List of the Top 13 Time Series Databases for Linux in 2025

RaimaDB is an embedded time series database designed specifically for Edge and IoT devices, capable of operating entirely in-memory. This powerful and lightweight relational database management system (RDBMS) is not only secure but has also been validated by over 20,000 developers globally, with deployments exceeding 25 million instances. It excels in high-performance environments and is tailored for critical applications across various sectors, particularly in edge computing and IoT. Its efficient architecture makes it particularly suitable for systems with limited resources, offering both in-memory and persistent storage capabilities. RaimaDB supports versatile data modeling, accommodating traditional relational approaches alongside direct relationships via network model sets. The database guarantees data integrity with ACID-compliant transactions and employs a variety of advanced indexing techniques, including B+Tree, Hash Table, R-Tree, and AVL-Tree, to enhance data accessibility and reliability. Furthermore, it is designed to handle real-time processing demands, featuring multi-version concurrency control (MVCC) and snapshot isolation, which collectively position it as a dependable choice for applications where both speed and stability are essential. This combination of features makes RaimaDB an invaluable asset for developers looking to optimize performance in their applications.

InfluxDB is a specialized data platform crafted to manage all types of time series data, encompassing users, sensors, applications, and infrastructure, allowing for the seamless collection, storage, visualization, and transformation of insights into actionable strategies. It features a comprehensive library of over 250 open-source Telegraf plugins, simplifying the process of importing and monitoring data from a variety of systems. By empowering developers, InfluxDB facilitates the creation of innovative IoT, monitoring, and analytics applications and services. Its adaptable architecture can accommodate various implementations, whether in the cloud, at the edge, or on-premises. Moreover, its versatility, ease of access, and an array of supporting tools such as client libraries and APIs enable developers of all experience levels to swiftly create applications and services utilizing time series data. The platform is optimized for enhancing developer productivity and efficiency, allowing builders to concentrate on the essential features that add value to their internal projects and provide their applications with a competitive advantage. To assist newcomers, InfluxData provides complimentary training through InfluxDB University, ensuring that anyone can quickly acquire the skills needed to leverage this powerful platform effectively.

Telegraf serves as an open-source server agent designed to efficiently gather metrics from various sensors, stacks, and systems. Acting as a plugin-centric agent, it not only collects but also transmits metrics and events from a diverse array of sources including systems, databases, and IoT devices. Engineered in Go, it compiles into a single binary, requiring no external dependencies and consuming minimal memory. Telegraf supports a vast range of input sources, allowing for the seamless writing of data to numerous output destinations. With its plugin architecture, it is effortlessly extendable for both data collection and output purposes. Additionally, Telegraf boasts over 300 plugins developed by community data experts, making the collection of metrics from your endpoints a straightforward task. This flexibility and community support make Telegraf an invaluable tool for monitoring and performance analysis.

VictoriaMetrics is an economical and scalable option for monitoring that also functions as a time series database. Additionally, it is suitable for the long-term storage of Prometheus data. This solution operates as a standalone executable without requiring any external dependencies. Configuration is managed through clear command-line flags along with sensible defaults. Notably, it offers a global query view, enabling multiple Prometheus instances or other data sources to input data into VictoriaMetrics. Users can later retrieve this data with a single query. It effectively addresses challenges related to high cardinality and high churn rates by implementing a series limiter, ensuring optimal performance and reliability. Furthermore, its user-friendly setup makes it accessible for a wide range of applications.

What contributes to the platform independence of eXtremeDB? It features a hybrid data storage approach, allowing for configurations that are entirely in-memory or fully persistent, as well as combinations of both, unlike many other IMDS databases. Additionally, eXtremeDB incorporates its proprietary Active Replication Fabric™, enabling not only bidirectional replication but also multi-tier replication, which can optimize data transfer across various network conditions through built-in compression techniques. Furthermore, it offers flexibility in structuring time series data by supporting both row-based and column-based formats, enhancing CPU cache efficiency. eXtremeDB can operate as either a client/server architecture or as an embedded system, providing adaptable and speedy data management solutions. With its design tailored for resource-limited, mission-critical embedded applications, eXtremeDB is utilized in over 30 million deployments globally, ranging from routers and satellites to trains and stock market operations, showcasing its versatility across diverse industries.

Elevate your monitoring and alerting strategies by utilizing a leading open-source tool known as Prometheus. This powerful platform organizes its data in the form of time series, which are essentially sequences of values linked to specific timestamps, metrics, and labeled dimensions. Beyond the stored time series, Prometheus can generate temporary derived time series based on the results of queries, enhancing versatility. Its querying capabilities are powered by PromQL (Prometheus Query Language), which enables users to real-time select and aggregate data from time series. The results from these queries can be visualized as graphs, presented in a table format via Prometheus's expression browser, or retrieved by external applications through its HTTP API. To configure Prometheus, users can employ both command-line flags and a configuration file, where flags define unchangeable system parameters such as storage locations and retention thresholds for disk and memory. This combination of configuration methods offers a customized monitoring experience that can accommodate a variety of user requirements. If you’re keen on delving deeper into this feature-rich tool, additional information is available at: https://sourceforge.net/projects/prometheus.mirror/. With Prometheus, you can achieve a level of monitoring sophistication that optimizes performance and responsiveness.

Riak® TS is a robust NoSQL Time Series Database tailored for handling IoT and Time Series data effectively. It excels at ingesting, transforming, storing, and analyzing vast quantities of time series information. Designed to outperform Cassandra, Riak TS utilizes a masterless architecture that allows for uninterrupted data read and write operations, even in the event of network partitions or hardware malfunctions. Data is systematically distributed across the Riak ring, with three copies of each dataset maintained by default to ensure at least one is available for access. This distributed system operates without a central coordinator, offering a seamless setup and user experience. The ability to easily add or remove nodes from the cluster enhances its flexibility, while the masterless architecture ensures this process is straightforward. Furthermore, incorporating nodes made from standard hardware can facilitate predictable and nearly linear scaling, making Riak TS an ideal choice for organizations looking to manage substantial time series datasets efficiently.

SiriDB is engineered for high-speed performance, ensuring that both data inserts and queries are processed rapidly. Its custom query language further accelerates development, allowing for more efficient coding. The flexibility of SiriDB enables on-the-fly scaling, which means you can expand your database without experiencing any downtime during updates. This capability ensures that the performance remains consistent even as your database grows. As it distributes your time series data across various pools, SiriDB optimally utilizes all available resources. Designed for exceptional performance, SiriDB minimizes downtime effectively. A cluster of SiriDB organizes time series data across several pools, each equipped with active replicas that facilitate load balancing and provide redundancy. Importantly, the database remains accessible even when one of the replicas is offline, ensuring continuity and reliability in data access. This architecture allows users to maintain high availability while benefiting from seamless scalability.

IBM Informix® stands out as a versatile and effective database solution, seamlessly integrating SQL, NoSQL/JSON, time series, and spatial data. Its user-friendly interface and adaptability make it an ideal option for a variety of environments, encompassing large enterprise data warehouses and smaller application development initiatives. The database's compact design and self-managing capabilities make it especially beneficial for applications involving embedded data management. As the need for IoT data processing continues to grow, Informix addresses this demand with its hybrid architecture, which minimizes administrative overhead while maintaining a small memory footprint and providing substantial functionality. It is particularly adept at supporting multi-tiered architectures that require data processing across different levels, including devices, gateways, and cloud infrastructures. Additionally, Informix features native encryption to protect data both at rest and during transmission. Its support for flexible schemas, along with various APIs and configurations, further enhances its versatility, making Informix a fitting solution for contemporary data management challenges. The combination of these attributes positions Informix as a leading contender in the database landscape, capable of meeting the evolving needs of businesses and developers alike.

An enterprise-grade database designed for handling time series, documents, and vectors. It allows for the storage of diverse data types while merging the ease and scalability of NoSQL with the capabilities of SQL. CrateDB stands out as a distributed database that executes queries in mere milliseconds, no matter the complexity, data volume, or speed of incoming data. This makes it an ideal solution for organizations that require rapid and efficient data processing.

Easily manage complex models with ArcadeDB without compromising on performance. There's no need to rely on Polyglot Persistence; you can store various data types without using multiple databases. In one ArcadeDB Multi-Model database, you can efficiently integrate graphs, documents, key-value pairs, and time series data seamlessly. Since each model is built directly into the database engine, worries about translation delays are a thing of the past. Designed with cutting-edge technology, ArcadeDB can handle millions of records per second effortlessly. A remarkable feature of ArcadeDB is its consistent traversal speed, which remains stable regardless of the database's size, whether it contains a handful of entries or billions. Additionally, ArcadeDB can function as an embedded database on a single server but is also capable of scaling across multiple servers with Kubernetes. Its adaptable nature allows it to run on any platform while using minimal resources. The security of your data is critical; our robust, fully transactional engine ensures durability for essential production databases. To maintain consistency across different servers, ArcadeDB utilizes a Raft Consensus Algorithm, guaranteeing that your data stays reliable and synchronized even in distributed settings. With ArcadeDB, you can effectively manage all your data requirements without the complications of handling multiple systems, ensuring a streamlined and efficient experience. Whether for small projects or large-scale applications, ArcadeDB provides the flexibility and performance needed to meet diverse data challenges.

The ITTIA DB suite unites sophisticated functionalities for time series analysis, real-time data streaming, and analytics specifically designed for embedded systems, thus simplifying development workflows while reducing costs. With ITTIA DB IoT, users benefit from a lightweight embedded database tailored for real-time tasks on constrained 32-bit microcontrollers (MCUs), whereas ITTIA DB SQL provides a powerful time-series embedded database that performs well on both single and multicore microprocessors (MPUs). These ITTIA DB solutions enable devices to efficiently monitor, process, and store real-time data. Furthermore, the products are meticulously crafted to cater to the requirements of Electronic Control Units (ECUs) in the automotive industry. To protect data integrity, ITTIA DB features robust security measures against unauthorized access, which include encryption, authentication, and the DB SEAL capability. In addition, ITTIA SDL complies with the IEC/ISO 62443 standards, underscoring its dedication to safety. By implementing ITTIA DB, developers are equipped to effortlessly gather, process, and refine incoming real-time data streams using a specialized Software Development Kit (SDK) designed for edge devices, enabling effective searching, filtering, joining, and aggregating of data directly at the edge. This all-encompassing strategy not only boosts performance but also addresses the increasing necessity for real-time data management in contemporary technological environments, ultimately benefiting a wide range of applications across various sectors.

A Time Series Database (TSDB) is designed to enable swift data reading and writing, effectively managing vast datasets with ease. It boasts remarkable compression ratios that significantly reduce storage costs. Furthermore, this service offers functionalities for visualizing precision reduction, conducting interpolation, and carrying out multi-metric aggregate computations in conjunction with query results. By minimizing storage expenses, the TSDB accelerates the processes of data writing, querying, and analysis. Consequently, it is adept at handling substantial amounts of data points, facilitating more frequent data acquisition. The adaptability of this system allows it to be utilized across various fields, such as IoT monitoring, enterprise energy management systems (EMSs), production security oversight, and power supply tracking. In addition, it enhances database architectures and algorithms, allowing for the reading and writing of millions of data points within mere seconds. Its implementation of a highly efficient compression algorithm reduces the size of each data point to just 2 bytes, achieving over 90% savings in storage costs. Thus, it serves as an essential resource for data-driven decision-making, operational efficiency, and advancing analytical capabilities in numerous applications. Ultimately, the integration of a TSDB can lead to improved performance and reliability in data handling across diverse industries.