BigQuery uses partitioning and clustering to optimize query performance and minimize the amount of data that needs to be scanned.
- Partitioning: Partitioning splits large tables into smaller, more manageable parts called partitions. BigQuery supports two types of partitioning:
- Time-based partitioning: Tables are partitioned based on a TIMESTAMP or DATE column, with each partition representing a contiguous range of time.
- Integer range partitioning: Tables are partitioned based on an INTEGER column, with each partition representing a contiguous range of values.
- Clustering: Clustering allows you to physically reorder the data within a partition based on the values of one or more columns. This reduces the amount of data that needs to be scanned and results in faster query performance. Clustering can only be performed on partitioned tables, and each partition can have a different set of clustered columns.
By using partitioning and clustering together, you can effectively reduce the amount of data that needs to be scanned, resulting in faster query performance. However, it is important to understand the trade-offs between partitioning and clustering, such as increased storage and ingestion costs, and to choose the right approach based on your specific use case.
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