Benchmarks (vs Meltano)

Honest, reproducible evidence for the “built for throughput” claim. Every number below comes from BENCHMARKS.md — identical workloads, one machine (Apple M3 Pro, 12 cores, 18 GiB RAM), 1M rows, seed 42, median of 5 timed runs. faucet-stream is compared against Meltano (the most common Singer runtime).

Read the caveats first

This measures single-machine batch throughput of three specific moves. It does not measure distributed throughput, connector breadth, or correctness. The CSV→JSONL figure is a best case (upper bound), not the typical case — see vs. Meltano and BENCHMARKS.md for the full methodology, hardware capture, and the Postgres-row measurement caveat.

~80×

faster on CSV → JSONL (best case, parse-bound)

~10×

faster on a realistic DB → DB move (sink-bound)

~40×

less peak memory (20 vs 819 MiB)

1:1

exact row-count parity, every scenario

faucet-stream Meltano (Singer)

Throughput — rows/second (higher is better)

1,000,000 rows. Meltano's bar is a sliver on purpose — that is the result.

A — CSV → JSONL · parse-bound (best case)

faucet

700,574

Meltano

8,698

B — Postgres → JSONL · typed row decode

faucet

191,124

Meltano

7,931

C — Postgres → Postgres · sink-bound (the realistic move)

faucet (COPY)

117,147

Meltano

12,186

Peak memory — MiB (lower is better)

Here faucet is the sliver: bounded-memory streaming holds flat while Meltano buffers.

A — CSV → JSONL

faucet

20.1

Meltano

818.8

B — Postgres → JSONL

faucet

21.7

Meltano

621.0

C — Postgres → Postgres

faucet

33.8

Meltano

690.3

The gap collapses as the workload gets more I/O-bound

Speed-up multiplier (faucet ÷ Meltano). The best case is not the typical case.

A · parse-bound

~80×

B · row decode

~24×

C · sink-bound

~10×

Show the raw numbers as a table

Scenario	Bottleneck	faucet (rows/s)	Meltano (rows/s)	Gap	faucet RSS (MiB)	Meltano RSS (MiB)
A — CSV → JSONL	parse/serialize (best case)	700,574	8,698	~80×	20.1	818.8
B — Postgres → JSONL	typed row decode	191,124	7,931	~24×	21.7	621.0
C — Postgres → Postgres	destination write (sink-bound)	117,147 (COPY)	12,186	~10×	33.8	690.3

All runs: 1,000,000 rows, seed 42, exact row-count parity (1,000,000 = 1,000,000). Measured 2026-10-16 against Meltano 4.4.1; earlier runs are in the History section of BENCHMARKS.md.

How to reproduce

The harness never fabricates a number — a tool that won’t install/run is recorded as such, not faked. Regenerate everything on your own hardware:

make bench            # Scenario A (CSV → JSONL, 1M rows) — no infra
make bench-smoke      # 100k-row smoke run
make bench-postgres   # adds Scenarios B & C (needs Docker)

Results land in benchmarks/results/. One independent confirmation on your own hardware is worth more to this project than a new connector — open an issue or PR with your output, especially if faucet does not win. See benchmarks/README.md and Performance tuning for the levers behind these numbers.