17 Free Open-source Time Series Database Solutions
A Time Series Database (TSDB) is a specialized database designed to efficiently store, retrieve, and manage time-stamped data points. These data points are usually recorded in sequential order over time, making TSDBs particularly suitable for applications that involve continuous data collection.
Use-Cases of Time Series Databases
IoT and Sensor Data:
TSDBs are widely used in the Internet of Things (IoT) to handle the high-frequency data generated by sensors. For example, smart home devices, industrial machinery, and environmental monitoring systems generate vast amounts of time-stamped data that need to be stored and analyzed.
Financial Market Data:
Financial institutions use TSDBs to manage stock prices, trading volumes, and other market-related data. This allows for real-time analysis, backtesting trading strategies, and detecting market trends.
DevOps and Monitoring:
TSDBs are crucial for monitoring IT infrastructure. They store metrics such as CPU usage, memory usage, and network activity, enabling real-time monitoring, anomaly detection, and performance optimization.
Scientific Research:
Researchers use TSDBs to store data from experiments and observations. Fields like astronomy, meteorology, and environmental science generate large volumes of time-stamped data that require efficient storage and retrieval.
Energy Management:
Energy companies use TSDBs to monitor power grids, track energy consumption, and optimize energy distribution.
The ability to store and analyze time-stamped data helps in forecasting demand and managing resources efficiently.
Healthcare:
In healthcare, TSDBs manage patient monitoring data, such as heart rates and blood pressure readings. This data helps in real-time patient monitoring, historical analysis, and improving patient care.
Transportation:
TSDBs are used to manage data from transportation systems, including vehicle tracking, traffic monitoring, and public transit data. This helps in optimizing routes, reducing congestion, and improving overall transportation efficiency.
Open-source Free Time Series Database Solutions
1. QuestDB
QuestDB is an open-source time-series database for high throughput ingestion and fast SQL queries with operational simplicity.
QuestDB is well-suited for financial market data, IoT sensor data and real-time dashboards. It shines for datasets with high cardinality and is a drop-in replacement for InfluxDB via support for the InfluxDB Line Protocol.
QuestDB implements ANSI SQL with native time-series SQL extensions. These SQL extensions make it simple to filter and downsample data, or correlate data from multiple sources using relational and time-series joins.
QuestDB supports schema-agnostic streaming ingestion using the InfluxDB line protocol and a REST API for bulk imports and exports. The QuestDB SQL Web Console is an interactive SQL editor facilitating CSV import. Finally, QuestDB also includes the Postgres Wire Protocol for programmatic queries.
questdb2. TimescaleDB
TimescaleDB is an open-source project that supercharges PostgreSQL with time series management tools, to speed up the data processing and provide faster queries than its parent (PostgreSQL) or its other counterparts.
It comes with two editions, a community edition that is fully open-source and hosted (managed) edition which features more tools, and options.
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3. Heroic
Heroic is a free and open-source A scalable time series database based on Bigtable, Cassandra, and Elasticsearch. However, it is important to note that it is no longer actively maintained.
4. KairosDB
KairosDB is a fast distributed scalable time series database written on top of Cassandra.
kairosdb5. Prometheus
Prometheus, a Cloud Native Computing Foundation project, is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts when specified conditions are observed.
The features that distinguish Prometheus from other metrics and monitoring systems are:
- A multi-dimensional data model (time series defined by metric name and set of key/value dimensions)
- PromQL, a powerful and flexible query language to leverage this dimensionality
- No dependency on distributed storage; single server nodes are autonomous
- An HTTP pull model for time series collection
- Pushing time series is supported via an intermediary gateway for batch jobs
- Targets are discovered via service discovery or static configuration
- Multiple modes of graphing and dashboarding support
- Support for hierarchical and horizontal federation
prometheus6. InfluxDB
InfluxDB is the most popular open source database for developers managing time series data. Unlock real-time insights from time series data at any scale in any environment – in the cloud, on-prem, or at the edge.
7. Thanos
Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity, which can be added seamlessly on top of existing Prometheus deployments. Thanos is a CNCF Incubating project.
Thanos leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies.
Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.
thanos-io8. Warp 10
Warp 10 is a modular open source platform shaped for the IoT that collects, stores and allows you to analyze sensor data. It offers both a Time Series Database and a powerful analysis environment that can be used together or independently.
Benefits
- Increase the storage capacity of your historical data and reduce your storage bill while preserving all analysis capabilities
- Deploy a real time database that scales with your time series needs
- Enhance your existing tools with a ubiquitous analysis environment dedicated to time series data
- Streamlining KPIs and data visualization across your organization
- Enable your business applications to interact easily with your system's data
senx9. Toshiba GridDB
Toshiba GridDB™ is a highly scalable, in-memory NoSQL time series database optimized for IoT and Big Data.
10. OpenTSDB
OpenTSDB is a scalable and distributed Time Series Database designed for storing, retrieving, and managing large volumes of time-stamped data. It is built on top of Apache HBase and is capable of handling billions of data points per day.
OpenTSDB supports complex queries, including aggregation and downsampling, making it suitable for use cases such as performance monitoring, IoT data collection, and financial market analysis.
It provides efficient data storage with customizable retention policies and real-time data analytics capabilities.
OpenTSDB also features easy integration with various data input sources and visualization tools, making it a robust solution for managing time series data.
11. Blueflood
Blueflood is a multi-tenant, distributed metric processing system. Blueflood is capable of ingesting, rolling up and serving metrics at a massive scale.
rax-maas12. VictoriaMetrics
VictoriaMetrics is a high-performance and cost-effective Time Series Database designed for large-scale monitoring and real-time analytics. It supports high ingestion rates, efficient storage, and fast querying of time series data. Key features include multi-tenancy, clustering for scalability, and support for Prometheus querying and ingestion protocols.
VictoriaMetrics is optimized for minimal resource usage, making it suitable for cloud-native environments.
It provides easy integration with existing monitoring and alerting systems, and its compatibility with Prometheus makes it a robust choice for DevOps, IoT, and other data-intensive applications.
13. SiriDB
SiriDB is a highly scalable and fast Time Series Database optimized for real-time data analysis. It supports clustering for high availability and horizontal scaling, enabling efficient handling of large volumes of time-stamped data.
SiriDB offers SQL-like query language for ease of use, automatic sharding, and data replication. Its high performance and low-latency query capabilities make it suitable for applications in IoT, monitoring, and analytics.
14. Akumuli
Akumuli is a high-performance Time Series Database designed for large-scale data ingestion and real-time analytics. It features a log-structured merge-tree architecture for efficient data storage and retrieval.
Akumuli supports high write and read throughput, with capabilities for data compression and retention policies. Its query language allows for complex time-series analysis, making it ideal for IoT, monitoring, and industrial applications. Akumuli is optimized for minimal resource usage and can be integrated easily into existing systems.
15. Graphite
Graphite is a free and open-source highly scalable real-time and time-series graphing system.
graphite-project16. M3
M3DB is a distributed Time Series Database designed for large-scale, high-throughput workloads. It offers a highly available and scalable storage solution with built-in aggregation and downsampling capabilities.
M3DB supports Prometheus querying and ingestion, making it ideal for monitoring and observability use cases.
Key features include efficient time series data compression, fast query performance, and flexible deployment options.
M3DB's architecture ensures reliability and durability, making it suitable for cloud-native environments and data-intensive applications.
m3db17. CockroachDB
CockroachDB is a distributed SQL database built on a transactional and strongly-consistent key-value store. It scales horizontally; survives disk, machine, rack, and even datacenter failures with minimal latency disruption and no manual intervention; supports strongly-consistent ACID transactions; and provides a familiar SQL API for structuring, manipulating, and querying data.
cockroachdbConclusion
Time Series Databases are essential for applications that require handling large volumes of sequential data. Their ability to efficiently store and query time-stamped data makes them invaluable across various industries, including IoT, finance, DevOps, scientific research, energy, healthcare, and transportation.
