Arc – high-throughput time-series warehouse with DuckDB analytics
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 &…
In plain words
Arc is a time-series data warehouse that combines fast data ingestion with analytical query capabilities. It accepts data through a binary MessagePack API or the standard Line Protocol format, stores information as Parquet files with hourly partitions, and enables SQL queries via DuckDB. Designed for teams managing time-series data, Arc addresses the typical trade-off between ingestion speed and analytical performance, delivering high-throughput writes alongside complex query support without sacrificing either capability.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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!
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