InfluxDB, an excellent open source DB to handle large amounts of data

When it comes to choosing a database for a new project or an existing one to replace the one you are working with, I have already mentioned here on the blog that the best website to find an option is DB Engines, in which we can find a large number of databases and of which I am sure you did not even know of their existence.

But moving on to the main topic, This article in which we will talk today is about InfluxDB which is an excellent option for handling large amounts of data without having to sacrifice performance.

We should know that InfluxDB is a database optimized for time series data and can be used in the on-premises data center or as a cloud solution on Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Computing.

The time series database (TSDB) can be operated without a server in the cloud or with its own servers in the data center. The database is being developed by the American company Influxdata.

InfluxDB focuses on storing large amounts of data in the scientific field and data sent by sensors. InfluxDB it is much faster than conventional databases when it comes to storing and managing time series. Real-time processing is also possible, as well as querying the data with the internal query language Flux, which is based on Javascript.

This looks more like a programming language than a SQL query language listening on port 8086, plus InfluxDB has no external dependencies and has time-focused built-in functions for querying a data structure composed of measures, series and points. Each point consists of several key-value pairs called a fieldset and a timestamp. When grouped by a set of key-value pairs called a tag set, they define a series. Finally, the series are grouped by a string identifier to form a measure.

Values ​​can be 64-bit integers, 64-bit floating points, strings, and Boolean values. Points are indexed by their time and tag set. Retention policies are defined in a metric and control how data is reduced and removed. Continuous queries run periodically and store the results in a target metric.

If the time series are to be stored in databases, for example when using Internet of Things infrastructures, InfluxDB can be used to save sensor information, including timestamps. Since timing plays an important role in InfluxDB, an internal timing service ensures that all nodes in the InfluxDB cluster run synchronously. Of course, InfluxDB is also suitable for storing monitoring data on company networks.

The databases in InfluxDB don't have to be complicated and provide dozens of columns. It makes sense to use it with only a few columns if, for example, certain measured values ​​from a sensor need to be saved as a function of time.

If data from many sources must be received and processed in parallel, for example in the case of sensors, it is necessary that the associated database can handle these parallel queries quickly. Since data is often received in real time, the write performance of the database must be tailored accordingly. In addition, there is the challenge that measurement data from sensors is not always accurately written and defined. Time series databases can still store this data and make it available.

In addition, once a time series data has been saved, it is seldom necessary to update it later. Therefore, it is not necessary to optimize a time series database for this. In addition, there are functions required to delete or compress outdated data that is no longer needed. These tasks are also part of rapid time series data processing.

InfluxDB consists of only a few components that are available for Linux and macOS. All functions are contained in one file, making it easy to install and operate.

Finally, if you are interested in knowing more about it, you can check the details in the following link.


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