What is the most efficient grouping for a parallel solution?

What is the most efficient grouping for a parallel solution?

Because the number of parallel units in a given platform is fixed, the number of grouped DFAs is preferably the same as (or a divisor of) the number of matching engines to maximize the use of the parallel platform.

What is Group optimization?

SGO is a novel optimization algorithm which focuses on the group-solving capability of humans to solve a given problem (Satapathy and Naik 2016. Naik.

Why do parallel algorithms have a limit?

Because some portions are always still sequential, the benefits of adding more processors will go down and eventually the speedup reaches a limit.

When does the algorithm process the first row in a group?

When the algorithm processes the first row in a group, it initializes a member holding the intermediate aggregate value with the relevant value (e.g., first row’s value for a MAX aggregate).

How is the hash aggregate algorithm used in optimization?

The Hash Aggregate algorithm organizes the groups and their aggregates in a hash table. It does not require the input to be ordered. With enough data, the optimizer considers parallelizing the work, applying what’s known as a local-global aggregate.

Which is the aggregating algorithm in SQL Server?

SQL Server supports two main algorithms for aggregating data: Stream Aggregate and Hash Aggregate. With grouped queries, the Stream Aggregate algorithm requires the data to be ordered by the grouped columns, so you need to distinguish between two cases. One is a preordered Stream Aggregate, e.g., when data is obtained preordered from an index.

When do you need to use stream aggregate algorithm?

Given a grouped query with a nonempty grouping set (the set of expressions that you group by), the Stream Aggregate algorithm requires the input rows to be ordered by the expressions forming the grouping set.