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test(perf): align BSS reader benchmarks with prior study
Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>
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+15
-3
@@ -407,11 +407,23 @@ query latency percentage is
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The configured three warmups occur after the initial query validation and before
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that query's 15 measured endpoint requests, so query latency is a warmed
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frontend/cache measurement. After the datanodes stop, the case also runs seven
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iterations of `parquetbench` for up to four inspected SST files and `scanbench`
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for their scan groups. Their per-run output and aggregate
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iterations of value-only `parquetbench` for up to four inspected SST files and
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sequential `scanbench` over the corresponding regions, with parallelism one.
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Their per-run output and aggregate
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`parquetbench_median_average_ms` and `scanbench_median_average_ms` are under
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`targets[].read_bench`; they are quiescent local-file read/scan diagnostics, not
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warmed frontend-query latency measurements.
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warmed frontend-query latency measurements. Bench averages include iteration one;
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no OS cache is dropped, and the driver runs base before candidate.
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Historical [PR #8548](https://github.com/GreptimeTeam/greptimedb/pull/8548)
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reported storage savings alongside warm-read slowdowns. Its mixed counter/gauge
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study used value-only and all-column projections, discarded the first iteration,
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and alternated target order. This smaller integer-counter case is not an exact
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reproduction: do not compare its inclusive averages directly with that study's
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warm medians. For a matching reader comparison, reuse the recorded bench commands
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against the stopped data directories, run both projections with eight iterations,
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alternate target order, and compare medians of iterations 2–8 from raw output.
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Keep post-flush and post-compaction measurements separate.
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The case's `-5.0` storage target and `25` query-latency guardrail are experimental
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acceptance targets, not observed-benefit claims; do not relax them if a run
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@@ -51,6 +51,10 @@ enabled = true
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parquetbench = true
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scanbench = true
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iterations = 7
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projection = ["greptime_value"]
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parquet_reader = "direct"
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scan_scanner = "seq"
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parallelism = 1
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max_files = 4
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[[scenario.queries]]
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