greptimedb/tests/perf

Query performance regression harness

This directory is for query performance cases that compare a base build with a candidate build. It is not a replacement for sqlness: the goal is to measure the effect of optimizer/query-engine changes on realistic scan work.

Phase 1: direct readable SST fixtures

Phase 1 should generate data by writing readable Mito SST files and matching manifest checkpoints directly. This follows the gc_readable_sst_fixture lab approach from ~/greptimedb-gc-huge-stress: use Mito's SST writer to create queryable files, then write a checkpoint and _last_checkpoint that reference those files.

The generator itself must be generic. It should not know about a specific issue such as #7913 or a specific PromQL query. Cases provide declarative table schema, data layout, distributions, and queries; the generator turns those declarations into readable SST fixtures.

The intended flow for each case is:

1. Start a GreptimeDB build and create an empty table to seed catalog/table metadata.
2. Stop the process.
3. Use the seed region metadata/manifest to generate deterministic readable SSTs and a replacement manifest checkpoint offline.
4. Start the same build on the generated data directory.
5. Run warmup and measured queries.
6. Repeat the same fixture/query process for the candidate build.
7. Compare base vs candidate metrics and write a regression report.

Direct SST fixtures are the default for phase 1 because they provide stable file counts, time ranges, row groups, and label distributions without spending CI time on ingestion and flush. Ingestion-path cases can be added later for nightly or release-level realism.

Prometheus remote-write scenario

The runner also supports scenario.kind = "prom_remote_write_then_query" for a bounded write-path smoke/regression flow. This path is explicit and separate from the direct-SST fixture path: it starts the base and candidate distributed clusters, writes deterministic Prometheus remote-write v1 samples through /v1/prometheus/write, flushes the configured physical metric table, then runs the configured SQL/TQL queries against the logical metric table.

Remote-write cases configure one database, one logical metric, and one physical table under [scenario.remote_write]:

[scenario]
kind = "prom_remote_write_then_query"

[scenario.remote_write]
database = "public"
metric = "prom_remote_write_smoke"
physical_table = "greptime_physical_table"
series_count = 8
samples_per_series = 30
start_unix_millis = 1_704_067_200_000
step_millis = 15_000
chunk_series_count = 4
visibility_timeout_seconds = 30
# Optional: split by time so each helper invocation writes only this many samples
# per series, then periodically flush the physical metric table to produce
# multiple time-interval SSTs.
# sample_chunk_size = 300
# flush_every_sample_chunks = 1

[scenario.remote_write.prom_store]
pending_rows_flush_interval = "1s"

The runner creates the configured database if needed, writes a per-target frontend config enabling [prom_store] with metric engine storage and a non-zero pending_rows_flush_interval, and validates that the logical metric table reaches series_count * samples_per_series rows before trusting the query measurements. Use --remote-write-generator /path/to/prom_remote_write_fixture to provide the Rust payload helper. --fixture-only is rejected for remote-write cases; use --dry-run for planning.

Large manual remote-write cases can set sample_chunk_size to split ingestion by time. For each chunk, the runner invokes prom_remote_write_fixture with the same series cardinality but a shorter --samples-per-series and an advanced --start-unix-millis. flush_every_sample_chunks controls periodic ADMIN FLUSH_TABLE('<physical_table>') calls; with flush_every_sample_chunks = 1, each time chunk is flushed separately. The final visible SST/file-range layout is still determined by the storage engine's normal compaction policy, so cases that need multi-window file distribution should span multiple compaction windows. If sample_chunk_size is omitted, the runner keeps the older single-helper-invocation behavior and flushes once at the end.

tests/perf/query_cases/prom_remote_write_7913/case.toml is a larger manual case for issue #7913. It writes 8192 series × 20160 samples through remote-write in 1440-sample daily time chunks, flushing after each chunk before running 1d/7d/14d TQL selectors. It is not included in the default case set because ingestion cost dominates routine CI validation.

Generator contract

The direct-SST generator should accept a case definition with:

• one or more table definitions: columns, semantic types, primary key, time index, SST format, append mode
• deterministic distributions: seed, series/tag cardinalities, label/value functions, timestamp layout
• physical layout: regions, SST count, rows per SST, row group size, time ranges per SST, optional overlap/skew
• output paths for object-store files, manifest checkpoints, and fixture metadata

This keeps query regression cases reusable: the same generator can produce PromQL, SQL, pruning, projection, join, or aggregation fixtures by changing only case config.

What a case owns

Each optimization PR should add or update the query case for the pattern it is expected to affect. A case should define:

• schema and seed table SQL
• deterministic data shape: seed, series count, rows per SST, SST count, time range layout, label distribution, region/partition layout
• queries to run
• warmup/measurement repetitions
• metrics to collect
• base-vs-candidate thresholds

The [case] table is metadata for reports. The executable regression config lives under [scenario]. A scenario owns data generation, queries, and thresholds:

[case]
name = "example"
description = "what this regression protects"

[scenario]
kind = "direct_readable_sst"
seed = 12345

[[scenario.tables]]
# table schema and distributions

[scenario.layout]
# SST and series layout

[[scenario.queries]]
# query, warmups, iterations, thresholds

The runner currently supports direct_readable_sst and prom_remote_write_then_query.

Metrics

Primary gates should compare query work rather than plan text:

• scanned files / file ranges
• scanned rows or row groups
• bytes read when available
• pruning ratio
• query latency median/p95
• output row count as a sanity check

Plan details such as pushed filters are useful diagnostics, but should not be the main pass/fail signal.

Runner MVP

query_regression_runner.py is the base-vs-candidate orchestration layer. The current MVP parses a case, creates per-target work directories, and in real query mode starts a local distributed cluster for each target: metasrv (memory-store, region failover disabled), one datanode (node_id=0), and one frontend. It creates the configured Mito table(s) through frontend HTTP SQL, discovers the real one-region-per-table metadata via information_schema, stops only the owning datanode, generates one shared direct-SST fixture per table using the discovered --region-id, --table-dir, and --table, injects those region subtrees into the datanode data home, restarts the datanode, then validates and measures through frontend. Reports are written as JSON under the work directory.

The runner intentionally keeps metasrv alive for the whole target run because memory-store metadata would otherwise be lost. It replaces only the discovered datanode region directory under data/greptime/<schema>/<table_id>/... with generated SST files and a manifest checkpoint. For multi-table cases this is repeated per table, enabling true JOIN fixtures while still requiring exactly one region per table. Base and candidate must discover identical per-table table_dir and region_id; otherwise the run fails.

Multi-table direct-SST cases must use unique table names as well as unique (database, name) pairs because the generator currently selects a table with --table <name>. Per-table fixture directories are derived from table index, database, and table name with path-unsafe characters sanitized.

Currently enforced threshold:

• max_candidate_latency_regression_pct, based on client-side median latency.

Server-side scan thresholds such as file ranges and scanned rows are planned for a follow-up PR that extracts them from structured EXPLAIN ANALYZE VERBOSE output. Do not add those threshold keys until the runner enforces them.

Dry-run example:

uv run --no-project python tests/perf/query_regression_runner.py \
  --case tests/perf/query_cases/promql_pushdown_7913/case.toml \
  --base-bin /path/to/base/greptime \
  --candidate-bin /path/to/candidate/greptime \
  --work-dir /tmp/query-perf-work \
  --dry-run

With a fixture generator:

uv run --no-project python tests/perf/query_regression_runner.py \
  --case tests/perf/query_cases/promql_pushdown_7913/case.toml \
  --base-bin /path/to/base/greptime \
  --candidate-bin /path/to/candidate/greptime \
  --fixture-generator /path/to/query_perf_fixture \
  --fixture-cache-dir /mnt/query-regression-fixtures \
  --allow-large-fixture \
  --work-dir /tmp/query-perf-work

This mode launches metasrv, datanode, and frontend for each target with explicit localhost HTTP/gRPC/MySQL/Postgres ports and writes component stdout/stderr under each target's logs/ directory.

By default query mode requires fresh base/candidate work directories and fails if either target directory already exists with contents. Use --reuse-work-dir only when intentionally debugging an existing run directory. SQL HTTP requests default to a 120 second timeout; override with --http-timeout <seconds> for slow lab runs.

For large direct-SST fixtures, pass --fixture-cache-dir <dir> to store generated fixtures in a persistent content-addressed cache keyed by case name and fixture data configuration. Query and threshold edits reuse the same cached data as long as the scenario layout and table definitions do not change. Cached fixtures are reused automatically when their summary.json matches the discovered table/region metadata; incompatible entries are regenerated instead of reused. Fixture materialization keeps base and candidate data directories isolated, but copies files efficiently by trying filesystem reflinks first, hardlinks for immutable SST/object files next, and normal copies as a fallback. Manifest files are reflinked or copied, not hardlinked.

Fixture generator smoke test:

cargo run -p cmd --bin query_perf_fixture -- \
  --case tests/perf/query_cases/smoke_direct_sst/case.toml \
  --out-dir /tmp/query-perf-smoke

Runner smoke test with fixture generation only:

uv run --no-project python tests/perf/query_regression_runner.py \
  --case tests/perf/query_cases/smoke_direct_sst/case.toml \
  --base-bin /path/to/query_perf_fixture \
  --candidate-bin /path/to/query_perf_fixture \
  --fixture-generator /path/to/query_perf_fixture \
  --work-dir /tmp/query-perf-runner-smoke \
  --fixture-only

--fixture-only preserves the earlier smoke behavior: it does not start standalone servers, and it materializes the generated fixture into base and candidate data directories for plumbing validation.

Remote-write runner dry-run:

uv run --no-project python tests/perf/query_regression_runner.py \
  --case tests/perf/query_cases/prom_remote_write_smoke/case.toml \
  --base-bin /path/to/base/greptime \
  --candidate-bin /path/to/candidate/greptime \
  --remote-write-generator /path/to/prom_remote_write_fixture \
  --work-dir /tmp/query-perf-remote-write \
  --dry-run

GitHub Actions

.github/workflows/query-regression.yml provides an opt-in CI entrypoint for query regression runs. It builds its own binaries for now:

• base greptime from the PR base commit, or workflow_dispatch base_ref
• candidate greptime and query_perf_fixture from the PR merge ref/current candidate checkout
• runner and summary formatter from the candidate checkout

The workflow runs automatically only for non-draft PRs labeled query-regression (on label/ready-for-review/reopen events, not every push). PR runs build base/candidate once and then run the default case set with --allow-large-fixture. Manual workflow_dispatch runs can pass all, one case path, or a comma/whitespace-separated list of case paths, and can override refs. It always uploads query-regression-work/** and query-regression-summary.md, writes the Markdown summary to the workflow step summary, and updates a sticky PR comment through the trusted follow-up workflow.

Built-in cases

The promql_pushdown_7913 case is only one case using the generic fixture format. It generates a high-cardinality metric-like table with a nanosecond time index and many SSTs with non-overlapping time ranges. Its timestamp_major series layout writes one sample for every series at each scrape timestamp, so short PromQL/TQL selector windows still scan realistic raw sample volumes. The queries should show scan-level time filters, tight SST pruning, and enough raw rows to make distributed PromQL pipeline placement meaningful instead of a millisecond-scale canary.

Additional SQL optimizer cases:

• sql_topk_order_by: single-table TopK / ORDER BY on a DOUBLE field with time and tag predicates.
• sql_aggregate_order_by: grouped aggregate ordered by aggregate value with a LIMIT.
• sql_join_filter_order: two direct-SST tables joined on a shared tag with time filters, aggregate ordering, and LIMIT.