Join
Join is an operation to combine two data sets in a side-by-side manner. At least one column must be present in both data sets so the field values can be compared e.g. pump id.
There are 4 common types: inner, outer, left outer, and right outer.
Inner Joins
An inner join takes only the rows of data in which both data sets have common field values.
In the example below, the join takes place on the pump id column so the C and D rows are excluded from the joined data set as these values are not common between the two data sets.
Outer Joins
An outer join keeps all data from both data sets, regardless of the values. Common rows have columns filled from both data sets, whereas rows without commonality fill the blanks in with nulls.
In the example below, A and B rows have all the fields filled. C and D rows are included, but with nulls to replace the incomplete data.
Left Outer Joins
A left outer join keeps all data from the left data set. Common rows have columns filled from both data sets, whereas rows from the left without commonality fill the blanks from the right with nulls.
In the example below, A and B rows have all the fields filled. C row is included as it is from the left data set, but with nulls to replace the incomplete data from the right. The D row is excluded as there is no common match on the left.
Right Outer Joins
A right outer join keeps all data from the right data set. Common rows have columns filled from both data sets, whereas rows from the right without commonality fill the blanks from the left with nulls.
In the example below, A and B rows have all the fields filled. D row is included as it is from the right data set, but with nulls to replace the incomplete data from the left. The C row is excluded as there is no common match on the right.
XMPro provides a convenient Transformation to join data arriving from two payloads or legs (the left and the right) into a single payload in a Data Stream.
Details for an example and its configuration can be found in the How to Use section.
Pre-requisites
None at this time
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