Timelaketimelake
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Apache 2.0 ·
Roadmap

DoneIn progressPlanned

Tables and storage

Done:1. Iceberg tablesDone:2. CommitsDone:3. Object-store catalogIn progress:4. Time-series table contractIn progress:5. File layout and encodingsIn progress:6. Last-value indexDone:7. One-request table state and version checksDone:8. Table format versions and upgradesDone:9. SettingsPlanned:10. Supplemental timestamps

Writing

Done:11. AppendsDone:12. Late data and dedup on readDone:13. Writes from WebAssemblyPlanned:14. Hot tier and seconds-fresh readsPlanned:15. BackfillIn progress:16. Import and migrationPlanned:17. DeletesPlanned:18. GDPR erasure

Maintenance

Done:19. CompactionIn progress:20. Maintenance runs, retention and expiry

Reading

In progress:21. Timelake readerDone:22. Block cacheDone:23. Filter pushdownDone:24. Pruning with long IN listsDone:25. Newest-first scansDone:26. Scans in series orderDone:27. Materialized rollups

SQL functions

In progress:28. Function libraryDone:29. Time buckets and gap fillingDone:30. Signal aggregatesDone:31. Downsampling with m4

Integrations

Planned:32. REST catalogPlanned:33. DuckDB extensionPlanned:34. Snowflake connectionPlanned:35. Multitenancy

Services

Planned:36. Ingestion and rulesPlanned:37. Data to eventsPlanned:38. Static read modelsPlanned:39. Computed metricsPlanned:40. Agent access with permission setsPlanned:41. Device syncPlanned:42. Edge gatewayPlanned:43. Server mode

Benchmarks

In progress:44. Benchmark harness: timebenchIn progress:45. Industrial IoT benchmarkPlanned:46. Agent benchmark

Background

ArchitectureDeployment and cost modelEmbedded engine: DataFusionLandscape and positioningLicense and open corePurpose and requirementsReleases
TimelaketimelakeThe open time-series lakehouse.
v0 · Apache 2.0 · Contribute · GitHubBuilt on Apache Arrow, Apache DataFusion, Apache Parquet, and Apache Iceberg.
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Background

Deployment and cost model

Summary

The minimum deployment is a bucket, a catalog and the library in the user's app, plus scheduled maintenance; no always-on Timelake server. Cost comes down through fewer, bigger files, rollups and tiered retention.

Design

  • Minimum deployment: One bucket, one Iceberg catalog, and the Timelake library inside the user's app or one ingest container, plus a scheduled timelake-maintain. There is no always-on Timelake server.
  • Catalog options:
    • timelake-catalog (recommended), in one of three forms:
      • In-process library (single node / edge)
      • Scale-to-zero function (Lambda / Cloud Run)
      • Container Object-storage backend by default; Postgres optional.
    • Bring your own: S3 Tables, Glue, Lakekeeper, Polaris, Nessie, Unity, with the reduced capabilities listed in spec 32.
  • Cost levers: Fewer, bigger files (lower GET/PUT costs); rollups instead of raw scans; tiered retention; optional cold storage classes for old partitions.

Open questions and issues

  • #82 Spike: push new table versions and recent rows to subscribed readers
  • #87 Spike: serve readers in several regions from one bucket
  • #89 Research: tiered reads, pushed freshness and many regions: tracking issue
  • #93 Docs: cost on each cloud, measured where we can

History

  • 2026-10-11: specified (SPEC.md §15)
  • 2026-10-19: moved from SPEC.md into its own spec.
  • 2026-10-19: was spec 28; a background page since the specs were regrouped one per feature.