Downsample data with InfluxDB

This page documents an earlier version of InfluxDB. InfluxDB v2.7 is the latest stable version. View this page in the v2.7 documentation.

One of the most common use cases for InfluxDB tasks is downsampling data to reduce the overall disk usage as data collects over time. In previous versions of InfluxDB, continuous queries filled this role.

This article walks through creating a continuous-query-like task that downsamples data by aggregating data within windows of time, then storing the aggregate value in a new bucket.

Requirements

To perform a downsampling task, you need to the following:

A “source” bucket

The bucket from which data is queried.

A “destination” bucket

A separate bucket where aggregated, downsampled data is stored.

Some type of aggregation

To downsample data, it must be aggregated in some way. What specific method of aggregation you use depends on your specific use case, but examples include mean, median, top, bottom, etc. View Flux’s aggregate functions for more information and ideas.

Example downsampling task script

The example task script below is a very basic form of data downsampling that does the following:

1. Defines a task named “cq-mem-data-1w” that runs once a week.
2. Defines a data variable that represents all data from the last 2 weeks in the mem measurement of the system-data bucket.
3. Uses the aggregateWindow() function to window the data into 1 hour intervals and calculate the average of each interval.
4. Stores the aggregated data in the system-data-downsampled bucket under the my-org organization.

// Task Options
option task = {name: "cq-mem-data-1w", every: 1w}
// Defines a data source
data = from(bucket: "system-data")
    |> range(start: -duration(v: int(v: task.every) * 2))
    |> filter(fn: (r) => r._measurement == "mem")
data
    // Windows and aggregates the data in to 1h averages
    |> aggregateWindow(fn: mean, every: 1h)
    // Stores the aggregated data in a new bucket
    |> to(bucket: "system-data-downsampled", org: "my-org")

Again, this is a very basic example, but it should provide you with a foundation to build more complex downsampling tasks.

Add your task

Once your task is ready, see Create a task for information about adding it to InfluxDB.

Things to consider

• If there is a chance that data may arrive late, specify an offset in your task options long enough to account for late-data.
• If running a task against a bucket with a finite retention period, schedule tasks to run prior to the end of the retention period to let downsampling tasks complete before data outside of the retention period is dropped.

tasks