RawDuck Benchmark

August 22, 2026 · View on GitHub

Primary workload: OTEL telemetry (OTLP/JSON logs, metrics, traces). GH Archive is a wide-schema stress test in the appendix.

All published numbers: DuckDB v1.5.5, default RawDuck settings, Apple M3 Ultra (32 cores, 512 GiB) unless noted. Cold ingest = first raw_ingest_file in a fresh database; warm = second ingest in the same process after DELETE (columns_added = 0). Report the best of N sessions unless noted. See scripts/benchmark/README.md for metric definitions.

Harness

GEN=ninja make release

# OTEL bulk ingest (NDJSON file)
./scripts/benchmark/run_otel.sh --quick
./scripts/benchmark/run_otel.sh --records 1000000 --runs 5

# OTEL streaming ingest (OpenTelemetry SDK → raw_serve HTTP)
./scripts/benchmark/run_otel_streaming.sh --quick
./scripts/benchmark/run_otel_streaming.sh --workers 16 --spans-per-worker 60000

# VARIANT vs RawDuck (same trace dataset)
./scripts/benchmark/run_variant.sh --quick
./scripts/benchmark/run_variant.sh --records 1000000 --runs 3

OTEL bulk ingest

1,000,000 records per signal, OTLP/JSON export envelopes (collector POST bodies), best of 5 sessions:

signal	records	source NDJSON	cold ingest	records/s	throughput
traces	1,000,000	435 MB	1.19 s	841k	366 MB/s
logs	1,000,000	294 MB	0.87 s	1.15M	338 MB/s
metrics	1,000,000	353 MB	1.13 s	889k	314 MB/s

3M telemetry records in 3.2 s (~940k records/s average). Warm ingest matches cold within ~2% on each signal.

Query speed (1,000,000 spans)

Same spans — shredded typed columns vs one JSON object per span (->>), best of 3 runs:

query	JSON `->>`	RawDuck	speedup
error count by service (`status>=500`)	39 ms	1.5 ms	26×
p99 latency by route	99 ms	3.2 ms	31×
status-code distribution	35 ms	2.5 ms	14×
storage	143 MB	39.5 MB	3.6× smaller

Reproduce

./scripts/benchmark/run_otel.sh --records 1000000 --runs 5
./scripts/benchmark/run_variant.sh --records 1000000 --runs 3   # queries + storage above

OTEL streaming ingest

Real OTLP/HTTP protobuf traffic via the OpenTelemetry Python SDK into raw_serve() (concurrent exporter processes, not bulk NDJSON):

workers	spans	wall	spans/s
4	20,000	0.75 s	27k
16	960,000	3.85 s	250k

rows_ingested must equal total_spans_sent (checked by the harness).

Reproduce

./scripts/benchmark/run_otel_streaming.sh --workers 16 --spans-per-worker 60000

First run creates benchmark/work/otel-streaming-venv (OpenTelemetry SDK dependency).

VARIANT vs RawDuck (DuckDB v1.5.5)

Same 1,000,000 OTLP/JSON trace spans. Paths:

path	definition
RawDuck	`raw_ingest_file(..., transform := 'otlp-traces')` → typed columns
VARIANT OTLP	SQL unnest → one `VARIANT` `{resource, span}` per span (KeyValue arrays kept)
JSON OTLP	same exploded shape as `JSON`
VARIANT-flat	`to_json(traces)::VARIANT` of shredded RawDuck rows (encode/query only)
JSON-flat	same shredded rows as `JSON`, queried with `->>`

Disk = used_blocks × block_size after cold CHECKPOINT. Parenthetical file size is after warm re-ingest (includes free-list holes; not comparable across paths). VARIANT requires STORAGE_VERSION 'v1.5.0'.

Ingest + storage (1,000,000 spans)

path	M3 Ultra	Spark GB10 aarch64 (`--threads 8`)
RawDuck	0.99 s · 1.01M rec/s · 39.5 MB (108 MB file)	1.18 s · 850k rec/s · 35.5 MB (91 MB file)
VARIANT-flat	encode · 35.8 MB	encode · 39.5 MB
VARIANT OTLP	11.96 s · 84k · 53.5 MB (106 MB file)	7.96 s · 126k · 54.5 MB (114 MB file)
JSON-flat	encode · 143 MB	encode · 142 MB
JSON OTLP	4.72 s · 212k · 241 MB (484 MB file)	5.71 s · 175k · 242 MB (484 MB file)

Spark GB10: 20 cores, 122 GiB, Linux aarch64.

Queries (best of 3, ms)

encoding	M3 Ultra	Spark GB10
	errors / p99 / status	errors / p99 / status
RawDuck	1.5 / 3.2 / 2.5	1.3 / 4.9 / 5.1
JSON-flat	39 / 99 / 35	65 / 136 / 63
JSON OTLP positional	213 / 303 / 193	253 / 415 / 215
JSON OTLP key lookup	344 / 418 / 304	397 / 580 / 366
VARIANT-flat	436 / 1225 / 416	700 / 1988 / 696
VARIANT OTLP positional	1227 / 3479 / 1162	2013 / 5947 / 1852
VARIANT OTLP key lookup	1493 / 3658 / 1404	2107 / 5996 / 2049

Reproduce

./scripts/benchmark/run_variant.sh --records 1000000 --runs 3
./scripts/benchmark/run_variant.sh --records 1000000 --runs 3 --threads 8   # many-core ARM

Appendix: GH Archive (wide-schema stress test)

One hour of GH Archive data — 247,199 events / 956 MB NDJSON / 914 columns. Apple Silicon, 10 cores, DuckDB v1.5.5:

	JSON column	RawDuck
count by event type	231 ms	1 ms	231×
top repos by pushes	268 ms	3 ms	89×
distinct repos per actor	457 ms	10 ms	46×
sum of push payload sizes	265 ms	1 ms	265×
events per minute	236 ms	3 ms	79×
cold ingest	1.4 s	~13 s	one-time cost
warm re-ingest	—	~4.9 s	steady state
storage	1.05 GB	636 MB	40% smaller

Reproduce

curl -sL https://data.gharchive.org/2024-01-15-10.json.gz -o gh.json.gz

CALL raw_ingest_file('gh_events', 'gh.json.gz');
CHECKPOINT;

Pitfalls

Split NDJSON on \n only (not splitlines() — \u2028/\u2029 appear in strings).
Use .timer on via duckdb -f script.sql (not duckdb -c).
Shallow duckdb clones without tags report v0.0.1; fetch tag v1.5.5.