Hi all! While conducting benchmarking of Cassandra against other databases for a particular healthcare solution, I found some surprising anomalies in single-threaded write performance.
Analysis & tracing suggest there might be some inefficiencies in inter-thread execution in Cassandra;
- Tracing showed an average delay of 1.52 ms between StorageProxy.performLocally() being called, and the LocalMutationRunnable actually executing.
- Total operation time averaged 2.06 ms (measured at Message.Dispatcher processRequest()). This suggested ~72% of the being lost waiting for thread scheduling in SEPExecutor.
- With I tested with multiple threads, performance with 10 threads was 27x higher. This supports a hypothesis that scheduling delays may be hindering single-threaded progress.
- Transaction throughput for Cassandra with a single-threaded workload, measured far lower than that of PostgreSQL on the same hardware. Postgres achieved ~200k committed transactions/ minute including fsync; Cassandra achieves ~37k per minute. Given they are both essentially writing to a commit log, it may be informative to understand why the differences are arising.
Cassandra's architecture seems in theory like it might be aligned for our usecase, given the Commit Log and Log Structured Merge design. Some of our customers have configurations posing high single-threaded loads. Therefore I spent some time trying to understand why efficiency for such loads seemed less than expected.
My investigation so far:
- benchmarked Cassandra 3.11.10
- stack-dumped it under load & identified a pattern of threads waiting in AbstractWriteResponseHandler while nothing else is busy
- checked out Cassandra 3.11.10 source, built it, debugged & stepped through key areas to try and understand behavior.
- instrumented key areas with custom tracing code & timestamps to 0.01 millisecond.
- see patch attached.
- benchmarked Cassandra 4 beta 4 & verified delays also present
- shown & traced delays with my healthcare scenario benchmark
- shown & traced delays with the tool.
The configuration was:
- single-node Cassandra running locally, on a recent Dell laptop with NVmE SSD.
- for the healthcare scenario:
- Java client app running 1 or 10 threads;
- trialled LOCAL_ONE and ANY consistency levels;
- trialled unbatched, BatchType.UNLOGGED and BatchType.LOGGED.
- for 'cassandra-stress':
- cassandra-stress.bat write n=10000 -rate threads=1
Without deeply understanding the code, I have considered a couple of possible areas/ ideas as to improvement. I worked on the 3.11.10 codebase. I'd be interested to understand whether or not these might be sound or not; note that neither achieves as much improvement as might theoretically be hoped for.
My investigations based on the key observation of large delays between StorageProxy.performLocally() being invoked and the LocalMutationRunnable actually executing, for single-threaded workloads.
What I looked at:
- Without fully understanding SEPExecutor.takeWorkPermit() – it answers true to execute immediately, false if scheduled – for this workload scheduling seemed slow.
- takeWorkPermit() answers false if no work permits are available.
- I noticed takeWorkPermit() also answers false if no task permits are available,
- by changing this condition I was able to gain +45% performance.
- Without deeply understanding SEP Executor/ Worker or sleep algorithms, I noted that in a single-thread workload SEPWorkers would likely spin & be put to sleep for a period after completing each task.
- I did wonder if the park
timesor parking behavior (empirically observed at 10,000 - 20,000 nanos) could contribute to threads being more aggressively de-scheduled.
- an experiment in keeping 1 SEPWorker awake (not sleeping at all) gained +7.9% performance.
- Note: initial ticket misread code as requesting 500,000 nanosecond sleeps. This has now been corrected.
- I did wonder if the park
- Without deeply understanding SEP Executor/ Worker, I feel there may be more questions around how SEP Workers are brought out of SPINNING/ sleep state and whether this logic functions promptly & correctly.
- At a very initial stage of investigation:
- This is a very cursory "looking at the code" & initial debugging stage, but I'm not certain it's accurate. Attempted experiments to unpark for the SPINNING -> Work transition so far have caused lockups of 100% machine CPU use or dropped messages, rather than helping anything.
- If & when I can find out more, I'll post it here.
I will post the tracing code & traces I captured, and welcome some feedback and thoughts on these performance questions from the Cassandra dev community. Thanks all!