diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..43897854 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +.venv +*.yaml +.git +__pycache__ +*.pyc +.DS_Store \ No newline at end of file diff --git a/packages/pynumaflow/examples/batchmap/concurrent_sink/Dockerfile b/packages/pynumaflow/examples/batchmap/concurrent_sink/Dockerfile new file mode 100644 index 00000000..6a12a4df --- /dev/null +++ b/packages/pynumaflow/examples/batchmap/concurrent_sink/Dockerfile @@ -0,0 +1,38 @@ +#################################################################################################### +# Stage 1: Builder - installs all dependencies using uv +#################################################################################################### +FROM ghcr.io/astral-sh/uv:python3.13-trixie AS builder + +ENV PYSETUP_PATH="/opt/pysetup" +WORKDIR $PYSETUP_PATH + +COPY pyproject.toml uv.lock README.md ./ +COPY pynumaflow/ ./pynumaflow/ + +ENV EXAMPLE_PATH="$PYSETUP_PATH/examples/batchmap/concurrent_sink" +COPY examples/batchmap/concurrent_sink/ $EXAMPLE_PATH/ + +WORKDIR $EXAMPLE_PATH +RUN uv sync --no-dev --no-install-project --frozen + +#################################################################################################### +# Stage 2: Runtime - clean image with only installed packages +#################################################################################################### +FROM ghcr.io/astral-sh/uv:python3.13-trixie AS udf + +ENV PYSETUP_PATH="/opt/pysetup" +ENV EXAMPLE_PATH="$PYSETUP_PATH/examples/batchmap/concurrent_sink" + +WORKDIR $EXAMPLE_PATH +COPY --from=builder $EXAMPLE_PATH/.venv $EXAMPLE_PATH/.venv +COPY --from=builder $EXAMPLE_PATH/ $EXAMPLE_PATH/ + +# NOTE: We cannot use "uv run python example.py" here because uv run reads the +# example's pyproject.toml, finds the pynumaflow path source (path = "../../../"), +# and tries to resolve it. In the runtime stage, the parent pynumaflow source tree +# is not present (by design, to keep the image small), so uv run fails. +# Instead, we activate the pre-built .venv via PATH and run python directly. +ENV PATH="$EXAMPLE_PATH/.venv/bin:$PATH" +CMD ["python", "example.py"] + +EXPOSE 5000 diff --git a/packages/pynumaflow/examples/batchmap/concurrent_sink/Makefile b/packages/pynumaflow/examples/batchmap/concurrent_sink/Makefile new file mode 100644 index 00000000..eb040a1e --- /dev/null +++ b/packages/pynumaflow/examples/batchmap/concurrent_sink/Makefile @@ -0,0 +1,22 @@ +TAG ?= stable +PUSH ?= false +IMAGE_REGISTRY = quay.io/numaio/numaflow-python/batch-map-concurrent-sink:${TAG} +DOCKER_FILE_PATH = examples/batchmap/concurrent_sink/Dockerfile + +.PHONY: update +update: + uv lock --check || uv lock + +.PHONY: image-push +image-push: update + cd ../../../ && docker buildx build \ + -f ${DOCKER_FILE_PATH} \ + -t ${IMAGE_REGISTRY} \ + --platform linux/amd64,linux/arm64 . --push + +.PHONY: image +image: update + cd ../../../ && podman build --tls-verify=false \ + -f ${DOCKER_FILE_PATH} \ + -t ${IMAGE_REGISTRY} . + @if [ "$(PUSH)" = "true" ]; then docker push ${IMAGE_REGISTRY}; fi diff --git a/packages/pynumaflow/examples/batchmap/concurrent_sink/README.md b/packages/pynumaflow/examples/batchmap/concurrent_sink/README.md new file mode 100644 index 00000000..5c7e054b --- /dev/null +++ b/packages/pynumaflow/examples/batchmap/concurrent_sink/README.md @@ -0,0 +1,92 @@ +# Concurrent Sink Example + +Demonstrates concurrent processing of batch messages using `ProcessPoolExecutor` with `BatchMapAsyncServer`. +Use batch processing only when it makes sense. In some scenarios, batch processing may **not** be the most efficient approach, and processing data items one by one could be a better option. +The burden of concurrent processing of the data will rely on the UDF implementation in this use case. + +## Overview + +This example shows a workload by offloading CPU-intensive work to a pool of worker processes. Each message in a batch is processed independently and in parallel, improving throughput for compute-heavy operations. + +## Key Features + +- **Parallel Processing**: Uses `ProcessPoolExecutor` to process multiple messages concurrently +- **Async/Await**: Leverages `asyncio` for non-blocking task submission and collection +- **Cross-Process Safety**: Returns only primitive types from worker processes (strings, bytes, booleans) +- **Graceful Degradation**: Drops messages on processing errors +- **Configurable Workers**: Adjust worker count via `NUM_WORKERS` environment variable + +## Architecture + +1. **Main Process (AsyncIO Loop)** + - Receives batch of datums from Numaflow + - Submits each message to `ProcessPoolExecutor` via `loop.run_in_executor()` + - Collects results concurrently with `asyncio.gather()` + - Constructs and returns `BatchResponses` + +2. **Worker Processes** + - Execute `_process_message_task()` in isolation + - Perform CPU-intensive work (hash computation in this example) + - Return primitive types only (no Pynumaflow objects) + +## Usage + +```bash +# Start with default worker count (CPU count) +python example.py + +# Start with custom worker count +NUM_WORKERS=4 python example.py +``` + +## Message Flow + +``` +Input Batch → Async Drain → Submit to Workers → Collect Results → BatchResponses + ↓ ↓ ↓ ↓ + [D1, D2, D3, ...] Worker Pool (P1-Pn) Task Futures [BR1, BR2, BR3, ...] +``` + +## Processing Example + +**Input Message**: `"hello world"` + +**Processing**: +1. Decode UTF-8 +2. Compute SHA256 hash 100 times iteratively +3. Return processed result with truncated hash + +**Output**: `"PROCESSED[hello world]-HASH[]"` + +## Error Handling + +- **Process Timeout**: Drops message (returns empty payload) +- **Exception in Worker**: Logs error, drops message +- **Invalid Input**: Gracefully handled with try-except + +## Configuration + +### Environment Variables + +- `NUM_WORKERS`: Number of worker processes (default: CPU count) + +### Resource Tuning + +For CPU-bound work: +- Set `NUM_WORKERS` ≈ CPU cores +- Larger batches improve throughput +- Monitor worker process memory usage + +## Comparison to Alternatives + +| Approach | Pros | Cons | +|----------|------|------| +| **ProcessPool** (this) | True parallelism, CPU-bound work | Process overhead, IPC serialization | +| **ThreadPool** | Lower overhead | GIL contention, not for CPU work | +| **AsyncIO** | Lightweight, I/O-bound | Single-threaded, no true parallelism | + +## Related Examples + +- `examples/map/multiproc_map/`: Single-message multiprocessing mapper +- `examples/batchmap/flatmap/`: Basic batch mapping without parallelism +- `examples/sink/async_log/`: Async sink without process pool diff --git a/packages/pynumaflow/examples/batchmap/concurrent_sink/example.py b/packages/pynumaflow/examples/batchmap/concurrent_sink/example.py new file mode 100644 index 00000000..29f1ca4d --- /dev/null +++ b/packages/pynumaflow/examples/batchmap/concurrent_sink/example.py @@ -0,0 +1,147 @@ +import asyncio +import hashlib +import logging +import os +from collections.abc import AsyncIterable +from concurrent.futures import ProcessPoolExecutor +from typing import Tuple + +from pynumaflow.batchmapper import ( + BatchMapper, + BatchResponse, + BatchResponses, + Datum, +) +from pynumaflow.batchmapper import BatchMapAsyncServer +from pynumaflow.mapper import Message + +logging.basicConfig(level=logging.INFO) +_LOGGER = logging.getLogger(__name__) + +# Process-level executor +_EXECUTOR = None + + +def get_executor(max_workers: int = None) -> ProcessPoolExecutor: + """Get or create global ProcessPoolExecutor.""" + global _EXECUTOR + if _EXECUTOR is None: + if max_workers is None: + max_workers = os.cpu_count() or 2 + _EXECUTOR = ProcessPoolExecutor(max_workers=max_workers) + return _EXECUTOR + + +def _process_message_task(task_data: Tuple[str, list, bytes]) -> Tuple[str, list, bytes, bool]: + """Worker function executed in ProcessPoolExecutor child process. + + Returns primitive types (msg_id, keys, payload_bytes, should_drop) to safely + cross process boundaries. + """ + msg_id, keys, datum_bytes = task_data + pid = os.getpid() + + try: + # Decode message + message_str = ( + datum_bytes.decode("utf-8") + if isinstance(datum_bytes, bytes) + else datum_bytes + ) + _LOGGER.info(f"[Worker PID: {pid}] Processing message: {message_str}") + + # CPU-intensive operation: compute hash multiple times + processed = message_str + for _ in range(128): + processed = hashlib.sha256(processed.encode()).hexdigest() + + # Simulate transformation + result = f"PROCESSED[{message_str}]-HASH[{processed[:16]}]" + result_bytes = result.encode("utf-8") + + _LOGGER.info(f"[Worker PID: {pid}] Processed msg_id={msg_id}: {result}") + return msg_id, keys, result_bytes, False + + except Exception as e: + _LOGGER.error(f"[Worker PID: {pid}] Error processing msg_id={msg_id}: {e}") + return msg_id, keys, b"", True + + +class ConcurrentSink(BatchMapper): + """BatchMapper that processes messages concurrently using ProcessPoolExecutor.""" + + def __init__(self, max_workers: int = None): + """Initialize sink with process pool. + + Args: + max_workers: Number of worker processes. Defaults to CPU count. + """ + self.executor = get_executor(max_workers) + _LOGGER.info(f"Initialized ConcurrentSink with ProcessPoolExecutor (PID={os.getpid()})") + + async def handler(self, datums: AsyncIterable[Datum]) -> BatchResponses: + """Process batch of datums concurrently in worker processes. + + Args: + datums: AsyncIterable of Datum objects from the stream. + + Returns: + BatchResponses with results from all processed messages. + """ + responses = BatchResponses() + loop = asyncio.get_running_loop() + + # Collect all datums from the async iterable + datums_list = [] + async for datum in datums: + datums_list.append(datum) + + _LOGGER.info(f"Processing batch of {len(datums_list)} messages") + + if not datums_list: + return responses + + # Submit all tasks to process pool concurrently + async def submit_task(datum: Datum): + """Submit a single message to worker pool.""" + task_data = (datum.id, datum.keys(), datum.value) + return await loop.run_in_executor(self.executor, _process_message_task, task_data) + + # Wait for all results + results = await asyncio.gather( + *[submit_task(d) for d in datums_list], + return_exceptions=True, + ) + + # Construct responses from results + for datum, res in zip(datums_list, results): + batch_resp = BatchResponse.from_id(datum.id) + + if isinstance(res, Exception): + _LOGGER.error(f"Task failed for datum ID: {datum.id}, error: {res}") + batch_resp.append(Message.to_drop()) + else: + msg_id, keys, payload_bytes, should_drop = res + if should_drop: + batch_resp.append(Message.to_drop()) + else: + batch_resp.append(Message(value=payload_bytes, keys=keys)) + + responses.append(batch_resp) + + _LOGGER.info(f"Completed processing, generated {len(responses)} responses") + return responses + + +if __name__ == "__main__": + """ + Example of starting a concurrent sink with process pool. + + To configure the number of worker processes, set NUM_WORKERS env var: + NUM_WORKERS=4 python example.py + NB: NUM_WORKERS is a user created ENV variable, not a platform ENV varible + """ + max_workers = int(os.getenv("NUM_WORKERS", str(os.cpu_count() or 2))) + sink = ConcurrentSink(max_workers=max_workers) + grpc_server = BatchMapAsyncServer(sink) + grpc_server.start() diff --git a/packages/pynumaflow/examples/batchmap/concurrent_sink/pipeline.yaml b/packages/pynumaflow/examples/batchmap/concurrent_sink/pipeline.yaml new file mode 100644 index 00000000..5e53af91 --- /dev/null +++ b/packages/pynumaflow/examples/batchmap/concurrent_sink/pipeline.yaml @@ -0,0 +1,33 @@ +apiVersion: numaflow.numaproj.io/v1alpha1 +kind: Pipeline +metadata: + name: concurrent-sink +spec: + vertices: + - name: in + source: + # A self data generating source + generator: + rpu: 500 + duration: 1s + - name: batch-concurrent-sink + partitions: 2 + scale: + min: 1 + udf: + container: + image: quay.io/numaio/numaflow-python/batch-map-concurrent-sink:stable + imagePullPolicy: Always + env: + - name: NUM_WORKERS + value: "2" + - name: sink + scale: + min: 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