Staff Engineering Skills Backpressure

Backpressure Trap

The producer is faster than the consumer. The buffer between them grows until something breaks. Before connecting a producer to a consumer, ask: what happens when the consumer is 10x slower than the producer?

The Core Problem

Backpressure is not a problem to solve -- it's a signal to propagate. When a consumer can't keep up, you have three options:

What you must NOT do: add a bigger unbounded buffer. A bigger buffer just delays the failure and makes the OOM worse.

Detection: When Backpressure Is Missing

Stop and fix if you see:

1. `Promise.all(items.map(fn))` where `items` is dynamic-sized -- if items has 500,000 entries, this creates 500,000 concurrent operations. Connection pools saturate, memory spikes, downstream services are overwhelmed. Always use a concurrency limiter.

2. An array, queue, or channel that gets `.push()`/`enqueue()` but never has its size checked -- what bounds the growth? If the answer is "nothing," it grows until OOM.

3. A producer loop that writes to a queue without checking queue depth -- the producer runs at full speed regardless of whether the consumer is keeping up. The queue grows linearly with the rate difference.

4. Fire-and-forget async calls in a loop -- for (item of items) { db.insert(item).catch(log) } launches all inserts as fast as possible with no await. The event loop fills with pending operations, the connection pool is exhausted, and nothing completes.

5. A database table used as a queue with no depth management -- INSERT in the producer, SELECT+DELETE in the consumer. Under load, the table grows to millions of rows. SELECT ... ORDER BY ... LIMIT 1 slows down as the table bloats. The consumer gets slower, which makes the queue grow faster -- a positive feedback loop.

6. No monitoring or alerting on queue/buffer depth -- you won't know the buffer is filling up until it's too late. Queue depth is the single most important metric for any producer-consumer system.

Patterns

Bounded queue with rejection

class BoundedQueue<T> {
  private items: T[] = [];

  constructor(private maxSize: number) {}

  enqueue(item: T): boolean {
    if (this.items.length >= this.maxSize) return false; // Backpressure signal
    this.items.push(item);
    return true;
  }

  dequeue(): T | undefined {
    return this.items.shift();
  }

  get depth(): number {
    return this.items.length;
  }
}

// Producer respects backpressure
function handleRequest(req: Request, res: Response) {
  if (!taskQueue.enqueue(createTask(req))) {
    // Tell the caller immediately instead of buffering forever
    res.status(503).json({ error: "System busy. Try again later." });
    return;
  }
  res.status(202).json({ status: "accepted" });
}

A 503 in 5ms is better than a timeout after 60 seconds. The caller knows immediately and can retry with backoff. The system stays healthy.

Concurrency-limited parallel processing

import pLimit from "p-limit";

const limit = pLimit(20); // Max 20 concurrent operations

async function processAllUsers(userIds: string[]) {
  return Promise.all(
    userIds.map((id) => limit(() => fetchAndProcessUser(id)))
  );
}

Without the limiter, 500,000 users = 500,000 concurrent requests. With it, at most 20 run at a time. The rest wait their turn.

Manual implementation without a library:

async function processWithConcurrency<T, R>(
  items: T[],
  fn: (item: T) => Promise<R>,
  concurrency: number,
): Promise<R[]> {
  const results: R[] = [];
  const executing = new Set<Promise<void>>();

  for (const item of items) {
    const promise = fn(item).then((result) => {
      results.push(result);
      executing.delete(promise);
    });
    executing.add(promise);

    if (executing.size >= concurrency) {
      await Promise.race(executing); // Wait for one to finish before starting next
    }
  }

  await Promise.all(executing);
  return results;
}

Pull-based consumption

// Consumer pulls work when ready -- natural backpressure
async function consumeQueue(queue: MessageQueue) {
  while (true) {
    // Consumer asks for work only when it's ready for more
    const message = await queue.receive({ waitTimeout: 5000 });
    if (!message) continue;

    try {
      await processMessage(message);
      await queue.ack(message.id);
    } catch (err) {
      await queue.nack(message.id); // Return to queue for retry
    }
  }
}

Pull-based has slightly higher latency than push-based (polling vs notification), but the consumer never takes more than it can handle. This is the standard pattern for SQS, RabbitMQ, and most message queues.

Node.js streams (backpressure built in)

import { pipeline, Transform } from "node:stream";
import { createReadStream, createWriteStream } from "node:fs";

const transform = new Transform({
  highWaterMark: 16 * 1024, // 16KB buffer max
  transform(chunk, encoding, callback) {
    const processed = processChunk(chunk);
    callback(null, processed);
    // If the writable side is full, the readable side automatically pauses.
    // Backpressure is handled by the stream machinery -- no manual management.
  },
});

pipeline(
  createReadStream("input.csv"),
  transform,
  createWriteStream("output.csv"),
  (err) => { if (err) console.error("Pipeline failed:", err); }
);

pipeline handles backpressure automatically. If the writer can't keep up, the transform pauses, which pauses the reader. Use pipeline instead of manually piping .pipe() -- it also handles cleanup on error.

Producer that checks consumer health

async function ingestEvents(stream: EventStream, queue: BoundedQueue<Event>) {
  for await (const event of stream) {
    // Check queue depth before producing
    while (!queue.enqueue(event)) {
      // Queue is full -- slow down instead of dropping or buffering in memory
      await new Promise((r) => setTimeout(r, 100));

      // Or: if events are time-sensitive and stale ones are worthless, drop
      // logger.warn("Queue full, dropping event", { eventId: event.id });
      // break;
    }
  }
}

The Cascading Failure Connection

Missing backpressure is how one slow service takes down an entire system:

1. Service B gets slow (database issue, GC pause, whatever) 2. Service A calls Service B. Requests pile up in A's outbound connection pool. 3. Service A's connection pool fills. A can't make any new connections -- to B or anyone else. 4. Service A becomes slow for ALL callers, not just the ones that need B. 5. Services C, D, E that call A now have the same problem. 6. The entire system is down because one database query got slow.

Circuit breakers (see Distributed System Fallacies skill) cut the cascade by failing fast. Backpressure prevents the buffer accumulation that leads to the cascade in the first place.

Anti-Patterns

// Unbounded queue: grows until OOM
const queue: Task[] = [];
function enqueue(task: Task) { queue.push(task); } // No size check

// Promise.all on dynamic array: 500k concurrent operations
await Promise.all(userIds.map(id => processUser(id)));

// Fire-and-forget in a loop: saturates connection pool
for (const row of rows) { db.insert(row).catch(console.error); }

// Producer ignores consumer: pushes to Redis queue without checking depth
for await (const event of stream) { await redis.lpush("queue", JSON.stringify(event)); }

// Database as unbounded queue: table bloat causes positive feedback loop
// INSERT ... (fast) vs SELECT ... ORDER BY created_at LIMIT 1 (slow on 10M rows)

Related Traps

• Memory Leaks -- an unbounded buffer is a memory leak. Backpressure and memory leaks are two views of the same problem: data accumulating without bound.
• Distributed System Fallacies -- fallacy #3 (bandwidth is infinite) is a backpressure problem. Circuit breakers complement backpressure by failing fast when a dependency is overwhelmed.
• Thundering Herd -- when a backed-up queue finally drains (consumer recovers or scales up), the burst of processing can overwhelm downstream services.
• Retry Storms -- retries without backoff are a backpressure violation. Each retry adds more work to an already-overwhelmed system.