Get started with InfluxDB Cloud Serverless

InfluxDB Cloud Serverless is the platform purpose-built to collect, store, process and visualize time series data. The InfluxDB IOx storage engine 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 InfluxDB Cloud Serverless, 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 data model organizes time series data into buckets and measurements. A bucket can contain multiple measurements. Measurements contain multiple tags and fields.

• Bucket: Named location where time series data is stored. A bucket 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 interact with InfluxDB Cloud Serverless. Examples are provided for each of the following:

• InfluxDB user interface
• influx CLI
• InfluxDB HTTP API

InfluxDB user interface

The InfluxDB user interface (UI) provides a web-based visual interface for interacting with and managing InfluxDB. To access the InfluxDB Cloud Serverless UI, log into your InfluxDB Cloud account.

influx CLI

The influx CLI lets you interact with and manage InfluxDB Cloud Serverless from a command line. For detailed CLI installation instructions, see the influx CLI reference.

InfluxDB HTTP API

The InfluxDB API provides a simple way to interact with the InfluxDB Cloud Serverless using HTTP(S) clients. Examples in this tutorial use cURL, but any HTTP(S) client will work.

Authorization

InfluxDB Cloud Serverless requires authentication using API tokens. Each API token is associated with a user and a specific set of permissions for InfluxDB resources.