It is unknown whether this operator is actually implemented to test the logical operation and then skip these steps, or whether Microsoft chose to simply run these steps in all cases. Execution continues as long as the left input has data, even if the right input is already fully processed. Execution continues as long as the right input has data, even if the left input is already fully processed.
Execution continues until both inputs are fully processed. The data returned will be from the left input only. The remaining rows from the right input that would have matched the last value from left will now be treated as unmatched. As with the other logical operations, the logic associated with testing and tracking matches has no effect and may or may not be skipped during processing.
The Merge Join operator does not have an explicit property to show when a left semi join actually executes as a probed left semi join. The only indication is the presence of a column in the Output List that does not come from the left input; this column is typically named Expr nnnn where nnnn is a 4-digit number that is unique within the execution plan.
There is no Defined Values property for this new column. Execution after returning a row always moves on to advance the left input. In other words, it immediately moves to the next left row upon finding but a single match in the right input.
This test and the logic to track this can be skipped or executed, the effect is the same. The data returned will be from the right input only.
In other words, it immediately moves to the next right row upon finding but a single match in the right input. It is rare to see Merge Join Concatenation in an execution plan. When the concatenated results are required to be ordered, and both inputs are already sorted in that order, then Merge Join Concatenation is more effective than combining a Concatenation operator and a Sort operator to restore the correct order. It is, as always, not clear whether this test is still performed or whether it is simply skipped for this logical operation.
A Merge Join Union is often a more effective way to obtain the union of two input sets than using a normal Concatenation and then adding e. This is especially the case if the inputs are already sorted and guaranteed to have no duplicates. If one or both of the inputs can have duplicates, then the optimizer has to add extra steps to remove these before inputting the data into the Merge Join operator.
The union logic of Merge Join removes duplicates between the two sets, but does not remove duplicates within either of the sets. Other than for concatenation, it does mark the left row as matched.
This is called a one to many join even when the right input has no duplicates either. The optimizer uses trusted constraints and logic of plan elements e. If it finds one such input, it arranges that input to be on the left side and marks the Merge Join as one to many.
If neither input is guaranteed to have no duplicates, the join is many to many. For a many to many merge join, a worktable is used in tempdb to store values from the right input that need to be used multiple times. This is only done when it is really needed.
What happens next depends on whether or not they are the same. When the values in the join columns of the current row from the right input and the peek-ahead buffer are not equal, the current row is unique in the input. The operator returns the combined results. This would normally direct the algorithm to run GetNext on the right input. In this case that has already been done and that row is stored in the peek-ahead buffer. So instead of calling its child operator, the Merge Join now promotes the peek-ahead buffer to be the current row and then resumes from the top.
When the values in the join columns of the current row from the right input and the peek-ahead buffer are the same, these two and possibly more rows in the right input are duplicates on the join columns. In this case, the Merge Join stores both the current row from the right input and the row in the peek-ahead buffer in a worktable in tempdb , then continues to request rows from the right input and add them to the worktable.
This continues until a row is returned with a different value in the join columns, which is then stored in the peek-ahead buffer. Before illustrating how to use the Merge transformation, we will explain several approaches to sort data sources in SSIS. This means that — in general — sorting data at its source is preferable to using this component.
The reason is that sorting data at the source level decreases the workload on the ETL server. Besides, it can benefit from indexes or other helpers found at the data source. One important note mentioned in the SSIS toolbox is that the Sort component is not recommended for large data since it requires loading all data in memory before generating the sorted output.
The Sort component is very simple and easy to use; you need to specify the columns used in the sort operation with the sorting order for each one of them.
Caution : Removing duplicates can result in a data loss since duplicates are compared based on the sorting columns, not the whole data row. As we mentioned before, when it can sort the data at its source, there is no need to use the Sort component. SSIS engine does not automatically detect our data source is sorted, and the user must tell it so. The next step is to configure the sorting key position for all columns used in the sort operation.
As shown in the image below, the merge transformation editor is very simple. You only need to specify the component output columns and their corresponding column from each input. After executing the package, we can note that the count of Merge transformation output rows is equal to the sum of both input rows. Figure 12 — Merge transformation Inputs and output rows count. Send documentation feedback.
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Hash Joins Versus Merge Joins The Vertica optimizer implements a join with one of the following algorithms: Merge join is used when projections of the joined tables are sorted on the join columns. Merge joins are faster and uses less memory than hash joins. Hash join is used when projections of the joined tables are not already sorted on the join columns.
In this case, the optimizer builds an in-memory hash table on the inner table's join column. The optimizer then scans the outer table for matches to the hash table, and joins data from the two tables accordingly.
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