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Scalable and reliable time-series SQL database optimized for fast ingest and complex queries. Built on PostgreSQL.

What is TimescaleDB?

TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
TimescaleDB is a tool in the Databases category of a tech stack.
TimescaleDB is an open source tool with 10.5K GitHub stars and 563 GitHub forks. Here’s a link to TimescaleDB's open source repository on GitHub

Who uses TimescaleDB?

36 companies reportedly use TimescaleDB in their tech stacks, including LaunchDarkly, Kasa, and wadiz.

82 developers on StackShare have stated that they use TimescaleDB.

TimescaleDB Integrations

Python, PostgreSQL, Kubernetes, Django, and Ruby are some of the popular tools that integrate with TimescaleDB. Here's a list of all 35 tools that integrate with TimescaleDB.
Pros of TimescaleDB
Open source
Easy Query Language
Time-series data analysis
Established postgresql API and support
Paid support for automatic Retention Policy
Fast and scalable
Chunk-based compression
Postgres integration
Case studies
Decisions about TimescaleDB

Here are some stack decisions, common use cases and reviews by companies and developers who chose TimescaleDB in their tech stack.

Hi, I need advice on which Database tool to use in the following scenario:

I work with Cesium, and I need to save and load CZML snapshot and update objects for a recording program that saves files containing several entities (along with the time of the snapshot or update). I need to be able to easily load the files according to the corresponding timeline point (for example, if the update was recorded at 13:15, I should be able to easily load the update file when I click on the 13:15 point on the timeline). I should also be able to make geo-queries relatively easily.

I am currently thinking about Elasticsearch or PostgreSQL, but I am open to suggestions. I tried looking into Time Series Databases like TimescaleDB but found that it is unnecessarily powerful than my needs since the update time is a simple variable.

Thanks for your advice in advance!

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Umair Iftikhar
Technical Architect at Vappar · | 3 upvotes · 13.5K views

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Blog Posts

TimescaleDB's Features

  • Packaged as a PostgreSQL extension
  • Full ANSI SQL
  • JOINs (e.g., across PostgreSQL tables)
  • Complex queries
  • Secondary indexes
  • Composite indexes
  • Support for very high cardinality data
  • Triggers
  • Constraints
  • Ability to ingest out of order data
  • Ability to perform accurate rollups
  • Data retention policies
  • Fast deletes
  • Integration with PostGIS and the rest of the PostgreSQL ecosystem

TimescaleDB Alternatives & Comparisons

What are some alternatives to TimescaleDB?
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.
See all alternatives

TimescaleDB's Followers
200 developers follow TimescaleDB to keep up with related blogs and decisions.