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DBMS > TimescaleDB

TimescaleDB System Properties

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Editorial information provided by DB-Engines
NameTimescaleDB
DescriptionA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.32
Rank#110  Overall
#6  Time Series DBMS
Websitewww.timescale.com
Technical documentationdocs.timescale.com
DeveloperTimescale
Initial release2017
License infoCommercial or Open SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC
Server operating systemsLinux
OS X
Windows
Data schemeyes
Typing infopredefined data types such as float or datenumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yes
Secondary indexesyes
SQL infoSupport of SQLyes infofull PostgreSQL SQL syntax
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes
Partitioning methods infoMethods for storing different data on different nodesyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replication with hot standby and reads on slaves info
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID
Concurrency infoSupport for concurrent manipulation of datayes
Durability infoSupport for making data persistentyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlfine grained access rights according to SQL-standard
More information provided by the system vendor
TimescaleDB
Specific characteristics

Timescale is the creator of TimescaleDB, the first open-source time-series database to offer the reliability, flexibility, query power, ease-of-use, and scalability that today's software applications, data analytics infrastructure, and complex systems require. 

All data is time-series data, and Timescale offers cloud-hosted, self-managed, and open source versions of TimescaleDB to help developers and data teams across all industries and use cases store, monitor, and analyze their time-series data (common scenarios include: DevOps, IT Monitoring, and IoT). 

Built on PostgreSQL and supporting full SQL, TimescaleDB works with the tools and skills teams already have – and TimescaleDB has an active community of developers and robust technical resources for ongoing support and education.

TimescaleDB at-a-glance

Supports full SQL and JOINS

TimescaleDB natively supports full SQL and adds specialized functions (and query optimizations) for working with time-series data. Support for SQL, a query language your developers and business analysts already know, means that teams don’t have to spend time learning a "SQL-like" syntax or custom query language. 

This also means JOINs with metadata are possible, so you simplify your stack and eliminate data silos between their relational and non-relational databases. More specifically, you’re able to consolidate time-series data (stored in hypertables) with the relational metadata, such as customer data, that gives it meaning (standard tables).

Looks and feels like PostgreSQL 

TimescaleDB scales PostgreSQL in ways previously reserved for non-relational systems and optimizes for fast ingestion on big data. In order to scale far beyond their relational counterparts, non-relational systems sacrifice query power and flexibility. 

TimescaleDB is the best of both worlds: ingests millions of data points per second; scales tables to 100s of billions of rows; and returns quick responses to complex queries.

Works with the entire PostgreSQL ecosystem and offers full extensibility

With its roots in PostgreSQL, TimescaleDB inherits an operational interface familiar to developers, 20+ years of reliability work, and ability to integrate with PostgreSQL extensions. This includes PostGIS for geo-temporal analytics and building a data pipeline with other open-source tools like Kafka, Grafana, Prometheus, and many more.

If it works with PostgreSQL, it works with Timescale.

Optimizes storage and query performance 

With built-in features like native compression, data retention policies, and real-time aggregates, you can apply best-in-class compression algorithms (internal benchmark reports show 90%+ lossless compression rates), execute queries against pre-calculated results (v. querying all underlying raw data), and automatically drop data as it ages. 


For more information about which TimescaleDB version is right for you and to get started, please view our product detail page, documentation, and join us in our active Slack Community.

Competitive advantages

TimescaleDB is purpose-built to scale and handle time-series data workloads and is intentionally designed as a PostgreSQL extension. 

Additionally, with fully managed (cloud), self-managed, and open source versions, developers can opt for the solution that works for their needs.

TimescaleDB is a category-defining time-series database for many reasons, including features like:

  • Native support for ANSI SQL, including JOINs with relational metadata that can be stored right in standard PostgreSQL tables
  • time_bucket and gap_fill, allowing users to query data at any time interval (v. SQL limitations) and choose how they solve for NULL values and missing data
  • GIS support (via PostGIS, another PostgreSQL extension) to roll your own geo-temporal database
  • Support for ACID operations and transactions 
  • Secondary indexes, including composite indexes
  • Secondary JOINs
  • Transparent resource utilization and performance (e.g., many options for administration and tooling, offers the ability to run EXPLAIN, etc.)
  • Native compression and real-time aggregation capabilities to reduce data stored on disk, while still allowing users to analyze historical data
  • Automated data retention policies to remove the burden of manual data lifecycle management
  • Integration with Grafana and Prometheus for data visualization and long-term data storage (roll your own IT Monitoring stack)

TimescaleDB combines the reliability and tooling of PostgreSQL with enterprise-grade security, production-ready SLAs, and community-based and professional support services. 

TimescaleDB is also flexible and customizable, supporting thousands of extensions, products, and integrations critical for data analysis, including: Tableau, Looker, Mode, Grafana, PowerBI, Apache Kafka, RabbitMQ, Apache Spark, Prometheus, Zabbix, JDBC/ODBC, Telegraf, and more.

Typical application scenarios

IoT, Industrial Machine, and Sensor Data

Various industrial IoT use cases across manufacturing, mining, oil & gas, energy, retail, healthcare, and more.

Customer and Internal-facing SaaS applications and Data Pipelines

Surface time-series metrics to end-users, or use to power internal applications, business operations, and data intelligence (Machine Learning pipelines).

DevOps and Infrastructure Monitoring

DevOps and infrastructure monitoring, especially custom initiatives, where siloed metrics are a concern and metadata and trend analysis is important.

Financial Information

Financial information and services across segments such as market research, commodities, and cryptocurrencies.

Key customers

Comcast, Cray, Cree, DNSFilter, LAIKA, Nutanix, Sakura Internet, Schneider Electric, TransferWise, and many more.

Market metrics

With over 8.5K GitHub stars and growing, 1.8 million+ downloads, and an active Slack community, TimescaleDB is trusted by developers and teams around the world.

Licensing and pricing models

TimescaleDB comes in 3 versions, including a fully managed Cloud-hosted solution, licensed under Apache 2 and Timescale License (TSL).

To learn more and find the version that’s best for you, please see our product matrix here (and read more about our support options).

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
TimescaleDB
Recent citations in the news

TimescaleDB Delivers Another Option for Time-Series Analytics
17 October 2019, Datanami

TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
22 August 2019, Packt Hub

Timescale announces $15M investment and new enterprise version of TimescaleDB
29 January 2019, TechCrunch

TimescaleDB Readies its 1.0 Launch for Primetime
13 September 2018, RTInsights

The First Complete Life-Cycle Database Management Solution for TimescaleDB
16 April 2019, PRNewswire

provided by Google News

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