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Create clustered tables | BigQuery

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You can reduce the amount of data processed by a query by using clustered tables in BigQuery.

With clustered tables, table data is organized based on the values of specified columns, also called the clustering columns. BigQuery sorts the data by the clustered columns, then stores the rows that have similar values in the same or nearby physical blocks. When a query filters on a clustered column, BigQuery efficiently scans only the relevant blocks and skips the data that doesn't match the filter.

For more information, see the following:

• To learn more about clustered tables in BigQuery, see Introduction to clustered tables.
• To learn about working with and controlling access to clustered tables, see Manage clustered tables.

To create a table, you need the following IAM permissions:

• bigquery.tables.create
• bigquery.tables.updateData
• bigquery.jobs.create

Additionally, you might require the bigquery.tables.getData permission to access the data that you write to the table.

Each of the following predefined IAM roles includes the permissions that you need in order to create a table:

• roles/bigquery.dataEditor
• roles/bigquery.dataOwner
• roles/bigquery.admin (includes the bigquery.jobs.create permission)
• roles/bigquery.user (includes the bigquery.jobs.create permission)
• roles/bigquery.jobUser (includes the bigquery.jobs.create permission)

Additionally, if you have the bigquery.datasets.create permission, you can create and update tables in the datasets that you create.

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

When you create a table in BigQuery, the table name must be unique per dataset. The table name can:

• Contain characters with a total of up to 1,024 UTF-8 bytes.
• Contain Unicode characters in category L (letter), M (mark), N (number), Pc (connector, including underscore), Pd (dash), Zs (space). For more information, see General Category.

The following are all examples of valid table names: table 01, ग्राहक, 00_お客様, étudiant-01.

Caveats:

• Table names are case-sensitive by default. mytable and MyTable can coexist in the same dataset, unless they are part of a dataset with case-sensitivity turned off.
• Some table names and table name prefixes are reserved. If you receive an error saying that your table name or prefix is reserved, then select a different name and try again.

• If you include multiple dot operators (.) in a sequence, the duplicate operators are implicitly stripped.

  For example, this: project_name....dataset_name..table_name

  Becomes this: project_name.dataset_name.table_name

You can specify the columns used to create the clustered table when you create a table in BigQuery. After the table is created, you can modify the columns used to create the clustered table. For details, see Modifying the clustering specification.

Clustering columns must be top-level, non-repeated columns, and they must be one of the following data types:

• BIGNUMERIC
• BOOL
• DATE
• DATETIME
• GEOGRAPHY
• INT64
• NUMERIC
• RANGE
• STRING
• TIMESTAMP

You can specify up to four clustering columns. When you specify multiple columns, the order of the columns determines how the data is sorted. For example, if the table is clustered by columns a, b and c, the data is sorted in the same order: first by column a, then by column b, and then by column c. As a best practice, place the most frequently filtered or aggregated column first.

The order of your clustering columns also affects query performance and pricing. For more information about query best practices for clustered tables, see Querying clustered tables.

To create an empty clustered table with a schema definition:

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

   Go to BigQuery

2. In the Explorer pane, expand your project, and then select a dataset.
3. In the Dataset info section, click add_box Create table.
4. In the Create table panel, specify the following details:
1. In the Source section, select Empty table in the Create table from list.
2. In the Destination section, specify the following details:
   1. For Dataset, select the dataset in which you want to create the table.
   2. In the Table field, enter the name of the table that you want to create.
   3. Verify that the Table type field is set to Native table.
3. In the Schema section, enter the schema definition. You can enter schema information manually by using one of the following methods:
   • Option 1: Click Edit as text and paste the schema in the form of a JSON array. When you use a JSON array, you generate the schema using the same process as creating a JSON schema file. You can view the schema of an existing table in JSON format by entering the following command:

         bq show --format=prettyjson dataset.table
         

   • Option 2: Click add_box Add field and enter the table schema. Specify each field's Name, Type, and Mode.
4. For Clustering order, enter between one and four comma-separated column names.
5. Optional: In the Advanced options section, if you want to use a customer-managed encryption key, then select the Use a customer-managed encryption key (CMEK) option. By default, BigQuery encrypts customer content stored at rest by using a Google-owned and Google-managed encryption key.
6. Click Create table.

Use the CREATE TABLE DDL statement command with the CLUSTER BY option. The following example creates a clustered table named myclusteredtable in mydataset:

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

   Go to BigQuery

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

   CREATE TABLE mydataset.myclusteredtable
   (
     customer_id STRING,
     transaction_amount NUMERIC
   )
   CLUSTER BY
     customer_id
     OPTIONS (
       description = 'a table clustered by customer_id');

3. Click play_circle Run.

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

Use the bq mk command with the following flags:

• --table (or the -t shortcut).
• --schema. You can supply the table's schema definition inline or use a JSON schema file.
• --clustering_fields. You can specify up to four clustering columns.

Optional parameters include --expiration, --description, --time_partitioning_type, --time_partitioning_field, --time_partitioning_expiration, --destination_kms_key, and --label.

If you are creating a table in a project other than your default project, add the project ID to the dataset in the following format: project_id:dataset.

--destination_kms_key is not demonstrated here. For information about using --destination_kms_key, see customer-managed encryption keys.

Enter the following command to create an empty clustered table with a schema definition:

bq mk \
    --table \
    --expiration INTEGER1 \
    --schema SCHEMA \
    --clustering_fields CLUSTER_COLUMNS \
    --description "DESCRIPTION" \
    --label KEY:VALUE,KEY:VALUE \
    PROJECT_ID:DATASET.TABLE

Replace the following:

• INTEGER1: the default lifetime, in seconds, for the table. The minimum value is 3,600 seconds (one hour). The expiration time evaluates to the current UTC time plus the integer value. If you set the table's expiration time when you create a table, the dataset's default table expiration setting is ignored. Setting this value deletes the table after the specified time.
• SCHEMA: an inline schema definition in the format COLUMN:DATA_TYPE,COLUMN:DATA_TYPE or the path to the JSON schema file on your local machine.
• CLUSTER_COLUMNS: a comma-separated list of up to four clustering columns. The list cannot contain any spaces.
• DESCRIPTION: a description of the table, in quotes.
• KEY:VALUE: the key-value pair that represents a label. You can enter multiple labels using a comma-separated list.
• PROJECT_ID: your project ID.
• DATASET: a dataset in your project.
• TABLE: the name of the table you're creating.

When you specify the schema on the command line, you cannot include a RECORD (STRUCT) type, you cannot include a column description, and you cannot specify the column's mode. All modes default to NULLABLE. To include descriptions, modes, and RECORD types, supply a JSON schema file instead.

Examples:

Enter the following command to create a clustered table named myclusteredtable in mydataset in your default project. The table's expiration is set to 2,592,000 (1 30-day month), the description is set to This is my clustered table, and the label is set to organization:development. The command uses the -t shortcut instead of --table.

The schema is specified inline as: timestamp:timestamp,customer_id:string,transaction_amount:float. The specified clustering field customer_id is used to cluster the table.

bq mk \
    -t \
    --expiration 2592000 \
    --schema 'timestamp:timestamp,customer_id:string,transaction_amount:float' \
    --clustering_fields customer_id \
    --description "This is my clustered table" \
    --label org:dev \
    mydataset.myclusteredtable

Enter the following command to create a clustered table named myclusteredtable in myotherproject, not your default project. The description is set to This is my clustered table, and the label is set to organization:development. The command uses the -t shortcut instead of --table. This command does not specify a table expiration. If the dataset has a default table expiration, it is applied. If the dataset has no default table expiration, the table never expires.

The schema is specified in a local JSON file: /tmp/myschema.json. The customer_id field is used to cluster the table.

bq mk \
    -t \
    --expiration 2592000 \
    --schema /tmp/myschema.json \
    --clustering_fields=customer_id \
    --description "This is my clustered table" \
    --label org:dev \
    myotherproject:mydataset.myclusteredtable

After the table is created, you can update the table's description and labels.

Use the google_bigquery_table resource.

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

The following example creates a table named mytable that is clustered on the ID and Created columns:

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

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.

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

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.

Call the tables.insert method with a defined table resource that specifies the clustering.fields property and the schema property.

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

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

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

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

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

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

There are two ways to create a clustered table from a query result:

• Write the results to a new destination table and specify the clustering columns.
• By using a DDL CREATE TABLE AS SELECT statement. For more information about this method, see Creating a clustered table from the result of a query on the Using data definition language statements page.

You can create a clustered table by querying either a partitioned table or a non-partitioned table. You cannot change an existing table to a clustered table by using query results.

When you create a clustered table from a query result, you must use standard SQL. Currently, legacy SQL is not supported for querying clustered tables or for writing query results to clustered tables.

To create a clustered table from a query result, use the CREATE TABLE DDL statement with the CLUSTER BY option. The following example creates a new table clustered by customer_id by querying an existing unclustered 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 TABLE mydataset.clustered_table
   (
     customer_id STRING,
     transaction_amount NUMERIC
   )
   CLUSTER BY
     customer_id
   AS (
     SELECT * FROM mydataset.unclustered_table
   );

3. Click play_circle Run.

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

Enter the following command to create a new, clustered destination table from a query result:

bq --location=LOCATION query \
    --use_legacy_sql=false 'QUERY'

Replace the following:

• LOCATION: the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• QUERY: a query in GoogleSQL syntax. Currently, you cannot use legacy SQL to query clustered tables or to write query results to clustered tables. The query can contain a CREATE TABLE DDL statement that specifies the options for creating your clustered table. You can use DDL rather than specifying the individual command-line flags.

Examples:

Enter the following command to write query results to a clustered destination table named myclusteredtable in mydataset. mydataset is in your default project. The query retrieves data from a non-partitioned table: mytable. The table's customer_id column is used to cluster the table. The table's timestamp column is used to create a partitioned table.

bq query --use_legacy_sql=false \
    'CREATE TABLE
       mydataset.myclusteredtable
     PARTITION BY
       DATE(timestamp)
     CLUSTER BY
       customer_id
     AS (
       SELECT
         *
       FROM
         `mydataset.mytable`
     );'

To save query results to a clustered table, call the jobs.insert method, configure a query job, and include a CREATE TABLE DDL statement that creates your clustered table.

Specify your location in the location property in the jobReference section of the job resource.

You can create a clustered table by specifying clustering columns when you load data into a new table. You do not need to create an empty table before loading data into it. You can create the clustered table and load your data at the same time.

For more information about loading data, see Introduction to loading data into BigQuery.

To define clustering when defining a load job:

Use the LOAD DATA statement. The following example loads AVRO data to create a table that is partitioned by the transaction_date field and clustered by the customer_id field. It also configures the partitions to expire after three days.

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

   Go to BigQuery

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

   LOAD DATA INTO mydataset.mytable
   PARTITION BY transaction_date
   CLUSTER BY customer_id
     OPTIONS (
       partition_expiration_days = 3)
   FROM FILES(
     format = 'AVRO',
     uris = ['gs://bucket/path/file.avro']);

3. Click play_circle Run.

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

To define a clustering configuration when creating a table through a load job, you can populate the Clustering properties for the table.

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

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

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

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

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

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

• For information about working with clustered tables, see Manage clustered tables.
• For information about querying clustered tables, see Querying clustered tables.
• For an overview of partitioned table support in BigQuery, see Introduction to partitioned tables.
• To learn how to create partitioned tables, see Creating partitioned tables.
• To see an overview of INFORMATION_SCHEMA, see Introduction to BigQuery INFORMATION_SCHEMA.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["This document provides a comprehensive guide on creating and utilizing clustered tables in BigQuery, detailing various methods, including SQL, bq command-line tool, API calls, and client libraries."],["Clustered tables can be created from query results, by loading data, or by defining an empty table with a schema and clustering specifications using up to four non-repeated columns of specified data types."],["Table names must be unique per dataset, up to 1,024 UTF-8 bytes, and can include Unicode characters from specific categories, with certain caveats regarding case-sensitivity and reserved names."],["Proper IAM permissions are required to create and access tables, including `bigquery.tables.create`, `bigquery.tables.updateData`, and `bigquery.jobs.create`, with predefined roles like `roles/bigquery.dataEditor` providing necessary permissions."],["Clustering specifications of tables can be modified or removed even after the table is created by using `bq update` command or `tables.update` or `tables.patch` API method, then running the `UPDATE` statement to enforce the changes."]]],[]]

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