A time series database provides real-time applications with recordsets based on a time frame. While all databases allow for storing data with a timestamp, a time series database is optimised for recordsets where the data tells a story in chronological order. The duration of a set of data could be within a few milliseconds of each other, or a time series database could be used for hourly, daily, monthly, or yearly data points.
What Is a Time Series Database?
While every database engine lets developers store a timestamp in a record, a time series database (TSDB) is specifically designed for recordsets focused on a time frame. For example, you might need minute-to-minute updates in a weather application. A time series database can store data for every minute to display to users. A TSDB can be an option instead of standard NoSQL databases like MongoDB when you need more refined time-based queries.
Because a TSDB is mainly used to retrieve data, these databases are often optimised for reads and creating new records. Because TSDBs rarely update records, they’re built for better retrieval of records based on a timestamp compared to other common databases. TSDBs are also best for real-time applications where users are shown a linear set of information based on the timestamp in each record.
How Does a Time Series Database Work?
The best way to understand how a time series database works is to use an example. Suppose that you have sensors on a machine that relies on temperature. Temperatures change minute by minute, so a time series database can be used to track the temperature and the timestamp. Other machine properties could be recorded, including the location of the machine, humidity, and ambient temperature.
As the machine runs, sensors store data in a TSDB. The TSDB stores each record in order as the sensors collect information. You wouldn’t need to update records, but you would need to retrieve them to monitor the system. The TSDB storage engine works by optimizing queries for large data sets and retrieving data based on record timestamps. Streams of timestamped data are sent to analysts to monitor machinery and make decisions for maintenance.
Benefits of Using a Time Series Database
A TSDB engine is built for storing large volumes of data and delivering large recordsets. Real-time applications benefit from a TSDB's optimised engine for data retrieval every second or minute. Monitoring solutions requiring predictions from large data volumes or real-time information benefit from the scale and speed of a time series database.
Because records usually contain a small number of fields, querying is simplified for developers. A TSDB might return thousands of records, but the recordset often contains only a few fields. The limited number of fields and simplified queries speed up the performance of real-time applications and reduce overhead on the database server.
Use Cases for Time Series Databases
Industries where IoT is common or large data points with timestamps are necessary for predictions and monitoring are ideal for a time series database. Manufacturing and other industries where IoT sensors are critical for uptime work with time series databases to predict issues before they interrupt services. For example, a manufacturer uses IoT sensor data to predict when machinery needs maintenance or could soon suffer from hardware failure.
Large networking applications can work with time series databases to predict outages or identify potential threats. Critical network infrastructure could support a large internet service provider or a data centre, and timestamped data could alert a security analyst to a potential outage when ambient temperatures are too high or hardware sends corrupted information, indicating potential downtime. Any real-time critical application works with a time series database for performance and scalability. For example, if server CPU usage spikes frequently, it might be time to update it or replace a CPU to avoid downtime and system crashes.
Popular Time Series Database Solutions
If you haven’t worked with a time series database, there are several popular options to choose from. Here are a few TSDB solutions for your applications:
- InfluxDB: This columnar solution is for optimised real-time queries and columnar analytics.
- Prometheus: This TSDB is ideal for dimensional data and visualization analytics. Prometheus developers provide an open source monitoring tool and database engine.
- Timescale: This popular TSDB is used for IoT, sensors, crypto, and AI analytics.
Conclusion
Since it’s difficult to change database providers, you’ll want to use the right one from the start. If your application requires real-time analytics or output based on time-stamped data, a time series database is probably right for you. Whether you use a time series database, big data analytics, or traditional database engines, Pure Storage has the storage solution to support your real-time, high-volume applications.
