59+ Time-Series Databases Ranked & Compared

Compare time-series databases ranked by GitHub stars, ingestion speed, and observability adoption.

Last updated: May 20, 2026
51 databases
1Redis
Redis
74.4k+529 30d

In-memory data store used as a database, cache, message broker, and streaming engine

Key-Value·2009·RSALv2 / SSPLv1 / AGPLv3 (triple-licensed)·C
2Prometheus
Prometheus
64.1k+436 30d

Open-source monitoring system and time-series database with a powerful query language and alerting

Time-Series·2012·Apache-2.0·Go
3ClickHouse
ClickHouse
47.5k+534 30d

Blazing-fast open-source column-oriented database for real-time analytics and OLAP

Analytics·2016·Apache-2.0·C++
4SurrealDB
SurrealDB
32.1k+246 30d

Multi-model database combining documents, graphs, vectors, and time-series with built-in API layer and real-time capabilities

Multi-Model·2022·BSL-1.1·Rust
5InfluxDB
InfluxDB
31.5k+57 30d

Scalable time-series database built in Rust for metrics, events, and real-time analytics

Time-Series·2013·Apache-2.0·Rust
6MongoDB
MongoDB
28.3k+87 30d

The most popular document database for modern applications

Document·2009·SSPL·C++, JavaScript, Python
7TDengine
TDengine
24.9k+21 30d

High-performance open-source time-series database designed for Industrial IoT and real-time analytics

Time-Series·2019·AGPL-3.0·C
8TimescaleDB
TimescaleDB
22.7k+241 30d

Time-series database for high-performance real-time analytics, packaged as a PostgreSQL extension

Time-Series·2017·Timescale License (Apache-2.0 for core)·C
9VictoriaMetrics
VictoriaMetrics
17.0k+208 30d

Fast, cost-effective time-series database and monitoring solution compatible with Prometheus

Time-Series·2018·Apache-2.0·Go
10QuestDB
QuestDB
17.0k+92 30d

High-performance time-series database with SQL and column-oriented storage for fast analytics

Time-Series·2014·Apache-2.0·Java, C++, Rust
11Thanos
Thanos
14.1k+48 30d

Highly available Prometheus setup with unlimited long-term storage on object storage

Time-Series·2017·Apache-2.0·Go
12Apache Druid
Apache Druid
14.0k+21 30d

High-performance real-time analytics database for sub-second OLAP queries at scale

Analytics·2012·Apache-2.0·Java
13Apache IoTDB
Apache IoTDB
6.3k+11 30d

High-performance time-series database for IoT data with lightweight architecture and high compression

Time-Series·2019·Apache-2.0·Java
14GreptimeDB
GreptimeDB
6.3k+107 30d

Open-source unified observability database for metrics, logs, and traces built in Rust

Time-Series·2022·Apache-2.0·Rust
15Cortex
Cortex
5.8k+18 30d

Horizontally scalable, multi-tenant long-term storage for Prometheus metrics

Time-Series·2016·Apache-2.0·Go
16OpenTSDB
OpenTSDB
5.1k+4 30d

Distributed, scalable time-series database built on top of HBase for monitoring at massive scale

Time-Series·2010·LGPL-2.1·Java
17M3DB
M3DB
4.9k−6 30d

Distributed time-series database built by Uber for large-scale metrics with Prometheus and Graphite compatibility

Time-Series·2018·Apache-2.0·Go
18CrateDB
CrateDB
4.4k+3 30d

Distributed SQL database for real-time analytics on massive datasets with PostgreSQL compatibility

Multi-Model·2014·Apache-2.0·Java
19RavenDB
RavenDB
3.9k+5 30d

ACID document database with integrated full-text search, time series, and distributed counters

Document·2010·AGPL-3.0 / Commercial·C#
20Netflix Atlas
Netflix Atlas
3.6k−1 30d

In-memory dimensional time-series database built for operational metrics at Netflix scale

Time-Series·2014·Apache-2.0·Scala, Java
21Roshi
Roshi
3.2k+1 30d

Large-scale CRDT set implementation for timestamped events backed by Redis

Time-Series·2014·BSD-2-Clause·Go
22LinDB
LinDB
3.1k−1 30d

Scalable, high-performance distributed time-series database with multi-IDC replication

Time-Series·2019·Apache-2.0·Go
23Apache HoraeDB
Apache HoraeDB
2.8k−3 30d

High-performance distributed cloud-native time-series database for analytics and time-series workloads

Time-Series·2022·Apache-2.0·Rust
24GridDB
GridDB
2.5k0 30d

IoT-optimized time-series database with hybrid in-memory and disk storage from Toshiba

Time-Series·2013·AGPL-3.0·C++, Java
25KairosDB
KairosDB
1.8k+4 30d

Fast distributed scalable time-series database built on top of Apache Cassandra

Time-Series·2013·Apache-2.0·Java
26CnosDB
CnosDB
1.8k+3 30d

Cloud-native open-source distributed time-series database with high performance and compression

Time-Series·2022·AGPL-3.0·Rust
27OpenMLDB
OpenMLDB
1.7k+1 30d

Open-source machine learning database providing consistent feature engineering for training and inference

Time-Series·2021·Apache-2.0·C++, Java, Python
28Graphite
Graphite
1.5k0 30d

Scalable real-time graphing and metrics storage with Whisper time-series database and Carbon ingestion daemon

Time-Series·2008·Apache-2.0·Python
29GeoMesa
GeoMesa
1.5k+3 30d

Distributed spatio-temporal indexing on top of Accumulo, HBase, Cassandra, and Kafka

Multi-Model·2014·Apache-2.0·Scala, Java
30openGemini
openGemini
1.2k+15 30d

Cloud-native distributed time-series database by Huawei for IoT and observability at massive scale

Time-Series·2022·Apache-2.0·Go
31RRDtool
RRDtool
1.1k+4 30d

Industry-standard round-robin database for high-performance time-series data logging and graphing

Time-Series·1999·GPL-2.0·C
32ArcadeDB
ArcadeDB
888+74 30d

Multi-model database supporting graphs, documents, key-value, vectors, time-series, and search in one engine

Multi-Model·2021·Apache-2.0·Java
33Arc
Arc
597+22 30d

High-performance columnar analytical database built on DuckDB SQL, Parquet storage, and Arrow format

Analytics·2024·AGPL-3.0·Go
34SiriDB
SiriDB
512+1 30d

Highly scalable and super fast open-source time-series database with dynamic grouping

Time-Series·2017·MIT·C
35Warp 10
Warp 10
415+2 30d

Advanced open-source time-series platform with native geo-temporal support and WarpScript analytics

Time-Series·2015·Apache-2.0·Java
36Gnocchi
Gnocchi
3210 30d

Scalable time-series database with pre-computed aggregations for cloud metrics and resource indexing

Time-Series·2017·Apache-2.0·Python
37ReductStore
ReductStore
290+7 30d

Time-series database for blob data designed for robotics and industrial IoT with edge-to-cloud replication

Time-Series·2022·BUSL-1.1·Rust
38Machbase Neo
Machbase Neo
1960 30d

IoT-optimized time-series database with built-in MQTT broker and HTTP API for edge-to-cloud data

Time-Series·2023·source-available·Go, C
39RayforceDB
RayforceDB
130+6 30d

SIMD-accelerated columnar database for analytics written in pure C with zero dependencies

Analytics·2023·MIT·C
40Alibaba Cloud Log Service

Cloud-native observability platform for PB-scale log collection, analysis, and visualization

Analytics·2016·proprietary
41Alibaba Cloud Table Store

Serverless NoSQL wide-column and time-series storage with auto-scaling to 10 PB

Wide-Column·2016·proprietary
42Amazon Timestream

Serverless time-series database for IoT and operational applications with built-in analytics

Time-Series·2020·proprietary
43Axibase Time Series Database

Special-purpose time-series database for IT infrastructure, industrial equipment, and financial market data

Time-Series·2004·proprietary·Java
44DolphinDB

High-performance time-series database with built-in analytics for finance and IoT

Time-Series·2018·Proprietary·C++
45IBM Informix

Enterprise object-relational database with seamless SQL, NoSQL, time-series, and spatial data integration

Relational·1980·proprietary·C
46ITTIA DB

Real-time embedded database with time-series and streaming for IoT and edge AI devices

Embedded·2003·proprietary·C, C++
47kdb+

Ultra-high-performance columnar time-series database with the vector language q, built for capital markets and real-time analytics

Time-Series·2003·proprietary·C
48Kinetica

GPU-accelerated real-time analytics database for spatial, temporal, graph, and AI workloads at scale

Analytics·2016·proprietary·C++
49Microsoft Azure Data Explorer

Fast and scalable data analytics service for real-time analysis of streaming and time-series data using Kusto Query Language

Analytics·2019·proprietary
50QuasarDB

High-performance distributed column-oriented time-series database with native transactional support

Time-Series·2009·proprietary·C++
51Transwarp Hippo

Enterprise cloud-native distributed vector database with GPU acceleration and multi-model support

Vector·proprietary

What is a Time-Series Database?

A time-series database (TSDB) is optimized for storing, querying, and analyzing data that is indexed by time — metrics, events, sensor readings, and logs. Unlike general-purpose databases, TSDBs are designed for high write throughput (millions of data points per second), efficient time-range queries, automatic data downsampling, and retention policies that age out old data. They power observability stacks (monitoring, alerting, dashboards), IoT platforms, financial data systems, and any application where understanding trends over time is critical. Popular options include TimescaleDB, InfluxDB, Prometheus, QuestDB, and VictoriaMetrics.

When to Use a Time-Series Database

Use a time-series database when your data is timestamped and append-heavy — server metrics, application logs, IoT sensor data, financial tick data, or user analytics. TSDBs excel at queries like 'show me the average CPU usage per minute for the last 24 hours' and can ingest millions of points per second. For observability, Prometheus and VictoriaMetrics are the standard. For analytical queries on time-stamped data, TimescaleDB (built on PostgreSQL) and QuestDB offer SQL interfaces. Consider general-purpose databases if your time-series data is a small part of a larger application with complex relational needs.

Frequently Asked Questions

What is the difference between a time-series database and a relational database?
Relational databases treat all rows equally and optimize for random reads, updates, and joins. Time-series databases optimize for a specific access pattern: high-speed sequential writes of timestamped data and fast time-range queries with aggregations. TSDBs use columnar storage, time-based partitioning, and compression algorithms tuned for time-series data. TimescaleDB bridges both worlds — it's built as a PostgreSQL extension, so you get time-series optimizations with full SQL and relational capabilities.
Is TimescaleDB better than InfluxDB?
TimescaleDB is built on PostgreSQL, giving you full SQL support, joins, and the PostgreSQL ecosystem. InfluxDB uses its own query language (Flux/InfluxQL) and is more narrowly focused on metrics and monitoring. TimescaleDB is often preferred when you need time-series alongside relational data or want SQL familiarity. InfluxDB is strong for pure observability workloads and has a large monitoring ecosystem. QuestDB is another contender, offering very fast ingestion with SQL support and a lightweight footprint.
Can I use PostgreSQL for time-series data?
Yes, especially with the TimescaleDB extension which adds automatic time-based partitioning, compression, continuous aggregates, and retention policies to PostgreSQL. For small to medium time-series workloads (under 1 billion rows), vanilla PostgreSQL with proper partitioning and indexing can work. For high-volume ingestion or long-term retention, TimescaleDB or a dedicated TSDB will significantly outperform plain PostgreSQL.
What is the best time-series database for IoT?
For IoT workloads with high-volume sensor ingestion, QuestDB and TimescaleDB are strong choices — both offer high write throughput and SQL queries. InfluxDB is widely used in IoT with its Telegraf agent for data collection. VictoriaMetrics offers excellent compression and performance for resource-constrained environments. For edge deployments where data must be collected locally before syncing, SQLite with time-based partitioning or InfluxDB's edge agent are common approaches.
What is the difference between Prometheus and a time-series database?
Prometheus is both a monitoring system and a time-series database, specifically designed for infrastructure metrics and alerting. It uses a pull-based model (scraping endpoints) rather than push-based ingestion. General-purpose TSDBs like TimescaleDB, InfluxDB, and QuestDB accept push-based writes and are designed for longer-term storage and analytical queries. Many production setups use Prometheus for short-term metrics collection and alerting, with VictoriaMetrics or Thanos as a long-term storage backend.

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