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Memory Performance Evaluation

Offline baseline harness for issue #288.

What it measures

For each configured dataset size, the harness seeds a synthetic SQLite memory store and reports:

  • seed time
  • database size on disk
  • search_memories() unscoped latency
  • search_memories() track-scoped latency
  • search_memories_batch() track latency
  • search_memories_batch() paper latency

Latency summaries include avg_ms, p50_ms, p95_ms, and p99_ms.

Default sizes

The CLI defaults to the three baseline scales requested by the issue:

  • 10,000
  • 100,000
  • 1,000,000

Run locally

PYTHONPATH=src python scripts/benchmark_memory_performance.py \
  --sizes 10000,100000,1000000 \
  --query-count 25 \
  --output output/reports/memory_performance_baseline.json

For a faster smoke run during development:

PYTHONPATH=src python scripts/benchmark_memory_performance.py \
  --sizes 1000,5000 \
  --query-count 5

Notes

  • The benchmark uses deterministic synthetic data and disables embedding generation so the baseline is stable and offline.
  • It is intended as a reporting baseline, not a strict CI latency gate, because runtime varies across machines.