Published at Jun 6, 2024
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Written by Junaid Ahmed
Time-series data has become a valuable asset in various industries and businesses. By recording data points indexed in time order, this type of data is helpful in observing, analyzing, and predicting trends. However, one of the downsides of time-series data is that it accumulates fast and grows incessantly, which has driven demand for dedicated databases over the years.
Source: DB Engines
Originally born in the IoT space (sensor data is time-series data), Timescale is an open-source time-series database management system built on top of PostgreSQL. It combines PostgreSQL's power and reliability with state-of-the-art capabilities to handle time-series data, events, and analytics.
In this article, we’ll compare Timescale with other key time-series DBMSs. We’ll start by looking at Timescale and its features before seeing how it stands up against other options.
Timescale is an advanced cloud database platform designed to handle events, analytics, and time-series data. It offers a stable and mature PostgreSQL base while providing features to handle demanding workloads.
Due to its many optimizations, Timescale excels at working with timestamped data, such as logs, metrics, sensor data, and financial data, along with other large and resource-intensive datasets. Its efficient query execution engine is also optimized for fast, complex analysis, which helps extract insights and action information from massive datasets. If you’re new to Timescale, the Timescale documentation offers a thorough guide to get started.
As it is built on top of PostgreSQL, users can benefit from the wide-reaching PostgreSQL ecosystem of tools and libraries. PostgreSQL is well known for its reliability, scalability, and robust feature set. Timescale inherits the same robustness and offers specialized functionality specifically attuned to time-series data.
Timescale includes many solid capabilities that provide high performance and unparalleled developer experience:
Automated table partitioning with hypertables: You can automatically partition a regular PostgreSQL table into smaller and more manageable units, called hypertables, instantly improving data management and query performance. The data partitions within hypertables are called chunks.
Advanced compression: Timescale offers advanced compression techniques like delta encoding, delta-of-delta, simple-8b, and run-length encoding. These methods conserve storage space and are essential for handling larger datasets for extended periods.
Incremental up-to-date materializations: Timescale provides up-to-date materialized views of data that are incrementally refreshed. This capability is crucial for updating data insights without re-computation.
Specialized analysis functions: Timescale also has specialized hyperfunctions that assist in analyzing time-series data with fewer lines of code. These functions include the time_bucket, rollup, and aggregate functions.
Timescale is known for effectively handling time-series data in a PostgreSQL environment, helping you build faster, scale further, and stay under budget. Yet, other Timescale alternatives have some notable features and merits. Let’s look at them.
InfluxDB is a well-known time-series database system specialized in managing large amounts of time-series data. It recently released version 3.0, which introduced many new significant improvements and features.
InfluxDB has always been built with a custom query language. It is highly optimized for real-time analytics, monitoring, and working with IoT applications.
InfluxDB's high performance in writing and querying data and flexibility are some of the core reasons why it is favored by developers and businesses that work with time-series data.
Together, Prometheus and Grafana make a robust monitoring and visualization system. Prometheus is a monitoring system, with the alerting system being its second essential component. Prometheus can store and query metrics as time-series data. It excels at scraping data points from numerous endpoints and alerting based on user-defined rules.
Grafana complements Prometheus by providing rich visualization capabilities. Users can create interactive and dynamic dashboards representing the metrics scraped by Prometheus. Although Grafana is not a time-series database, combining Prometheus for monitoring and Grafana for visualization can be an effective solution for many use cases.
MongoDB is a popular NoSQL database known for its scalability and flexibility. Although it is not tailored to be used for time-series data, its document-oriented nature and support for time-series collections can make it an efficient solution.
MongoDB provides dynamic schemas that can easily change with evolving data structures. Its horizontal scalability using sharding can effectively manage massive datasets.
MongoDB's general-purpose nature makes it a versatile choice for various data requirements.
Amazon Aurora is an AWS-managed relational database service that simplifies database scaling and management. It is fully compatible with MySQL and PostgreSQL and is designed as a drop-in replacement for users of these databases.
It simplifies and automates many of the most time-consuming and complicated administrative tasks, including scaling, backup, and patching. This frees customers to focus on optimizing their application performance with higher availability and resilience.
Amazon Aurora Database Structure
Aurora can offer up to five times the performance of standard MySQL databases and three times that of standard PostgreSQL databases.
ClickHouse is a column-based database management system optimized for online analytical processing (OLAP) operations. The database is known for high-speed query execution. It is particularly suited for real-time data reporting.
The query language is SQL-based, which makes it easy for users to use SQL. The storage structure of the ClickHouse is designed with read-intensive operations. The horizontal scaling and distributed processing of the ClickHouse enable the efficient processing of extensive data.
Now, let’s look at the strengths and weaknesses of each alternative compared to Timescale. We’ll list some of the key benefits and drawbacks of leading solutions and compare them to Timescale.
InfluxDB's time-series nature gives it certain advantages:
Specialization: InfluxDB is designed to handle time-series data and offers enhanced efficiency for such tasks.
IoT workloads: It is optimal for high ingestion rates, especially in IoT applications, due to its design.
However, InfluxDB’s use of a custom query language can be a barrier:
Query language: InfluxDB’s query language is unique to the system and may not be well-known by many engineers so more learning will be required.
Community and extensions: On the other hand, Timescale, which is built on PostgreSQL, doesn’t require a learning curve. Most developers know SQL. Plus, it enjoys the support of a large community of SQL developers and a wide range of PostgreSQL extensions.
Check out this article for five InfluxDB alternatives.
Wide table, IoT workloads: InfluxDB performs well in scenarios requiring high data input rates and wide tables, which are typical of IoT use cases. Its architecture is suited for handling time-series data, enabling high data ingestion rates and fast query response times. This makes it suitable for applications like sensor data, where data is continuously produced, as well as for analytics and monitoring. However, it may not be ideal for applications with complex queries and connections between different kinds of data, as it is not as flexible as SQL in querying data.
Varied workloads: Timescale, backed by all the PostgreSQL features, is compatible with numerous extensions and well-suited for various workloads. It is best used in applications that require a combination of capabilities for real-time analysis, historical analysis, and relational data.
Generally combined to build alerting and visualization systems, Prometheus and Grafana have some pros and cons:
Monitoring and visualization: While it is impressively bad for data visualization, Prometheus provides great tools with persistent tracking and alerting capabilities. Combined, Prometheus and Grafana are an ideal couple that is generally used for visualization purposes, creating a real-time monitoring stack. They are standard in communities like DevOps and IT operations.
Data storage limitation: Prometheus is not meant for long-term data storage and is terrible for lengthy, extended historical analysis. By design, Prometheus stores data for very short durations but at high resolutions. As such, it lacks the long-term storage capabilities exhibited by other alternatives, like Timescale.
Timescale can handle alerts and visualizations pretty well using PostgreSQL libraries as a foundation. It can integrate quickly with any visualization tool, such as Grafana, to provide a complete monitoring and alerting solution.
Timescale also provides a long-term store for practical data analysis, which Prometheus alone can’t deliver. This makes Timescale vastly better for use cases wherein one needs to serve real-time and historical data analysis.
Lightweight alerting setups: Both Prometheus and Grafana perform well in creating lightweight alert setups, especially when data does not need to be stored for an extended period. This setup is most suitable for application performance monitoring and infrastructure monitoring with real-time alerts, where rapid detection and response to problems are critical.
Complex workloads: Timescale is ideal for scenarios requiring both alerting and historical data storage, allowing for the analysis of past data. It is a sufficient solution for organizational needs involving long-term historical metrics and deep analytical pattern trends over an extended period. This makes it an excellent choice for applications that require immediate alerting and long-term data analysis, such as financial analytics, health monitoring, and industrial IoT.
Let’s now focus on MongoDB and its advantages and disadvantages:
Generalistic database: MongoDB is a general-purpose database solution that accommodates heterogeneous application workloads. It is not specifically designed for time-series data, which allows for agility in schema design with its document-oriented structure. However, storing time-series data in MongoDB can present challenges. Read our article to learn how to store time-series data in MongoDB and why this is a bad idea.
MongoDB is best suited for applications requiring horizontal scalability with massive data handling for high throughput and low latency. This differs slightly from time-series data handling, where Timescale excels.
Performance: Timescale offers improved performance for time-series workloads, as it is built on PostgreSQL. Key features include native partitioning, efficient data compression, and advanced indexing for time. This makes Timescale more storage-efficient and capable of advanced compression without sacrificing performance, reducing the cost of storing large amounts of data and enhancing read and write performance.
Scaling database solution: For large-scale database solutions with a wide variety of use cases, MongoDB is highly effective. It performs well in applications with variable data structures and those requiring significant horizontal scaling.
Time-series-specific: Timescale is a powerful tool specifically designed for time-series data. It is engineered to support high-throughput, time-based queries and efficiently manage large volumes of timestamped data. This makes it ideal for applications such as financial market analysis, telemetry data processing, and real-time analytics.
It is easy to integrate a cloud-native relational database compatible with PostgreSQL and the AWS environment using Amazon Aurora:
PostgreSQL hosting: Amazon Aurora supports PostgreSQL databases and time-series applications and offers several managed services. This includes automated backups and high availability, making it serviceable for AWS users.
AWS integration: It has a fully integrated relationship with the AWS ecosystem. It comes with IAM authentication and VPC support and is compatible with other AWS services such as Lambda, S3, and CloudWatch.
But for time-series workloads, Timescale outperforms Aurora on many fronts:
Data compression: Timescale users often see compression rates of 95 % or more.
Cost-efficiency: Timescale is 52% cheaper on compute costs per hour, offering users considerable savings for long data-series applications.
Ingest speed: Timescale ingests data on average at 35 % better speeds to cope with higher-volume data ingest.
General use: Aurora best suits applications requiring PostgreSQL or MySQL databases with cloud-native features and AWS integration.
Time-series data processing: For applications that process time-series data and other general-purpose workloads, Timescale is a good option due to its performance and cost-efficiency. It suits large-scale time-series data analysis applications such as IoT, business intelligence, finance, crypto, and environmental monitoring, among others. It’s not just PostgreSQL in the cloud, it’s supercharged PostgreSQL in the cloud.
ClickHouse is a high-performance column-oriented database management system for OLAP (OnLine Analytical Processing) applications.
Performance: ClickHouse is optimized for processing huge volumes of data that do not change much over time. Its columnar storage format uses a highly advanced and efficient data compression method. This makes data storage and query performance reliable.
SQL features: It is a great choice for fast analytical workloads in aggregation and reporting.
However, it lacks some fundamental features and robustness when compared to Timescale:
Feature limitations: Although ClickHouse has numerous features, it is explicitly restricted to OLAP uses. One notable feature it lacks compared to Timescale is support for joins.
Consistency: ClickHouse does not follow ACID principles, making it less consistent and robust compared to Timescale. This can be a concern for applications that require rigorous transactional guarantees and data integrity.
OLAP workloads: ClickHouse is highly competent as an OLAP work tool. It is suitable for businesses that require high performance with analytical queries, including data warehousing, real-time analytics, and business intelligence.
General time-series workloads: Timescale is the better option for businesses that require more complex queries on time-series data. It provides tools for historical data analysis, real-time monitoring, and execution of complex queries. That sets Timescale up for broad adoption across diverse industries, such as finance, healthcare, and manufacturing, for which performance and flexibility are relevant.
Many time-series database systems are available on the market, all optimized for different functions and use cases. While some systems are more effective in specific areas, Timescale’s performant PostgreSQL scaling offers the most efficient, robust, and usable time-series database management system on the market.
Timescale allows users to analyze large amounts of time-series data and perform complex queries in a worry-free experience. For monitoring, analytics, or even storing historical data, Timescale offers a flexible and scalable solution that aligns with the changing needs of today’s demanding applications.
See if Timescale is the right choice for you by signing up for a free trial. This is free for 30 days; no credit card required.
Want to see what Timescale is really capable of? Read how we scaled PostgreSQL to more than 350 TB (currently waaaay more than that) while handling 10 billion new records daily.