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Merged
not-napoleon merged 7 commits into from
Feb 4, 2026
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Merged

More count csv tests #141781

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84e6c33
tdigest tests
not-napoleon Feb 3, 2026
11de35b
histogram tests
not-napoleon Feb 3, 2026
d137df0
fix copy/pasta error
not-napoleon Feb 3, 2026
30974d8
exponential histogram tests
not-napoleon Feb 3, 2026
e297fa3
Merge branch 'main' into more-count-csv-tests
not-napoleon Feb 3, 2026
34d9a34
tests for a specific processing rule
not-napoleon Feb 3, 2026
0a86c6e
Merge branch 'main' into more-count-csv-tests
not-napoleon Feb 4, 2026
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228 changes: 228 additions & 0 deletions x-pack/plugin/esql/qa/testFixtures/src/main/resources/exponential_histogram.csv-spec
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Original file line number Diff line number Diff line change
Expand Up @@ -734,3 +734,231 @@ TS exp_histo_sample
cnt:long
0
;

// -------------------------------------------------------------------------------

All aggs grouped by TBucket and Instance (time series mode)
required_capability: exponential_histogram_tech_preview
required_capability: ts_command_v0
required_capability: count_of_histogram_types

TS exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS min = MIN(responseTime),
max = MAX(responseTime),
p90 = PERCENTILE(responseTime,90),
sum = SUM(responseTime),
avg = AVG(responseTime),
count = COUNT(responseTime)
BY instance, time=TBUCKET(10m)
| EVAL p90 = ROUND(p90, 1), sum = ROUND(sum, 7), avg = ROUND(avg, 7) // rounding to avoid floating point precision issues
| KEEP instance, time, min, max, p90, sum, avg, count
| SORT instance, time
;

instance:keyword | time:datetime | min:double | max:double | p90:double | sum:double | avg:double | count:long
instance-0 | 2025-09-25T00:30:00.000Z | 2.5E-4 | 1.291403 | 0.5 | 128.220162 | 0.1582965 | 810
instance-0 | 2025-09-25T00:40:00.000Z | 2.93E-4 | 1.381711 | 0.5 | 139.229386 | 0.1567898 | 888
instance-0 | 2025-09-25T00:50:00.000Z | 2.57E-4 | 1.337726 | 0.5 | 146.914416 | 0.1680943 | 874
instance-0 | 2025-09-25T01:00:00.000Z | 3.58E-4 | 1.384036 | 0.6 | 15.216617 | 0.1729161 | 88
instance-1 | 2025-09-25T00:30:00.000Z | 2.37E-4 | 0.144625 | 0.0 | 1.323601 | 0.0046119 | 287
instance-1 | 2025-09-25T00:40:00.000Z | 2.33E-4 | 0.189058 | 0.0 | 1.422738 | 0.0049746 | 286
instance-1 | 2025-09-25T00:50:00.000Z | 2.29E-4 | 0.06199 | 0.0 | 0.737455 | 0.0025695 | 287
instance-1 | 2025-09-25T01:00:00.000Z | 3.31E-4 | 0.252402 | 0.0 | 0.399745 | 0.0133248 | 30
instance-2 | 2025-09-25T00:30:00.000Z | 2.21E-4 | 0.136683 | 0.0 | 1.710478 | 0.0057982 | 295
instance-2 | 2025-09-25T00:40:00.000Z | 2.25E-4 | 0.098632 | 0.0 | 1.278917 | 0.0041255 | 310
instance-2 | 2025-09-25T00:50:00.000Z | 2.2E-4 | 0.092109 | 0.0 | 0.946027 | 0.0032622 | 290
instance-2 | 2025-09-25T01:00:00.000Z | 2.45E-4 | 0.008362 | 0.0 | 0.033433 | 0.0011529 | 29
;

// -------------------------------------------------------------------------------

All aggs grouped by Date Trunc and Instance (time series mode)
required_capability: exponential_histogram_tech_preview
required_capability: ts_command_v0
required_capability: count_of_histogram_types

TS exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS min = MIN(responseTime),
max = MAX(responseTime),
p90 = PERCENTILE(responseTime,90),
sum = SUM(responseTime),
avg = AVG(responseTime),
count = COUNT(responseTime)
BY instance, time=date_trunc(10m, @timestamp)
| EVAL p90 = ROUND(p90, 1), sum = ROUND(sum, 7), avg = ROUND(avg, 7) // rounding to avoid floating point precision issues
| KEEP instance, time, min, max, p90, sum, avg, count
| SORT instance, time
;

instance:keyword | time:datetime | min:double | max:double | p90:double | sum:double | avg:double | count:long
instance-0 | 2025-09-25T00:30:00.000Z | 2.5E-4 | 1.291403 | 0.5 | 128.220162 | 0.1582965 | 810
instance-0 | 2025-09-25T00:40:00.000Z | 2.93E-4 | 1.381711 | 0.5 | 139.229386 | 0.1567898 | 888
instance-0 | 2025-09-25T00:50:00.000Z | 2.57E-4 | 1.337726 | 0.5 | 146.914416 | 0.1680943 | 874
instance-0 | 2025-09-25T01:00:00.000Z | 3.58E-4 | 1.384036 | 0.6 | 15.216617 | 0.1729161 | 88
instance-1 | 2025-09-25T00:30:00.000Z | 2.37E-4 | 0.144625 | 0.0 | 1.323601 | 0.0046119 | 287
instance-1 | 2025-09-25T00:40:00.000Z | 2.33E-4 | 0.189058 | 0.0 | 1.422738 | 0.0049746 | 286
instance-1 | 2025-09-25T00:50:00.000Z | 2.29E-4 | 0.06199 | 0.0 | 0.737455 | 0.0025695 | 287
instance-1 | 2025-09-25T01:00:00.000Z | 3.31E-4 | 0.252402 | 0.0 | 0.399745 | 0.0133248 | 30
instance-2 | 2025-09-25T00:30:00.000Z | 2.21E-4 | 0.136683 | 0.0 | 1.710478 | 0.0057982 | 295
instance-2 | 2025-09-25T00:40:00.000Z | 2.25E-4 | 0.098632 | 0.0 | 1.278917 | 0.0041255 | 310
instance-2 | 2025-09-25T00:50:00.000Z | 2.2E-4 | 0.092109 | 0.0 | 0.946027 | 0.0032622 | 290
instance-2 | 2025-09-25T01:00:00.000Z | 2.45E-4 | 0.008362 | 0.0 | 0.033433 | 0.0011529 | 29
;


// -------------------------------------------------------------------------------

All aggs grouped by Bucket and Instance (time series mode)
required_capability: exponential_histogram_tech_preview
required_capability: ts_command_v0
required_capability: count_of_histogram_types

TS exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS min = MIN(responseTime),
max = MAX(responseTime),
p90 = PERCENTILE(responseTime,90),
sum = SUM(responseTime),
avg = AVG(responseTime),
count = COUNT(responseTime)
BY instance, time=Bucket(@timestamp, 10m)
| EVAL p90 = ROUND(p90, 1), sum = ROUND(sum, 7), avg = ROUND(avg, 7) // rounding to avoid floating point precision issues
| KEEP instance, time, min, max, p90, sum, avg, count
| SORT instance, time
;

instance:keyword | time:datetime | min:double | max:double | p90:double | sum:double | avg:double | count:long
instance-0 | 2025-09-25T00:30:00.000Z | 2.5E-4 | 1.291403 | 0.5 | 128.220162 | 0.1582965 | 810
instance-0 | 2025-09-25T00:40:00.000Z | 2.93E-4 | 1.381711 | 0.5 | 139.229386 | 0.1567898 | 888
instance-0 | 2025-09-25T00:50:00.000Z | 2.57E-4 | 1.337726 | 0.5 | 146.914416 | 0.1680943 | 874
instance-0 | 2025-09-25T01:00:00.000Z | 3.58E-4 | 1.384036 | 0.6 | 15.216617 | 0.1729161 | 88
instance-1 | 2025-09-25T00:30:00.000Z | 2.37E-4 | 0.144625 | 0.0 | 1.323601 | 0.0046119 | 287
instance-1 | 2025-09-25T00:40:00.000Z | 2.33E-4 | 0.189058 | 0.0 | 1.422738 | 0.0049746 | 286
instance-1 | 2025-09-25T00:50:00.000Z | 2.29E-4 | 0.06199 | 0.0 | 0.737455 | 0.0025695 | 287
instance-1 | 2025-09-25T01:00:00.000Z | 3.31E-4 | 0.252402 | 0.0 | 0.399745 | 0.0133248 | 30
instance-2 | 2025-09-25T00:30:00.000Z | 2.21E-4 | 0.136683 | 0.0 | 1.710478 | 0.0057982 | 295
instance-2 | 2025-09-25T00:40:00.000Z | 2.25E-4 | 0.098632 | 0.0 | 1.278917 | 0.0041255 | 310
instance-2 | 2025-09-25T00:50:00.000Z | 2.2E-4 | 0.092109 | 0.0 | 0.946027 | 0.0032622 | 290
instance-2 | 2025-09-25T01:00:00.000Z | 2.45E-4 | 0.008362 | 0.0 | 0.033433 | 0.0011529 | 29
;
// -------------------------------------------------------------------------------

Group Aggs By Date_Trunc and Instance (standard mode)
required_capability: exponential_histogram_tech_preview
required_capability: count_of_histogram_types

FROM exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS min = MIN(responseTime),
max = MAX(responseTime),
p90 = PERCENTILE(responseTime,90),
sum = SUM(responseTime),
avg = AVG(responseTime),
count = COUNT(responseTime)
BY instance, time=date_trunc(10m, @timestamp)
| EVAL p90 = ROUND(p90, 1), sum = ROUND(sum, 7), avg = ROUND(avg, 7) // rounding to avoid floating point precision issues
| KEEP instance, time, min, max, p90, sum, avg, count
| SORT instance, time
;

instance:keyword | time:datetime | min:double | max:double | p90:double | sum:double | avg:double | count:long
instance-0 | 2025-09-25T00:30:00.000Z | 2.5E-4 | 1.291403 | 0.5 | 128.220162 | 0.1582965 | 810
instance-0 | 2025-09-25T00:40:00.000Z | 2.93E-4 | 1.381711 | 0.5 | 139.229386 | 0.1567898 | 888
instance-0 | 2025-09-25T00:50:00.000Z | 2.57E-4 | 1.337726 | 0.5 | 146.914416 | 0.1680943 | 874
instance-0 | 2025-09-25T01:00:00.000Z | 3.58E-4 | 1.384036 | 0.6 | 15.216617 | 0.1729161 | 88
instance-1 | 2025-09-25T00:30:00.000Z | 2.37E-4 | 0.144625 | 0.0 | 1.323601 | 0.0046119 | 287
instance-1 | 2025-09-25T00:40:00.000Z | 2.33E-4 | 0.189058 | 0.0 | 1.422738 | 0.0049746 | 286
instance-1 | 2025-09-25T00:50:00.000Z | 2.29E-4 | 0.06199 | 0.0 | 0.737455 | 0.0025695 | 287
instance-1 | 2025-09-25T01:00:00.000Z | 3.31E-4 | 0.252402 | 0.0 | 0.399745 | 0.0133248 | 30
instance-2 | 2025-09-25T00:30:00.000Z | 2.21E-4 | 0.136683 | 0.0 | 1.710478 | 0.0057982 | 295
instance-2 | 2025-09-25T00:40:00.000Z | 2.25E-4 | 0.098632 | 0.0 | 1.278917 | 0.0041255 | 310
instance-2 | 2025-09-25T00:50:00.000Z | 2.2E-4 | 0.092109 | 0.0 | 0.946027 | 0.0032622 | 290
instance-2 | 2025-09-25T01:00:00.000Z | 2.45E-4 | 0.008362 | 0.0 | 0.033433 | 0.0011529 | 29
;

// -------------------------------------------------------------------------------

Group Aggs By Bucket and Instance (standard mode)
required_capability: exponential_histogram_tech_preview
required_capability: count_of_histogram_types

FROM exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS min = MIN(responseTime),
max = MAX(responseTime),
p90 = PERCENTILE(responseTime,90),
sum = SUM(responseTime),
avg = AVG(responseTime),
count = COUNT(responseTime)
BY instance, time=bucket(@timestamp, 10m)
| EVAL p90 = ROUND(p90, 1), sum = ROUND(sum, 7), avg = ROUND(avg, 7) // rounding to avoid floating point precision issues
| KEEP instance, time, min, max, p90, sum, avg, count
| SORT instance, time
;

instance:keyword | time:datetime | min:double | max:double | p90:double | sum:double | avg:double | count:long
instance-0 | 2025-09-25T00:30:00.000Z | 2.5E-4 | 1.291403 | 0.5 | 128.220162 | 0.1582965 | 810
instance-0 | 2025-09-25T00:40:00.000Z | 2.93E-4 | 1.381711 | 0.5 | 139.229386 | 0.1567898 | 888
instance-0 | 2025-09-25T00:50:00.000Z | 2.57E-4 | 1.337726 | 0.5 | 146.914416 | 0.1680943 | 874
instance-0 | 2025-09-25T01:00:00.000Z | 3.58E-4 | 1.384036 | 0.6 | 15.216617 | 0.1729161 | 88
instance-1 | 2025-09-25T00:30:00.000Z | 2.37E-4 | 0.144625 | 0.0 | 1.323601 | 0.0046119 | 287
instance-1 | 2025-09-25T00:40:00.000Z | 2.33E-4 | 0.189058 | 0.0 | 1.422738 | 0.0049746 | 286
instance-1 | 2025-09-25T00:50:00.000Z | 2.29E-4 | 0.06199 | 0.0 | 0.737455 | 0.0025695 | 287
instance-1 | 2025-09-25T01:00:00.000Z | 3.31E-4 | 0.252402 | 0.0 | 0.399745 | 0.0133248 | 30
instance-2 | 2025-09-25T00:30:00.000Z | 2.21E-4 | 0.136683 | 0.0 | 1.710478 | 0.0057982 | 295
instance-2 | 2025-09-25T00:40:00.000Z | 2.25E-4 | 0.098632 | 0.0 | 1.278917 | 0.0041255 | 310
instance-2 | 2025-09-25T00:50:00.000Z | 2.2E-4 | 0.092109 | 0.0 | 0.946027 | 0.0032622 | 290
instance-2 | 2025-09-25T01:00:00.000Z | 2.45E-4 | 0.008362 | 0.0 | 0.033433 | 0.0011529 | 29
;

// -------------------------------------------------------------------------------

// this specifically tests the interaction with PushCountQueryAndTagsToSource rule
Top level date group with no other groups (standard index)
required_capability: exponential_histogram_tech_preview
required_capability: count_of_histogram_types

FROM exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS count = Count(responseTime) by time = Bucket(@timestamp, 10m)
| KEEP time, count
;
ignoreOrder:true

time:datetime | count:long
2025-09-25T00:30:00.000Z | 1392
2025-09-25T00:40:00.000Z | 1484
2025-09-25T00:50:00.000Z | 1451
2025-09-25T01:00:00.000Z | 147
;

// -------------------------------------------------------------------------------

// this specifically tests the interaction with PushCountQueryAndTagsToSource rule
Top level date group with no other groups (timeseries index)
required_capability: ts_command_v0
required_capability: exponential_histogram_tech_preview
required_capability: count_of_histogram_types

TS exp_histo_sample
| WHERE NOT STARTS_WITH(instance, "hand-rolled")
| WHERE TRANGE(to_datetime("2025-09-25T00:30:00Z"), to_datetime("2025-09-25T01:00:00Z"))
| STATS count = Count(responseTime) by time = Bucket(@timestamp, 10m)
| KEEP time, count
;
ignoreOrder:true

time:datetime | count:long
2025-09-25T00:30:00.000Z | 1392
2025-09-25T00:40:00.000Z | 1484
2025-09-25T00:50:00.000Z | 1451
2025-09-25T01:00:00.000Z | 147
;
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