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cb10be710d
This PR - fixes smgr metrics https://github.com/neondatabase/neon/issues/9925 - adds an additional startup log line logging the current batching config - adds a histogram of batch sizes global and per-tenant - adds a metric exposing the current batching config The issue described #9925 is that before this PR, request latency was only observed *after* batching. This means that smgr latency metrics (most importantly getpage latency) don't account for - `wait_lsn` time - time spent waiting for batch to fill up / the executor stage to pick up the batch. The fix is to use a per-request batching timer, like we did before the initial batching PR. We funnel those timers through the entire request lifecycle. I noticed that even before the initial batching changes, we weren't accounting for the time spent writing & flushing the response to the wire. This PR drive-by fixes that deficiency by dropping the timers at the very end of processing the batch, i.e., after the `pgb.flush()` call. I was **unable to maintain the behavior that we deduct time-spent-in-throttle from various latency metrics. The reason is that we're using a *single* counter in `RequestContext` to track micros spent in throttle. But there are *N* metrics timers in the batch, one per request. As a consequence, the practice of consuming the counter in the drop handler of each timer no longer works because all but the first timer will encounter error `close() called on closed state`. A failed attempt to maintain the current behavior can be found in https://github.com/neondatabase/neon/pull/9951. So, this PR remvoes the deduction behavior from all metrics. I started a discussion on Slack about it the implications this has for our internal SLO calculation: https://neondb.slack.com/archives/C033RQ5SPDH/p1732910861704029 # Refs - fixes https://github.com/neondatabase/neon/issues/9925 - sub-issue https://github.com/neondatabase/neon/issues/9377 - epic: https://github.com/neondatabase/neon/issues/9376
How to reproduce benchmark results / run these benchmarks interactively.
- Get an EC2 instance with Instance Store. Use the same instance type as used for the benchmark run.
- Mount the Instance Store =>
neon.git/scripts/ps_ec2_setup_instance_store - Use a pytest command line (see other READMEs further up in the pytest hierarchy).
For tests that take a long time to set up / consume a lot of storage space,
we use the test suite's repo_dir snapshotting functionality (from_repo_dir).
It supports mounting snapshots using overlayfs, which improves iteration time.
Here's a full command line.
RUST_BACKTRACE=1 NEON_ENV_BUILDER_USE_OVERLAYFS_FOR_SNAPSHOTS=1 DEFAULT_PG_VERSION=16 BUILD_TYPE=release \
./scripts/pytest test_runner/performance/pageserver/pagebench/test_pageserver_max_throughput_getpage_at_latest_lsn.py