Streaming Systems: Scenario-Based Questions

Backpressure occurs when a downstream consumer can’t keep up with the rate of incoming data — leading to dropped messages, high memory usage, or pipeline stalls. Proper design is essential to prevent system overload.

📉 What Causes Backpressure?

• Slow consumers or processors
• Network bottlenecks
• Data spikes exceeding buffer or queue limits

🔧 Techniques to Manage Backpressure

• Buffering: Use bounded queues or ring buffers with monitoring
• Rate Limiting: Apply token buckets or leaky bucket algorithms
• Flow Control Protocols: e.g., gRPC streaming with built-in backpressure
• Scaling Consumers: Autoscale or shard processors to match load

🛠️ Tooling & Framework Support

• Apache Kafka: backpressure handled via topic offsets + consumer lag
• Apache Flink: backpressure tracking + task chaining
• Reactive Streams (e.g., Akka, RxJava, Project Reactor): built-in support for backpressure

✅ Best Practices

• Monitor consumer lag, memory, and CPU across stages
• Drop or sample non-critical data when overloaded
• Design for elasticity — not peak load handling alone

🚫 Common Pitfalls

• Unbounded queues → memory bloat and crashes
• Retry loops with no backoff → amplify traffic
• Ignoring signs of overload (e.g., high GC, lag)

📌 Final Insight

Backpressure is a natural part of streaming — not an anomaly. Build observability, apply flow control, and scale gracefully to maintain real-time performance under pressure.