Intraquery Parallelism

Intraquery Parallelism refers to the execution of a single query in parallel across multiple processors. This approach is particularly beneficial for accelerating long-running, resource-intensive queries, such as those involving large datasets. By dividing the tasks associated with a single query, intraquery parallelism significantly reduces query execution time, making it valuable in high-performance and large-scale database systems.

There are two main types of intraquery parallelism:

1. Intraoperation Parallelism
2. Interoperation Parallelism

Intraoperation Parallelism

Intraoperation parallelism involves parallelizing individual operations within a single query, such as sorting, selection, and joins. This approach is most effective when there is a large number of data entries (tuples) relative to the number of operations, as it allows the workload for each operation to be distributed across multiple processors.

Key Examples:

• Selection: A query may require retrieving specific rows based on certain conditions (e.g., WHERE Age > 30). If there are millions of records, a parallel scan can be employed where different processors scan different parts of the table simultaneously, significantly speeding up the selection process.
• Sorting: Sorting is a computation-heavy operation, especially with large datasets. In intraoperation parallelism, a large dataset can be split into smaller chunks that are sorted independently across multiple processors. The sorted chunks are then merged to form a single sorted dataset.
• Join Operations: Join operations, such as equi-joins or hash joins, are some of the most intensive database operations. For a join between two large tables, the operation can be divided across processors where each processor joins a portion of the data. For instance, a hash join can partition data from both tables into chunks and then join those chunks in parallel.

When Intraoperation Parallelism is Useful:

This form of parallelism is particularly beneficial when:

• The number of tuples (rows) is much larger than the number of operations to be performed.
• The operations are compute-intensive and involve scanning or manipulating large datasets.

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-- Suppose we have a query that involves sorting and filtering a large dataset
SELECT * 
FROM Orders
WHERE OrderDate > '2023-01-01'
ORDER BY OrderAmount DESC;

In this case, the database can:

• Use multiple processors to scan the Orders table in parallel, each applying the WHERE OrderDate > '2023-01-01' condition to their respective portions.
• Each processor sorts its result subset by OrderAmount.
• Finally, a merge operation brings together the sorted subsets to return a single, ordered result set.

Interoperation Parallelism

Interoperation parallelism executes different operations of a query simultaneously. This type of parallelism allows for independent operations within a query plan (i.e., different steps required to complete the query) to be processed in parallel.

Key Examples: Consider a query with multiple operations, such as a WHERE clause (filtering), a JOIN operation, and an AGGREGATE function (e.g., SUM, COUNT):

• Selection and Join: The selection operation can filter rows concurrently while the join operation matches data from different tables.
• Join and Aggregation: After filtering, the join and aggregation can be processed simultaneously if they don’t depend on each other.

When Interoperation Parallelism is Useful:

This form of parallelism is most effective when:

• The query has multiple independent steps, allowing them to run in parallel without dependencies.
• Different stages of the query plan can be executed independently and simultaneously.

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-- Suppose we have a complex query with a join, selection, and aggregation
SELECT c.CustomerID, COUNT(o.OrderID) AS OrderCount
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
WHERE c.City = 'New York'
GROUP BY c.CustomerID;

In this case:

• The WHERE c.City = 'New York' selection could be processed in parallel across different processors scanning the Customers table.
• While selection is happening, another processor could begin working on the join condition (Customers.CustomerID = Orders.CustomerID) in parallel.
• Another processor could calculate the count of OrderID for each customer in parallel once it has the joined data.

Comparison of Intraoperation and Interoperation Parallelism

Intraoperation parallelism generally provides better performance for operations on very large tables with minimal interdependencies. Interoperation parallelism, on the other hand, is ideal for complex queries with multiple distinct operations.