DBMS > TimescaleDB
TimescaleDB System Properties
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|Editorial information provided by DB-Engines|
|Description||A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL|
|Primary database model||Time Series DBMS|
|Secondary database models||Relational DBMS|
|License Commercial or Open Source||Open Source Apache 2.0|
|Cloud-based only Only available as a cloud service||no|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems||Linux|
|Typing predefined data types such as float or date||numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||yes|
|SQL Support of SQL||yes full PostgreSQL SQL syntax|
|APIs and other access methods||ADO.NET|
native C library
streaming API for large objects
|Supported programming languages||.Net|
|Server-side scripts Stored procedures||user 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 Methods for storing different data on different nodes||yes, across time and space (hash partitioning) attributes|
|Replication methods Methods for redundantly storing data on multiple nodes||Master-slave replication with hot standby and reads on slaves|
|MapReduce Offers an API for user-defined Map/Reduce methods||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency|
|Foreign keys Referential integrity||yes|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||ACID|
|Concurrency Support for concurrent manipulation of data||yes|
|Durability Support for making data persistent||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||no|
|User concepts Access control||fine grained access rights according to SQL-standard|
|More information provided by the system vendor|
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.
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.
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:
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 and services across segments such as market research, commodities, and cryptocurrencies.
Comcast, Cray, Cree, DNSFilter, LAIKA, Nutanix, Sakura Internet, Schneider Electric, TransferWise, and many more.
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).
Related products and services
We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
|Recent citations in the news|
TimescaleDB Delivers Another Option for Time-Series Analytics
TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
Timescale announces $15M investment and new enterprise version of TimescaleDB
TimescaleDB Readies its 1.0 Launch for Primetime
The First Complete Life-Cycle Database Management Solution for TimescaleDB
provided by Google News
Systems Engineer - Monitoring
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