Time Series Database

This is a list of the top commercial, financial and open source time-series databases available as of January 2023.

What is a time-series Database?

A time-series database is specialized to quickly and efficiently answer queries involving:

  • Time-Joins - e.g. Event X occurred at 9am, when was the closest Y event to that.
  • Time-Aggregations - specialized functions to allow handling date/time types well.
Additionally they typically add:
  • Compression - As the data is large and often repeating
  • Support Nanoseconds - As for some industries e.g. trading, the exact timing of events matter.
To find out more see our full article on what a time-series database is? What it is for etc.
The table below shows the support each database has in this area.

Time-series Databases

Product Score Released Speed SQL Compression Time-Joins Time-Aggregations Nanoseconds Popularity Description License
Timescale (wp) 6 2018 Yes Medium+ Yes Full + Extensions N/A Basic No No No N/A Micros N/A Unknown Postgres for time-series Apache License 2.0
Clickhouse (wp) 8 2016 Yes Fast Some + Custom Yes Many Yes asof No N/A Partial Yes Popular Very fast OLAP database with cloud version available. Started 10 years ago at Yandex to store the russian equivalent of google analytics. Apache License 2.0
QuestDB 7 2014 Yes Fast High + Extensions Yes Many Yes asof+ Yes Good N/A Partial N/A New Fast database with strong focus on time-series. Very similar ideas to kdb+ but open source. Apache License 2.0
InfluxDB (wp) 6 2013 Yes Medium N/A Some + Custom Yes N/A Some No No Yes Yes Yes IoT/monitoring Originally built for monitoring and alerting. Now specializing in time-series analysis and IoT. Uses an SQL-like language. MIT License
Druid (wp) 6 2011 N/A Medium N/A Some + custom Yes No No No No Milliseconds Yes Click analytics A distributed data store written in Java. Druid is designed to quickly ingest massive quantities of event data, and provide aggregated queries ontop. Apache License 2.0
kdb+ (wp) 8 2003 Yes Fast++ No Some + qSQL Yes Many Yes Yes. AJ/WJ Yes Yes Yes Finance Very fast column-oriented database with custom language q and custom time-series joins.
Steep learning curve and difficult to find experts.

Time-Series Benchmarks

For more information see our Time-Series benchmarks article

Clickbench results:

System & MachineRelative time (lower is better)Note
ClickHouse (c6a.metal, 500gb gp2):×1.59
SelectDB (c6a.metal, 500gb gp2):×1.88
ClickHouse (m5d.24xlarge):×2.15
StarRocks (c6a.metal, 500gb gp2):×2.16
Redshift (4×ra3.16xlarge):×2.20
DuckDB (c6a.metal, 500gb gp2):×2.74
QuestDB (partitioned) (c6a.metal, 500gb gp2)†:×24.24
MariaDB ColumnStore (c6a.4xlarge, 500gb gp2)†:×59.27
TimescaleDB (compression) (c6a.4xlarge, 500gb gp2):×86.91
Druid (c6a.4xlarge, 500gb gp2)†:×150.50
PostgreSQL (c6a.4xlarge, 500gb gp2):×883.89NOT column oriented

Results reproduced from Mark Litwintschik's excellent article.

SetupTotal Query Time (lower = better)Note
kdb+/q & 4 Intel Xeon Phi 7210 CPUs1.04 
ClickHouse, 3 x c5d.9xlarge cluster4.06 
Clickhouse on DoubleCloud, s1-c32-m1285.77 

Financial Tick Databases

Product Vendor (release year)Description
One Tick Database Onetick
Column/Row oriented database targeted at the financial sector and specialised for tick data, created by Leonid Frants that had built a tick solution while at Goldman Sachs.
eXtremeDB McObject
A fast embedded, mostly in-memory database targeted for financial firms and time series data. It's raw API and ability to be embedded within a process makes it fast, however this means a higher configuration cost and learning curve to get started.