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Db Scaling — 20260823T184745Z

Run 20260823T184745Z-b16f52f5-db-scaling · commit a309863d585b (working tree dirty) · 2026-08-23 22:01 UTC

Validity guards passed

No swapping, no OOM kills, no unexpected restarts, disk headroom kept, load generator below its own ceiling, monitoring coverage complete.

Headline

finding value evidence
peak single-replica throughput 230.7 conc-D1-c4-r1 at 4 clients, p95 28 ms
sustainable single-replica capacity (C1) 230.7 highest successful rps with p95 within the 2.0s SLO and errors under 1%

What was measured

dataset size ObsCore rows total rows index/table generation
D1 2.06 GiB 600,120 8,624,396 0.63 2 s
D2 10.26 GiB 3,000,213 43,120,494 0.63 517 s

Sizes are pg_database_size() with indexes — not row counts times an assumed row width. One database is grown through every target, so each tier is a genuine prefix of the next.

Throughput and latency against concurrency

dataset clients reps requests/s p95 (ms) errors bottleneck
D1 1 3 177.0 ± 1.3 12 0.00% UNKNOWN
D1 2 3 224.9 ± 7.2 15 0.00% UNKNOWN
D1 4 8 223.0 ± 5.2 29 0.00% TAP_CPU_BOUND
D2 1 3 144.4 ± 3.1 13 0.00% UNKNOWN
D2 2 3 169.6 ± 5.2 20 0.00% UNKNOWN
D2 4 3 195.6 ± 6.1 33 0.00% TAP_CPU_BOUND
D2 8 8 184.9 ± 5.4 66 0.00% TAP_CPU_BOUND

Intervals are 95% Student-t across repetitions. The sweep stops when two saturation signals agree, so the last row of a dataset is where that dataset's ceiling was found rather than where the ladder ran out.

Where the limit was

classification measurements meaning
TAP_CPU_BOUND 25 The API's own CPU is the constraint: it sat at its ceiling, or was CFS-throttled, for a material part of the window. The ceiling compared against is min(pod CPU limit, workers), not the pod limit: ADQL translation is pure-Python and holds the GIL, so one worker cannot exceed one core whatever the cgroup allows. Relieved by more processes (tapApi.workers) or more pods, never by a bigger limit on one worker.
UNKNOWN 12 Nothing saturated by these rules. Either the offered load was below every ceiling, or the limit is somewhere this suite does not instrument — which is itself worth knowing.
DATABASE_CPU_BOUND 2 PostgreSQL is CPU-bound. Adding API replicas cannot help and will make it worse; this needs cheaper plans, fewer rows, or a bigger database.
SERIALIZATION_BOUND 2 Large responses with a busy API and an idle database: the time is going into formatting and streaming rows, not into finding them. This is a result-pipeline cost, not a query cost.

Query plans

flag plans why it matters
bad_cardinality_estimate 14 The planner's row estimate was an order of magnitude out, which is how good indexes get ignored.
large_rows_removed_by_filter 1 The plan fetched rows only to throw them away - work done for nothing, and usually a missing or wrong-ordered index.

Graphs

Throughput against offered concurrency

Throughput against offered concurrency

Latency percentiles against concurrency

Latency percentiles against concurrency

Errors against concurrency

Errors against concurrency

Throughput against database size

Throughput against database size

Latency against database size

Latency against database size

Buffer cache hit ratio against database size

Buffer cache hit ratio against database size

API CPU against throughput

API CPU against throughput

PostgreSQL CPU against throughput

PostgreSQL CPU against throughput

PostgreSQL read I/O against throughput

PostgreSQL read I/O against throughput

Throughput by query class

Throughput by query class

Latency by query class

Latency by query class

Query class against database size

Query class against database size

Latency against response size

Latency against response size

Run-to-run variability

Run-to-run variability

Environment

property value
host macOS-26.6.2-arm64-arm-64bit-Mach-O (14 CPUs)
Kubernetes v1.36.1
node capacity 14 CPU, 16354760Ki
KEDA ghcr.io/kedacore/keda:2.18.1
PostgreSQL PostgreSQL 18.6 (Debian 18.6-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit
extensions pg_sphere 1.5.2, pg_stat_statements 1.12, plpgsql 1.0
seed 20260823
corpus sha256 483dc27a6f2749a7
chart values sha256 b6e9c1b32e19c965

Download the per-measurement CSV · environment.json · dataset.json

Raw per-request samples and Prometheus series stay with the run that produced them, under benchmarks/tap-performance/results/20260823T184745Z-b16f52f5-db-scaling/: Parquet for every request and every metric, PostgreSQL statistics before and after, EXPLAIN plans, and the exact ScaledObject and HPA YAML the measurement ran against.