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cluster_keys: ["customer_city", "customer_state"]
AcceptsOne or more clustered column names
Google BigQuery has the ability to cluster partitioned tables. Clustering sorts the data in a partition based on the values in the clustered columns and organizes the clustered columns in optimally sized storage blocks. Clustering can improve the performance and reduce the cost of queries that filter on or aggregate by the clustered columns.
To add a clustered column to a persistent derived table (PDT), use the
cluster_keys parameter and supply the names of the columns you want clustered in the database table.
customer_order_facts native derived table on a Google BigQuery database, partitioned on the
date column and clustered on the
gender columns to optimize queries that are filtered or aggregated on those columns:
cluster_keys Only Works with Derived Tables That Are Persisted and Partitioned
Derived tables can be calculated at query time, or they can be made persistent using
cluster_keys parameter works only with persistent derived tables.
In addition, Google BigQuery supports clustering on partitioned tables only. The
cluster_keys parameter only works with PDTs that are also partitioned using the
Google BigQuery Tables Can Partition Only on Date Fields
Google BigQuery tables can only be partitioned on a date or timestamp column. If you want to add clustered columns to a PDT that does not include date or time-based data, one way to do that is to add a date column using a SQL statement such as
SELECT CURRENT_DATETIME() as now, and then use
partition_keys to partition on the new column. You can then use clustering on other columns in your PDT.