TimescaleDB is a relational database for time-series data.
It is implemented as an extension to PostgreSQL, which means that it runs within a PostgreSQL server as part of the same process, with code that introduces new capabilities for time-series data management, new functions for data analytics, new query planner and query execution optimizations, and new storage mechanisms for more cost effective and performant analytics. Any operations to a PostgreSQL database that includes TimescaleDB (whether SELECTs or INSERTs, or schema management like creating indexes) are first processed by TimescaleDB to determine how they should be planned or executed against TimescaleDB's data structures.
This extension model allows the database to take advantage of the richness of PostgreSQL, from 40+ data types (integers, floats, strings, timestamps, to arrays and JSON data types), to a dozen types of indexes, to complex schemas, to an advanced query planner, and to a larger extension ecosystem that plays nicely with TimescaleDB (including geo-spatial support through PostGIS, monitoring with pg_stat_statements, foreign data wrappers, and more).
This design also allows TimescaleDB to take advantage of PostgreSQL's maturity from a reliability, robustness, security, and ecosystem perspective. One can use PostgreSQL's physical replication to create multiple replicas for higher- availability and scaling read queries; snapshots and incremental WAL streaming for continuous backups to support full point-in-time recovery; role-based access control at the schema, table, or even row-level; and integrations with a huge number of third-party connectors and systems that speak the standard PostgreSQL protocol, such as popular programming languages, ORMs, data science tools, streaming systems, ETL tools, visualization libraries and dashboards, and more.
At the same time, TimescaleDB leverages the high degree of customization available to extensions by adding hooks deep into PostgreSQL's query planner, data and storage model, and execution engine. This allows for new, advanced capabilities designed specifically for time-series data, including:
Transparent and automated time partitioning, where time-series tables are automatically and continuously "chunked" into smaller intervals to improve performance and to unlock various data-management capabilities. Data and indexes for the latest chunks naturally remain in memory, insuring fast inserts and performant queries to recent data.
Native columnar compression with advanced datatype-specific compression, employing various best-in-class algorithms based on whether the data are timestamps, integers, floats, strings, or others. Users typically report 94-97% storage reduction and faster queries to compressed data.
Continuous and real-time aggregations, in which the database continually and incrementally maintains a materialized view of aggregate time-series data to improve query performance, while properly handling late data or data backfills. TimescaleDB even enables queries to transparently merge pre- computed aggregates with the latest raw data to ensure always up-to-date answers.
Automated time-series data management features, such as explicit or policy-based data retention policies, data reordering policies, aggregation and compression policies, downsampling policies, and more.
In-database job-scheduling framework to power both native policies and to support user-defined actions, including those written in SQL or PL/pgSQL.
Horizontally-scalable multi-node operation to automatically and elastically scale your time-series data across many TimescaleDB databases, while still giving the abstraction of a single time-series table.
To better understand TimescaleDB, we first want to better explain two main concepts of how TimescaleDB scales: its data abstractions of hypertables and chunks (and how these are stored and processed), and how TimescaleDB can be deployed as either a single-node, with physical replicas, or as a multi- node cluster to enable distributed hypertables.
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