Published at May 6, 2024
Written by Team Timescale
Time-series data has become integral to today’s business data infrastructure. From monitoring system metrics and tracking user behavior to analyzing financial data and sensor readings, time-series data enables organizations to make informed decisions and gain valuable insights.
To effectively store, manage, and analyze this ever-growing volume of data, business implementations are increasingly turning to time-series databases (TSDBs). These specialized databases are designed to handle the unique characteristics of time-series data, such as high write throughput, efficient compression, and optimized query performance.
While proprietary time-series database solutions are available, open-source TSDBs have gained significant popularity due to their flexibility, cost-effectiveness, and community-driven development. Open-source TSDBs offer extensible and customizable solutions that can be tailored to meet an organization's specific needs.
However, with the abundance of open-source time-series database options available, it can take time to determine which one best fits your requirements. Each TSDB has its strengths, limitations, and use cases, making the selection process a critical decision for your use case.
In this article, we will:
Explore the options for open-source time-series databases, including specialized solutions and PostgreSQL-based extensions.
Examine the pros and cons of each option, considering factors such as scalability, performance, ease of use, and community support.
Guide to help you choose an open-source time-series database for your specific needs.
By the end of this article, you will have a clearer understanding of the open-source TSDB landscape and be better equipped to select the right solution for your time-series data management and analysis requirements.
A time-series database (TSDB) is a specialized database designed to store, manage, and analyze time-series data efficiently. Time-series data consists of data points collected at regular intervals, enabling you to monitor and track changes over time. Sensors, IoT devices, financial systems, and monitoring tools commonly generate this data type.
Examples of time-series data include the following:
Sensor readings from industrial equipment
Stock prices and trading volumes
User engagement metrics for web and mobile applications
Server performance metrics like CPU usage and memory utilization
Time-series data has unique characteristics that make it challenging for traditional databases to handle efficiently. TSDBs are optimized to address these challenges:
High-volume writes: Time-series data is often generated in high volumes, with data points continuously being collected. TSDBs are designed to handle high write throughput, allowing for efficient ingestion of large amounts of data.
Time-based queries: Queries on time-series data often involve time-based filtering, aggregation, and analysis. TSDBs provide optimized query performance for time-based queries, leveraging the data's inherent time-based structure.
Scalability: As time-series data accumulates, TSDBs must scale to accommodate growing data storage needs. They offer scalable architectures that allow for horizontal scaling and distributed storage.
When evaluating time-series databases, there are several fundamental properties to consider:
Scalability: The TSDB should be able to handle high-volume data ingestion and scale horizontally to accommodate growing data storage needs. It should maintain performance and reliability as the dataset grows over time.
Maintainability: The database should be easy to maintain, with clear documentation, community support, and streamlined processes for upgrades and migrations. It should provide tools and features to simplify tasks like data retention, compression, and backup/recovery.
Reliability: The TSDB should provide high availability and fault tolerance to ensure data integrity and minimize downtime. It should have mechanisms to handle node failures, data replication, and disaster recovery.
Usability (query language): The TSDB's query language and APIs should be intuitive and easy to use. They should support common time-series query patterns and provide a familiar interface for developers (e.g., SQL or a SQL-like language).
When it comes to open-source time-series databases, there are several types of options available:
1. Specialized solutions: Specialized TSDBs offer purpose-built features and optimizations for handling time-series data efficiently. They often have query languages, storage engines, and data models explicitly tailored for time series workloads.
Examples of specialized TSDBs include:
2. PostgreSQL: An open-source, industry-standard relational database with extensive community support. While not explicitly designed for time-series data, PostgreSQL's flexibility and robustness make it a viable option for storing and querying time-series data, especially for smaller-scale use cases or when integration with other relational data is required.
3. PostgreSQL extensions: With the right extensions, PostgreSQL becomes a powerful and versatile platform, becoming a key-value store, a geospatial database, a graph database, or even a vector database. To bridge the gap between PostgreSQL's general-purpose capabilities and the specific requirements of time-series workloads, several extensions have been developed that build on top of PostgreSQL and provide additional functionality and optimizations. Such extensions, like TimescaleDB, leverage PostgreSQL's extensibility and add time series-specific features, such as:
Optimized storage: optimized storage formats and compression techniques to efficiently store large volumes of time-series data.
Automated partitioning: automation of the partitioning of time-series data based on time intervals, improving query performance and simplifying data management.
Time series-specific functions: extensions like TimescaleDB provide additional functions and operators specifically designed for time-series analysis (hyperfunctions), such as time-based aggregations, interpolation, and downsampling.
Each option has strengths and considerations, which we will explore in more detail in the following sections.
Specialized time-series databases are purpose-built database systems designed from the ground up specifically for storing, managing, and analyzing time-series data. Dedicated companies or teams create these databases to address the unique challenges and requirements of time-series workloads.
Key characteristics of specialized TSDBs include:
Optimized storage engines that efficiently handle high write throughput and large data volumes
Specific data models and schemas tailored for time-series data
Query languages and APIs designed for time series analysis and aggregation
Built-in features for data retention, downsampling, and data compression
In addition to the core database functionality, the companies or teams behind these specialized solutions may offer additional services, such as:
Managed cloud hosting and infrastructure provisioning
Enterprise support and consulting services
Integration with other tools and platforms in the time-series ecosystem
Examples of specialized time-series databases include:
InfluxDB
Developed by InfluxData
SQL-like query language called InfluxQL (in InfluxDB v2, the language is Flux, a functional data scripting language)
Supports high write throughput and fast query performance
Provides features like continuous queries (tasks in InfluxDB v2), data retention policies, and downsampling
Prometheus
Created by SoundCloud, now maintained by the Cloud Native Computing Foundation (CNCF)
Powerful query language called PromQL for time-series data analysis
Designed for monitoring and alerting in cloud-native environments; it is mostly an observability store to collect metrics, enable quick integration with other tools, and swift dashboard building and alerting, offering little flexibility if you want to merge business data and other types of data into a single database
Integrates well with Kubernetes and other cloud-native tools
Pros of specialized solutions:
Purpose-built for time-series data, offering optimized performance and storage efficiency
Provide a set of features and functionalities specifically designed for time-series workloads
Often have robust ecosystem integrations and managed service offerings
Cons of specialized solutions:
May have a steeper learning curve due to non-standard query languages and APIs
For example, some users have found Prometheus' query language (PromQL) opaque and difficult to adapt.
Limited community support compared to more widely used general-purpose databases
InfluxDB users have reported challenges in finding debugging solutions and resources.
Original designs may have limitations or untested features
For instance, InfluxDB has had issues with changing schemas or updating existing entries.
PostgreSQL is a widely used, open-source relational database management system (RDBMS) known for its reliability, efficiency, and strong community support. While not explicitly designed as a time-series database, PostgreSQL's flexibility and robustness make it a viable option for storing and querying time-series data, particularly for smaller-scale use cases or when integration with other relational data is necessary.
Key characteristics of PostgreSQL include:
ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring data integrity and reliability
Extensive SQL support and advanced query capabilities
A rich ecosystem of extensions and tools for various use cases
Cross-platform compatibility and wide industry adoption
Pros of using PostgreSQL for time-series data:
Proven and reliable
PostgreSQL is a mature and battle-tested database that meets industry standards.
It has been widely adopted and is trusted by organizations of all sizes.
Large community support
PostgreSQL has a vast and active community, providing extensive learning, troubleshooting, and optimization resources.
Numerous extensions and tools are available to extend PostgreSQL's functionality and cater to specific use cases.
Familiar SQL interface
PostgreSQL uses standard SQL, making it easy for developers and analysts familiar with SQL to work with the database.
There is no need to learn a new query language specific to a particular time-series database.
Cons of using PostgreSQL for time-series data:
Not optimized for time series workloads
PostgreSQL's general-purpose design may provide a different level of performance than specialized time-series databases.
It may need help with the high write throughput and large data volumes typical in time-series scenarios.
Scalability challenges
PostgreSQL's performance may degrade when faced with the high-volume inserts and queries typical of time-series data.
Scaling PostgreSQL horizontally can be complex and require additional engineering effort.
Lack of native time-series optimizations
PostgreSQL does not have built-in features designed explicitly for time-series data, such as automatic data retention, downsampling, or time-based partitioning.
Implementing these optimizations may require manual effort or the use of external extensions.
To mitigate performance and scalability challenges, developers can leverage PostgreSQL extensions. PostgreSQL extensions specifically designed for time-series data, such as TimescaleDB, can provide additional optimizations and features while retaining compatibility with the PostgreSQL ecosystem.
PostgreSQL extensions are additional modules that can be installed on top of a PostgreSQL database to provide enhanced functionality and performance. These extensions leverage PostgreSQL's extensibility and add features designed to handle time series workloads more efficiently.
Key characteristics of PostgreSQL extensions for time-series data:
Seamless integration with the PostgreSQL database engine
Enhanced performance and scalability for time-series workloads
Additional features and optimizations not available in core PostgreSQL
PostgreSQL extensions build upon the reliability and flexibility of PostgreSQL while addressing some of its limitations when dealing with time-series data. They introduce specialized data types, indexing techniques, and query optimizations to improve the storage and retrieval of time-series data.
TimescaleDB
TimescaleDB is an open-source PostgreSQL extension that transforms PostgreSQL into a highly performant time-series database.
It provides automatic partitioning, optimized data storage, and fast query performance for time series workloads.
Key features of TimescaleDB:
High-performance time-series data storage and retrieval
TimescaleDB's hypertable abstraction automatically partitions data based on time, enabling efficient storage and retrieval of large time-series datasets.
It offers performance that matches or surpasses specialized time-series databases like InfluxDB.
Full SQL support
TimescaleDB retains full compatibility with PostgreSQL's SQL interface, allowing developers to use familiar SQL syntax and tools.
It extends SQL with additional time series-specific functions and operators, making performing complex time series queries and aggregations easier.
Columnar compression
TimescaleDB introduces columnar compression, which leverages the properties of time-series data to achieve high compression ratios.
By storing data in a columnar format and applying advanced compression techniques, TimescaleDB significantly reduces storage requirements and improves query performance.
The benefits of using PostgreSQL extensions for time-series data include the following:
Leveraging the reliability, flexibility, and ecosystem of PostgreSQL
Improving performance and scalability for time series workloads without sacrificing SQL compatibility
Accessing a wide range of time series-specific features and optimizations
Simplifying database management and reducing the need for multiple specialized databases
Overall, PostgreSQL extensions provide a compelling option for organizations looking to handle time-series data while leveraging the strength and familiarity of PostgreSQL. They balance the benefits of a specialized time-series database and the advantages of using a well-established relational database system.
In this article, we have explored various open-source time-series databases and options available to you if you want to efficiently store, manage, and analyze your time-series data.
We started by discussing the importance of time-series data and the need for specialized databases to handle the unique characteristics of this data. We then discussed the fundamental properties to consider when evaluating time-series databases: scalability, maintainability, reliability, and usability.
Next, we examined the different categories of open-source time-series databases:
Specialized solutions like InfluxDB and Prometheus are purpose-built for time-series workloads and offer optimized performance and features.
PostgreSQL is a popular open-source relational database that, while not explicitly designed for time-series data, can still be viable in specific scenarios.
PostgreSQL extensions, such as TimescaleDB, build upon PostgreSQL's capabilities and provide enhanced functionality and optimizations for time-series data.
We discussed each category's key characteristics, pros, and cons, providing insights to help readers make informed decisions based on their specific requirements.
Throughout the article, we have emphasized the importance of considering scalability, performance, ease of use, community support, and integration factors when selecting an open-source time-series database.
If you're looking for a high-performance, SQL-compatible time-series database, TimescaleDB is an excellent choice. Built on the foundation of PostgreSQL, TimescaleDB offers seamless integration, powerful features, and optimized performance for time-series workloads.
Sign up for a free trial to experience TimescaleDB's benefits firsthand and unlock the full potential of your time-series data.