Documentation

Join data

Use the Flux join package to join two data sets based on common values. Learn how join two data sets using the following join methods:

When to use the join package

We recommend using the join package to join streams that have mostly different schemas or that come from two separate data sources. If you’re joining data from the same data source with the same schema, using union() and pivot() to combine the data will likely be more performant.

For more information, see When to use union and pivot instead of join functions.

How join functions work

join functions join two streams of tables together based on common values in each input stream.

Input streams

Each input stream is assigned to the left or right parameter. Input streams can be defined from any valid data source. For more information, see:

Data requirements

To join data, each input stream must have the following:

  • One or more columns with common values to join on.
    Columns do not need identical labels, but they do need to have comparable values.

  • Identical group keys.
    Functions in the join package use group keys to quickly determine what tables from each input stream should be paired and evaluated for the join operation.
    Both input streams should have the same group key. If they don’t, your join operation may not find any matching tables and will return unexpected output. If the group keys of your input streams are not identical, use group() to regroup each input stream before joining them together.

    Only tables with the same group key instance are joined.

Join predicate function (on)

join package functions require the on parameter to compare values from each input stream (represented by l (left) and r (right)) and returns true or false. Rows that return true are joined. This parameter is a predicate function.

(l, r) => l.column == r.column
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Join output function (as)

join package functions (except join.time()) require the as parameter to define the output schema of the join. The as parameter returns a new record using values from joined rows–left (l) and right (r).

(l, r) => ({l with name: r.name, location: r.location})
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Do not modify group key columns

Do not modify group key columns. The as function must return the same group key as both input streams to successfully perform a join.

Perform join operations

The join package supports the following join types and special use cases:

Troubleshoot join operations

For information about unexpected behaviors and errors when using the join package, see Troubleshoot join operations.


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Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

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InfluxDB 3 Core and Enterprise are now in Beta

InfluxDB 3 Core and Enterprise are now available for beta testing, available under MIT or Apache 2 license.

InfluxDB 3 Core is a high-speed, recent-data engine that collects and processes data in real-time, while persisting it to local disk or object storage. InfluxDB 3 Enterprise is a commercial product that builds on Core’s foundation, adding high availability, read replicas, enhanced security, and data compaction for faster queries. A free tier of InfluxDB 3 Enterprise will also be available for at-home, non-commercial use for hobbyists to get the full historical time series database set of capabilities.

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