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 | 4,520 | 1,011 | 247 | +25% |
| Developer Experience | 1 | 458 | 191 | 107 | +30% |
| LLM | 1 | 4,654 | 532 | 180 | +51% |
| RAG | 1 | 2,191 | 260 | 96 | +39% |
| Vector Search | 1 | 2,885 | 343 | 120 | -34% |
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