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distillate

Methodology

How RESULTS.md is produced. Run it yourself with pnpm bench (src/report.ts).

Fairness

All classic-Bloom libraries are configured for the same target FPR (1%) over the same key set, and every filter is measured by the same vendored code (src/harness.ts), so differences reflect the implementation, not the setup.

Configuration at matched FPR

  • distillate/bloom: BloomFilter.create(n, 0.01).
  • bloom-filters: BloomFilter.create(n, 0.01).
  • bloomfilter (jasondavies): takes bits m and hashes k directly, so it is given (m, k) computed from (n, 0.01) by the standard optimal-sizing formulas (m = ceil(-n·ln ε / ln²2), k = round((m/n)·ln 2)). See optimalMK in src/adapters.ts.

bits/key is read from each filter’s actual allocated bit count divided by n, not from the requested target, so all three land at the same ~9.59 bits/key.

Keys

hitMissPools(n) builds two disjoint sets: inserted “hit” keys 0:0 … 0:(n-1) and never-inserted “miss” keys 1:0 … 1:(n-1). The prefixes guarantee the miss set shares no member with the hit set.

Measured FPR

After inserting the hit set, measureFpr queries a disjoint miss set of 1,000,000 keys and reports the fraction that return true. A well-built 1%-target filter lands near 1%; blocked/fuse sit lower by design.

Throughput

Measured with mitata at n = 100,000. To keep the numbers honest against dead-code elimination:

  • results are fed through mitata’s do_not_optimize;
  • lookups cycle through a key pool (cycle) instead of repeating one key;
  • hit and miss paths are benched separately;
  • add inserts distinct keys each iteration.

Reported as ops/sec (1e9 / avg_ns).

Structures

  • Classic Bloom is a head-to-head: distillate/bloom vs bloom-filters vs bloomfilter.
  • blocked, fuse8, fuse16 are distillate-only and shown standalone; no audited incumbent offers an equivalent, so there is nothing fair to compare them to. fuse8 targets 2⁻⁸, fuse16 targets 2⁻¹⁶.

Portability caveat

bloomfilter hashes strings via charCodeAt: fast, but ASCII-lossy (it ignores the high bytes of non-ASCII characters) and not reproducible in another language. distillate hashes the UTF-8 bytes with MurmurHash3_x64_128, so its filters serialize and re-read across languages. The throughput gap is that tradeoff.

Scope

Node only, single machine (disclosed in the banner). No Bun/Deno, no CI runs, no charts. Capacities: 100k and 1M for space/accuracy, 100k for throughput.