InfluxDB is an open-source time series database developed by InfluxData. It is written in Go and optimized for fast,…
InfluxDB is an open-source time series database developed by InfluxData. It is written in Go and optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics. It also has support for processing data from Graphite.
InfluxDB has no external dependencies and provides a SQL-like language with builtin time-centric functions for querying a data structure composed of measurements, series, and points. Each point consists of several key-value pairs called the fieldset and a timestamp. When grouped together by a set of key-value pairs called the tagset, these define a series. Finally, series are grouped together by a string identifier to form a measurement.
Values can be 64-bit integers, 64-bit floating points, strings, and booleans.
Points are indexed by their time and tagset.
Retention policies are defined on a measurement and control how data is downsampled and deleted.
Continuous Queries run periodically, storing results in a target measurement.
InfluxDB provides InfluxQL as a SQL-like query language for interacting with your data. It has been lovingly crafted to feel familiar to those coming from other SQL or SQL-like environments while also providing features specific to storing and analyzing time series data. InfluxQL also supports regular expressions, arithmetic expressions, and time series specific functions to speed up data processing.
InfluxDB can handle millions of data points per second. Working with that much data over a long period of time can create storage concerns. InfluxDB will automatically compact the data to minimize your storage space. In addition, you can easily downsample the data; keeping the high precision raw data for only a limited time, and storing the lower precision, summarized data for much longer or forever. InfluxDB offers two features—Continuous Queries (CQ) and Retention Policies (RP)—that help you automate the process of downsampling data and expiring old data.
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