This Video Shows How to Speed Up PostgreSQL Queries by 7x

Are your PostgreSQL queries taking too long to execute? 

Building demanding applications on PostgreSQL—such as applications with real-time dashboards—requires scanning huge datasets. 

That can slow down queries—but not if you use Timescale’s chunk–skipping indexes. 

Watch the chunk-skipping indexes video

This 2.5-minute video shows how chunk-skipping indexes in TimescaleDB can speed up your time-series queries by up to 7x in PostgreSQL. Rather than scanning everything, PostgreSQL can intelligently prune irrelevant data, making real-time analytics more efficient.

Chunk-skipping indexes are a TimescaleDB-specific feature and apply to hypertables, which automatically partition your data into smaller partitions or chunks. (You must use TimescaleDB’s hypertables to benefit from this optimization). 

Timescale's row-columnar engine, hypercore, already delivers excellent performance, balancing efficient writes with powerful compression for reads, and chunk-skipping indexes take that performance even further.

The video walks through:

• What chunk-skipping indexes are
• How they work in TimescaleDB
• How to enable them
• Considerations to keep in mind

A new level of "fast" for query performance speed

What's new with chunk-skipping indexes is that previously, we only used chunk-skipping for partition columns, but with chunk-skipping indexes, we store the min/max values for non-partition columns as well, allowing queries to skip irrelevant chunks even if the query is not using a partition column.

Defining a chunk-skipping index takes query performance to a whole new level—faster than anything we could achieve before with PostgreSQL (even with that index). Plus, it comes with considerable storage savings! 💰In this case, 7x better performance while using 87% less storage, thanks to compression. - Source

Best of all, you can implement this optimization with minimal effort—just a couple of SQL commands to create specialized indexes on your hypertables. 

This optimization is particularly beneficial for any workload involving large volumes of time-series data across many devices or sources. The performance gains become even more significant as your data volume grows. They don't come at the expense of tradeoffs, leaving developers free to power the real-time, customer-facing dashboards and build the AI agents that depend on real-time analytics to drive critical functionality.

Experience the query speed difference

You can try this indexing technique in TimescaleDB today and test the query speed difference for your use case. Timescale Cloud is the easiest way to get started.

For the technical deep dive on this feature, read how chunk-skipping indexes boost PostgreSQL performance by 7x.