Timelaketimelake
DocsBenchmarksRoadmapSpecContribute
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.
DocsBenchmarksRoadmapSpecContribute

Spec 23 · Reading

Filter pushdown

DoneUpdated 2026-10-17View on GitHub
Versions
0.7.0
Roadmap
Fast first queries on object storage
Related
21. Timelake reader, 22. Block cache

Summary

Late materialization: the reader applies filters while decoding Parquet and decodes the other columns only for rows that pass. It helps on local disk and costs a round trip per row group on object storage, so the default turns it on for local files only.

Design

  • Setting filter-pushdown (auto, true, false; table and session). auto, the default, turns it on for local files and off on object storage. Timelake's scans don't follow DataFusion's datafusion.execution.parquet.pushdown_filters (0.7.0, breaking).
  • Measured (2026-10-17, bench/results/PERFORMANCE.md, P8): TSBS geomeans 4–5% faster on local disk, selective group-bys up to 40%; first queries 12–17% slower at 20 ms per request. The Phase 0 baseline found the same direction in plain DataFusion: 1.2–1.6× slower on object storage (extra range requests), slightly faster locally (spec 44).

Open questions and issues

  • #75 Query: re-measure Parquet filter pushdown (late materialization)

History

  • 2026-10-17: built and measured (P8)
  • 2026-10-19: moved from SPEC.md into spec 20, then into this spec when the specs were regrouped one per feature.