Does my channel buffer size choice hide backpressure problems, and how do I decide it?

Short answer: yes, a large buffer hides backpressure.

Short answer

Keep the buffer small, let blocking propagate all the way to the Kafka poll loop, and scale the number of consumers by measured consumer lag — not by buffer size.

Why

1. Backpressure is blocking, by definition. The whole point of an unbuffered or small-buffered channel is that a slow consumer blocks the producer — that IS the signal you want to feel. A 10,000-slot buffer mutes that signal; it turns a throughput problem into a latency + memory problem and merely delays the moment you notice.

2. A buffer adds no throughput, it only absorbs bursts. If sustained production rate > sustained consumption rate, no buffer size saves you: it fills and you block anyway, just later. A buffer is only meaningful when average consumption keeps up with average production but arrivals are bursty/uneven.

3. In Kafka, real backpressure = slowing the poll. When the channel blocks, your consumer loop stops fetching the next message, and consumer lag grows on the Kafka side. That’s exactly the signal you want — and it’s durable, living in the broker. Buffering tens of thousands of messages in memory decouples in-flight work from committed offsets: if you commit the offset before processing finishes (auto-commit, essentially), a crash loses those messages; if you commit only after processing completes — as in the example below — a crash just gets you redelivery and reprocessing, not loss.

What to do

1. Sizing rule: 0 or small (1-2x the number of workers). The goal is to smooth scheduling jitter, not to hide a rate mismatch. A bounded worker pool + a small channel gives you parallelism while still propagating blocking backwards.

   jobs := make(chan Event, len(workers)) // buffer ≈ worker count, not huge
   for i := 0; i < len(workers); i++ {
       go func() {
           for e := range jobs {
               process(e)
           }
       }()
   }
   for msg := range consumer.Poll() {
       jobs <- decode(msg) // blocks here when full → poll slows → Kafka lag grows
       consumer.Commit(msg) // commit only after processing to keep at-least-once
   }

2. Measure the signal, or it stays hidden. Export len(ch)/cap(ch), Kafka consumer lag, and processing latency as metrics. If the channel sits persistently near capacity, consumers can’t keep up — and the answer is more workers (or a faster processing step), not a bigger buffer.

3. Keep the signal in Kafka, not in the channel. Lag is durable in the broker and visible on your dashboard; an in-memory buffer vanishes on crash. When you get to choose where backpressure accumulates, choose the durable, observable place.

Bottom line: personally I’d shrink that 10,000 buffer right away, pulling channel capacity down near the worker count. I’d let blocking propagate up to the poll loop and make Kafka consumer lag my primary backpressure metric. If lag climbs persistently under load, I’d grow the worker pool or the processing speed — never the buffer. A big buffer isn’t a fix; it’s the window in which the problem is invisible.