diff --git a/bases/bot_detector/worker_ml/core.py b/bases/bot_detector/worker_ml/core.py index 554df534..34438d0b 100644 --- a/bases/bot_detector/worker_ml/core.py +++ b/bases/bot_detector/worker_ml/core.py @@ -3,57 +3,44 @@ import traceback import aiohttp -from bot_detector.database import Settings as DBSettings -from bot_detector.database import get_session_factory -from bot_detector.database.prediction import PredictionLatestRepo, PredictionRepo from bot_detector.event_queue.adapters.kafka import ( KafkaConfig, KafkaConsumerConfig, KafkaProducerConfig, KafkaSettings, ) -from bot_detector.event_queue.core import Queue +from bot_detector.event_queue.core import Queue, QueueProducer from bot_detector.event_queue.factory import QueueFactory -from bot_detector.event_queue.structs import DataToPredictStruct, ScrapedStruct +from bot_detector.event_queue.structs import ( + DataToPredictStruct, + PredictionsToInsertStruct, + ScrapedStruct, +) from bot_detector.ml_api import InputData, MLApiClient, Prediction -from bot_detector.structs import PredictionCreate from bot_detector.worker_ml.metrics import ( api_errors_counter, batches_consumed_counter, messages_requeued_counter, - predictions_inserted_counter, + predictions_published_counter, start_metrics_server, ) from bot_detector.worker_ml.settings import Settings from bot_detector.worker_ml.settings import Settings as MLSettings -from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker logger = logging.getLogger(__name__) -async def insert_prediction_results( - session_factory: async_sessionmaker[AsyncSession], - predictions: list[PredictionCreate], -) -> None: - pred_repo = PredictionRepo() - pred_latest_repo = PredictionLatestRepo() - async with session_factory() as session: - await pred_repo.insert(session, predictions) - await pred_latest_repo.insert(session, predictions) - return - - def transform_prediction( player_id: int, prediction: Prediction, model_name: str, -) -> PredictionCreate: +) -> PredictionsToInsertStruct: _pred_dict: dict[str, float] = prediction.model_dump() _max_prob_key = max(_pred_dict, key=_pred_dict.get) # type: ignore _confidence = round(_pred_dict[_max_prob_key], 4) _predictions = {k: round(v, 4) for k, v in prediction.model_dump().items()} - return PredictionCreate( + return PredictionsToInsertStruct( model_name=model_name, player_id=player_id, prediction=_max_prob_key, @@ -101,7 +88,7 @@ async def consume_data_to_predict( max_messages: int, data_to_predict_queue: Queue[DataToPredictStruct], api: MLApiClient, - session_factory: async_sessionmaker[AsyncSession], + predictions_queue: QueueProducer[PredictionsToInsertStruct], model_name: str = "multi_model_v1", ): while True: @@ -169,25 +156,22 @@ async def consume_data_to_predict( for b, pred in zip(_batch, _predictions) ] - # database call - try: - await insert_prediction_results( - session_factory=session_factory, - predictions=_predictions, - ) - except Exception as e: + # publish prediction results to kafka + put_result = await predictions_queue.put(_predictions) + if isinstance(put_result, Exception): logger.error( { + "error": "failed to publish predictions", + "reason": str(put_result), "_predictions": _predictions[:3], - "error": str(e), } ) - put_result = await data_to_predict_queue.put(_batch) - if isinstance(put_result, Exception): + requeue_result = await data_to_predict_queue.put(_batch) + if isinstance(requeue_result, Exception): logger.error( { "error": "failed to requeue consumed records", - "reason": str(put_result), + "reason": str(requeue_result), } ) await asyncio.sleep(15) @@ -205,7 +189,7 @@ async def consume_data_to_predict( continue await asyncio.sleep(15) continue - predictions_inserted_counter.inc(len(_predictions)) + predictions_published_counter.inc(len(_predictions)) commit_result = await data_to_predict_queue.commit() if isinstance(commit_result, Exception): logger.error( @@ -221,7 +205,7 @@ async def consume_player_scraped( max_messages: int, player_sc_queue: Queue[ScrapedStruct], api: MLApiClient, - session_factory: async_sessionmaker[AsyncSession], + predictions_queue: QueueProducer[PredictionsToInsertStruct], model_name: str = "multi_model_v1", ): while True: @@ -307,12 +291,41 @@ async def consume_player_scraped( await asyncio.sleep(15) continue - # insert prediction results into mysql - await insert_prediction_results( - session_factory=session_factory, - predictions=combined_predictions, - ) - predictions_inserted_counter.inc(len(combined_predictions)) + # publish prediction results to kafka + put_result = await predictions_queue.put(combined_predictions) + if isinstance(put_result, Exception): + logger.error( + { + "error": "failed to publish predictions", + "reason": str(put_result), + "_predictions": combined_predictions[:3], + } + ) + requeue_result = await player_sc_queue.put(batch) + if isinstance(requeue_result, Exception): + logger.error( + { + "error": "failed to requeue consumed records", + "reason": str(requeue_result), + } + ) + await asyncio.sleep(15) + continue + messages_requeued_counter.labels(loop="player_scraped").inc(len(batch)) + commit_result = await player_sc_queue.commit() + if isinstance(commit_result, Exception): + logger.error( + { + "error": "failed to commit offset after requeue", + "reason": str(commit_result), + } + ) + await asyncio.sleep(15) + continue + await asyncio.sleep(15) + continue + + predictions_published_counter.inc(len(combined_predictions)) commit_result = await player_sc_queue.commit() if isinstance(commit_result, Exception): logger.error( @@ -351,8 +364,30 @@ async def consume_player_scraped( await asyncio.sleep(15) +async def main_predictions_producer() -> QueueProducer[PredictionsToInsertStruct]: + predictions_queue = QueueFactory.create_queue( + model=PredictionsToInsertStruct, + queue_type="producer", + backend_type="kafka", + config=KafkaConfig( + topic="predictions.to_insert", + bootstrap_servers=KafkaSettings().bootstrap_servers, + producer=True, + producer_config=KafkaProducerConfig(partition_key_fn=None), + ), + ) + if isinstance(predictions_queue, Exception): + raise predictions_queue + + assert isinstance(predictions_queue, QueueProducer) + + await predictions_queue.start() + return predictions_queue + + async def main_player_scraped( - api: MLApiClient, session_factory: async_sessionmaker[AsyncSession] + api: MLApiClient, + predictions_queue: QueueProducer[PredictionsToInsertStruct], ) -> tuple[asyncio.Task, Queue]: player_sc_queue = QueueFactory.create_queue( model=ScrapedStruct, @@ -382,7 +417,7 @@ async def main_player_scraped( max_messages=MLSettings().MAX_MESSAGES, player_sc_queue=player_sc_queue, api=api, - session_factory=session_factory, # type: ignore + predictions_queue=predictions_queue, model_name=MLSettings().MODEL_NAME, ) ) @@ -390,7 +425,8 @@ async def main_player_scraped( async def main_data_to_predict( - api: MLApiClient, session_factory: async_sessionmaker[AsyncSession] + api: MLApiClient, + predictions_queue: QueueProducer[PredictionsToInsertStruct], ) -> tuple[asyncio.Task, Queue]: data_to_predict_queue = QueueFactory.create_queue( model=DataToPredictStruct, @@ -418,7 +454,7 @@ async def main_data_to_predict( max_messages=Settings().MAX_MESSAGES, data_to_predict_queue=data_to_predict_queue, api=api, - session_factory=session_factory, # type: ignore + predictions_queue=predictions_queue, model_name=Settings().MODEL_NAME, ) ) @@ -427,8 +463,9 @@ async def main_data_to_predict( async def main(): start_metrics_server(port=MLSettings().METRICS_PORT) - ## database - session_factory, engine = get_session_factory(SETTINGS=DBSettings()) + + ## predictions producer + predictions_queue = await main_predictions_producer() ## api client http_session = aiohttp.ClientSession() @@ -438,14 +475,14 @@ async def main(): if Settings().CONSUME_PLAYER_SCRAPED: task_ps, player_sc_queue = await main_player_scraped( api=api, - session_factory=session_factory, + predictions_queue=predictions_queue, ) tasks.append(task_ps) if Settings().CONSUME_DATA_TO_PREDICT: task_dtp, data_to_predict_queue = await main_data_to_predict( api=api, - session_factory=session_factory, + predictions_queue=predictions_queue, ) tasks.append(task_dtp) await asyncio.gather(*tasks) @@ -456,8 +493,8 @@ async def main(): if Settings().CONSUME_DATA_TO_PREDICT: await data_to_predict_queue.stop() + await predictions_queue.stop() await http_session.close() - await engine.dispose() async def run_async(): diff --git a/bases/bot_detector/worker_ml/metrics.py b/bases/bot_detector/worker_ml/metrics.py index 36823d96..4ad75319 100644 --- a/bases/bot_detector/worker_ml/metrics.py +++ b/bases/bot_detector/worker_ml/metrics.py @@ -16,9 +16,9 @@ def start_metrics_server(port: int) -> None: labelnames=["loop"], ) -predictions_inserted_counter = Counter( - name="worker_ml_predictions_inserted", - documentation="Number of predictions inserted into the database", +predictions_published_counter = Counter( + name="worker_ml_predictions_published", + documentation="Number of predictions published to kafka", ) api_errors_counter = Counter( diff --git a/bases/bot_detector/worker_prediction/README.md b/bases/bot_detector/worker_prediction/README.md new file mode 100644 index 00000000..803576fc --- /dev/null +++ b/bases/bot_detector/worker_prediction/README.md @@ -0,0 +1,66 @@ +# worker_prediction + +Consumes ML prediction messages from the `predictions.to_insert` Kafka topic and inserts them into the `prediction` and `prediction_latest` tables. + +## Architecture + +``` +┌─────────────┐ +│ main.py │ wiring & lifecycle +├─────────────┤ +│ settings.py│ pydantic-settings (N_WORKERS, MAX_BATCH_SIZE, MAX_INTERVAL_MS) +├─────────────┤ +│ worker.py │ PredictionWorker.handle() + insert_batch() +└─────────────┘ +``` + +### Dependency flow + +``` +main.py + → worker component (WorkerRunner) + → database component (PredictionRepo, PredictionLatestRepo, session factory) + → event_queue component (KafkaConfig, PredictionsToInsertStruct) +``` + +## Flow + +1. `main()` creates `N_WORKERS` instances of `PredictionWorker`, each wired to a `WorkerRunner`. +2. Each `WorkerRunner` creates a Kafka queue (consumer + producer), starts it, and enters the consume loop. +3. On each iteration, `WorkerRunner` fetches a batch of `PredictionsToInsertStruct` messages from Kafka. +4. `PredictionWorker.handle()` inserts the batch into `prediction` + `prediction_latest` via `insert_batch()`. +5. On success, `WorkerRunner` commits the Kafka offset. +6. On failure, `WorkerRunner` requeues the batch and retries. + +## Sequence diagram + +```mermaid +sequenceDiagram + participant M as main.py + participant WR as WorkerRunner + participant K as Kafka + participant W as PredictionWorker + participant DB as MySQL + + M->>WR: runner.run() (per worker) + WR->>K: queue.start() + loop until stop_event + WR->>K: get_many(batch_size) + K-->>WR: PredictionsToInsertStruct[] + WR->>W: handle(batch) + W->>DB: insert_batch(batch) + DB-->>W: ok + W-->>WR: return [] + WR->>K: commit() + end + WR->>K: queue.stop() +``` + +## Configuration + +| Variable | Default | Description | +|---|---|---| +| `N_WORKERS` | `1` | Number of parallel worker instances | +| `MAX_BATCH_SIZE` | `10_000` | Max messages fetched per consume iteration | +| `MAX_INTERVAL_MS` | `1_000` | Kafka consumer timeout in milliseconds | +| `METRICS_PORT` | `8000` | Prometheus metrics port | diff --git a/bases/bot_detector/worker_prediction/__init__.py b/bases/bot_detector/worker_prediction/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bases/bot_detector/worker_prediction/main.py b/bases/bot_detector/worker_prediction/main.py new file mode 100644 index 00000000..eb3c9ab7 --- /dev/null +++ b/bases/bot_detector/worker_prediction/main.py @@ -0,0 +1,77 @@ +import asyncio +import logging + +from bot_detector import database as db +from bot_detector.database import Settings as DBSettings +from bot_detector.database.prediction import PredictionLatestRepo, PredictionRepo +from bot_detector.event_queue.adapters.kafka import ( + KafkaConfig, + KafkaConsumerConfig, + KafkaProducerConfig, + KafkaSettings, +) +from bot_detector.event_queue.structs import PredictionsToInsertStruct +from bot_detector.worker.core import WorkerRunner +from bot_detector.worker_prediction.metrics import start_metrics_server +from bot_detector.worker_prediction.settings import Settings +from bot_detector.worker_prediction.worker import PredictionWorker + +logger = logging.getLogger(__name__) + +SETTINGS = Settings() +KAFKA_SETTINGS = KafkaSettings() + + +async def main(): + start_metrics_server(port=SETTINGS.METRICS_PORT) + session_factory, async_engine = db.get_session_factory(SETTINGS=DBSettings()) + prediction_repo = PredictionRepo() + prediction_latest_repo = PredictionLatestRepo() + stop_event = asyncio.Event() + tasks = [] + for worker_id in range(SETTINGS.N_WORKERS): + worker = PredictionWorker( + worker_id=worker_id, + session_factory=session_factory, + prediction_repo=prediction_repo, + prediction_latest_repo=prediction_latest_repo, + ) + kafka_config = KafkaConfig( + topic="predictions.to_insert", + bootstrap_servers=KAFKA_SETTINGS.bootstrap_servers, + producer=True, + consumer=True, + producer_config=KafkaProducerConfig(partition_key_fn=None), + consumer_config=KafkaConsumerConfig( + group_id="prediction_worker", + consume_timeout_ms=SETTINGS.MAX_INTERVAL_MS, + ), + ) + runner = WorkerRunner( + worker=worker, + config=kafka_config, + model=PredictionsToInsertStruct, + batch_size=SETTINGS.MAX_BATCH_SIZE, + stop_event=stop_event, + name="worker-prediction", + ) + task = asyncio.create_task(runner.run()) + tasks.append(task) + + try: + await asyncio.gather(*tasks) + finally: + stop_event.set() + await async_engine.dispose() + + +async def run_async(): + await main() + + +def run(): + asyncio.run(run_async()) + + +if __name__ == "__main__": + run() diff --git a/bases/bot_detector/worker_prediction/metrics.py b/bases/bot_detector/worker_prediction/metrics.py new file mode 100644 index 00000000..f9aa7e5b --- /dev/null +++ b/bases/bot_detector/worker_prediction/metrics.py @@ -0,0 +1,16 @@ +import logging + +from prometheus_client import Counter, start_http_server + +logger = logging.getLogger(__name__) + + +def start_metrics_server(port: int) -> None: + start_http_server(port) + logger.info(f"metrics server started on port {port}") + + +predictions_inserted_counter = Counter( + name="worker_prediction_predictions_inserted", + documentation="Number of predictions inserted into the database", +) diff --git a/bases/bot_detector/worker_prediction/settings.py b/bases/bot_detector/worker_prediction/settings.py new file mode 100644 index 00000000..36c1c231 --- /dev/null +++ b/bases/bot_detector/worker_prediction/settings.py @@ -0,0 +1,8 @@ +from pydantic_settings import BaseSettings + + +class Settings(BaseSettings): + N_WORKERS: int = 1 + MAX_BATCH_SIZE: int = 10_000 + MAX_INTERVAL_MS: int = 1_000 + METRICS_PORT: int = 8000 diff --git a/bases/bot_detector/worker_prediction/worker.py b/bases/bot_detector/worker_prediction/worker.py new file mode 100644 index 00000000..456dadde --- /dev/null +++ b/bases/bot_detector/worker_prediction/worker.py @@ -0,0 +1,61 @@ +import logging + +from bot_detector.database.prediction import PredictionLatestRepo, PredictionRepo +from bot_detector.event_queue.structs import PredictionsToInsertStruct +from bot_detector.worker.core import Worker +from bot_detector.worker_prediction.metrics import predictions_inserted_counter +from sqlalchemy.exc import OperationalError +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker + +logger = logging.getLogger(__name__) + + +async def insert_batch( + prediction_repo: PredictionRepo, + prediction_latest_repo: PredictionLatestRepo, + batch: list[PredictionsToInsertStruct], + session_factory: async_sessionmaker[AsyncSession], +) -> None: + logger.debug(f"batch inserting: {len(batch)}") + try: + async with session_factory() as session: + await prediction_repo.insert(async_session=session, predictions=batch) + await prediction_latest_repo.insert( + async_session=session, + predictions=batch, + ) + except OperationalError as e: + logger.error(f"OperationalError during batch insert: {e}") + raise + predictions_inserted_counter.inc(len(batch)) + logger.info(f"inserted: {len(batch)}") + + +class PredictionWorker(Worker[PredictionsToInsertStruct]): + def __init__( + self, + worker_id: int, + session_factory: async_sessionmaker[AsyncSession], + prediction_repo: PredictionRepo, + prediction_latest_repo: PredictionLatestRepo, + ) -> None: + self._id = worker_id + self._session_factory = session_factory + self._prediction_repo = prediction_repo + self._prediction_latest_repo = prediction_latest_repo + + async def handle( + self, batch: list[PredictionsToInsertStruct] + ) -> list[PredictionsToInsertStruct]: + logger.info(f"[{self._id}] consumed {len(batch)} predictions") + if not batch: + logger.info("No predictions to process.") + return [] + await insert_batch( + prediction_repo=self._prediction_repo, + prediction_latest_repo=self._prediction_latest_repo, + batch=batch, + session_factory=self._session_factory, + ) + logger.info(f"[{self._id}] processed {len(batch)} predictions") + return [] diff --git a/components/bot_detector/database/prediction/interface.py b/components/bot_detector/database/prediction/interface.py index a80eda76..16046667 100644 --- a/components/bot_detector/database/prediction/interface.py +++ b/components/bot_detector/database/prediction/interface.py @@ -1,4 +1,5 @@ from abc import ABC, abstractmethod +from collections.abc import Sequence from bot_detector.structs import ( PredictionCreate, @@ -20,7 +21,7 @@ async def select( async def insert( self, async_session: AsyncSession, - predictions: list[PredictionCreate], + predictions: Sequence[PredictionCreate], ) -> None: raise NotImplementedError() @@ -37,6 +38,6 @@ async def select( async def insert( self, async_session: AsyncSession, - predictions: list[PredictionCreate], + predictions: Sequence[PredictionCreate], ) -> None: raise NotImplementedError() diff --git a/components/bot_detector/database/prediction/repository.py b/components/bot_detector/database/prediction/repository.py index 4b5a1adc..3c006405 100644 --- a/components/bot_detector/database/prediction/repository.py +++ b/components/bot_detector/database/prediction/repository.py @@ -1,4 +1,5 @@ import logging +from collections.abc import Sequence from dataclasses import asdict import sqlalchemy as sqla @@ -45,7 +46,7 @@ async def select( async def insert( self, async_session: AsyncSession, - predictions: list[PredictionCreate], + predictions: Sequence[PredictionCreate], ) -> None: logger.info(f"Inserting {len(predictions)} rows into prediction_latest") @@ -91,7 +92,7 @@ async def select( async def insert( self, async_session: AsyncSession, - predictions: list[PredictionCreate], + predictions: Sequence[PredictionCreate], ) -> None: logger.info(f"Inserting {len(predictions)} rows into prediction") diff --git a/projects/worker_ml/pyproject.toml b/projects/worker_ml/pyproject.toml index ab70f1af..2f1c15d2 100644 --- a/projects/worker_ml/pyproject.toml +++ b/projects/worker_ml/pyproject.toml @@ -8,12 +8,9 @@ version = "0.1.0" requires-python = ">=3.10" dependencies = [ - "asyncmy==0.2.11", - "cryptography==46.0.3", "pydantic-settings==2.7.0", "pydantic==2.10.4", 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------------------------------------------------------------ +# Base (shared env only, no filesystem assumptions) +# ------------------------------------------------------------ +FROM python:3.12-slim-bookworm AS base + +ENV PYTHONUNBUFFERED=1 +ENV UV_COMPILE_BYTECODE=1 + +WORKDIR /project + + +# ------------------------------------------------------------ +# Builder: production dependencies only +# ------------------------------------------------------------ +FROM base AS builder + +COPY --from=ghcr.io/astral-sh/uv:0.5.13 /uv /bin/uv +COPY ./bases ./bases +COPY ./components ./components +COPY ./projects/worker_prediction/Dockerfile ./projects/worker_prediction/Dockerfile +COPY ./projects/worker_prediction/pyproject.toml ./projects/worker_prediction/pyproject.toml +COPY ./projects/worker_prediction/uv.lock ./projects/worker_prediction/uv.lock +COPY workspace.toml workspace.toml + +# IMPORTANT: venv path is fixed here +WORKDIR /project/projects/worker_prediction + +RUN --mount=type=cache,id=uv_cache,target=/root/.cache/uv \ + uv sync --frozen --no-dev + + +# ------------------------------------------------------------ +# Dev: development environment with dev dependencies +# ------------------------------------------------------------ +FROM base AS dev + +COPY --from=ghcr.io/astral-sh/uv:0.5.13 /uv /uvx /bin/ + +ENV PYTHONPATH="/project" + +COPY ./bases ./bases +COPY ./components ./components +COPY ./projects/worker_prediction/Dockerfile ./projects/worker_prediction/Dockerfile +COPY ./projects/worker_prediction/pyproject.toml ./projects/worker_prediction/pyproject.toml +COPY ./projects/worker_prediction/uv.lock ./projects/worker_prediction/uv.lock +COPY workspace.toml workspace.toml + +WORKDIR /project/projects/worker_prediction + +RUN --mount=type=cache,id=uv_cache,target=/root/.cache/uv \ + uv sync --dev + +ENV PATH="/project/projects/worker_prediction/.venv/bin:$PATH" + +CMD ["worker_prediction"] + + +# ------------------------------------------------------------ +# Prod: minimal, secure, venv-only runtime +# ------------------------------------------------------------ +FROM python:3.12-slim-bookworm AS prod + +# Non-root user +RUN addgroup --system appuser && \ + adduser --system --ingroup appuser \ + --uid 1000 --disabled-password \ + --no-create-home appuser + +# SAME PATH as builder +WORKDIR /project/projects/worker_prediction + +# Copy sealed venv (no relocation!) +COPY --from=builder \ + --chown=appuser:appuser \ + /project/projects/worker_prediction/.venv \ + /project/projects/worker_prediction/.venv + +ENV PATH="/project/projects/worker_prediction/.venv/bin:$PATH" + +USER appuser + +CMD ["worker_prediction"] diff --git a/projects/worker_prediction/pyproject.toml b/projects/worker_prediction/pyproject.toml new file mode 100644 index 00000000..f4f2eeb1 --- /dev/null +++ b/projects/worker_prediction/pyproject.toml @@ -0,0 +1,36 @@ +[build-system] +requires = ["hatchling", 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+requires-dist = [ + { name = "aiokafka", specifier = "==0.12.0" }, + { name = "asyncmy", specifier = "==0.2.11" }, + { name = "cryptography", specifier = "==46.0.3" }, + { name = "orjson", specifier = "==3.11.5" }, + { name = "prometheus-client", specifier = "==0.22.0" }, + { name = "pydantic", specifier = "==2.10.4" }, + { name = "pydantic-settings", specifier = "==2.7.0" }, + { name = "sqlalchemy", specifier = "==2.0.44" }, +] diff --git a/test/bases/bot_detector/worker_ml/test_core.py b/test/bases/bot_detector/worker_ml/test_core.py index 493711f7..dc094941 100644 --- a/test/bases/bot_detector/worker_ml/test_core.py +++ b/test/bases/bot_detector/worker_ml/test_core.py @@ -69,7 +69,7 @@ async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_ max_messages=10, data_to_predict_queue=queue, api=AsyncMock(), - session_factory=AsyncMock(), + predictions_queue=AsyncMock(), ) queue.put.assert_awaited_once_with(data_to_predict_batch) @@ -77,7 +77,7 @@ async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_ @pytest.mark.asyncio -async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_db_error( +async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_publish_error( monkeypatch: pytest.MonkeyPatch, data_to_predict_batch: list[DataToPredictStruct], ) -> None: @@ -86,15 +86,43 @@ async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_ queue.put = AsyncMock(return_value=Exception("requeue failed")) queue.commit = AsyncMock() + predictions_queue = AsyncMock() + predictions_queue.put = AsyncMock(return_value=Exception("publish failed")) + monkeypatch.setattr( core, "predict", AsyncMock(return_value=[AsyncMock(model_dump=lambda: {"bot": 1.0})]), ) + monkeypatch.setattr(core.asyncio, "sleep", AsyncMock(side_effect=_StopLoop())) + + with pytest.raises(_StopLoop): + await core.consume_data_to_predict( + max_messages=10, + data_to_predict_queue=queue, + api=AsyncMock(), + predictions_queue=predictions_queue, + ) + + queue.put.assert_awaited_once_with(data_to_predict_batch) + queue.commit.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_consume_data_to_predict_commits_after_publish( + monkeypatch: pytest.MonkeyPatch, + data_to_predict_batch: list[DataToPredictStruct], +) -> None: + queue = AsyncMock() + queue.get_many = AsyncMock(side_effect=[data_to_predict_batch, []]) + queue.commit = AsyncMock() + + predictions_queue = AsyncMock() + monkeypatch.setattr( core, - "insert_prediction_results", - AsyncMock(side_effect=Exception("db failed")), + "predict", + AsyncMock(return_value=[AsyncMock(model_dump=lambda: {"bot": 1.0})]), ) monkeypatch.setattr(core.asyncio, "sleep", AsyncMock(side_effect=_StopLoop())) @@ -103,11 +131,11 @@ async def test_consume_data_to_predict_does_not_commit_when_requeue_fails_after_ max_messages=10, data_to_predict_queue=queue, api=AsyncMock(), - session_factory=AsyncMock(), + predictions_queue=predictions_queue, ) - queue.put.assert_awaited_once_with(data_to_predict_batch) - queue.commit.assert_not_awaited() + predictions_queue.put.assert_awaited_once() + queue.commit.assert_awaited_once() @pytest.mark.asyncio @@ -128,7 +156,7 @@ async def test_consume_player_scraped_does_not_commit_when_requeue_fails_after_p max_messages=10, player_sc_queue=queue, api=AsyncMock(), - session_factory=AsyncMock(), + predictions_queue=AsyncMock(), ) queue.put.assert_awaited_once_with(scraped_batch) @@ -136,7 +164,7 @@ async def test_consume_player_scraped_does_not_commit_when_requeue_fails_after_p @pytest.mark.asyncio -async def test_consume_player_scraped_does_not_commit_when_requeue_fails_in_outer_handler( +async def test_consume_player_scraped_does_not_commit_when_requeue_fails_after_publish_error( monkeypatch: pytest.MonkeyPatch, scraped_batch: list[ScrapedStruct], ) -> None: @@ -145,16 +173,14 @@ async def test_consume_player_scraped_does_not_commit_when_requeue_fails_in_oute queue.put = AsyncMock(return_value=Exception("requeue failed")) queue.commit = AsyncMock() + predictions_queue = AsyncMock() + predictions_queue.put = AsyncMock(return_value=Exception("publish failed")) + monkeypatch.setattr( core, "predict", AsyncMock(return_value=[AsyncMock(model_dump=lambda: {"bot": 1.0})]), ) - monkeypatch.setattr( - core, - "insert_prediction_results", - AsyncMock(side_effect=Exception("db failed")), - ) monkeypatch.setattr(core.asyncio, "sleep", AsyncMock(side_effect=_StopLoop())) with pytest.raises(_StopLoop): @@ -162,7 +188,7 @@ async def test_consume_player_scraped_does_not_commit_when_requeue_fails_in_oute max_messages=10, player_sc_queue=queue, api=AsyncMock(), - session_factory=AsyncMock(), + predictions_queue=predictions_queue, ) queue.put.assert_awaited_once_with(scraped_batch) diff --git a/test/bases/bot_detector/worker_ml/test_metrics.py b/test/bases/bot_detector/worker_ml/test_metrics.py index b20148b0..4a852a81 100644 --- a/test/bases/bot_detector/worker_ml/test_metrics.py +++ b/test/bases/bot_detector/worker_ml/test_metrics.py @@ -33,7 +33,7 @@ async def test_consume_data_to_predict_increments_prediction_counters( monkeypatch: pytest.MonkeyPatch, ) -> None: batch = [DataToPredictStruct(player_id=123, data={"attack": 1})] - inserted_before = _sample("worker_ml_predictions_inserted_total") or 0 + published_before = _sample("worker_ml_predictions_published_total") or 0 batches_before = ( _sample("worker_ml_batches_consumed_total", {"loop": "data_to_predict"}) or 0 ) @@ -42,9 +42,10 @@ async def test_consume_data_to_predict_increments_prediction_counters( queue.get_many = AsyncMock(side_effect=[batch, []]) queue.commit = AsyncMock() + predictions_queue = AsyncMock() + monkeypatch_predict = AsyncMock(return_value=[_prediction()]) monkeypatch.setattr(core, "predict", monkeypatch_predict) - monkeypatch.setattr(core, "insert_prediction_results", AsyncMock()) monkeypatch.setattr(core.asyncio, "sleep", AsyncMock(side_effect=_StopLoop())) with pytest.raises(_StopLoop): @@ -52,12 +53,14 @@ async def test_consume_data_to_predict_increments_prediction_counters( max_messages=10, data_to_predict_queue=queue, api=AsyncMock(), - session_factory=AsyncMock(), + predictions_queue=predictions_queue, ) assert monkeypatch_predict.await_count == 1 assert queue.commit.await_count == 1 - assert (_sample("worker_ml_predictions_inserted_total") or 0) == inserted_before + 1 + assert ( + _sample("worker_ml_predictions_published_total") or 0 + ) == published_before + 1 assert ( _sample("worker_ml_batches_consumed_total", {"loop": "data_to_predict"}) or 0 ) == batches_before + 1 diff --git a/test/bases/bot_detector/worker_prediction/__init__.py b/test/bases/bot_detector/worker_prediction/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/bases/bot_detector/worker_prediction/test_core.py b/test/bases/bot_detector/worker_prediction/test_core.py new file mode 100644 index 00000000..89cf8ef3 --- /dev/null +++ b/test/bases/bot_detector/worker_prediction/test_core.py @@ -0,0 +1,84 @@ +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from bot_detector.event_queue.structs import PredictionsToInsertStruct +from bot_detector.worker_prediction.worker import PredictionWorker, insert_batch + + +def _build_prediction(player_id: int = 123) -> PredictionsToInsertStruct: + return PredictionsToInsertStruct( + model_name="multi_model_v1", + player_id=player_id, + prediction="Real_Player", + confidence=0.9, + predictions={"Real_Player": 0.9}, + ) + + +def _mock_session_factory() -> MagicMock: + session = AsyncMock() + session_factory = MagicMock() + session_factory.return_value.__aenter__ = AsyncMock(return_value=session) + session_factory.return_value.__aexit__ = AsyncMock(return_value=None) + return session_factory + + +@pytest.mark.asyncio +async def test_insert_batch_calls_both_repos(): + batch = [_build_prediction(), _build_prediction(player_id=456)] + prediction_repo = AsyncMock() + prediction_latest_repo = AsyncMock() + + await insert_batch( + prediction_repo=prediction_repo, + prediction_latest_repo=prediction_latest_repo, + batch=batch, + session_factory=_mock_session_factory(), + ) + + prediction_repo.insert.assert_awaited_once() + prediction_latest_repo.insert.assert_awaited_once() + assert prediction_repo.insert.await_args.kwargs["predictions"] == batch + assert prediction_latest_repo.insert.await_args.kwargs["predictions"] == batch + + +@pytest.mark.asyncio +async def test_handle_calls_insert_batch(): + batch = [_build_prediction()] + prediction_repo = AsyncMock() + prediction_latest_repo = AsyncMock() + + w = PredictionWorker( + worker_id=0, + session_factory=MagicMock(), + prediction_repo=prediction_repo, + prediction_latest_repo=prediction_latest_repo, + ) + + with patch( + "bot_detector.worker_prediction.worker.insert_batch", new_callable=AsyncMock + ) as mock_insert: + result = await w.handle(batch) + + mock_insert.assert_awaited_once() + assert result == [] + prediction_repo.insert.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_handle_empty_batch_is_noop(): + prediction_repo = AsyncMock() + prediction_latest_repo = AsyncMock() + + w = PredictionWorker( + worker_id=0, + session_factory=MagicMock(), + prediction_repo=prediction_repo, + prediction_latest_repo=prediction_latest_repo, + ) + + result = await w.handle([]) + + assert result == [] + prediction_repo.insert.assert_not_awaited() + prediction_latest_repo.insert.assert_not_awaited() diff --git a/test/bases/bot_detector/worker_prediction/test_metrics.py b/test/bases/bot_detector/worker_prediction/test_metrics.py new file mode 100644 index 00000000..97df5254 --- /dev/null +++ b/test/bases/bot_detector/worker_prediction/test_metrics.py @@ -0,0 +1,43 @@ +from unittest.mock import AsyncMock, MagicMock + +import pytest +from bot_detector.event_queue.structs import PredictionsToInsertStruct +from bot_detector.worker_prediction.worker import insert_batch +from prometheus_client import REGISTRY + + +def _build_prediction(player_id: int = 123) -> PredictionsToInsertStruct: + return PredictionsToInsertStruct( + model_name="multi_model_v1", + player_id=player_id, + prediction="Real_Player", + confidence=0.9, + predictions={"Real_Player": 0.9}, + ) + + +def _sample(name: str) -> float | None: + return REGISTRY.get_sample_value(name) + + +def _mock_session_factory() -> MagicMock: + session = AsyncMock() + session_factory = MagicMock() + session_factory.return_value.__aenter__ = AsyncMock(return_value=session) + session_factory.return_value.__aexit__ = AsyncMock(return_value=None) + return session_factory + + +@pytest.mark.asyncio +async def test_insert_batch_increments_inserted_counter(): + batch = [_build_prediction(), _build_prediction(player_id=456)] + before = _sample("worker_prediction_predictions_inserted_total") or 0 + + await insert_batch( + prediction_repo=AsyncMock(), + prediction_latest_repo=AsyncMock(), + batch=batch, + session_factory=_mock_session_factory(), + ) + + assert (_sample("worker_prediction_predictions_inserted_total") or 0) == before + 2