[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

Run all tests on some custom configuration

Start two servers manually on ports 9001 (old) and 9002 (new). Change to a new directory to be used as workspace for tests, and try something like this:

$ PATH=$PATH:~/ch4/build-gcc9-rel/programs \
    CHPC_TEST_PATH=~/ch3/ch/tests/performance \
    CHPC_TEST_GREP=visit_param \
    stage=run_tests \
    ~/ch3/ch/docker/test/performance-comparison/compare.sh

PATH must contain clickhouse-local and clickhouse-client.
CHPC_TEST_PATH -- path to performance test cases, e.g. tests/performance.
CHPC_TEST_GREP -- a filter for which tests to run, as a grep pattern.
stage -- from which execution stage to start. To run the tests, use run_tests stage.

The tests will run, and the report.html will be generated in the current directory.

More complex setup is possible, but inconvenient and requires some scripting. See manual-run.sh for inspiration.

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."

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