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Partial Aggregation

In some of my past posts, I've discussed how SQL Server implements aggregation including the stream aggregate and hash aggregate operators. I also used hash aggregation as an initial example in my introductory post on parallel query execution . In this
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Parallel Query Execution Presentation

For those of you readers who've been wondering whatever happened to me, I've been rather busy. Among other activities, I've been writing a chapter for Kalen Delaney's upcoming fourth book in the Inside SQL Server 2005 series : Query Tuning and Optimization
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Attachment(s): Parallel Query Execution.pdf

Parallel Hash Join

SQL Server uses one of two different strategies to parallelize a hash join . The more common strategy uses hash partitioning. In some cases, we use broadcast partitioning; this strategy is often called a “broadcast hash join.” Hash Partitioning The more
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Parallel Nested Loops Join

SQL Server parallelizes a nested loops join by distributing the outer rows (i.e., the rows from the first input) randomly among the nested loops threads. For example, if we have two threads running a nested loops join, we send about half of the rows to
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Parallel Scan

In this post, I’m going to take a look at how SQL Server parallelizes scans. The scan operator is one of the few operators that is parallel “aware.” Most operators neither need to know nor care whether they are executing in parallel; the scan is an exception.
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The Parallelism Operator (aka Exchange)

As I noted in my Introduction to Parallel Query Execution post , the parallelism (or exchange) iterator actually implements parallelism in query execution. The optimizer places exchanges at the boundaries between threads; the exchange moves the rows between
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Introduction to Parallel Query Execution

SQL Server has the ability to execute queries using multiple CPUs simultaneously. We refer to this capability as parallel query execution. Parallel query execution can be used to reduce the response time of (i.e., speed up) a large query. It can also
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