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DBMS > TimescaleDB

TimescaleDB System Properties

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Editorial information provided by DB-Engines
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
Rank#128  Overall
#8  Time Series DBMS
Technical documentationdocs.timescale.com
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
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 methodsnative C library
streaming API for large objects
Supported programming languages.Net
Java infoJDBC
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
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
Specific characteristics

TimescaleDB is the first open-source time-series database that combines the reliability, query power, ease-of-use, and scalability required by today's software applications and data analytics infrastructure. All data can fundamentally be collected as time-series data and developers across all industries globally are recognizing this capability. TimescaleDB enables developers and data analysts to work with time-series data in more powerful and flexible ways by offering a system that:

Supports Full SQL
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 your team does not have to spend time learning a "SQL-like" syntax or custom query language. It also means JOINs with metadata are possible, allowing developers to simplify their stack and eliminate data silos.

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 the flexibility that comes with the relational schema. TimescaleDB ingests millions of data points per second; scales tables to 100s of billions of rows; and returns quick responses to complex queries.

Works with entire PostgreSQL ecosystem
As a PostgreSQL extension, TimescaleDB inherits an operational interface familiar to developers, 20+ years of reliability work and allows users to leverage best-of-breed technologies by integrating with the entire PostgreSQL extension. This includes PostGIS, which adds GIS support for geo-temporal analytics. Additionally, with support for JOINs, users are able to consolidate their time-series data (stored in hypertables) with the relational metadata that gives it meaning (stored in standard tables), eliminating the data silos resulting from deployments where two different databases are used: one for time-series data and one for metadata. 

For more information, please view our documentation or 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.

TimescaleDB provides developers with capabilities that no other time-series database today can offer:

  • Native support for ANSI SQL, including JOINs with relational metadata that can be stored right in standard PostgreSQL tables
  • GIS support (via PostGIS, another PostgreSQL extension), which allows users to roll their own geo-temporal database
  • Consistent, with support for ACID operations and transactions 
  • Secondary indexes, including composite indexes
  • Secondary JOINs
  • Transparent in its resource utilization and performance (e.g., many options for administration and tooling, offers the ability to run EXPLAIN)

TimescaleDB is proven and enterprise ready, providing the reliability and tooling of PostgreSQL, enterprise-grade security, production-ready SLAs, and support. TimescaleDB offers the largest ecosystem of any time-series database including support for Tableau, Looker, Mode, Grafana, PowerBI, Apache Kafka, RabbitMQ, Apache Spark, Prometheus, Zabbix, JDBC/ODBC, and more.

Typical application scenarios

IoT, Industrial Machine, and Sensor Data
Various industrial IoT use cases across manufacturing, mining, oil & gas, retail, healthcare, and more.

Modern Applications, Business Operations, and Data Pipelines
Developing modern SaaS applications, powering business operations and intelligence, and machine learning pipelines.

DevOps and Infrastructure Monitoring
DevOps and infrastructure monitoring, especially where siloed metrics are a concern and metadata is important.

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

Key customers

Altair Engineering Inc., Bloomberg, Comcast, Cray, Cree, DNSFilter, Laika, LaunchDarkly, Nutanix, Sailthru, ShiftLeft, SoFi, TransferWise, and many more.

Market metrics

1 million+ downloads, 6,500+ Github stars

Licensing and pricing models

Apache 2 and Timescale License (TSL); information on pricing for support and enterprise version available upon request

Related products and services

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

More resources
Recent citations in the news

Timescale Raises Additional $15M in Series A1 Funding
29 January 2019, FinSMEs

Why time series databases are exploding in popularity
26 June 2019, TechRepublic

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

Introducing Timescale Cloud: The First Fully-Managed, Multi-Cloud Time-Series Database Service
18 June 2019, Business Wire

Timescale and Microsoft Azure Join Forces to Power IoT and Time-Series Workloads
18 March 2019, Business Wire

provided by Google News

Job opportunities

Network Controller Software Engineer
Fungible Inc., Santa Clara, CA

Principal Engineer - Data Pipeline
Tagup Inc., Somerville, MA

Developer Evangelist
Timescale, New York, NY

Solutions Engineer
Timescale, New York, NY

Business Development Representative
Timescale, New York, NY

jobs by Indeed

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