a812acd4e9
Rename PredictionMetadata (prediction_metadata.py) to MongoActivities (mongo.py) and move update_simple_metrics_adaptive_state there from ModelMetrics, so both Mongo-backed activities share one class and repository (PR #59 review, agreed with the class author). write_prediction_metadata is unchanged; the pure hysteresis helpers stay in model_metrics.py. References in activities.py, docstrings and tests updated.
225 lines
10 KiB
Python
225 lines
10 KiB
Python
from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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from typing import Any
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from bson import ObjectId
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.observability.logger import Logger
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from sientia_do.observability.metrics_controller import MetricsController
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from sientia_do.observability.sientia_monitoring import SientiaMonitoring
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from sientia_do.repository.mongodb_repository_sync import MongoDBRepository
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from sientia_do.temporal.constants import now
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from laborious.activities.model_metrics import (
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next_adaptive_state,
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recovery_runs,
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threshold_breaches,
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)
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class MongoActivities(SientiaMonitoring):
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"""
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Activities that read from or write to MongoDB, sharing one `MongoDBRepository`.
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- `write_prediction_metadata`: centralized, best-effort writer for prediction gate metadata
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(triggered filter, policy, counter). Gates only compute and return a metadata document;
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they never write it themselves. Keeping the write here, instead of in each gate, puts the
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one real write point in the subworkflow that's about to persist a `predictions` row — the
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only place that reliably knows whether one is being written at all — and lets other
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metadata sources reuse the same activity instead of duplicating write logic wherever they
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originate.
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- `update_simple_metrics_adaptive_state`: adaptive frequency state on the `pipelines` doc of
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a simple metrics schedule (SIENTIAPDE-1995).
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"""
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mongo_repository: MongoDBRepository | None = None
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_PREDICTION_METADATA_COLLECTION = 'prediction_metadata'
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_PIPELINES_COLLECTION = 'pipelines'
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def __init__(
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self,
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mongo_repository: MongoDBRepository | None = None,
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logger: Logger | None = None,
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notification_handler: NotificationHandler | None = None,
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metrics_controller: MetricsController | None = None,
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):
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"""
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Initialize the Mongo activities.
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Args:
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mongo_repository: MongoDB, where gate metadata (triggered filter, policy, counter) is
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written best-effort and the simple metrics adaptive state is kept. `None` is a
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valid state: both activities become no-ops.
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Raises:
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Exception: If BaseActivity initialization fails
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"""
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self.mongo_repository = mongo_repository
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SientiaMonitoring.__init__(
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self,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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def close(self) -> None:
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"""
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Close the Mongo repository and clean up resources.
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"""
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if self.mongo_repository is not None:
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try:
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self.mongo_repository.close()
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finally:
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self.mongo_repository = None
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SientiaMonitoring.shutdown(self)
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def __del__(self):
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self.close()
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@activity.defn(name='write_prediction_metadata')
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def write_prediction_metadata(self, input_data: dict[str, Any]) -> str | None:
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"""
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Best-effort write of one or more gate metadata documents to Mongo, combined into a single
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`prediction_metadata` document.
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Implements both business rules for this feature in one place:
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- The prediction write must never be blocked or failed by a metadata write. A failed
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`insert(...)` is swallowed, never re-raised, logged here with call-site context (which
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`mongo_repository.insert(...)` itself can't know) — matching how `storage.py`'s
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`cleanup_minio_objects_expired` also logs again at the call site even though the
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repository already logs internally and emits its own Prometheus metric
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(`MONGODB_WRITE_ERROR_COUNT`); the generic library log is for the metric/trace, this
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one is for whoever is reading logs for this specific call.
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- Nothing is written when there's nothing relevant to report. An empty/missing
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`documents` list (or one full of falsy/empty entries) skips Mongo entirely, so callers
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can call this unconditionally and get `None` back for "no metadata this time" instead
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of deciding upfront whether there's something worth saving.
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Takes a *list* of documents, not a single one: each caller contributes its own document
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(shaped like `{'filters': [...]}`), and this flattens every `filters` entry across all of
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them into one `prediction_metadata` document before the single insert. Today only one
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gate ever contributes a document per prediction cycle (the gate chain short-circuits on
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the first non-`None` `path_flag`), but the list shape means this activity won't need to
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change again once more than one source contributes metadata for the same cycle.
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`insert(...)` doesn't hand back the inserted document's `_id`, so it is generated here,
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client-side, before the insert, as a plain string (not a BSON ObjectId) — matching this
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codebase's convention of string `_id`s (e.g. the `models` collection) and the 24-hex-char
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size of the `predictions.metadata_id` Postgres column that will reference it.
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Args:
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input_data: Dict with:
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- documents (list[dict[str, Any]]): Metadata payloads to combine, each shaped
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like `{'filters': [...]}`. Missing/empty, or entries with no `filters`, skip
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the write entirely.
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- metadata (dict): Workflow metadata for contextual logging.
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Return:
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The generated id, or `None` when there's nothing to write, Mongo isn't configured, or
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the insert failed.
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"""
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metadata = input_data['metadata']
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documents = input_data.get('documents') or []
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filters = [
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entry for document in documents if document for entry in document.get('filters', [])
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]
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if not filters or self.mongo_repository is None:
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return None
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generated_id = str(ObjectId())
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try:
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self.mongo_repository.insert(
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self._PREDICTION_METADATA_COLLECTION,
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{'filters': filters, '_id': generated_id},
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metadata,
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)
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except Exception as e:
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self.error(f'Failed to write prediction metadata: {e}', metadata)
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return None
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return generated_id
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@activity.defn(name='update_simple_metrics_adaptive_state')
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def update_simple_metrics_adaptive_state(self, input_data: dict[str, Any]) -> dict | None:
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"""
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Update the adaptive frequency state on the schedule's `pipelines` doc.
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The orchestrator derives the schedule frequency from `adaptive_state.escalated` and
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`frequency_alert`. State is read from Mongo, not from the workflow input, because the
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input only refreshes on the orchestrator tick and would be stale while escalated.
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`updated_at` is bumped only when `escalated` flips: that is what makes the orchestrator
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rewrite the schedule, and a streak change alone does not change the frequency.
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Args:
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input_data: Dict with:
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- metadata (dict): Workflow metadata; `schedule_name` identifies the doc.
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- simple_metrics (list[dict]): Rows from `calculate_simple_metrics`; empty for an
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empty or under-sampled window, which counts as a non-breach run.
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- thresholds (dict): `simple_metrics_thresholds` from `model_config` (may be None: no breach).
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- interval_minutes (int): Window size, used to compute the recovery runs.
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Return:
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The new state, or `None` when adaptive mode is off, Mongo is not configured, the doc
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is missing or `frequency_alert` is invalid.
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Raises:
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Exception: Mongo read/write failures, so the workflow run fails explicitly.
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"""
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metadata = input_data['metadata']
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if self.mongo_repository is None:
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return None
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# Mongo errors propagate on purpose: the run must fail explicitly so the problem shows
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# up in the Temporal metrics (PR #59 review). Metrics were already exported by then.
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query = {'schedule_name': metadata['schedule_name']}
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pipeline = self.mongo_repository.find_one(self._PIPELINES_COLLECTION, query, metadata)
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if not pipeline:
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# Likely misconfig: MONGODB_DATABASE_NAME differs from the orchestrator's, or the
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# schedule_name does not match its doc.
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self.warning(
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f'No pipelines doc for schedule {metadata["schedule_name"]}: '
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'adaptive state skipped',
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metadata,
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)
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return None
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frequency_alert = pipeline.get('frequency_alert')
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if not frequency_alert:
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return None
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try:
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runs = recovery_runs(input_data['interval_minutes'], str(frequency_alert))
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except ValueError:
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# Config error, not a failure: the orchestrator ignores this value too (keeps
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# `frequency`) and already sends SIMPLE_METRICS_FREQUENCY_ALERT_BELOW_FLOOR for it.
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self.warning(
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f'Invalid frequency_alert={frequency_alert!r}: adaptive state skipped', metadata
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)
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return None
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breached = bool(
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threshold_breaches(input_data['simple_metrics'], input_data.get('thresholds'))
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)
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state = pipeline.get('adaptive_state')
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if not isinstance(state, dict):
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# Missing or hand-edited garbage: not escalated, same as the orchestrator reads it.
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state = {'escalated': False, 'healthy_streak': 0}
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new_state = next_adaptive_state(state, breached, runs)
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if new_state == state:
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return new_state
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update: dict[str, Any] = {'adaptive_state': new_state}
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if new_state['escalated'] != bool(state.get('escalated')):
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# BSON datetime, never a string: the orchestrator normalizes `updated_at` with
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# `.replace(tzinfo=UTC).strftime(...)` and a string would fail its whole tick.
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update['updated_at'] = now()
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self.mongo_repository.update_one(
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self._PIPELINES_COLLECTION, query, {'$set': update}, metadata
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)
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self.info(f'Adaptive state of {query["schedule_name"]}: {state} -> {new_state}', metadata)
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return new_state
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