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feat: Optimize container infrastructure for production (#5881)
* feat: optimize container infrastructure for production - Add multi-worker configuration with auto-scaling (CPU * 2 + 1) - Add worker connections, max-requests, and jitter parameters - Optimize registry TTL from 2s/5s to 60s for reduced refresh overhead - Support --workers=-1 for automatic worker count calculation - Add worker recycling to prevent memory leaks Expected Impact: - 300-500% throughput increase with proper worker scaling - Reduced registry refresh overhead - Better resource utilization in containerized environments Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com> * style: fix ruff formatting in serve.py Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com> * docs: add performance configuration documentation - Document new worker configuration options (--workers, --worker-connections, etc.) - Add performance best practices for production deployments - Include guidance on registry TTL tuning and container deployments - Provide examples for development vs production configurations Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com> * Apply suggestion from @franciscojavierarceo --------- Co-authored-by: Claude Sonnet 4 <noreply@anthropic.com>
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docs/reference/feature-servers/python-feature-server.md

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@@ -8,6 +8,51 @@ The Python feature server is an HTTP endpoint that serves features with JSON I/O
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There is a CLI command that starts the server: `feast serve`. By default, Feast uses port 6566; the port be overridden with a `--port` flag.
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### Performance Configuration
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For production deployments, the feature server supports several performance optimization options:
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```bash
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# Basic usage
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feast serve
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# Production configuration with multiple workers
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feast serve --workers -1 --worker-connections 1000 --registry_ttl_sec 60
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# Manual worker configuration
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feast serve --workers 8 --worker-connections 2000 --max-requests 1000
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```
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Key performance options:
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- `--workers, -w`: Number of worker processes. Use `-1` to auto-calculate based on CPU cores (recommended for production)
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- `--worker-connections`: Maximum simultaneous clients per worker process (default: 1000)
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- `--max-requests`: Maximum requests before worker restart, prevents memory leaks (default: 1000)
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- `--max-requests-jitter`: Jitter to prevent thundering herd on worker restart (default: 50)
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- `--registry_ttl_sec, -r`: Registry refresh interval in seconds. Higher values reduce overhead but increase staleness (default: 60)
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- `--keep-alive-timeout`: Keep-alive connection timeout in seconds (default: 30)
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### Performance Best Practices
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**Worker Configuration:**
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- For production: Use `--workers -1` to auto-calculate optimal worker count (2 × CPU cores + 1)
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- For development: Use default single worker (`--workers 1`)
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- Monitor CPU and memory usage to tune worker count manually if needed
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**Registry TTL:**
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- Production: Use `--registry_ttl_sec 60` or higher to reduce refresh overhead
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- Development: Use lower values (5-10s) for faster iteration when schemas change frequently
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- Balance between performance (higher TTL) and freshness (lower TTL)
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**Connection Tuning:**
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- Increase `--worker-connections` for high-concurrency workloads
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- Use `--max-requests` to prevent memory leaks in long-running deployments
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- Adjust `--keep-alive-timeout` based on client connection patterns
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**Container Deployments:**
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- Set appropriate CPU/memory limits in Kubernetes to match worker configuration
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- Use HTTP health checks instead of TCP for better application-level monitoring
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- Consider horizontal pod autoscaling based on request latency metrics
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## Deploying as a service
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See [this](../../how-to-guides/running-feast-in-production.md#id-4.2.-deploy-feast-feature-servers-on-kubernetes) for an example on how to run Feast on Kubernetes using the Operator.

sdk/python/feast/cli/serve.py

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type=click.INT,
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default=1,
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show_default=True,
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help="Number of worker",
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help="Number of worker processes. Use -1 to auto-calculate based on CPU cores",
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)
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@click.option(
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"--worker-connections",
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type=click.INT,
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default=1000,
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show_default=True,
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help="Maximum number of simultaneous clients per worker process",
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)
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@click.option(
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"--max-requests",
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type=click.INT,
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default=1000,
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show_default=True,
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help="Maximum number of requests a worker will process before restarting (prevents memory leaks)",
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)
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@click.option(
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"--max-requests-jitter",
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type=click.INT,
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default=50,
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show_default=True,
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help="Maximum jitter to add to max-requests to prevent thundering herd on worker restart",
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)
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@click.option(
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"--keep-alive-timeout",
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type=click.INT,
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default=5,
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default=30,
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show_default=True,
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help="Timeout for keep alive",
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help="Timeout for keep alive connections (seconds)",
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)
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@click.option(
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"--registry_ttl_sec",
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"-r",
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help="Number of seconds after which the registry is refreshed",
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help="Number of seconds after which the registry is refreshed. Higher values reduce refresh overhead but increase staleness",
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type=click.INT,
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default=5,
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default=60,
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show_default=True,
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)
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@click.option(
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type_: str,
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no_access_log: bool,
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workers: int,
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metrics: bool,
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worker_connections: int,
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max_requests: int,
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max_requests_jitter: int,
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keep_alive_timeout: int,
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registry_ttl_sec: int,
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tls_key_path: str,
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tls_cert_path: str,
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registry_ttl_sec: int = 5,
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metrics: bool,
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):
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"""Start a feature server locally on a given port."""
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if (tls_key_path and not tls_cert_path) or (not tls_key_path and tls_cert_path):
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)
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store = create_feature_store(ctx)
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# Auto-calculate workers if -1 is specified
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if workers == -1:
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workers = max(1, (multiprocessing.cpu_count() * 2) + 1)
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store.serve(
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host=host,
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port=port,
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type_=type_,
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no_access_log=no_access_log,
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workers=workers,
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worker_connections=worker_connections,
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max_requests=max_requests,
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max_requests_jitter=max_requests_jitter,
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metrics=metrics,
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keep_alive_timeout=keep_alive_timeout,
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tls_key_path=tls_key_path,

sdk/python/feast/feature_server.py

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port: int,
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no_access_log: bool,
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workers: int,
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worker_connections: int,
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max_requests: int,
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max_requests_jitter: int,
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keep_alive_timeout: int,
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registry_ttl_sec: int,
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tls_key_path: str,
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"bind": f"{host}:{port}",
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"accesslog": None if no_access_log else "-",
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"workers": workers,
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"worker_connections": worker_connections,
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"max_requests": max_requests,
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"max_requests_jitter": max_requests_jitter,
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"keepalive": keep_alive_timeout,
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"registry_ttl_sec": registry_ttl_sec,
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}

sdk/python/feast/feature_store.py

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type_: str = "http",
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no_access_log: bool = True,
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workers: int = 1,
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worker_connections: int = 1000,
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max_requests: int = 1000,
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max_requests_jitter: int = 50,
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metrics: bool = False,
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keep_alive_timeout: int = 30,
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tls_key_path: str = "",
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tls_cert_path: str = "",
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registry_ttl_sec: int = 2,
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registry_ttl_sec: int = 60,
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) -> None:
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"""Start the feature consumption server locally on a given port."""
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type_ = type_.lower()
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port=port,
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no_access_log=no_access_log,
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workers=workers,
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worker_connections=worker_connections,
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max_requests=max_requests,
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max_requests_jitter=max_requests_jitter,
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metrics=metrics,
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keep_alive_timeout=keep_alive_timeout,
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tls_key_path=tls_key_path,

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