Perform a full outer join
Use join.full()
to perform an full outer join of two streams of data.
Full outer joins output a row for all rows in both the left and right input streams
and join rows that match according to the on
predicate.
Use join.full to join your data
-
Import the
join
package. -
Define the left and right data streams to join:
- Each stream must have one or more columns with common values. Column labels do not need to match, but column values do.
- Each stream should have identical group keys.
For more information, see join data requirements.
-
Use
join.full()
to join the two streams together. Provide the following required parameters:-
left
: Stream of data representing the left side of the join. -
right
: Stream of data representing the right side of the join. -
on
: Join predicate. For example:(l, r) => l.column == r.column
. -
as
: Join output function that returns a record with values from each input stream.Account for missing, non-group-key values
In a full outer join, it’s possible for either the left (
l
) or right (r
) to contain null values for the columns used in the join operation and default to a default record (group key columns are populated and other columns are null).l
andr
will never both use default records at the same time.To ensure non-null values are included in the output for non-group-key columns, check for the existence of a value in the
l
orr
record, and return the value that exists:(l, r) => { id = if exists l.id then l.id else r.id return {_time: l.time, location: r.location, id: id} }
-
The following example uses a filtered selection from the
machineProduction sample data set
as the left data stream and an ad-hoc table created with array.from()
as the right data stream.
Example data grouping
The example below ungroups the left stream to match the grouping of the right stream.
After the two streams are joined together, the joined data is grouped by stationID
and sorted by _time
.
import "array"
import "influxdata/influxdb/sample"
import "join"
left =
sample.data(set: "machineProduction")
|> filter(fn: (r) => r.stationID == "g1" or r.stationID == "g2" or r.stationID == "g3")
|> filter(fn: (r) => r._field == "oil_temp")
|> limit(n: 5)
right =
array.from(
rows: [
{station: "g1", opType: "auto", last_maintained: 2021-07-15T00:00:00Z},
{station: "g2", opType: "manned", last_maintained: 2021-07-02T00:00:00Z},
{station: "g4", opType: "auto", last_maintained: 2021-08-04T00:00:00Z},
],
)
join.full(
left: left |> group(),
right: right,
on: (l, r) => l.stationID == r.station,
as: (l, r) => {
stationID = if exists l.stationID then l.stationID else r.station
return {
stationID: stationID,
_time: l._time,
_field: l._field,
_value: l._value,
opType: r.opType,
maintained: r.last_maintained,
}
},
)
|> group(columns: ["stationID"])
|> sort(columns: ["_time"])
Was this page helpful?
Thank you for your feedback!
Support and feedback
Thank you for being part of our community! We welcome and encourage your feedback and bug reports for Flux and this documentation. To find support, use the following resources:
Customers with an annual or support contract can contact InfluxData Support.