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test(perf): cover rate and increase window and reset shapes

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Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>
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discord9
2026-09-10 17:24:47 +08:00
parent 0f625a7e92
commit 29870a776a
3 changed files with 486 additions and 0 deletions
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tests/perf/query_cases/promql_rate_dense_resets/case.toml
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# PromQL rate/increase window-width and evaluation-step benchmark with dense resets.
#
# Remote-write `modulo` values use cardinality 15 and step 1. Because 780 is
# divisible by 15, every one of the 128 series resets from 14 to 0 exactly every
# 15 local samples (225 seconds) at the 15-second cadence: 51 resets
# per series in 780 samples. The `resets` HTTP control is manually inspected in
# its retained response: each aligned one-hour window contains 240 samples and
# should report 16 resets per series. The runner does not assert numeric values.
[case]
name = "promql_rate_dense_resets"
description = "PromQL rate and increase across windows and steps for counters resetting every 15 samples"
issue = "https://github.com/GreptimeTeam/greptimedb/pull/9089"
[scenario]
kind = "prom_remote_write_then_query"
[scenario.remote_write]
database = "public"
metric = "promql_rate_dense_resets"
physical_table = "greptime_physical_table"
series_count = 128
samples_per_series = 780
sample_chunk_size = 195
flush_every_sample_chunks = 1
start_unix_millis = 1704067200000
step_millis = 15000
chunk_series_count = 128
timeout_seconds = 180
visibility_timeout_seconds = 120
[scenario.remote_write.value]
pattern = "modulo"
base = 0
step = 1
cardinality = 15
[scenario.remote_write.prom_store]
pending_rows_flush_interval = "1s"
max_batch_rows = 100000
[[scenario.queries]]
name = "selector_control_2h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') promql_rate_dense_resets{host=~'host.*'}"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_dense_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_dense_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_dense_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_dense_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_dense_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_dense_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Twelve evaluations per 1h window: representative larger overlapping step.
[[scenario.queries]]
name = "rate_1h_5m_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '5m') rate(promql_rate_dense_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# One evaluation per 1h window: representative non-overlapping step.
[[scenario.queries]]
name = "increase_1h_1h_non_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '1h') increase(promql_rate_dense_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "resets_1h_1h_expected_sixteen"
kind = "prom_http"
query = "resets(promql_rate_dense_resets{host=~\"host.*\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Manual response check: at 1704070800, 1704074400, and 1704078000, the
# left-open 1h windows contain samples 1..240, 241..480, and 481..720. Each has
# 16 resets and extrapolated increase 53520 / 239 = 223.93305439330544. The runner
# retains but does not assert it.
[[scenario.queries]]
name = "increase_1h_host0000_manual_response_check"
kind = "prom_http"
query = "increase(promql_rate_dense_resets{host=\"host0000\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
tests/perf/query_cases/promql_rate_no_resets/case.toml
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# PromQL rate/increase window-width and evaluation-step benchmark without resets.
#
# Remote-write `linear` values increase by one at every 15-second sample for
# each series. 128 series × 780 samples = 99,840 rows over 3h14m45s.
# The `resets` HTTP control below is manually inspected in its retained response:
# it should report 0 for every series at all three one-hour, non-overlapping
# evaluations. The runner records the response but does not assert its numeric values.
[case]
name = "promql_rate_no_resets"
description = "PromQL rate and increase across windows and steps for monotonic counters without resets"
issue = "https://github.com/GreptimeTeam/greptimedb/pull/9089"
[scenario]
kind = "prom_remote_write_then_query"
[scenario.remote_write]
database = "public"
metric = "promql_rate_no_resets"
physical_table = "greptime_physical_table"
series_count = 128
samples_per_series = 780
sample_chunk_size = 195
flush_every_sample_chunks = 1
start_unix_millis = 1704067200000
step_millis = 15000
chunk_series_count = 128
timeout_seconds = 180
visibility_timeout_seconds = 120
[scenario.remote_write.value]
pattern = "linear"
base = 0
step = 1
[scenario.remote_write.prom_store]
pending_rows_flush_interval = "1s"
max_batch_rows = 100000
[[scenario.queries]]
name = "selector_control_2h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') promql_rate_no_resets{host=~'host.*'}"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_no_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_no_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_no_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_no_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_no_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_no_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Twelve evaluations per 1h window: representative larger overlapping step.
[[scenario.queries]]
name = "rate_1h_5m_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '5m') rate(promql_rate_no_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# One evaluation per 1h window: representative non-overlapping step.
[[scenario.queries]]
name = "increase_1h_1h_non_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '1h') increase(promql_rate_no_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "resets_1h_1h_expected_zero"
kind = "prom_http"
query = "resets(promql_rate_no_resets{host=~\"host.*\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Manual response check: at 1704070800, 1704074400, and 1704078000, the
# left-open 1h windows contain samples 1..240, 241..480, and 481..720. Each
# extrapolated increase is exactly 240. The runner retains but does not assert it.
[[scenario.queries]]
name = "increase_1h_host0000_manual_response_check"
kind = "prom_http"
query = "increase(promql_rate_no_resets{host=\"host0000\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
tests/perf/query_cases/promql_rate_sparse_resets/case.toml
+163
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# PromQL rate/increase window-width and evaluation-step benchmark with sparse resets.
#
# Remote-write `modulo` values use cardinality 195 and step 1. Because 780 is
# divisible by 195, every one of the 128 series resets from 194 to 0 exactly at
# local sample indexes 195, 390, and 585 (15-second cadence): three resets per
# series. The `resets` HTTP control is manually inspected in its retained response:
# each aligned one-hour window should report 1 reset per series. The runner does
# not assert numeric response values.
[case]
name = "promql_rate_sparse_resets"
description = "PromQL rate and increase across windows and steps for counters resetting every 195 samples"
issue = "https://github.com/GreptimeTeam/greptimedb/pull/9089"
[scenario]
kind = "prom_remote_write_then_query"
[scenario.remote_write]
database = "public"
metric = "promql_rate_sparse_resets"
physical_table = "greptime_physical_table"
series_count = 128
samples_per_series = 780
sample_chunk_size = 195
flush_every_sample_chunks = 1
start_unix_millis = 1704067200000
step_millis = 15000
chunk_series_count = 128
timeout_seconds = 180
visibility_timeout_seconds = 120
[scenario.remote_write.value]
pattern = "modulo"
base = 0
step = 1
cardinality = 195
[scenario.remote_write.prom_store]
pending_rows_flush_interval = "1s"
max_batch_rows = 100000
[[scenario.queries]]
name = "selector_control_2h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') promql_rate_sparse_resets{host=~'host.*'}"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_sparse_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_sparse_resets{host=~'host.*'}[1m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_sparse_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_5m_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_sparse_resets{host=~'host.*'}[5m])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "rate_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(promql_rate_sparse_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "increase_1h_15s"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(promql_rate_sparse_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Twelve evaluations per 1h window: representative larger overlapping step.
[[scenario.queries]]
name = "rate_1h_5m_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '5m') rate(promql_rate_sparse_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# One evaluation per 1h window: representative non-overlapping step.
[[scenario.queries]]
name = "increase_1h_1h_non_overlapping"
kind = "tql"
query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '1h') increase(promql_rate_sparse_resets{host=~'host.*'}[1h])"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
[[scenario.queries]]
name = "resets_1h_1h_expected_one"
kind = "prom_http"
query = "resets(promql_rate_sparse_resets{host=~\"host.*\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10
# Manual response check: at 1704070800, 1704074400, and 1704078000, the
# left-open 1h windows contain samples 1..240, 241..480, and 481..720. Each has
# one reset and extrapolated increase 57120 / 239 = 238.9958158995816. The runner
# retains but does not assert it.
[[scenario.queries]]
name = "increase_1h_host0000_manual_response_check"
kind = "prom_http"
query = "increase(promql_rate_sparse_resets{host=\"host0000\"}[1h])"
start = "1704070800"
end = "1704078000"
step = "1h"
warmup = 3
iterations = 9
[scenario.queries.thresholds]
max_candidate_latency_regression_pct = 10