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Introduction to external tables

This page introduces external tables and provides guidance on querying data stored outside of BigQuery.

Non-BigLake external tables let you to query structured data in external data stores. To query a non-BigLake external table, you must have permissions to both the external table and the external data source. For example, to query a non-BigLake external table that uses a data source in Cloud Storage, you must have the following permissions:

  • bigquery.tables.getData
  • bigquery.jobs.create
  • storage.buckets.get
  • storage.objects.get

Supported data stores

You can use non-BigLake external tables with the following data stores:

Temporary table support

You can query an external data source in BigQuery by using a permanent table or a temporary table. A permanent table is a table that is created in a dataset and is linked to your external data source. Because the table is permanent, you can use access controls to share the table with others who also have access to the underlying external data source, and you can query the table at any time.

When you query an external data source using a temporary table, you submit a command that includes a query and creates a non-permanent table linked to the external data source. When you use a temporary table, you do not create a table in one of your BigQuery datasets. Because the table is not permanently stored in a dataset, it cannot be shared with others. Querying an external data source using a temporary table is useful for one-time, ad-hoc queries over external data, or for extract, transform, and load (ETL) processes.

Multiple source files

If you create a non-BigLake external table based on Cloud Storage, you can use multiple external data sources, provided those data sources have the same schema. This isn't supported for non-BigLake external table based on Bigtable or Drive.

Limitations

The following limitations apply to external tables:

  • BigQuery does not guarantee data consistency for external data tables. Changes to the underlying data while a query is running can result in unexpected behavior.
  • Query performance for external tables might be low compared to querying data in a native BigQuery table. If query speed is a priority, load the data into BigQuery instead of setting up an external data source. The performance of a query that includes an external table depends on the external storage type. For example, querying data stored in Cloud Storage is faster than querying data stored in Drive. In general, the query performance for an external table should be equivalent to reading the data directly from the data source.
  • You cannot modify external data tables using DML or other methods. External tables are read-only for BigQuery.
  • You cannot use the TableDataList JSON API method to retrieve data from external tables. For more information, see tabledata.list.

    To work around this limitation, you can save query results in a destination table. You can then use the TableDataList method on the results table.

  • You cannot run a BigQuery job that exports data from an external table.

    To work around this limitation, you can save query results in a destination table. Then, run an export job against the results table.

  • You cannot reference an external table in a wildcard table query.

  • External tables do not support clustering. They support partitioning in limited ways. For details, see Querying externally partitioned data.

  • When you query an external data source other than Cloud Storage, the results are not cached. (Google Standard SQL queries on Cloud Storage are supported.) You are charged for each query against an external table even if you issue the same query multiple times. If you need to repeatedly issue a query against an external table that does not change frequently, consider writing the query results to a permanent table and run the queries against the permanent table instead.

  • You are limited to 4 concurrent queries against a Cloud Bigtable external data source.

  • A dry run of a federated query that uses an external table might report a lower bound of 0 bytes of data, even if rows are returned. This is because the amount of data processed from the external table can't be determined until the actual query completes. Running the federated query incurs a cost for processing this data.

Location considerations

When you choose a location for your data, consider the following:

  • Colocate your BigQuery dataset when using external data sources.
    • Cloud Storage: When you query data in Cloud Storage through a BigQuery external table, the data you query must be colocated with your BigQuery dataset. For example:
      • Single region: If your BigQuery dataset is in the Warsaw (europe-central2) region, the corresponding Cloud Storage bucket must also be in the Warsaw region because there is currently no Cloud Storage dual-region that includes Warsaw.
      • If your BigQuery dataset is in one of the multi-regions, then using a Cloud Storage bucket in a location that is contained within the multi-region of the dataset is not supported. For example, if the external tables are in the EU multi-region but the Cloud Storage bucket is in europe-west1, then the job fails.
      • Dual-region: If your BigQuery dataset is in the Tokyo (asia-northeast1) region, the corresponding Cloud Storage bucket must be a bucket in the Tokyo region or in the ASIA1 dual-region (which includes Tokyo).
      • Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations with multi-region Cloud Storage buckets is not recommended for external tables.
      View the supported Cloud Storage locations.
    • Bigtable: When you query data in Bigtable through a BigQuery external table, your Bigtable instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Bigtable instance must be in the Belgium region.
      • Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations is not recommended for external tables on Bigtable.
      View the supported Bigtable locations.
    • Google Drive: Location considerations do not apply to Google Drive external data sources.
    • Cloud SQL: When you query data in Cloud SQL through a BigQuery federated query, your Cloud SQL instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Cloud SQL instance must be in the Belgium region.
      • Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Cloud SQL instance must be in single region in the US geographic area.
      View the supported Cloud SQL locations.
    • Cloud Spanner: When you query data in Spanner through a BigQuery federated query, your Cloud Spanner instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Spanner instance must be in the Belgium region.
      • Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Spanner instance must be in single region in the US geographic area.
      View the supported Spanner locations.
  • Develop a data management plan.

For more information on Cloud Storage locations, see Bucket Locations in the Cloud Storage documentation.

To manually move a dataset from one location to another, follow this process:

  1. Export the data from your BigQuery tables to a Cloud Storage bucket in either the same location as your dataset or in a location contained within your dataset's location. For example, if your dataset is in the EU multi-region location, you could export your data to the europe-west1 Belgium location, which is part of the EU.

    There are no charges for exporting data from BigQuery, but you do incur charges for storing the exported data in Cloud Storage. BigQuery exports are subject to the limits on export jobs.

  2. Copy or move the data from your export Cloud Storage bucket to a new bucket you created in the destination location. For example, if you are moving your data from the US multi-region to the asia-northeast1 Tokyo region, you would transfer the data to a bucket you created in Tokyo. For information on transferring Cloud Storage objects, see Copying, renaming, and moving objects in the Cloud Storage documentation.

    Note that transferring data between regions incurs network egress charges in Cloud Storage.

  3. After you transfer the data to a Cloud Storage bucket in the new location, create a new BigQuery dataset (in the new location). Then, load your data from the Cloud Storage bucket into BigQuery.

    You are not charged for loading the data into BigQuery, but you will incur charges for storing the data in Cloud Storage until you delete the data or the bucket. You are also charged for storing the data in BigQuery after it is loaded. Loading data into BigQuery is subject to the limits on load jobs.

You can also use Cloud Composer to move and copy large datasets programmatically.

For more information on using Cloud Storage to store and move large datasets, see Using Cloud Storage with big data.

Pricing

When querying an external table from BigQuery, you are charged for running the query.

If your data is stored in ORC or Parquet on Cloud Storage, see Data size calculation.

You are also charged for storing the data and any resources used by the source application, subject to the application's pricing guidelines:

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