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:
- Intraoperation Parallelism
- 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.
-- 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.
-- 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
Feature | Intraoperation Parallelism | Interoperation Parallelism |
---|---|---|
Goal | Parallelize each individual operation | Execute different operations in parallel |
Optimal Scenario | High number of tuples, fewer operations | Multiple independent operations in query plan |
Benefit | Reduces time for data-intensive operations | Shortens time for complex queries with many steps |
Example Operations | Sorting, selection, joins | Selection + join, join + aggregation |
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.