Using Partitions and Clusters to Improve Query Performance
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Question of the Month: How can I improve query performance on large datasets and help reduce usage costs in PAD?
Bendesk Answer: Partitioning and clustering are effective strategies for optimizing query performance and reducing usage costs in BigQuery. In fact, CTA routinely uses these techniques to deliver faster and more cost-effective data solutions for our partners every day!
Partitioning in BigQuery refers to dividing a table into smaller, more manageable segments called partitions. Each partition can be queried independently, improving query performance and reducing costs. Partitioning essentially ‘slices’ your table into segments, allowing BigQuery to skip over slices that do not meet your query criteria. Learn more about how to create partitioned tables here.
Clustering in BigQuery organizes data within a table by grouping it based on the values of one or more specified columns. This helps improve query efficiency because related data is stored together, reducing the amount of data scanned during queries. Similar to partitioning, clustering allows BigQuery to skip chunks of rows that do not meet your query criteria based on the specified columns. Learn more about how to create clustered tables here.
Best Practices on Partitioning and Clustering:
Select a partitioning strategy that aligns with your most frequent queries and prioritizes commonly accessed timeframes when possible.
Reduce the number of partitions to balance query performance and data management; excessive partitions can lead to increased overhead in your code.
Cluster columns that are frequently used in WHERE statements to enhance performance.
Before executing your query on partitioned or clustered tables, check the BigQuery UI to monitor the amount of data that will be scanned. This information can provide insight into expected performance and allow you to make necessary adjustments.
For more tips on partitioning and clustering data in PAD, check out our NotePAD article here.