chore: bump version to 0.9.0-rc2

Rc/sientiapde 2173 - Mover repeat_last_prediction do sientia-dataops-library para o laborious, com suporte a metadata_id

e2e/db_schema.sql

@@ -40,6 +40,7 @@ CREATE TABLE IF NOT EXISTS sientia_data.predictions (
     "timestamp"           timestamptz NOT NULL,
     created_at            timestamptz DEFAULT CURRENT_TIMESTAMP NOT NULL,
     "comments"            text NULL,
+    metadata_id           varchar(24) NULL,
     CONSTRAINT unique_model_id_timestamp
         UNIQUE (model_id, "timestamp")
 );

laborious/activities/storage.py

@@ -2,6 +2,7 @@ from temporalio import activity, workflow
 
 with workflow.unsafe.imports_passed_through():
     # Extend the Temporal Postgres activities for convenient query -> MinIO export
+    import time
     import traceback
     from datetime import timedelta
     from typing import Any
@@ -9,12 +10,14 @@ with workflow.unsafe.imports_passed_through():
     import pandas as pd
     from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
     from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability import metrics
     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.temporal.activities.postgres_sync import Postgres
     from sientia_do.temporal.constants import now
+    from sqlalchemy import text
 
     from laborious.utils.messages import MISSING_MINIO_REPOSITORY
     from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
@@ -26,7 +29,9 @@ _LOAD_QUERY_OFFLOAD_SKIP_KEYS = frozenset({'model_name', 'key_prefix', 'size_thr
 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.
+    directly to MinIO as Parquet and return the object name, and with
+    ``repeat_last_prediction``, a query specific to this repo's REPEAT path
+    (moved here from the shared library — nothing else consumes it).
     """
 
     minio_repository: MinioRepository | None = None
@@ -141,6 +146,110 @@ class Storage(Postgres, SientiaMonitoring):
             }
         )
 
+    def _build_repeat_last_prediction_query(self, schema: str, table_name: str):
+        table_identifier = f'"{schema}".{table_name}'
+        return text(  # noqa: S608
+            f"""
+        INSERT INTO {table_identifier} (
+            model_id,
+            prediction,
+            timestamp,
+            response_time,
+            prediction_status,
+            prediction_confidence,
+            metadata_id,
+            created_at
+        )
+        SELECT
+            model_id,
+            prediction,
+            :last_timestamp,
+            response_time,
+            prediction_status,
+            prediction_confidence,
+            :metadata_id,
+            NOW()
+        FROM {table_identifier}
+        WHERE model_id = :model
+        ORDER BY timestamp DESC
+        LIMIT 1;
+        """  # nosec B608
+        )
+
+    @activity.defn(name='repeat_last_prediction')
+    def repeat_last_prediction(self, query_items: dict[str, Any]):
+        """
+        Insert a new `predictions` row that copies the last one for a model forward in time.
+
+        REPEAT persists a new row (unlike STOP, which persists nothing), reusing the previous
+        row's `prediction`/`response_time`/`prediction_status`/`prediction_confidence` values with
+        a fresh `timestamp`/`created_at`. `metadata_id` references the `prediction_metadata`
+        document for this cycle, if any (see `PredictionMetadata.write_prediction_metadata`);
+        `None` is a valid value — the column is nullable.
+
+        Args (query_items):
+            metadata (dict): Workflow metadata for logging and metrics.
+            schema (str): Postgres schema of the `predictions` table.
+            table_name (str): `predictions` table name.
+            model (str): `model_id` to repeat the last prediction for.
+            last_timestamp: Timestamp to stamp the new row with.
+            metadata_id (str | None): `prediction_metadata` document id, or `None`.
+
+        Raises:
+            Exception: Re-raised after logging/metrics if the insert fails.
+        """
+        metadata = query_items['metadata']
+        schema = query_items['schema']
+        table_name = query_items['table_name']
+        model = query_items['model']
+        last_timestamp = query_items['last_timestamp']
+        metadata_id = query_items.get('metadata_id')
+
+        repeat_query = self._build_repeat_last_prediction_query(schema, table_name)
+        self.info(f'Repeating last prediction for model {model}', metadata)
+        self.debug(
+            f'Query parameters: schema={schema}, model={model}, last_timestamp={last_timestamp}',
+            metadata,
+        )
+        core_labels = {
+            **self.get_core_labels(metadata, 'execute_sql'),
+            'conflict_mode': 'error',
+        }
+
+        with self.session_factory.begin() as session:
+            try:
+                start_time = time.time()
+                session.execute(
+                    repeat_query,
+                    {
+                        'last_timestamp': last_timestamp,
+                        'model': model,
+                        'metadata_id': metadata_id,
+                    },
+                )
+            except Exception as e:
+                trace = traceback.format_exc()
+                self.send_notification(
+                    metadata=metadata,
+                    notification_id='ERROR_REPEATING_LAST_PREDICTION',
+                    message=f'Error repeating last prediction: {e}',
+                    block='repeat_last_prediction',
+                    level=NotificationLevel.ERROR,
+                    attachment_content=trace,
+                )
+                self.error(trace, metadata)
+                self.emit_metric_sync(
+                    metric_object=metrics.POSTGRES_WRITE_ERROR_COUNT,
+                    tags=core_labels,
+                )
+                raise
+            else:
+                self.observe_lag_sync(start_time, metrics.POSTGRES_WRITE_LAG, core_labels)
+                self.emit_metric_sync(
+                    metric_object=metrics.POSTGRES_WRITE_COUNT,
+                    tags=core_labels,
+                )
+
     @activity.defn(name='cleanup_minio_objects_expired')
     def cleanup_minio_objects_expired(self, input_data: dict[str, Any]) -> dict[str, Any]:
         """

laborious/workflows/sub_workflows/prediction_process.py

@@ -360,10 +360,9 @@ class PredictionProcess:
             return True
         elif path_flag == 'REPEAT':
             metadata_id = await self._write_prediction_metadata(metadata, document)
-            # Repeat last prediction if available. `metadata_id` isn't consumed by
+            # Repeat last prediction, copying the previous row's values forward with a new
-            # `repeat_last_prediction` yet (its INSERT...SELECT has no `metadata_id` column —
+            # timestamp. `repeat_last_prediction` now lives in `Storage` (laborious), not the
-            # pending a change in `sientia-dataops-library`); passed through already so no further
+            # shared library — its INSERT...SELECT references `metadata_id` directly.
-            # change is needed here once that lands.
             await workflow.execute_activity_method(
                 Activities.repeat_last_prediction,
                 {

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "laborious"
-version = "0.9.0"
+version = "0.9.0-rc2"
 description = "Sientia DataOps Laborious - ML Model Orchestration System"
 readme = "README.md"
 requires-python = ">=3.11"

tests/laborious/activities/test_storage.py

@@ -257,6 +257,86 @@ def test_export_payload_to_postgres(storage):
     assert result == {'success': True}
 
 
+def test_build_repeat_last_prediction_query(storage):
+    query = storage._build_repeat_last_prediction_query('public', 'predictions')
+    query_str = str(query)
+
+    assert 'INSERT INTO "public".predictions' in query_str
+    assert 'metadata_id' in query_str
+    assert ':last_timestamp' in query_str
+    assert ':metadata_id' in query_str
+    assert ':model' in query_str
+
+
+@patch('laborious.activities.storage.text')
+def test_repeat_last_prediction_success(text_mock, storage):
+    storage.emit_metric_sync = MagicMock()
+    storage.observe_lag_sync = MagicMock()
+    query_items = {
+        **metadata,
+        'schema': 'public',
+        'table_name': 'predictions',
+        'model': 1,
+        'last_timestamp': '2022-01-01',
+        'metadata_id': 'abc123',
+    }
+
+    with patch('sqlalchemy.orm.session.Session.execute') as mock_execute:
+        storage.repeat_last_prediction(query_items)
+
+    text_mock.assert_called_once()
+    mock_execute.assert_called_once_with(
+        text_mock.return_value,
+        {'last_timestamp': '2022-01-01', 'model': 1, 'metadata_id': 'abc123'},
+    )
+    storage.observe_lag_sync.assert_called_once()
+    storage.emit_metric_sync.assert_called_once()
+
+
+@patch('laborious.activities.storage.text')
+def test_repeat_last_prediction_without_metadata_id(text_mock, storage):
+    storage.emit_metric_sync = MagicMock()
+    storage.observe_lag_sync = MagicMock()
+    query_items = {
+        **metadata,
+        'schema': 'public',
+        'table_name': 'predictions',
+        'model': 1,
+        'last_timestamp': '2022-01-01',
+    }
+
+    with patch('sqlalchemy.orm.session.Session.execute') as mock_execute:
+        storage.repeat_last_prediction(query_items)
+
+    mock_execute.assert_called_once_with(
+        text_mock.return_value,
+        {'last_timestamp': '2022-01-01', 'model': 1, 'metadata_id': None},
+    )
+
+
+@patch('laborious.activities.storage.text')
+def test_repeat_last_prediction_error(text_mock, storage):
+    storage.emit_metric_sync = MagicMock()
+    storage.send_notification = MagicMock()
+    query_items = {
+        **metadata,
+        'schema': 'public',
+        'table_name': 'predictions',
+        'model': 1,
+        'last_timestamp': '2022-01-01',
+        'metadata_id': 'abc123',
+    }
+
+    with (
+        patch('sqlalchemy.orm.session.Session.execute', side_effect=ValueError('db')),
+        raises(ValueError),
+    ):
+        storage.repeat_last_prediction(query_items)
+
+    storage.send_notification.assert_called_once()
+    storage.emit_metric_sync.assert_called()
+
+
 def test_cleanup_minio_objects_expired_minio_not_initialized(storage):
     storage.minio_repository = None