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feat(perf): add nullable_wave distribution and nullable range-fn case
Add a nullable_wave value distribution to the direct-SST fixture
generator: every null_every-th sample of each series is NULL, counted
per series so every series carries the same null ratio on every layout.
New case tests/perf/query_cases/promql_nullable_range_fn: 128 series x
780 timestamps at 15s cadence, nullable_table (25% NULL, null_every=4)
and dense_table (identical, 0 NULL) as control. Seven TQL queries cover
rate/increase/delta and count/last_over_time at [5m], rate at [1m],
and a dense control query. Every window of every series takes the
nullable branch (no window is entirely NULL or NULL-free).
Regression evidence for #9234. The case is selectable by explicit path;
not added to DEFAULT_CASES.
Review: .e-agent/greptimedb-query-optimization-review-2026-09-17.md §4.4.
Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>
(cherry picked from commit 5a73f6f21f)
Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>
This commit is contained in:
discord9
@@ -58,6 +58,34 @@ warmup = 0
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iterations = 1
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```
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### Value distributions
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Tag columns use `cardinality` and produce one label per series. Double field
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columns accept either a NULL-free wave:
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```toml
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distribution = { kind = "deterministic_wave", min = 0.0, max = 100.0 }
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```
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or a wave with periodic NULLs:
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```toml
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distribution = { kind = "nullable_wave", min = 0.0, max = 100.0, null_every = 4 }
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```
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`nullable_wave` also accepts `null_every = 0` to disable NULLs. It writes NULL on
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every `null_every`-th sample of each series and wave values everywhere else, so a
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NULL is one missing sample of one series rather than a missing row. The cadence
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is counted in samples of the series, not in generated rows, so every series
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carries the same null ratio under any `series_layout`; when one SST holds a
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multiple of `null_every` samples per series (`rows_per_sst / series_count`), the
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pattern also repeats with the same period in every SST. Counting the cadence in
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generated rows instead would make it constant per series whenever `series_count`
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is a multiple of `null_every` (128 and 4, for example), leaving series either
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entirely NULL or entirely NULL-free. Counting per series keeps NULL and non-NULL
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samples mixed inside range-function windows, which is what the nullable branches
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of the PromQL range functions observe.
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### Query kinds
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`kind = "prom_http"` runs a Prometheus range query by POSTing form fields to
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@@ -0,0 +1,183 @@
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# PromQL nullable range-function benchmark: regression evidence for the indexed
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# valid-sample lookup in nullable range functions (#9234).
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#
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# `rate`/`increase`/`delta` and the presence evaluator behind
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# `count_over_time`/`last_over_time` fall back to a per-window validity scan once
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# any sample of a series is NULL. `nullable_table` writes NULL on every fourth
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# sample of every series, so 25% of its samples are missing (one missing sample
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# per minute at the 15s cadence) and every window of every series contains both
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# NULL and non-NULL samples instead of being entirely NULL or entirely dense.
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# `dense_table` is the same fixture without NULLs; it keeps the no-NULL fast path
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# measured next to the nullable path.
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#
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# 128 series x 780 timestamps at 15s cadence = 99,840 rows per table, written as
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# 13 SSTs x 7,680 rows. Data starts one hour before evaluation; 481 evaluations
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# span two hours. Range windows are left-open and every series is dense on the
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# 15s grid, so a fully covered 5m window holds 20 slots and a 1m window holds 4.
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# Five of the 20 slots and one of the 4 slots are NULL, which makes
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# `count_over_time(nullable_table[5m])` return exactly 15 and
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# `count_over_time(nullable_table[1m])` return exactly 3 at every one of the 481
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# evaluations: no window is entirely NULL and none is NULL-free, so every window
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# of every series takes the nullable branch.
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[case]
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name = "promql_nullable_range_fn"
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description = "PromQL range functions over partially NULL series at 15-second scrape and evaluation cadence"
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[scenario]
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kind = "direct_readable_sst"
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seed = 9234
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[[scenario.tables]]
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database = "public"
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name = "nullable_table"
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engine = "mito"
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append_mode = true
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sst_format = "flat"
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primary_key = ["host", "instance"]
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time_index = "ts"
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[[scenario.tables.columns]]
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name = "host"
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type = "STRING"
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semantic = "tag"
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distribution = { kind = "cardinality", values = 16, prefix = "host" }
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[[scenario.tables.columns]]
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name = "instance"
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type = "STRING"
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semantic = "tag"
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distribution = { kind = "cardinality", values = 128, prefix = "instance" }
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[[scenario.tables.columns]]
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name = "value"
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type = "DOUBLE"
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semantic = "field"
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distribution = { kind = "nullable_wave", min = 0.0, max = 1000.0, null_every = 4 }
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[[scenario.tables.columns]]
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name = "ts"
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type = "TIMESTAMP(9)"
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semantic = "timestamp"
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[[scenario.tables]]
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database = "public"
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name = "dense_table"
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engine = "mito"
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append_mode = true
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sst_format = "flat"
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primary_key = ["host", "instance"]
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time_index = "ts"
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[[scenario.tables.columns]]
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name = "host"
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type = "STRING"
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semantic = "tag"
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distribution = { kind = "cardinality", values = 16, prefix = "host" }
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[[scenario.tables.columns]]
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name = "instance"
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type = "STRING"
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semantic = "tag"
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distribution = { kind = "cardinality", values = 128, prefix = "instance" }
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[[scenario.tables.columns]]
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name = "value"
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type = "DOUBLE"
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semantic = "field"
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distribution = { kind = "deterministic_wave", min = 0.0, max = 1000.0 }
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[[scenario.tables.columns]]
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name = "ts"
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type = "TIMESTAMP(9)"
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semantic = "timestamp"
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[scenario.layout]
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regions = 1
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sst_count = 13
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rows_per_sst = 7680
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row_group_size = 1920
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series_count = 128
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start_unix_nanos = 1704067200000000000
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step_nanos = 15000000000
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time_range_layout = "non_overlapping_per_sst"
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series_layout = "timestamp_major"
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# Twenty slots per window, five of them NULL: the counter correction and the
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# valid-sample lookup both walk a mixed window on every series.
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[[scenario.queries]]
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name = "rate_nullable_5m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(nullable_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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[[scenario.queries]]
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name = "increase_nullable_5m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') increase(nullable_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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# `delta` shares the nullable window-bounds path but skips the counter correction.
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[[scenario.queries]]
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name = "delta_nullable_5m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') delta(nullable_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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# The presence evaluator counts valid slots per window instead of scanning bounds.
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[[scenario.queries]]
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name = "count_over_time_nullable_5m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') count_over_time(nullable_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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# `last_over_time` searches backwards for the last valid slot of every window,
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# which the NULL at the window end forces for one evaluation in every four.
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[[scenario.queries]]
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name = "last_over_time_nullable_5m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') last_over_time(nullable_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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# Four slots per window, one of them NULL: fixed overhead and short-window control
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# for the same nullable path.
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[[scenario.queries]]
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name = "rate_nullable_1m"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(nullable_table{host=~'host.*'}[1m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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# Identical shape and values without NULLs: the fast path must not move.
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[[scenario.queries]]
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name = "rate_dense_5m_control"
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kind = "tql"
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query = "TQL ANALYZE VERBOSE (1704070800, 1704078000, '15s') rate(dense_table{host=~'host.*'}[5m])"
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warmup = 3
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iterations = 9
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[scenario.queries.thresholds]
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max_candidate_latency_regression_pct = 20
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