mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-13 02:53:38 +00:00
79 lines
4.0 KiB
Markdown
79 lines
4.0 KiB
Markdown
# [draft] Performance comparison test
|
|
|
|
This is an experimental mode that compares performance of old and new server
|
|
side by side. Both servers are run, and the query is executed on one then another,
|
|
measuring the times. This setup should remove much of the variability present in
|
|
the current performance tests, which only run the new version and compare with
|
|
the old results recorded some time in the past.
|
|
|
|
To interpret the observed results, we build randomization distribution for the
|
|
observed difference of median times between old and new server, under the null
|
|
hypothesis that the performance distribution is the same (for the details of the
|
|
method, see [1]). We consider the observed difference in performance significant,
|
|
if it is above 5% and above the 95th percentile of the randomization distribution.
|
|
We also consider the test to be unstable, if the observed difference is less than
|
|
5%, but the 95th percentile is above 5% -- this means that we are likely to observe
|
|
performance differences above 5% more often than in 5% runs, so the test is likely
|
|
to have false positives.
|
|
|
|
### How to read the report
|
|
|
|
Should add inline comments there, because who reads the docs anyway. They must
|
|
be collapsible and I am afraid of Javascript, so I'm going to do it later.
|
|
|
|
### How to run
|
|
Run the entire docker container, specifying PR number (0 for master)
|
|
and SHA of the commit to test. The reference revision is determined as a nearest
|
|
ancestor testing release tag. It is possible to specify the reference revision and
|
|
pull requests (0 for master) manually.
|
|
|
|
```
|
|
docker run --network=host --volume=$(pwd)/workspace:/workspace --volume=$(pwd)/output:/output
|
|
[-e REF_PR={} -e REF_SHA={} -e ]
|
|
-e PR_TO_TEST={} -e SHA_TO_TEST={}
|
|
yandex/clickhouse-performance-comparison
|
|
```
|
|
|
|
Then see the `report.html` in the `output` directory.
|
|
|
|
There are some environment variables that influence what the test does:
|
|
* `-e CHCP_RUNS` -- the number of runs;
|
|
* `-e CHPC_TEST_GREP` -- the names of the tests (xml files) to run, interpreted
|
|
as a grep pattern.
|
|
|
|
#### Re-genarate report with your tweaks
|
|
From the workspace directory (extracted test output archive):
|
|
```
|
|
stage=report compare.sh
|
|
```
|
|
More stages are available, e.g. restart servers or run the tests. See the code.
|
|
|
|
#### Run a single test on the already configured servers
|
|
```
|
|
docker/test/performance-comparison/perf.py --host=localhost --port=9000 --runs=1 tests/performance/logical_functions_small.xml
|
|
```
|
|
|
|
#### Statistical considerations
|
|
Generating randomization distribution for medians is tricky. Suppose we have N
|
|
runs for each version, and then use the combined 2N run results to make a
|
|
virtual experiment. In this experiment, we only have N possible values for
|
|
median of each version. This becomes very clear if you sort those 2N runs and
|
|
imagine where a window of N runs can be -- the N/2 smallest and N/2 largest
|
|
values can never be medians. From these N possible values of
|
|
medians, you can obtain (N/2)^2 possible values of absolute median difference.
|
|
These numbers are +-1, I'm making an off-by-one error somewhere. So, if your
|
|
number of runs is small, e.g. 7, you'll only get 16 possible differences, so
|
|
even if you make 100k virtual experiments, the randomization distribution will
|
|
have only 16 steps, so you'll get weird effects. So you also have to have
|
|
enough runs. You can observe it on real data if you add more output to the
|
|
query that calculates randomization distribution, e.g., add a number of unique
|
|
median values. Looking even more closely, you can see that the exact
|
|
values of medians don't matter, and the randomization distribution for
|
|
difference of medians devolves into some kind of ranked test. We could probably
|
|
skip all these virtual experiments and calculate the resulting distribution
|
|
analytically, but I don't know enough math to do it. It would be something
|
|
close to Wilcoxon test distribution.
|
|
|
|
### References
|
|
1\. Box, Hunter, Hunter "Statictics for exprerimenters", p. 78: "A Randomized Design Used in the Comparison of Standard and Modified Fertilizer Mixtures for Tomato Plants."
|