Obviously, you should choose hypertables for time-series data, and regular PostgreSQL tables for relational data. You can have both hypertables and regular PostgreSQL tables in the same database. They are automatically set up and maintained by the database, meanwhile you insert and read the data as if it stored in a single and standard PostgreSQL table. Each chunk is a partition based on a time constraint and only contains data from that time range. The hypertables are actually parent tables made of many regular PostgreSQL child tables, called “chunks”. Hypertables are designed to improve insert and query performance by partitioning time-series data on its time parameter. TimescaleDB works with a specific type of tables called hypertables. – To scale the the time-series data across many databases Horizontally-scalable multi-node operation.– Retention policies, reordering policies, compression policies, aso… – By maintaining a materialized view of aggregate time-series data to improve query performance – Compression according to data type (up to 97% storage reduction) – To improve performance by keeping latest data and indexes in memory TimescaleDB features have been designed specifically for time-series data management.

Some of them are particularly well adapted to work with TimescaleDB, I am thinking in particular of the well known PostGIS and pg_stat_statement. It’is packaged as an extension of PostgreSQL, bringing new capabilities and features for data management and analytics as well as new optimizations and mechanisms for storage and query planner/execution.īeing integrated to PostgreSQL as an extension allows TimescaleDB to take advantage of all the possibilities PostgreSQL offers in terms of data (data types, index types, schema, etc,…) but also of all the other available extensions. TimescaleDB is actually a relational database for time-series data. daily visitors on a blog)Įxample of the monthly orders quantity – including forecasts

Here are some other areas where this kind of data is used : IoT is thus one of the main use cases of time-series data, but it is obviously not the only one. The major reason is that we live surrounded by objects that are more and more connected and therefore have more and more sensors. Today, time-series data are everywhere and we can find them in several applications and across various industries and business. The first time-series databases were mainly used for storing financial data. In other words, time-series data is data that collectively represents how a system, process, or behavior changes over time.Īs you can imagine, time-series database are not new. Time-series data is a collection of metrics (regular) or measurements (irregular) that are tracked over time. This kind of databases are optimized for storing, manipulating and querying time-series data. TimescaleDB is a “time-series” database (TSDB).

Timescaledb postgresql 13