Backpressure: What It Is and How to Tame It

Backpressure occurs when producers outpace consumers and work piles up. It is a naturally occurring phenomenon driven by architecture choices like fan-out depth, batch sizing, retry policy, and shared dependencies rather than a one-off bug. It most often appears when bursty or sustained load runs X times faster than the capacity of Y constrained resources (threads, DB IOPS, partition throughput, or external APIs). With careful planning you can keep flow steady instead of spiky: size buffers deliberately, cap concurrency, shape ingress, and degrade gracefully. In the worst case, queues explode, leaving users with an unknown amount of loading time.

Where It Shows Up

• APIs & gateways: client bursts overwhelm downstreams; 429s are common indicators.
• Streams & queues: hot partitions, unbalanced consumers, and small batch sizes starve throughput.
• Datastores: write amplification or index contention increases latency per-request.
• Async workers: fan-out without capping the amount of workers. One slow dependency stalls the whole pool.

Common Relief Valves

• Gate ingress: rate-limit by tenant.
• Bound concurrency: fixed worker pools with backoff instead of unbounded goroutines/promises.
• Shape work: coalesce, batch, and prefer idempotent retries. Favor adaptive batching.
• Protect dependencies: circuit breakers + timeouts, load-shedding early when detection signs are clear.
• Surface signals: track queue depth, service time histograms, and drop/timeout counts.