Console

1. Go to theBigQuery page.

   Go to BigQuery

2. In the left pane, clickexploreExplorer:

   

   If you don't see the left pane, clicklast_pageExpand left pane to open the pane.

3. In theExplorer pane, expand your project, clickDatasets, andthen select a dataset.

4. Expand themore_vertActions option and clickCreate table.

5. In theSource section, specify the following details:

   1. ForCreate table from, selectGoogle Cloud Storage

   2. ForSelect file from GCS bucket or use a URI pattern, browse toselect a bucket and file to use, or type the path in the formatgs://bucket_name/[folder_name/]file_name.

      You can't specify multiple URIs in the Google Cloud console, butyou can select multiple files by specifying one asterisk (*)wildcard character. For example,gs://mybucket/file_name*. For moreinformation, seeWildcard support for Cloud Storage URIs.

      The Cloud Storage bucketmust be in the same location as the dataset that contains the tableyou're creating.

   3. ForFile format, select the format that matches your file.

6. In theDestination section, specify the following details:

   1. ForProject, choose the project in which to create the table.

   2. ForDataset, choose the dataset in which to create the table.

   3. ForTable, enter the name of the table you are creating.

   4. ForTable type, selectExternal table.

7. In theSchema section, you can either enableschema auto-detection or manually specifya schema if you have a source file. If you don't have a source file, youmust manually specify a schema.

   • To enable schema auto-detection, select theAuto-detect option.

   • To manually specify a schema, leave theAuto-detect optionunchecked. EnableEdit as text and enter the table schema as aJSON array.

8. To ignore rows with extra column values that do not match the schema,expand theAdvanced options section and selectUnknown values.

9. ClickCreate table.

After the permanent table is created, you can run a query against the tableas if it were a native BigQuery table. After your querycompletes, you canexport the resultsas CSV or JSON files, save the resultsas a table, or save the results to Google Sheets.

SQL

You can create a permanent external table by running theCREATE EXTERNAL TABLE DDL statement.You can specify the schema explicitly, or useschema auto-detection to infer the schemafrom the external data.

1. In the Google Cloud console, go to theBigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   CREATEEXTERNALTABLE`PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`OPTIONS(format="TABLE_FORMAT",uris=['BUCKET_PATH'[,...]]);

   Replace the following:

   • PROJECT_ID: the name of your project in which you want to create the table—for example,myproject
   • DATASET: the name of the BigQuery dataset that you want to create the table in—for example,mydataset
   • EXTERNAL_TABLE_NAME: the name of the table that you want to create—for example,mytable
   • TABLE_FORMAT: the format of the table that you want to create—for example,PARQUET
   • BUCKET_PATH: the path to the Cloud Storage bucket that contains the data for the external table, in the format['gs://bucket_name/[folder_name/]file_name'].

     You can select multiple files from the bucket by specifying one asterisk (*) wildcard character in the path. For example,['gs://mybucket/file_name*']. For more information, seeWildcard support for Cloud Storage URIs.

     You can specify multiple buckets for theuris option by providing multiple paths.

     The following examples show validuris values:

     • ['gs://bucket/path1/myfile.csv']
     • ['gs://bucket/path1/*.csv']
     • ['gs://bucket/path1/*', 'gs://bucket/path2/file00*']

     When you specifyuris values that target multiple files, all of those files must share a compatible schema.

     For more information about using Cloud Storage URIs in BigQuery, seeCloud Storage resource path.

3. Clickplay_circleRun.

For more information about how to run queries, seeRun an interactive query.

Examples

The following example uses schema auto-detection to create an external tablenamedsales that is linked to a CSV file stored in Cloud Storage:

CREATEORREPLACEEXTERNALTABLEmydataset.salesOPTIONS(format='CSV',uris=['gs://mybucket/sales.csv']);

The next example specifies a schema explicitly and skips the first row inthe CSV file:

CREATEORREPLACEEXTERNALTABLEmydataset.sales(RegionSTRING,QuarterSTRING,Total_SalesINT64)OPTIONS(format='CSV',uris=['gs://mybucket/sales.csv'],skip_leading_rows=1);

bq

To create an external table, use thebq mk command with the--external_table_definitionflag. This flag contains either a path to atable definition file or an inlinetable definition.

Option 1: Table definition file

Use thebq mkdefcommand to create a table definition file, and then pass the file path tothebq mk command as follows:

bqmkdef--source_format=SOURCE_FORMAT\BUCKET_PATH >DEFINITION_FILEbqmk--table\--external_table_definition=DEFINITION_FILE\DATASET_NAME.TABLE_NAME\SCHEMA

Replace the following:

• SOURCE_FORMAT: the format of the external data source.For example,CSV.

• BUCKET_PATH: the path to theCloud Storage bucket that contains the data for thetable, in the formatgs://bucket_name/[folder_name/]file_pattern.

  You can select multiple files from the bucket by specifying one asterisk (*)wildcard character in thefile_pattern. For example,gs://mybucket/file00*.parquet. For moreinformation, seeWildcard support for Cloud Storage URIs.

  You can specify multiple buckets for theuris option by providing multiplepaths.

  The following examples show validuris values:

  • gs://bucket/path1/myfile.csv
  • gs://bucket/path1/*.parquet
  • gs://bucket/path1/file1*,gs://bucket1/path1/*

  When you specifyuris values that target multiple files, all of thosefiles must share a compatible schema.

  For more information about using Cloud Storage URIs inBigQuery, seeCloud Storage resource path.

• DEFINITION_FILE: the path to thetable definition fileon your local machine.

• DATASET_NAME: the name of the dataset that contains thetable.

• TABLE_NAME: the name of the table you're creating.

• SCHEMA: specifies a path to aJSON schema file,or specifies the schema in the formfield:data_type,field:data_type,....

Example:

bq mkdef --source_format=CSV gs://mybucket/sales.csv > mytable_defbq mk --table --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

To use schema auto-detection, set the--autodetect=true flag in themkdef command and omit the schema:

bq mkdef --source_format=CSV --autodetect=true \  gs://mybucket/sales.csv > mytable_defbq mk --table --external_table_definition=mytable_def \  mydataset.mytable

Option 2: Inline table definition

Instead of creating a table definition file, you can pass the tabledefinition directly to thebq mk command:

bqmk--table\--external_table_definition=@SOURCE_FORMAT=BUCKET_PATH\DATASET_NAME.TABLE_NAME\SCHEMA

Replace the following:

• SOURCE_FORMAT: the format of the external data source

  For example,CSV.

• BUCKET_PATH: the path to theCloud Storage bucket that contains the data for thetable, in the formatgs://bucket_name/[folder_name/]file_pattern.

  You can select multiple files from the bucket by specifying one asterisk (*)wildcard character in thefile_pattern. For example,gs://mybucket/file00*.parquet. For moreinformation, seeWildcard support for Cloud Storage URIs.

  You can specify multiple buckets for theuris option by providing multiplepaths.

  The following examples show validuris values:

  • gs://bucket/path1/myfile.csv
  • gs://bucket/path1/*.parquet
  • gs://bucket/path1/file1*,gs://bucket1/path1/*

  When you specifyuris values that target multiple files, all of thosefiles must share a compatible schema.

  For more information about using Cloud Storage URIs inBigQuery, seeCloud Storage resource path.

• DATASET_NAME: the name of the dataset that contains the table.

• TABLE_NAME: the name of the table you're creating.

• SCHEMA: specifies a path to aJSON schema file,or specifies the schema in the formfield:data_type,field:data_type,.... To use schemaauto-detection, omit this argument.

Example:

bq mkdef --source_format=CSV gs://mybucket/sales.csv > mytable_defbq mk --table --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

API

Call thetables.insert methodAPI method, and create anExternalDataConfigurationin theTable resourcethat you pass in.

Specify theschema property or set theautodetect property totrue to enable schema auto detection forsupported data sources.

Java

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.CsvOptions;importcom.google.cloud.bigquery.ExternalTableDefinition;importcom.google.cloud.bigquery.Field;importcom.google.cloud.bigquery.QueryJobConfiguration;importcom.google.cloud.bigquery.Schema;importcom.google.cloud.bigquery.StandardSQLTypeName;importcom.google.cloud.bigquery.TableId;importcom.google.cloud.bigquery.TableInfo;importcom.google.cloud.bigquery.TableResult;// Sample to queries an external data source using a permanent tablepublicclassQueryExternalGCSPerm{publicstaticvoidrunQueryExternalGCSPerm(){// TODO(developer): Replace these variables before running the sample.StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";StringsourceUri="gs://cloud-samples-data/bigquery/us-states/us-states.csv";Schemaschema=Schema.of(Field.of("name",StandardSQLTypeName.STRING),Field.of("post_abbr",StandardSQLTypeName.STRING));Stringquery=String.format("SELECT * FROM %s.%s WHERE name LIKE 'W%%'",datasetName,tableName);queryExternalGCSPerm(datasetName,tableName,sourceUri,schema,query);}publicstaticvoidqueryExternalGCSPerm(StringdatasetName,StringtableName,StringsourceUri,Schemaschema,Stringquery){try{// Initialize client that will be used to send requests. This client only needs to be created// once, and can be reused for multiple requests.BigQuerybigquery=BigQueryOptions.getDefaultInstance().getService();// Skip header row in the file.CsvOptionscsvOptions=CsvOptions.newBuilder().setSkipLeadingRows(1).build();TableIdtableId=TableId.of(datasetName,tableName);// Create a permanent table linked to the GCS fileExternalTableDefinitionexternalTable=ExternalTableDefinition.newBuilder(sourceUri,csvOptions).setSchema(schema).build();bigquery.create(TableInfo.of(tableId,externalTable));// Example query to find states starting with 'W'TableResultresults=bigquery.query(QueryJobConfiguration.of(query));results.iterateAll().forEach(row->row.forEach(val->System.out.printf("%s,",val.toString())));System.out.println("Query on external permanent table performed successfully.");}catch(BigQueryException|InterruptedExceptione){System.out.println("Query not performed \n"+e.toString());}}}

Node.js

// Import the Google Cloud client library and create a clientconst{BigQuery}=require('@google-cloud/bigquery');constbigquery=newBigQuery();asyncfunctionqueryExternalGCSPerm(){// Queries an external data source using a permanent table/**   * TODO(developer): Uncomment the following lines before running the sample.   */// const datasetId = "my_dataset";// const tableId = "my_table";// Configure the external data sourceconstdataConfig={sourceFormat:'CSV',sourceUris:['gs://cloud-samples-data/bigquery/us-states/us-states.csv'],// Optionally skip header rowcsvOptions:{skipLeadingRows:1},};// For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resourceconstoptions={schema:schema,externalDataConfiguration:dataConfig,};// Create an external table linked to the GCS fileconst[table]=awaitbigquery.dataset(datasetId).createTable(tableId,options);console.log(`Table${table.id} created.`);// Example query to find states starting with 'W'constquery=`SELECT post_abbr  FROM \`${datasetId}.${tableId}\`  WHERE name LIKE 'W%'`;// Run the query as a jobconst[job]=awaitbigquery.createQueryJob(query);console.log(`Job${job.id} started.`);// Wait for the query to finishconst[rows]=awaitjob.getQueryResults();// Print the resultsconsole.log('Rows:');console.log(rows);}

Python

fromgoogle.cloudimportbigquery# Construct a BigQuery client object.client=bigquery.Client()# TODO(developer): Set table_id to the ID of the table to create.table_id="your-project.your_dataset.your_table_name"# TODO(developer): Set the external source format of your table.# Note that the set of allowed values for external data sources is# different than the set used for loading data (see :class:`~google.cloud.bigquery.job.SourceFormat`).external_source_format="AVRO"# TODO(developer): Set the source_uris to point to your data in Google Cloudsource_uris=["gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/a-twitter.avro","gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/b-twitter.avro","gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/c-twitter.avro",]# Create ExternalConfig object with external source formatexternal_config=bigquery.ExternalConfig(external_source_format)# Set source_uris that point to your data in Google Cloudexternal_config.source_uris=source_uris# TODO(developer) You have the option to set a reference_file_schema_uri, which points to# a reference file for the table schemareference_file_schema_uri="gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/b-twitter.avro"external_config.reference_file_schema_uri=reference_file_schema_uritable=bigquery.Table(table_id)# Set the external data configuration of the tabletable.external_data_configuration=external_configtable=client.create_table(table)# Make an API request.print(f"Created table with external source format{table.external_data_configuration.source_format}")

Console

1. In the Google Cloud console, go toBigQuery.

   Go to BigQuery

2. In the left pane, clickexploreExplorer.
3. In theExplorer pane, expand your project, clickDatasets, and then select a dataset.
4. Clickmore_vertActions, and then clickCreate table. This opens theCreate table pane.
5. In theSource section, specify the following details:
1. ForCreate table from, selectGoogle Cloud Storage.
2. ForSelect file from Cloud Storage bucket, enter the path to the Cloud Storage folder, usingwildcards. For example,my_bucket/my_files*. The Cloud Storage bucket must be in the same location as the dataset that contains the table you want to create, append, or overwrite.
3. From theFile format list, select the file type.
4. Select theSource data partitioning checkbox, and then forSelect Source URI Prefix, enter the Cloud Storage URI prefix. For example,gs://my_bucket/my_files.
5. In thePartition inference mode section, select one of the following options:
   • Automatically infer types: set the partition schema detection mode toAUTO.
   • All columns are strings: set the partition schema detection mode toSTRINGS.
   • Provide my own: set the partition schema detection mode toCUSTOM and manually enter the schema information for the partition keys. For more information, seeProvide a custom partition key schema.
6. Optional: To require a partition filter on all queries for this table, select theRequire partition filter checkbox. Requiring a partition filter can reduce cost and improve performance. For more information, seeRequiring predicate filters on partition keys in queries.
6. In theDestination section, specify the following details:
   1. ForProject, select the project in which you want to create the table.
   2. ForDataset, select the dataset in which you want to create the table.
   3. ForTable, enter the name of the table that you want to create.
   4. ForTable type, selectExternal table.
7. In theSchema section, enter theschema definition.
8. To enable theauto detection of schema, selectAuto detect.
9. To ignore rows with extra column values that do not match the schema, expand theAdvanced options section and selectUnknown values.
10. ClickCreate table.

SQL

Use theCREATE EXTERNAL TABLE DDL statement.

The following example uses automatic detection of Hive partition keys:

CREATEEXTERNALTABLE`PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`WITHPARTITIONCOLUMNSOPTIONS(format='SOURCE_FORMAT',uris=['GCS_URIS'],hive_partition_uri_prefix='GCS_URI_SHARED_PREFIX',require_hive_partition_filter=BOOLEAN);

Replace the following:

• SOURCE_FORMAT: the format of the external datasource, such asPARQUET
• GCS_URIS: the path to the Cloud Storagefolder, using wildcard format
• GCS_URI_SHARED_PREFIX: the source URI prefix withoutthe wildcard
• BOOLEAN: whether to require a predicate filter atquery time. This flag is optional. The default value isfalse.

The following example uses custom Hive partition keys and types by listingthem in theWITH PARTITION COLUMNS clause:

CREATEEXTERNALTABLE`PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`WITHPARTITIONCOLUMNS(PARTITION_COLUMN_LIST)OPTIONS(format='SOURCE_FORMAT',uris=['GCS_URIS'],hive_partition_uri_prefix='GCS_URI_SHARED_PREFIX',require_hive_partition_filter=BOOLEAN);

Replace the following:

• PARTITION_COLUMN_LIST: a list of columns followingthe same order in the path of Cloud Storage folder, in the formatof:

KEY1TYPE1,KEY2TYPE2

The following example creates an externally partitioned table. It uses schemaauto-detection to detect both the file schema and the hive partitioninglayout. If the external path isgs://bucket/path/field_1=first/field_2=1/data.parquet, the partition columnsare detected asfield_1 (STRING) andfield_2 (INT64).

CREATEEXTERNALTABLEdataset.AutoHivePartitionedTableWITHPARTITIONCOLUMNSOPTIONS(uris=['gs://bucket/path/*'],format='PARQUET',hive_partition_uri_prefix='gs://bucket/path',require_hive_partition_filter=false);

The following example creates an externally partitioned table by explicitlyspecifying the partition columns. This example assumes that the external filepath has the patterngs://bucket/path/field_1=first/field_2=1/data.parquet.

CREATEEXTERNALTABLEdataset.CustomHivePartitionedTableWITHPARTITIONCOLUMNS(field_1STRING,-- column order must match the external pathfield_2INT64)OPTIONS(uris=['gs://bucket/path/*'],format='PARQUET',hive_partition_uri_prefix='gs://bucket/path',require_hive_partition_filter=false);

bq

First, use thebq mkdef command tocreate a table definition file:

bqmkdef\--source_format=SOURCE_FORMAT\--hive_partitioning_mode=PARTITIONING_MODE\--hive_partitioning_source_uri_prefix=GCS_URI_SHARED_PREFIX\--require_hive_partition_filter=BOOLEAN\GCS_URIS>DEFINITION_FILE

Replace the following:

• SOURCE_FORMAT: the format of the external data source. Forexample,CSV.
• PARTITIONING_MODE: the Hive partitioning mode. Use one of thefollowing values:
  • AUTO: Automatically detect the key names and types.
  • STRINGS: Automatically convert the key names to strings.
  • CUSTOM: Encode the key schema in the source URI prefix.
• GCS_URI_SHARED_PREFIX: the source URI prefix.
• BOOLEAN: specifies whether to require a predicate filter at querytime. This flag is optional. The default value isfalse.
• GCS_URIS: the path to the Cloud Storage folder, usingwildcard format.
• DEFINITION_FILE: the path to thetable definition file on your localmachine.

IfPARTITIONING_MODE isCUSTOM, include the partition key schemain the source URI prefix, using the following format:

--hive_partitioning_source_uri_prefix=GCS_URI_SHARED_PREFIX/{KEY1:TYPE1}/{KEY2:TYPE2}/...

After you create the table definition file, use thebq mk command tocreate the external table:

bqmk--external_table_definition=DEFINITION_FILE\DATASET_NAME.TABLE_NAME\SCHEMA

Replace the following:

• DEFINITION_FILE: the path to the table definition file.
• DATASET_NAME: the name of the dataset that contains thetable.
• TABLE_NAME: the name of the table you're creating.
• SCHEMA: specifies a path to aJSON schema file,or specifies the schema in the formfield:data_type,field:data_type,.... To use schemaauto-detection, omit this argument.

Examples

The following example usesAUTO Hive partitioning mode:

bq mkdef --source_format=CSV \  --hive_partitioning_mode=AUTO \  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable \  gs://myBucket/myTable/* > mytable_defbq mk --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example usesSTRING Hive partitioning mode:

bq mkdef --source_format=CSV \  --hive_partitioning_mode=STRING \  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable \  gs://myBucket/myTable/* > mytable_defbq mk --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example usesCUSTOM Hive partitioning mode:

bq mkdef --source_format=CSV \  --hive_partitioning_mode=CUSTOM \  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable/{dt:DATE}/{val:STRING} \  gs://myBucket/myTable/* > mytable_defbq mk --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

API

To set Hive partitioning using the BigQuery API, include ahivePartitioningOptionsobject in theExternalDataConfigurationobject when you create thetable definition file.

If you set thehivePartitioningOptions.mode field toCUSTOM, you mustencode the partition key schema in thehivePartitioningOptions.sourceUriPrefix field as follows:gs://BUCKET/PATH_TO_TABLE/{KEY1:TYPE1}/{KEY2:TYPE2}/...

To enforce the use of a predicate filter at query time, set thehivePartitioningOptions.requirePartitionFilter field totrue.

Java

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.ExternalTableDefinition;importcom.google.cloud.bigquery.FormatOptions;importcom.google.cloud.bigquery.HivePartitioningOptions;importcom.google.cloud.bigquery.TableId;importcom.google.cloud.bigquery.TableInfo;// Sample to create external table using hive partitioningpublicclassSetHivePartitioningOptions{publicstaticvoidmain(String[]args){// TODO(developer): Replace these variables before running the sample.StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";StringsourceUri="gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/*";StringsourceUriPrefix="gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/{pkey:STRING}/";setHivePartitioningOptions(datasetName,tableName,sourceUriPrefix,sourceUri);}publicstaticvoidsetHivePartitioningOptions(StringdatasetName,StringtableName,StringsourceUriPrefix,StringsourceUri){try{// Initialize client that will be used to send requests. This client only needs to be created// once, and can be reused for multiple requests.BigQuerybigquery=BigQueryOptions.getDefaultInstance().getService();// Configuring partitioning optionsHivePartitioningOptionshivePartitioningOptions=HivePartitioningOptions.newBuilder().setMode("CUSTOM").setRequirePartitionFilter(true).setSourceUriPrefix(sourceUriPrefix).build();TableIdtableId=TableId.of(datasetName,tableName);ExternalTableDefinitioncustomTable=ExternalTableDefinition.newBuilder(sourceUri,FormatOptions.parquet()).setAutodetect(true).setHivePartitioningOptions(hivePartitioningOptions).build();bigquery.create(TableInfo.of(tableId,customTable));System.out.println("External table created using hivepartitioningoptions");}catch(BigQueryExceptione){System.out.println("External table was not created"+e.toString());}}}

SQL

Use theCREATE OR REPLACE EXTERNAL TABLE DDL statementto update a table:

1. In the Google Cloud console, go to theBigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   CREATEORREPLACEEXTERNALTABLE`PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`WITHCONNECTION{`REGION.CONNECTION_ID`|DEFAULT}OPTIONS(format="TABLE_FORMAT",uris=['BUCKET_PATH'],max_staleness=STALENESS_INTERVAL,metadata_cache_mode='CACHE_MODE');

   Replace the following:

   • PROJECT_ID: the name of the project that contains the table
   • DATASET: the name of the dataset that contains the table
   • EXTERNAL_TABLE_NAME: the name of the table
   • REGION: the region that contains the connection
   • CONNECTION_ID: the name of the connection to use

     To use a default connection, specifyDEFAULT instead of the connection string containingREGION.CONNECTION_ID.

   • TABLE_FORMAT: the format used by the table

     You can't change this when updating the table.

   • BUCKET_PATH: the path to the Cloud Storage bucket that contains the data for the external table, in the format['gs://bucket_name/[folder_name/]file_name'].

     You can select multiple files from the bucket by specifying one asterisk (*) wildcard character in the path. For example,['gs://mybucket/file_name*']. For more information, seeWildcard support for Cloud Storage URIs.

     You can specify multiple buckets for theuris option by providing multiple paths.

     The following examples show validuris values:

     • ['gs://bucket/path1/myfile.csv']
     • ['gs://bucket/path1/*.csv']
     • ['gs://bucket/path1/*', 'gs://bucket/path2/file00*']

     When you specifyuris values that target multiple files, all of those files must share a compatible schema.

     For more information about using Cloud Storage URIs in BigQuery, seeCloud Storage resource path.

   • STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the table, and how fresh the cached metadata must be in order for the operation to use it

     For more information about metadata caching considerations, seeMetadata caching for performance.

     To disable metadata caching, specify 0. This is the default.

     To enable metadata caching, specify aninterval literal value between 30 minutes and 7 days. For example, specifyINTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Cloud Storage instead.

   • CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually

     For more information on metadata caching considerations, seeMetadata caching for performance.

     Set toAUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.

     Set toMANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call theBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.

     You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a value greater than 0.

3. Clickplay_circleRun.

For more information about how to run queries, seeRun an interactive query.

bq

Use thebq mkdef andbq update commandsto update a table:

1. Generate anexternal table definition,that describes the aspects of the table to change:

   bqmkdef--connection_id=PROJECT_ID.REGION.CONNECTION_ID\--source_format=TABLE_FORMAT\--metadata_cache_mode=CACHE_MODE\"BUCKET_PATH">/tmp/DEFINITION_FILE

   Replace the following:

   • PROJECT_ID: the name of the project thatcontains the connection
   • REGION: the region that contains theconnection
   • CONNECTION_ID: the name of the connectionto use
   • TABLE_FORMAT: the format used by thetable. You can't change this when updating the table.

   • CACHE_MODE: specifies whether the metadatacache is refreshed automatically or manually. For more informationon metadata caching considerations, seeMetadata caching for performance.

     Set toAUTOMATIC for the metadata cache to be refreshed at asystem-defined interval, usually somewhere between 30 and60 minutes.

     Set toMANUAL if you want to refresh the metadata cache on aschedule you determine. In this case, you can call theBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refreshthe cache.

     You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a valuegreater than 0.

   • BUCKET_PATH: the path to theCloud Storage bucket that contains the data for theexternal table, in the formatgs://bucket_name/[folder_name/]file_name.

     You can limit the files selected from the bucket by specifying one asterisk (*)wildcard character in the path. For example,gs://mybucket/file_name*. For moreinformation, seeWildcard support for Cloud Storage URIs.

     You can specify multiple buckets for theuris option by providing multiplepaths.

     The following examples show validuris values:

     • gs://bucket/path1/myfile.csv
     • gs://bucket/path1/*.csv
     • gs://bucket/path1/*,gs://bucket/path2/file00*

     When you specifyuris values that target multiple files, all of thosefiles must share a compatible schema.

     For more information about using Cloud Storage URIs inBigQuery, seeCloud Storage resource path.

   • DEFINITION_FILE: the name of the tabledefinition file that you are creating.

2. Update the table using the new external table definition:

   bqupdate--max_staleness=STALENESS_INTERVAL\--external_table_definition=/tmp/DEFINITION_FILE\PROJECT_ID:DATASET.EXTERNAL_TABLE_NAME

   Replace the following:

   • STALENESS_INTERVAL: specifies whethercached metadata is used by operations against thetable, and how fresh the cached metadata must be in order forthe operation to use it. For more information about metadatacaching considerations, seeMetadata caching for performance.

     To disable metadata caching, specify 0. This is the default.

     To enable metadata caching, specify an interval value between 30minutes and 7 days, using theY-M D H:M:S format described in theINTERVAL data typedocumentation. For example, specify0-0 0 4:0:0 for a 4hour staleness interval.With this value, operations against the table use cached metadata ifit has been refreshed within the past 4 hours. If the cached metadatais older than that, the operation retrieves metadata fromCloud Storage instead.

   • DEFINITION_FILE: the name of the tabledefinition file that you created or updated.

   • PROJECT_ID: the name of the projectthat contains the table

   • DATASET: the name of the dataset thatcontains the table

   • EXTERNAL_TABLE_NAME: the name of the table