Timescale Documentation

Time-series data represents how a system, process, or behavior changes over time. For example, if you are taking measurements from a temperature guage every five minutes, you are collecting time-series data. Another common example is stock price changes, or even the battery life of your smart phone. As these measurements change over time, each data point is recorded alongside its timestamp, allowing it to be measured, analyzed, and visualized.

Time-series data can be collected very frequently, such as financial data, or infrequently, such as weather or system measurements. It can also be collected regularly, such as every millisecond or every hour, or irregularly, such as only when a change occurs.

Databases have always had time fields, but using a special database for handling time-series data can make your database work much more effectively. Specialized time-series databases, like Timescale, are designed to handle large amounts of database writes, so they work much faster. They are also optimized to handle schema changes, and use more flexible indexing, so you don't need to spend time migrating your data whenever you make a change.

Time-series data is everywhere, but there are some environments where it is especially important to use a specialized time-series database, like Timescale:

• Monitoring computer systems: virtual machines, servers, container metrics, CPU, free memory, net/disk IOPs, service and application metrics such as request rates, and request latency.
• Financial trading systems: securities, cryptocurrencies, payments, and transaction events.
• Internet of things: data from sensors on industrial machines and equipment, wearable devices, vehicles, physical containers, pallets, and consumer devices for smart homes.
• Eventing applications: user or customer interaction data such as clickstreams, pageviews, logins, and signups.
• Business intelligence: Tracking key metrics and the overall health of the business.
• Environmental monitoring: temperature, humidity, pressure, pH, pollen count, air flow, carbon monoxide, nitrogen dioxide, or particulate matter.

Hypertables are PostgreSQL tables that automatically partition your data by time. You interact with hypertables in the same way as regular PostgreSQL tables, but with extra features that makes managing your time-series data much easier.

In Timescale, hypertables exist alongside regular PostgreSQL tables. Use hypertables to store time-series data. This gives you improved insert and query performance, and access to useful time-series features. Use regular PostgreSQL tables for other relational data.

With hypertables, Timescale makes it easy to improve insert and query performance by partitioning time-series data on its time parameter. Behind the scenes, the database performs the work of setting up and maintaining the hypertable's partitions. Meanwhile, you insert and query your data as if it all lives in a single, regular PostgreSQL table.

For more information about Timescale hypertables, see Using Timescale

Time-series data usually grows very quickly. And that means that aggregating the data into useful summaries can become very slow. Continuous aggregates makes aggregating data lightning fast.

If you are collecting data very frequently, you might want to aggregate your data into minutes or hours instead. For example, if you have a table of temperature readings taken every second, you can find the average temperature for each hour. Every time you run this query, the database needs to scan the entire table and recalculate the average every time.

Continuous aggregate views are refreshed automatically in the background as new data is added, or old data is modified. Timescale tracks these changes to the dataset, and automatically updates the view in the background. This does not add any maintenance burden to your database, and does not slow down INSERT operations.

By default, querying continuous aggregates provides you with real-time data. Pre-aggregated data from the materialized view is combined with recent data that hasn't been aggregated yet. This gives you up-to-date results on every query.

For more information about Timescale continuous aggregates, see Using Timescale

Compressing your time-series data allows you to reduce your chunk size by more than 90%. This saves on storage costs, and keeps your queries operating at lightning speed.

When you enable compression, the data in your hypertable is compressed chunk by chunk. When the chunk is compressed, multiple records are grouped into a single row. The columns of this row hold an array-like structure that stores all the data. This means that instead of using lots of rows to store the data, it stores the same data in a single row. Because a single row takes up less disk space than many rows, it decreases the amount of disk space required, and can also speed up your queries.

For more information about Timescale compression, see Using Timescale