Pchelkin & Ding
Pchelkin Pchelkin
Ding Ding
Ding Ding
Hey Pchelkin, I've been staring at this performance bottleneck in our event stream processor, and I think there might be some hidden latency in the way the windowing logic is implemented. Have you encountered a similar issue, and how would you go about profiling and tightening that down?
Pchelkin Pchelkin
Sounds like a classic windowing hiccup. Grab a profiler first – Java Flight Recorder or your cloud’s native traces work well for streamers. Turn on the “time spent in user code” metric and watch the window operator’s CPU span. If you see the “merge” or “purge” steps ballooning, you’re probably allocating too many objects per event. Next, check your window type. Sliding windows with a very small slide and large size will churn out a lot of partial aggregates. Switching to a tumbling window or using a fixed‑size ring buffer for incremental aggregation can cut that down. Make sure you’re not rebuilding the window from scratch on every event; incremental aggregations that update the state in place are far cheaper. Look at the state store access pattern too. If you’re hitting the disk for every key, you’ll get latency spikes. In RocksDB, keep the write buffer large enough and batch writes – that reduces I/O contention. Also, tune the checkpoint interval; too frequent checkpoints will add overhead, too infrequent will leave you vulnerable to long recovery times. Finally, profile the JIT. If you’re in a tight loop, force a hotspot compilation by running a warm‑up batch. After that, tweak the data structure: a Long2ObjectMap or a primitive‑based array can beat a generic HashMap for high‑frequency keys. So: start with a trace, isolate the window operator, switch to incremental aggregation, batch state writes, tune checkpoints, and let the JIT do its magic. Coffee in hand, we’ll shave milliseconds into microseconds.
Ding Ding
That’s a solid outline. I’ll start by grabbing a JFR snapshot, but I’m still worried about the warm‑up period; if we’re not hitting a hotspot, the JIT won’t optimize the merge step. Also, switching to a tumbling window feels like a band‑aid—maybe we can tweak the slide granularity instead and see if that keeps the partial aggregates in check without changing the semantics. If the state store is still a bottleneck, I’ll experiment with a custom serializer to reduce the write overhead, even if it means writing a bit more code. Let’s keep the profiler open and iterate quickly; a 10‑ms improvement is still a win.
Pchelkin Pchelkin
Sounds good, no band‑aid needed if we fine‑tune the slide. Warm‑up is key—just run a few thousand events through the merge path before you snap the JFR; that’ll push it into hotspot territory. A custom serializer can cut the state write size, but keep it lean—avoid reflection, use fixed‑length fields if possible. Keep the profiler tab open, log the merge time slice, tweak slide size, and see if the partial aggregates drop under 10 ms. Coffee? Grab a shot, let the code breathe. We’ll squeeze that extra latency out together.
Ding Ding
Sounds good, let’s give the warm‑up a try and keep an eye on the merge timing. I’ll tweak the slide granularity first, and if the partial aggregates stay above 10 ms I’ll pull in a lightweight serializer. Coffee’s on me—just hit the code, let it breathe, and we’ll see the numbers improve.
Pchelkin Pchelkin
Nice, go ahead with the warm‑up and monitor that merge step. If the slide tweak still pushes above 10 ms, hit the serializer next. Keep the profiler open, note the times, and iterate. Coffee’s on you – let’s see those numbers drop.
Ding Ding
Got it, I’ll fire up the warm‑up loop, keep JFR running, and log every merge timestamp. If the 10 ms target stays out of reach after adjusting the slide, I’ll switch to a lean, fixed‑field serializer. Coffee’s on me—let’s watch those numbers drop.