Time-Series and Observability: PostgreSQL Stats to Embedded TS Engines

Time-series and observability are colliding in the database stack. Pg_statviz 0.8 now ships with PostgreSQL 18 support, delivering timeseries analysis of Postgres internal stats ^[1].

Pg_statviz and Postgres Observability That makes Postgres even more friendly to observability workflows. The tool tracks internal stats as time-series data, nudging teams toward richer Postgres-native visibility ^[1].

Dimension-DB: Embedded TS and SQL Acceleration Meet Dimension-DB, an embedded, pure-Java time-series/columnar DB with local storage and SQL acceleration ^[2]. It runs on Berkeley DB Java Edition as its storage backend and uses adaptive columnar storage that toggles between RAW, dictionary-encoded ENUM, or run-length-encoded HISTOGRAM on-the-fly for compression and speed ^[2]. It can also act as a read-only query accelerator for external databases via JDBC, auto-generating efficient SQL for time-series queries when pointed at a PostgreSQL, Oracle, or ClickHouse table ^[2]. In a NYC Taxi benchmark, it sustained ingest at roughly 55,000 rows/sec and showed strong gains on complex aggregations ^[2].

CDC/Streaming Time-Series Pipelines On the streaming side, Supermetal centers on transactional databases data replication and CDC, illustrating how streaming time-series pipelines take shape in practice ^[3].

Bottom line: observability is expanding in PostgreSQL, but Dimension-DB and Supermetal show how embedded time-series engines and CDC-first pipelines can coexist with traditional Postgres workloads ^[3].

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