Get started with InfluxDB tasks

An InfluxDB task is a scheduled Flux script that takes a stream of input data, modifies or analyzes it in some way, then writes the modified data back to InfluxDB or performs other actions.

This article walks through writing a basic InfluxDB task that downsamples data and stores it in a new bucket.

Components of a task

Every InfluxDB task needs the following components. Their form and order can vary, but they are all essential parts of a task.

• Task options
• A data source
• Data processing or transformation
• A destination

Skip to the full example task script

Define task options

Task options define the schedule, name, and other information about the task. The following example shows how to set task options in a Flux script:

option task = {name: "downsample_5m_precision", every: 1h, offset: 0m}

See Task configuration options for detailed information about each option.

Note that InfluxDB doesn’t guarantee that a task will run at the scheduled time. See View task run logs for a task for detailed information on task service-level agreements (SLAs).

Retrieve and filter data

A minimal Flux script uses the following functions to retrieve a specified amount of data from a data source and then filter the data based on time or column values:

1. from(): queries data from InfluxDB .
2. range(): defines the time range to return data from.
3. filter(): filters data based on column values.

The following sample Flux retrieves data from an InfluxDB bucket and then filters by the _measurement and host columns:

from(bucket: "example-bucket")
    |> range(start: -task.every)
    |> filter(fn: (r) => r._measurement == "mem" and r.host == "myHost")

To retrieve data from other sources, see Flux input functions.

from(bucket: "example-bucket")
    |> range(start: -task.every)
    |> filter(fn: (r) => r._measurement == "mem" and r.host == "myHost")

Process or transform your data

Tasks run scripts automatically at regular intervals. Scripts process or transform data in some way–for example: downsampling, detecting anomalies, or sending notifications.

Consider a task that runs hourly and downsamples data by calculating the average of set intervals. It uses aggregateWindow() to group points into 5-minute (5m) windows and calculate the average of each window with mean().

The following sample code shows the Flux script with task options:

option task = {name: "downsample_5m_precision", every: 1h, offset: 0m}

from(bucket: "example-bucket")
    |> range(start: -task.every)
    |> filter(fn: (r) => r._measurement == "mem" and r.host == "myHost")
    |> aggregateWindow(every: 5m, fn: mean)

See Common tasks for examples of tasks commonly used with InfluxDB.

Define a destination

In most cases, you’ll want to send and store data after the task has transformed it. The destination could be a separate InfluxDB measurement or bucket.

The example below uses to() to write the transformed data back to another InfluxDB bucket:

// ...
    |> to(bucket: "example-downsampled", org: "my-org")

To write data into InfluxDB, to() requires the following columns:

• _time
• _measurement
• _field
• _value

To write data to other destinations, see Flux output functions.

Full example Flux task script

The following sample Flux combines all the components described in this guide:

// Task options
option task = {name: "downsample_5m_precision", every: 1h, offset: 0m}

// Data source
from(bucket: "example-bucket")
    |> range(start: -task.every)
    |> filter(fn: (r) => r._measurement == "mem" and r.host == "myHost")
    // Data processing
    |> aggregateWindow(every: 5m, fn: mean)
    // Data destination
    |> to(bucket: "example-downsampled")

To learn more about InfluxDB tasks and how they work, watch the following video: