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Show HN: Arc – high-throughput time-series warehouse with DuckDB analytics

(github.com)
29 points ignaciovdk | 2 comments | 07 Oct 25 16:40 UTC | HN request time: 0.55s | source

Hi HN, I’m Ignacio, founder at Basekick Labs.

Over the past months I’ve been building Arc, a time-series data platform designed to combine very fast ingestion with strong analytical queries.

What Arc does? Ingest via a binary MessagePack API (fast path), Compatible with Line Protocol for existing tools (Like InfluxDB, I'm ex Influxer), Store data as Parquet with hourly partitions, Query via DuckDB engine using SQL

Why I built it:

Many systems force you to trade retention, throughput, or complexity. I wanted something where ingestion performance doesn’t kill your analytics.

Performance & benchmarks that I have so far.

Write throughput: ~1.88M records/sec (MessagePack, untuned) in my M3 Pro Max (14 cores, 36gb RAM) ClickBench on AWS c6a.4xlarge: 35.18 s cold, ~0.81 s hot (43/43 queries succeeded) In those runs, caching was disabled to match benchmark rules; enabling cache in production gives ~20% faster repeated queries

I’ve open-sourced the Arc repo so you can dive into implementation, benchmarks, and code. Would love your thoughts, critiques, and use-case ideas.

Thanks!

1. whalesalad ◴[07 Oct 25 19:05 UTC] No.45507372[source]▶
> Arc Core is designed with MinIO as the primary storage backend

Noticing that all the benchmarking is being done with MinIO which I presume is also running alongside/locally so there is no latency and it will be roughly as fast as whatever underlying disk its operating from.

Are there any benchmarks for using actual S3 as the storage layer?

How does Arc decide what to keep hot and local? TTL based? Frequency of access based?

We're going to be evaluating Clickhouse with this sort of hot (local), cold (S3) configuration soon (https://clickhouse.com/docs/guides/separation-storage-comput...) but would like to evaluate other platforms if they are relevant.

replies(1): >>45507538 #
2. ignaciovdk ◴[07 Oct 25 19:18 UTC] No.45507538[source]▶
Hey there, great questions.

The benchmarks weren’t run on the same machine as MinIO, but on the same network, connected over a 1 Gbps switch, so there’s a bit of real network latency, though still close to local-disk performance.

We’ve also tried a true remote setup before (compute around ~160 ms away from AWS S3). I plan to rerun that scenario soon and publish the updated results for transparency.

Regarding “hot vs. cold” data, Arc doesn’t maintain separate tiers in the traditional sense. All data lives in the S3-compatible storage (MinIO or AWS S3), and we rely on caching for repeated query patterns instead of a separate local tier.

In practice, Arc performs better than ClickHouse when using S3 as the primary storage layer. ClickHouse can scan faster in pure analytical workloads, but Arc tends to outperform it on time-range–based queries (typical in observability and IoT).

I’ll post the new benchmark numbers in the next few days, they should give a clearer picture of the trade-offs.