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“Handle unmatched left row” and “Handle unmatched right row” do nothing for the inner join operation. Execution only continues as long as both inputs still have data. The “Done?” test for an inner join results in yes if either of the inputs is depleted. On the next call, the “Next?” test directs to read the next row from the right input, because a one to many merge join always arranges the side with potential duplicates as the right input.
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Inner Joinįor an inner join, “Handle matching rows” returns the combined data to the calling operator. Depending on the requested join type (the logical operation), some of the steps will change. The flowchart above shows the basic outline of Merge Join processing. In this post you can see some nice (slightly simplified) animations showing the algorithm in action. This ensures that matching rows are always processed simultaneously.
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It compares the values from the two inputs, handles the data as appropriate, and then advances the input with the lowest value. The flowchart shows how the operator processes the data from two inputs at the same time. It also does not show some of the extra logic required to handle many to many joins. It doesn’t show that execution stops whenever a row is returned, and resumes where it was upon next being called. Note that this flowchart is a simplification. The basic algorithm for the Merge Join operator is as shown below: Visual appearance in execution plansĭepending on the tool being used, a Merge Join operator is displayed in a graphical execution plan as shown below: The algorithm requires at least one equality-based join predicate.
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The Merge Join operator supports all ten logical join operations: inner join left, right, and full outer join left and right semi and anti semi join as well as concatenation and union. The alternatives are Nested Loops (ideal for joining a small data stream with a cheap input), Hash Match (most effective for joining large unsorted sets), and Adaptive Join (which can be used when the optimizer finds viable plans for both Nested Loops and Hash Match and wants to postpone the final choice until run time). In such cases, the optimizer tends to choose other join operators instead. These extra sorts increase the total plan cost. Often this means that a Merge Join can’t be used without adding extra Sort operators. However, it requires all input data to be sorted by the join columns. Merge Join is the most effective of all join operators. In a graphical execution plan, the left input is displayed on the top. As such, it has two inputs, called the left and right input. The Merge Join operator is one of four operators that join data from two input streams into a single combined output stream.