Use externally partitioned data

You can use BigQuery external tables to query partitioned data in the following data stores:

The external partitioned data must use a default Hive partitioning layout and be in one of the following formats:

  • Avro
  • CSV
  • JSON
  • ORC
  • Parquet

To query externally partitioned data, you must create a BigLake table or an external table. We recommend using BigLake tables because they let you enforce fine-grained security at the table level. For information about BigLake and external tables, see Introduction to BigLake tables and Introduction to external tables.

You enable Hive partitioning support by setting the appropriate options in the table definition file. For instructions about querying managed partitioned tables, see Introduction to partitioned tables.

Partition schema

The following sections explain the default Hive partitioned layout and the schema detection modes that BigQuery supports.

To avoid reading unnecessary files and to improve performance, you can use predicate filters on partition keys in queries.

Supported data layouts

Hive partition keys appear as normal columns when you query data from Cloud Storage. The data must follow a default Hive partitioned layout. For example, the following files follow the default layout—the key-value pairs are configured as directories with an equal sign (=) as a separator, and the partition keys are always in the same order:


The common source URI prefix in this example is gs://my_bucket/my_table.

Unsupported data layouts

If the partition key names are not encoded in the directory path, partition schema detection fails. For example, consider the following path, which does not encode the partition key names:


Files where the schema is not in a consistent order also fail detection. For example, consider the following two files with inverted partition key encodings:


Detection modes

BigQuery supports three modes of Hive partition schema detection:

  • AUTO: Key names and types are automatically detected. The following types can be detected:

  • STRINGS: Key names are automatically converted to STRING type.

  • CUSTOM: Partition key schema is encoded as specified in the source URI prefix.

Custom partition key schema

To use a CUSTOM schema, you must specify the schema in the source URI prefix field. Using a CUSTOM schema lets you specify the type for each partition key. The values must validly parse as the specified type or the query fails.

For example, if you set the source_uri_prefix flag to gs://my_bucket/my_table/{dt:DATE}/{val:STRING}, BigQuery treats val as a STRING, dt as a DATE, and uses gs://my_bucket/my_table as the source URI prefix for the matched files.

Partition pruning

BigQuery prunes partitions when possible using query predicates on the partition keys. This lets BigQuery avoid reading unnecessary files, which helps improve performance.

Predicate filters on partition keys in queries

When you create an externally partitioned table, you can require the use of predicate filters on partition keys by enabling the requirePartitionFilter option under HivePartitioningOptions.

When this option is enabled, attempts to query the externally partitioned table without specifying a WHERE clause produce the following error: Cannot query over table <table_name> without a filter over column(s) <partition key names> that can be used for partition elimination.


  • Hive partitioning support is built assuming a common source URI prefix for all URIs that ends immediately before partition encoding, as follows: gs://BUCKET/PATH_TO_TABLE/.
  • The directory structure of a Hive partitioned table is assumed to have the same partitioning keys appear in the same order, with a maximum of ten partition keys per table.
  • The data must follow a default Hive partitioning layout.
  • The Hive partitioning keys and the columns in the underlying files cannot overlap.
  • Support is for GoogleSQL only.

  • All limitations for querying external data sources stored on Cloud Storage apply.

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