lakehq/sail
Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads. observed · 2026-08-28
Health v2 · maintenance only
93/100
- Activity 99
- Release rhythm 99
- Longevity 70
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 14
- age_days: 986
- days_rel: 9
- days_push: 7
- n_releases_24m: 28
Adoption not part of the score
3333 stars · 210 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Sail is an open-source, Rust-native multimodal compute engine that serves as a drop-in replacement for Apache Spark, unifying batch processing, stream processing, and compute-intensive AI workloads. It is compatible with the Spark Connect protocol, so existing PySpark and Spark SQL code runs unchanged, with no JVM overhead, and supports Delta Lake and Apache Iceberg lakehouse formats.
Use cases
- replace Apache Spark with a faster Rust engine without rewriting PySpark code
- run Spark SQL and DataFrame workloads with no JVM overhead
- unify batch and stream processing on a single compute engine
- run compute-intensive AI and machine learning workloads on a lakehouse
- query Delta Lake and Apache Iceberg tables natively
- deploy distributed data processing on Kubernetes
- cut infrastructure costs for large-scale Spark deployments
When to choose
- you have existing PySpark or Spark SQL code and want better performance without rewrites
- you want to avoid JVM tuning, slow startup, and memory overhead
- you need a lakehouse engine supporting Delta Lake and Iceberg
- you want to run batch, streaming, and AI workloads on one engine
- you want to self-host a Spark-compatible engine or deploy on Kubernetes
When to avoid
- you rely on Spark features or third-party libraries not yet supported by Sail's compatibility layer
- you need the full mature ecosystem and long-term battle-tested stability of Apache Spark
- your workloads depend on JVM-specific integrations or custom Scala/Java UDFs
- you need guaranteed enterprise support without a commercial plan
Facets
framework · maturity active
etl streaming machine-learning data-science big-data microservices machine-learning artificial-intelligence rust python cloud self-hosted cli apache-spark spark-connect pyspark datafusion arrow delta-lake apache-iceberg lakehouse parquet distributed-computing batch-processing stream-processing no-jvm tpc-h clickbench sql data-engineering kubernetes docker
6 sources
- readme: https://github.com/lakehq/sail · fetched 2026-08-28 · a3d5abef5703
- homepage: https://lakesail.com · fetched 2026-08-29 · 8b933ba724c3
- site_page: https://docs.lakesail.com · fetched 2026-08-29 · 8bafaca71566
- site_page: https://docs.lakesail.com/platform · fetched 2026-08-29 · d9fc6bf28231
- site_page: https://lakesail.com/about · fetched 2026-08-29 · 0c9e6e3d26a1
- site_page: https://lakesail.com/pricing · fetched 2026-08-29 · 320d198ed7aa
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| lakehq/sail | main | 93 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem