C++ is heavily customized C. The heavy customization make Redshift a columnar database and more ideal for querying large amounts of data quickly. How does Timescale help Postgres in this area?
Timescale is built around a concept they call "hypertables", which automatically partition data into a set of smaller tables segmented by time range. Timescale exposes the time-series data as if it was a single table, but behind the scenes is managing queries against the individual table partitions and automatically creating new partitions as data is inserted.
By tuning the chunk sizes so their data fits in memory, many common queries gain a lot of efficiency. It's built around some assumptions of time-series data: Most inserts and queries are for recent data and are generally ordered.
I've had great experience with TimescaleDB for small-medium time-series loads such as sensor or analytics data; I've found it's pretty plug-and-play and have used it to store tables with ~1B time-series rows of geospatial data, sensor values, etc.
chrisdalke|3 years ago
By tuning the chunk sizes so their data fits in memory, many common queries gain a lot of efficiency. It's built around some assumptions of time-series data: Most inserts and queries are for recent data and are generally ordered.
I've had great experience with TimescaleDB for small-medium time-series loads such as sensor or analytics data; I've found it's pretty plug-and-play and have used it to store tables with ~1B time-series rows of geospatial data, sensor values, etc.