Differences between a merge and a join

Below are the some differences between a merge and a join :
MERGE
JOIN
Default is one-to-one “outer-join” using the given order of observations in the source datasets
Default is Cartesian Product; joining all rows from one table with all the rows in the other table
Match-merge results by default in a special case of “full-outer-join”
Match-join results by default in an inner-join
Outer-joins may be done on multiple source datasets
Outer-joins can be done only on two source tables
Need to sort or index the source datasets before the data step match-merge
No need to sort or index the source tables before join
Requires variable or variables that are identical on all datasets
May join on columns which are named differently
In a one-to-one match-merge, common
variables that are not included in the
match condition (i.e. are not part of the
BY statement): the value from the latter
dataset sometimes overwrites the value
coming from the left-more dataset
Common columns are not overwritten unless
this is specifically managed by the code via
the use of the COALESCE function
Impossible to do a many-to-many merge
due to one-to-one behavior of MERGE
Relatively easy to do a many-to-many join
Fewer advantages when working with
tables stored in database servers since
databases are designed for extensive
matching tools
Most advantageous when working with tables
stored in Database Servers because
databases are designed for SQL processing
Usually more efficient when combining
small datasets but too much overhead on
large unsorted datasets!
More efficient than Merge when dealing with
multiple large datasets (or when combining an
indexed large table with a small table)

 

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2 thoughts on “Differences between a merge and a join

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