OpenSearch Dashboards
Three releases in a year, all of them about the Vega dependency — the feed shows nothing else.
Comparison · Analytics
TimescaleDB vs Citus
A side-by-side editorial comparison of TimescaleDB and Citus — release velocity, themes, recent moves, and the top alternatives to consider.
TimescaleDB vs Citus: at a glance
What is TimescaleDB?
Every release in this window is columnstore work — compression is where TimescaleDB is spending
TimescaleDB is on a roughly two-week cadence and the releases are dominated by one subsystem. 2.28.0 made first() and last() far cheaper on compressed data by deriving the aggregates straight from columnstore batch metadata rather than decompressing. 2.29.0 added chunk exclusion for DML, so UPDATE and DELETE on hypertables take row exclusive locks only on the chunks actually being modified. The patch releases in between are almost entirely columnar correctness: wrong results from functions returning NULL in the columnar execution pipeline, sort transformation errors on negative constants, column ordering on first/last sparse indexes, incompatible smallint bloom filters, and crashes grouping by columns absent from the SELECT list under vectorized aggregation.
What is Citus?
Citus keeps three Postgres branches alive while chasing each new major
Citus publishes synchronized maintenance releases across at least three live branches — 14.1, 13.3 and 12.1.13 all went out on the same day in July 2026 — with contents that are almost entirely backports of the same handful of upstream fixes. The substantive release in the window is Citus 14.0 from February, which added PostgreSQL 18.1 support. Release titles are inconsistent, several carrying release dates that contradict their publication date.
TimescaleDB vs Citus: editorial side-by-side
TimescaleDB
5.0ANALYTICS
Every release in this window is columnstore work — compression is where TimescaleDB is spending
◆ Current state
TimescaleDB is on a roughly two-week cadence and the releases are dominated by one subsystem. 2.28.0 made first() and last() far cheaper on compressed data by deriving the aggregates straight from columnstore batch metadata rather than decompressing. 2.29.0 added chunk exclusion for DML, so UPDATE and DELETE on hypertables take row exclusive locks only on the chunks actually being modified. The patch releases in between are almost entirely columnar correctness: wrong results from functions returning NULL in the columnar execution pipeline, sort transformation errors on negative constants, column ordering on first/last sparse indexes, incompatible smallint bloom filters, and crashes grouping by columns absent from the SELECT list under vectorized aggregation.
◆ Where it's heading
The compression layer is no longer a storage option bolted onto hypertables — it is being turned into a full query path, with its own aggregate pushdowns, sparse indexes, bloom filters and vectorized execution. The bug pattern confirms how new that path still is: several patches fix wrong results rather than crashes, which is what a young execution engine produces as it meets real query shapes. The DML chunk-exclusion work in 2.29.0 shows the other half of the effort, reducing the lock footprint of writes so compressed hypertables stay usable under mutation, not just under read.
◆ Prediction
Given that every release in this window touches the columnstore and several fix correctness rather than performance, the next releases should continue hardening that path — more vectorized-aggregation and sparse-index fixes alongside further pushdowns. The entries give no signal of work outside compression.
Citus
0.0ANALYTICS
Citus keeps three Postgres branches alive while chasing each new major
◆ Current state
Citus publishes synchronized maintenance releases across at least three live branches — 14.1, 13.3 and 12.1.13 all went out on the same day in July 2026 — with contents that are almost entirely backports of the same handful of upstream fixes. The substantive release in the window is Citus 14.0 from February, which added PostgreSQL 18.1 support. Release titles are inconsistent, several carrying release dates that contradict their publication date.
◆ Where it's heading
The pattern is a distributed-Postgres extension whose roadmap is set by Postgres itself: a major Citus version tracks a major Postgres version, then a long tail of branch releases carries fixes backward to users who cannot upgrade. Recent backport content is concentrated on correctness in edge cases — role propagation with missing grantor dependencies, deadlocks when adding named constraints with long partition names, crashes in CREATE STATISTICS — plus tightening ownership checks on citus-internal UDFs.
◆ Prediction
Expect the next Citus major to line up with the next PostgreSQL major, with 12.1 continuing to receive backports until it is retired. The entries do not indicate which branch is nearest end-of-life.
Alternatives to TimescaleDB and Citus
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either TimescaleDB or Citus.
Apache Kylin
Kylin ships once a year with empty release notes and a native engine nobody is being told about.
Apache Iceberg
Iceberg's release cadence is now backports and CVE patches across three live minor lines.
Mage
Feature releases every two months in 2024; one bugfix release in the last twelve.
OpenMetadata
MCP servers became first-class governed assets in 1.13.0 — and 2.0 is now in release candidate.
Polars
The streaming engine is stable and the API is being narrowed — Polars is clearing ground for a breaking release
See all TimescaleDB alternatives → · See all Citus alternatives →
Recent activity from TimescaleDB and Citus
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3d agoTimescaleDBChunk exclusion narrows UPDATE and DELETE locking on hypertables
- 17d agoTimescaleDBColumnar pipeline NULL and sort-key correctness fixes
- 1mo agoCitusCitus 14.1 backport release
- 1mo agoCitusDeadlock, crash and role-propagation fixes for 12.1
- 1mo agoCitusCitus 13.3 backport release
- 1mo agoTimescaleDBMigration and sparse-index fixes after 2.28.1
- 1mo agoTimescaleDBCrash and constraint-enforcement fixes on compressed tables
- 1mo agoTimescaleDBfirst() and last() answered from columnstore metadata
- 2mo agoTimescaleDBVectorized aggregation grouping correctness fixes
- 5mo agoCitusCitus 14.0 adds PostgreSQL 18.1 support ⚡
Frequently asked questions
What is the difference between TimescaleDB and Citus?
They serve adjacent needs but don't currently overlap on shipped themes. TimescaleDB is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is TimescaleDB better than Citus?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. TimescaleDB is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to TimescaleDB?
Top TimescaleDB alternatives in Analytics are ranked by recent ship velocity. Browse the "TimescaleDB alternatives" section above for the current picks, or visit /alternatives/timescaledb for the full list with editorial commentary on each.
What are the best alternatives to Citus?
Top Citus alternatives in Analytics are ranked by recent ship velocity. Browse the "Citus alternatives" section above for the current picks, or visit /alternatives/citus for the full list with editorial commentary on each.