Get started with InfluxDB Clustered

InfluxDB Clustered is a highly available InfluxDB 3.0 cluster hosted and managed on your own infrastructure and is the platform purpose-built to collect, store, and query time series data. It is powered by the InfluxDB 3.0 storage engine which provides a number of benefits including nearly unlimited series cardinality, improved query performance, and interoperability with widely used data processing tools and platforms.

Time series data is a sequence of data points indexed in time order. Data points typically consist of successive measurements made from the same source and are used to track changes over time. Examples of time series data include:

• Industrial sensor data
• Server performance metrics
• Heartbeats per minute
• Electrical activity in the brain
• Rainfall measurements
• Stock prices

This multi-part tutorial walks you through writing time series data to your InfluxDB cluster, querying, and then visualizing that data.

Key concepts before you get started

Before you get started using InfluxDB, it’s important to understand how time series data is organized and stored in InfluxDB and some key definitions that are used throughout this documentation.

• Data organization
• Schema on write
• Important definitions

Data organization

The InfluxDB Clustered data model organizes time series data into databases and measurements. A database can contain multiple measurements. Measurements contain multiple tags and fields.

• Database: Named location where time series data is stored. A database can contain multiple measurements.
  • Measurement: Logical grouping for time series data. All points in a given measurement should have the same tags. A measurement contains multiple tags and fields.
    • Tags: Key-value pairs that provide metadata for each point–for example, something to identify the source or context of the data like host, location, station, etc. Tag values may be null.
    • Fields: Key-value pairs with values that change over time–for example, temperature, pressure, stock price, etc. Field values may be null, but at least one field value is not null on any given row.
    • Timestamp: Timestamp associated with the data. When stored on disk and queried, all data is ordered by time. A timestamp is never null.

Schema on write

When using InfluxDB, you define your schema as you write your data. You don’t need to create measurements (equivalent to a relational table) or explicitly define the schema of the measurement. Measurement schemas are defined by the schema of data as it is written to the measurement.

Important definitions

The following definitions are important to understand when using InfluxDB:

• Point: Single data record identified by its measurement, tag keys, tag values, field key, and timestamp.
• Series: A group of points with the same measurement, tag keys and values, and field key.
• Primary key: Columns used to uniquely identify each row in a table. Rows are uniquely identified by their timestamp and tag set. A row’s primary key tag set does not include tags with null values.

Example InfluxDB query results

name: weather

Tools to use

Throughout this tutorial, there are multiple tools you can use to manage and interact with your InfluxDB cluster. Examples are provided for each of the following:

Authorization

InfluxDB Clustered requires authentication using tokens.

There are two types of tokens:

• Database token: A token that grants read and write access to InfluxDB databases.

• Management token: A short-lived (1 hour) Auth0 token used to administer your InfluxDB cluster. These are generated by the influxctl and do not require any direct management. Management tokens authorize a user to perform tasks related to:

  • Account management
  • Database management
  • Database token management
  • Pricing