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Changelog

All notable changes to LakeTS are documented in this file.

Format follows Keep a Changelog, versioning follows Semantic Versioning.

[Unreleased]​

[0.1.0] - 2026-06-05​

Initial public release. LakeTS turns Databricks Lakebase (PostgreSQL 17+) into a time-series database — pure PL/pgSQL, no custom extensions.

Added​

  • ChronoTables — automatic time-based partitioning via create_chronotable(), plus multi-metric (tag + field) tables via create_metric_table().
  • Time-series functions — time_bucket, time_bucket_gapfill, first/last aggregates, locf, interpolate, delta, rate, and histogram.
  • RollUp engine — incremental aggregates with watermark-driven refresh, per-bucket invalidation tracking, chunk-skip pruning, and DAG-ordered cascade refresh.
  • Last Value Cache — sub-10ms latest-state lookups via enable_lvc().
  • Lifecycle — policy-driven tiering that flags chunks once durable in Unity Catalog, and retention that drops Lakebase partitions only after CDF confirms durability (with a force override).
  • Unity Catalog sync — enable_sync() mirrors ChronoTables and RollUps to Unity Catalog through Lakebase CDF via an unpartitioned shadow table.
  • Alerts — SQL-native alert_check() and alert_deadman().
  • Bulk ingest — ingest_batch() for JSONB arrays and ingest_prometheus().
  • Monitoring — Prometheus-compatible metrics, chunk_health(), and a Databricks AI/BI dashboard.
  • Databricks jobs — serverless maintenance for partitioning, RollUp refresh, tiering, and retention.