## Problem
For PRs with `run-benchmarks` label, we don't upload results to the db,
making it harder to debug such tests. The only way to see some
numbers is by examining GitHub Action output which is really
inconvenient.
This PR adds zenbenchmark metrics to Allure reports.
## Summary of changes
- Create a json file with zenbenchmark results and attach it to allure
report
- use parse_metrics() in all places where we parse Prometheus metrics
- query_all: make `filter` argument optional
- encourage using properly parsed, typed metrics by changing get_metrics()
to return already-parsed metrics. The new get_metric_str() method,
like in the Safekeeper type, returns the raw text response.
Commit f081419e68 moved all the prometheus counters to `metrics.rs`,
but accidentally replaced a couple of `register_int_counter!(...)`
calls with just `IntCounter::new(...)`. Because of that, the counters
were not registered in the metrics registry, and were not exposed
through the metrics HTTP endpoint.
Fixes failures we're seeing in a bunch of 'performance' tests because
of the missing metrics.
For better ergonomics. I always found it weird that we used UUID to
actually mean a tenant or timeline ID. It worked because it happened
to have the same length, 16 bytes, but it was hacky.
- Enabled process exporter for storage services
- Changed zenith_proxy prefix to just proxy
- Removed old `monitoring` directory
- Removed common prefix for metrics, now our common metrics have `libmetrics_` prefix, for example `libmetrics_serve_metrics_count`
- Added `test_metrics_normal_work`
- Remove batch_others/test_pgbench.py. It was a quick check that pgbench
works, without actually recording any performance numbers, but that
doesn't seem very interesting anymore. Remove it to avoid confusing it
with the actual pgbench benchmarks
- Run pgbench with "-n" and "-S" options, for two different workloads:
simple-updates, and SELECT-only. Previously, we would only run it with
the "default" TPCB-like workload. That's more or less the same as the
simple-update (-n) workload, but I think the simple-upload workload
is more relevant for testing storage performance. The SELECT-only
workload is a new thing to measure.
- Merge test_perf_pgbench.py and test_perf_pgbench_remote.py. I added
a new "remote" implementation of the PgCompare class, which allows
running the same tests against an already-running Postgres instance.
- Make the PgBenchRunResult.parse_from_output function more
flexible. pgbench can print different lines depending on the
command-line options, but the parsing function expected a particular
set of lines.
tests are based on self-hosted runner which is physically close
to our staging deployment in aws, currently tests consist of
various configurations of pgbenchi runs.
Also these changes rework benchmark fixture by removing globals and
allowing to collect reports with desired metrics and dump them to json
for further analysis. This is also applicable to usual performance tests
which use local zenith binaries.
Instead of having a lot of separate fixtures for setting up the page
server, the compute nodes, the safekeepers etc., have one big ZenithEnv
object that encapsulates the whole environment. Every test either uses
a shared "zenith_simple_env" fixture, which contains the default setup
of a pageserver with no authentication, and no safekeepers. Tests that
want to use safekeepers or authentication set up a custom test-specific
ZenithEnv fixture.
Gathering information about the whole environment into one object makes
some things simpler. For example, when a new compute node is created,
you no longer need to pass the 'wal_acceptors' connection string as
argument to the 'postgres.create_start' function. The 'create_start'
function fetches that information directly from the ZenithEnv object.
* Fix bugs found by mypy
* Add some missing types and runtime checks, remove unused code
* Make ZenithPageserver start right away for better type safety
* Add `types-*` packages to Pipfile
* Pin mypy version and run it on CircleCI
* Add yapf run to CircleCI
* Pin yapf version
* Enable `SPLIT_ALL_TOP_LEVEL_COMMA_SEPARATED_VALUES` setting
* Reformat all existing code with slight manual adjustments
* test_runner/README: note that yapf is forced
* Use logging in python tests
* Use f-strings for logs
* Don't log test output while running
* Use only pytest logging handler
* Add more info about pytest logging
Now that the page server collects this metric (since commit 212920e47e),
let's include it in the performance test results
The new metric looks like this:
performance/test_perf_pgbench.py . [100%]
--------------- Benchmark results ----------------
test_pgbench.init: 6.784 s
test_pgbench.pageserver_writes: 466 MB <---- THIS IS NEW
test_pgbench.5000_xacts: 8.196 s
test_pgbench.size: 163 MB
=============== 1 passed in 21.00s ===============
This provides a pytest fixture to record metrics from pytest tests. The
The recorded metrics are printed out at the end of the tests.
As a starter, this includes on small test, using pgbench. It prints out
three metrics: the initialization time, runtime of 5000 xacts, and the
repository size after the tests.
