ClickHouse/docker/test/performance-comparison/eqmed.sql

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-- input is table(query text, run UInt32, version int, time float)
select
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floor(original_medians_array.time_by_version[1], 4) l,
floor(original_medians_array.time_by_version[2], 4) r,
floor((r - l) / l, 3) diff_percent,
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floor(threshold / l, 3) threshold_percent,
query
from
(
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-- quantiles of randomization distributions
select quantileExact(0.999)(abs(time_by_label[1] - time_by_label[2]) as d) threshold
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---- uncomment to see what the distribution is really like
--, uniqExact(d) u
--, arraySort(x->x.1,
-- arrayZip(
-- (sumMap([d], [1]) as f).1,
-- f.2)) full_histogram
from
(
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select virtual_run, groupArrayInsertAt(median_time, random_label) time_by_label -- make array 'random label' -> 'median time'
from (
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select medianExact(time) median_time, virtual_run, random_label -- get median times, grouping by random label
from (
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select *, toUInt32(rowNumberInAllBlocks() % 2) random_label -- randomly relabel measurements
from (
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select time, number virtual_run
from
-- strip the query away before the join -- it might be several kB long;
(select time, run, version from table) no_query,
-- duplicate input measurements into many virtual runs
numbers(1, 100000) nn
-- for each virtual run, randomly reorder measurements
order by virtual_run, rand()
) virtual_runs
) relabeled
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group by virtual_run, random_label
) virtual_medians
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group by virtual_run -- aggregate by random_label
) virtual_medians_array
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-- this select aggregates by virtual_run
) rd,
(
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select groupArrayInsertAt(median_time, version) time_by_version
from
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(
select medianExact(time) median_time, version
from table
group by version
) original_medians
) original_medians_array,
(
select any(query) query from table
) any_query,
(
select throwIf(uniq(query) != 1) from table
) check_single_query -- this subselect checks that there is only one query in the input table;
-- written this way so that it is not optimized away (#10523)
;