How can you speed up data warehouse query processing with query parallelism?

1 What is query parallelism?

Query parallelism is the ability to execute different parts of a query or multiple queries simultaneously on different processing units. For example, if you have a query that joins two large tables, you can split the tables into smaller chunks and assign each chunk to a different processor or node. Then, each processor or node can perform the join operation on its own chunk and return the partial result to a coordinator node, which can merge them into the final result. This way, you can reduce the overall execution time and resource consumption of the query.

3 What are the challenges of query parallelism?

Query parallelism is not a guaranteed solution for data warehouse performance tuning, as it comes with complexities and potential drawbacks. Implementing query parallelism requires a careful design and configuration of the data warehouse architecture, data distribution, query optimization, and parallel execution plans. This can lead to more overhead and coordination costs for managing and synchronizing the parallel tasks and results. Additionally, depending on the workload and resource availability, query parallelism may cause contention and interference among the parallel tasks and queries, such as competing for locks, buffers, or bandwidth. Furthermore, query parallelism may not always improve the performance of the data warehouse queries; in some cases, the benefits may be outweighed by the costs and challenges of implementing it. For example, if the queries are already simple or fast, or if the data warehouse is already well-optimized or underutilized, adding more parallelism may not make a significant difference or may even worsen the performance.

4 How to implement query parallelism?

Query parallelism can be implemented at various levels of the data warehouse architecture. At the data level, you can partition or segment the data into smaller and independent units, using hash, range, list, round-robin, or random partitioning. At the query level, you can split the query into smaller subqueries and execute them on different processing units. You can also take advantage of platform level query parallelism by selecting or configuring a data warehouse platform that supports it. This could be a parallel or distributed data warehouse platform, such as Amazon Redshift or Microsoft Azure Synapse Analytics, or a traditional or centralized one, such as Oracle or SQL Server. In the latter case, you can manually or dynamically adjust the degree and mode of parallelism for the queries.

5 How to measure and optimize query parallelism?

Query parallelism is a powerful technique for improving data warehouse query processing by utilizing multiple processing units. However, it is not a universal solution and requires careful monitoring and evaluation to ensure that it is delivering the expected results. To measure and optimize query parallelism, you can use metrics such as execution time, speedup, cost, and tuning. Execution time measures the total time that it takes to execute a query or set of queries, while speedup compares the performance of different levels of parallelism. Cost examines the resources consumed or allocated to execute a query, while tuning adjusts and optimizes parameters and settings that affect query parallelism. By understanding how query parallelism works and how to implement, measure, and optimize it, you can make the most of this technique and improve your data warehouse performance and productivity.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?