Usage
view: my_view {
  derived_table: {
    table_format: PARQUET
    ...
  }
}
| 
       Hierarchy 
      
      
      
      table_format | 
    
       Default Value 
      PARQUET
      Accepts 
      PARQUET, ORC, AVRO, JSON, or TEXTFILE
      Special Rules 
      table_format is supported only on specific dialects
     | 
  
Definition
The table_format parameter specifies the format that a persistent derived table (PDT) will have in an Athena database, which can be one of the following:
- Parquet
 - Optimized Row Columnar (ORC)
 - Avro
 - JavaScript Object Notation (JSON)
 - Text file
 
See the Amazon Athena documentation for details.
See the Dialect support for
table_formatsection for the list of dialects that supporttable_format.
The
table_formatparameter works only with tables that are persistent, such as PDTs and aggregate tables.table_formatis not supported for derived tables without a persistence strategy.
Examples
Create a customer_order_facts PDT on an Amazon Athena database using ORC format:
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 }
      derived_column: num_orders {
        sql: COUNT(order.customer_id) ;;
      }
    }
    table_format: ORC
    table_compression: SNAPPY
    datagroup_trigger: daily_datagroup
  }
}
Dialect support for table_format
The ability to use table_format depends on the database dialect your Looker connection is using. In the latest release of Looker, the following dialects support table_format:
| Dialect | Supported? | 
|---|---|
| Actian Avalanche | No  | 
| Amazon Athena | Yes  | 
| Amazon Aurora MySQL | No  | 
| Amazon Redshift | No  | 
| Amazon Redshift 2.1+ | No  | 
| Amazon Redshift Serverless 2.1+ | No  | 
| Apache Druid | No  | 
| Apache Druid 0.13+ | No  | 
| Apache Druid 0.18+ | No  | 
| Apache Hive 2.3+ | No  | 
| Apache Hive 3.1.2+ | No  | 
| Apache Spark 3+ | No  | 
| ClickHouse | No  | 
| Cloudera Impala 3.1+ | No  | 
| Cloudera Impala 3.1+ with Native Driver | No  | 
| Cloudera Impala with Native Driver | No  | 
| DataVirtuality | No  | 
| Databricks | No  | 
| Denodo 7 | No  | 
| Denodo 8 | No  | 
| Dremio | No  | 
| Dremio 11+ | No  | 
| Exasol | No  | 
| Firebolt | No  | 
| Google BigQuery Legacy SQL | No  | 
| Google BigQuery Standard SQL | No  | 
| Google Cloud PostgreSQL | No  | 
| Google Cloud SQL | No  | 
| Google Spanner | No  | 
| Greenplum | No  | 
| HyperSQL | No  | 
| IBM Netezza | No  | 
| MariaDB | No  | 
| Microsoft Azure PostgreSQL | No  | 
| Microsoft Azure SQL Database | No  | 
| Microsoft Azure Synapse Analytics | No  | 
| Microsoft SQL Server 2008+ | No  | 
| Microsoft SQL Server 2012+ | No  | 
| Microsoft SQL Server 2016 | No  | 
| Microsoft SQL Server 2017+ | No  | 
| MongoBI | No  | 
| MySQL | No  | 
| MySQL 8.0.12+ | No  | 
| Oracle | No  | 
| Oracle ADWC | No  | 
| PostgreSQL 9.5+ | No  | 
| PostgreSQL pre-9.5 | No  | 
| PrestoDB | No  | 
| PrestoSQL | No  | 
| SAP HANA | No  | 
| SAP HANA 2+ | No  | 
| SingleStore | No  | 
| SingleStore 7+ | No  | 
| Snowflake | No  | 
| Teradata | No  | 
| Trino | No  | 
| Vector | No  | 
| Vertica | No  |