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DataStream SDK: Connection Pooling Guide

Overview

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Connection pooling is critical for production DataStream deployments. Without pooling, each API call creates a new TCP connection, completes the TLS handshake, sends the request, and tears down the connection. For applications making hundreds or thousands of API calls per second, this overhead dominates actual request time.

The DataStream SDK includes a built-in connection pool that reuses connections across requests. This guide covers pool configuration, sizing, health checks, and monitoring.

Default Pool Configuration

The SDK creates a default connection pool when you initialize a client:

from datastream import Client

# Default pool: 10 connections, 30s idle timeout
client = Client(api_key="your-api-key")

Default settings:

Sizing Your Pool

Pool size depends on your concurrency model and throughput requirements. A pool that's too small causes request queuing; too large wastes memory and may trigger server-side rate limiting.

Rule of Thumb

Start with pool_size = expected_concurrent_requests * 1.5, rounded up to the nearest 5. For most applications, 10-25 connections is sufficient. Applications with high fan-out (sending to many streams simultaneously) may need 50-100.

# High-throughput configuration
client = Client(
    api_key="your-api-key",
    pool_size=50,
    pool_timeout=10,  # Fail fast if pool is exhausted
    idle_timeout=120,  # Reclaim idle connections sooner
    max_lifetime=900,  # Recycle connections every 15 minutes
)

Monitoring Pool Usage

The SDK exposes pool metrics via the pool_stats() method:

stats = client.pool_stats()
print(f"Active connections: {stats.active}")
print(f"Idle connections: {stats.idle}")
print(f"Waiting requests: {stats.waiting}")
print(f"Total checkouts: {stats.total_checkouts}")
print(f"Checkout timeouts: {stats.timeouts}")
print(f"Stale connections recycled: {stats.recycled}")

Key signals to watch:

• waiting > 0 sustained: Pool is too small. Increase pool_size or reduce request concurrency.
• timeouts > 0: Requests are failing because no connection became available within pool_timeout. This is a capacity issue.
• recycled increasing rapidly: Connections are hitting max_lifetime frequently. This is normal but if it spikes, check whether the server is closing connections prematurely.

Connection Health Checks

The pool periodically verifies that idle connections are still usable. A health check sends a lightweight ping to the DataStream API and expects a response within 5 seconds. Failed connections are removed from the pool and replaced.

# Custom health check configuration
client = Client(
    api_key="your-api-key",
    health_check_interval=30,  # Check every 30 seconds
    health_check_timeout=3,    # Fail health check after 3 seconds
)

Connection Lifecycle

Each connection in the pool follows this lifecycle:

1. Creation: TCP connection established, TLS handshake completed, HTTP/2 negotiated (if supported).
2. Active use: Checked out from the pool, used for one or more requests, then returned.
3. Idle: Sitting in the pool waiting for the next checkout. Subject to health checks.
4. Recycling: Connection exceeds max_lifetime or fails a health check. Gracefully closed and replaced.

Multi-Region Pools

If your application connects to multiple DataStream regions, create separate clients (and therefore separate pools) for each region:

us_client = Client(api_key="key", region="us-east-1", pool_size=20)
eu_client = Client(api_key="key", region="eu-west-1", pool_size=10)
ap_client = Client(api_key="key", region="ap-southeast-1", pool_size=5)

Do not share a single client across regions. Each region has different endpoints, and mixing them in one pool causes connection resets and increased latency.

Troubleshooting

Connection Reset Errors

If you see ConnectionResetError or BrokenPipeError, the server closed the connection while the client thought it was still valid. This usually happens when:

• The server's idle timeout is shorter than the client's idle_timeout. Reduce the client's idle_timeout to be shorter than the server's (DataStream's server timeout is 300s).
• A load balancer between the client and server is closing idle connections. Set idle_timeout to less than the load balancer's timeout.

Pool Exhaustion

If all connections are in use and a new request arrives, the SDK waits up to pool_timeout seconds for a connection to become available. If none does, a PoolExhaustedError is raised. Solutions:

• Increase pool_size
• Reduce request concurrency
• Add request queuing with backpressure
• Check for connection leaks (requests that check out connections but never return them due to unhandled exceptions)