Documentation

experimental.join() function

experimental.join() is subject to change at any time.

experimental.join() joins two streams of tables on the group key and _time column.

Deprecated

experimental.join() is deprecated in favor of join.time(). The join package provides support for multiple join methods.

Use the fn parameter to map new output tables using values from input tables.

Note: To join streams of tables with different fields or measurements, use group() or drop() to remove _field and _measurement from the group key before joining.

Function type signature
(fn: (left: A, right: B) => C, left: stream[A], right: stream[B]) => stream[C] where A: Record, B: Record, C: Record
  • Copy
  • Fill window

For more information, see Function type signatures.

Parameters

left

(Required) First of two streams of tables to join.

(Required) Second of two streams of tables to join.

fn

(Required) Function with left and right arguments that maps a new output record using values from the left and right input records. The return value must be a record.

Examples

Join two streams of tables

import "array"
import "experimental"

left =
    array.from(
        rows: [
            {_time: 2021-01-01T00:00:00Z, _field: "temp", _value: 80.1},
            {_time: 2021-01-01T01:00:00Z, _field: "temp", _value: 80.6},
            {_time: 2021-01-01T02:00:00Z, _field: "temp", _value: 79.9},
            {_time: 2021-01-01T03:00:00Z, _field: "temp", _value: 80.1},
        ],
    )
right =
    array.from(
        rows: [
            {_time: 2021-01-01T00:00:00Z, _field: "temp", _value: 75.1},
            {_time: 2021-01-01T01:00:00Z, _field: "temp", _value: 72.6},
            {_time: 2021-01-01T02:00:00Z, _field: "temp", _value: 70.9},
            {_time: 2021-01-01T03:00:00Z, _field: "temp", _value: 71.1},
        ],
    )

experimental.join(
    left: left,
    right: right,
    fn: (left, right) =>
        ({left with lv: left._value, rv: right._value, diff: left._value - right._value}),
)
  • Copy
  • Fill window

View example output

Join two streams of tables with different fields and measurements

import "experimental"

s1 =
    from(bucket: "example-bucket")
        |> range(start: -1h)
        |> filter(fn: (r) => r._measurement == "foo" and r._field == "bar")
        |> group(columns: ["_time", "_measurement", "_field", "_value"], mode: "except")

s2 =
    from(bucket: "example-bucket")
        |> range(start: -1h)
        |> filter(fn: (r) => r._measurement == "baz" and r._field == "quz")
        |> group(columns: ["_time", "_measurement", "_field", "_value"], mode: "except")

experimental.join(
    left: s1,
    right: s2,
    fn: (left, right) => ({left with bar_value: left._value, quz_value: right._value}),
)
  • Copy
  • Fill window

Was this page helpful?

Thank you for your feedback!


The future of Flux

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

Read more

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.

For more information, check out: