15 +305,12 @@ async def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
     assert response_data == mock_from_dataframe.return_value
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_request_predict_success_dataframe_and_meta(
-    mock_to_datetime, mock_from_dataframe, mlflow
-):
+def test_request_predict_success_dataframe_and_meta(mock_to_datetime, mock_from_dataframe, mlflow):
     wrapper = MagicMock()
     pred_df = pd.DataFrame({'raw': [0.3]})
     wrapper.predict.return_value = (pred_df, {'m': 1})
@@ -326,8 +318,8 @@ async def test_request_predict_success_dataframe_and_meta(
 
     data_mock = MagicMock()
     data_mock.index = pd.DatetimeIndex([pd.Timestamp('2020-01-01', tz='UTC')])
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=data_mock)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=data_mock)
 
     input_data = {
         **metadata,
@@ -336,25 +328,24 @@ async def test_request_predict_success_dataframe_and_meta(
         'model_config': {},
     }
 
-    await mlflow.request_predict(input_data)
+    mlflow.request_predict(input_data)
 
     assert list(pred_df.columns) == ['prediction', 'response_time']
     mlflow.info.assert_any_call("Wrapper predict metadata: {'m': 1}", metadata['metadata'])
     mock_from_dataframe.assert_called_once()
 
 
-@mark.asyncio
 @patch(
     'laborious.activities.mlflow.MinioDataFramePayload.from_dataframe',
-    new_callable=AsyncMock,
+    new_callable=MagicMock,
 )
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_request_predict_failure(mock_to_datetime, mock_from_dataframe, mlflow):
+def test_request_predict_failure(mock_to_datetime, mock_from_dataframe, mlflow):
     mlflow.mlflow_repository.get_cached_model.side_effect = RuntimeError('predict boom')
 
     data_mock = MagicMock()
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=data_mock)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=data_mock)
 
     input_data = {
         **metadata,
@@ -363,7 +354,7 @@ async def test_request_predict_failure(mock_to_datetime, mock_from_dataframe, ml
         'model_config': {},
     }
 
-    await mlflow.request_predict(input_data)
+    mlflow.request_predict(input_data)
 
     mock_from_dataframe.assert_called_once_with(
         dataframe=None,
@@ -377,12 +368,11 @@ async def test_request_predict_failure(mock_to_datetime, mock_from_dataframe, ml
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.mlflow.mlflow.log_artifact')
 @patch('laborious.activities.mlflow.tempfile.mkdtemp')
 @patch('laborious.activities.mlflow.rmtree')
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_retrain_model_success_data_success_retrain(
+def test_retrain_model_success_data_success_retrain(
     mock_to_datetime, mock_rmtree, mock_mkdtemp, mock_log_artifact, mlflow
 ):
     mock_mkdtemp.return_value = 'tmp'
@@ -408,10 +398,10 @@ async def test_retrain_model_success_data_success_retrain(
         }
     )
 
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw_data)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw_data)
 
-    response = await mlflow.retrain_model(
+    response = mlflow.retrain_model(
         {
             **metadata,
             'data': payload,
@@ -428,12 +418,11 @@ async def test_retrain_model_success_data_success_retrain(
     assert response['experiment']['run_id'] == 'new-run'
 
 
-@mark.asyncio
 @patch('laborious.activities.mlflow.mlflow.log_artifact')
 @patch('laborious.activities.mlflow.tempfile.mkdtemp')
 @patch('laborious.activities.mlflow.rmtree')
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_retrain_model_success_with_payload_data(
+def test_retrain_model_success_with_payload_data(
     mock_to_datetime, mock_rmtree, mock_mkdtemp, mock_log_artifact, mlflow
 ):
     mock_mkdtemp.return_value = 'tmp'
@@ -448,8 +437,8 @@ async def test_retrain_model_success_with_payload_data(
 
     raw_data = MagicMock(columns=['variable', 'timestamp', 'value', 'created_at'])
     raw_data.__getitem__.return_value.max.return_value = 'ts'
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw_data)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw_data)
 
     pivoted = MagicMock()
     raw_data.sort_values.return_value = raw_data
@@ -460,7 +449,7 @@ async def test_retrain_model_success_with_payload_data(
     pivoted.index = MagicMock()
     pivoted.__setitem__ = MagicMock()
 
-    response = await mlflow.retrain_model(
+    response = mlflow.retrain_model(
         {
             **metadata,
             'data': payload,
@@ -472,12 +461,11 @@ async def test_retrain_model_success_with_payload_data(
     assert response['success'] is True
 
 
-@mark.asyncio
 @patch('laborious.activities.mlflow.mlflow.log_artifact')
 @patch('laborious.activities.mlflow.tempfile.mkdtemp')
 @patch('laborious.activities.mlflow.rmtree')
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_retrain_model_always_uses_retrain_even_with_full_retrain_flag(
+def test_retrain_model_always_uses_retrain_even_with_full_retrain_flag(
     mock_to_datetime, mock_rmtree, mock_mkdtemp, mock_log_artifact, mlflow
 ):
     mock_mkdtemp.return_value = 'tmp'
@@ -498,10 +486,10 @@ async def test_retrain_model_always_uses_retrain_even_with_full_retrain_flag(
             'value': [1.0, 2.0, 3.0, 4.0],
         }
     )
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw_data)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw_data)
 
-    response = await mlflow.retrain_model(
+    response = mlflow.retrain_model(
         {
             **metadata,
             'data': payload,
@@ -515,17 +503,16 @@ async def test_retrain_model_always_uses_retrain_even_with_full_retrain_flag(
     assert response['success'] is True
 
 
-@mark.asyncio
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow):
+def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow):
     mv_alias = MagicMock(run_id='src')
     mlflow.mlflow_repository._client.get_model_version_by_alias.return_value = mv_alias
     mlflow.mlflow_repository.get_cached_model.side_effect = RuntimeError('retrain failed')
 
     raw_data = MagicMock(columns=['variable', 'timestamp', 'value', 'created_at'])
     raw_data.__getitem__.return_value.max.return_value = 'tsmax'
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw_data)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw_data)
 
     pivoted = MagicMock()
     raw_data.sort_values.return_value = raw_data
@@ -536,7 +523,7 @@ async def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow)
     pivoted.index = MagicMock()
     pivoted.__setitem__ = MagicMock()
 
-    response = await mlflow.retrain_model(
+    response = mlflow.retrain_model(
         {
             **metadata,
             'data': payload,
@@ -549,9 +536,8 @@ async def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow)
     assert 'retrain failed' in response['message']
 
 
-@mark.asyncio
-async def test_retrain_model_data_error(mlflow):
-    response = await mlflow.retrain_model(
+def test_retrain_model_data_error(mlflow):
+    response = mlflow.retrain_model(
         {
             **metadata,
             'model_name': 'test_model',
@@ -565,8 +551,7 @@ async def test_retrain_model_data_error(mlflow):
     assert 'data' in response['message'].lower() or 'loading' in response['message'].lower()
 
 
-@mark.asyncio
-async def test_retrain_model_missing_target(mlflow):
+def test_retrain_model_missing_target(mlflow):
     ts = pd.Timestamp('2020-01-01', tz='UTC')
     raw_data = pd.DataFrame(
         {
@@ -575,10 +560,10 @@ async def test_retrain_model_missing_target(mlflow):
             'value': [1.0, 2.0],
         }
     )
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=raw_data)
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=raw_data)
 
-    response = await mlflow.retrain_model(
+    response = mlflow.retrain_model(
         {
             **metadata,
             'data': payload,
@@ -591,12 +576,11 @@ async def test_retrain_model_missing_target(mlflow):
     assert 'target' in response['message']
 
 
-@mark.asyncio
-async def test_retrain_model_data_error_no_minio_repository(mlflow):
+def test_retrain_model_data_error_no_minio_repository(mlflow):
     mlflow.minio_repository = None
 
     with raises(ValueError) as e:
-        await mlflow.retrain_model(
+        mlflow.retrain_model(
             {
                 **metadata,
                 'object_key': 'test_object_key',
@@ -610,8 +594,7 @@ async def test_retrain_model_data_error_no_minio_repository(mlflow):
     assert str(e.value) == 'Minio repository not initialized'
 
 
-@mark.asyncio
-async def test_update_production_model(mlflow):
+def test_update_production_model(mlflow):
     mlflow.mlflow_repository._client.search_model_versions.return_value = [
         MagicMock(version='3', run_id='run-x'),
         MagicMock(version='2', run_id='run-x'),
@@ -626,7 +609,7 @@ async def test_update_production_model(mlflow):
         'status': 'success',
     }
 
-    response = await mlflow.update_production_model(input_data)
+    response = mlflow.update_production_model(input_data)
 
     mlflow.mlflow_repository.promote_to_alias.assert_called_once_with(
         model_name='test_model',
@@ -639,8 +622,7 @@ async def test_update_production_model(mlflow):
     assert response['version'] == '3'
 
 
-@mark.asyncio
-async def test_update_production_model_error(mlflow):
+def test_update_production_model_error(mlflow):
     mlflow.mlflow_repository._client.search_model_versions.return_value = []
 
     input_data = {
@@ -653,9 +635,9 @@ async def test_update_production_model_error(mlflow):
     }
 
     try:
-        await mlflow.update_production_model(input_data)
+        mlflow.update_production_model(input_data)
     except Exception:
-        mlflow.send_notification_async.assert_called_once_with(
+        mlflow.send_notification.assert_called_once_with(
             metadata=metadata['metadata'],
             notification_id='UPDATE_PRODUCTION_MODEL_ERROR',
             message=ANY,
@@ -667,9 +649,8 @@ async def test_update_production_model_error(mlflow):
         raise AssertionError('Expected exception')
 
 
-@mark.asyncio
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_get_reference_data_success(mock_to_datetime, mlflow):
+def test_get_reference_data_success(mock_to_datetime, mlflow):
     input_data = {
         **metadata,
         'model_name': 'test_model',
@@ -690,14 +671,13 @@ async def test_get_reference_data_success(mock_to_datetime, mlflow):
             with patch('laborious.activities.mlflow.rmtree'):
                 with patch('laborious.activities.mlflow.Path') as mp:
                     mp.return_value.rglob.return_value = [MagicMock()]
-                    result = await mlflow.get_reference_data(input_data)
+                    result = mlflow.get_reference_data(input_data)
 
     mock_reference_data.to_dict.assert_called_once_with(orient='records')
     assert result == mock_reference_data.to_dict.return_value
 
 
-@mark.asyncio
-async def test_get_reference_data_not_found(mlflow):
+def test_get_reference_data_not_found(mlflow):
     input_data = {
         **metadata,
         'model_name': 'test_model',
@@ -705,14 +685,13 @@ async def test_get_reference_data_not_found(mlflow):
 
     mlflow.mlflow_repository._client.get_model_version_by_alias.side_effect = Exception('missing')
 
-    result = await mlflow.get_reference_data(input_data)
+    result = mlflow.get_reference_data(input_data)
 
     mlflow.warning.assert_called()
     assert result is None
 
 
-@mark.asyncio
-async def test_get_reference_data_missing_csv_file_returns_none(mlflow):
+def test_get_reference_data_missing_csv_file_returns_none(mlflow):
     input_data = {
         **metadata,
         'model_name': 'test_model',
@@ -724,13 +703,12 @@ async def test_get_reference_data_missing_csv_file_returns_none(mlflow):
         with patch('laborious.activities.mlflow.rmtree'):
             with patch('laborious.activities.mlflow.Path') as mp:
                 mp.return_value.rglob.return_value = []
-                result = await mlflow.get_reference_data(input_data)
+                result = mlflow.get_reference_data(input_data)
 
     assert result is None
 
 
-@mark.asyncio
-async def test_get_reference_data_exception(mlflow):
+def test_get_reference_data_exception(mlflow):
     input_data = {
         **metadata,
         'model_name': 'test_model',
@@ -740,6 +718,6 @@ async def test_get_reference_data_exception(mlflow):
     mlflow.mlflow_repository._client.get_model_version_by_alias.return_value = mv
     mlflow.mlflow_repository.download_artifacts.side_effect = Exception('dl fail')
 
-    result = await mlflow.get_reference_data(input_data)
+    result = mlflow.get_reference_data(input_data)
 
     assert result is None

tests/laborious/activities/test_model_metrics.py

@@ -1,7 +1,7 @@
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
+from unittest.mock import ANY, MagicMock, patch
 
 from pandas import DataFrame
-from pytest import fixture, mark
+from pytest import fixture
 from sientia_do.notifications.models import NotificationLevel
 
 from laborious.activities.model_metrics import ModelMetrics
@@ -12,7 +12,7 @@ def model_metrics_activity():
     model_metrics = ModelMetrics(
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     model_metrics.error = MagicMock()
     model_metrics.debug = MagicMock()
@@ -20,8 +20,7 @@ def model_metrics_activity():
     model_metrics.warning = MagicMock()
     model_metrics.critical = MagicMock()
     model_metrics.send_notification = MagicMock()
-    model_metrics.send_notification_async = AsyncMock()
-    model_metrics.emit_metric = AsyncMock()
+    model_metrics.emit_metric_sync = MagicMock()
     model_metrics.get_core_labels = MagicMock(
         return_value={
             'pod_id': 'test_pod',
@@ -29,7 +28,7 @@ def model_metrics_activity():
             'workflow_name': 'test_workflow',
         }
     )
-    model_metrics.observe_lag = AsyncMock()
+    model_metrics.observe_lag_sync = MagicMock()
     model_metrics.pod_id = 'test_pod'
     return model_metrics
 
@@ -44,8 +43,7 @@ metadata = {
 }
 
 
-@mark.asyncio
-async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
+def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
     # Arrange
     input_data = {
         **metadata,
@@ -64,7 +62,7 @@ async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
 
     # Act & Assert
     try:
-        await model_metrics_activity.calculate_drift(input_data)
+        model_metrics_activity.calculate_drift(input_data)
     except ValueError as e:
         assert str(e) == 'Invalid chunk period: invalid, must be "min" or "s"'
         model_metrics_activity.error.assert_called_once_with(
@@ -74,10 +72,9 @@ async def test_calculate_drift_invalid_chunk_period(model_metrics_activity):
         raise AssertionError('Expected ValueError')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_with_reference_data(
+def test_calculate_drift_with_reference_data(
     mock_to_datetime, mock_dataframe, model_metrics_activity
 ):
     # Arrange
@@ -107,7 +104,7 @@ async def test_calculate_drift_with_reference_data(
         }
     ]
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
 
     reference_data = DataFrame(
         {
@@ -142,7 +139,7 @@ async def test_calculate_drift_with_reference_data(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert isinstance(result, list)
@@ -150,10 +147,18 @@ async def test_calculate_drift_with_reference_data(
     model_metrics_activity.info.assert_called()
     model_metrics_activity.get_drift_metrics.assert_called_once()
     # Verify transformations were called
-    mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
+    mock_drift_df.drop.assert_called_once_with(
+        columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
+    )
     mock_drift_df.__getitem__.assert_called()
     mock_drift_df.rename.assert_called_once_with(
-        columns={'metric': 'method', 'statistic': 'value'}, inplace=True
+        columns={
+            'metric': 'method',
+            'statistic': 'value',
+            'alert': 'drift',
+            'chunk_index': 'chunk',
+            'chunk_end_date': 'timestamp_end',
+        }
     )
     mock_drift_df.drop_duplicates.assert_called_once_with(
         subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -161,10 +166,9 @@ async def test_calculate_drift_with_reference_data(
     mock_drift_df.to_dict.assert_called_once_with(orient='records')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_without_reference_data(
+def test_calculate_drift_without_reference_data(
     mock_to_datetime, mock_dataframe, model_metrics_activity
 ):
     # Arrange
@@ -194,7 +198,7 @@ async def test_calculate_drift_without_reference_data(
         }
     ]
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
 
     target_data_dict = {
         'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29'],
@@ -232,13 +236,13 @@ async def test_calculate_drift_without_reference_data(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert isinstance(result, list)
     assert result == mock_drift_df.to_dict.return_value
     model_metrics_activity.warning.assert_called()
-    model_metrics_activity.send_notification_async.assert_called_once_with(
+    model_metrics_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='MODEL_METRICS_REFERENCE_DATA_WARNING',
         message='Using 30% first rows of target data as reference data',
@@ -247,10 +251,18 @@ async def test_calculate_drift_without_reference_data(
         attachment_content=ANY,
     )
     # Verify transformations were called
-    mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
+    mock_drift_df.drop.assert_called_once_with(
+        columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
+    )
     mock_drift_df.__getitem__.assert_called()
     mock_drift_df.rename.assert_called_once_with(
-        columns={'metric': 'method', 'statistic': 'value'}, inplace=True
+        columns={
+            'metric': 'method',
+            'statistic': 'value',
+            'alert': 'drift',
+            'chunk_index': 'chunk',
+            'chunk_end_date': 'timestamp_end',
+        }
     )
     mock_drift_df.drop_duplicates.assert_called_once_with(
         subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -258,16 +270,13 @@ async def test_calculate_drift_without_reference_data(
     mock_drift_df.to_dict.assert_called_once_with(orient='records')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_empty_drift_df(
-    mock_to_datetime, mock_dataframe, model_metrics_activity
-):
+def test_calculate_drift_empty_drift_df(mock_to_datetime, mock_dataframe, model_metrics_activity):
     # Arrange
     mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=DataFrame())
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=DataFrame())
 
     reference_data = DataFrame(
         {
@@ -302,7 +311,7 @@ async def test_calculate_drift_empty_drift_df(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert result == []
@@ -311,10 +320,9 @@ async def test_calculate_drift_empty_drift_df(
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_empty_after_timestamp_filter(
+def test_calculate_drift_empty_after_timestamp_filter(
     mock_to_datetime, mock_dataframe, model_metrics_activity
 ):
     # Arrange
@@ -340,7 +348,7 @@ async def test_calculate_drift_empty_after_timestamp_filter(
 
     mock_drift_df.__getitem__.side_effect = getitem_side_effect
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
 
     reference_data = DataFrame(
         {
@@ -375,7 +383,7 @@ async def test_calculate_drift_empty_after_timestamp_filter(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert result == []
@@ -383,17 +391,16 @@ async def test_calculate_drift_empty_after_timestamp_filter(
         'No drift metrics found after dropping rows where timestamp is not in target data',
         metadata['metadata'],
     )
-    # Verify transformations were called
-    mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
+    # When the timestamp filter empties the dataframe, the rename/drop pipeline
+    # is short-circuited, so neither ``drop`` nor ``rename`` should run.
+    mock_drift_df.drop.assert_not_called()
+    mock_drift_df.rename.assert_not_called()
     mock_drift_df.__getitem__.assert_called()
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_success_min(
-    mock_to_datetime, mock_dataframe, model_metrics_activity
-):
+def test_calculate_drift_success_min(mock_to_datetime, mock_dataframe, model_metrics_activity):
     # Arrange
     mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
     mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
@@ -421,7 +428,7 @@ async def test_calculate_drift_success_min(
         }
     ]
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
 
     reference_data = DataFrame(
         {
@@ -456,7 +463,7 @@ async def test_calculate_drift_success_min(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert isinstance(result, list)
@@ -464,10 +471,18 @@ async def test_calculate_drift_success_min(
     model_metrics_activity.info.assert_called()
     model_metrics_activity.get_drift_metrics.assert_called_once()
     # Verify transformations were called
-    mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
+    mock_drift_df.drop.assert_called_once_with(
+        columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
+    )
     mock_drift_df.__getitem__.assert_called()
     mock_drift_df.rename.assert_called_once_with(
-        columns={'metric': 'method', 'statistic': 'value'}, inplace=True
+        columns={
+            'metric': 'method',
+            'statistic': 'value',
+            'alert': 'drift',
+            'chunk_index': 'chunk',
+            'chunk_end_date': 'timestamp_end',
+        }
     )
     mock_drift_df.drop_duplicates.assert_called_once_with(
         subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -475,10 +490,9 @@ async def test_calculate_drift_success_min(
     mock_drift_df.to_dict.assert_called_once_with(orient='records')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metrics_activity):
+def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model_metrics_activity):
     # Arrange
     mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
     mock_to_datetime.return_value.dt.tz_localize.return_value.dt.strftime.return_value = (
@@ -506,7 +520,7 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
         }
     ]
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(return_value=mock_drift_df)
+    model_metrics_activity.get_drift_metrics = MagicMock(return_value=mock_drift_df)
 
     reference_data = DataFrame(
         {
@@ -541,7 +555,7 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert isinstance(result, list)
@@ -549,10 +563,18 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
     model_metrics_activity.info.assert_called()
     model_metrics_activity.get_drift_metrics.assert_called_once()
     # Verify transformations were called
-    mock_drift_df.drop.assert_called_once_with(columns=['p_value'], inplace=True)
+    mock_drift_df.drop.assert_called_once_with(
+        columns=['p_value', 'chunk_start_date'], inplace=True, errors='ignore'
+    )
     mock_drift_df.__getitem__.assert_called()
     mock_drift_df.rename.assert_called_once_with(
-        columns={'metric': 'method', 'statistic': 'value'}, inplace=True
+        columns={
+            'metric': 'method',
+            'statistic': 'value',
+            'alert': 'drift',
+            'chunk_index': 'chunk',
+            'chunk_end_date': 'timestamp_end',
+        }
     )
     mock_drift_df.drop_duplicates.assert_called_once_with(
         subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
@@ -560,16 +582,15 @@ async def test_calculate_drift_success_s(mock_to_datetime, mock_dataframe, model
     mock_drift_df.to_dict.assert_called_once_with(orient='records')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.DataFrame')
 @patch('laborious.activities.model_metrics.to_datetime')
-async def test_calculate_drift_get_drift_metrics_error(
+def test_calculate_drift_get_drift_metrics_error(
     mock_to_datetime, mock_dataframe, model_metrics_activity
 ):
     # Arrange
     mock_to_datetime.return_value.dt.strftime.return_value = '2023-05-26 11:12:27'
 
-    model_metrics_activity.get_drift_metrics = AsyncMock(
+    model_metrics_activity.get_drift_metrics = MagicMock(
         side_effect=Exception('Get drift metrics error')
     )
 
@@ -606,14 +627,14 @@ async def test_calculate_drift_get_drift_metrics_error(
     }
 
     # Act
-    result = await model_metrics_activity.calculate_drift(input_data)
+    result = model_metrics_activity.calculate_drift(input_data)
 
     # Assert
     assert result == []
     model_metrics_activity.error.assert_called_once_with(
         'Error getting drift metrics: Get drift metrics error', metadata['metadata']
     )
-    model_metrics_activity.send_notification_async.assert_called_once_with(
+    model_metrics_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='MODEL_METRICS_GET_DRIFT_METRICS_ERROR',
         message='Error getting drift metrics: Get drift metrics error',
@@ -623,12 +644,11 @@ async def test_calculate_drift_get_drift_metrics_error(
     )
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.to_datetime')
 @patch('laborious.activities.model_metrics.time.time')
 @patch('laborious.activities.model_metrics.ModelAnalysis')
 @patch('laborious.activities.model_metrics.metrics')
-async def test_get_drift_metrics_success(
+def test_get_drift_metrics_success(
     mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
 ):
     # Arrange
@@ -668,7 +688,7 @@ async def test_get_drift_metrics_success(
     ).columns
 
     # Act
-    result = await model_metrics_activity.get_drift_metrics(
+    result = model_metrics_activity.get_drift_metrics(
         reference_data=reference_data,
         target_data=target_data,
         target_name='target',
@@ -681,16 +701,15 @@ async def test_get_drift_metrics_success(
     # Assert
     assert isinstance(result, DataFrame)
     model_metrics_activity.debug.assert_called()
-    model_metrics_activity.observe_lag.assert_called()
-    model_metrics_activity.emit_metric.assert_called()
+    model_metrics_activity.observe_lag_sync.assert_called()
+    model_metrics_activity.emit_metric_sync.assert_called()
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.to_datetime')
 @patch('laborious.activities.model_metrics.time.time')
 @patch('laborious.activities.model_metrics.ModelAnalysis')
 @patch('laborious.activities.model_metrics.metrics')
-async def test_get_drift_metrics_univariate_error(
+def test_get_drift_metrics_univariate_error(
     mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
 ):
     # Arrange
@@ -722,7 +741,7 @@ async def test_get_drift_metrics_univariate_error(
 
     # Act & Assert
     try:
-        await model_metrics_activity.get_drift_metrics(
+        model_metrics_activity.get_drift_metrics(
             reference_data=reference_data,
             target_data=target_data,
             target_name='target',
@@ -736,19 +755,18 @@ async def test_get_drift_metrics_univariate_error(
         model_metrics_activity.error.assert_called_once_with(
             'Error detecting univariate drift: Univariate drift error', metadata['metadata']
         )
-        model_metrics_activity.emit_metric.assert_called_with(
+        model_metrics_activity.emit_metric_sync.assert_called_with(
             metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
         )
     else:
         raise AssertionError('Expected Exception')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.to_datetime')
 @patch('laborious.activities.model_metrics.time.time')
 @patch('laborious.activities.model_metrics.ModelAnalysis')
 @patch('laborious.activities.model_metrics.metrics')
-async def test_get_drift_metrics_multivariate_error(
+def test_get_drift_metrics_multivariate_error(
     mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
 ):
     mock_time.return_value = 1000.0
@@ -769,7 +787,7 @@ async def test_get_drift_metrics_multivariate_error(
     ).columns
 
     try:
-        await model_metrics_activity.get_drift_metrics(
+        model_metrics_activity.get_drift_metrics(
             reference_data=reference_data,
             target_data=target_data,
             target_name='target',
@@ -783,19 +801,18 @@ async def test_get_drift_metrics_multivariate_error(
         model_metrics_activity.error.assert_called_once_with(
             'Error detecting multivariate drift: Multivariate drift error', metadata['metadata']
         )
-        model_metrics_activity.emit_metric.assert_called_with(
+        model_metrics_activity.emit_metric_sync.assert_called_with(
             metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
         )
     else:
         raise AssertionError('Expected Exception')
 
 
-@mark.asyncio
 @patch('laborious.activities.model_metrics.to_datetime')
 @patch('laborious.activities.model_metrics.time.time')
 @patch('laborious.activities.model_metrics.ModelAnalysis')
 @patch('laborious.activities.model_metrics.metrics')
-async def test_get_drift_metrics_dataframe_error(
+def test_get_drift_metrics_dataframe_error(
     mock_metrics, mock_model_analysis, mock_time, mock_to_datetime, model_metrics_activity
 ):
     mock_time.return_value = 1000.0
@@ -817,7 +834,7 @@ async def test_get_drift_metrics_dataframe_error(
     ).columns
 
     try:
-        await model_metrics_activity.get_drift_metrics(
+        model_metrics_activity.get_drift_metrics(
             reference_data=reference_data,
             target_data=target_data,
             target_name='target',
@@ -831,15 +848,14 @@ async def test_get_drift_metrics_dataframe_error(
         model_metrics_activity.error.assert_called_once_with(
             'Error getting drift metrics: Dataframe error', metadata['metadata']
         )
-        model_metrics_activity.emit_metric.assert_called_with(
+        model_metrics_activity.emit_metric_sync.assert_called_with(
             metric_object=mock_metrics.MODEL_ANALYZE_ERROR_COUNT, tags=ANY
         )
     else:
         raise AssertionError('Expected Exception')
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activity):
+def test_calculate_simple_metrics_success_all_metrics(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -858,7 +874,7 @@ async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activi
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 4
@@ -877,8 +893,7 @@ async def test_calculate_simple_metrics_success_all_metrics(model_metrics_activi
     model_metrics_activity.debug.assert_called_once()
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity):
+def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -897,7 +912,7 @@ async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 1
@@ -911,8 +926,7 @@ async def test_calculate_simple_metrics_success_rmse_only(model_metrics_activity
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity):
+def test_calculate_simple_metrics_success_mse_only(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -931,7 +945,7 @@ async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity)
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 1
@@ -945,8 +959,7 @@ async def test_calculate_simple_metrics_success_mse_only(model_metrics_activity)
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity):
+def test_calculate_simple_metrics_success_mae_only(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -965,7 +978,7 @@ async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity)
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 1
@@ -979,8 +992,7 @@ async def test_calculate_simple_metrics_success_mae_only(model_metrics_activity)
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
+def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -999,7 +1011,7 @@ async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 1
@@ -1013,8 +1025,7 @@ async def test_calculate_simple_metrics_success_r2_only(model_metrics_activity):
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
+def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
     # Arrange
     # All target values are the same, so ss_tot will be 0
     target_data = DataFrame(
@@ -1034,7 +1045,7 @@ async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 1
@@ -1049,8 +1060,7 @@ async def test_calculate_simple_metrics_r2_zero_ss_tot(model_metrics_activity):
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_metrics_activity):
+def test_calculate_simple_metrics_success_multiple_metrics_subset(model_metrics_activity):
     # Arrange
     target_data = DataFrame(
         {
@@ -1069,7 +1079,7 @@ async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_me
     }
 
     # Act
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     # Assert
     assert len(result['metric']) == 2
@@ -1084,8 +1094,7 @@ async def test_calculate_simple_metrics_success_multiple_metrics_subset(model_me
     )
 
 
-@mark.asyncio
-async def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_activity):
+def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_activity):
     target_data = DataFrame(
         {
             'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
@@ -1102,7 +1111,7 @@ async def test_calculate_simple_metrics_unknown_metric_ignored(model_metrics_act
         'interval_minutes': 5,
     }
 
-    result = DataFrame(await model_metrics_activity.calculate_simple_metrics(input_data))
+    result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
 
     assert len(result['metric']) == 1
     assert result['metric'].values[0] == 'rmse'

tests/laborious/activities/test_opc.py

@@ -1,6 +1,6 @@
-from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
+from unittest.mock import ANY, MagicMock, call, patch
 
-import pytest_asyncio
+import pytest
 from pandas import DataFrame
 from pytest import mark
 from sientia_do.notifications.models import NotificationLevel
@@ -23,39 +23,32 @@ def test__init__():
         opc_servers=servers,
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
 
     assert opc.opc_servers == servers
     assert opc.opc_repository == {}
 
 
-@mark.asyncio
 @patch('laborious.activities.opc.OpcRepository')
-@patch('laborious.activities.opc.OPC.send_notification_async')
-async def test_init_opc(mock_send_notification, mock_opc_repository):
+@patch('laborious.activities.opc.OPC.send_notification')
+def test_init_opc(mock_send_notification, mock_opc_repository):
     mock_logger = MagicMock()
-    mock_metrics_controller = AsyncMock()
-    server1 = MagicMock(
-        connect=AsyncMock(return_value=(True, {})), write_data=AsyncMock(return_value=(True, {}))
-    )
-    server2 = MagicMock(
-        connect=AsyncMock(return_value=(True, {})), write_data=AsyncMock(return_value=(True, {}))
-    )
-    server3 = MagicMock(
-        connect=AsyncMock(
-            return_value=(
-                False,
-                {
-                    'notification_id': 'OPC_CONNECTION_ERROR_server3',
-                    'message': 'Failed to connect to OPC server: Test error',
-                    'block': 'opc_repository',
-                    'level': NotificationLevel.ERROR,
-                    'attachment_content': 'Test error',
-                },
-            )
-        ),
-        write_data=AsyncMock(return_value=(True, {})),
+    mock_metrics_controller = MagicMock()
+    server1 = MagicMock()
+    server1.connect.return_value = (True, {})
+    server2 = MagicMock()
+    server2.connect.return_value = (True, {})
+    server3 = MagicMock()
+    server3.connect.return_value = (
+        False,
+        {
+            'notification_id': 'OPC_CONNECTION_ERROR_server3',
+            'message': 'Failed to connect to OPC server: Test error',
+            'block': 'opc_repository',
+            'level': NotificationLevel.ERROR,
+            'attachment_content': 'Test error',
+        },
     )
     mock_opc_repository.side_effect = [server1, server2, server3]
     mock_notification_handler = MagicMock()
@@ -97,7 +90,7 @@ async def test_init_opc(mock_send_notification, mock_opc_repository):
         notification_handler=mock_notification_handler,
         metrics_controller=mock_metrics_controller,
     )
-    await opc.init_opc()
+    opc.init_opc()
 
     assert opc.opc_servers == servers
     assert opc.logger == mock_logger
@@ -112,13 +105,12 @@ async def test_init_opc(mock_send_notification, mock_opc_repository):
                 server_name='server1',
                 url='http://localhost:8080',
                 logger=mock_logger,
+                notification_handler=mock_notification_handler,
+                metrics_controller=mock_metrics_controller,
                 server_uri='opc.tcp://localhost:4840',
                 cert_path='',
                 private_key_path='',
                 server_cert_path='',
-                notification_handler=mock_notification_handler,
-                reconnection_interval=60,
-                metrics_controller=mock_metrics_controller,
             ),
         ]
     )
@@ -129,13 +121,12 @@ async def test_init_opc(mock_send_notification, mock_opc_repository):
                 server_name='server2',
                 url='http://localhost:8080',
                 logger=mock_logger,
+                notification_handler=mock_notification_handler,
+                metrics_controller=mock_metrics_controller,
                 server_uri='opc.tcp://localhost:4840',
                 cert_path='',
                 private_key_path='',
                 server_cert_path='',
-                notification_handler=mock_notification_handler,
-                reconnection_interval=60,
-                metrics_controller=mock_metrics_controller,
             )
         ]
     )
@@ -162,51 +153,49 @@ async def test_init_opc(mock_send_notification, mock_opc_repository):
     )
 
 
-@pytest_asyncio.fixture
-@patch('laborious.activities.opc.OpcRepository')
-async def opc(mock_opc_repository):
-    servers = {
-        'server1': {
-            'id': 'server1',
-            'server_name': 'server1',
-            'url': 'http://localhost:8080',
-            'server_uri': 'opc.tcp://localhost:4840',
-            'cert_path': '',
-            'private_key_path': '',
-            'server_cert_path': '',
-            'reconnection_interval': 60,
+@pytest.fixture
+def opc():
+    with patch('laborious.activities.opc.OpcRepository') as mock_opc_repository:
+        mock_opc_repository.return_value.write_data = MagicMock(return_value=(True, {}))
+        mock_opc_repository.return_value.connect = MagicMock(return_value=(True, {}))
+        servers = {
+            'server1': {
+                'id': 'server1',
+                'server_name': 'server1',
+                'url': 'http://localhost:8080',
+                'server_uri': 'opc.tcp://localhost:4840',
+                'cert_path': '',
+                'private_key_path': '',
+                'server_cert_path': '',
+                'reconnection_interval': 60,
+            }
         }
-    }
 
-    mock_opc_repository.return_value.write_data = AsyncMock(return_value=(True, {}))
-    mock_opc_repository.return_value.connect = AsyncMock(return_value=(True, {}))
-    opc = OPC(
-        opc_servers=servers,
-        logger=MagicMock(),
-        notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
-    )
-    await opc.init_opc()
-    opc.send_notification = MagicMock()
-    opc.send_notification_async = AsyncMock()
-    opc.emit_metric = AsyncMock()
-    return opc
+        opc_instance = OPC(
+            opc_servers=servers,
+            logger=MagicMock(),
+            notification_handler=MagicMock(),
+            metrics_controller=MagicMock(),
+        )
+        opc_instance.init_opc()
+        opc_instance.send_notification = MagicMock()
+        opc_instance.emit_metric_sync = MagicMock()
+        yield opc_instance
 
 
 WRITE_DATA_CASES = [
     ('tag1', 'int', 50),
     ('tag2', 'float', 50.5),
     ('tag3', 'bool', True),
-    ('tag4', 'string', 'test'),
+    ('tag4', 'str', 'test'),
 ]
 
 
 @mark.parametrize('tag,data_type,data', WRITE_DATA_CASES)
-@mark.asyncio
-async def test_write_data_success(opc, tag, data_type, data):
+def test_write_data_success(opc, tag, data_type, data):
     opc.opc_repository['server1'].write_data.return_value = (True, {'response_time': 0.1})
 
-    result = await opc.write_data(
+    result = opc.write_data(
         server_id='server1',
         tag=tag,
         data=data,
@@ -220,8 +209,7 @@ async def test_write_data_success(opc, tag, data_type, data):
     )
 
 
-@mark.asyncio
-async def test_write_data_failed(opc):
+def test_write_data_failed(opc):
     opc.opc_repository['server1'].write_data.return_value = (
         False,
         {
@@ -233,7 +221,7 @@ async def test_write_data_failed(opc):
         },
     )
 
-    result = await opc.write_data(
+    result = opc.write_data(
         server_id='server1',
         tag='tag1',
         data=50,
@@ -243,7 +231,7 @@ async def test_write_data_failed(opc):
     )
     assert result is None
 
-    opc.send_notification_async.assert_called_once_with(
+    opc.send_notification.assert_called_once_with(
         metadata=metadata,
         notification_id='OPC_WRITE_DATA_ERROR_server1',
         message='Failed to write data to OPC server: Test error',
@@ -253,12 +241,11 @@ async def test_write_data_failed(opc):
     )
 
 
-@mark.asyncio
-async def test_write_data_exception(opc):
+def test_write_data_exception(opc):
     opc.opc_repository['server1'].write_data.side_effect = Exception('Test error')
 
     try:
-        await opc.write_data(
+        opc.write_data(
             server_id='server1',
             tag='tag1',
             data=50,
@@ -268,7 +255,7 @@ async def test_write_data_exception(opc):
         )
 
     except Exception:
-        opc.send_notification_async.assert_called_once_with(
+        opc.send_notification.assert_called_once_with(
             metadata=metadata,
             notification_id='WRITE_OPC_PREDICTION_ERROR',
             message='Error writing data to OPC server: Test error',
@@ -281,9 +268,8 @@ async def test_write_data_exception(opc):
         raise AssertionError('Expected an exception to be raised')
 
 
-@mark.asyncio
-async def test_manage_output_tags_success(opc):
-    opc.write_data = AsyncMock(return_value=0.1)
+def test_manage_output_tags_success(opc):
+    opc.write_data = MagicMock(return_value=0.1)
 
     data = DataFrame({'prediction': [0.75], 'prediction_confidence': [0.95]})
     config = {
@@ -291,7 +277,7 @@ async def test_manage_output_tags_success(opc):
         'confidence_tags': {'tag2': {'data_type': 'float'}},
     }
 
-    output_data, opc_metrics = await opc.manage_output_tags(
+    output_data, opc_metrics = opc.manage_output_tags(
         server_id='server1',
         config=config,
         data=data,
@@ -322,16 +308,15 @@ async def test_manage_output_tags_success(opc):
     )
 
 
-@mark.asyncio
 @mark.parametrize('side_effect', [[0.1, None], [None, 0.2]])
-async def test_manage_output_tags_failed(opc, side_effect):
-    opc.write_data = AsyncMock(side_effect=side_effect)
+def test_manage_output_tags_failed(opc, side_effect):
+    opc.write_data = MagicMock(side_effect=side_effect)
     data = DataFrame({'prediction': [0.75], 'prediction_confidence': [0.95]})
     config = {
         'prediction_tags': {'tag1': {'data_type': 'float'}},
         'confidence_tags': {'tag2': {'data_type': 'float'}},
     }
-    output_data, opc_metrics = await opc.manage_output_tags(
+    output_data, opc_metrics = opc.manage_output_tags(
         server_id='server1',
         config=config,
         data=data,
@@ -361,14 +346,13 @@ async def test_manage_output_tags_failed(opc, side_effect):
     )
 
 
-@mark.asyncio
-async def test_manage_output_tags_do_nothing(opc):
-    opc.write_data = AsyncMock(return_value=0.1)
+def test_manage_output_tags_do_nothing(opc):
+    opc.write_data = MagicMock(return_value=0.1)
     data = DataFrame({'prediction': [0.75], 'prediction_confidence': [0.95]})
     config = {
         '_invalid_key': {'tag1': {'data_type': 'float'}},
     }
-    output_data, opc_metrics = await opc.manage_output_tags(
+    output_data, opc_metrics = opc.manage_output_tags(
         server_id='server1',
         config=config,
         data=data,
@@ -379,11 +363,9 @@ async def test_manage_output_tags_do_nothing(opc):
     opc.write_data.assert_not_called()
 
 
-@mark.asyncio
 @patch('laborious.activities.opc.DataFrame')
-async def test_write_opc_data_success(mock_dataframe, opc):
-    # Arrange
-    input_data = {
+def test_write_opc_data_success(mock_dataframe, opc):
+    input_data: dict[str, object] = {
         **metadata,
         'data': {'prediction': [0.75], 'prediction_confidence': [0.95]},
         'opc_output_config': {
@@ -393,19 +375,19 @@ async def test_write_opc_data_success(mock_dataframe, opc):
             }
         },
     }
+    opc_output_config = input_data['opc_output_config']
+    assert isinstance(opc_output_config, dict)
 
-    # Act
-    opc.manage_output_tags = AsyncMock(return_value=(True, {'tag1': 0.1, 'tag2': 0.2}))
+    opc.manage_output_tags = MagicMock(return_value=(True, {'tag1': 0.1, 'tag2': 0.2}))
 
     opc.process_confidence = MagicMock(return_value={'data': 'data'})
-    output_data, opc_metrics = await opc.write_opc_data(input_data)
+    output_data, opc_metrics = opc.write_opc_data(input_data)
 
-    # Assert
     assert output_data == {'data': 'data'}
     assert opc_metrics == {'server1': {'tag1': 0.1, 'tag2': 0.2}}
     opc.manage_output_tags.assert_called_once_with(
         'server1',
-        input_data['opc_output_config']['server1'],
+        opc_output_config['server1'],
         mock_dataframe.return_value,
         metadata['metadata'],
     )
@@ -416,9 +398,7 @@ async def test_write_opc_data_success(mock_dataframe, opc):
     )
 
 
-@mark.asyncio
-async def test_write_opc_data_empty_config(opc):
-    # Arrange
+def test_write_opc_data_empty_config(opc):
     input_data = {
         **metadata,
         'data': {'prediction': [0.75], 'prediction_confidence': [0.95]},
@@ -426,16 +406,13 @@ async def test_write_opc_data_empty_config(opc):
         'opc_output_config': {'server1': {'prediction_tags': {}, 'confidence_tags': {}}},
     }
 
-    # Act
-    await opc.write_opc_data(input_data)
+    opc.write_opc_data(input_data)
 
-    # Assert
     opc.opc_repository['server1'].write_data.assert_not_called()
 
 
-@mark.asyncio
-async def test_write_opc_data_no_validate_server(opc):
-    opc.validate_server = AsyncMock(return_value=False)
+def test_write_opc_data_no_validate_server(opc):
+    opc.validate_server = MagicMock(return_value=False)
     input_data = {
         **metadata,
         'data': {'prediction': [0.75], 'prediction_confidence': [0.95]},
@@ -447,10 +424,8 @@ async def test_write_opc_data_no_validate_server(opc):
         },
     }
 
-    # Act
-    await opc.write_opc_data(input_data)
+    opc.write_opc_data(input_data)
 
-    # Assert
     opc.opc_repository['server1'].write_data.assert_not_called()
 
 
@@ -462,21 +437,18 @@ async def test_write_opc_data_no_validate_server(opc):
     ],
 )
 def test_process_confidence(opc, data, success, expected):
-    # Act
     result = opc.process_confidence(data, success, metadata)
 
-    # Assert
     assert result['prediction_confidence'][0] == expected
 
 
-@mark.asyncio
-async def test_validate_server(opc):
-    assert await opc.validate_server('server1', metadata) is True
-    assert await opc.validate_server('server2', metadata) is False
+def test_validate_server(opc):
+    assert opc.validate_server('server1', metadata) is True
+    assert opc.validate_server('server2', metadata) is False
 
 
-@mark.asyncio
-async def test_close(opc):
-    opc.opc_repository['server1'].disconnect = AsyncMock(return_value=True)
-    await opc.close()
-    opc.opc_repository['server1'].disconnect.assert_called_once()
+def test_close(opc):
+    disconnect_mock = MagicMock(return_value=None)
+    opc.opc_repository['server1'].disconnect = disconnect_mock
+    opc.close()
+    disconnect_mock.assert_called_once()

tests/laborious/activities/test_storage.py

@@ -1,10 +1,11 @@
 import datetime
 import os
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
+from unittest.mock import ANY, MagicMock, patch
 
-from pytest import fixture, mark, raises
+from pytest import fixture, raises
 from sientia_do.notifications.models import NotificationLevel
-from sientia_do.temporal.activities.postgres import Postgres
+from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+from sientia_do.temporal.activities.postgres_sync import Postgres
 
 from laborious.activities.storage import Storage
 
@@ -17,6 +18,15 @@ def _passthrough_from_dict():
         yield
 
 
+@fixture(autouse=True)
+def _patch_monitoring_shutdown():
+    """
+    Avoid running real async SientiaMonitoring.shutdown when Storage.close runs inside tests.
+    """
+    with patch.object(SientiaMonitoring, 'shutdown') as mock_shutdown:
+        yield mock_shutdown
+
+
 metadata = {
     'metadata': {
         'model_id': 'test_model_id',
@@ -42,7 +52,7 @@ def storage(mock_minio_repository):
         minio_repository=mock_minio_repository.return_value,
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
 
 
@@ -50,7 +60,7 @@ def storage(mock_minio_repository):
 def test___init___not_hasattr(mock_minio_repository):
     logger = MagicMock()
     notification_handler = MagicMock()
-    metrics_controller = AsyncMock()
+    metrics_controller = MagicMock()
     minio_repo = mock_minio_repository.return_value
     storage = Storage(
         host='localhost',
@@ -77,7 +87,7 @@ def test___init___none_minio_repository(mock_minio_repository, storage):
     storage.minio_repository = None
     logger = MagicMock()
     notification_handler = MagicMock()
-    metrics_controller = AsyncMock()
+    metrics_controller = MagicMock()
     storage.__init__(
         host='localhost',
         port=5432,
@@ -111,54 +121,55 @@ def test___init___done_repository(mock_minio_repository, storage):
         minio_repository=mock_minio_repository.return_value,
         logger=MagicMock(),
         notification_handler=MagicMock(),
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     mock_minio_repository.assert_not_called()
     assert storage.minio_repository is not None
 
 
-def test_close(storage):
+def test_close(storage, _patch_monitoring_shutdown):
     storage.minio_repository = MagicMock()
 
     storage.close()
 
     assert storage.minio_repository is None
+    _patch_monitoring_shutdown.assert_called_once_with(storage)
 
 
-def test___del__(storage):
-    storage.close = MagicMock()
+def test_close_when_minio_repository_already_none(storage, _patch_monitoring_shutdown):
+    """Closing without an initialized MinIO repository skips MinIO teardown."""
+    storage.minio_repository = None
 
-    storage.__del__()
+    storage.close()
 
-    storage.close.assert_called_once()
+    assert storage.minio_repository is None
+    _patch_monitoring_shutdown.assert_called_once_with(storage)
 
 
-@mark.asyncio
-async def test_load_query_with_minio_offload_no_rows(storage):
-    storage.load_custom_query = AsyncMock(return_value=None)
+def test_load_query_with_minio_offload_no_rows(storage):
+    storage.load_custom_query = MagicMock(return_value=None)
     storage_result = {'success': False}
     with patch(
         'laborious.activities.storage.MinioDataFramePayload.from_dataframe',
-        new_callable=AsyncMock,
+        new_callable=MagicMock,
         return_value=storage_result,
     ) as mock_from_dataframe:
-        result = await storage.load_query_with_minio_offload(
+        result = storage.load_query_with_minio_offload(
             {**metadata, 'query': 'SELECT 1', 'model_name': 'm', 'key_prefix': 'predictions/s'}
         )
     assert result == storage_result
-    mock_from_dataframe.assert_awaited_once()
+    mock_from_dataframe.assert_called_once()
 
 
-@mark.asyncio
-async def test_load_query_with_minio_offload_inline(storage):
-    storage.load_custom_query = AsyncMock(return_value=[{'a': 1}])
+def test_load_query_with_minio_offload_inline(storage):
+    storage.load_custom_query = MagicMock(return_value=[{'a': 1}])
     storage_result = {'success': True, 'data': {'a': [1]}, 'object_key': None}
     with patch(
         'laborious.activities.storage.MinioDataFramePayload.from_dataframe',
-        new_callable=AsyncMock,
+        new_callable=MagicMock,
         return_value=storage_result,
     ) as mock_from_dataframe:
-        result = await storage.load_query_with_minio_offload(
+        result = storage.load_query_with_minio_offload(
             {
                 **metadata,
                 'query': 'SELECT 1',
@@ -167,44 +178,42 @@ async def test_load_query_with_minio_offload_inline(storage):
             }
         )
     assert result == storage_result
-    mock_from_dataframe.assert_awaited_once()
+    mock_from_dataframe.assert_called_once()
 
 
-@mark.asyncio
-async def test_load_query_with_minio_offload_minio(storage):
-    storage.load_custom_query = AsyncMock(return_value=[{'a': 1}])
+def test_load_query_with_minio_offload_minio(storage):
+    storage.load_custom_query = MagicMock(return_value=[{'a': 1}])
     storage_result = {'success': True, 'data': None, 'object_key': 'object-key'}
     with patch(
         'laborious.activities.storage.MinioDataFramePayload.from_dataframe',
-        new_callable=AsyncMock,
+        new_callable=MagicMock,
         return_value=storage_result,
     ) as mock_from_dataframe:
-        result = await storage.load_query_with_minio_offload(
+        result = storage.load_query_with_minio_offload(
             {**metadata, 'query': 'SELECT 1', 'model_name': 'm', 'key_prefix': 'predictions/s'}
         )
 
     assert result == storage_result
-    mock_from_dataframe.assert_awaited_once()
+    mock_from_dataframe.assert_called_once()
 
 
-@mark.asyncio
 @patch.dict(os.environ, {'SIENTIA_MINIO_RETENTION_HOURS': '1'})
 @patch('laborious.activities.storage.now')
-async def test_cleanup_minio_objects_expired(mock_now, storage):
+def test_cleanup_minio_objects_expired(mock_now, storage):
     mock_now.return_value = datetime.datetime(2025, 1, 10, 12, 0, 0)
-    storage.minio_repository.list_objects = AsyncMock(
+    storage.minio_repository.list_objects = MagicMock(
         return_value=[
             'sientia/streamlit-connectors/training_datasets/m/m-initial-2024-12-01_00-00-00.parquet',
             'sientia/streamlit-connectors/training_datasets/m/m-initial-2025-01-10_12-00-00.parquet',
         ]
     )
-    storage.minio_repository.delete_file = AsyncMock()
-    storage.send_notification_async = AsyncMock()
+    storage.minio_repository.delete_file = MagicMock()
+    storage.send_notification = MagicMock()
 
     data_mock = MagicMock()
     data_mock.cleanup_prefix.return_value = 'training_datasets/m'
 
-    result = await storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
+    result = storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
 
     assert result['deleted_count'] == 1
     assert result['failed_count'] == 0
@@ -224,72 +233,65 @@ async def test_cleanup_minio_objects_expired(mock_now, storage):
     )
 
 
-@mark.asyncio
-async def test_load_query_with_minio_offload_minio_not_initialized(storage):
+def test_load_query_with_minio_offload_minio_not_initialized(storage):
     storage.minio_repository = None
 
     with raises(ValueError, match='Minio repository not initialized'):
-        await storage.load_query_with_minio_offload(
-            {**metadata, 'query': 'SELECT 1', 'model_name': 'm'}
-        )
+        storage.load_query_with_minio_offload({**metadata, 'query': 'SELECT 1', 'model_name': 'm'})
 
 
-@mark.asyncio
-async def test_export_payload_to_postgres(storage):
-    payload = AsyncMock()
-    payload.retrieve = AsyncMock(return_value=MagicMock())
-    storage.export_data_to_postgres = AsyncMock(return_value={'success': True})
+def test_export_payload_to_postgres(storage):
+    payload = MagicMock()
+    payload.retrieve = MagicMock(return_value=MagicMock())
+    storage.export_data_to_postgres = MagicMock(return_value={'success': True})
 
-    result = await storage.export_payload_to_postgres(
+    result = storage.export_payload_to_postgres(
         {**metadata, 'data': payload, 'schema': 'public', 'table': 't'}
     )
 
-    payload.retrieve.assert_awaited_once_with(storage.minio_repository, metadata['metadata'])
-    storage.export_data_to_postgres.assert_awaited_once()
+    payload.retrieve.assert_called_once_with(storage.minio_repository, metadata['metadata'])
+    storage.export_data_to_postgres.assert_called_once()
     assert result == {'success': True}
 
 
-@mark.asyncio
-async def test_cleanup_minio_objects_expired_minio_not_initialized(storage):
+def test_cleanup_minio_objects_expired_minio_not_initialized(storage):
     storage.minio_repository = None
 
     data_mock = MagicMock()
     data_mock.cleanup_prefix.return_value = 'test'
     with raises(ValueError, match='Minio repository not initialized'):
-        await storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
+        storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
 
 
-@mark.asyncio
 @patch('laborious.activities.storage.now')
-async def test_cleanup_minio_objects_expired_unparseable_key(mock_now, storage):
+def test_cleanup_minio_objects_expired_unparseable_key(mock_now, storage):
     mock_now.return_value = datetime.datetime(2025, 1, 10, 12, 0, 0)
-    storage.minio_repository.list_objects = AsyncMock(
+    storage.minio_repository.list_objects = MagicMock(
         return_value=['some/random/key-without-timestamp.parquet']
     )
-    storage.minio_repository.delete_file = AsyncMock()
-    storage.send_notification_async = AsyncMock()
+    storage.minio_repository.delete_file = MagicMock()
+    storage.send_notification = MagicMock()
 
     data_mock = MagicMock()
     data_mock.cleanup_prefix.return_value = 'test'
-    result = await storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
+    result = storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
 
     assert result['deleted_count'] == 0
     assert result['failed_count'] == 0
     storage.minio_repository.delete_file.assert_not_called()
 
 
-@mark.asyncio
 @patch('laborious.activities.storage.now')
-async def test_cleanup_minio_objects_expired_delete_fails(mock_now, storage):
+def test_cleanup_minio_objects_expired_delete_fails(mock_now, storage):
     mock_now.return_value = datetime.datetime(2025, 1, 10, 12, 0, 0)
     old_key = 'training_datasets/m/m-initial-2024-12-01_00-00-00.parquet'
-    storage.minio_repository.list_objects = AsyncMock(return_value=[old_key])
-    storage.minio_repository.delete_file = AsyncMock(side_effect=Exception('delete error'))
-    storage.send_notification_async = AsyncMock()
+    storage.minio_repository.list_objects = MagicMock(return_value=[old_key])
+    storage.minio_repository.delete_file = MagicMock(side_effect=Exception('delete error'))
+    storage.send_notification = MagicMock()
 
     data_mock = MagicMock()
     data_mock.cleanup_prefix.return_value = 'training_datasets/m'
-    result = await storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
+    result = storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
 
     assert result['deleted_count'] == 0
     assert result['failed_count'] == 1
@@ -298,21 +300,20 @@ async def test_cleanup_minio_objects_expired_delete_fails(mock_now, storage):
     assert result['failed'][old_key]['message'] == 'delete error'
 
 
-@mark.asyncio
 @patch('laborious.activities.storage.now')
-async def test_cleanup_minio_objects_expired_list_objects_error(mock_now, storage):
+def test_cleanup_minio_objects_expired_list_objects_error(mock_now, storage):
     mock_now.return_value = datetime.datetime(2025, 1, 10, 12, 0, 0)
-    storage.minio_repository.list_objects = AsyncMock(side_effect=Exception('list error'))
-    storage.send_notification_async = AsyncMock()
+    storage.minio_repository.list_objects = MagicMock(side_effect=Exception('list error'))
+    storage.send_notification = MagicMock()
     storage.error = MagicMock()
 
     data_mock = MagicMock()
     data_mock.cleanup_prefix.return_value = 'training_datasets/m'
-    result = await storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
+    result = storage.cleanup_minio_objects_expired({**metadata, 'data': data_mock})
 
     assert result['deleted_count'] == 0
     assert result['failed_count'] == 0
-    storage.send_notification_async.assert_called_once_with(
+    storage.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='ERROR_CLEANUP_MINIO_OBJECTS_EXPIRED',
         message='Error cleaning up MinIO objects: list error',

tests/laborious/utils/models/test_minio_dataframe_payload.py

@@ -1,8 +1,7 @@
 from datetime import datetime
 from io import BytesIO
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
-import pytest
 from pandas import DataFrame
 
 from laborious.utils.models.minio_dataframe_payload import (
@@ -54,17 +53,15 @@ def test_has_data_true_when_object_key_set():
     assert payload.has_data() is True
 
 
-@pytest.mark.asyncio
-async def test_retrieve_inline_dict_as_dataframe():
+def test_retrieve_inline_dict_as_dataframe():
     payload = MinioDataFramePayload(last_timestamp='t', data={'a': [1, 2]})
-    minio = AsyncMock()
-    out = await payload.retrieve(minio, {'metadata': {}})
+    minio = MagicMock()
+    out = payload.retrieve(minio, {'metadata': {}})
     assert list(out.columns) == ['a']
     minio.download_file.assert_not_called()
 
 
-@pytest.mark.asyncio
-async def test_retrieve_downloads_parquet_when_offloaded():
+def test_retrieve_downloads_parquet_when_offloaded():
     source = DataFrame({'a': [1, 2]})
     buf = BytesIO()
     source.to_parquet(buf, engine='pyarrow', index=True)
@@ -76,12 +73,12 @@ async def test_retrieve_downloads_parquet_when_offloaded():
         object_key='training_datasets/m/f.parquet',
         object_prefix='training_datasets/m',
     )
-    minio = AsyncMock()
-    minio.download_file = AsyncMock(return_value=file_bytes)
+    minio = MagicMock()
+    minio.download_file = MagicMock(return_value=file_bytes)
 
-    out = await payload.retrieve(minio, {'metadata': {}})
+    out = payload.retrieve(minio, {'metadata': {}})
 
-    minio.download_file.assert_awaited_once_with(
+    minio.download_file.assert_called_once_with(
         object_name='training_datasets/m/f.parquet',
         metadata={'metadata': {}},
     )
@@ -113,21 +110,19 @@ def test_parse_object_timestamp_bad_datetime():
     assert MinioDataFramePayload.parse_object_timestamp(key) is None
 
 
-@pytest.mark.asyncio
-async def test_retrieve_empty_when_no_data():
+def test_retrieve_empty_when_no_data():
     payload = MinioDataFramePayload(last_timestamp='t', data=None, object_key=None)
-    minio = AsyncMock()
-    out = await payload.retrieve(minio, {})
+    minio = MagicMock()
+    out = payload.retrieve(minio, {})
     assert out.empty
     minio.download_file.assert_not_called()
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.models.minio_dataframe_payload.now')
-async def test_from_dataframe_none(mock_now):
+def test_from_dataframe_none(mock_now):
     mock_now.return_value = datetime(2024, 1, 1, 0, 0, 0)
-    minio = AsyncMock()
-    result = await MinioDataFramePayload.from_dataframe(
+    minio = MagicMock()
+    result = MinioDataFramePayload.from_dataframe(
         dataframe=None,
         minio_repo=minio,
         model_name='m',
@@ -139,15 +134,14 @@ async def test_from_dataframe_none(mock_now):
     assert result.object_key is None
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.models.minio_dataframe_payload.now')
-async def test_from_dataframe_empty(mock_now):
+def test_from_dataframe_empty(mock_now):
     mock_now.return_value = datetime(2024, 1, 1, 0, 0, 0)
-    minio = AsyncMock()
+    minio = MagicMock()
     mock_df = MagicMock()
     mock_df.__bool__ = MagicMock(return_value=True)
     mock_df.empty = True
-    result = await MinioDataFramePayload.from_dataframe(
+    result = MinioDataFramePayload.from_dataframe(
         dataframe=mock_df,
         minio_repo=minio,
         model_name='m',
@@ -174,12 +168,11 @@ def _mock_dataframe(data_dict, timestamp_values=None):
     return mock_df
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.models.minio_dataframe_payload.OFFLOAD_THRESHOLD_BYTES', 10**9)
-async def test_from_dataframe_inline():
-    minio = AsyncMock()
+def test_from_dataframe_inline():
+    minio = MagicMock()
     df = _mock_dataframe({'timestamp': ['2024-01-01'], 'value': [42]})
-    result = await MinioDataFramePayload.from_dataframe(
+    result = MinioDataFramePayload.from_dataframe(
         dataframe=df,
         minio_repo=minio,
         model_name='m',
@@ -190,12 +183,11 @@ async def test_from_dataframe_inline():
     assert result.last_timestamp == '2024-01-01'
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.models.minio_dataframe_payload.OFFLOAD_THRESHOLD_BYTES', 10**9)
-async def test_from_dataframe_inline_uses_provided_last_timestamp():
-    minio = AsyncMock()
+def test_from_dataframe_inline_uses_provided_last_timestamp():
+    minio = MagicMock()
     df = _mock_dataframe({'timestamp': ['2024-01-01'], 'value': [42]})
-    result = await MinioDataFramePayload.from_dataframe(
+    result = MinioDataFramePayload.from_dataframe(
         dataframe=df,
         minio_repo=minio,
         model_name='m',
@@ -205,17 +197,16 @@ async def test_from_dataframe_inline_uses_provided_last_timestamp():
     assert result.last_timestamp == '2024-01-02'
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.models.minio_dataframe_payload.now')
 @patch('laborious.utils.models.minio_dataframe_payload.OFFLOAD_THRESHOLD_BYTES', 0)
-async def test_from_dataframe_offloaded(mock_now):
+def test_from_dataframe_offloaded(mock_now):
     mock_now.return_value = datetime(2024, 1, 1, 0, 0, 0)
-    minio = AsyncMock()
-    minio.upload_file = AsyncMock(return_value={'minio_object_name': 'full/key.parquet'})
+    minio = MagicMock()
+    minio.upload_file = MagicMock(return_value={'minio_object_name': 'full/key.parquet'})
     minio.bucket = 'test-bucket'
 
     df = _mock_dataframe({'timestamp': ['2024-01-01'], 'value': [42]})
-    result = await MinioDataFramePayload.from_dataframe(
+    result = MinioDataFramePayload.from_dataframe(
         dataframe=df,
         minio_repo=minio,
         model_name='m',
@@ -226,7 +217,7 @@ async def test_from_dataframe_offloaded(mock_now):
     assert result.object_key == 'full/key.parquet'
     assert result.bucket == 'test-bucket'
     assert result.uri == 's3://test-bucket/full/key.parquet'
-    minio.upload_file.assert_awaited_once()
+    minio.upload_file.assert_called_once()
 
 
 def test_from_dict_inline():

tests/laborious/utils/repository/test_opc_repository.py

@@ -1,9 +1,9 @@
 import json
 from datetime import datetime
-from unittest.mock import ANY, AsyncMock, MagicMock, Mock, patch
+from unittest.mock import ANY, MagicMock, Mock, patch
 
 import pytest
-from asyncua.crypto.security_policies import SecurityPolicyBasic256
+from opcua.crypto import security_policies
 from sientia_do.notifications.models import NotificationLevel
 
 from laborious.utils.repository.opc_repository import OpcRepository
@@ -27,19 +27,18 @@ def opc_repository(mock_logger):
         cert_path='/path/to/cert.pem',
         private_key_path='/path/to/key.pem',
         server_cert_path='/path/to/server_cert.pem',
-        metrics_controller=AsyncMock(),
+        metrics_controller=MagicMock(),
     )
     repository.disconnection_interval = 0.1
     repository.send_notification = MagicMock()
-    repository.send_notification_async = AsyncMock()
-    repository.emit_metric = AsyncMock()
+    repository.emit_metric_sync = MagicMock()
     return repository
 
 
 @pytest.fixture
 def mock_client():
     with patch('laborious.utils.repository.opc_repository.Client') as mock:
-        client_instance = AsyncMock()
+        client_instance = MagicMock()
         mock.return_value = client_instance
         yield client_instance
 
@@ -68,58 +67,48 @@ def test_init(opc_repository):
     assert opc_repository.error_count == 0
 
 
-@pytest.mark.asyncio
-async def test_set_security(opc_repository, mock_client):
+def test_set_security(opc_repository, mock_client):
     opc_repository.client = mock_client
-    await opc_repository.set_security()
+    opc_repository.set_security()
 
-    mock_client.application_uri = 'urn:test:server'
     mock_client.set_security.assert_called_once_with(
-        SecurityPolicyBasic256,
-        certificate='/path/to/cert.pem',
-        private_key='/path/to/key.pem',
-        server_certificate='/path/to/server_cert.pem',
+        security_policies.SecurityPolicyBasic256,
+        '/path/to/cert.pem',
+        '/path/to/key.pem',
+        '/path/to/server_cert.pem',
     )
     assert mock_client.secure_channel_timeout == 10000000
     assert mock_client.session_timeout == 10000000
 
 
-@pytest.mark.asyncio
-async def test_set_security_missing_certificates(opc_repository):
+def test_set_security_missing_certificates(opc_repository):
     opc_repository.cert_path = None
     opc_repository.private_key_path = None
 
-    try:
-        await opc_repository.set_security()
-    except ValueError as e:
-        assert str(e) == 'Certificate and private key paths must be provided for secure connection.'
+    with pytest.raises(ValueError, match='Certificate and private key paths'):
+        opc_repository.set_security()
 
 
-@pytest.mark.asyncio
-async def test_set_security_missing_client(opc_repository):
+def test_set_security_missing_client(opc_repository):
     opc_repository.client = None
-    try:
-        await opc_repository.set_security()
-    except ValueError as e:
-        assert str(e) == 'Client must be initialized before setting security'
+    with pytest.raises(ValueError, match='Client must be initialized'):
+        opc_repository.set_security()
 
 
-@pytest.mark.asyncio
-async def test_connect_with_security(opc_repository, mock_client):
-    opc_repository.try_connect = AsyncMock(return_value=(True, {}))
-    result = await opc_repository.connect()
+def test_connect_with_security(opc_repository, mock_client):
+    opc_repository.try_connect = MagicMock(return_value=(True, {}))
+    result = opc_repository.connect()
 
     opc_repository.try_connect.assert_called_once()
     assert opc_repository.client == mock_client
     assert result == (True, {})
 
 
-@pytest.mark.asyncio
-async def test_connect_without_security(opc_repository, mock_client):
+def test_connect_without_security(opc_repository, mock_client):
     opc_repository.cert_path = None
-    opc_repository.try_connect = AsyncMock(return_value=(True, {}))
-    opc_repository.set_security = AsyncMock()
-    result = await opc_repository.connect()
+    opc_repository.try_connect = MagicMock(return_value=(True, {}))
+    opc_repository.set_security = MagicMock()
+    result = opc_repository.connect()
 
     opc_repository.try_connect.assert_called_once()
     opc_repository.set_security.assert_not_called()
@@ -127,25 +116,23 @@ async def test_connect_without_security(opc_repository, mock_client):
     assert result == (True, {})
 
 
-@pytest.mark.asyncio
-async def test_try_connect_success(opc_repository):
+def test_try_connect_success(opc_repository):
     opc_repository.last_reconnection_time = None
-    opc_repository.client = AsyncMock()
-    result = await opc_repository.try_connect()
+    opc_repository.client = MagicMock()
+    result = opc_repository.try_connect()
 
     opc_repository.client.connect.assert_called_once()
     assert opc_repository.last_reconnection_time is not None
     assert result == (True, {})
 
 
-@pytest.mark.asyncio
-async def test_try_connect_fail(opc_repository):
+def test_try_connect_fail(opc_repository):
     opc_repository.last_reconnection_time = None
-    opc_repository.disconnect = AsyncMock()
+    opc_repository.disconnect = MagicMock()
     opc_repository.client = MagicMock()
     opc_repository.client.connect.side_effect = Exception('Test error')
 
-    is_connected, error_data = await opc_repository.try_connect()
+    is_connected, error_data = opc_repository.try_connect()
 
     opc_repository.disconnect.assert_called_once()
     opc_repository.client.connect.assert_called_once()
@@ -157,10 +144,9 @@ async def test_try_connect_fail(opc_repository):
     assert error_data['attachment_content'] is not None
 
 
-@pytest.mark.asyncio
-async def test_try_connect_no_client(opc_repository):
+def test_try_connect_no_client(opc_repository):
     opc_repository.client = None
-    result = await opc_repository.try_connect()
+    result = opc_repository.try_connect()
     assert result == (
         False,
         {
@@ -172,21 +158,24 @@ async def test_try_connect_no_client(opc_repository):
     )
 
 
-@pytest.mark.asyncio
-async def test_disconnection_fallback_success(opc_repository, mock_client):
+def test_session_alive_returns_false_when_client_none(opc_repository):
+    opc_repository.client = None
+    assert opc_repository._session_alive() is False
+
+
+def test_disconnection_fallback_success(opc_repository, mock_client):
     opc_repository.client = mock_client
-    mock_client.disconnect.return_value = True
-    result = await opc_repository.disconnection_fallback()
+    mock_client.disconnect.return_value = None
+    result = opc_repository.disconnection_fallback()
 
     mock_client.disconnect.assert_called_once()
     assert result == []
 
 
-@pytest.mark.asyncio
-async def test_disconnection_fallback_fail(opc_repository, mock_client):
+def test_disconnection_fallback_fail(opc_repository, mock_client):
     opc_repository.client = mock_client
     mock_client.disconnect.side_effect = Exception('Test error')
-    result = await opc_repository.disconnection_fallback()
+    result = opc_repository.disconnection_fallback()
     assert result == [
         {'attempt': 1, 'error': 'Test error', 'traceback': ANY},
         {'attempt': 2, 'error': 'Test error', 'traceback': ANY},
@@ -197,32 +186,29 @@ async def test_disconnection_fallback_fail(opc_repository, mock_client):
     assert mock_client.disconnect.call_count == 5
 
 
-@pytest.mark.asyncio
-async def test_disconnect(opc_repository, mock_client):
+def test_disconnect(opc_repository, mock_client):
     opc_repository.client = mock_client
-    opc_repository.disconnection_fallback = AsyncMock(return_value=[])
-    await opc_repository.disconnect()
+    opc_repository.disconnection_fallback = MagicMock(return_value=[])
+    opc_repository.disconnect()
 
     opc_repository.disconnection_fallback.assert_called_once()
     assert opc_repository.client is None
 
 
-@pytest.mark.asyncio
-async def test_disconnect_no_client(opc_repository):
+def test_disconnect_no_client(opc_repository):
     opc_repository.client = None
-    assert await opc_repository.disconnect() is None
+    assert opc_repository.disconnect() is None
 
 
-@pytest.mark.asyncio
-async def test_disconnect_error(opc_repository, mock_client):
+def test_disconnect_error(opc_repository, mock_client):
     opc_repository.client = mock_client
-    opc_repository.disconnection_fallback = AsyncMock(
+    opc_repository.disconnection_fallback = MagicMock(
         return_value=[{'attempt': 1, 'error': 'Test error', 'traceback': 'text'}]
     )
-    await opc_repository.disconnect()
+    opc_repository.disconnect()
 
     opc_repository.disconnection_fallback.assert_called_once()
-    opc_repository.send_notification_async.assert_called_once_with(
+    opc_repository.send_notification.assert_called_once_with(
         metadata=opc_repository.metadata,
         notification_id=f'OPC_DISCONNECTION_ERROR_{opc_repository.id}',
         message='Failed to disconnect from OPC server in 5 attempts.',
@@ -235,63 +221,39 @@ async def test_disconnect_error(opc_repository, mock_client):
     assert opc_repository.client is None
 
 
-@pytest.mark.asyncio
-async def test_validate_connection_none_client(opc_repository):
+def test_validate_connection_none_client(opc_repository):
     opc_repository.client = None
-    opc_repository.connect = AsyncMock(return_value=(True, {}))
-    response = await opc_repository.validate_connection()
+    opc_repository.connect = MagicMock(return_value=(True, {}))
+    response = opc_repository.validate_connection()
     assert response == (True, {})
     opc_repository.connect.assert_called_once()
 
 
-# @pytest.mark.asyncio
-# async def test_validate_connection_error_count_disconnect_error(opc_repository):
-#     opc_repository.error_count = 6
-#     opc_repository.client = AsyncMock()
-#     opc_repository.disconnect = AsyncMock(side_effect=Exception('Test error'))
-#     opc_repository.connect = AsyncMock(return_value=(True, {}))
+def test_validate_connection_disconnect_raises(opc_repository):
+    """Outer except path when reconnect cleanup fails mid-validation."""
 
-#     response = await opc_repository.validate_connection()
-#     assert response == opc_repository.connect.return_value
-#     opc_repository.disconnect.assert_called_once()
-#     opc_repository.connect.assert_called_once()
-#     opc_repository.logger.custom_error.assert_has_calls(
-#         [
-#             call('Failed to disconnect from OPC server: Test error', ANY),
-#         ]
-#     )
+    opc_repository.client = MagicMock()
+    opc_repository._session_alive = MagicMock(return_value=False)
+    opc_repository.last_reconnection_time = datetime(2020, 1, 1, 0, 0, 0)
+    opc_repository.disconnect = MagicMock(side_effect=RuntimeError('disconnect failed'))
+
+    response = opc_repository.validate_connection()
+
+    assert response[0] is False
+    assert response[1]['notification_id'] == f'OPC_CONNECTION_CHECK_ERROR_{opc_repository.id}'
+    assert 'disconnect failed' in response[1]['message']
 
 
-@pytest.mark.asyncio
-async def test_validate_connection_error_validate_connection_error(opc_repository):
-    opc_repository.client = MagicMock(uaclient=Exception('Test error'))
-    opc_repository.error_count = 0
-
-    response = await opc_repository.validate_connection()
-
-    assert response == (
-        False,
-        {
-            'notification_id': f'OPC_CONNECTION_CHECK_ERROR_{opc_repository.id}',
-            'message': "Failed to validate connection to OPC server: 'Exception' object has no attribute 'protocol'",
-            'block': 'opc_repository',
-            'level': NotificationLevel.ERROR,
-            'attachment_content': ANY,
-        },
-    )
-
-
-@pytest.mark.asyncio
 @patch('laborious.utils.repository.opc_repository.datetime')
-async def test_validate_connection_lost_not_time_to_reconnect(_mock_datetime, opc_repository):
+def test_validate_connection_lost_not_time_to_reconnect(_mock_datetime, opc_repository):
     _mock_datetime.now = MagicMock(return_value=datetime(2025, 1, 1, 0, 0, 0))
     opc_repository.error_count = 0
     opc_repository.client = MagicMock()
-    opc_repository.client.uaclient.protocol = None
+    opc_repository.client.get_root_node.side_effect = RuntimeError('down')
     opc_repository.last_reconnection_time = datetime(2025, 1, 1, 0, 0, 0)
     opc_repository.connect = MagicMock(return_value=(True, {}))
 
-    response = await opc_repository.validate_connection()
+    response = opc_repository.validate_connection()
     opc_repository.connect.assert_not_called()
     assert response == (
         False,
@@ -304,55 +266,51 @@ async def test_validate_connection_lost_not_time_to_reconnect(_mock_datetime, op
     )
 
 
-@pytest.mark.asyncio
 @patch('laborious.utils.repository.opc_repository.datetime')
-async def test_validate_connection_lost_time_to_reconnect(mock_datetime, opc_repository):
+def test_validate_connection_lost_time_to_reconnect(mock_datetime, opc_repository):
     mock_datetime.now = MagicMock(return_value=datetime(2025, 1, 1, 1, 0, 0))
     opc_repository.error_count = 0
-    opc_repository.client = AsyncMock()
-    opc_repository.client.uaclient.protocol = None
+    opc_repository.client = MagicMock()
+    opc_repository.client.get_root_node.side_effect = RuntimeError('down')
     opc_repository.last_reconnection_time = datetime(2025, 1, 1, 0, 0, 0)
-    opc_repository.connect = AsyncMock(return_value=(True, {}))
+    opc_repository.connect = MagicMock(return_value=(True, {}))
 
-    response = await opc_repository.validate_connection()
+    response = opc_repository.validate_connection()
     opc_repository.connect.assert_called_once()
     assert response == opc_repository.connect.return_value
 
 
-@pytest.mark.asyncio
-async def test_validate_connection_success(opc_repository):
+def test_validate_connection_success(opc_repository):
     opc_repository.client = MagicMock()
     opc_repository.error_count = 0
-    opc_repository.client.uaclient.protocol = MagicMock()
-    opc_repository.client.uaclient.protocol.state = 'open'
+    opc_repository.client.get_root_node.return_value = MagicMock()
 
-    output = await opc_repository.validate_connection()
+    output = opc_repository.validate_connection()
     assert output == (True, {})
 
 
-@pytest.mark.asyncio
-async def test_write_data_validate_connection_do_nothing(opc_repository):
-    opc_repository.validate_connection = AsyncMock(return_value=(True, {}))
-    opc_repository.client = AsyncMock(get_node=MagicMock())
-    mock_node = AsyncMock()
+def test_write_data_validate_connection_do_nothing(opc_repository):
+    opc_repository.validate_connection = MagicMock(return_value=(True, {}))
+    opc_repository.client = MagicMock()
+    mock_node = MagicMock()
     opc_repository.client.get_node.return_value = mock_node
 
-    result = await opc_repository.write_data(
+    result = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'float', opc_repository.logger, metadata['metadata']
     )
 
     opc_repository.validate_connection.assert_called_once()
     opc_repository.client.get_node.assert_called_once_with('ns=2;s=TestNode')
+    mock_node.set_value.assert_called_once()
     assert result == (True, {'response_time': ANY})
 
 
-@pytest.mark.asyncio
-async def test_write_data_validate_connection_failed(opc_repository):
-    opc_repository.validate_connection = AsyncMock(return_value=(False, {}))
-    opc_repository.client = AsyncMock()
+def test_write_data_validate_connection_failed(opc_repository):
+    opc_repository.validate_connection = MagicMock(return_value=(False, {}))
+    opc_repository.client = MagicMock()
     opc_repository.error_count = 0
 
-    result = await opc_repository.write_data(
+    result = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'float', opc_repository.logger, metadata['metadata']
     )
 
@@ -361,14 +319,13 @@ async def test_write_data_validate_connection_failed(opc_repository):
     assert result == (False, {})
 
 
-@pytest.mark.asyncio
-async def test_write_data_get_node_failed(opc_repository):
-    opc_repository.validate_connection = AsyncMock(return_value=(True, {}))
-    opc_repository.client = AsyncMock()
+def test_write_data_get_node_failed(opc_repository):
+    opc_repository.validate_connection = MagicMock(return_value=(True, {}))
+    opc_repository.client = MagicMock()
     opc_repository.error_count = 0
     opc_repository.client.get_node = MagicMock(side_effect=Exception('Test error'))
 
-    is_success, error_data = await opc_repository.write_data(
+    is_success, error_data = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'float', opc_repository.logger, metadata['metadata']
     )
 
@@ -376,23 +333,15 @@ async def test_write_data_get_node_failed(opc_repository):
     opc_repository.client.get_node.assert_called_once_with('ns=2;s=TestNode')
     assert is_success is False
     assert error_data['notification_id'] == f'OPC_WRITE_GET_NODE_ERROR_{opc_repository.id}'
-    assert (
-        error_data['message']
-        == "Failed to get node from OPC server: Test error | metadata: {'model_id': 'test_model', 'model_name': 'test_model', 'workflow_name': 'test_workflow', 'schema_name': 'test_schedule'}"
-    )
-    assert error_data['block'] == 'opc_repository'
-    assert error_data['level'] == NotificationLevel.ERROR
-    assert error_data['attachment_content'] is not None
 
 
-@pytest.mark.asyncio
-async def test_write_data_invalid_data_type(opc_repository, mock_client):
-    opc_repository.validate_connection = AsyncMock(return_value=(True, {}))
+def test_write_data_invalid_data_type(opc_repository, mock_client):
+    opc_repository.validate_connection = MagicMock(return_value=(True, {}))
     opc_repository.client = mock_client
-    mock_node = AsyncMock()
+    mock_node = MagicMock()
     mock_client.get_node = MagicMock(return_value=mock_node)
 
-    is_success, error_data = await opc_repository.write_data(
+    is_success, error_data = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'invalid_type', opc_repository.logger, metadata['metadata']
     )
 
@@ -401,53 +350,37 @@ async def test_write_data_invalid_data_type(opc_repository, mock_client):
 
     assert is_success is False
     assert error_data['notification_id'] == f'OPC_WRITE_DATA_TYPE_ERROR_{opc_repository.id}'
-    assert (
-        error_data['message']
-        == "Unsupported data type: invalid_type | metadata: {'model_id': 'test_model', 'model_name': 'test_model', 'workflow_name': 'test_workflow', 'schema_name': 'test_schedule'}"
-    )
-    assert error_data['block'] == 'opc_repository'
-    assert error_data['level'] == NotificationLevel.ERROR
-    assert error_data.get('attachment_content') is None
 
 
-@pytest.mark.asyncio
-async def test_write_data(opc_repository, mock_client):
-    opc_repository.validate_connection = AsyncMock(return_value=(True, {}))
+def test_write_data(opc_repository, mock_client):
+    opc_repository.validate_connection = MagicMock(return_value=(True, {}))
     opc_repository.client = mock_client
-    mock_node = AsyncMock()
+    mock_node = MagicMock()
     mock_client.get_node = MagicMock(return_value=mock_node)
 
-    result = await opc_repository.write_data(
+    result = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'float', opc_repository.logger, metadata['metadata']
     )
 
     mock_client.get_node.assert_called_once_with('ns=2;s=TestNode')
-    mock_node.write_value.assert_called_once()
+    mock_node.set_value.assert_called_once()
     assert result == (True, {'response_time': ANY})
 
 
-@pytest.mark.asyncio
-async def test_write_data_write_value_failed(opc_repository, mock_client):
-    opc_repository.validate_connection = AsyncMock(return_value=(True, {}))
+def test_write_data_write_value_failed(opc_repository, mock_client):
+    opc_repository.validate_connection = MagicMock(return_value=(True, {}))
     opc_repository.client = mock_client
-    mock_node = AsyncMock()
+    mock_node = MagicMock()
     opc_repository.error_count = 0
     mock_client.get_node = MagicMock(return_value=mock_node)
-    mock_node.write_value.side_effect = Exception('Test error')
+    mock_node.set_value.side_effect = Exception('Test error')
 
-    is_success, error_data = await opc_repository.write_data(
+    is_success, error_data = opc_repository.write_data(
         'ns=2;s=TestNode', 42.0, 'float', opc_repository.logger, metadata['metadata']
     )
 
     opc_repository.validate_connection.assert_called_once()
     mock_client.get_node.assert_called_once_with('ns=2;s=TestNode')
-    mock_node.write_value.assert_called_once()
+    mock_node.set_value.assert_called_once()
     assert is_success is False
     assert error_data['notification_id'] == f'OPC_WRITE_DATA_ERROR_{opc_repository.id}'
-    assert (
-        error_data['message']
-        == "Failed to write data to OPC server: Test error | metadata: {'model_id': 'test_model', 'model_name': 'test_model', 'workflow_name': 'test_workflow', 'schema_name': 'test_schedule'}"
-    )
-    assert error_data['block'] == 'opc_repository'
-    assert error_data['level'] == NotificationLevel.ERROR
-    assert error_data['attachment_content'] is not None

tests/laborious/worker/test_worker.py

@@ -7,8 +7,8 @@ from laborious.worker import worker
 
 def _build_fake_activities():
     inst = MagicMock()
-    inst.init_opc = AsyncMock()
-    inst.shutdown = AsyncMock()
+    inst.init_opc = MagicMock()
+    inst.shutdown = MagicMock()
     inst.load_query_with_minio_offload = MagicMock()
     inst.retrain_model = MagicMock()
     inst.update_production_model = MagicMock()
@@ -176,7 +176,7 @@ async def test_main_success_exit_zero(monkeypatch):
 
     notif = m_notif_cls.return_value
     notif.shutdown.assert_called_once()
-    fake_activities.shutdown.assert_awaited_once()
+    fake_activities.shutdown.assert_called_once()
     assert m_prepare.call_count == 4
     prepare_calls = m_prepare.call_args_list
     assert prepare_calls[0].kwargs['runtime'] == 'single'

values.yaml

@@ -1,120 +1,265 @@
-# Default values for sientia-module.
+#
+# Default values for sientia-laborious-worker using the sientia-module chart (0.6.x).
 # This is a YAML-formatted file.
 # Declare variables to be passed into your templates.
+#
 
-# This will set the replicaset count more information can be found here: https://kubernetes.io/docs/concepts/workloads/controllers/replicaset/
-replicaCount: 1
+projectName: &projectName "sientia-laborious-worker"
 
-# This sets the container image more information can be found here: https://kubernetes.io/docs/concepts/containers/images/
-image:
-  repository: aignosi.azurecr.io/sientia-module
-  # This sets the pull policy for images.
-  pullPolicy: Always
-  # Overrides the image tag whose default is the chart appVersion.
-  tag: "1.1.2"
+# -----------------------------------------------------------------------------
+# Global configuration shared by all runtimes
+# -----------------------------------------------------------------------------
+global:
+  namespace: sientia
 
-# This is for the secrets for pulling an image from a private repository more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
+  image:
+    repository: aignosi.azurecr.io/sientia-module
+    pullPolicy: Always
+    tag: "1.2.0"
+
+  commonLabels: {}
+
+  resources:
+    # Resource limits and requests are important for ResourceBasedTuner to work correctly.
+    # The tuner monitors system CPU and memory usage, so proper resource limits must be set.
+    limits:
+      cpu: 2000m
+      memory: 20Gi
+    requests:
+      cpu: 1000m
+      memory: 2Gi
+
+  livenessProbe:
+    exec:
+      command:
+        - sh
+        - -c
+        - |
+          curl -sf http://localhost:9090/metrics | grep -q '^app_up{.*} 1'
+    initialDelaySeconds: 1260
+    periodSeconds: 15
+    timeoutSeconds: 5
+    failureThreshold: 3
+
+  readinessProbe:
+    exec:
+      command:
+        - sh
+        - -c
+        - |
+          curl -sf http://localhost:9090/metrics | grep -q '^app_up{.*} 1'
+    initialDelaySeconds: 1200
+    periodSeconds: 10
+    timeoutSeconds: 3
+    failureThreshold: 2
+
+  autoscaling:
+    enabled: false
+    minReplicas: 1
+    maxReplicas: 100
+    targetCPUUtilizationPercentage: 80
+    # targetMemoryUtilizationPercentage: 80
+
+  # Environment variables shared by all runtimes.
+  env:
+    # Entrypoint variables
+    - name: GITHUB_REPO_URL
+      value: "git@github.com:Aignosi/sientia-dataops-laborious_temporal.git"
+    - name: GITHUB_BRANCH
+      value: "feature/SIENTIAPDE-1646"
+    - name: PYTHON_APP
+      value: "laborious.worker.worker"
+    - name: PYPI_SERVER
+      value: "http://library-distribution-server.library.svc.cluster.local:5000"
+
+    # Application variables
+    - name: POSTGRES_HOST
+      value: "paradedb-rw.paradedb.svc.cluster.local"
+    - name: POSTGRES_PORT
+      value: "5432"
+    - name: POSTGRES_USER
+      value: "postgres"
+    - name: POSTGRES_PASSWORD
+      value: "nFqc81y6kwmr2zuAIx43DhiOosFCVPpeEfTtTWZflkNjB2j1KtEeIANkhFR9mAX3"
+    - name: POSTGRES_DBNAME
+      value: "sientia"
+    - name: POSTGRES_MIN_CONNECTIONS
+      value: "20"
+    # max_connections = number_of_workers * max_concurrent_activities * safety_factor
+    # Example: 4 workers * 50 activities * 0.5 = 100 connections
+    - name: POSTGRES_MAX_CONNECTIONS
+      value: "100"
+
+    - name: MLFLOW_URL
+      value: "http://sientia-tracker-mlflow-tracking.sientia-tracker.svc.cluster.local:80"
+    - name: MLFLOW_USERNAME
+      value: "aignosi"
+    - name: MLFLOW_PASSWORD
+      value: "1L0FP50j3ncp123"
+
+    # Plugin store (model-library-store Git + runtime packages).
+    - name: STORE_BASE_URL
+      value: "http://gitea-http.gitea.svc.cluster.local:3000"
+    - name: STORE_OWNER
+      value: "aignosi"
+    - name: STORE_REPO
+      value: "suse-model-store"
+    - name: STORE_USERNAME
+      valueFrom:
+        secretKeyRef:
+          name: sientia-plugin-store-credentials
+          key: username
+    - name: STORE_PASSWORD
+      valueFrom:
+        secretKeyRef:
+          name: sientia-plugin-store-credentials
+          key: password
+    - name: STORE_CACHE_TTL_SECONDS
+      value: "3600"
+
+    - name: OPC_ID
+      value: "1"
+    - name: OPC_SERVER_NAME
+      value: "default_server"
+    - name: OPC_URL
+      value: "opc.tcp://sientia-opc-simulator-opc.sientia.svc.cluster.local:4840"
+
+    - name: LOG_LEVEL
+      value: "DEBUG"
+    - name: HTTP_METRICS_PORT
+      value: "9090"
+    - name: HTTP_SDK_METRICS_PORT
+      value: "9091"
+    - name: PROJECT_NAME
+      value: "sientia-laborious"
+
+    - name: TEMPORAL_HOST
+      value: "temporal-frontend.temporal.svc.cluster.local:7233"
+    - name: TEMPORAL_NAMESPACE
+      value: "laborious"
+
+    - name: MONGODB_USERNAME
+      value: "root"
+    - name: MONGODB_PASSWORD
+      value: "wKZDbMNU1c"
+    - name: MONGODB_URL
+      value: "my-release-mongodb.mongodb.svc.cluster.local:27017"
+    - name: MONGODB_DATABASE
+      value: "sientia"
+    - name: MONGODB_TTL_INDEX_HOURS
+      value: "1"
+
+    - name: MINIO_ENDPOINT_URL
+      value: "minio.minio.svc.cluster.local:9000"
+    - name: MINIO_ACCESS_KEY
+      value: "admin"
+    - name: MINIO_SECRET_KEY
+      value: "LiArt4eNmJ"
+    - name: MINIO_DEFAULT_BUCKET
+      value: "sientia"
+    - name: MINIO_RETENTION_HOURS
+      value: "24"
+    - name: SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES
+      value: "0.5"
+
+    # Temporal worker tuning for PredictionsBatch.
+    # IMPORTANT: prefix must be PREDICTIONSBATCH_ (from class name PredictionsBatch).
+    - name: PREDICTIONSBATCH_MAX_CONCURRENT_WORKFLOW_TASKS
+      value: "20"
+    - name: PREDICTIONSBATCH_MAX_CONCURRENT_ACTIVITIES
+      value: "60"
+    - name: PREDICTIONSBATCH_ACTIVITY_EXECUTOR_MAX_WORKERS
+      value: "10"
+    - name: PREDICTIONSBATCH_MAX_CONCURRENT_LOCAL_ACTIVITIES
+      value: "20"
+    - name: PREDICTIONSBATCH_MAX_CACHED_WORKFLOWS
+      value: "200"
+    - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_MINIMUM
+      value: "3"
+    - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_INITIAL
+      value: "5"
+    - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM
+      value: "15"
+    - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_MINIMUM
+      value: "3"
+    - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_INITIAL
+      value: "10"
+    - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM
+      value: "30"
+
+    - name: MINIMALRETRAIN_MAX_CONCURRENT_ACTIVITIES
+      value: "5"
+    - name: MINIMALRETRAIN_ACTIVITY_EXECUTOR_MAX_WORKERS
+      value: "5"
+    - name: MINIMALRETRAIN_MAX_CONCURRENT_LOCAL_ACTIVITIES
+      value: "5"
+    - name: MINIMALRETRAIN_MAX_CACHED_WORKFLOWS
+      value: "5"
+    - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_MINIMUM
+      value: "5"
+    - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_INITIAL
+      value: "5"
+    - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM
+      value: "5"
+    - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_MINIMUM
+      value: "5"
+    - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_INITIAL
+      value: "5"
+    - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM
+      value: "5"
+
+    - name: PI_WEB_API_BASE_URL
+      value: "https://pivision.votorantimcimentos.com/piwebapi"
+    - name: PI_WEB_API_AUTH_TYPE
+      value: "basic"
+    - name: PI_WEB_API_AUTH_TOKEN
+      valueFrom:
+        secretKeyRef:
+          name: pi-web-api-auth-token
+          key: token
+
+    # Thread-pool size for non-runtime workers that also use prepare_worker.
+    - name: SIMPLEMETRICS_ACTIVITY_EXECUTOR_MAX_WORKERS
+      value: "20"
+    - name: DRIFT_ACTIVITY_EXECUTOR_MAX_WORKERS
+      value: "20"
+
+# -----------------------------------------------------------------------------
+# Runtimes configuration
+# -----------------------------------------------------------------------------
+# IMPORTANT:
+# - The runtime name is used by the worker bootstrap to resolve plugins and task queues.
+# - Keep runtime names in sync with the plugin-store runtime names.
+runtimes:
+  - name: "single"
+    replicas: 1
+
+# -----------------------------------------------------------------------------
+# Chart-level configuration (applies to all runtimes)
+# -----------------------------------------------------------------------------
 imagePullSecrets:
-- name: docker-hub-secret
-# This is to override the chart name.
-nameOverride: "sientia-laborious-worker"
-fullnameOverride: "sientia-laborious-worker"
-namespace: sientia
+  - name: docker-hub-secret
+
+nameOverride: *projectName
+fullnameOverride: *projectName
 
-# This section builds out the service account more information can be found here: https://kubernetes.io/docs/concepts/security/service-accounts/
 serviceAccount:
-  # Specifies whether a service account should be created
   create: true
-  # Automatically mount a ServiceAccount's API credentials?
   automount: true
-  # Annotations to add to the service account
   annotations: {}
-  # The name of the service account to use.
-  # If not set and create is true, a name is generated using the fullname template
-  name: "sientia-laborious-worker"
+  name: *projectName
 
-# This is for setting Kubernetes Annotations to a Pod.
-# For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
 podAnnotations: {}
-# This is for setting Kubernetes Labels to a Pod.
-# For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/
 podLabels: {}
 
 podSecurityContext: {}
-  # fsGroup: 2000
-
 securityContext: {}
-  # capabilities:
-  #   drop:
-  #   - ALL
-  # readOnlyRootFilesystem: true
-  # runAsNonRoot: true
-  # runAsUser: 1000
 
-
-resources:
-  # Resource limits and requests are important for ResourceBasedTuner to work correctly.
-  # The tuner monitors system CPU and memory usage, so proper resource limits must be set.
-  limits:
-    cpu: 2000m  # 2 CPU cores
-    memory: 20Gi  # 20 GB memory
-  requests:
-    cpu: 1000m  # 1 CPU core
-    memory: 2Gi  # 2 GB memory
-
-# This is to setup the liveness and readiness probes more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/
-# This is to setup the liveness and readiness probes more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/
-livenessProbe:
-  exec:
-    command:
-      - sh
-      - -c
-      - |
-        curl -sf http://localhost:9090/metrics | grep -q '^app_up{.*} 1'
-  initialDelaySeconds: 1260
-  periodSeconds: 15
-  timeoutSeconds: 5
-  failureThreshold: 3
-
-readinessProbe:
-  exec:
-    command:
-      - sh
-      - -c
-      - |
-        curl -sf http://localhost:9090/metrics | grep -q '^app_up{.*} 1'
-  initialDelaySeconds: 1200
-  periodSeconds: 10
-  timeoutSeconds: 3
-  failureThreshold: 2
-
-
-
-# This section is for setting up autoscaling more information can be found here: https://kubernetes.io/docs/concepts/workloads/autoscaling/
-autoscaling:
-  enabled: false
-  minReplicas: 1
-  maxReplicas: 100
-  targetCPUUtilizationPercentage: 80
-  # targetMemoryUtilizationPercentage: 80
-
-# Additional volumes on the output Deployment definition.
 volumes: []
-# - name: foo
-#   secret:
-#     secretName: mysecret
-#     optional: false
-
-# Additional volumeMounts on the output Deployment definition.
 volumeMounts: []
-# - name: foo
-#   mountPath: "/etc/foo"
-#   readOnly: true
 
 nodeSelector: {}
-
 tolerations: []
-
 affinity: {}
 
 services:
@@ -124,7 +269,6 @@ services:
     port: 9091
     targetPort: 9091
     name: sdk-metrics
-
   metrics:
     enabled: true
     type: ClusterIP
@@ -135,195 +279,18 @@ services:
 # Configuração do ServiceMonitor para o Prometheus Operator
 # ref: https://github.com/prometheus-operator/prometheus-operator
 serviceMonitor:
-  # Se true, um recurso ServiceMonitor será criado.
   enabled: true
-  # O intervalo no qual as métricas devem ser coletadas (ex: 30s, 1m).
   endpoints:
-  - port: metrics
-    path: /metrics
-    interval: 30s
-    relabelings: []
-  - port: sdk-metrics
-    path: /metrics
-    interval: 30s
-    relabelings: []
-
+    - port: metrics
+      path: /metrics
+      interval: 30s
+      relabelings: []
+    - port: sdk-metrics
+      path: /metrics
+      interval: 30s
+      relabelings: []
   additionalLabels:
     release: kube-prometheus-stack
-  
-
-env:
-  # Entrypoint variables 
-  - name: GITHUB_REPO_URL
-    value: "git@github.com:Aignosi/sientia-dataops-laborious_temporal.git"
-  - name: GITHUB_BRANCH
-    value: "feature/SIENTIAPDE-1712"
-  - name: PYTHON_APP
-    value: "laborious.worker.worker"
-
-  # Application variables
-  - name: POSTGRES_HOST
-    value: "paradedb-rw.paradedb.svc.cluster.local"
-  - name: POSTGRES_PORT
-    value: "5432"
-  - name: POSTGRES_USER
-    value: "postgres"
-  - name: POSTGRES_PASSWORD
-    value: "nFqc81y6kwmr2zuAIx43DhiOosFCVPpeEfTtTWZflkNjB2j1KtEeIANkhFR9mAX3"
-  - name: POSTGRES_DBNAME
-    value: "sientia"
-  - name: POSTGRES_MIN_CONNECTIONS
-    value: "20"
-  # max_connections = number_of_workers * max_concurrent_activities * safety_factor
-  # Example: 4 workers * 50 activities * 0.5 = 100 connections
-  - name: POSTGRES_MAX_CONNECTIONS
-    value: "100"
-
-  - name: MLFLOW_HOST
-    value: "http://sientia-tracker-mlflow-tracking.sientia-tracker.svc.cluster.local"
-  - name: MLFLOW_PORT
-    value: "80"
-  - name: MLFLOW_USERNAME
-    value: "aignosi"
-  - name: MLFLOW_PASSWORD
-    value: "1L0FP50j3ncp123"
-
-  # Worker runtime (PluginStore): required for PredictionsBatch / MinimalRetrain workers.
-  - name: RUNTIME
-    value: "single"
-
-  # Plugin store (model-library-store Git + runtime packages).
-  - name: STORE_BASE_URL
-    value: "http://gitea.sientia.svc.cluster.local:3000"
-  - name: STORE_OWNER
-    value: "sientia"
-  - name: STORE_REPO
-    value: "model-library-store"
-  - name: STORE_BRANCH
-    value: "main"
-  - name: STORE_USERNAME
-    valueFrom:
-      secretKeyRef:
-        name: store-credentials
-        key: username
-  - name: STORE_PASSWORD
-    valueFrom:
-      secretKeyRef:
-        name: store-credentials
-        key: password
-  - name: STORE_CACHE_TTL_SECONDS
-    value: ""
-
-  - name: OPC_ID
-    value: "1"
-  - name: OPC_SERVER_NAME
-    value: "default_server"
-  - name: OPC_URL
-    value: "opc.tcp://sientia-opc-simulator-opc.sientia.svc.cluster.local:4840"
-
-
-  - name: LOG_LEVEL
-    value: "DEBUG"
-  - name: HTTP_METRICS_PORT
-    value: "9090"
-  - name: HTTP_SDK_METRICS_PORT
-    value: "9091"
-  - name: PROJECT_NAME
-    value: "sientia-laborious"
-
-  - name: TEMPORAL_HOST
-    value: "temporal-frontend.temporal.svc.cluster.local:7233"
-  - name: TEMPORAL_NAMESPACE
-    value: "laborious"
-
-  - name: MONGODB_USERNAME
-    value: "root"
-  - name: MONGODB_PASSWORD
-    value: "wKZDbMNU1c"
-  - name: MONGODB_URL
-    value: "my-release-mongodb.mongodb.svc.cluster.local:27017"
-  - name: MONGODB_DATABASE
-    value: "sientia"
-  - name: MONGODB_TTL_INDEX_HOURS
-    value: "1"
-
-  - name: MINIO_ENDPOINT_URL
-    value: "minio.minio.svc.cluster.local:9000"
-  - name: MINIO_ACCESS_KEY
-    value: "admin"
-  - name: MINIO_SECRET_KEY
-    value: "LiArt4eNmJ"
-  - name: MINIO_DEFAULT_BUCKET
-    value: "sientia"
-  - name: MINIO_RETENTION_HOURS
-    value: "24"
-  - name: SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES
-    value: "0.5"
-
-  # Temporal worker tuning for PredictionsBatch.
-  # IMPORTANT: prefix must be PREDICTIONSBATCH_ (from class name PredictionsBatch).
-  # Keep workflow-task concurrency moderate to reduce task completion races under load.
-  - name: PREDICTIONSBATCH_MAX_CONCURRENT_WORKFLOW_TASKS
-    value: "20"
-  # Allow higher activity parallelism because most activities are I/O-bound, but keep headroom.
-  - name: PREDICTIONSBATCH_MAX_CONCURRENT_ACTIVITIES
-    value: "60"
-  # Keep local activities controlled so they do not monopolize the event loop.
-  - name: PREDICTIONSBATCH_MAX_CONCURRENT_LOCAL_ACTIVITIES
-    value: "20"
-  # Cache enough workflows for reuse without excessive memory growth.
-  - name: PREDICTIONSBATCH_MAX_CACHED_WORKFLOWS
-    value: "200"
-  # Start with one workflow poller to avoid burst contention at startup.
-  - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_MINIMUM
-    value: "3"
-  # Small initial poller count warms up gradually instead of spiking task fetches.
-  - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_INITIAL
-    value: "5"
-  # Cap workflow pollers to limit scheduling pressure and avoid over-polling.
-  - name: PREDICTIONSBATCH_WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM
-    value: "15"
-  # Keep at least two activity pollers so activity queues do not starve during spikes.
-  - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_MINIMUM
-    value: "3"
-  # Moderate initial activity pollers for faster ramp-up with controlled pressure.
-  - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_INITIAL
-    value: "10"
-  # Limit max activity pollers to preserve CPU for workflow-task completion.
-  - name: PREDICTIONSBATCH_ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM
-    value: "30"
-
-  - name: MINIMALRETRAIN_MAX_CONCURRENT_ACTIVITIES
-    value: "1"
-  - name: MINIMALRETRAIN_MAX_CONCURRENT_LOCAL_ACTIVITIES
-    value: "1"
-  - name: MINIMALRETRAIN_MAX_CACHED_WORKFLOWS
-    value: "1"
-  - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_MINIMUM
-    value: "1"
-  - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_INITIAL
-    value: "1"
-  - name: MINIMALRETRAIN_WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM
-    value: "1"
-  - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_MINIMUM
-    value: "1"
-  - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_INITIAL
-    value: "1"
-  - name: MINIMALRETRAIN_ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM
-    value: "1"
-
-  - name: PI_WEB_API_BASE_URL
-    value: "https://pivision.votorantimcimentos.com/piwebapi"
-  - name: PI_WEB_API_AUTH_TYPE
-    value: "basic"
-  - name: PI_WEB_API_AUTH_TOKEN
-    valueFrom:
-      secretKeyRef:
-        name: pi-web-api-auth-token
-        key: token
-
-  - name: PYPI_SERVER
-    value: "http://library-distribution-server.library.svc.cluster.local:5000"
 
 ssh:
   enabled: true
@@ -331,10 +298,10 @@ ssh:
   sshPath: /mnt/.ssh
   knownHostsPath: /mnt/known_hosts
 
-# kubectl create secret docker-registry docker-hub-secret --namespace sientia --docker-server=http://aignosi.azurecr.io --docker-username=aignosi --docker-password=5I5zpQ6sRaHqX1hD3dr+2mo647yO3FRc359/wu6gsP+ACRDRz5mp
-
-# helm upgrade --install sientia-laborious-worker sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.6.0
-
+# kubectl create secret docker-registry docker-hub-secret --namespace sientia --docker-server=http://aignosi.azurecr.io --docker-username=aignosi --docker-password=<pwd>
+#
+# helm upgrade --install sientia-laborious-worker sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.6.1
+#
 # kubectl create secret generic git-ssh-key-sientia-laborious-worker \
 #   --namespace sientia \
 #   --from-file=ssh-privatekey=git_key \