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Scalable and reliable time-series SQL database optimized for fast ingest and complex queries. Built on PostgreSQL.
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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 9K GitHub stars and 488 GitHub forks. Here’s a link to TimescaleDB's open source repository on GitHub

Who uses TimescaleDB?

32 companies reportedly use TimescaleDB in their tech stacks, including LaunchDarkly, Qubitro, and wadiz.

63 developers on StackShare have stated that they use TimescaleDB.

TimescaleDB Integrations

Python, PostgreSQL, Kubernetes, Ruby, and Django are some of the popular tools that integrate with TimescaleDB. Here's a list of all 34 tools that integrate with TimescaleDB.
Public Decisions about TimescaleDB

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

John Kodumal
John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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Mauro Bennici
Mauro Bennici
CTO at You Are My GUide · | 7 upvotes · 34.8K views

PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

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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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SciFin Technologies
SciFin Technologies
Quantitative Developer at SciFin Technologies · | 1 upvotes · 18.8K views

Python Sanic PostgreSQL TimescaleDB Redis

Simple, yet, astonishingly fast and powerful stack to handle huge load of data feed from cryptocurrency exchanges across the globe.

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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
147 developers follow TimescaleDB to keep up with related blogs and decisions.
Sergey Rodovinsky
Eugene van der Watt
Viet Hung Nguyen
Eder Ferreira Dias
Joao Lages
Paul Tom
Matteo Visonà