Usage
view: view_name {
derived_table: {
cluster_keys: ["customer_city", "customer_state"]
...
}
}
|
Hierarchy
cluster_keys- or - cluster_keys |
Default Value
None
Accepts
One or more clustered column names
Special Rules
cluster_keys is supported only on specific dialects
|
Definition
Clustering a partitioned table 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.
See the Dialect support for cluster_keys section for the list of dialects that support cluster_keys.
To add a clustered column to a persistent derived table (PDT) or an aggregate table, use the cluster_keys parameter and supply the names of the columns you want clustered in the database table.
Examples
Create a customer_order_facts native derived table on a BigQuery database, partitioned on the date column and clustered on the city, age_tier, and gender columns to optimize queries that are filtered or aggregated on those columns:
view: customer_order_facts {
derived_table: {
explore_source: order {
column: customer_id { field: order.customer_id }
column: date { field: order.order_time }
column: city { field: users.city}
column: age_tier { field: users.age_tier }
column: gender { field: users.gender }
derived_column: num_orders {
sql: COUNT(order.customer_id) ;;
}
}
partition_keys: [ "date" ]
cluster_keys: [ "city", "age_tier", "gender" ]
datagroup_trigger: daily_datagroup
}
}
Dialect support for cluster_keys
The ability to use cluster_keys depends on the database dialect your Looker connection is using. In the latest release of Looker, the following dialects support cluster_keys:
| Dialect | Supported? |
|---|---|
| Actian Avalanche | |
| Amazon Athena | |
| Amazon Aurora MySQL | |
| Amazon Redshift | |
| Amazon Redshift 2.1+ | |
| Amazon Redshift Serverless 2.1+ | |
| Apache Druid | |
| Apache Druid 0.13+ | |
| Apache Druid 0.18+ | |
| Apache Hive 2.3+ | |
| Apache Hive 3.1.2+ | |
| Apache Spark 3+ | |
| ClickHouse | |
| Cloudera Impala 3.1+ | |
| Cloudera Impala 3.1+ with Native Driver | |
| Cloudera Impala with Native Driver | |
| DataVirtuality | |
| Databricks | |
| Denodo 7 | |
| Denodo 8 & 9 | |
| Dremio | |
| Dremio 11+ | |
| Exasol | |
| Google BigQuery Legacy SQL | |
| Google BigQuery Standard SQL | |
| Google Cloud PostgreSQL | |
| Google Cloud SQL | |
| Google Spanner | |
| Greenplum | |
| HyperSQL | |
| IBM Netezza | |
| MariaDB | |
| Microsoft Azure PostgreSQL | |
| Microsoft Azure SQL Database | |
| Microsoft Azure Synapse Analytics | |
| Microsoft SQL Server 2008+ | |
| Microsoft SQL Server 2012+ | |
| Microsoft SQL Server 2016 | |
| Microsoft SQL Server 2017+ | |
| MongoBI | |
| MySQL | |
| MySQL 8.0.12+ | |
| Oracle | |
| Oracle ADWC | |
| PostgreSQL 9.5+ | |
| PostgreSQL pre-9.5 | |
| PrestoDB | |
| PrestoSQL | |
| SAP HANA | |
| SAP HANA 2+ | |
| SingleStore | |
| SingleStore 7+ | |
| Snowflake | |
| Teradata | |
| Trino | |
| Vector | |
| Vertica |