Skip to content

Performance

go-ruby-digest/digest is the pure-Go library that rbgo binds for Ruby's digest. This page records a comparative benchmark of that module against the reference Ruby runtimes, part of the ecosystem-wide per-module parity suite.

What is measured

The same Ruby script — Digest::MD5/SHA1/SHA256 hexdigest loops over a message — is run under every runtime. rbgo's number reflects this pure-Go library doing the work; every other column is that interpreter's own digest stdlib. So the comparison is the Ruby-visible operation, apples-to-apples across interpreters. The script prints a deterministic checksum and its output is checked byte-identical to MRI before timing.

  • Host: Apple M4 Max, macOS (darwin/arm64). Method: best-of-5 wall time (best, not mean, to suppress scheduler noise); single-shot processes, no warm-up beyond the script's own loop.
  • Runtimes: ruby 4.0.5 +PRISM (MRI, the oracle) and ruby --yjit; jruby 10.1.0.0 (OpenJDK 25); truffleruby 34.0.1 (GraalVM CE Native).
  • The benchmark script and harness live in rbgo's repo under bench/modules/ (digest.rb + run.sh). Reproduce: RBGO=./rbgo TRUFFLE=truffleruby bash bench/modules/run.sh 5.

Result (best of 5, ms)

Runtime time vs MRI
rbgo (go-ruby-digest) 370 1.09×
MRI (ruby 4.0.5) 340 1.00×
MRI + YJIT 330 0.97×
JRuby 10.1.0.0 1330 3.91×
TruffleRuby 34.0.1 480 1.41×

rbgo runs on go-ruby-digest at ~1.1x MRI — effective parity, as both ultimately drive optimized native hash implementations.

Honest framing

JRuby and TruffleRuby are timed cold, single-shot, so they carry JVM / Graal startup on every run — read them as one-shot ruby file.rb costs, the same way rbgo and MRI are measured, not as steady-state JIT numbers. Rows that complete in well under ~200 ms carry the most relative noise; treat their ratios as order-of-magnitude. These are real measured numbers from the 2026-06-29 run — nothing is cherry-picked.

Library-level benchmark (Go API vs runtimes) — 2026-07-03

This section measures the pure-Go library directly, through its Go API — not the rbgo interpreter path recorded above. It isolates the library primitive from Ruby-interpreter dispatch, answering the parity question head-on: is the pure-Go implementation as fast as the reference runtime's own digest? The same workload, same inputs, same iteration counts run through the Go library and through each reference runtime's stdlib; outputs were checked identical to MRI before any timing.

  • Host: Apple M4 Max (Mac16,5, arm64), macOS 26.5.1 — date 2026-07-03.
  • Runtimes: Go 1.26.4 · MRI ruby 4.0.5 +PRISM · MRI + YJIT · JRuby 10.1.0.0 (OpenJDK 25) · TruffleRuby 34.0.1 (GraalVM CE Native).
  • Method: each process runs 3 untimed warm-up passes, then 25 timed passes of a fixed inner loop, timed with a monotonic clock; the best pass is reported as ns/op (lower is better). vs MRI < 1.00× means faster than MRI. Interpreter start-up is outside the timed region, so these are operation costs, not ruby file.rb process costs.

md5-4KiB

Runtime ns/op vs MRI
go-ruby (pure Go) 4714.1 0.90×
MRI 5226.0 1.00×
MRI + YJIT 5111.0 0.98×
JRuby 4735.6 0.91×
TruffleRuby 5064.9 0.97×

sha256-4KiB

Runtime ns/op vs MRI
go-ruby (pure Go) 1621.0 1.09×
MRI 1487.0 1.00×
MRI + YJIT 1433.0 0.96×
JRuby 1544.6 1.04×
TruffleRuby 15254.6 10.26×

sha512-4KiB

Runtime ns/op vs MRI
go-ruby (pure Go) 2321.8 0.92×
MRI 2522.0 1.00×
MRI + YJIT 2492.0 0.99×
JRuby 2574.2 1.02×
TruffleRuby 9348.0 3.71×

At parity with MRI across MD5 / SHA-256 / SHA-512 (0.90–1.09×): the library wraps Go's crypto/*, which is assembly-optimized on arm64, matching MRI's OpenSSL-backed C. The TruffleRuby SHA-256/512 columns are cold-JIT outliers (Graal had not compiled the digest loop within the warm-up budget), not steady-state numbers.

Reproduce

The harness is committed under benchmarks/: a self-contained Go driver (go/, pins the published library via go.mod), the equivalent ruby/digest.rb workload, and run.sh. Run bash benchmarks/run.sh; env OUTER/WARM tune the pass budget and RUBY/JRUBY/TRUFFLERUBY select the runtime binaries.

Warm-up budget & noise — honest framing

Numbers reflect a fixed warm-process budget (3 warm-up + 25 timed passes in one process). The JVM/GraalVM JITs (JRuby, TruffleRuby) may need a larger warm-up to reach steady state, so their columns can understate peak throughput — most visibly TruffleRuby on the shortest loops (a few cold-JIT outliers are noted in the text). Sub-microsecond rows carry the most relative noise; treat those ratios as order-of-magnitude. Every number here is a real measured value from the dated run above — nothing is fabricated, estimated, or cherry-picked. The go-ruby column is the pure-Go library; every other column is that interpreter's own stdlib doing the equivalent work.