Create BigLake external tables for Cloud Storage

This document describes how to create a Cloud Storage BigLake table. A BigLake table lets you use access delegation to query structured data in Cloud Storage. Access delegation decouples access to the BigLake table from access to the underlying data store.

Before you begin

  1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  2. Make sure that billing is enabled for your Google Cloud project.

  3. Enable the BigQuery Connection API.

    Enable the API

    If you want to read BigLake tables from open source engines such as Apache Spark, then you need to enable the BigQuery Storage Read API.

  4. In the Google Cloud console, activate Cloud Shell.

    Activate Cloud Shell

  5. Ensure that you have a BigQuery dataset.

  6. Ensure that your version of the Google Cloud SDK is 366.0.0 or later:

    gcloud version

    If needed, update the Google Cloud SDK.

    1. Optional: For Terraform, terraform-provider-google version 4.25.0 or later is required. terraform-provider-google releases are listed on GitHub. You can download the latest version of Terraform from HashiCorp Terraform downloads.

  7. Create a Cloud resource connection based on your external data source, and grant that connection access to Cloud Storage. If you don't have the appropriate permissions to create a connection, ask your BigQuery administrator to create a connection and share it with you.

Required roles

To create a BigLake table, you need the following BigQuery Identity and Access Management (IAM) permissions:

  • bigquery.tables.create
  • bigquery.connections.delegate

The BigQuery Admin (roles/bigquery.admin) predefined Identity and Access Management role includes these permissions.

If you are not a principal in this role, ask your administrator to grant you access or to create the BigLake table for you.

For more information on Identity and Access Management roles and permissions in BigQuery, see Predefined roles and permissions.

Location consideration

When you use Cloud Storage to store data files, you can improve performance by using Cloud Storage single-region or dual-region buckets instead of multi-region buckets.

Create BigLake tables on unpartitioned data

If you're familiar with creating tables in BigQuery, the process of creating a BigLake table is similar. Your table can use any file format that BigLake supports. For more information, see Limitations.

Before you create a BigLake table, you need to have a dataset and a Cloud resource connection that can access Cloud Storage.

To create a BigLake table, select one of the following options:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, expand your project and select a dataset.

  3. Expand the Actions option and click Create table.

  4. In the Source section, specify the following details:

    1. For Create table from, select Google Cloud Storage

    2. For Select file from GCS bucket or use a URI pattern, browse to select a bucket and file to use, or type the path in the format gs://bucket_name/[folder_name/]file_name.

      You can't specify multiple URIs in the Google Cloud console, but you can select multiple files by specifying one asterisk (*) wildcard character. For example, gs://mybucket/file_name*. For more information, see Wildcard support for Cloud Storage URIs.

      The Cloud Storage bucket must be in the same location as the dataset that contains the table you're creating.

    3. For File format, select the format that matches your file.

  5. In the Destination section, specify the following details:

    1. For Project, choose the project in which to create the table.

    2. For Dataset, choose the dataset in which to create the table.

    3. For Table, enter the name of the table you are creating.

    4. For Table type, select External table.

    5. Select Create a BigLake table using a Cloud Resource connection.

    6. For Connection ID, select the connection that you created earlier.

  6. In the Schema section, you can either enable schema auto-detection or manually specify a schema if you have a source file. If you don't have a source file, you must manually specify a schema.

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

    • To manually specify a schema, leave the Auto-detect option unchecked. Enable Edit as text and enter the table schema as a JSON array.

  7. To ignore rows with extra column values that do not match the schema, expand the Advanced options section and select Unknown values.

  8. Click Create table.

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

SQL

Use the CREATE EXTERNAL TABLE DDL statement. You can specify the schema explicitly, or use schema auto-detection to infer the schema from the external data.

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

    Go to BigQuery

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

    CREATE EXTERNAL TABLE `PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`
  WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`
  OPTIONS (
    format ="TABLE_FORMAT",
    uris = ['BUCKET_PATH'[,...]],
    max_staleness = STALENESS_INTERVAL,
    metadata_cache_mode = 'CACHE_MODE'
    );

    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
    • REGION: the region that contains the connection—for example, us
    • CONNECTION_ID: the connection ID—for example, myconnection

      When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

    • TABLE_FORMAT: the format of the table that you want to create—for example, PARQUET

      For more information about supported formats, see Limitations.

    • 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, see Wildcard support for Cloud Storage URIs.

      You can specify multiple buckets for the uris option by providing multiple paths.

      The following examples show valid uris values:

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

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

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

    • STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.

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

      To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 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, see Metadata caching for performance.

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

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

      You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

  3. Click Run.

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

bq

Option 1: Table definition file

Use the bq mkdef command to create a table definition file, and then pass the file path to the bq mk command as follows:

bq mkdef \
    --connection_id=CONNECTION_ID \
    --source_format=SOURCE_FORMAT \
  BUCKET_PATH > DEFINITION_FILE

bq mk --table \
    --external_table_definition=DEFINITION_FILE \
    --max_staleness=STALENESS_INTERVAL \
    PROJECT_ID:DATASET.EXTERNAL_TABLE_NAME \
    SCHEMA

Replace the following:

  • CONNECTION_ID: the connection ID—for example, myconnection

    When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

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

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

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

    You can specify multiple buckets for the uris option by providing multiple paths.

    The following examples show valid uris values:

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

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

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

  • DEFINITION_FILE: the path to the table definition file on your local machine.

  • STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.

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

    To enable metadata caching, specify an interval value between 30 minutes and 7 days, using the Y-M D H:M:S format described in the INTERVAL data type documentation. For example, specify 0-0 0 4:0:0 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.

  • DATASET: the name of the BigQuery dataset that you want to create a table in—for example, mydataset

  • EXTERNAL_TABLE_NAME: the name of the table that you want to create—for example, mytable

  • SCHEMA: the schema for the BigLake table

Example:

bq mkdef
    --connection_id=myconnection
    --metadata_cache_mode=CACHE_MODE
    --source_format=CSV 'gs://mybucket/*.csv' > mytable_def

bq mk
    --table
    --external_table_definition=mytable_def='gs://mybucket/*.csv'
    --max_staleness=0-0 0 4:0:0
    myproject:mydataset.mybiglaketable
    Region:STRING,Quarter:STRING,Total_sales:INTEGER

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

bq mkdef \
    --connection_id=myconnection \
    --metadata_cache_mode=CACHE_MODE \
    --source_format=CSV --autodetect=true \
    gs://mybucket/*.csv > mytable_def

bq mk \
    --table \
    --external_table_definition=mytable_def=gs://mybucket/*.csv \
    --max_staleness=0-0 0 4:0:0 \
    myproject:mydataset.myexternaltable

Option 2: Inline table definition

Instead of creating a table definition file, you can pass the table definition directly to the bq mk command. Use the @connection decorator to specify the connection to use at the end of the --external_table_definition flag.

bq mk --table \
  --external_table_definition=@SOURCE_FORMAT=BUCKET_PATH@projects/PROJECT_ID/locations/REGION/connections/CONNECTION_ID \
  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 the Cloud Storage bucket that contains the data for the table, in the format gs://bucket_name/[folder_name/]file_pattern.

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

    You can specify multiple buckets for the uris option by providing multiple paths.

    The following examples show valid uris values:

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

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

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

  • PROJECT_ID: the name of your project in which you want to create the table—for example, myproject

  • REGION: the region that contains the connection, us

  • CONNECTION_ID: the connection ID—for example, myconnection

    When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

  • DATASET_NAME: the name of the dataset where you want to create the BigLake table

  • TABLE_NAME: the name of the BigLake table

  • SCHEMA: the schema for the BigLake table

Example:

bq mk --table \
    --external_table_definition=@CSV=gs://mybucket/*.parquet@projects/myproject/locations/us/connections/myconnection \
    --max_staleness=0-0 0 4:0:0 \
    myproject:mydataset.myexternaltable \
    Region:STRING,Quarter:STRING,Total_sales:INTEGER

API

Call the tables.insert method API method, and create an ExternalDataConfiguration in the Table resource that you pass in.

Specify the schema property or set the autodetect property to true to enable schema auto detection for supported data sources.

Specify the connectionId property to identify the connection to use for connecting to Cloud Storage.

Terraform

This example creates a BigLake Table on unpartitioned data.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

# This creates a bucket in the US region named "my-bucket" with a pseudorandom suffix.
resource "random_id" "default" {
  byte_length = 8
}
resource "google_storage_bucket" "default" {
  name                        = "my-bucket-${random_id.default.hex}"
  location                    = "US"
  force_destroy               = true
  uniform_bucket_level_access = true
}

# This queries the provider for project information.
data "google_project" "project" {}

# This creates a connection in the US region named "my-connection".
# This connection is used to access the bucket.
resource "google_bigquery_connection" "default" {
  connection_id = "my-connection"
  location      = "US"
  cloud_resource {}
}

# This grants the previous connection IAM role access to the bucket.
resource "google_project_iam_member" "default" {
  role    = "roles/storage.objectViewer"
  project = data.google_project.project.id
  member  = "serviceAccount:${google_bigquery_connection.default.cloud_resource[0].service_account_id}"
}

# This makes the script wait for seven minutes before proceeding.
# This lets IAM permissions propagate.
resource "time_sleep" "default" {
  create_duration = "7m"

  depends_on = [google_project_iam_member.default]
}

# This defines a Google BigQuery dataset with
# default expiration times for partitions and tables, a
# description, a location, and a maximum time travel.
resource "google_bigquery_dataset" "default" {
  dataset_id                      = "my_dataset"
  default_partition_expiration_ms = 2592000000  # 30 days
  default_table_expiration_ms     = 31536000000 # 365 days
  description                     = "My dataset description"
  location                        = "US"
  max_time_travel_hours           = 96 # 4 days

  # This defines a map of labels for the bucket resource,
  # including the labels "billing_group" and "pii".
  labels = {
    billing_group = "accounting",
    pii           = "sensitive"
  }
}


# This creates a BigQuery Table with automatic metadata caching.
resource "google_bigquery_table" "default" {
  dataset_id = google_bigquery_dataset.default.dataset_id
  table_id   = "my_table"
  schema = jsonencode([
    { "name" : "country", "type" : "STRING" },
    { "name" : "product", "type" : "STRING" },
    { "name" : "price", "type" : "INT64" }
  ])
  external_data_configuration {
    # This defines an external data configuration for the BigQuery table
    # that reads Parquet data from the publish directory of the default
    # Google Cloud Storage bucket.
    autodetect    = false
    source_format = "PARQUET"
    connection_id = google_bigquery_connection.default.name
    source_uris   = ["gs://${google_storage_bucket.default.name}/data/*"]
    # This enables automatic metadata refresh.
    metadata_cache_mode = "AUTOMATIC"
  }

  # This sets the maximum staleness of the metadata cache to 10 hours.
  max_staleness = "0-0 0 10:0:0"

  deletion_protection = false

  depends_on = [time_sleep.default]
}

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell

  1. Launch Cloud Shell.

  2. Set the default Google Cloud project where you want to apply your Terraform configurations.

    You only need to run this command once per project, and you can run it in any directory.

    export GOOGLE_CLOUD_PROJECT=PROJECT_ID

    Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. If you are following a tutorial, you can copy the sample code in each section or step.

    Copy the sample code into the newly created main.tf.

    Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. Initialize Terraform. You only need to do this once per directory. 
    terraform init

    Optionally, to use the latest Google provider version, include the -upgrade option: 

    terraform init -upgrade

Apply the changes

  1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: 
    terraform plan

    Make corrections to the configuration as necessary.

  2. Apply the Terraform configuration by running the following command and entering yes at the prompt: 
    terraform apply

    Wait until Terraform displays the "Apply complete!" message.

  3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.

BigLake supports schema autodetection. However, if you did not provide a schema and the service account was not granted access in the previous steps, these steps fail with an access denied message if you try to autodetect the schema.

Create BigLake tables on Hive partitioned data

You can create a BigLake table for Hive partitioned data in Cloud Storage. After you create an externally partitioned table, you can't change the partition key. You need to recreate the table to change the partition key.

To create a BigLake table based on Hive partitioned data in Cloud Storage, select one of the following options:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, expand your project and select a dataset.

  3. Click View actions, and then click Create table. This opens the Create table pane.

  4. In the Source section, specify the following details:

    1. For Create table from, select Google Cloud Storage.

    2. Provide the path to the folder, using wildcards. For example, my_bucket/my_files*. The folder must be in the same location as the dataset that contains the table you want to create, append, or overwrite.

    3. From the File format list, select the file type.

    4. Select the Source data partitioning checkbox, and then specify the following details:

      1. For Select Source URI Prefix, enter the URI prefix. For example, gs://my_bucket/my_files.
      2. Optional: To require a partition filter on all queries for this table, select the Require partition filter checkbox. Requiring a partition filter can reduce cost and improve performance. For more information, see Requiring predicate filters on partition keys in queries.

      3. In the Partition inference mode section, select one of the following options:

        • Automatically infer types: set the partition schema detection mode to AUTO.
        • All columns are strings: set the partition schema detection mode to STRINGS.
        • Provide my own: set the partition schema detection mode to CUSTOM and manually enter the schema information for the partition keys. For more information, see Provide a custom partition key schema.

  5. In the Destination section, specify the following details:

    1. For Project, select the project in which you want to create the table.
    2. For Dataset, select the dataset in which you want to create the table.
    3. For Table, enter the name of the table that you want to create.
    4. For Table type, select External table.
    5. Select the Create a BigLake table using a Cloud Resource connection checkbox.
    6. For Connection ID, select the connection that you created earlier.

  6. In the Schema section, enable schema auto-detection by selecting the Auto detect option.

  7. To ignore rows with extra column values that do not match the schema, expand the Advanced options section and select Unknown values.

  8. Click Create table.

SQL

Use the CREATE EXTERNAL TABLE DDL statement:

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

    Go to BigQuery

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

    CREATE EXTERNAL TABLE `PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`
WITH PARTITION COLUMNS
(
  PARTITION_COLUMN PARTITION_COLUMN_TYPE,
)
WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`
OPTIONS (
  hive_partition_uri_prefix = "HIVE_PARTITION_URI_PREFIX",
  uris=['FILE_PATH'],
  max_staleness = STALENESS_INTERVAL,
  metadata_cache_mode = 'CACHE_MODE',
  format ="TABLE_FORMAT"
);

    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
    • PARTITION_COLUMN: the name of the partitioning column
    • PARTITION_COLUMN_TYPE: the type of the partitioning column
    • REGION: the region that contains the connection—for example, us
    • CONNECTION_ID: the connection ID—for example, myconnection

      When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

    • HIVE_PARTITION_URI_PREFIX: hive partitioning uri prefix–for example, gs://mybucket/
    • FILE_PATH: path to the data source for the external table that you want to create—for example, gs://mybucket/*.parquet
    • STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.

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

      To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 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 about metadata caching considerations, see Metadata caching for performance.

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

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

      You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

    • TABLE_FORMAT: the format of the table that you want to create—for example, PARQUET

  3. Click Run.

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

Examples

The following example creates a BigLake table over partitioned data where:

  • The schema is autodetected.
  • The metadata cache staleness interval for the table is 1 day.
  • The metadata cache refreshes automatically.
CREATE EXTERNAL TABLE `my_dataset.my_table`
WITH PARTITION COLUMNS
(
  sku STRING,
)
WITH CONNECTION `us.my-connection`
OPTIONS(
  hive_partition_uri_prefix = "gs://mybucket/products",
  uris = ['gs://mybucket/products/*'],
  max_staleness = INTERVAL 1 DAY,
  metadata_cache_mode = 'AUTOMATIC'
);

The following example creates a BigLake table over partitioned data where:

  • The schema is specified.
  • The metadata cache staleness interval for the table is 8 hours.
  • The metadata cache must be manually refreshed.
CREATE EXTERNAL TABLE `my_dataset.my_table`
(
  ProductId INTEGER,
  ProductName STRING,
  ProductType STRING
)
WITH PARTITION COLUMNS
(
  sku STRING,
)
WITH CONNECTION `us.my-connection`
OPTIONS(
  hive_partition_uri_prefix = "gs://mybucket/products",
  uris = ['gs://mybucket/products/*'],
  max_staleness = INTERVAL 8 HOUR,
  metadata_cache_mode = 'MANUAL'
);

bq

First, use the bq mkdef command to create a table definition file:

bq mkdef \
--source_format=SOURCE_FORMAT \
--connection_id=REGION.CONNECTION_ID \
--hive_partitioning_mode=PARTITIONING_MODE \
--hive_partitioning_source_uri_prefix=GCS_URI_SHARED_PREFIX \
--require_hive_partition_filter=BOOLEAN \
--metadata_cache_mode=CACHE_MODE \
 GCS_URIS > DEFINITION_FILE

Replace the following:

  • SOURCE_FORMAT: the format of the external data source. For example, CSV.
  • REGION: the region that contains the connection—for example, us.

  • CONNECTION_ID: the connection ID—for example, myconnection.

    When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

  • PARTITIONING_MODE: the Hive partitioning mode. Use one of the following 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 query time. This flag is optional. The default value is false.

  • CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. You only need to include this flag if you also plan to use the --max_staleness flag in the subsequent bq mk command to enable metadata caching. For more information on metadata caching considerations, see Metadata caching for performance.

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

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

    You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

  • GCS_URIS: the path to the Cloud Storage folder, using wildcard format.

  • DEFINITION_FILE: the path to the table definition file on your local machine.

If PARTITIONING_MODE is CUSTOM, include the partition key schema in 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 the bq mk command to create the BigLake table:

bq mk --external_table_definition=DEFINITION_FILE \
--max_staleness=STALENESS_INTERVAL \
DATASET_NAME.TABLE_NAME \
SCHEMA

Replace the following:

  • DEFINITION_FILE: the path to the table definition file.

  • STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. If you include this flag, you must have also specified a value for the --metadata_cache_mode flag in the preceding bq mkdef command. For more information on metadata caching considerations, see Metadata caching for performance.

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

    To enable metadata caching, specify an interval value between 30 minutes and 7 days, using the Y-M D H:M:S format described in the INTERVAL data type documentation. For example, specify 0-0 0 4:0:0 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.

  • 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 a JSON schema file, or specifies the schema in the form field:data_type,field:data_type,.... To use schema auto-detection, omit this argument.

Examples

The following example uses AUTO Hive partitioning mode, and also sets the metadata cache to have a 12 hour staleness interval and to get refreshed automatically:

bq mkdef --source_format=CSV \
  --connection_id=us.my-connection \
  --hive_partitioning_mode=AUTO \
  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable \
  --metadata_cache_mode=AUTOMATIC \
  gs://myBucket/myTable/* > mytable_def

bq mk --external_table_definition=mytable_def \
  --max_staleness=0-0 0 12:0:0 \
  mydataset.mytable \
  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example uses STRING Hive partitioning mode:

bq mkdef --source_format=CSV \
  --connection_id=us.my-connection \
  --hive_partitioning_mode=STRING \
  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable \
  gs://myBucket/myTable/* > mytable_def

bq mk --external_table_definition=mytable_def \
  mydataset.mytable \
  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example uses CUSTOM Hive partitioning mode:

bq mkdef --source_format=CSV \
  --connection_id=us.my-connection \
  --hive_partitioning_mode=CUSTOM \
  --hive_partitioning_source_uri_prefix=gs://myBucket/myTable/{dt:DATE}/{val:STRING} \
  gs://myBucket/myTable/* > mytable_def

bq 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 the hivePartitioningOptions object in the ExternalDataConfiguration object when you create the table definition file. To create a BigLake table, you must also specify a value for the connectionId field.

If you set the hivePartitioningOptions.mode field to CUSTOM, you must encode the partition key schema in the hivePartitioningOptions.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 the hivePartitioningOptions.requirePartitionFilter field to true.

Terraform

This example creates a BigLake Table on partitioned data.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.


# This creates a bucket in the US region named "my-bucket" with a pseudorandom
# suffix.
resource "random_id" "default" {
  byte_length = 8
}
resource "google_storage_bucket" "default" {
  name                        = "my-bucket-${random_id.default.hex}"
  location                    = "US"
  force_destroy               = true
  uniform_bucket_level_access = true
}

resource "google_storage_bucket_object" "default" {
  # This creates a fake message to create partition locations on the table.
  # Otherwise, the table deployment fails.
  name    = "publish/dt=2000-01-01/hr=00/min=00/fake_message.json"
  content = "{\"column1\": \"XXX\"}"
  bucket  = google_storage_bucket.default.name
}

# This queries the provider for project information.
data "google_project" "default" {}

# This creates a connection in the US region named "my-connection".
# This connection is used to access the bucket.
resource "google_bigquery_connection" "default" {
  connection_id = "my-connection"
  location      = "US"
  cloud_resource {}
}

# This grants the previous connection IAM role access to the bucket.
resource "google_project_iam_member" "default" {
  role    = "roles/storage.objectViewer"
  project = data.google_project.default.id
  member  = "serviceAccount:${google_bigquery_connection.default.cloud_resource[0].service_account_id}"
}

# This makes the script wait for seven minutes before proceeding. This lets IAM
# permissions propagate.
resource "time_sleep" "default" {
  create_duration = "7m"

  depends_on = [google_project_iam_member.default]
}

# This defines a Google BigQuery dataset with default expiration times for
# partitions and tables, a description, a location, and a maximum time travel.
resource "google_bigquery_dataset" "default" {
  dataset_id                      = "my_dataset"
  default_partition_expiration_ms = 2592000000  # 30 days
  default_table_expiration_ms     = 31536000000 # 365 days
  description                     = "My dataset description"
  location                        = "US"
  max_time_travel_hours           = 96 # 4 days

  # This defines a map of labels for the bucket resource,
  # including the labels "billing_group" and "pii".
  labels = {
    billing_group = "accounting",
    pii           = "sensitive"
  }
}

# This creates a BigQuery table with partitioning and automatic metadata
# caching.
resource "google_bigquery_table" "default" {
  dataset_id = google_bigquery_dataset.default.dataset_id
  table_id   = "my_table"
  schema     = jsonencode([{ "name" : "column1", "type" : "STRING", "mode" : "NULLABLE" }])
  external_data_configuration {
    # This defines an external data configuration for the BigQuery table
    # that reads Parquet data from the publish directory of the default
    # Google Cloud Storage bucket.
    autodetect    = false
    source_format = "PARQUET"
    connection_id = google_bigquery_connection.default.name
    source_uris   = ["gs://${google_storage_bucket.default.name}/publish/*"]
    # This configures Hive partitioning for the BigQuery table,
    # partitioning the data by date and time.
    hive_partitioning_options {
      mode                     = "CUSTOM"
      source_uri_prefix        = "gs://${google_storage_bucket.default.name}/publish/{dt:STRING}/{hr:STRING}/{min:STRING}"
      require_partition_filter = false
    }
    # This enables automatic metadata refresh.
    metadata_cache_mode = "AUTOMATIC"
  }


  # This sets the maximum staleness of the metadata cache to 10 hours.
  max_staleness = "0-0 0 10:0:0"

  deletion_protection = false

  depends_on = [
    time_sleep.default,
    google_storage_bucket_object.default
  ]
}

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell

  1. Launch Cloud Shell.

  2. Set the default Google Cloud project where you want to apply your Terraform configurations.

    You only need to run this command once per project, and you can run it in any directory.

    export GOOGLE_CLOUD_PROJECT=PROJECT_ID

    Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. If you are following a tutorial, you can copy the sample code in each section or step.

    Copy the sample code into the newly created main.tf.

    Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. Initialize Terraform. You only need to do this once per directory. 
    terraform init

    Optionally, to use the latest Google provider version, include the -upgrade option: 

    terraform init -upgrade

Apply the changes

  1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: 
    terraform plan

    Make corrections to the configuration as necessary.

  2. Apply the Terraform configuration by running the following command and entering yes at the prompt: 
    terraform apply

    Wait until Terraform displays the "Apply complete!" message.

  3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.

Set up access control policies

You can use several methods to control access to BigLake tables:

For example, let's say you want to limit row access for the table mytable in the dataset mydataset:

+---------+---------+-------+
| country | product | price |
+---------+---------+-------+
| US      | phone   |   100 |
| JP      | tablet  |   300 |
| UK      | laptop  |   200 |
+---------+---------+-------+

You can create a row-level filter for Kim (kim@example.com) that restricts their access to rows where country is equal to US.

CREATE ROW ACCESS POLICY only_us_filter
ON mydataset.mytable
GRANT TO ('user:kim@example.com')
FILTER USING (country = 'US');

Then, Kim runs the following query:

SELECT * FROM projectid.mydataset.mytable;

The output shows only the rows where country is equal to US:

+---------+---------+-------+
| country | product | price |
+---------+---------+-------+
| US      | phone   |   100 |
+---------+---------+-------+

Query BigLake tables

For more information, see Query Cloud Storage data in BigLake tables.

Update BigLake tables

You can update BigLake tables if necessary, for example to change their metadata caching. To get table details such as source format and source URI, see Get table information.

You can also use this same procedure to upgrade Cloud Storage-based external tables to BigLake tables by associating the external table to a connection. For more information, see Upgrade external tables to BigLake tables.

To update a BigLake table, select one of the following options:

SQL

Use the CREATE OR REPLACE EXTERNAL TABLE DDL statement to update a table:

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

    Go to BigQuery

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

    CREATE OR REPLACE EXTERNAL TABLE
  `PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`
  WITH CONNECTION `REGION.CONNECTION_ID`
  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
    • 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, see Wildcard support for Cloud Storage URIs.

      You can specify multiple buckets for the uris option by providing multiple paths.

      The following examples show valid uris values:

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

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

      For more information about using Cloud Storage URIs in BigQuery, see Cloud 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, see Metadata caching for performance.

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

      To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 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, see Metadata caching for performance.

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

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

      You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

  3. Click Run.

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

bq

Use the bq mkdef and bq update commands to update a table:

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

    bq mkdef --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 that contains the connection
    • REGION: the region that contains the connection
    • CONNECTION_ID: the name of the connection to use
    • TABLE_FORMAT: the format used by the table. You can't change this when updating the table.

    • CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. For more information on metadata caching considerations, see Metadata caching for performance.

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

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

      You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

    • 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 limit the files selected from the bucket by specifying one asterisk (*) wildcard character in the path. For example, gs://mybucket/file_name*. For more information, see Wildcard support for Cloud Storage URIs.

      You can specify multiple buckets for the uris option by providing multiple paths.

      The following examples show valid uris values:

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

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

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

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

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

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

    Replace the following:

    • 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, see Metadata caching for performance.

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

      To enable metadata caching, specify an interval value between 30 minutes and 7 days, using the Y-M D H:M:S format described in the INTERVAL data type documentation. For example, specify 0-0 0 4:0:0 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.

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

    • 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

Example

The following example updates mytable to use cached metadata as long as it has been refreshed in the last 4.5 hours, and also to refresh cached metadata automatically:

bq update --project_id=myproject --max_staleness='0-0 0 4:30:0' \
--external_table_definition=enable_metadata.json mydataset.mytable

Where enable_metadata.json has the following contents: { "metadataCacheMode": "AUTOMATIC" }

Audit logging

For information about logging in BigQuery, see Introduction to BigQuery monitoring. To learn more about logging in Google Cloud, see Cloud Logging.

What's next