Streaming API Patterns

Streaming API Patterns

Overview

Modern applications require real-time data delivery. This skill covers Server-Sent Events (SSE) for server-to-client streaming, WebSockets for bidirectional communication, and the Streams API for handling backpressure and efficient data flow.

When to use this skill:

• Streaming LLM responses (ChatGPT-style interfaces)
• Real-time notifications and updates
• Live data feeds (stock prices, analytics)
• Chat applications
• Progress updates for long-running tasks
• Collaborative editing features

Core Technologies

1. Server-Sent Events (SSE)

Best for: Server-to-client streaming (LLM responses, notifications)

// Next.js Route Handler
export async function GET(req: Request) {
  const encoder = new TextEncoder()

  const stream = new ReadableStream({
    async start(controller) {
      // Send data
      controller.enqueue(encoder.encode('data: Hello\n\n'))

      // Keep connection alive
      const interval = setInterval(() => {
        controller.enqueue(encoder.encode(': keepalive\n\n'))
      }, 30000)

      // Cleanup
      req.signal.addEventListener('abort', () => {
        clearInterval(interval)
        controller.close()
      })
    }
  })

  return new Response(stream, {
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive',
    }
  })
}

// Client
const eventSource = new EventSource('/api/stream')
eventSource.onmessage = (event) => {
  console.log(event.data)
}

2. WebSockets

Best for: Bidirectional real-time communication (chat, collaboration)

// WebSocket Server (Next.js with ws)
import { WebSocketServer } from 'ws'

const wss = new WebSocketServer({ port: 8080 })

wss.on('connection', (ws) => {
  ws.on('message', (data) => {
    // Broadcast to all clients
    wss.clients.forEach((client) => {
      if (client.readyState === WebSocket.OPEN) {
        client.send(data)
      }
    })
  })
})

// Client
const ws = new WebSocket('ws://localhost:8080')
ws.onmessage = (event) => console.log(event.data)
ws.send(JSON.stringify({ type: 'message', text: 'Hello' }))

3. ReadableStream API

Best for: Processing large data streams with backpressure

async function* generateData() {
  for (let i = 0; i < 1000; i++) {
    await new Promise(resolve => setTimeout(resolve, 100))
    yield `data-${i}`
  }
}

const stream = new ReadableStream({
  async start(controller) {
    for await (const chunk of generateData()) {
      controller.enqueue(new TextEncoder().encode(chunk + '\n'))
    }
    controller.close()
  }
})

LLM Streaming Pattern

// Server
import OpenAI from 'openai'

const openai = new OpenAI()

export async function POST(req: Request) {
  const { messages } = await req.json()

  const stream = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages,
    stream: true
  })

  const encoder = new TextEncoder()

  return new Response(
    new ReadableStream({
      async start(controller) {
        for await (const chunk of stream) {
          const content = chunk.choices[0]?.delta?.content
          if (content) {
            controller.enqueue(encoder.encode(`data: ${JSON.stringify({ content })}\n\n`))
          }
        }
        controller.enqueue(encoder.encode('data: [DONE]\n\n'))
        controller.close()
      }
    }),
    {
      headers: {
        'Content-Type': 'text/event-stream',
        'Cache-Control': 'no-cache'
      }
    }
  )
}

// Client
async function streamChat(messages) {
  const response = await fetch('/api/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ messages })
  })

  const reader = response.body.getReader()
  const decoder = new TextDecoder()

  while (true) {
    const { done, value } = await reader.read()
    if (done) break

    const chunk = decoder.decode(value)
    const lines = chunk.split('\n')

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = line.slice(6)
        if (data === '[DONE]') return

        const json = JSON.parse(data)
        console.log(json.content) // Stream token
      }
    }
  }
}

Reconnection Strategy

class ReconnectingEventSource {
  private eventSource: EventSource | null = null
  private reconnectDelay = 1000
  private maxReconnectDelay = 30000

  constructor(private url: string, private onMessage: (data: string) => void) {
    this.connect()
  }

  private connect() {
    this.eventSource = new EventSource(this.url)

    this.eventSource.onmessage = (event) => {
      this.reconnectDelay = 1000 // Reset on success
      this.onMessage(event.data)
    }

    this.eventSource.onerror = () => {
      this.eventSource?.close()

      // Exponential backoff
      setTimeout(() => this.connect(), this.reconnectDelay)
      this.reconnectDelay = Math.min(this.reconnectDelay * 2, this.maxReconnectDelay)
    }
  }

  close() {
    this.eventSource?.close()
  }
}

Python Async Generator Cleanup (2025 Best Practice)

CRITICAL: Async generators can leak resources if not properly cleaned up. Python 3.10+ provides aclosing() from contextlib to guarantee cleanup.

The Problem

# ❌ DANGEROUS: Generator not closed if exception occurs mid-iteration
async def stream_analysis():
    async for chunk in external_api_stream():  # What if exception here?
        yield process(chunk)  # Generator may be garbage collected without cleanup

# ❌ ALSO DANGEROUS: Using .aclose() manually is error-prone
gen = stream_analysis()
try:
    async for chunk in gen:
        process(chunk)
finally:
    await gen.aclose()  # Easy to forget, verbose

The Solution: aclosing()

from contextlib import aclosing

# ✅ CORRECT: aclosing() guarantees cleanup
async def stream_analysis():
    async with aclosing(external_api_stream()) as stream:
        async for chunk in stream:
            yield process(chunk)

# ✅ CORRECT: Using aclosing() at consumption site
async def consume_stream():
    async with aclosing(stream_analysis()) as gen:
        async for chunk in gen:
            handle(chunk)

Real-World Pattern: LLM Streaming

from contextlib import aclosing
from langchain_core.runnables import RunnableConfig

async def stream_llm_response(prompt: str, config: RunnableConfig | None = None):
    """Stream LLM tokens with guaranteed cleanup."""
    async with aclosing(llm.astream(prompt, config=config)) as stream:
        async for chunk in stream:
            yield chunk.content

# Consumption with proper cleanup
async def generate_response(user_input: str):
    result_chunks = []
    async with aclosing(stream_llm_response(user_input)) as response:
        async for token in response:
            result_chunks.append(token)
            yield token  # Stream to client

    # Post-processing after stream completes
    full_response = "".join(result_chunks)
    await log_response(full_response)

Database Connection Pattern

from contextlib import aclosing
from typing import AsyncIterator
from sqlalchemy.ext.asyncio import AsyncSession

async def stream_large_query(
    session: AsyncSession,
    batch_size: int = 1000
) -> AsyncIterator[Row]:
    """Stream large query results with automatic connection cleanup."""
    result = await session.execute(
        select(Model).execution_options(stream_results=True)
    )

    async with aclosing(result.scalars()) as stream:
        async for row in stream:
            yield row

When to Use aclosing()

Anti-Patterns to Avoid

# ❌ NEVER: Consuming without aclosing
async for chunk in stream_analysis():
    process(chunk)

# ❌ NEVER: Manual try/finally (verbose, error-prone)
gen = stream_analysis()
try:
    async for chunk in gen:
        process(chunk)
finally:
    await gen.aclose()

# ❌ NEVER: Assuming GC will handle cleanup
gen = stream_analysis()
# ... later gen goes out of scope without close

Testing Async Generators

import pytest
from contextlib import aclosing

@pytest.mark.asyncio
async def test_stream_cleanup_on_error():
    """Test that cleanup happens even when exception raised."""
    cleanup_called = False

    async def stream_with_cleanup():
        nonlocal cleanup_called
        try:
            yield "data"
            yield "more"
        finally:
            cleanup_called = True

    with pytest.raises(ValueError):
        async with aclosing(stream_with_cleanup()) as gen:
            async for chunk in gen:
                raise ValueError("simulated error")

    assert cleanup_called, "Cleanup must run even on exception"

Best Practices

SSE

• ✅ Use for one-way server-to-client streaming
• ✅ Implement automatic reconnection
• ✅ Send keepalive messages every 30s
• ✅ Handle browser connection limits (6 per domain)
• ✅ Use HTTP/2 for better performance

WebSockets

• ✅ Use for bidirectional real-time communication
• ✅ Implement heartbeat/ping-pong
• ✅ Handle reconnection with exponential backoff
• ✅ Validate and sanitize messages
• ✅ Implement message queuing for offline periods

Backpressure

• ✅ Use ReadableStream with proper flow control
• ✅ Monitor buffer sizes
• ✅ Pause production when consumer is slow
• ✅ Implement timeouts for slow consumers

Performance

• ✅ Compress data (gzip/brotli)
• ✅ Batch small messages
• ✅ Use binary formats (MessagePack, Protobuf) for large data
• ✅ Implement client-side buffering
• ✅ Monitor connection count and resource usage

Resources

• Server-Sent Events Specification
• WebSocket Protocol
• Streams API
• Vercel AI SDK

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