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timescale/timescaledb

📈 Trending★ 22,659CAudience · developerComplexity · 3/5ActiveLicenseSetup · hard

TLDR

PostgreSQL extension that makes storing and querying time-series data (sensor readings, metrics, logs) fast and efficient by automatically partitioning data and compressing historical records.

Mindmap

mindmap
  root((TimescaleDB))
    What it does
      Time-series storage
      Automatic partitioning
      Data compression
      SQL queries
    Key features
      time_bucket function
      Columnstore format
      PostgreSQL compatible
      Transparent chunking
    Use cases
      IoT sensor data
      Financial tick data
      Infrastructure monitoring
      Application logs
    Tech stack
      PostgreSQL
      C language
      SQL interface
    Who uses it
      DevOps teams
      Data engineers
      IoT developers

Things people build with this

USE CASE 1

Store and query millions of sensor readings from IoT devices with fast response times.

USE CASE 2

Analyze stock prices and financial tick data across years without database slowdown.

USE CASE 3

Monitor server performance metrics and application logs at scale with efficient compression.

USE CASE 4

Build real-time dashboards that aggregate timestamped events into hourly or daily summaries.

Tech stack

PostgreSQLCSQL

Getting it running

Difficulty · hard Time to first run · 1day+

Requires compiling C extension against PostgreSQL source, setting up a test database, and understanding extension installation procedures.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

TimescaleDB is a database extension for PostgreSQL, the popular open-source database, that makes it fast and efficient to store and query time-series data. Time-series data is any information recorded with a timestamp, like sensor readings from IoT devices, stock prices, server performance metrics, or application logs. Regular databases can handle this kind of data, but they tend to slow down significantly as the data grows because they were not designed for it. TimescaleDB solves this by transparently dividing your time-stamped data into time-based chunks behind the scenes, you write normal SQL queries and it handles the partitioning automatically. It also includes a columnstore feature that compresses data and reorganizes it to speed up analytical queries that scan large amounts of historical data. A special function called time_bucket lets you easily group data into intervals like "every hour" or "every day" with a single SQL command. Since it runs as an extension inside PostgreSQL, you do not need to learn a new database system or query language. Your existing PostgreSQL tools, clients, and knowledge carry over. You just get time-series-specific capabilities added on top. You would use TimescaleDB when you need to store and analyze large volumes of data that has a time dimension, IoT sensor streams, financial tick data, infrastructure monitoring, or any application that keeps growing a timestamped log. It is written in C.

Copy-paste prompts

Prompt 1
Show me how to set up TimescaleDB as a PostgreSQL extension and create a hypertable for storing sensor data with timestamps.
Prompt 2
Write a SQL query using time_bucket to group IoT sensor readings into 5-minute intervals and calculate the average temperature.
Prompt 3
How do I enable compression on a TimescaleDB hypertable to reduce storage for historical data older than 30 days?
Prompt 4
Give me an example of migrating an existing PostgreSQL table with timestamp columns into a TimescaleDB hypertable.
Prompt 5
Explain how TimescaleDB's automatic chunking improves query performance compared to a regular PostgreSQL table with millions of rows.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.