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Are you looking for a solution to process a large volume of time-stamped data, or do you have issues handling high data processing? Now more than ever, companies collect and store a lot of data for analytical queries and real-time analytics, therefore choosing the right time-series database is crucial to handle this growing demand. With so many databases in the market, each with its own specific features, it can be quite overwhelming to make the right decision. This article will guide you through the importance, key features, scalable open-source time-series databases, managed solutions, and general recommendations to make your decision process easier and efficient.
Scalable time-series databases are fast becoming essential for various industries that require real-time analytics, efficient processing of large amounts of high-velocity time-series data, and quick insights for better decision-making. From monitoring devices to analyzing financial data applications, energy management, the Internet of Things (IoT), manufacturing, and many other industries, the significance of time-series databases cannot be overstated.
The handling of time-series data requires specialized databases that can collect data at speed and scale, handle millions or even billions of data points, support maintenance and scalability, and leverage time-based aggregation or time-based aggregate queries. Time-series data is a unique category with fields varying from industrial machine metrics to social media mentions, resulting in the need for a specialized set of features for processing. For instance, data retention, summarization, automatic downsampling, continuous aggregates, and data compression among others.
In the current technical discussion, choosing the right time-series database can be overwhelming, with the database market featuring various options. The top 15 time-series databases based on search engine popularity and social media mentions are InfluxDB, Prometheus, TimescaleDB, Graphite, QuestDB, AWS Timestream, OpenTSDB, and VictoriaMetrics, just to mention a few. However, different databases have different features, and the right database for your application largely depends on the requirements and features you need. In the next sections, we’re going to go through what you need to look for when choosing a scalable time-series database, as well as the solutions available.##Current State of Scalable Time-Series Databases
Scalable time-series databases are crucial for various industries, including finance, manufacturing, logistics, and IoT, that need to analyze large amounts of time-based data quickly and efficiently. Selecting the right scalable time-series database can make a big difference in terms of developer productivity, efficiency, and cost-effectiveness.
Some of the most popular scalable time-series databases are InfluxDB, Prometheus, TimescaleDB, Graphite, QuestDB, AWS Timestream, OpenTSDB, and VictoriaMetrics. These databases can handle vast amounts of data at high rates and scales, enabling real-time analytics and decision-making.
For instance, InfluxDB is unique in that it was built from the ground up as a purpose-built time-series database that can handle machine data monitoring and sensor data collection, with an advanced query engine in the Flux query language and support for custom query languages. TimescaleDB is an open-source PostgreSQL extension that provides full SQL support and comes with native compression and automatic downsampling features. Prometheus is an open-source monitoring system that provides an efficient time-series database to collect metrics from a variety of sources. AWS Timestream is a managed serverless database that automatically scales to handle thousands of requests per second and millions of metrics per second, providing fast and predictable performance.
Choosing the right time-series database depends on various factors, including scalability, maintainability, reliability, query language, specialized functionalities, and cost of scale. On the other hand, hardware resources are key to ensuring that a database can operate fast and scale according to your application’s needs. Object storage versus the local file system should also be evaluated.
Choosing a Scalable Time-Series Database: Considerations and Benefits
With so many databases to choose from, it can be quite confusing to make the right choice. Here are some essential considerations and benefits to consider when selecting a scalable time-series database.
Scalability and Availability
Scalability is a critical aspect when selecting a time-series database, especially for applications that require processing a large volume of data. Scalable time-series databases should also support cluster scalability, which means they can run on multiple nodes and handle multiple data types. These clusters must provide high availability to ensure that your data remains accessible and that your queries are answered, even during phases of high load.
Maintainability is the ease with which a database is managed by administrators, developers, and operators. It includes components like data lifecycle management, data compression, automated monitoring, and resiliency. Automated monitoring helps in the maintenance of uptime and query performance while tracking resource usage, query return rates, and disk usage.
Reliability refers to the ability to handle large amounts of data and ensure that the data remains accurate, accessible, and coherent. When a database fails, it needs to be able to recover from its state without loss of data or critical downtime.
Specialized databases like time-series databases need specialized query languages to handle time-stamped data efficiently. Query languages should enable efficient querying of recent data while ingesting large amounts of data.
Scalable time-series databases allow for more efficient processing of large amounts of data, especially time-series data, allowing real-time analytics and faster insights into your data.
Joining Time-Series Data
The ability to join time-series data with other business data allows for more relevant insights into your data, leading to more informed decisions.
Downsampling is the ability to aggregate data over time intervals in real-time. By handling data aggregation in real-time, scalable time-series databases have the ability to handle volumes of data.
Scalable time-series databases enable efficient processing of large amounts of data, ensuring optimal performance, and lower costs.
Scalable time-series databases enable real-time analytics, which is perfect for applications that require monitoring, alerts, and insights.