TimescaleDB in 2024: Making Postgres Faster
Blog post from Tiger Data
The year 2024 was marked by significant advancements in Postgres for AI, with the launch of performance-boosting extensions like pgvectorscale and pgai. TimescaleDB's time-series capabilities also evolved dramatically, enabling faster real-time analytics. Key features introduced or improved include chunk skipping, which allows queries to exclude certain chunks, reducing data access; continuous aggregates (CAggs), which automatically materialize queries in the background for faster results; hypercore, a hybrid row-columnar storage engine with enhancements like segment_by and order_by settings; and support for foreign keys on hypertables. These features aim to optimize query performance, reduce storage footprint, and make real-time analytics more efficient.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Real-time | 13 | 4,354 | 979 | 240 | +27% |
| Developer Experience | 1 | 453 | 188 | 105 | +32% |
| LLM | 1 | 4,587 | 525 | 176 | +56% |
| RAG | 1 | 2,188 | 259 | 95 | +39% |
| Vector Search | 1 | 2,869 | 338 | 116 | -34% |
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