Data definition language (DDL) statements in GoogleSQL

GoogleSQL is the new name for Google Standard SQL! New name, same great SQL dialect.

Data definition language (DDL) statements let you create and modify BigQuery resources using GoogleSQL query syntax. You can use DDL commands to create, alter, and delete resources, such as tables, table clones, table snapshots, views, user-defined functions (UDFs), and row-level access policies.

Required permissions

To create a job that runs a DDL statement, you must have the bigquery.jobs.create permission for the project where you are running the job. Each DDL statement also requires specific permissions on the affected resources, which are documented under each statement.

IAM roles

The predefined IAM roles bigquery.user, bigquery.jobUser, and bigquery.admin include the required bigquery.jobs.create permission.

For more information about IAM roles in BigQuery, see Predefined roles and permissions or the IAM permissions reference.

Run DDL statements

You can run DDL statements by using the Google Cloud console, by using the bq command-line tool, by calling the jobs.query REST API, or programmatically using the BigQuery API client libraries.

Console

1. Go to the BigQuery page in the Google Cloud console.

   Go to BigQuery

2. Click Compose new query.

3. Enter the DDL statement into the Query editor text area. For example:

    CREATE TABLE mydataset.newtable ( x INT64 )
    

4. Click Run.

bq

Enter the bq query command and supply the DDL statement as the query parameter. Set the use_legacy_sql flag to false.

bq query --use_legacy_sql=false \
  'CREATE TABLE mydataset.newtable ( x INT64 )'

API

Call the jobs.query method and supply the DDL statement in the request body's query property.

DDL functionality extends the information returned by a Jobs resource. statistics.query.statementType includes the following additional values for DDL support:

• CREATE_TABLE
• CREATE_TABLE_AS_SELECT
• DROP_TABLE
• CREATE_VIEW
• DROP_VIEW

statistics.query has 2 additional fields:

• ddlOperationPerformed: The DDL operation performed, possibly dependent on the existence of the DDL target. Current values include:
  • CREATE: The query created the DDL target.
  • SKIP: No-op. Examples — CREATE TABLE IF NOT EXISTS was submitted, and the table exists. Or DROP TABLE IF EXISTS was submitted, and the table does not exist.
  • REPLACE: The query replaced the DDL target. Example — CREATE OR REPLACE TABLE was submitted, and the table already exists.
  • DROP: The query deleted the DDL target.
• ddlTargetTable: When you submit a CREATE TABLE/VIEW statement or a DROP TABLE/VIEW statement, the target table is returned as an object with 3 fields:
• "projectId": string
• "datasetId": string
• "tableId": string

Java

Call the BigQuery.create() method to start a query job. Call the Job.waitFor() method to wait for the DDL query to finish.

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.

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobInfo;
import com.google.cloud.bigquery.QueryJobConfiguration;

// Sample to create a view using DDL
public class DDLCreateView {

  public static void runDDLCreateView() {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "MY_PROJECT_ID";
    String datasetId = "MY_DATASET_ID";
    String tableId = "MY_VIEW_ID";
    String ddl =
        "CREATE VIEW "
            + "`"
            + projectId
            + "."
            + datasetId
            + "."
            + tableId
            + "`"
            + " OPTIONS("
            + " expiration_timestamp=TIMESTAMP_ADD("
            + " CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),"
            + " friendly_name=\"new_view\","
            + " description=\"a view that expires in 2 days\","
            + " labels=[(\"org_unit\", \"development\")]"
            + " )"
            + " AS SELECT name, state, year, number"
            + " FROM `bigquery-public-data.usa_names.usa_1910_current`"
            + " WHERE state LIKE 'W%'`";
    ddlCreateView(ddl);
  }

  public static void ddlCreateView(String ddl) {
    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.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      QueryJobConfiguration config = QueryJobConfiguration.newBuilder(ddl).build();

      // create a view using query and it will wait to complete job.
      Job job = bigquery.create(JobInfo.of(config));
      job = job.waitFor();
      if (job.isDone()) {
        System.out.println("View created successfully");
      } else {
        System.out.println("View was not created");
      }
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("View was not created. \n" + e.toString());
    }
  }
}

Node.js

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

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

// Import the Google Cloud client library and create a client
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function ddlCreateView() {
  // Creates a view via a DDL query

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const projectId = "my_project"
  // const datasetId = "my_dataset"
  // const tableId = "my_new_view"

  const query = `
  CREATE VIEW \`${projectId}.${datasetId}.${tableId}\`
  OPTIONS(
      expiration_timestamp=TIMESTAMP_ADD(
          CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
      friendly_name="new_view",
      description="a view that expires in 2 days",
      labels=[("org_unit", "development")]
  )
  AS SELECT name, state, year, number
      FROM \`bigquery-public-data.usa_names.usa_1910_current\`
      WHERE state LIKE 'W%'`;

  // For all options, see https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/query
  const options = {
    query: query,
  };

  // Run the query as a job
  const [job] = await bigquery.createQueryJob(options);

  job.on('complete', metadata => {
    console.log(`Created new view ${tableId} via job ${metadata.id}`);
  });
}

Python

Call the Client.query() method to start a query job. Call the QueryJob.result() method to wait for the DDL query to finish.

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.

# from google.cloud import bigquery
# project = 'my-project'
# dataset_id = 'my_dataset'
# table_id = 'new_view'
# client = bigquery.Client(project=project)

sql = """
CREATE VIEW `{}.{}.{}`
OPTIONS(
    expiration_timestamp=TIMESTAMP_ADD(
        CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
    friendly_name="new_view",
    description="a view that expires in 2 days",
    labels=[("org_unit", "development")]
)
AS SELECT name, state, year, number
    FROM `bigquery-public-data.usa_names.usa_1910_current`
    WHERE state LIKE 'W%'
""".format(
    project, dataset_id, table_id
)

job = client.query(sql)  # API request.
job.result()  # Waits for the query to finish.

print(
    'Created new view "{}.{}.{}".'.format(
        job.destination.project,
        job.destination.dataset_id,
        job.destination.table_id,
    )
)

On-demand query size calculation

If you use on-demand billing, BigQuery charges for data definition language (DDL) queries based on the number of bytes processed by the query.

DDL statement	Bytes processed
CREATE TABLE	None.
CREATE TABLE ... AS SELECT ...	The sum of bytes processed for all the columns referenced from the tables scanned by the query.
CREATE VIEW	None.
DROP TABLE	None.
DROP VIEW	None.

For more information about cost estimation, see Estimate and control costs.

CREATE SCHEMA statement

Creates a new dataset.

Key Point: This SQL statement uses the term SCHEMA to refer to a logical collection of tables, views, and other resources. The equivalent concept in BigQuery is a dataset. In this context, SCHEMA does not refer to BigQuery table schemas.

Syntax

CREATE SCHEMA [ IF NOT EXISTS ]
[project_name.]dataset_name
[DEFAULT COLLATE collate_specification]
[OPTIONS(schema_option_list)]

Arguments

• IF NOT EXISTS: If any dataset exists with the same name, the CREATE statement has no effect.

• DEFAULT COLLATE collate_specification: When a new table is created in the dataset, the table inherits a default collation specification unless a collation specification is explicitly specified for a table or a column.

  If you remove or change this collation specification later with the ALTER SCHEMA statement, this will not change existing collation specifications in this dataset. If you want to update an existing collation specification in a dataset, you must alter the column that contains the specification.

• project_name: The name of the project where you are creating the dataset. Defaults to the project that runs this DDL statement.

• dataset_name: The name of the dataset to create.

• schema_option_list: A list of options for creating the dataset.

Details

The dataset is created in the location that you specify in the query settings. For more information, see Specifying your location.

For more information about creating a dataset, see Creating datasets. For information about quotas, see Dataset limits.

schema_option_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
default_kms_key_name	STRING	Specifies the default Cloud KMS key for encrypting table data in this dataset. You can override this value when you create a table.
default_partition_expiration_days	FLOAT64	Specifies the default expiration time, in days, for table partitions in this dataset. You can override this value when you create a table.
default_rounding_mode	STRING	Example: default_rounding_mode = "ROUND_HALF_EVEN" This specifies the defaultRoundingMode that is used for new tables created in this dataset. It does not impact existing tables. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.25 is rounded to 2.3, and -2.25 is rounded to -2.3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.25 is rounded to 2.2 and -2.25 is rounded to -2.2.
default_table_expiration_days	FLOAT64	Specifies the default expiration time, in days, for tables in this dataset. You can override this value when you create a table.
description	STRING	The description of the dataset.
failover_reservation	STRING	Associates the dataset to a reservation in the case of a failover scenario.
friendly_name	STRING	A descriptive name for the dataset.
is_case_insensitive	BOOL	TRUE if the dataset and its table names are case-insensitive, otherwise FALSE. By default, this is FALSE, which means the dataset and its table names are case-sensitive. Datasets: mydataset and MyDataset can coexist in the same project, unless one of them has case-sensitivity turned off. Tables: mytable and MyTable can coexist in the same dataset if case-sensitivity for the dataset is turned on.
is_primary	BOOLEAN	Declares if the dataset is the primary replica.
labels	<ARRAY<STRUCT<STRING, STRING>>>	An array of labels for the dataset, expressed as key-value pairs.
location	STRING	The location in which to create the dataset. If you don't specify this option, the dataset is created in the location where the query runs. If you specify this option and also explicitly set the location for the query job, the two values must match; otherwise the query fails.
max_time_travel_hours	SMALLINT	Specifies the duration in hours of the time travel window for the dataset. The max_time_travel_hours value must be an integer expressed in multiples of 24 (48, 72, 96, 120, 144, 168) between 48 (2 days) and 168 (7 days). 168 hours is the default if this option isn't specified.
primary_replica	STRING	The replica name to set as the primary replica.
storage_billing_model	STRING	Alters the storage billing model for the dataset. Set the storage_billing_model value to PHYSICAL to use physical bytes when calculating storage charges, or to LOGICAL to use logical bytes. LOGICAL is the default. The storage_billing_model option is only available for datasets that have been updated after December 1, 2022. For datasets that were last updated before that date, the storage billing model is LOGICAL. When you change a dataset's billing model, it takes 24 hours for the change to take effect. Once you change a dataset's storage billing model, you must wait 14 days before you can change the storage billing model again.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.create	The project where you create the dataset.

Examples

Creating a new dataset

The following example creates a dataset with a default table expiration and a set of labels.

CREATE SCHEMA mydataset
OPTIONS(
  location="us",
  default_table_expiration_days=3.75,
  labels=[("label1","value1"),("label2","value2")]
  )

Creating a case-insensitive dataset

The following example creates a case-insensitive dataset. Both the dataset name and table names inside the dataset are case-insensitive.

CREATE SCHEMA mydataset
OPTIONS(
  is_case_insensitive=TRUE
)

Creating a dataset with collation support

The following example creates a dataset with a collation specification.

CREATE SCHEMA mydataset
DEFAULT COLLATE 'und:ci'

CREATE TABLE statement

Creates a new table.

Syntax

CREATE [ OR REPLACE ] [ TEMP | TEMPORARY ] TABLE [ IF NOT EXISTS ]
table_name
[(
  column | constraint_definition[, ...]
)]
[DEFAULT COLLATE collate_specification]
[PARTITION BY partition_expression]
[CLUSTER BY clustering_column_list]
[WITH CONNECTION connection_name]
[OPTIONS(table_option_list)]
[AS query_statement]

column:=
column_definition

constraint_definition:=
[primary_key]
| [[CONSTRAINT constraint_name] foreign_key, ...]

primary_key :=
PRIMARY KEY (column_name[, ...]) NOT ENFORCED

foreign_key :=
FOREIGN KEY (column_name[, ...]) foreign_reference

foreign_reference :=
REFERENCES primary_key_table(column_name[, ...]) NOT ENFORCED

Arguments

• OR REPLACE: Replaces any table with the same name if it exists. Cannot appear with IF NOT EXISTS.

• TEMP | TEMPORARY: Creates a temporary table.

• IF NOT EXISTS: If any table exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• table_name: The name of the table to create. See Table path syntax. For temporary tables, do not include the project name or dataset name.

• column: The table's schema information.

• constraint_definition: An expression that defines a table constraint.

• collation_specification: When a new column is added to the table without an explicit collation specification, the column inherits this collation specification for STRING types.

  If you remove or change this collation specification later with the ALTER TABLE statement, this will not change existing collation specifications in this table. If you want to update an existing collation specification in a table, you must alter the column that contains the specification.

  If the table is part of a dataset, the default collation specification for this table overrides the default collation specification for the dataset.

• partition_expression: An expression that determines how to partition the table.

• clustering_column_list: A comma-separated list of column references that determine how to cluster the table. You cannot have collation on columns in this list.

• connection_name: Specifies a connection resource that has credentials for accessing the external data. Specify the connection name in the form PROJECT_ID.LOCATION.CONNECTION_ID. If the project ID or location contains a dash, enclose the connection name in backticks (`).

• table_option_list: A list of options for creating the table.

• query_statement: The query from which the table should be created. For the query syntax, see SQL syntax reference. If a collation specification is used on this table, collation passes through this query statement.

• primary_key: An expression that defines a primary key table constraint.

• foreign_key: An expression that defines a foreign key table constraint.

Details

CREATE TABLE statements must comply with the following rules:

• Only one CREATE statement is allowed.
• Either the column list, the AS query_statement clause, or both must be present.
• When both the column list and the AS query_statement clause are present, BigQuery ignores the names in the AS query_statement clause and matches the columns with the column list by position.
• When the AS query_statement clause is present and the column list is absent, BigQuery determines the column names and types from the AS query_statement clause.
• Column names must be specified either through the column list, the AS query_statement clause or schema of the table in the LIKE clause.
• Duplicate column names are not allowed.
• When both the LIKE and the AS query_statement clause are present, the column list in the query statement must match the columns of the table referenced by the LIKE clause.
• Table names are case-sensitive unless the dataset they belong to is not. To create a case-insensitive dataset, see Creating a case-insensitive dataset. To alter a dataset to make it case-insensitive dataset, see Turning on case insensitivity for a dataset.

Limitations:

• It is not possible to create an ingestion-time partitioned table from the result of a query. Instead, use a CREATE TABLE DDL statement to create the table, and then use an INSERT DML statement to insert data into it.
• It is not possible to use the OR REPLACE modifier to replace a table with a different kind of partitioning. Instead, DROP the table, and then use a CREATE TABLE ... AS SELECT ... statement to recreate it.

This statement supports the following variants, which have the same limitations:

• CREATE TABLE LIKE: Create a table with the same schema as an existing table.
• CREATE TABLE COPY: Create a table by copying schema and data from an existing table.

column

(column_name column_schema[, ...]) contains the table's schema information in a comma-separated list.

Note: Constraints cannot be specified on ARRAY or STRUCT elements.

column :=
  column_name column_schema

column_schema :=
   {
     simple_type
     | STRUCT<field_list>
     | ARRAY<array_element_schema>
   }
   [PRIMARY KEY NOT ENFORCED | REFERENCES table_name(column_name) NOT ENFORCED]
   [DEFAULT default_expression]
   [NOT NULL]
   [OPTIONS(column_option_list)]

simple_type :=
  { data_type | STRING COLLATE collate_specification }

field_list :=
  field_name column_schema [, ...]

array_element_schema :=
  { simple_type | STRUCT<field_list> }
  [NOT NULL]

• column_name is the name of the column. A column name:

  • Must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_)
  • Must start with a letter or underscore
  • Can be up to 300 characters

• column_schema: Similar to a data type, but supports an optional NOT NULL constraint for types other than ARRAY. column_schema also supports options on top-level columns and STRUCT fields.

  column_schema can be used only in the column definition list of CREATE TABLE statements. It cannot be used as a type in expressions.

• simple_type: Any supported data type aside from STRUCT and ARRAY.

  If simple_type is a STRING, it supports an additional clause for collation, which defines how a resulting STRING can be compared and sorted. The syntax looks like this:

  STRING COLLATE collate_specification
  

  If you have DEFAULT COLLATE collate_specification assigned to the table, the collation specification for a column overrides the specification for the table.

• default_expression: The default value assigned to the column.

• field_list: Represents the fields in a struct.

• field_name: The name of the struct field. Struct field names have the same restrictions as column names.

• NOT NULL: When the NOT NULL constraint is present for a column or field, the column or field is created with REQUIRED mode. Conversely, when the NOT NULL constraint is absent, the column or field is created with NULLABLE mode.

  Columns and fields of ARRAY type do not support the NOT NULL modifier. For example, a column_schema of ARRAY<INT64> NOT NULL is invalid, since ARRAY columns have REPEATED mode and can be empty but cannot be NULL. An array element in a table can never be NULL, regardless of whether the NOT NULL constraint is specified. For example, ARRAY<INT64> is equivalent to ARRAY<INT64 NOT NULL>.

  The NOT NULL attribute of a table's column_schema does not propagate through queries over the table. If table T contains a column declared as x INT64 NOT NULL, for example, CREATE TABLE dataset.newtable AS SELECT x FROM T creates a table named dataset.newtable in which x is NULLABLE.

partition_expression

PARTITION BY is an optional clause that controls table partitioning. partition_expression is an expression that determines how to partition the table. The partition expression can contain the following values:

• _PARTITIONDATE. Partition by ingestion time with daily partitions. This syntax cannot be used with the AS query_statement clause.
• DATE(_PARTITIONTIME). Equivalent to _PARTITIONDATE. This syntax cannot be used with the AS query_statement clause.
• <date_column>. Partition by a DATE column with daily partitions.
• DATE({ <timestamp_column> | <datetime_column> }). Partition by a TIMESTAMP or DATETIME column with daily partitions.
• DATETIME_TRUNC(<datetime_column>, { DAY | HOUR | MONTH | YEAR }). Partition by a DATETIME column with the specified partitioning type.
• TIMESTAMP_TRUNC(<timestamp_column>, { DAY | HOUR | MONTH | YEAR }). Partition by a TIMESTAMP column with the specified partitioning type.
• TIMESTAMP_TRUNC(_PARTITIONTIME, { DAY | HOUR | MONTH | YEAR }). Partition by ingestion time with the specified partitioning type. This syntax cannot be used with the AS query_statement clause.
• DATE_TRUNC(<date_column>, { MONTH | YEAR }). Partition by a DATE column with the specified partitioning type.

• RANGE_BUCKET(<int64_column>, GENERATE_ARRAY(<start>, <end>[, <interval>])). Partition by an integer column with the specified range, where:

  • start is the start of range partitioning, inclusive.
  • end is the end of range partitioning, exclusive.
  • interval is the width of each range within the partition. Defaults to 1.

clustering_column_list

CLUSTER BY is an optional clause that controls table clustering. clustering_column_list is a comma-separated list that determines how to cluster the table. The clustering column list can contain a list of up to four clustering columns.

Note: You cannot have collation on a column in clustering_column_list.

table_option_list

The option list lets you set table options such as a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a table option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property.
partition_expiration_days	FLOAT64	Example: partition_expiration_days=7 Sets the partition expiration in days. For more information, see Set the partition expiration. By default, partitions don't expire. This property is equivalent to the timePartitioning.expirationMs table resource property but uses days instead of milliseconds. One day is equivalent to 86400000 milliseconds, or 24 hours. This property can only be set if the table is partitioned.
require_partition_filter	BOOL	Example: require_partition_filter=true Specifies whether queries on this table must include a a predicate filter that filters on the partitioning column. For more information, see Set partition filter requirements. The default value is false. This property is equivalent to the timePartitioning.requirePartitionFilter table resource property. This property can only be set if the table is partitioned.
kms_key_name	STRING	Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys.
friendly_name	STRING	Example: friendly_name="my_table" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a table that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.
default_rounding_mode	STRING	Example: default_rounding_mode = "ROUND_HALF_EVEN" This specifies the default rounding mode that's used for values written to any new NUMERIC or BIGNUMERIC type columns or STRUCT fields in the table. It does not impact existing fields in the table. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.5 is rounded to 3.0, and -2.5 is rounded to -3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.5 is rounded to 2.0 and -2.5 is rounded to -2.0. This property is equivalent to the defaultRoundingMode table resource property.
enable_change_history	BOOL	In preview. Example: enable_change_history=TRUE Set this property to TRUE in order to capture change history on the table, which you can then view by using the CHANGES function. Enabling this table option has an impact on costs; for more information see Pricing and costs. The default is FALSE.
max_staleness	INTERVAL	Example: max_staleness=INTERVAL "4:0:0" HOUR TO SECOND The maximum interval behind the current time where it's acceptable to read stale data. For example, with change data capture, when this option is set, the table copy operation is denied if data is more stale than the max_staleness value. max_staleness is disabled by default.
enable_fine_grained_mutations	BOOL	In preview. Example: enable_fine_grained_mutations=TRUE Set this property to TRUE to enable fine-grained DML optimization on the table. The default is FALSE.
storage_uri	STRING	In preview. Example: storage_uri=gs://BUCKET_DIRECTORY/TABLE_DIRECTORY/ A fully qualified location prefix for the external folder where data is stored. Supports gs: buckets. Required for managed tables.
table_format	STRING	In preview. Example: table_format=PARQUET The open-source file format in which the table data is stored. Only PARQUET is supported. Required for managed tables. The default is PARQUET.
file_format	STRING	In preview. Example: file_format=ICEBERG The open table format in which metadata-only snapshots are stored. Only ICEBERG is supported. Required for managed tables. The default is ICEBERG.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

If VALUE evaluates to NULL, the corresponding option NAME in the CREATE TABLE statement is ignored.

column_option_list

Specify a column option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
description	STRING	Example: description="a unique id" This property is equivalent to the schema.fields[].description table resource property.
rounding_mode	STRING	Example: rounding_mode = "ROUND_HALF_EVEN" This specifies the rounding mode that's used for values written to a NUMERIC or BIGNUMERIC type column or STRUCT field. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.25 is rounded to 2.3, and -2.25 is rounded to -2.3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.25 is rounded to 2.2 and -2.25 is rounded to -2.2. This property is equivalent to the roundingMode table resource property.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

Setting the VALUE replaces the existing value of that option for the column, if there was one. Setting the VALUE to NULL clears the column's value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the table.

In addition, the OR REPLACE clause requires bigquery.tables.update and bigquery.tables.updateData permissions.

If the OPTIONS clause includes any expiration options, then the bigquery.tables.delete permission is also required.

Examples

Creating a new table

The following example creates a partitioned table named newtable in mydataset:

CREATE TABLE mydataset.newtable
(
  x INT64 OPTIONS(description="An optional INTEGER field"),
  y STRUCT<
    a ARRAY<STRING> OPTIONS(description="A repeated STRING field"),
    b BOOL
  >
)
PARTITION BY _PARTITIONDATE
OPTIONS(
  expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC",
  partition_expiration_days=1,
  description="a table that expires in 2025, with each partition living for 24 hours",
  labels=[("org_unit", "development")]
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.newtable, your table qualifier might be `myproject.mydataset.newtable`.

If the table name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.table

The table uses the following partition_expression to partition the table: PARTITION BY _PARTITIONDATE. This expression partitions the table using the date in the _PARTITIONDATE pseudocolumn.

The table schema contains two columns:

• x: An integer, with description "An optional INTEGER field"

• y: A STRUCT containing two columns:

  • a: An array of strings, with description "A repeated STRING field"
  • b: A boolean
  Note: When you examine the table schema in the Google Cloud console, a STRUCT is displayed as a RECORD column, and an ARRAY is displayed as a REPEATED column. The STRUCT and ARRAY data types are used to create nested and repeated data in BigQuery. For more information, see Specifying nested and repeated fields.

The table option list specifies the:

• Table expiration time: January 1, 2025 at 00:00:00 UTC
• Partition expiration time: 1 day
• Description: A table that expires in 2025
• Label: org_unit = development

Creating a new table from an existing table

The following example creates a table named top_words in mydataset from a query:

CREATE TABLE mydataset.top_words
OPTIONS(
  description="Top ten words per Shakespeare corpus"
) AS
SELECT
  corpus,
  ARRAY_AGG(STRUCT(word, word_count) ORDER BY word_count DESC LIMIT 10) AS top_words
FROM bigquery-public-data.samples.shakespeare
GROUP BY corpus;

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.top_words, your table qualifier might be `myproject.mydataset.top_words`.

If the table name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.table

The table schema contains 2 columns:

• corpus: Name of a Shakespeare corpus

• top_words: An ARRAY of STRUCTs containing 2 fields: word (a STRING) and word_count (an INT64 with the word count)

  Note: When you examine the table schema in the Google Cloud console, a STRUCT is displayed as a RECORD column, and an ARRAY is displayed as a REPEATED column. The STRUCT and ARRAY data types are used to create nested and repeated data in BigQuery. For more information, see Specifying nested and repeated fields.

The table option list specifies the:

• Description: Top ten words per Shakespeare corpus

Creating a table only if the table doesn't exist

The following example creates a table named newtable in mydataset only if no table named newtable exists in mydataset. If the table name exists in the dataset, no error is returned, and no action is taken.

CREATE TABLE IF NOT EXISTS mydataset.newtable (x INT64, y STRUCT<a ARRAY<STRING>, b BOOL>)
OPTIONS(
  expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC",
  description="a table that expires in 2025",
  labels=[("org_unit", "development")]
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.newtable, your table qualifier might be `myproject.mydataset.newtable`.

The table schema contains 2 columns:

• x: An integer

• y: A STRUCT containing a (an array of strings) and b (a boolean)

  Note: When you examine the table schema in the Google Cloud console, a STRUCT is displayed as a RECORD, and an ARRAY is displayed as REPEATED. The STRUCT and ARRAY data types are used to create nested and repeated data in BigQuery. For more information, see Specifying nested and repeated fields.

The table option list specifies the:

• Expiration time: January 1, 2025 at 00:00:00 UTC
• Description: A table that expires in 2025
• Label: org_unit = development

Creating or replacing a table

The following example creates a table named newtable in mydataset, and if newtable exists in mydataset, it is overwritten with an empty table.

CREATE OR REPLACE TABLE mydataset.newtable (x INT64, y STRUCT<a ARRAY<STRING>, b BOOL>)
OPTIONS(
  expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC",
  description="a table that expires in 2025",
  labels=[("org_unit", "development")]
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.newtable, your table qualifier might be `myproject.mydataset.newtable`.

The table schema contains 2 columns:

• x: An integer

• y: A STRUCT containing a (an array of strings) and b (a boolean)

  Note: When you examine the table schema in the Google Cloud console, a STRUCT is displayed as a RECORD, and an ARRAY is displayed as REPEATED. The STRUCT and ARRAY data types are used to create nested and repeated data in BigQuery. For more information, see Specifying nested and repeated fields.

The table option list specifies the:

• Expiration time: January 1, 2025 at 00:00:00 UTC
• Description: A table that expires in 2025
• Label: org_unit = development

Creating a table with REQUIRED columns

The following example creates a table named newtable in mydataset. The NOT NULL modifier in the column definition list of a CREATE TABLE statement specifies that a column or field is created in REQUIRED mode.

CREATE TABLE mydataset.newtable (
  x INT64 NOT NULL,
  y STRUCT<
    a ARRAY<STRING>,
    b BOOL NOT NULL,
    c FLOAT64
  > NOT NULL,
  z STRING
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.newtable, your table qualifier might be `myproject.mydataset.newtable`.

If the table name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.table

The table schema contains 3 columns:

• x: A REQUIRED integer
• y: A REQUIRED STRUCT containing a (an array of strings), b (a REQUIRED boolean), and c (a NULLABLE float)

• z: A NULLABLE string

  Note: When you examine the table schema in the Google Cloud console, a STRUCT is displayed as a RECORD, and an ARRAY is displayed as REPEATED. The STRUCT and ARRAY data types are used to create nested and repeated data in BigQuery. For more information, see Specifying nested and repeated fields.

Creating a table with collation support

The following examples create a table named newtable in mydataset with columns a, b, c, and a struct with fields x and y.

All STRING column schemas in this table are collated with 'und:ci':

CREATE TABLE mydataset.newtable (
  a STRING,
  b STRING,
  c STRUCT<
    x FLOAT64
    y ARRAY<STRING>
  >
)
DEFAULT COLLATE 'und:ci';

Only b and y are collated with 'und:ci':

CREATE TABLE mydataset.newtable (
  a STRING,
  b STRING COLLATE 'und:ci',
  c STRUCT<
    x FLOAT64
    y ARRAY<STRING COLLATE 'und:ci'>
  >
);

Creating a table with parameterized data types

The following example creates a table named newtable in mydataset. The parameters in parentheses specify that the column contains a parameterized data type. See Parameterized Data Types for more information about parameterized types.

CREATE TABLE mydataset.newtable (
  x STRING(10),
  y STRUCT<
    a ARRAY<BYTES(5)>,
    b NUMERIC(15, 2) OPTIONS(rounding_mode = 'ROUND_HALF_EVEN'),
    c FLOAT64
  >,
  z BIGNUMERIC(35)
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. Instead of mydataset.newtable, your table qualifier should be `myproject.mydataset.newtable`.

If the table name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.table

The table schema contains 3 columns:

• x: A parameterized string with a maximum length of 10
• y: A STRUCT containing a (an array of parameterized bytes with a maximum length of 5), b (a parameterized NUMERIC with a maximum precision of 15, maximum scale of 2, and rounding mode set to 'ROUND_HALF_EVEN'), and c (a float)
• z: A parameterized BIGNUMERIC with a maximum precision of 35 and maximum scale of 0

Creating a partitioned table

The following example creates a partitioned table named newtable in mydataset using a DATE column:

CREATE TABLE mydataset.newtable (transaction_id INT64, transaction_date DATE)
PARTITION BY transaction_date
OPTIONS(
  partition_expiration_days=3,
  description="a table partitioned by transaction_date"
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.newtable, your table qualifier might be `myproject.mydataset.newtable`.

The table schema contains 2 columns:

• transaction_id: An integer
• transaction_date: A date

The table option list specifies the:

• Partition expiration: Three days
• Description: A table partitioned by transaction_date

Creating a partitioned table from the result of a query

The following example creates a partitioned table named days_with_rain in mydataset using a DATE column:

CREATE TABLE mydataset.days_with_rain
PARTITION BY date
OPTIONS (
  partition_expiration_days=365,
  description="weather stations with precipitation, partitioned by day"
) AS
SELECT
  DATE(CAST(year AS INT64), CAST(mo AS INT64), CAST(da AS INT64)) AS date,
  (SELECT ANY_VALUE(name) FROM `bigquery-public-data.noaa_gsod.stations` AS stations
   WHERE stations.usaf = stn) AS station_name,  -- Stations can have multiple names
  prcp
FROM `bigquery-public-data.noaa_gsod.gsod2017` AS weather
WHERE prcp != 99.9  -- Filter unknown values
  AND prcp > 0      -- Filter stations/days with no precipitation

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.days_with_rain, your table qualifier might be `myproject.mydataset.days_with_rain`.

The table schema contains 2 columns:

• date: The DATE of data collection
• station_name: The name of the weather station as a STRING
• prcp: The amount of precipitation in inches as a FLOAT64

The table option list specifies the:

• Partition expiration: One year
• Description: Weather stations with precipitation, partitioned by day

Creating a clustered table

Example 1

The following example creates a clustered table named myclusteredtable in mydataset. The table is a partitioned table, partitioned by a TIMESTAMP column and clustered by a STRING column named customer_id.

CREATE TABLE mydataset.myclusteredtable
(
  timestamp TIMESTAMP,
  customer_id STRING,
  transaction_amount NUMERIC
)
PARTITION BY DATE(timestamp)
CLUSTER BY customer_id
OPTIONS (
  partition_expiration_days=3,
  description="a table clustered by customer_id"
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.myclusteredtable, your table qualifier might be `myproject.mydataset.myclusteredtable`.

The table schema contains 3 columns:

• timestamp: The time of data collection as a TIMESTAMP
• customer_id: The customer ID as a STRING
• transaction_amount: The transaction amount as NUMERIC

The table option list specifies the:

• Partition expiration: 3 days
• Description: A table clustered by customer_id

Example 2

The following example creates a clustered table named myclusteredtable in mydataset. The table is an ingestion-time partitioned table.

CREATE TABLE mydataset.myclusteredtable
(
  customer_id STRING,
  transaction_amount NUMERIC
)
PARTITION BY DATE(_PARTITIONTIME)
CLUSTER BY
  customer_id
OPTIONS (
  partition_expiration_days=3,
  description="a table clustered by customer_id"
)

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.myclusteredtable, your table qualifier might be `myproject.mydataset.myclusteredtable`.

The table schema contains 2 columns:

• customer_id: The customer ID as a STRING
• transaction_amount: The transaction amount as NUMERIC

The table option list specifies the:

• Partition expiration: 3 days
• Description: A table clustered by customer_id

Example 3

The following example creates a clustered table named myclusteredtable in mydataset. The table is not partitioned.

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

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.myclusteredtable, your table qualifier might be `myproject.mydataset.myclusteredtable`.

The table schema contains 2 columns:

• customer_id: The customer ID as a STRING
• transaction_amount: The transaction amount as NUMERIC

The table option list specifies the:

• Description: A table clustered by customer_id

Creating a clustered table from the result of a query

Example 1

The following example creates a clustered table named myclusteredtable in mydataset using the result of a query. The table is a partitioned table, partitioned by a TIMESTAMP column.

CREATE TABLE mydataset.myclusteredtable
(
  timestamp TIMESTAMP,
  customer_id STRING,
  transaction_amount NUMERIC
)
PARTITION BY DATE(timestamp)
CLUSTER BY
  customer_id
OPTIONS (
  partition_expiration_days=3,
  description="a table clustered by customer_id"
)
AS SELECT * FROM mydataset.myothertable

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.myclusteredtable, your table qualifier might be `myproject.mydataset.myclusteredtable`.

The table schema contains 3 columns:

• timestamp: The time of data collection as a TIMESTAMP
• customer_id: The customer ID as a STRING
• transaction_amount: The transaction amount as NUMERIC

The table option list specifies the:

• Partition expiration: 3 days
• Description: A table clustered by customer_id

Example 2

The following example creates a clustered table named myclusteredtable in mydataset using the result of a query. The table is not partitioned.

CREATE TABLE mydataset.myclusteredtable
(
  customer_id STRING,
  transaction_amount NUMERIC
)
CLUSTER BY
  customer_id
OPTIONS (
  description="a table clustered by customer_id"
)
AS SELECT * FROM mydataset.myothertable

If you haven't configured a default project, prepend a project ID to the dataset name in the example SQL, and enclose the name in backticks if project_id contains special characters: `project_id.dataset.table`. So, instead of mydataset.myclusteredtable, your table qualifier might be `myproject.mydataset.myclusteredtable`.

The table schema contains 2 columns:

• customer_id: The customer ID as a STRING
• transaction_amount: The transaction amount as NUMERIC

The table option list specifies the:

• Description: A table clustered by customer_id

Creating a temporary table

The following example creates a temporary table named Example and inserts values into it.

CREATE TEMP TABLE Example
(
  x INT64,
  y STRING
);

INSERT INTO Example
VALUES (5, 'foo');

INSERT INTO Example
VALUES (6, 'bar');

SELECT *
FROM Example;

This script returns the following output:

+-----+---+-----+
| Row | x | y   |
+-----+---|-----+
| 1   | 5 | foo |
| 2   | 6 | bar |
+-----+---|-----+

Load data across clouds

Example 1

Suppose you have a BigLake table named myawsdataset.orders that references data from Amazon S3. You want to transfer data from that table to a BigQuery table myotherdataset.shipments in the US multi-region.

First, display information about the myawsdataset.orders table:

    bq show myawsdataset.orders;

The output is similar to the following:

  Last modified             Schema              Type     Total URIs   Expiration
----------------- -------------------------- ---------- ------------ -----------
  31 Oct 17:40:28   |- l_orderkey: integer     EXTERNAL   1
                    |- l_partkey: integer
                    |- l_suppkey: integer
                    |- l_linenumber: integer
                    |- l_returnflag: string
                    |- l_linestatus: string
                    |- l_commitdate: date

Next, display information about the myotherdataset.shipments table:

  bq show myotherdataset.shipments

The output is similar to the following. Some columns are omitted to simplify the output.

  Last modified             Schema             Total Rows   Total Bytes   Expiration   Time Partitioning   Clustered Fields   Total Logical
 ----------------- --------------------------- ------------ ------------- ------------ ------------------- ------------------ ---------------
  31 Oct 17:34:31   |- l_orderkey: integer      3086653      210767042                                                         210767042
                    |- l_partkey: integer
                    |- l_suppkey: integer
                    |- l_commitdate: date
                    |- l_shipdate: date
                    |- l_receiptdate: date
                    |- l_shipinstruct: string
                    |- l_shipmode: string

Now, using the CREATE TABLE AS SELECT statement you can selectively load data to the myotherdataset.orders table in the US multi-region:

CREATE OR REPLACE TABLE
  myotherdataset.orders
  PARTITION BY DATE_TRUNC(l_commitdate, YEAR) AS
SELECT
  *
FROM
  myawsdataset.orders
WHERE
  EXTRACT(YEAR FROM l_commitdate) = 1992;

Note: If you get a ResourceExhausted error, retry after sometime. If the issue persists, you can contact support.

You can then perform a join operation with the newly created table:

SELECT
  orders.l_orderkey,
  orders.l_orderkey,
  orders.l_suppkey,
  orders.l_commitdate,
  orders.l_returnflag,
  shipments.l_shipmode,
  shipments.l_shipinstruct
FROM
  myotherdataset.shipments
JOIN
  `myotherdataset.orders` as orders
ON
  orders.l_orderkey = shipments.l_orderkey
AND orders.l_partkey = shipments.l_partkey
AND orders.l_suppkey = shipments.l_suppkey
WHERE orders.l_returnflag = 'R'; -- 'R' means refunded.

When new data is available, append the data of the 1993 year to the destination table using the INSERT INTO SELECT statement:

INSERT INTO
   myotherdataset.orders
 SELECT
   *
 FROM
   myawsdataset.orders
 WHERE
   EXTRACT(YEAR FROM l_commitdate) = 1993;

Example 2

The following example inserts data into an ingestion-time partitioned table:

CREATE TABLE
 mydataset.orders(id String, numeric_id INT)
PARTITION BY _PARTITIONDATE;

After creating a partitioned table, you can insert data into the ingestion-time partitioned table:

INSERT INTO
 mydataset.orders(
   _PARTITIONTIME,
   id,
   numeric_id)
SELECT
 TIMESTAMP("2023-01-01"),
 id,
 numeric_id,
FROM
 mydataset.ordersof23
WHERE
 numeric_id > 4000000;

CREATE TABLE LIKE statement

Creates a new table with all of the same metadata of another table.

Syntax

CREATE [ OR REPLACE ] TABLE [ IF NOT EXISTS ]
table_name
LIKE [[project_name.]dataset_name.]source_table_name
...
[OPTIONS(table_option_list)]

Details

This statement is a variant of the CREATE TABLE statement and has the same limitations. Other than the use of the LIKE clause in place of a column list, the syntax is identical to the CREATE TABLE syntax.

The CREATE TABLE LIKE statement copies only the metadata of the source table. You can use the AS query_statement clause to include data into the new table.

The new table has no relationship to the source table after creation; thus modifications to the source table will not propagate to the new table.

By default, the new table inherits partitioning, clustering, and options metadata from the source table. You can customize metadata in the new table by using the optional clauses in the SQL statement. For example, if you want to specify a different set of options for the new table, then include the OPTIONS clause with a list of options and values. This behavior matches that of ALTER TABLE SET OPTIONS.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the table.
bigquery.tables.get	The source table.

In addition, the OR REPLACE clause requires bigquery.tables.update and bigquery.tables.updateData permissions.

If the OPTIONS clause includes any expiration options, then the bigquery.tables.delete permission is also required.

Examples

Example 1

The following example creates a new table named newtable in mydataset with the same metadata as sourcetable:

CREATE TABLE mydataset.newtable
LIKE mydataset.sourcetable

Example 2

The following example creates a new table named newtable in mydataset with the same metadata as sourcetable and the data from the SELECT statement:

CREATE TABLE mydataset.newtable
LIKE mydataset.sourcetable
AS SELECT * FROM mydataset.myothertable

CREATE TABLE COPY statement

Creates a table that has the same metadata and data as another table. The source table can be a table, a table clone, or a table snapshot.

Syntax

CREATE [ OR REPLACE ] TABLE [ IF NOT EXISTS ] table_name
COPY source_table_name
...
[OPTIONS(table_option_list)]

Details

This statement is a variant of the CREATE TABLE statement and has the same limitations. Other than the use of the COPY clause in place of a column list, the syntax is identical to the CREATE TABLE syntax.

The CREATE TABLE COPY statement copies both the metadata and data from the source table.

The new table inherits partitioning and clustering from the source table. By default, the table options metadata from the source table are also inherited, but you can override table options by using the OPTIONS clause. The behavior is equivalent to running ALTER TABLE SET OPTIONS after the table is copied.

The new table has no relationship to the source table after creation; modifications to the source table are not propagated to the new table.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the table copy.
bigquery.tables.get	The source table.
bigquery.tables.getData	The source table.

In addition, the OR REPLACE clause requires bigquery.tables.update and bigquery.tables.updateData permissions.

If the OPTIONS clause includes any expiration options, then the bigquery.tables.delete permission is also required.

CREATE SNAPSHOT TABLE statement

Creates a table snapshot based on a source table. The source table can be a table, a table clone, or a table snapshot.

Syntax

CREATE SNAPSHOT TABLE [ IF NOT EXISTS ] table_snapshot_name
CLONE source_table_name
[FOR SYSTEM_TIME AS OF time_expression]
[OPTIONS(snapshot_option_list)]

Arguments

• IF NOT EXISTS: If a table snapshot or other table resource exists with the same name, the CREATE statement has no effect.

• table_snapshot_name: The name of the table snapshot that you want to create. The table snapshot name must be unique per dataset. See Table path syntax.

• source_table_name: The name of the table that you want to snapshot or the table snapshot that you want to copy. See Table path syntax.

  If the source table is a standard table, then BigQuery creates a table snapshot of the source table. If the source table is a table snapshot, then BigQuery creates a copy of the table snapshot.

• FOR SYSTEM_TIME AS OF: Lets you select the version of the table that was current at the time specified by timestamp_expression. It can only be used when creating a snapshot of a table; it can't be used when making a copy of a table snapshot.

• snapshot_option_list: Additional table snapshot creation options such as a label and an expiration time.

Details

CREATE SNAPSHOT TABLE statements must comply with the following rules:

• Only one CREATE statement is allowed.
• The source table must be one of the following:
  • A table
  • A table clone
  • A table snapshot
• The FOR SYSTEM_TIME AS OF clause can only be used when creating a snapshot of a table or table clone; it can't be used when making a copy of a table snapshot.

snapshot_option_list

The option list lets you set table snapshot options such as a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a table snapshot option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property.
friendly_name	STRING	Example: friendly_name="my_table_snapshot" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="A table snapshot that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.

VALUE is a constant expression that contains only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, and UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

If VALUE evaluates to NULL, the corresponding option NAME in the CREATE SNAPSHOT TABLE statement is ignored.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the table snapshot.
bigquery.tables.createSnapshot	The source table.
bigquery.tables.get	The source table.
bigquery.tables.getData	The source table.

Examples

Create a table snapshot: fail if it already exists

The following example creates a table snapshot of the table myproject.mydataset.mytable. The table snapshot is created in the dataset mydataset and is named mytablesnapshot:

CREATE SNAPSHOT TABLE `myproject.mydataset.mytablesnapshot`
CLONE `myproject.mydataset.mytable`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="my_table_snapshot",
  description="A table snapshot that expires in 2 days",
  labels=[("org_unit", "development")]
)

If the table snapshot name already exists in the dataset, then the following error is returned:

Already Exists: myproject.mydataset.mytablesnapshot

The table snapshot option list specifies the following:

• Expiration time: 48 hours after the time the table snapshot is created
• Friendly name: my_table_snapshot
• Description: A table snapshot that expires in 2 days
• Label: org_unit = development

Create a table snapshot: ignore if it already exists

The following example creates a table snapshot of the table myproject.mydataset.mytable. The table snapshot is created in the dataset mydataset and is named mytablesnapshot:

CREATE SNAPSHOT TABLE IF NOT EXISTS `myproject.mydataset.mytablesnapshot`
CLONE `myproject.mydataset.mytable`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="my_table_snapshot",
  description="A table snapshot that expires in 2 days"
  labels=[("org_unit", "development")]
)

The table snapshot option list specifies the following:

• Expiration time: 48 hours after the time the table snapshot is created
• Friendly name: my_table_snapshot
• Description: A table snapshot that expires in 2 days
• Label: org_unit = development

If the table snapshot name already exists in the dataset, then no action is taken, and no error is returned.

For information about restoring table snapshots, see CREATE TABLE CLONE.

For information about removing table snapshots, see DROP SNAPSHOT TABLE.

CREATE TABLE CLONE statement

Creates a table clone based on a source table. The source table can be a table, a table clone, or a table snapshot.

Syntax

CREATE TABLE [ IF NOT EXISTS ]
destination_table_name
CLONE source_table_name [FOR SYSTEM_TIME AS OF time_expression]
...
[OPTIONS(table_option_list)]

Details

Other than the use of the CLONE clause in place of a column list, the syntax is identical to the CREATE TABLE syntax.

Arguments

• IF NOT EXISTS: If the specified destination table name already exists, the CREATE statement has no effect.

• destination_table_name: The name of the table that you want to create. The table name must be unique per dataset. The table name can contain the following:

  • Up to 1,024 characters
  • Letters (upper or lower case), numbers, and underscores

• OPTIONS(table_option_list): Lets you specify additional table creation options such as a label and an expiration time.

• source_table_name: The name of the source table.

CREATE TABLE CLONE statements must comply with the following rules:

• Only one CREATE statement is allowed.
• The table that is being cloned must be a table, a table clone, or a table snapshot.

OPTIONS

CREATE TABLE CLONE options are the same as CREATE TABLE options.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the table clone.
bigquery.tables.get	The source table.
bigquery.tables.getData	The source table.
bigquery.tables.restoreSnapshot	The source table (required only if the source table is a table snapshot).

If the OPTIONS clause includes any expiration options, then the bigquery.tables.delete permission is also required.

Examples

Restore a table snapshot: fail if destination table already exists

The following example creates the table myproject.mydataset.mytable from the table snapshot myproject.mydataset.mytablesnapshot:

CREATE TABLE `myproject.mydataset.mytable`
CLONE `myproject.mydataset.mytablesnapshot`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 365 DAY),
  friendly_name="my_table",
  description="A table that expires in 1 year",
  labels=[("org_unit", "development")]
)

If the table name exists in the dataset, then the following error is returned:

Already Exists: myproject.mydataset.mytable.

The table option list specifies the following:

• Expiration time: 365 days after the time that the table is created
• Friendly name: my_table
• Description: A table that expires in 1 year
• Label: org_unit = development

Create a clone of a table: ignore if the destination table already exists

The following example creates the table clone myproject.mydataset.mytableclone based on the table myproject.mydataset.mytable:

CREATE TABLE IF NOT EXISTS `myproject.mydataset.mytableclone`
CLONE `myproject.mydataset.mytable`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 365 DAY),
  friendly_name="my_table",
  description="A table that expires in 1 year",
  labels=[("org_unit", "development")]
)

The table option list specifies the following:

• Expiration time: 365 days after the time the table is created
• Friendly name: my_table
• Description: A table that expires in 1 year
• Label: org_unit = development

If the table name exists in the dataset, then no action is taken, and no error is returned.

For information about creating a copy of a table, see CREATE TABLE COPY.

For information about creating a snapshot of a table, see CREATE SNAPSHOT TABLE.

CREATE VIEW statement

Creates a new view.

Syntax

CREATE [ OR REPLACE ] VIEW [ IF NOT EXISTS ] view_name
[(view_column_name_list)]
[OPTIONS(view_option_list)]
AS query_expression

view_column_name_list :=
  view_column[, ...]

view_column :=
  column_name [OPTIONS(view_column_option_list)]

Arguments

• OR REPLACE: Replaces any view with the same name if it exists. Cannot appear with IF NOT EXISTS.

• IF NOT EXISTS: If a view or other table resource exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• view_name: The name of the view you're creating. See Table path syntax.

• view_column_name_list: Lets you explicitly specify the column names of the view, which may be aliases to the column names in the underlying SQL query.

• view_option_list: Additional view creation options such as a label and an expiration time.

• query_expression: The GoogleSQL query expression used to define the view.

Details

CREATE VIEW statements must comply with the following rules:

• Only one CREATE statement is allowed.

view_column_name_list

The view's column name list is optional. The names must be unique but do not have to be the same as the column names of the underlying SQL query. For example, if your view is created with the following statement:

CREATE VIEW mydataset.age_groups(age, count) AS SELECT age, COUNT(*)
FROM mydataset.people
group by age;

Then you can query it with:

SELECT age, count from mydataset.age_groups;

The number of columns in the column name list must match the number of columns in the underlying SQL query. If the columns in the table of the underlying SQL query is added or dropped, the view becomes invalid and must be recreated. For example, if the age column is dropped from the mydataset.people table, then the view created in the previous example becomes invalid.

view_column_option_list

The view_column_option_list lets you specify optional top-level column options. Column options for a view have the same syntax and requirements as for a table, but with a different list of NAME and VALUE fields:

NAME	VALUE	Details
description	STRING	Example: description="a unique id"

view_option_list

The option list allows you to set view options such as a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a view option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property.
friendly_name	STRING	Example: friendly_name="my_view" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a view that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.
privacy_policy	JSON-formatted STRING	The policies to enforce when anyone queries the view. To learn more about the policies available for a view, see the privacy_policy view option. Note: Time travel is disabled on any view that has an analysis rule.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

If VALUE evaluates to NULL, the corresponding option NAME in the CREATE VIEW statement is ignored.

privacy_policy

The following policies are available in the privacy_policy view option to create analysis rules. A policy represents a condition that needs to be met before a query can be run.

Policy	Details
aggregation_threshold_policy	The aggregation threshold policy to enforce when a view is queried. Syntax: '{ "aggregation_threshold_policy": { "threshold": value, "privacy_unit_columns": value } }' Parameters: aggregation_threshold_policy: An aggregation threshold policy for the view. When this parameter is included, a minimum number of distinct entities must be present in a set of data in the view. threshold: The minimum number of distinct privacy units (privacy unit column values) that need to contribute to each row in the query results. If a potential row doesn't satisfy this threshold, that row is omitted from the query results. value is a positive JSON integer. privacy_unit_columns: The columns that represents the privacy unit columns in a view. At this time, a view can have only one privacy unit column. value is a JSON string. Example: privacy_policy='{"aggregation_threshold_policy": {"threshold" : 50, "privacy_unit_columns": "ID"}}'
differential_privacy_policy	A differential privacy policy for the view. When this parameter is included, only differentially private queries can be run on the view. Syntax: '{ "differential_privacy_policy": { "privacy_unit_column": value, "max_epsilon_per_query": value, "epsilon_budget": value, "delta_per_query": value, "delta_budget": value, "max_groups_contributed": value } }' Parameters: differential_privacy_policy: The differential privacy policy for the view. privacy_unit_column: The column that represents the privacy unit column for differentially private queries on the view. value is a JSON string. max_epsilon_per_query: The maximum amount of epsilon that can be specified for a differentially private query on the view. value is a JSON number from 0.001 to 1e+15. epsilon_budget: The amount of epsilon that can be used in totality for all differentially private queries on the view. value is JSON number from 0.001 to 1e+15. delta_per_query: The maximum amount of delta that can be specified for a differentially private query on the view. value is a JSON number from 1e-15 to 1. delta_budget: The amount of delta that can be used in totality for all differentially private queries on the view. The budget must be larger than the delta for any differentially private query on the view. value is a JSON number from 1e-15 to `1000`. max_groups_contributed: The maximum number of groups to which each protected entity can contribute in a differentially private query. value is a non-negative JSON integer. Example: privacy_policy='{"differential_privacy_policy": { "privacy_unit_column": "contributor_id", "max_epsilon_per_query": 0.01, "epsilon_budget": 25.6, "delta_per_query": 0.005, "delta_budget": 9.6, "max_groups_contributed": 2}}'
join_restriction_policy	A join restriction policy for the view. When this parameter is included, only the specified joins can be run on the specified columns in the view. This policy can be used alone or with other policies, such as the aggregation threshold or differential privacy policy. Syntax: '{ "join_restriction_policy": { "join_condition": value, "join_allowed_columns": value } }' Parameters: join_restriction_policy: The join restriction policy for the view. join_condition: The type of join condition to enforce on the view. value can be one of the following JSON strings: JOIN_ALL: All columns in join_allowed_columns must be inner joined upon for this view to be queried. JOIN_ANY: At least one column in join_allowed_columns must be joined upon for this view to be queried. JOIN_BLOCKED: This view can't be joined along any column. Don't set join_allowed_columns in this case. This can be used with all analysis rules except for the list overlap analysis rule. JOIN_NOT_REQUIRED: A join is not required to query this view. If a join is used, only the columns in join_allowed_columns can be used. This can be used with all analysis rules except for the list overlap analysis rule. join_allowed_columns: A list of columns that can be part of a join operation. value is a JSON array. Example: privacy_policy='{"join_restriction_policy": { "join_condition": 'JOIN_ANY', "join_allowed_columns": ['col1', 'col2']}}'

Note: Time travel is disabled on any view that has a policy.

Default project in view body

If the view is created in the same project used to run the CREATE VIEW statement, the view body query_expression can reference entities without specifying the project; the default project is the project which owns the view. Consider the sample query below.

CREATE VIEW myProject.myDataset.myView AS SELECT * FROM anotherDataset.myTable;

After running the above CREATE VIEW query in the project myProject, you can run the query SELECT * FROM myProject.myDataset.myView. Regardless of the project you choose to run this SELECT query, the referenced table anotherDataset.myTable is always resolved against project myProject.

If the view is not created in the same project used to run the CREATE VIEW statement, then all references in the view body query_expression must be qualified with project IDs. For instance, the preceding sample CREATE VIEW query is invalid if it runs in a project different from myProject.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the view.

In addition, the OR REPLACE clause requires bigquery.tables.update permission.

If the OPTIONS clause includes an expiration time, then the bigquery.tables.delete permission is also required.

Examples

Creating a new view

The following example creates a view named newview in mydataset:

CREATE VIEW `myproject.mydataset.newview`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="newview",
  description="a view that expires in 2 days",
  labels=[("org_unit", "development")]
)
AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

If the view name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.table

The view is defined using the following GoogleSQL query:

SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The view option list specifies the:

• Expiration time: 48 hours from the time the view is created
• Friendly name: newview
• Description: A view that expires in 2 days
• Label: org_unit = development

Creating a view only if the view doesn't exist

The following example creates a view named newview in mydataset only if no view named newview exists in mydataset. If the view name exists in the dataset, no error is returned, and no action is taken.

CREATE VIEW IF NOT EXISTS `myproject.mydataset.newview`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="newview",
  description="a view that expires in 2 days",
  labels=[("org_unit", "development")]
)
AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The view is defined using the following GoogleSQL query:

SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The view option list specifies the:

• Expiration time: 48 hours from the time the view is created
• Friendly name: newview
• Description: A view that expires in 2 days
• Label: org_unit = development

Creating or replacing a view

The following example creates a view named newview in mydataset, and if newview exists in mydataset, it is overwritten using the specified query expression.

CREATE OR REPLACE VIEW `myproject.mydataset.newview`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="newview",
  description="a view that expires in 2 days",
  labels=[("org_unit", "development")]
)
AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The view is defined using the following GoogleSQL query:

SELECT column_1, column_2, column_3 FROM myproject.mydataset.mytable

The view option list specifies the:

• Expiration time: 48 hours from the time the view is created
• Friendly name: newview
• Description: A view that expires in 2 days
• Label: org_unit = development

Creating a view with column descriptions

The following example creates a view named newview in mydataset. This view definition provides the column description for each column in mytable. You can rename columns from the original query.

CREATE VIEW `myproject.mydataset.newview` (
  column_1_new_name OPTIONS (DESCRIPTION='Description of the column 1 contents'),
  column_2_new_name OPTIONS (DESCRIPTION='Description of the column 2 contents'),
  column_3_new_name OPTIONS (DESCRIPTION='Description of the column 3 contents')
)
AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

CREATE MATERIALIZED VIEW statement

Creates a new materialized view.

Syntax

CREATE [ OR REPLACE ] MATERIALIZED VIEW [ IF NOT EXISTS ] materialized_view_name
[PARTITION BY partition_expression]
[CLUSTER BY clustering_column_list]
[OPTIONS(materialized_view_option_list)]
AS query_expression

Arguments

• OR REPLACE: Replaces a materialized view with the same name if it exists. Cannot appear with IF NOT EXISTS.

• IF NOT EXISTS: If a materialized view or other table resource exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• materialized_view_name: The name of the materialized view you're creating. See Table path syntax.

  If the project_name is omitted from the materialized view name, or it is the same as the project that runs this DDL query, then the latter is also used as the default project for references to tables, functions, and other resources in query_expression. The default project of the references is fixed and does not depend on the future queries that invoke the new materialized view. Otherwise, all references in query_expression must be qualified with project names.

  The materialized view name must be unique per dataset.

• partition_expression: An expression that determines how to partition the table. A materialized view can only be partitioned in the same way as the table in query expression (the base table) is partitioned.

• clustering_column_list: A comma-separated list of column references that determine how to cluster the materialized view.

• materialized_view_option_list: Allows you to specify additional materialized view options such as a whether refresh is enabled, the refresh interval, a label, and an expiration time.

• query_expression: The GoogleSQL query expression used to define the materialized view.

Details

CREATE MATERIALIZED VIEW statements must comply with the following rules:

• Only one CREATE statement is allowed.

Default project in materialized view body

If the materialized view is created in the same project used to run the CREATE MATERIALIZED VIEW statement, the materialized view body query_expression can reference entities without specifying the project; the default project is the project which owns the materialized view. Consider the sample query below.

CREATE MATERIALIZED VIEW myProject.myDataset.myView AS SELECT * FROM anotherDataset.myTable;

After running the above CREATE MATERIALIZED VIEW query in the project myProject, you can run the query SELECT * FROM myProject.myDataset.myView. Regardless of the project you choose to run this SELECT query, the referenced table anotherDataset.myTable is always resolved against project myProject.

If the materialized view is not created in the same project used to run the CREATE VIEW statement, then all references in the materialized view body query_expression must be qualified with project IDs. For instance, the preceding sample CREATE MATERIALIZED VIEW query is invalid if it runs in a project different from myProject.

materialized_view_option_list

The option list allows you to set materialized view options such as a whether refresh is enabled. the refresh interval, a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a materialized view option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
enable_refresh	BOOLEAN	Example: enable_refresh=false Default: true
refresh_interval_minutes	FLOAT64	Example: refresh_interval_minutes=20 Default: refresh_interval_minutes=30
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property. expiration_timestamp is optional and not used by default.
max_staleness	INTERVAL	Example: max_staleness=INTERVAL "4:0:0" HOUR TO SECOND The max_staleness property provides consistently high performance with controlled costs when processing large, frequently changing datasets. max_staleness is disabled by default.
allow_non_incremental_definition	BOOLEAN	Example: allow_non_incremental_definition=true The allow_non_incremental_definition property supports an expanded range of SQL queries to create materialized views. allow_non_incremental_definition=true is disabled by default.
kms_key_name	STRING	Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys.
friendly_name	STRING	Example: friendly_name="my_mv" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a materialized view that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the materialized view.

In addition, the OR REPLACE clause requires bigquery.tables.update permission.

If the OPTIONS clause includes any expiration options, then the bigquery.tables.delete permission is also required.

Examples

Creating a new materialized view

The following example creates a materialized view named new_mv in mydataset:

CREATE MATERIALIZED VIEW `myproject.mydataset.new_mv`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="new_mv",
  description="a materialized view that expires in 2 days",
  labels=[("org_unit", "development")],
  enable_refresh=true,
  refresh_interval_minutes=20
)
AS SELECT column_1, SUM(column_2) AS sum_2, AVG(column_3) AS avg_3
FROM `myproject.mydataset.mytable`
GROUP BY column_1

If the materialized view name exists in the dataset, the following error is returned:

Already Exists: project_id:dataset.materialized_view

When you use a DDL statement to create a materialized view, you must specify the project, dataset, and materialized view in the following format: `project_id.dataset.materialized_view` (including the backticks if project_id contains special characters); for example, `myproject.mydataset.new_mv`.

The materialized view is defined using the following GoogleSQL query:

SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The materialized view option list specifies the:

• Expiration time: 48 hours from the time the materialized view is created
• Friendly name: new_mv
• Description: A materialized view that expires in 2 days
• Label: org_unit = development
• Refresh enabled: true
• Refresh interval: 20 minutes

Creating a materialized view only if the materialized view doesn't exist

The following example creates a materialized view named new_mv in mydataset only if no materialized view named new_mv exists in mydataset. If the materialized view name exists in the dataset, no error is returned, and no action is taken.

CREATE MATERIALIZED VIEW IF NOT EXISTS `myproject.mydataset.new_mv`
OPTIONS(
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
  friendly_name="new_mv",
  description="a view that expires in 2 days",
  labels=[("org_unit", "development")],
  enable_refresh=false
)
AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The materialized view is defined using the following GoogleSQL query:

SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`

The materialized view option list specifies the:

• Expiration time: 48 hours from the time the view is created
• Friendly name: new_mv
• Description: A view that expires in 2 days
• Label: org_unit = development
• Refresh enabled: false

Creating a materialized view with partitioning and clustering

The following example creates a materialized view named new_mv in mydataset, partitioned by the col_datetime column and clustered by the col_int column:

CREATE MATERIALIZED VIEW `myproject.mydataset.new_mv`
PARTITION BY DATE(col_datetime)
CLUSTER BY col_int
AS SELECT col_int, col_datetime, COUNT(1) as cnt
   FROM `myproject.mydataset.mv_base_table`
   GROUP BY col_int, col_datetime

The base table, mv_base_table, must also be partitioned by the col_datetime column. For more information, see Working with partitioned and clustered tables.

CREATE MATERIALIZED VIEW AS REPLICA OF statement

Creates a replica of a materialized view. The source materialized view must be over an Amazon Simple Storage Service (Amazon S3) BigLake table. You can use the materialized view replica to make Amazon S3 data available locally for joins.

For more information, see Create materialized view replicas.

Syntax

CREATE MATERIALIZED VIEW replica_name
[OPTIONS(materialized_view_replica_option_list)]
AS REPLICA OF source_materialized_view_name

Arguments

• replica_name: The name of the materialized view replica you're creating, in table path syntax. If the project name is omitted from the materialized view replica name, the current project is used as the default.

  The materialized view replica name must be unique for each dataset.

• materialized_view_replica_option_list: Allows you to specify options such as the replication interval.

• source_materialized_view_name: The name of the materialized view you are replicating, in table path syntax. The source materialized view must be over an Amazon S3 BigLake table, and must be authorized on the dataset that contains that table.

materialized_view_replica_option_list

The option list lets you set materialized view replica options.

Specify a materialized view replica option list in the following format:

NAME=VALUE, ...

NAME	VALUE	Details
replication_interval_seconds	INT64	Specifies how often to replicate the data from the source materialized view to the replica. Must be a value between 60 and 3,600, inclusive. Defaults to 300 (5 minutes). Example: replication_interval_seconds=900

Required permissions

This statement requires the following IAM permissions:

• bigquery.tables.create
• bigquery.tables.get
• bigquery.tables.getData
• bigquery.tables.replicateData
• bigquery.jobs.create

Example

The following example creates a materialized view replica named mv_replica in bq_dataset:

CREATE MATERIALIZED VIEW `myproject.bq_dataset.mv_replica`
OPTIONS(
  replication_interval_seconds=600
)
AS REPLICA OF `myproject.s3_dataset.my_s3_mv`

CREATE EXTERNAL SCHEMA statement

Creates a new federated dataset.

A federated dataset is a connection between BigQuery and an external data source at the dataset level. For an example, see Create AWS Glue federated datasets.

Syntax

CREATE EXTERNAL SCHEMA [ IF NOT EXISTS ] dataset_name
[WITH CONNECTION connection_name]
[OPTIONS(external_schema_option_list)]

Arguments

• IF NOT EXISTS: If any dataset exists with the same name, the CREATE statement has no effect.

• dataset_name: The name of the dataset to create.

• connection_name: Specifies a connection resource that has credentials for accessing the external data. Specify the connection name in the form PROJECT_ID.LOCATION.CONNECTION_ID. If the project ID or location contains a dash, enclose the connection name in backticks (`).

• external_schema_option_list: A list of options for creating the federated dataset.

Details

The dataset is created in the location that you specify in the query settings. For more information, see Specify locations. The location must support the kind of federated dataset that you are creating, for example, you can only create AWS Glue federated datasets in AWS locations.

For more information about creating a dataset, see Create datasets. For information about quotas, see dataset limits.

external_schema_option_list

The option list specifies options for the federated dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
description	STRING	The description of the dataset.
friendly_name	STRING	A descriptive name for the dataset.
labels	<ARRAY<STRUCT<STRING, STRING>>>	An array of labels for the dataset, expressed as key-value pairs.
location	STRING	The location in which to create the dataset. If you don't specify this option, the dataset is created in the location where the query runs. If you specify this option and also explicitly set the location for the query job, the two values must match; otherwise the query fails. The location must support the kind of federated dataset that you are creating, for example, you can only create AWS Glue federated datasets in AWS locations.
external_source	STRING	The source of the external dataset, such as the Amazon Resource Name (ARN), with a prefix identifying the source, such as aws-glue://.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.create	The project where you create the federated dataset.
bigquery.connections.use	The project where you create the federated dataset.
bigquery.connections.delegate	The project where you create the federated dataset.

Examples

The following example creates an AWS Glue federated dataset:

CREATE EXTERNAL SCHEMA mydataset
WITH CONNECTION myproject.`aws-us-east-1`.myconnection
  OPTIONS (
    external_source = 'aws-glue://arn:aws:glue:us-east-1:123456789:database/test_database',
    location = 'aws-us-east-1');

CREATE EXTERNAL TABLE statement

Creates a new external table.

External tables let BigQuery query data that is stored outside of BigQuery storage. For more information about external tables, see Introduction to external data sources.

Syntax

CREATE [ OR REPLACE ] EXTERNAL TABLE [ IF NOT EXISTS ] table_name
[(
  column_name column_schema,
  ...
)]
[WITH CONNECTION connection_name]
[WITH PARTITION COLUMNS
  [(
      partition_column_name partition_column_type,
      ...
  )]
]
OPTIONS (
  external_table_option_list,
  ...
);

Arguments

• OR REPLACE: Replaces any external table with the same name if it exists. Cannot appear with IF NOT EXISTS.

• IF NOT EXISTS: If an external table or other table resource exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• table_name: The name of the external table. See Table path syntax.

• column_name: The name of a column in the table.

• column_schema: Specifies the schema of the column. It uses the same syntax as the column_schema definition in the CREATE TABLE statement. If you don't include this clause, BigQuery detects the schema automatically.

• connection_name: Specifies a connection resource that has credentials for accessing the external data. Specify the connection name in the form PROJECT_ID.LOCATION.CONNECTION_ID. If the project ID or location contains a dash, enclose the connection name in backticks (`).

• partition_column_name: The name of a partition column. Include this field if your external data uses a hive-partitioned layout. For more information, see: Supported data layouts.

• partition_column_type: The partition column type.

• external_table_option_list: A list of options for creating the external table.

Details

The CREATE EXTERNAL TABLE statement does not support creating temporary external tables.

To create an externally partitioned table, use the WITH PARTITION COLUMNS clause to specify the partition schema details. BigQuery validates the column definitions against the external data location. The schema declaration must strictly follow the ordering of the fields in the external path. For more information about external partitioning, see Querying externally partitioned data.

external_table_option_list

The option list specifies options for creating the external table. The format and uris options are required. Specify the option list in the following format: NAME=VALUE, ...

Options
allow_jagged_rows	BOOL If true, allow rows that are missing trailing optional columns. Applies to CSV data.
allow_quoted_newlines	BOOL If true, allow quoted data sections that contain newline characters in the file. Applies to CSV data.
bigtable_options	STRING Only required when creating a Bigtable external table. Specifies the schema of the Bigtable external table in JSON format. For a list of Bigtable table definition options, see BigtableOptions in the REST API reference.
compression	STRING The compression type of the data source. Supported values include: GZIP. If not specified, the data source is uncompressed. Applies to CSV and JSON data.
decimal_target_types	ARRAY<STRING> Determines how to convert a Decimal type. Equivalent to ExternalDataConfiguration.decimal_target_types Example: ["NUMERIC", "BIGNUMERIC"].
description	STRING A description of this table.
enable_list_inference	BOOL If true, use schema inference specifically for Parquet LIST logical type. Applies to Parquet data.
enable_logical_types	BOOL If true, convert Avro logical types into their corresponding SQL types. For more information, see Logical types. Applies to Avro data.
encoding	STRING The character encoding of the data. Supported values include: UTF8 (or UTF-8), ISO_8859_1 (or ISO-8859-1). Applies to CSV data.
enum_as_string	BOOL If true, infer Parquet ENUM logical type as STRING instead of BYTES by default. Applies to Parquet data.
expiration_timestamp	TIMESTAMP The time when this table expires. If not specified, the table does not expire. Example: "2025-01-01 00:00:00 UTC".
field_delimiter	STRING The separator for fields in a CSV file. Applies to CSV data.
format	STRING The format of the external data. Supported values for CREATE EXTERNAL TABLE include: AVRO, CLOUD_BIGTABLE, CSV, DATASTORE_BACKUP, DELTA_LAKE (preview), GOOGLE_SHEETS, NEWLINE_DELIMITED_JSON (or JSON), ORC, PARQUET. Supported values for LOAD DATA include: AVRO, CSV, DELTA_LAKE (preview) NEWLINE_DELIMITED_JSON (or JSON), ORC, PARQUET. The value JSON is equivalent to NEWLINE_DELIMITED_JSON.
hive_partition_uri_prefix	STRING A common prefix for all source URIs before the partition key encoding begins. Applies only to hive-partitioned external tables. Applies to Avro, CSV, JSON, Parquet, and ORC data. Example: "gs://bucket/path".
file_set_spec_type	STRING Specifies how to interpret source URIs for load jobs and external tables. Supported values include: FILE_SYSTEM_MATCH. Expands source URIs by listing files from the object store. This is the default behavior if FileSetSpecType is not set. NEW_LINE_DELIMITED_MANIFEST. Indicates that the provided URIs are newline-delimited manifest files, with one URI per line. Wildcard URIs are not supported in the manifest files, and all referenced data files must be in the same bucket as the manifest file. For example, if you have a source URI of "gs://bucket/path/file" and the file_set_spec_type is FILE_SYSTEM_MATCH, then the file is used directly as a data file. If the file_set_spec_type is NEW_LINE_DELIMITED_MANIFEST, then each line in the file is interpreted as a URI that points to a data file.
ignore_unknown_values	BOOL If true, ignore extra values that are not represented in the table schema, without returning an error. Applies to CSV and JSON data.
json_extension	STRING For JSON data, indicates a particular JSON interchange format. If not specified, BigQuery reads the data as generic JSON records. Supported values include: GEOJSON. Newline-delimited GeoJSON data. For more information, see Creating an external table from a newline-delimited GeoJSON file.
max_bad_records	INT64 The maximum number of bad records to ignore when reading the data. Applies to: CSV, JSON, and Google Sheets data.
max_staleness	INTERVAL Applicable for BigLake tables and object tables. 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. 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 falls back to retrieving metadata from Cloud Storage instead.
null_marker	STRING The string that represents NULL values in a CSV file. Applies to CSV data.
object_metadata	STRING Only required when creating an object table. Set the value of this option to SIMPLE when creating an object table.
preserve_ascii_control_characters	BOOL If true, then the embedded ASCII control characters which are the first 32 characters in the ASCII table, ranging from '\x00' to '\x1F', are preserved. Applies to CSV data.
projection_fields	STRING A list of entity properties to load. Applies to Datastore data.
quote	STRING The string used to quote data sections in a CSV file. If your data contains quoted newline characters, also set the allow_quoted_newlines property to true. Applies to CSV data.
reference_file_schema_uri	STRING User provided reference file with the table schema. Applies to Parquet/ORC/AVRO data. Example: "gs://bucket/path/reference_schema_file.parquet".
require_hive_partition_filter	BOOL If true, all queries over this table require a partition filter that can be used to eliminate partitions when reading data. Applies only to hive-partitioned external tables. Applies to Avro, CSV, JSON, Parquet, and ORC data.
sheet_range	STRING Range of a Google Sheets spreadsheet to query from. Applies to Google Sheets data. Example: "sheet1!A1:B20",
skip_leading_rows	INT64 The number of rows at the top of a file to skip when reading the data. Applies to CSV and Google Sheets data.
uris	For external tables, including object tables, that aren't Bigtable tables: ARRAY<STRING> An array of fully qualified URIs for the external data locations. Each URI can contain one asterisk (*) wildcard character, which must come after the bucket name. When you specify uris values that target multiple files, all of those files must share a compatible schema. The following examples show valid uris values: ['gs://bucket/path1/myfile.csv'] ['gs://bucket/path1/*.csv'] ['gs://bucket/path1/*', 'gs://bucket/path2/file00*'] For Bigtable tables: STRING The URI identifying the Bigtable table to use as a data source. You can only specify one Bigtable URI. Example: https://googleapis.com/bigtable/projects/project_id/instances/instance_id[/appProfiles/app_profile]/tables/table_name For more information on constructing a Bigtable URI, see Retrieving the Bigtable URI.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.create	The dataset where you create the external table.

In addition, the OR REPLACE clause requires bigquery.tables.update permission.

If the OPTIONS clause includes an expiration time, then the bigquery.tables.delete permission is also required.

Examples

The following example creates a BigLake table and explicitly specifies the schema. It also specifies refreshing metadata cache automatically at a system-defined interval.

CREATE OR REPLACE EXTERNAL TABLE mydataset.newtable (x INT64, y STRING, z BOOL)
  WITH CONNECTION myconnection
  OPTIONS(
    format ="PARQUET",
    max_staleness = STALENESS_INTERVAL,
    metadata_cache_mode = 'AUTOMATIC');

The following example creates an external table from multiple URIs. The data format is CSV. This example uses schema auto-detection.

CREATE EXTERNAL TABLE dataset.CsvTable OPTIONS (
  format = 'CSV',
  uris = ['gs://bucket/path1.csv', 'gs://bucket/path2.csv']
);

The following example creates an external table from a CSV file and explicitly specifies the schema. It also specifies the field delimiter ('|') and sets the maximum number of bad records allowed.

CREATE OR REPLACE EXTERNAL TABLE dataset.CsvTable
(
  x INT64,
  y STRING
)
OPTIONS (
  format = 'CSV',
  uris = ['gs://bucket/path1.csv'],
  field_delimiter = '|',
  max_bad_records = 5
);

The following example creates an externally partitioned table. It uses schema auto-detection to detect both the file schema and the hive partitioning layout. If the external path is gs://bucket/path/field_1=first/field_2=1/data.parquet, the partition columns are detected as field_1 (STRING) and field_2 (INT64).

CREATE EXTERNAL TABLE dataset.AutoHivePartitionedTable
WITH PARTITION COLUMNS
OPTIONS (
  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 explicitly specifying the partition columns. This example assumes that the external file path has the pattern gs://bucket/path/field_1=first/field_2=1/data.parquet.

CREATE EXTERNAL TABLE dataset.CustomHivePartitionedTable
WITH PARTITION COLUMNS (
  field_1 STRING, -- column order must match the external path
  field_2 INT64)
OPTIONS (
  uris = ['gs://bucket/path/*'],
  format = 'PARQUET',
  hive_partition_uri_prefix = 'gs://bucket/path',
  require_hive_partition_filter = false);

CREATE FUNCTION statement

Creates a new user-defined function (UDF). BigQuery supports UDFs written in either SQL or JavaScript.

Syntax

To create a SQL UDF, use the following syntax:

CREATE [ OR REPLACE ] [ TEMPORARY | TEMP ] FUNCTION [ IF NOT EXISTS ]
    [[project_name.]dataset_name.]function_name
    ([named_parameter[, ...]])
     ([named_parameter[, ...]])
  [RETURNS data_type]
  AS (sql_expression)
  [OPTIONS (function_option_list)]

named_parameter:
  param_name param_type

To create a JavaScript UDF, use the following syntax:

CREATE [OR REPLACE] [TEMPORARY | TEMP] FUNCTION [IF NOT EXISTS]
    [[project_name.]dataset_name.]function_name
    ([named_parameter[, ...]])
  RETURNS data_type
  [determinism_specifier]
  LANGUAGE js
  [OPTIONS (function_option_list)]
  AS javascript_code

named_parameter:
  param_name param_type

determinism_specifier:
  { DETERMINISTIC | NOT DETERMINISTIC }

To create a remote function, use the following syntax:

CREATE [OR REPLACE] FUNCTION [IF NOT EXISTS]
    [[project_name.]dataset_name.]function_name
    ([named_parameter[, ...]])
  RETURNS data_type
  REMOTE WITH CONNECTION connection_path
  [OPTIONS (function_option_list)]

named_parameter:
  param_name param_type

Routine names must contain only letters, numbers, and underscores, and be at most 256 characters long.

Arguments

• OR REPLACE: Replaces any function with the same name if it exists. Cannot appear with IF NOT EXISTS.

• IF NOT EXISTS: If any dataset exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• TEMP or TEMPORARY: Creates a temporary function. If the clause is not present, the statement creates a persistent UDF. You can reuse persistent UDFs across multiple queries, whereas you can only use temporary UDFs in a single query, script, session, or procedure.

• project_name: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Do not include the project name for temporary functions.

• dataset_name: For persistent functions, the name of the dataset where you are creating the function. Defaults to the defaultDataset in the request. Do not include the dataset name for temporary functions.

• function_name: The name of the function.

• named_parameter: A comma-separated param_name and param_type pair. The value of param_type is a BigQuery data type. For a SQL UDF, the value of param_type can also be ANY TYPE.

• determinism_specifier: Applies only to JavaScript UDFs. Provides a hint to BigQuery as to whether the query result can be cached. Can be one of the following values:

  • DETERMINISTIC: The function always returns the same result when passed the same arguments. The query result is potentially cacheable. For example, if the function add_one(i) always returns i + 1, the function is deterministic.

  • NOT DETERMINISTIC: The function does not always return the same result when passed the same arguments, and therefore is not cacheable. For example, if the functionj add_random(i) returns i + rand(), the function is not deterministic and BigQuery does not use cached results.

    If all of the invoked functions are DETERMINISTIC, BigQuery tries to cache the result, unless the results can't be cached for other reasons. For more information, see Using cached query results.

• data_type: The data type that the function returns.

  • If the function is defined in SQL, then the RETURNS clause is optional. If the RETURNS clause is omitted, then BigQuery infers the result type of the function from the SQL function body when a query calls the function.
  • If the function is defined in JavaScript, then the RETURNS clause is required. For more information about allowed values for data_type, see Supported JavaScript UDF data types.

• sql_expression: The SQL expression that defines the function.

• function_option_list: A list of options for creating the function.

• javascript_code: The definition of a JavaScript function. The value is a string literal. If the code includes quotes and backslashes, it must be either escaped or represented as a raw string. For example, the code return "\n"; can be represented as one of the following:

  • Quoted string"return \"\\n\";". Both quotes and backslashes need to be escaped.
  • Triple quoted string: """return "\\n";""". Backslashes need to be escaped while quotes do not.
  • Raw string: r"""return "\n";""". No escaping is needed.

• connection_name: Specifies a connection resource that has credentials for accessing the remote endpoint. Specify the connection name in the form project_name.location.connection_id: If the project name or location contains a dash, enclose the connection name in backticks (`).

function_option_list

The option list specifies options for creating a UDF. The following options are supported:

NAME	VALUE	Details
description	STRING	A description of the UDF. This option isn't supported when creating a temporary function.
library	ARRAY<STRING>	An array of JavaScript libraries to include in the function definition. Applies only to JavaScript UDFs. For more information, see Including JavaScript libraries. Example: ["gs://my-bucket/lib1.js", "gs://my-bucket/lib2.js"]
endpoint	STRING	A HTTP endpoint of Cloud Functions. Applies only to remote functions. Example: "https://us-east1-your-project.cloudfunctions.net/foo" For more information, see Creating a Remote Function.
user_defined_context	ARRAY<STRUCT<STRING,STRING>>	A list of key-value pairs that will be sent with every HTTP request when the function is invoked. Applies only to remote functions. Example: [("key1","value1"),("key2", "value2")]
max_batching_rows	INT64	The maximum number of rows in each HTTP request. If not specified, BigQuery decides how many rows are included in a HTTP request. Applies only to remote functions.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.create	The dataset where you create the function.

In addition, the OR REPLACE clause requires bigquery.routines.update permission.

To create a remote function, additional IAM permissions are needed:

Permission	Resource
bigquery.connections.delegate	The connection which you use to create the remote function.

Examples

Create a SQL UDF

The following example creates a persistent SQL UDF named multiplyInputs in a dataset named mydataset.

CREATE FUNCTION mydataset.multiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
AS (x * y);

Create a JavaScript UDF

The following example creates a temporary JavaScript UDF named multiplyInputs and calls it from inside a SELECT statement.

CREATE TEMP FUNCTION multiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
LANGUAGE js
AS r"""
  return x*y;
""";


SELECT multiplyInputs(a, b) FROM (SELECT 3 as a, 2 as b);

Create a remote function

The following example creates a persistent remote function named remoteMultiplyInputs in a dataset named mydataset, assuming mydataset is in US location and there is a connection myconnection in the same location and same project.

CREATE FUNCTION mydataset.remoteMultiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
REMOTE WITH CONNECTION us.myconnection
OPTIONS(endpoint="https://us-central1-myproject.cloudfunctions.net/multiply");

CREATE AGGREGATE FUNCTION statement (SQL)

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

For support during the preview, email [e-mail].

Creates a new SQL user-defined aggregate function (UDAF).

Syntax

To create a SQL UDAF, use the following syntax:

CREATE
  [ OR REPLACE ]
  [ { TEMPORARY | TEMP } ]
  AGGREGATE FUNCTION
  [ IF NOT EXISTS ]
  function_path ( [ function_parameter[, ...] ] )
  [ RETURNS data_type ]
  AS ( sql_function_body )
  [ OPTIONS ( function_option_list ) ]

function_path:
  [[project_name.]dataset_name.]function_name

function_parameter:
  parameter_name
  data_type
  [ NOT AGGREGATE ]

Arguments

• OR REPLACE: Replaces any function with the same name if it exists. OR REPLACE can't appear with IF NOT EXISTS.
• IF NOT EXISTS: If any dataset exists with the same name, the CREATE statement has no effect. IF NOT EXISTS can't appear with OR REPLACE.
• TEMP or TEMPORARY: The function is temporary; that is, it exists for the lifetime of a single query, script, session, or procedure. A temporary function can't have the same name as a built-in function. If the names match, an error is produced. If TEMP or TEMPORARY is not included, a persistent function is created. You can reuse persistent functions across multiple queries.
• function_path: The path where the function must be created and the name of the function.
  • project_name: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Don't include the project name for temporary functions.
  • dataset_name: For persistent functions, the name of the dataset where you are creating the function. Defaults to defaultDataset in the request. Don't include the dataset name for temporary functions.
  • function_name: The name of the function. Function names must contain only letters, numbers, and underscores, and be at most 256 characters long.
• function_parameter: A parameter for the function.
  • parameter_name: The name of the function parameter.
  • parameter_data_type: The GoogleSQL data type for the function parameter.
  • NOT AGGREGATE: The function parameter is not an aggregate. A non-aggregate function parameter can appear anywhere in the function definition.
• return_data_type: The GoogleSQL data type that the function should return. GoogleSQL infers the result data type of the function from the function body when the RETURN clause is omitted.
• function_body: The SQL expression that defines the function body.
• function_option_list: A list of options for creating the function. For more information, see function_option_list.

function_option_list

The option list specifies options for creating a SQL UDAF. The following options are supported:

NAME	VALUE	Details
description	STRING	A description of the UDAF.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.create	The dataset where you create the function.

In addition, the OR REPLACE clause requires the bigquery.routines.update permission.

Examples

Create and call a SQL UDAF

The following example shows a persistent SQL UDAF that includes a non-aggregate function parameter. Inside the function definition, the aggregate SUM method takes the aggregate function parameter dividend, while the non-aggregate division operator ( / ) takes the non-aggregate function parameter divisor.

CREATE AGGREGATE FUNCTION myProject.myDataset.ScaledSum(
  dividend FLOAT64,
  divisor FLOAT64 NOT AGGREGATE)
RETURNS FLOAT64
AS (
  SUM(dividend) / divisor
);

-- Call the SQL UDAF.
SELECT ScaledSum(col1, 2) AS scaled_sum
FROM (
  SELECT 1 AS col1 UNION ALL
  SELECT 3 AS col1 UNION ALL
  SELECT 5 AS col1
);

/*------------*
 | scaled_sum |
 +------------+
 | 4.5        |
 *------------*/

CREATE AGGREGATE FUNCTION statement (JavaScript)

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

For support during the preview, email [e-mail].

Creates a new JavaScript user-defined aggregate function (UDAF).

Syntax

To create a JavaScript UDAF, use the following syntax:

CREATE
  [ OR REPLACE ]
  [ { TEMPORARY | TEMP } ]
  AGGREGATE FUNCTION
  [ IF NOT EXISTS ]
  function_path([ function_parameter[, ...] ])
  RETURNS return_data_type
  LANGUAGE js
  [ OPTIONS ( function_option_list ) ]
  AS function_body

function_path:
  [[project_name.]dataset_name.]function_name

function_parameter:
  parameter_name parameter_data_type [ NOT AGGREGATE ]

Arguments

• OR REPLACE: Replaces any function with the same name if it exists. OR REPLACE can't appear with IF NOT EXISTS.
• IF NOT EXISTS: If any dataset exists with the same name, the CREATE statement has no effect. IF NOT EXISTS can't appear with OR REPLACE.
• TEMP or TEMPORARY: The function is temporary; that is, it exists for the lifetime of a single query, script, session, or procedure. A temporary function can't have the same name as a built-in function. If the names match, an error is produced. If TEMP or TEMPORARY is not included, a persistent function is created. You can reuse persistent functions across multiple queries.
• function_path: The path where the function must be created and the name of the function.
  • project_name: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Don't include the project name for temporary functions.
  • dataset_name: For persistent functions, the name of the dataset where you are creating the function. Defaults to defaultDataset in the request. Don't include the dataset name for temporary functions.
  • function_name: The name of the function. Function names must contain only letters, numbers, and underscores, and be at most 256 characters long.
• function_parameter: A parameter for the function.
  • parameter_name: The name of the function parameter.
  • parameter_data_type: The GoogleSQL data type for the function parameter.
  • NOT AGGREGATE: The function parameter is not an aggregate. Only one non-aggregate function parameter is allowed per JavaScript UDAF, and it must be the last parameter in the list.
• return_data_type: The GoogleSQL data type that the function should return.
• function_body: The JavaScript expression that defines the function body. For more information, see function_body.
• function_option_list: A list of options for creating the function. For more information, see function_option_list.

function_body

The body of the JavaScript function must be a quoted string literal that represents the JavaScript code. To learn more about the different types of quoted string literals you can use, see Formats for quoted literals.

Only certain type encodings are allowed. To learn more, see SQL type encodings in a JavaScript UDAF.

The JavaScript function body must include four JavaScript functions that initialize, aggregate, merge, and finalize the results for the JavaScript UDAF. To learn more about the initialState, aggregate, merge, and finalize JavaScript functions, see Required aggregate functions in a JavaScript UDAF.

Only serialized data can be passed into the JavaScript aggregate functions. If you need to serialize data such as functions or symbols to pass them into the aggregate functions, use the JavaScript serialization functions. For more information, see Serialization functions for a JavaScript UDAF.

function_option_list

The option list specifies options for creating a JavaScript UDAF. The following options are supported:

NAME	VALUE	Details
description	STRING	A description of the UDAF.
library	ARRAY<STRING>	An array of JavaScript libraries to include in the JavaScript UDAF function body. Example: ["gs://my-bucket/lib1.js", "gs://my-bucket/lib2.js"]

SQL type encodings in a JavaScript UDAF

In JavaScript UDAFs, GoogleSQL data types represent JavaScript data types in the following manner:

GoogleSQL data type	JavaScript data type	Notes
ARRAY	Array	An array of arrays is not supported. To get around this limitation, use the Array<Object<Array>> (JavaScript) and ARRAY<STRUCT<ARRAY>> (GoogleSQL) data types.
BIGNUMERIC	Number or String	Same as NUMERIC.
BOOL	Boolean
BYTES	Uint8Array
DATE	Date
FLOAT64	Number
INT64	BigInt
JSON	Various types	The GoogleSQL JSON data type can be converted into a JavaScript Object, Array, or other GoogleSQL-supported JavaScript data type.
NUMERIC	Number or String	If a NUMERIC value can be represented exactly as an IEEE 754 floating-point value (range [-2^53, 2^53]), and has no fractional part, it is encoded as a Number data type, otherwise it is encoded as a String data type.
STRING	String
STRUCT	Object	Each STRUCT field is a named property in the Object data type. An unnamed STRUCT field is not supported.
TIMESTAMP	Date	Date contains a microsecond field with the microsecond fraction of TIMESTAMP.

Note: The SQL encodings for JavaScript UDAFs are different from those for JavaScript UDFs.

Required aggregation functions in a JavaScript UDAF

The JavaScript function body must include the following exportable JavaScript functions:

• initialState function: Sets up the initial aggregation state of the UDAF and then returns the initial aggregation state.

  Syntax:

  export function initialState([nonAggregateParam]){...}
  

  Parameters:

  • nonAggregateParam: Replace this parameter with a NOT AGGREGATE function parameter name.

  Examples:

  export function initialState(){...}
  

  export function initialState(initialSum){...}
  

• aggregate function: Aggregates one row of data, updating state to store the result of the aggregation. Doesn't return a value.

  Syntax:

  export function aggregate(state, aggregateParam[, ...][, nonAggregateParam]){...}
  

  Parameters:

  • state: The aggregate state, which is initialState on the first invocation, and then the return value of the previous call to aggregate thereafter.

  • aggregateParam: The name of an aggregation parameter in the JavaScript UDAF. The argument for this parameter will be aggregated.

  • nonAggregateParam: Replace with a NOT AGGREGATE function parameter name.

  Example:

  export function aggregate(currentState, aggX, aggWeight, initialSum)
  

• merge function: Combines two aggregation states from a prior call to the aggregate, merge, or initialState function. This function does not return a value.

  Syntax:

  export function merge(state, partialState[, nonAggregateParam]){...}
  

  Parameters:

  • state: The state into which partialState is merged.

  • partialState: The second aggregation state to merge.

  • nonAggregateParam: Replace with a NOT AGGREGATE function parameter name.

  Details:

  Depending on the size and organization of the underlying data being queried, the merge function might or might not be called. For example, if a particular set of data is small, or the data is partitioned in a way that results in small sets of data, the merge function won't be called.

  Example:

  export function merge(currentState, partialState, initialSum)
  

• finalize function: Computes the final aggregation result and then returns this result for the UDAF.

  Syntax:

  export function finalize(state[, nonAggregateParam]){...}
  

  Parameters:

  • state: The final aggregation state.

  • nonAggregateParam: Replace with a NOT AGGREGATE function parameter name.

  The final aggregation state is returned by the merge function (or aggregate function if merge is never invoked). If the input is empty after NULL filtering, the final aggregation state is initialState.

  Example:

  export function finalize(finalState, initialSum)
  

Serialization functions for a JavaScript UDAF

If you want to work with non-serializable aggregation states, the JavaScript UDAF must provide the serialize and deserialize functions:

• serialize function: Converts an aggregation state into a BigQuery-serializable object. An object in JavaScript is BigQuery-serializable if all fields are a JavaScript primitive data type (for example, String, Number, null, undefined), another BigQuery-serializable object, or a JavaScript Array, where all elements are either primitives or BigQuery-serializable objects.

  Syntax:

  export function serialize(state[, nonAggregateParam]){...}
  

  Arguments:

  • state: The aggregation state to serialize.

  • nonAggregateParam: Replace with a NOT AGGREGATE function parameter name.

  Example:

  export function serialize(stateToSerialize, initialSum)
  

• deserialize function: Converts a serialized state into an aggregation state. An aggregated state can be passed into the serialize, aggregate, merge, and finalize functions.

  Syntax:

  export function deserialize(serializedState[, nonAggregateParam]){...}
  

  Arguments:

  • serializedState: The serialized state to convert into the aggregation state.

  • nonAggregateParam: Replace with a NOT AGGREGATE function parameter name.

  Example:

  export function deserialize(stateToDeserialize, initialSum)
  

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.create	The dataset where you create the function.

In addition, the OR REPLACE clause requires the bigquery.routines.update permission.

Examples

Calculate the positive sum of all rows

A JavaScript UDAF is similar to a JavaScript UDF, but defines an aggregate function instead of a scalar function. In the following example, a temporary JavaScript UDAF calculates the sum of all rows that have a positive value. The JavaScript UDAF body is quoted within a raw string:

CREATE TEMP AGGREGATE FUNCTION SumPositive(x FLOAT64)
RETURNS FLOAT64
LANGUAGE js
AS r'''
  export function initialState() {
    return {sum: 0}
  }
  export function aggregate(state, x) {
    if (x > 0) {
      state.sum += x;
    }
  }
  export function merge(state, partialState) {
    state.sum += partialState.sum;
  }
  export function finalize(state) {
    return state.sum;
  }
''';

-- Call the JavaScript UDAF.
WITH numbers AS (
  SELECT * FROM UNNEST([1.0, -1.0, 3.0, -3.0, 5.0, -5.0]) AS x)
SELECT SumPositive(x) AS sum
FROM numbers;

/*-----*
 | sum |
 +-----+
 | 9.0 |
 *-----*/

Get the weighted average of all rows

A JavaScript UDAF can have aggregate and non-aggregate parameters. In the following example, the JavaScript UDAF calculates the weighted average for x after starting with an initial sum (initialSum). x and weight are aggregate parameters, and initialSum is a non-aggregate parameter:

CREATE OR REPLACE AGGREGATE FUNCTION my_project.my_dataset.WeightedAverage(
    x INT64,
    weight FLOAT64,
    initialSum FLOAT64 NOT AGGREGATE)
RETURNS INT64
LANGUAGE js
AS '''
   export function initialState(initialSum) {
     return {count: 0, sum: initialSum}
   }
   export function aggregate(state, x, weight) {
     state.count += 1;
     state.sum += Number(x) * weight;
   }
   export function merge(state, partialState) {
     state.sum += partialState.sum;
     state.count += partialState.count;
   }
   export function finalize(state) {
     return state.sum / state.count;
   }
''';

SELECT my_project.my_dataset.WeightedAverage(item, weight, 2) AS weighted_average
FROM (
  SELECT 1 AS item, 2.45 AS weight UNION ALL
  SELECT 3 AS item, 0.11 AS weight UNION ALL
  SELECT 5 AS item, 7.02 AS weight
);

/*------------------*
 | weighted_average |
 +------------------+
 | 13               |
 *------------------*/

CREATE TABLE FUNCTION statement

Creates a new table function, also called a table-valued function (TVF).

Syntax

CREATE [ OR REPLACE ] TABLE FUNCTION [ IF NOT EXISTS ]
  [[project_name.]dataset_name.]function_name
  ( [ function_parameter [, ...] ] )
  [RETURNS TABLE < column_declaration [, ...] > ]
  [OPTIONS (table_function_options_list) ]
  AS sql_query

function_parameter:
  parameter_name { data_type | ANY TYPE }

column_declaration:
  column_name data_type

Arguments

• OR REPLACE: Replaces any table function with the same name if it exists. Cannot appear with IF NOT EXISTS.
• IF NOT EXISTS: If any table function exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.
• project_name: The name of the project where you are creating the function. Defaults to the project that runs this DDL statement.
• dataset_name: The name of the dataset where you are creating the function.
• function_name: The name of the function to create.
• function_parameter: A parameter for the function, specified as a parameter name and a data type. The value of data_type is a scalar BigQuery data type or ANY TYPE.
• RETURNS TABLE: The schema of the table that the function returns, specified as a comma-separated list of column name and data type pairs. If RETURNS TABLE is absent, BigQuery infers the output schema from the query statement in the function body. If RETURNS TABLE is included, the names in the returned table type must match column names from the SQL query.
• sql_query: Specifies the SQL query to run. The SQL query must include names for all columns.

table_function_options_list

The table_function_options_list lets you specify table function options. Table function options have the same syntax and requirements as table options but with a different list of NAMEs and VALUEs:

NAME	VALUE	Details
description	STRING	The description of the table function.

Details

BigQuery coerces argument types when possible. For example, if the parameter type is FLOAT64 and you pass an INT64 value, then BigQuery coerces it to a FLOAT64.

If a parameter type is ANY TYPE, the function accepts an input of any type for this argument. The type that you pass to the function must be compatible with the function definition. If you pass an argument with an incompatible type, the query returns an error. If more than one parameter has type ANY TYPE, BigQuery does not enforce any type relationship between them.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.create	The dataset where you create the table function.

In addition, the OR REPLACE clause requires bigquery.routines.update permission.

Examples

The following table function takes an INT64 parameter that is used to filter the results of a query:

CREATE OR REPLACE TABLE FUNCTION mydataset.names_by_year(y INT64)
AS
  SELECT year, name, SUM(number) AS total
  FROM `bigquery-public-data.usa_names.usa_1910_current`
  WHERE year = y
  GROUP BY year, name

The following example specifies the return TABLE type in the RETURNS clause:

CREATE OR REPLACE TABLE FUNCTION mydataset.names_by_year(y INT64)
RETURNS TABLE<name STRING, year INT64, total INT64>
AS
  SELECT year, name, SUM(number) AS total
  FROM `bigquery-public-data.usa_names.usa_1910_current`
  WHERE year = y
  GROUP BY year, name

CREATE PROCEDURE statement

Creates a new procedure, which is a block of statements that can be called from other queries. Procedures can call themselves recursively.

Syntax

To create a GoogleSQL stored procedure, use the following syntax:

CREATE [OR REPLACE] PROCEDURE [IF NOT EXISTS]
[[project_name.]dataset_name.]procedure_name (procedure_argument[, ...] )
[OPTIONS(procedure_option_list)]
BEGIN
multi_statement_query
END;

procedure_argument: [procedure_argument_mode] argument_name argument_type

procedure_argument_mode: IN | OUT | INOUT

To create a stored procedure for Apache Spark, use the following syntax:

CREATE [OR REPLACE] PROCEDURE [IF NOT EXISTS]
[[project_name.]dataset_name.]procedure_name (procedure_argument[, ...] )
[EXTERNAL SECURITY external_security]
WITH CONNECTION connection_project_id.connection_region.connection_id
[OPTIONS(procedure_option_list)]
LANGUAGE language [AS pyspark_code]

procedure_argument: [procedure_argument_mode] argument_name argument_type

procedure_argument_mode: IN | OUT | INOUT

external_security: INVOKER

Arguments

• OR REPLACE: Replaces any procedure with the same name if it exists. Cannot appear with IF NOT EXISTS.

• IF NOT EXISTS: If any procedure exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• project_name: The name of the project where you are creating the procedure. Defaults to the project that runs this DDL query. If the project name contains special characters such as colons, it should be quoted in backticks ` (example: `google.com:my_project`).

• dataset_name: The name of the dataset where you are creating the procedure. Defaults to the defaultDataset in the request.

• procedure_name: The name of the procedure to create.

• external_security: The procedure to be executed with the privileges of the user that calls it.

• connection_project_id: the project that contains the connection to run Spark procedures—for example, myproject.

• connection_region: the region that contains the connection to run Spark procedures—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.

  For more information, see Create a stored procedure for Apache Spark.

• multi_statement_query: The multi-statement query to run.

• language: The language in which the stored procedure for Apache Spark is written. BigQuery supports stored procedures for Apache Spark that are written in Python, Java, or Scala.

• pyspark_code: The PySpark code for the stored procedure for Apache Spark if you want to pass the body of the procedure inline. Cannot appear with main_file_uri in procedure_option_list.

• argument_type: Any valid BigQuery type.

• procedure_argument_mode: Specifies whether an argument is an input, an output, or both.

procedure_option_list

The procedure_option_list lets you specify procedure options. Procedure options have the same syntax and requirements as table options but with a different list of NAMEs and VALUEs:

NAME	VALUE	Details
strict_mode	BOOL	It is useful for catching many common types of errors. The errors are not exhaustive, and successful creation of a procedure with strict_mode doesn't guarantee that the procedure will successfully execute at runtime. If strict_mode is TRUE, the procedure body undergoes additional checks for errors such as non-existent tables or columns. The CREATE PROCEDURE statement fails if the body fails any of these checks. If strict_mode is FALSE, the procedure body is checked only for syntax. Procedures which invoke themselves recursively should be created with strict_mode=FALSE to avoid errors caused by the procedure not yet existing while it is being validated. Default value is TRUE. Example: strict_mode=FALSE
description	STRING	A description of the procedure. Example: description="A procedure that runs a query."
engine	STRING	The engine type for processing stored procedures for Apache Spark. Must be specified for stored procedures for Spark. Valid value: engine="SPARK"
runtime_version	STRING	The runtime version of stored procedures for Spark. If not specified, the system default runtime version is used. Stored procedures for Spark support the same list of runtime versions as Dataproc Serverless. However, we recommend to specify a runtime version. For more information, see Dataproc Serverless Spark runtime releases. Example: runtime_version="1.1"
container_image	STRING	Custom container image for the runtime environment of the stored procedure for Spark. If not specified, the system default container image that includes the default Spark, Java, and Python packages associated with a runtime version is used. You can provide a custom container Docker image that includes your own built Java or Python dependencies. As Spark is mounted into your custom container at runtime, you must omit Spark in your custom container image. For optimized performance, we recommend you to host your image in Artifact Registry. For more information, see Use custom containers with Dataproc Serverless for Spark. Example: container_image="us-docker.pkg.dev/my-project-id/my-images/my-image”
properties	ARRAY<STRUCT<STRING, STRING>>	A key-value pair to include properties for stored procedures for Spark. Stored procedures for Spark support most of the Spark properties and a list of custom Dataproc Serverless properties. If you specify unsupported Spark properties such as YARN-related Spark properties, BigQuery fails to create the stored procedure. You can add Spark properties using the following format: [("key1","value1"),("key2", "value2")] For example: bq query --nouse_legacy_sql --dry_run 'CREATE PROCEDURE my_bq_project.my_dataset.spark_proc() WITH CONNECTION `my-project-id.us.my-connection` OPTIONS( engine="SPARK", main_file_uri="gs://my-bucket/my-pyspark-main.py", properties=[ ("spark.executor.instances", "3"), ("spark.yarn.am.memory", "3g") ]) LANGUAGE PYTHON' # Error in query string: Invalid value: \ Invalid properties: \ Attempted to set unsupported properties: \ [spark:spark.yarn.am.memory] at [1:1] Note: You can use the BigQuery dry run feature to validate your stored procedure without creating it.
main_file_uri	STRING	The Cloud Storage URI of the main Python, Scala, or Java JAR file of the Spark application. Applies only to stored procedures for Spark. Alternatively, if you want to add the body of the stored procedure that's written in Python in the CREATE PROCEDURE statement, add the code after LANGUAGE PYTHON AS as shown in the example in Use inline code. Example: main_file_uri="gs://my-bucket/my-pyspark-main.py" For Scala and Java languages, this field contains a path to only one JAR file. You can set only one value for main_file_uri and main_class. Example: main_file_uri="gs://my-bucket/my-scala-main.jar"
main_class	STRING	Applies only to stored procedures for Spark written in Java and Scala. Specify a fully-qualified class name in a JAR set with the jar_uris option. You can set only one value for main_file_uri and main_class. Example: main_class=”com.example.wordcount”
py_file_uris	ARRAY<STRING>	Python files to be placed on the PYTHONPATH for a PySpark application. Applies only to stored procedures for Apache Spark written in Python. Optional. Cloud Storage URIs of Python files to pass to the PySpark framework. Supported file formats include the following: .py, .egg, and .zip. Example: py_file_uris=[ "gs://my-bucket/my-pyspark-file1.py", "gs://my-bucket/my-pyspark-file2.py" ]
jar_uris	ARRAY<STRING>	Path to the JAR files to include on the driver and executor classpaths. Applies only to stored procedures for Apache Spark. Optional. Cloud Storage URIs of JAR files to add to the classpath of the Spark driver and tasks. Example: jar_uris=["gs://my-bucket/my-lib1.jar", "gs://my-bucket/my-lib2.jar"]
file_uris	ARRAY<STRING>	Files to be placed in the working directory of each executor. Applies only to stored procedures for Apache Spark. Optional. Cloud Storage URIs of files to be placed in the working directory of each executor. Example: file_uris=["gs://my-bucket/my-file1", "gs://my-bucket/my-file2"]
archive_uris	ARRAY<STRING>	Archive files to be extracted into the working directory of each executor. Applies only to stored procedures for Apache Spark. Optional. Cloud Storage URIs of archives to be extracted into the working directory of each executor. Supported file formats include the following: .jar, .tar, .tar.gz, .tgz, and .zip. Example: archive_uris=["gs://my-bucket/my-archive1.zip", "gs://my-bucket/my-archive2.zip"]

Argument mode

IN indicates that the argument is only an input to the procedure. You can specify either a variable or a value expression for IN arguments.

OUT indicates that the argument is an output of the procedure. An OUT argument is initialized to NULL when the procedure starts. You must specify a variable for OUT arguments.

INOUT indicates that the argument is both an input to and an output from the procedure. You must specify a variable for INOUT arguments. An INOUT argument can be referenced in the body of a procedure as a variable and assigned new values.

If neither IN, OUT, nor INOUT is specified, the argument is treated as an IN argument.

Variable scope

If a variable is declared outside a procedure, passed as an INOUT or OUT argument to a procedure, and the procedure assigns a new value to that variable, that new value is visible outside of the procedure.

Variables declared in a procedure are not visible outside of the procedure, and vice versa.

An OUT or INOUT argument can be assigned a value using SET, in which case the modified value is visible outside of the procedure. If the procedure exits successfully, then the value of the OUT or INOUT argument is the final value assigned to that INOUT variable.

Temporary tables exist for the duration of the script, so if a procedure creates a temporary table, the caller of the procedure will be able to reference the temporary table as well.

Default project in procedure body

Procedure bodies can reference entities without specifying the project; the default project is the project which owns the procedure, not necessarily the project used to run the CREATE PROCEDURE statement. Consider the sample query below.

CREATE PROCEDURE myProject.myDataset.QueryTable()
BEGIN
  SELECT * FROM anotherDataset.myTable;
END;

After creating the above procedure, you can run the query CALL myProject.myDataset.QueryTable(). Regardless of the project you choose to run this CALL query, the referenced table anotherDataset.myTable is always resolved against project myProject.

Required permissions

This statement requires the following IAM permission:

Permission	Resource
bigquery.routines.create	The dataset where you create the procedure.

To create a stored procedure for Apache Spark, additional IAM permission are needed:

Permission	Resource
bigquery.connections.delegate	The connection which you use to create the stored procedure for Apache Spark.

In addition, the OR REPLACE clause requires bigquery.routines.update permission.

SQL examples

You can also see examples of stored procedures for Apache Spark.

The following example creates a SQL procedure that both takes x as an input argument and returns x as output; because no argument mode is present for the argument delta, it is an input argument. The procedure consists of a block containing a single statement, which assigns the sum of the two input arguments to x.

CREATE PROCEDURE mydataset.AddDelta(INOUT x INT64, delta INT64)
BEGIN
  SET x = x + delta;
END;

The following example calls the AddDelta procedure from the example above, passing it the variable accumulator both times; because the changes to x within AddDelta are visible outside of AddDelta, these procedure calls increment accumulator by a total of 8.

DECLARE accumulator INT64 DEFAULT 0;
CALL mydataset.AddDelta(accumulator, 5);
CALL mydataset.AddDelta(accumulator, 3);
SELECT accumulator;

This returns the following:

+-------------+
| accumulator |
+-------------+
|           8 |
+-------------+

The following example creates the procedure SelectFromTablesAndAppend, which takes target_date as an input argument and returns rows_added as an output. The procedure creates a temporary table DataForTargetDate from a query; then, it calculates the number of rows in DataForTargetDate and assigns the result to rows_added. Next, it inserts a new row into TargetTable, passing the value of target_date as one of the column names. Finally, it drops the table DataForTargetDate and returns rows_added.

CREATE PROCEDURE mydataset.SelectFromTablesAndAppend(
  target_date DATE, OUT rows_added INT64)
BEGIN
  CREATE TEMP TABLE DataForTargetDate AS
  SELECT t1.id, t1.x, t2.y
  FROM dataset.partitioned_table1 AS t1
  JOIN dataset.partitioned_table2 AS t2
  ON t1.id = t2.id
  WHERE t1.date = target_date
    AND t2.date = target_date;

  SET rows_added = (SELECT COUNT(*) FROM DataForTargetDate);

  SELECT id, x, y, target_date  -- note that target_date is a parameter
  FROM DataForTargetDate;

  DROP TABLE DataForTargetDate;
END;

The following example declares a variable rows_added, then passes it as an argument to the SelectFromTablesAndAppend procedure from the previous example, along with the value of CURRENT_DATE; then it returns a message stating how many rows were added.

DECLARE rows_added INT64;
CALL mydataset.SelectFromTablesAndAppend(CURRENT_DATE(), rows_added);
SELECT FORMAT('Added %d rows', rows_added);

CREATE ROW ACCESS POLICY statement

Creates or replaces a row-level access policy. Row-level access policies on a table must have unique names.

Syntax

CREATE [ OR REPLACE ] ROW ACCESS POLICY [ IF NOT EXISTS ]
row_access_policy_name ON table_name
[GRANT TO (grantee_list)]
FILTER USING (filter_expression);

Arguments

• IF NOT EXISTS: If any row-level access policy exists with the same name, the CREATE statement has no effect. Cannot appear with OR REPLACE.

• row_access_policy_name: The name of the row-level access policy that you are creating. The row-level access policy name must be unique for each table. The row-level access policy name can contain the following:

  • Up to 256 characters.
  • Letters (upper or lowercase), numbers, and underscores. Must start with a letter.

• table_name: The name of the table that you want to create a row-level access policy for. The table must already exist.

• GRANT TO grantee_list: An optional clause that specifies the initial members that the row-level access policy should be created with.

  Caution: If no grantee_list is provided, then the row-level access policy for the specified filter is initialized with no principals. This configuration prevents all data reads by everyone.

  grantee_list is a list of iam_member users or groups. Strings must be valid IAM principals, or members, following the format of an IAM Policy Binding member, and must be quoted. The following types are supported:

  grantee_list types
  user:{emailid}	An email address that represents a specific Google account. Example: user:[e-mail]
  serviceAccount:{emailid}	An email address that represents a service account. Example: serviceAccount:[e-mail]
  group:{emailid}	An email address that represents a Google group. Example: group:[e-mail]
  domain:{domain}	The Google Workspace domain (primary) that represents all the users of that domain. Example: domain:example.com
  allAuthenticatedUsers	A special identifier that represents all service accounts and all users on the internet who have authenticated with a Google Account. This identifier includes accounts that aren't connected to a Google Workspace or Cloud Identity domain, such as personal Gmail accounts. Users who aren't authenticated, such as anonymous visitors, aren't included.
  allUsers	A special identifier that represents anyone who is on the internet, including authenticated and unauthenticated users. Because BigQuery requires authentication before a user can access the service, allUsers includes only authenticated users.

  You can combine a series of iam_member values, if they are comma-separated and quoted separately. For example: "user:[e-mail]","group:[e-mail]","user:[e-mail]"

• filter_expression: Defines the subset of table rows to show only to the members of the grantee_list. The filter_expression is similar to the WHERE clause in a SELECT query.

  The following are valid filter expressions:

  • GoogleSQL scalar functions.
  • SESSION_USER(), to restrict access only to rows that belong to the user running the query. If none of the row-level access policies are applicable to the querying user, then the user has no access to the data in the table.
  • TRUE. Grants the principals in the grantee_list field access to all rows of the table.

  The filter expression cannot contain the following:

  • References to other tables, such as subqueries.
  • SQL statements such as SELECT, CREATE, or UPDATE.
  • User-defined functions.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.rowAccessPolicies.create	The target table.
bigquery.rowAccessPolicies.setIamPolicy	The target table.
bigquery.tables.getData	The target table.

Examples

CREATE CAPACITY statement

Purchases slots by creating a new capacity commitment.

Caution: Before you purchase slots, understand the details of the commitment plans and pricing.

Syntax

CREATE CAPACITY
`project_id.location_id.commitment_id`
OPTIONS (capacity_commitment_option_list);

Arguments

• project_id: The project ID of the administration project that will maintain ownership of this commitment.
• location_id: The location of the commitment.
• commitment_id: The ID of the commitment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.
• capacity_commitment_option_list: The options you can set to describe the capacity commitment.

capacity_commitment_option_list

The option list specifies options for the capacity commitment. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	TYPE	Details
plan	String	The commitment plan to purchase. Supported values include: ANNUAL, THREE_YEAR, and TRIAL. For more information, see Commitment plans.
renewal_plan	String	The commitment renewal plan. Applies only when plan is ANNUAL, THREE_YEAR, or TRIAL. For more information, see Renewing commitments.
slot_count	Integer	The number of slots in the commitment.
edition	String	The edition associated with this reservation. For more information about editions, see Introduction to BigQuery editions.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.capacityCommitments.create	The administration project that maintains ownership of the commitments.

Example

The following example creates a capacity commitment of 100 annual slots that are located in the region-us region and managed by a project admin_project:

CREATE CAPACITY `admin_project.region-us.my-commitment`
OPTIONS (
  slot_count = 100,
  plan = 'ANNUAL');

CREATE RESERVATION statement

Creates a reservation. For more information, see Introduction to Reservations.

Syntax

CREATE RESERVATION
`project_id.location_id.reservation_id`
OPTIONS (reservation_option_list);

Arguments

• project_id: The project ID of the administration project where the capacity commitment was created.
• location: The location of the reservation.
• reservation_id: The reservation ID.
• reservation_option_list: The options you can set to describe the reservation.

reservation_option_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	TYPE	Details
ignore_idle_slots	BOOLEAN	If the value is true, then the reservation uses only the slots that are provisioned to it. The default value is false. For more information, see Idle slots.
slot_capacity	INTEGER	The number of slots to allocate to the reservation. If this reservation was created with an edition, this is equivalent to the amount of baseline slots.
target_job_concurrency	INTEGER	A soft upper bound on the number of jobs that can run concurrently in this reservation.
edition	STRING	The edition associated with this reservation. For more information about editions, see Introduction to BigQuery editions.
autoscale_max_slots	INTEGER	The maximum number of slots that could be added to the reservation by autoscaling.
secondary_location	STRING	The secondary location to use in the case of disaster recovery.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.reservations.create	The administration project that maintains ownership of the commitments.

Example

The following example creates a reservation of 100 slots in the project admin_project:

CREATE RESERVATION `admin_project.region-us.prod`
OPTIONS (
  slot_capacity = 100);

CREATE ASSIGNMENT statement

Assigns a project, folder, or organization to a reservation.

Syntax

CREATE ASSIGNMENT
`project_id.location_id.reservation_id.assignment_id`
OPTIONS (assignment_option_list)

Arguments

• project_id: The project ID of the administration project where the reservation was created.
• location: The location of the reservation.
• reservation_id: The reservation ID.
• assignment_id: The ID of the assignment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.
• assignment_option_list: The options you can set to describe assignment.

To remove a project from any reservations and use on-demand billing instead, set reservation_id to none.

assignment_option_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	TYPE	Details
assignee	String	The ID of the project, folder, or organization to assign to the reservation.
job_type	String	The type of job to assign to this reservation. Supported values include QUERY, PIPELINE, ML_EXTERNAL, CONTINUOUS, and BACKGROUND. For more information, see Assignments.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.reservationAssignments.create	The administration project and the assignee.

Example

The following example assigns the project my_project to the prod reservation for query jobs:

CREATE ASSIGNMENT `admin_project.region-us.prod.my_assignment`
OPTIONS (
  assignee = 'projects/my_project',
  job_type = 'QUERY');

The following example assigns an organization to the prod reservation for pipeline jobs, such as load and export jobs:

CREATE ASSIGNMENT `admin_project.region-us.prod.my_assignment`
OPTIONS (
  assignee = 'organizations/1234',
  job_type = 'PIPELINE');

CREATE SEARCH INDEX statement

Creates a new search index on one or more columns of a table.

A search index enables efficient queries using the SEARCH function.

Syntax

CREATE SEARCH INDEX [ IF NOT EXISTS ] index_name
ON table_name({ALL COLUMNS | column_name [, ...]})
[OPTIONS(index_option_list)]

Arguments

• IF NOT EXISTS: If there is already a search index by that name on the table, do nothing. If the table has a search index by a different name, then return an error.

• index_name: The name of the search index you're creating. Since the search index is always created in the same project and dataset as the base table, there is no need to specify these in the name.

• table_name: The name of the table. See Table path syntax.

• ALL COLUMNS: If data types are not specified, creates a search index on every column in the table which contains a STRING field. If data types are specified, create a search index on every column in the table which matches any of the data types specified.

• column_name: The name of a top-level column in the table which is one of the following supported data types or contains a field with one of the supported data types:

  Supported data types	Notes
  STRING	Primitive data type.
  INT64	Primitive data type.
  TIMESTAMP	Primitive data type.
  ARRAY<PRIMITIVE_DATA_TYPE>	Must contain a primitive data type in this list.
  STRUCT	Must contain at least one nested field that is a primitive data type in this list or ARRAY<PRIMITIVE_DATA_TYPE>.
  JSON	Must contain at least one nested field of a type that matches any data types in this list.

• index_option_list: The list of options to set on the search index.

Details

You can create only one search index per base table. You cannot create a search index on a view or materialized view. To modify which columns are indexed, DROP the current index and create a new one.

BigQuery returns an error if any column_name is not a STRING or does not contain a STRING field, or if you call CREATE SEARCH INDEX on ALL COLUMNS of a table which contains no STRING fields.

Creating a search index fails on a table which has column ACLs or row filters; however, these may all be added to the table after creation of the index.

index_option_list

The option list specifies options for the search index. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
analyzer	STRING	Example: analyzer='LOG_ANALYZER' The text analyzer to use to generate tokens for the search index. The supported values are 'LOG_ANALYZER', 'NO_OP_ANALYZER', and 'PATTERN_ANALYZER'.
analyzer_options	JSON-formatted STRING	The text analyzer configurations to set when creating a search index. Supported when analyzer is equal to 'LOG_ANALYZER' or 'PATTERN_ANALYZER'. For examples of JSON-formatted strings with different text analyzers, see Work with text analyzers.
data_types	ARRAY<STRING>	Example: data_types=['STRING', 'INT64', 'TIMESTAMP'] An array of data types to set when creating a search index. Supported data types are STRING, INT64 and TIMESTAMP. If data_types is not set, STRING fields are indexed by default.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.createIndex	The base table where you create the index.

Examples

The following example creates a search index called my_index on all string columns of my_table. In this case, the index is only created on column a.

CREATE TABLE dataset.my_table(a STRING, b INT64);

CREATE SEARCH INDEX my_index
ON dataset.my_table(ALL COLUMNS);

The following example creates a search index on columns a, my_struct.string_field, and b that uses the NO_OP_ANALYZER text analyzer.

CREATE TABLE dataset.complex_table(
  a STRING,
  my_struct STRUCT<string_field STRING, int_field INT64>,
  b ARRAY<STRING>
);

CREATE SEARCH INDEX my_index
ON dataset.complex_table(a, my_struct, b)
OPTIONS (analyzer = 'NO_OP_ANALYZER');

CREATE VECTOR INDEX statement

Creates a new vector index on a column of a table.

A vector index lets you perform a vector search more quickly, with the trade-off of reducing recall and so returning more approximate results.

Syntax

CREATE [ OR REPLACE ] VECTOR INDEX [ IF NOT EXISTS ] index_name
ON table_name(column_name)
[STORING(stored_column_name [, ...])]
OPTIONS(index_option_list);

Arguments

• OR REPLACE: Replaces any vector index with the same name if it exists. Can't appear with IF NOT EXISTS.

• IF NOT EXISTS: If there is already a vector index by that name on the table, do nothing. If the table has a vector index by a different name, then return an error.

• index_name: The name of the vector index you're creating. Since the index is always created in the same project and dataset as the base table, there is no need to specify these in the name.

• table_name: The name of the table. See Table path syntax.

• column_name: The name of a column with a type of ARRAY<FLOAT64>. The column can't have any child fields. All elements in the array must be non-NULL, and all values in the column must have the same array dimensions.

• stored_column_name: The name of a top-level column in the table to store in the vector index. The column type can't be RANGE. Stored columns are not used if the table has a row-level access policy or the column has a policy tag. To learn more, see Store columns and pre-filter.

• index_option_list: The list of options to set on the vector index.

Details

You can only create vector indexes on standard tables.

You can create only one vector index per table. You can't create a vector index on a table that already has a search index with the same index name.

To modify which column is indexed, DROP the current index and create a new one.

index_option_list

The option list specifies options for the vector index. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
index_type	STRING	Required. The algorithm to use to build the vector index. The supported values are IVF and TREE_AH. IVF: Specifying IVF builds the vector index as an inverted file index (IVF). An IVF uses a k-means algorithm to cluster the vector data, and then partitions the vector data based on those clusters. When you use the VECTOR_SEARCH function to search the vector data, it can use these partitions to reduce the amount of data it needs to read in order to determine a result. TREE_AH (Preview): Uses Google's ScaNN algorithm. TREE_AH is a tree-quantization based index, leveraging k-means clustering for partitioning and asymmetric hashing (product quantization) for fast approximate distance computation. For more information, see TreeAH index.
distance_type	STRING	Specifies the default distance type to use when performing a vector search using this index. The supported values are EUCLIDEAN, COSINE, and DOT_PRODUCT. EUCLIDEAN is the default. The index creation itself always uses EUCLIDEAN distance for training but the distance used in the VECTOR_SEARCH function can be different. If you specify a value for the distance_type argument of the VECTOR_SEARCH function, that value is used instead of the vector index's distance_type value.
ivf_options	JSON-formatted STRING	The options to use with the IVF algorithm. Defaults to '{}' to denote that all underlying options use their corresponding default values. The only supported option is num_lists. Specify an INT64 less than or equal to 5,000 that determines how many lists the IVF algorithm creates. For example, ivf_options = '{"num_lists":1000}'. The IVF algorithm divides the whole data space into a number of lists equal to the num_lists value, with data points that are closer to each other being more likely to be put on the same list. If the num_lists value is small, you have fewer lists with more data points, while a larger value creates more lists with fewer data points. You can use num_lists in combination with the fractions_list_to_search argument of the VECTOR_SEARCH function to create an efficient vector search. When you have data that is distributed in many small groups in the embedding space, specify a high num_lists value to create an index with more lists and specify a lower fractions_list_to_search value to scan fewer of those lists in vector search. Use a lower num_lists value and a higher fractions_list_to_search value when your data is distributed in fewer, larger groups. Using a high num_lists value might make the vector index take longer to build. If you don't specify a value for num_lists, BigQuery calculates an appropriate value. The statement fails if ivf_options is specified and index_type is not IVF.
tree_ah_options (Preview)	JSON-formatted STRING	The options to use with the TREE_AH algorithm. Defaults to '{}' to denote that all underlying options use their corresponding default values. Two options are supported: leaf_node_embedding_node and normalization_type. leaf_node_embedding_count: the approximate number of vectors in each leaf node. The minimum value is 500. This is the inverse of num_lists in ivf_options. Defaults to 1000. normalization_type: the type of normalization performed on each base table and query vector prior to any processing. The supported values are NONE and L2. L2 is also referred to as the Euclidean norm. Defaults to NONE. For example tree_ah_options = '{"leaf_node_embedding_count": 1000, "normalization_type": "L2"}' The statement fails if tree_ah_options is specified and index_type is not TREE_AH.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.createIndex	The table where you create the vector index.

If you choose to use the OR REPLACE clause, you must also have the bigquery.tables.updateIndex permission.

Examples

The following example creates a vector index on the embedding column of my_table:

CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>);

CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding)
OPTIONS (index_type = 'IVF');

The following example creates a vector index on the embedding column of my_table, and specifies the distance type to use and the IVF options:

CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>);

CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding)
OPTIONS (
  index_type = 'IVF',
  distance_type = 'COSINE',
  ivf_options = '{"num_lists":2500}');

The following example creates a vector index on the embedding column of my_table, and specifies the distance type to use and the TREE_AH options:

CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>);

CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding)
OPTIONS (
  index_type = 'TREE_AH',
  distance_type = 'EUCLIDEAN',
  tree_ah_options = '{"normalization_type": "L2"}');

ALTER SCHEMA SET DEFAULT COLLATE statement

Sets collation specifications on a dataset.

Syntax

ALTER SCHEMA [IF EXISTS]
[project_name.]dataset_name
SET DEFAULT COLLATE collate_specification

Arguments

• IF EXISTS: If no dataset exists with that name, the statement has no effect.

• DEFAULT COLLATE collate_specification: When a new table is created in the dataset, the table inherits a default collation specification unless a collation specification is explicitly specified for a column.

  The updated collation specification only applies to tables created afterwards.

• project_name: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.

• dataset_name: The name of the dataset.

• collate_specification: Specifies the collation specifications to set.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.get	The dataset to alter.
bigquery.datasets.update	The dataset to alter.

Example

Assume you have an existing table, mytable_a, in a dataset called mydataset. For example:

CREATE SCHEMA mydataset

CREATE TABLE mydataset.mytable_a
(
  number INT64,
  word STRING
)

+----------------------+
| mydataset.mytable_a  |
|   number INT64       |
|   word STRING        |
+----------------------+

At a later time, you decide to add a collation specification to your dataset. For example:

ALTER SCHEMA mydataset
SET DEFAULT COLLATE 'und:ci'

If you create a new table for your dataset, it inherits COLLATE 'und:ci' for all STRING columns. For example, collation is added to characters when you create the mytable_b table in the mydataset dataset:

CREATE TABLE mydataset.mytable_b
(
  amount INT64,
  characters STRING
)

+--------------------------------------+
| mydataset.mytable_b                  |
|   amount INT64                       |
|   characters STRING COLLATE 'und:ci' |
+--------------------------------------+

However, although you have updated the collation specification for the dataset, your existing table, mytable_a, continues to use the previous collation specification. For example:

+---------------------+
| mydataset.mytable_a |
|   number INT64      |
|   word STRING       |
+---------------------+

ALTER SCHEMA SET OPTIONS statement

Sets options on a dataset.

The statement runs in the location of the dataset if the dataset exists, unless you specify the location in the query settings. For more information, see Specifying your location.

Syntax

ALTER SCHEMA [IF EXISTS]
[project_name.]dataset_name
SET OPTIONS(schema_set_options_list)

Arguments

• IF EXISTS: If no dataset exists with that name, the statement has no effect.

• project_name: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.

• dataset_name: The name of the dataset.

• schema_set_options_list: The list of options to set.

schema_set_options_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
default_kms_key_name	STRING	Specifies the default Cloud KMS key for encrypting table data in this dataset. You can override this value when you create a table.
default_partition_expiration_days	FLOAT64	Specifies the default expiration time, in days, for table partitions in this dataset. You can override this value when you create a table.
default_rounding_mode	STRING	Example: default_rounding_mode = "ROUND_HALF_EVEN" This specifies the defaultRoundingMode that is used for new tables created in this dataset. It does not impact existing tables. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.25 is rounded to 2.3, and -2.25 is rounded to -2.3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.25 is rounded to 2.2 and -2.25 is rounded to -2.2.
default_table_expiration_days	FLOAT64	Specifies the default expiration time, in days, for tables in this dataset. You can override this value when you create a table.
description	STRING	The description of the dataset.
failover_reservation	STRING	Associates the dataset to a reservation in the case of a failover scenario.
friendly_name	STRING	A descriptive name for the dataset.
is_case_insensitive	BOOL	TRUE if the dataset and its table names are case-insensitive, otherwise FALSE. By default, this is FALSE, which means the dataset and its table names are case-sensitive. Datasets: mydataset and MyDataset can coexist in the same project, unless one of them has case-sensitivity turned off. Tables: mytable and MyTable can coexist in the same dataset if case-sensitivity for the dataset is turned on.
is_primary	BOOLEAN	Declares if the dataset is the primary replica.
labels	<ARRAY<STRUCT<STRING, STRING>>>	An array of labels for the dataset, expressed as key-value pairs.
max_time_travel_hours	SMALLINT	Specifies the duration in hours of the time travel window for the dataset. The max_time_travel_hours value must be an integer expressed in multiples of 24 (48, 72, 96, 120, 144, 168) between 48 (2 days) and 168 (7 days). 168 hours is the default if this option isn't specified.
primary_replica	STRING	The replica name to set as the primary replica.
storage_billing_model	STRING	Alters the storage billing model for the dataset. Set the storage_billing_model value to PHYSICAL to use physical bytes when calculating storage charges, or to LOGICAL to use logical bytes. LOGICAL is the default. The storage_billing_model option is only available for datasets that have been updated after December 1, 2022. For datasets that were last updated before that date, the storage billing model is LOGICAL. When you change a dataset's billing model, it takes 24 hours for the change to take effect. Once you change a dataset's storage billing model, you must wait 14 days before you can change the storage billing model again.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.get	The dataset to alter.
bigquery.datasets.update	The dataset to alter.

Examples

Setting the default table expiration for a dataset

The following example sets the default table expiration.

ALTER SCHEMA mydataset
SET OPTIONS(
  default_table_expiration_days=3.75
  )

Turning on case insensitivity for a dataset

The following example turns on case insensitivity for the name of a dataset and the table names within that dataset.

ALTER SCHEMA mydataset
SET OPTIONS(
  is_case_insensitive=TRUE
)

ALTER SCHEMA ADD REPLICA statement

Adds a replica to a schema (preview).

Syntax

ALTER SCHEMA [IF EXISTS]
[project_name.]dataset_name
ADD REPLICA replica_name [OPTIONS(add_replica_options_list)]

Arguments

• IF EXISTS: If no dataset exists with that name, the statement has no effect.
• dataset_name: The name of the table to alter. See Table path syntax.
• replica_name: The name of the new replica. Conventionally, this is the same as the location you are creating the replica in.
• add_replica_option_list: The list of options to set.

add_replica_options_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
location	STRING	The location in which to create the replica.
replica_kms_key	STRING	The Cloud Key Management Service key set in the destination region. replica_kms_key is used as a substitute encryption key in the destination region for any keys used in the source region. Any table in the source region that's encrypted with a Cloud KMS key is encrypted with the replica_kms_key. This value must be a Cloud KMS key created in the replica dataset's region, not the source dataset's region. For more information about setting up a Cloud KMS key, see Grant encryption and decryption permission.

Required permissions

To get the permissions that you need to manage replicas, ask your administrator to grant you the BigQuery Data Editor (roles/bigquery.dataEditor) IAM role on your schema. For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Examples

The following example adds a secondary replica that is named EU in the EU multi-region to a schema that is named cross_region_dataset:

ALTER SCHEMA cross_region_dataset
ADD REPLICA `EU` OPTIONS(location=`eu`);

ALTER SCHEMA DROP REPLICA statement

Drops a replica from a schema (preview).

Syntax

ALTER SCHEMA [IF EXISTS] dataset_name
DROP REPLICA replica_name

• IF EXISTS: If no dataset exists with that name, the statement has no effect.
• dataset_name: The name of the table to alter. See Table path syntax.
• replica_name: The name of the replica to drop.

Required permissions

To get the permissions that you need to manage replicas, ask your administrator to grant you the BigQuery Data Editor (roles/bigquery.dataEditor) IAM role on your schema. For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Examples

The following example removes a replica that is located in the us-east4 region from the cross-region-dataset dataset:

ALTER SCHEMA [IF EXISTS] cross-region-dataset
DROP REPLICA `us-east4`

ALTER TABLE SET OPTIONS statement

Sets the options on a table.

Syntax

ALTER TABLE [IF EXISTS] table_name
SET OPTIONS(table_set_options_list)

Arguments

• IF EXISTS: If no table exists with that name, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• table_set_options_list: The list of options to set.

Details

This statement is not supported for external tables.

table_set_options_list

The option list lets you set table options such as a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a table option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property.
partition_expiration_days	FLOAT64	Example: partition_expiration_days=7 Sets the partition expiration in days. For more information, see Set the partition expiration. By default, partitions don't expire. This property is equivalent to the timePartitioning.expirationMs table resource property but uses days instead of milliseconds. One day is equivalent to 86400000 milliseconds, or 24 hours. This property can only be set if the table is partitioned.
require_partition_filter	BOOL	Example: require_partition_filter=true Specifies whether queries on this table must include a a predicate filter that filters on the partitioning column. For more information, see Set partition filter requirements. The default value is false. This property is equivalent to the timePartitioning.requirePartitionFilter table resource property. This property can only be set if the table is partitioned.
kms_key_name	STRING	Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys.
friendly_name	STRING	Example: friendly_name="my_table" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a table that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.
default_rounding_mode	STRING	Example: default_rounding_mode = "ROUND_HALF_EVEN" This specifies the default rounding mode that's used for values written to any new NUMERIC or BIGNUMERIC type columns or STRUCT fields in the table. It does not impact existing fields in the table. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.5 is rounded to 3.0, and -2.5 is rounded to -3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.5 is rounded to 2.0 and -2.5 is rounded to -2.0. This property is equivalent to the defaultRoundingMode table resource property.
enable_change_history	BOOL	In preview. Example: enable_change_history=TRUE Set this property to TRUE in order to capture change history on the table, which you can then view by using the CHANGES function. Enabling this table option has an impact on costs; for more information see Pricing and costs. The default is FALSE.
max_staleness	INTERVAL	Example: max_staleness=INTERVAL "4:0:0" HOUR TO SECOND The maximum interval behind the current time where it's acceptable to read stale data. For example, with change data capture, when this option is set, the table copy operation is denied if data is more stale than the max_staleness value. max_staleness is disabled by default.
enable_fine_grained_mutations	BOOL	In preview. Example: enable_fine_grained_mutations=TRUE Set this property to TRUE to enable fine-grained DML optimization on the table. The default is FALSE.
storage_uri	STRING	In preview. Example: storage_uri=gs://BUCKET_DIRECTORY/TABLE_DIRECTORY/ A fully qualified location prefix for the external folder where data is stored. Supports gs: buckets. Required for managed tables.
table_format	STRING	In preview. Example: table_format=PARQUET The open-source file format in which the table data is stored. Only PARQUET is supported. Required for managed tables. The default is PARQUET.
file_format	STRING	In preview. Example: file_format=ICEBERG The open table format in which metadata-only snapshots are stored. Only ICEBERG is supported. Required for managed tables. The default is ICEBERG.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

Setting the value replaces the existing value of that option for the table, if there was one. Setting the value to NULL clears the table's value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Setting the expiration timestamp and description on a table

The following example sets the expiration timestamp on a table to seven days from the execution time of the ALTER TABLE statement, and sets the description as well:

ALTER TABLE mydataset.mytable
SET OPTIONS (
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 7 DAY),
  description="Table that expires seven days from now"
)

Setting the require partition filter attribute on a partitioned table

The following example sets the timePartitioning.requirePartitionFilter attribute on a partitioned table:

ALTER TABLE mydataset.mypartitionedtable
SET OPTIONS (require_partition_filter=true)

Queries that reference this table must use a filter on the partitioning column, or else BigQuery returns an error. Setting this option to true can help prevent mistakes in querying more data than intended.

Clearing the expiration timestamp on a table

The following example clears the expiration timestamp on a table so that it will not expire:

ALTER TABLE mydataset.mytable
SET OPTIONS (expiration_timestamp=NULL)

ALTER TABLE ADD COLUMN statement

Adds one or more new columns to an existing table schema.

Syntax

ALTER TABLE table_name
ADD COLUMN [IF NOT EXISTS] column [, ...]

Arguments

• table_name: The name of the table. See Table path syntax.

• IF NOT EXISTS: If the column name already exists, the statement has no effect.

• column: The column to add. This includes the name of the column and schema to add. The column name and schema use the same syntax used in the CREATE TABLE statement.

Details

You cannot use this statement to create:

• Partitioned columns.
• Clustered columns.
• Nested columns inside existing RECORD fields.

You cannot add a REQUIRED column to an existing table schema. However, you can create a nested REQUIRED column as part of a new RECORD field.

This statement is not supported for external tables.

Without the IF NOT EXISTS clause, if the table already contains a column with that name, the statement returns an error. If the IF NOT EXISTS clause is included and the column name already exists, no error is returned, and no action is taken.

The value of the new column for existing rows is set to one of the following:

• NULL if the new column was added with NULLABLE mode. This is the default mode.
• An empty ARRAY if the new column was added with REPEATED mode.

For more information about schema modifications in BigQuery, see Modifying table schemas.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Adding columns

The following example adds the following columns to an existing table named mytable:

• Column A of type STRING.
• Column B of type GEOGRAPHY.
• Column C of type NUMERIC with REPEATED mode.
• Column D of type DATE with a description.

ALTER TABLE mydataset.mytable
  ADD COLUMN A STRING,
  ADD COLUMN IF NOT EXISTS B GEOGRAPHY,
  ADD COLUMN C ARRAY<NUMERIC>,
  ADD COLUMN D DATE OPTIONS(description="my description")

If any of the columns named A, C, or D already exist, the statement fails. If column B already exists, the statement succeeds because of the IF NOT EXISTS clause.

Adding a RECORD column

The following example adds a column named A of type STRUCT that contains the following nested columns:

• Column B of type GEOGRAPHY.
• Column C of type INT64 with REPEATED mode.
• Column D of type INT64 with REQUIRED mode.
• Column E of type TIMESTAMP with a description.

ALTER TABLE mydataset.mytable
   ADD COLUMN A STRUCT<
       B GEOGRAPHY,
       C ARRAY<INT64>,
       D INT64 NOT NULL,
       E TIMESTAMP OPTIONS(description="creation time")
       >

The query fails if the table already has a column named A, even if that column does not contain any of the nested columns that are specified.

The new STRUCT named A is nullable, but the nested column D within A is required for any STRUCT values of A.

Adding collation support to a column

When you create a new column for your table, you can specifically assign a new collation specification to that column.

ALTER TABLE mydataset.mytable
ADD COLUMN word STRING COLLATE 'und:ci'

ALTER TABLE ADD FOREIGN KEY statement

Adds a foreign key constraint to an existing table. You can add multiple foreign key constraints by using additional ADD FOREIGN KEY statements.

Syntax

ALTER TABLE [[project_name.]dataset_name.]fk_table_name
ADD [CONSTRAINT [IF NOT EXISTS] constraint_name] FOREIGN KEY (fk_column_name[, ...])
REFERENCES pk_table_name(pk_column_name[,...]) NOT ENFORCED
[ADD...];

Arguments

• project_name: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.
• dataset_name: The name of the dataset that contains the table with a primary key. Defaults to the project that runs this DDL statement if undefined.
• fk_table_name: The name of the existing table to add a foreign key to.
• IF NOT EXISTS: If a constraint of the same name already exists in the defined table, the statement has no effect.
• constraint_name: The name of the constraint to add.
• fk_column_name: In the foreign key table, the name of the foreign key column. Only top-level columns can be used as foreign key columns.
• pk_table_name: The name of the table that contains the primary key.
• pk_column_name: In the primary key table, the name of the primary key column. Only top-level columns can be used as primary key columns.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example adds the my_fk_name foreign key constraint to the fk_table table. This example depends on an existing table, pk_table.

1. Add a primary key to the pk_table table:

   ALTER TABLE pk_table
   ADD PRIMARY KEY (x,y) NOT ENFORCED;

2. Create a table named fk_table for the foreign key.

   CREATE TABLE fk_table(x int64, y int64, i int64, j int64, u int64, v int64);

3. Add the my_fk_name foreign key constraint to the fk_table.

   ALTER TABLE fk_table
   ADD CONSTRAINT my_fk_name FOREIGN KEY (u, v)
   REFERENCES pk_table(x, y) NOT ENFORCED

The following example adds the fk and fk2 foreign key constraints to the fk_table table in a single statement. This example depends on an existing table, pk_table.

1. Add a primary key to the pk_table table:

   ALTER TABLE pk_table
   ADD PRIMARY KEY (x,y) NOT ENFORCED;

2. Create a table named fk_table for multiple foreign key constraints.

   CREATE TABLE fk_table(x int64, y int64, i int64, j int64, u int64, v int64);

3. Add the fk and fk2 constraints to fk_table in one statement.

   ALTER TABLE fk_table
   ADD PRIMARY KEY (x,y) NOT ENFORCED,
   ADD CONSTRAINT fk FOREIGN KEY (u, v) REFERENCES pk_table(x, y) NOT ENFORCED,
   ADD CONSTRAINT fk2 FOREIGN KEY (i, j) REFERENCES pk_table(x, y) NOT ENFORCED;

ALTER TABLE ADD PRIMARY KEY statement

Adds a primary key to an existing table.

Syntax

ALTER TABLE [[project_name.]dataset_name.]table_name
ADD PRIMARY KEY(column_list) NOT ENFORCED;

Arguments

• project_name: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.
• dataset_name: The name of the dataset that contains the table with a primary key.
• table_name: The name of the existing table with a primary key.
• column_list: The list of columns to be added as primary keys.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example adds the primary key constraint of x and y to the pk_table table.

ALTER TABLE pk_table ADD PRIMARY KEY (x,y) NOT ENFORCED;

ALTER TABLE RENAME TO statement

Renames a clone, snapshot or table.

Caution: Currently, renaming a table deletes all tags that may be attached to it or its columns in Data Catalog.

Syntax

ALTER TABLE [IF EXISTS] table_name
RENAME TO new_table_name

Arguments

• IF EXISTS: If no table exists with that name, the statement has no effect.

• table_name: The name of the table to rename. See Table path syntax.

• new_table_name: The new name of the table. The new name cannot be an existing table name.

Details

• If you want to rename a table that has data streaming into it, you must stop the streaming, commit any pending streams, and wait for BigQuery to indicate that streaming is not in use.
• While a table can usually be renamed 5 hours after the last streaming operation, it might take longer.
• Existing table ACLs and row access policies are preserved, but table ACL and row access policy updates made during the table rename are not preserved.
• You can't concurrently rename a table and run a DML statement on that table.
• Renaming a table removes all Data Catalog tags on the table.
• You can't rename external tables.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Renaming a table

The following example renames the table mydataset.mytable to mydataset.mynewtable:

ALTER TABLE mydataset.mytable RENAME TO mynewtable

ALTER TABLE RENAME COLUMN statement

Caution: Renaming a column deletes all Data Catalog tags that are attached to it. Primary key columns can't be renamed.

Renames one or more columns in an existing table schema.

Syntax

ALTER TABLE [IF EXISTS] table_name
RENAME COLUMN [IF EXISTS] column_to_column[, ...]

column_to_column :=
    column_name TO new_column_name

Arguments

• (ALTER TABLE) IF EXISTS: If the specified table does not exist, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top-level column you're altering.

• new_column_name: The new name of the column. The new name cannot be an existing column name.

Details

This statement is not supported for external tables.

If the table to be modified has active row-level access policies, the statement returns an error.

Without the IF EXISTS clause, if the table does not contain a column with that name, then the statement returns an error. If the IF EXISTS clause is included and the column name does not exist, then no error is returned, and no action is taken.

This statement only renames the column from the table. Any objects that refer to the column, such as views or materialized views, must be updated or recreated separately.

You cannot use this statement to rename the following:

• Subfields, such as nested columns in a STRUCT
• Partitioning columns
• Clustering columns
• Fields that are part of primary key constraints or foreign key constraints
• Columns in a table that has row access policies

After one or more columns in a table are renamed, you cannot do the following:

• Query the table with legacy SQL.
• Query the table as a wildcard table.

Renaming the columns with their original names removes these restrictions.

Multiple RENAME COLUMN statements in one ALTER TABLE statement are supported. The sequence of renames are interpreted and validated in order. Each column_name must refer to a column name that exists after all preceding renames have been applied. RENAME COLUMN cannot be used with other ALTER TABLE actions in one statement.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Renaming columns

The following example renames columns from an existing table named mytable:

• Column A -> columnA
• Column B -> columnB

ALTER TABLE mydataset.mytable
  RENAME COLUMN A TO columnA,
  RENAME COLUMN IF EXISTS B TO columnB

If column A does not exist, then the statement fails. If column B does not exist, then the statement still succeeds because of the IF EXISTS clause.

The following example swaps the names of columnA and columnB:

ALTER TABLE mydataset.mytable
  RENAME COLUMN columnA TO temp,
  RENAME COLUMN columnB TO columnA,
  RENAME COLUMN temp TO columnB

ALTER TABLE DROP COLUMN statement

Drops one or more columns from an existing table schema.

Syntax

ALTER TABLE table_name
DROP COLUMN [IF EXISTS] column_name [, ...]

Arguments

• table_name: The name of the table to alter. See Table path syntax. The table must already exist and have a schema.

• IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the column to drop.

Details

Dropping a column is a metadata-only operation and does not immediately free up the storage that is associated with the dropped column. The storage is freed up the next time the table is written to, typically when you perform a DML operation on it or when a background optimzation job happens. Since DROP COLUMN is not a data cleanup operation, there is no guaranteed time window within which the data will be deleted.

There are two options for immediately reclaiming storage:

• Overwrite a table with a SELECT * EXCEPT query.
• Export the data to Cloud Storage, delete the unwanted columns, and then load the data into a new table with the correct schema.

You can restore a dropped column in a table using time travel. You cannot use this statement to drop the following:

• Partitioned columns
• Clustered columns
• Fields that are part of primary key constraints or foreign key constraints
• Nested columns inside existing RECORD fields
• Columns in a table that has row access policies

After one or more columns in a table are dropped you cannot do the following:

• Query the table with legacy SQL.
• Query the table as a wildcard table.

This statement is not supported for external tables.

Without the IF EXISTS clause, if the table does not contain a column with that name, then the statement returns an error. If the IF EXISTS clause is included and the column name does not exist, then no error is returned, and no action is taken.

This statement only removes the column from the table. Any objects that refer to the column, such as views or materialized views, must be updated or recreated separately.

For more information about schema modifications in BigQuery, see Modifying table schemas.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Dropping columns

The following example drops the following columns from an existing table named mytable:

• Column A
• Column B

ALTER TABLE mydataset.mytable
  DROP COLUMN A,
  DROP COLUMN IF EXISTS B

If the column named A does not exist, then the statement fails. If column B does not exist, then the statement still succeeds because of the IF EXISTS clause.

After one or more columns in a table are dropped, you cannot do the following:

• Query the table with legacy SQL.
• Accelerate queries on the table with BigQuery BI Engine.
• Query the table as a Wildcard Table.
• Copy the table in the Google Cloud console.
• Copy the table using the bq cp command.

Recreating the table using CREATE TABLE ... AS SELECT ... removes these restrictions.

ALTER TABLE DROP CONSTRAINT statement

Drops a constraint from an existing table. You can use this statement to drop foreign key constraints from a table.

Syntax

ALTER TABLE [[project_name.]dataset_name.]table_name
DROP CONSTRAINT [IF EXISTS] constraint_name;

Arguments

• project_name: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.
• dataset_name: The name of the dataset that contains the table with a primary key.
• table_name: The name of the existing table with a primary key.
• IF EXISTS: If no primary key exists in the defined table, the statement has no effect.
• constraint_name: The name of the constraint to drop.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example drops the constraint myConstraint from the existing table myTable.

ALTER TABLE mytable DROP CONSTRAINT myConstraint;

ALTER TABLE DROP PRIMARY KEY statement

Drops a primary key from an existing table.

Syntax

ALTER TABLE [[project_name.]dataset_name.]table_name
DROP PRIMARY KEY [IF EXISTS];

Arguments

• project_name: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.
• dataset_name: The name of the dataset that contains the table with a primary key.
• table_name: The name of the existing table with a primary key.
• IF EXISTS: If no primary key exists in the defined table, the statement has no effect.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example drops all primary keys from the existing table myTable.

ALTER TABLE myTable
DROP PRIMARY KEY;

ALTER TABLE SET DEFAULT COLLATE statement

Sets collation specifications on a table.

Syntax

ALTER TABLE
  table_name
  SET DEFAULT COLLATE collate_specification

Arguments

• table_name: The name of the table to alter. See Table path syntax. The table must already exist and have a schema.

• SET DEFAULT COLLATE collate_specification: When a new column is created in the schema, and if the column does not have an explicit collation specification, the column inherits this collation specification for STRING types. The updated collation specification only applies to columns added afterwards.

  If you want to add a collation specification on a new column in an existing table, you can do this when you add the column. If you add a collation specification directly on a column, the collation specification for the column has precedence over a table's default collation specification. You cannot update an existing collation specification on a column.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Example

Assume you have an existing table, mytable, in a schema called mydataset.

CREATE TABLE mydataset.mytable
(
  number INT64,
  word STRING
) DEFAULT COLLATE 'und:ci'

When you create mytable, all STRING columns inherit COLLATE 'und:ci'. The resulting table has this structure:

+--------------------------------+
| mydataset.mytable              |
|   number INT64                 |
|   word STRING COLLATE 'und:ci' |
+--------------------------------+

At a later time, you decide to change the collation specification for your table.

ALTER TABLE mydataset.mytable
SET DEFAULT COLLATE ''

Although you have updated the collation specification, your existing column, word, continues to use the previous collation specification.

+--------------------------------+
| mydataset.mytable              |
|   number INT64                 |
|   word STRING COLLATE 'und:ci' |
+--------------------------------+

However, if you create a new column for your table, the new column includes the new collation specification. In the following example a column called name is added. Because the new collation specification is empty, the default collation specification is used.

ALTER TABLE mydataset.mytable
ADD COLUMN name STRING

+--------------------------------+
| mydataset.mytable              |
|   number INT64                 |
|   word STRING COLLATE 'und:ci' |
|   name STRING COLLATE          |
+--------------------------------+

ALTER COLUMN SET OPTIONS statement

Sets options, such as the column description, on a column in a table or view in BigQuery.

Syntax

ALTER { TABLE | VIEW } [IF EXISTS] name
ALTER COLUMN [IF EXISTS] column_name
SET OPTIONS({ column_set_options_list | view_column_set_options_list })

Arguments

• (ALTER { TABLE | VIEW }) IF EXISTS: If no table or view exists with that name, then the statement has no effect.

• name: The name of the table or view to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top-level column you're altering. Modifying subfields, such as nested columns in a STRUCT, is not supported.

• column_set_options_list: The list of options to set on the column of the table. This option must be used with TABLE.

• view_column_set_options_list: The list of options to set on the column of the view. This option must be used with VIEW.

Details

This statement is not supported for external tables.

column_set_options_list

Specify a column option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
description	STRING	Example: description="a unique id" This property is equivalent to the schema.fields[].description table resource property.
rounding_mode	STRING	Example: rounding_mode = "ROUND_HALF_EVEN" This specifies the rounding mode that's used for values written to a NUMERIC or BIGNUMERIC type column or STRUCT field. The following values are supported: "ROUND_HALF_AWAY_FROM_ZERO": Halfway cases are rounded away from zero. For example, 2.25 is rounded to 2.3, and -2.25 is rounded to -2.3. "ROUND_HALF_EVEN": Halfway cases are rounded towards the nearest even digit. For example, 2.25 is rounded to 2.2 and -2.25 is rounded to -2.2. This property is equivalent to the roundingMode table resource property.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

Setting the VALUE replaces the existing value of that option for the column, if there was one. Setting the VALUE to NULL clears the column's value for that option.

view_column_set_options_list

The view_column_option_list lets you specify optional top-level column options. Column options for a view have the same syntax and requirements as for a table, but with a different list of NAME and VALUE fields:

NAME	VALUE	Details
description	STRING	Example: description="a unique id"

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example sets a new description on a table column called price:

ALTER TABLE mydataset.mytable
ALTER COLUMN price
SET OPTIONS (description = 'Price per unit');

The following example sets a new description on a view column called total:

ALTER VIEW mydataset.myview
ALTER COLUMN total
SET OPTIONS (description = 'Total sales of the product');

ALTER COLUMN DROP NOT NULL statement

Removes a NOT NULL constraint from a column in a table in BigQuery.

Syntax

ALTER TABLE [IF EXISTS] table_name
ALTER COLUMN [IF EXISTS] column DROP NOT NULL

Arguments

• (ALTER TABLE) IF EXISTS: If no table exists with that name, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top level column you're altering. Modifying subfields is not supported.

Details

If a column does not have a NOT NULL constraint the query returns an error.

This statement is not supported for external tables.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example removes the NOT NULL constraint from a column called mycolumn:

ALTER TABLE mydataset.mytable
ALTER COLUMN mycolumn
DROP NOT NULL

ALTER COLUMN SET DATA TYPE statement

Changes the data type of a column in a table in BigQuery to a less restrictive data type. For example, a NUMERIC data type can be changed to a BIGNUMERIC type but not the reverse.

Syntax

ALTER TABLE [IF EXISTS] table_name
ALTER COLUMN [IF EXISTS] column_name SET DATA TYPE column_schema

Arguments

• (ALTER TABLE) IF EXISTS: If no table exists with that name, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top level column you're altering. Modifying subfields is not supported.

• column_schema: The schema that you're converting the column to. This schema uses the same syntax used in the CREATE TABLE statement.

Details

The following data type conversions are supported: :

• INT64 to NUMERIC, BIGNUMERIC, FLOAT64
• NUMERIC to BIGNUMERIC, FLOAT64

You can also convert data types from more restrictive to less restrictive parameterized data types. For example, you can increase the maximum length of a string type or increase the precision or scale of a numeric type.

The following are examples of valid parameterized data type conversions:

• NUMERIC(10, 6) to NUMERIC(12, 8)
• NUMERIC to BIGNUMERIC(40, 20)
• STRING(5) to STRING(7)

This statement is not supported for external tables.

Without the IF EXISTS clause, if the table does not contain a column with that name, the statement returns an error. If the IF EXISTS clause is included and the column name does not exist, no error is returned, and no action is taken.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

Changing the data type for a column

The following example changes the data type of column c1 from an INT64 to NUMERIC:

CREATE TABLE dataset.table(c1 INT64);

ALTER TABLE dataset.table ALTER COLUMN c1 SET DATA TYPE NUMERIC;

Changing the data type for a field

The following example changes the data type of one of the fields in the s1 column:

CREATE TABLE dataset.table(s1 STRUCT<a INT64, b STRING>);

ALTER TABLE dataset.table ALTER COLUMN s1
SET DATA TYPE STRUCT<a NUMERIC, b STRING>;

Changing precision

The following example changes the precision of a parameterized data type column:

CREATE TABLE dataset.table (pt NUMERIC(7,2));

ALTER TABLE dataset.table
ALTER COLUMN pt
SET DATA TYPE NUMERIC(8,2);

ALTER COLUMN SET DEFAULT statement

Sets the default value of a column.

Syntax

ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column_name
SET DEFAULT default_expression;

Arguments

• (ALTER TABLE) IF EXISTS: If the specified table does not exist, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top-level column to add a default value to.

• default_expression: The default value assigned to the column. The expression must be a literal or one of the following functions:

  • CURRENT_DATE
  • CURRENT_DATETIME
  • CURRENT_TIME
  • CURRENT_TIMESTAMP
  • GENERATE_UUID
  • RAND
  • SESSION_USER
  • ST_GEOGPOINT

Details

Setting the default value for a column only affects future inserts to the table. It does not change any existing table data.

The type of the default value must match the type of the column. A STRUCT type can only have a default value set for the entire STRUCT field. You cannot set the default value for a subset of the fields. You cannot set the default value of an array to NULL or set an element within an array to NULL.

If the default value is a function, it is evaluated at the time that the value is written to the table, not the time the table is created.

You can't set default values on columns that are primary keys.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example sets the default value of the column mycolumn to the current time:

ALTER TABLE mydataset.mytable
ALTER COLUMN mycolumn
SET DEFAULT CURRENT_TIME();

ALTER COLUMN DROP DEFAULT statement

Removes the default value assigned to a column. This is the same as setting the default value to NULL.

Syntax

ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column_name
DROP DEFAULT;

Arguments

• (ALTER TABLE) IF EXISTS: If the specified table does not exist, the statement has no effect.

• table_name: The name of the table to alter. See Table path syntax.

• (ALTER COLUMN) IF EXISTS: If the specified column does not exist, the statement has no effect.

• column_name: The name of the top-level column to remove the default value from. If you drop the default value from a column that does not have a default set, an error is returned.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The table to alter.
bigquery.tables.update	The table to alter.

Examples

The following example removes the default value from the column mycolumn:

ALTER TABLE mydataset.mytable
ALTER COLUMN mycolumn
DROP DEFAULT;

ALTER VIEW SET OPTIONS statement

Sets the options on a view.

Syntax

ALTER VIEW [IF EXISTS] view_name
SET OPTIONS(view_set_options_list)

Arguments

• IF EXISTS: If no view exists with that name, the statement has no effect.

• view_name: The name of the view to alter. See Table path syntax.

• view_set_options_list: The list of options to set.

view_set_options_list

The option list allows you to set view options such as a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a view option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property.
friendly_name	STRING	Example: friendly_name="my_view" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a view that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.
privacy_policy	JSON-formatted STRING	The policies to enforce when anyone queries the view. To learn more about the policies available for a view, see the privacy_policy view option. Note: Time travel is disabled on any view that has an analysis rule.

VALUE is a constant expression containing only literals, query parameters, and scalar functions.

The constant expression cannot contain:

• A reference to a table
• Subqueries or SQL statements such as SELECT, CREATE, or UPDATE
• User-defined functions, aggregate functions, or analytic functions
• The following scalar functions:
  • ARRAY_TO_STRING
  • REPLACE
  • REGEXP_REPLACE
  • RAND
  • FORMAT
  • LPAD
  • RPAD
  • REPEAT
  • SESSION_USER
  • GENERATE_ARRAY
  • GENERATE_DATE_ARRAY

Setting the value replaces the existing value of that option for the view, if there was one. Setting the value to NULL clears the view's value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The view to alter.
bigquery.tables.update	The view to alter.

Examples

Setting the expiration timestamp and description on a view

The following example sets the expiration timestamp on a view to seven days from the execution time of the ALTER VIEW statement, and sets the description as well:

ALTER VIEW mydataset.myview
SET OPTIONS (
  expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 7 DAY),
  description="View that expires seven days from now"
)

ALTER MATERIALIZED VIEW SET OPTIONS statement

Sets the options on a materialized view.

Syntax

ALTER MATERIALIZED VIEW [IF EXISTS] materialized_view_name
SET OPTIONS(materialized_view_set_options_list)

Arguments

• IF EXISTS: If no materialized view exists with that name, the statement has no effect.

• materialized_view_name: The name of the materialized view to alter. See Table path syntax.

• materialized_view_set_options_list: The list of options to set.

materialized_view_set_options_list

The option list allows you to set materialized view options such as a whether refresh is enabled. the refresh interval, a label and an expiration time. You can include multiple options using a comma-separated list.

Specify a materialized view option list in the following format:

NAME=VALUE, ...

NAME and VALUE must be one of the following combinations:

NAME	VALUE	Details
enable_refresh	BOOLEAN	Example: enable_refresh=false Default: true
refresh_interval_minutes	FLOAT64	Example: refresh_interval_minutes=20 Default: refresh_interval_minutes=30
expiration_timestamp	TIMESTAMP	Example: expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC" This property is equivalent to the expirationTime table resource property. expiration_timestamp is optional and not used by default.
max_staleness	INTERVAL	Example: max_staleness=INTERVAL "4:0:0" HOUR TO SECOND The max_staleness property provides consistently high performance with controlled costs when processing large, frequently changing datasets. max_staleness is disabled by default.
allow_non_incremental_definition	BOOLEAN	Example: allow_non_incremental_definition=true The allow_non_incremental_definition property supports an expanded range of SQL queries to create materialized views. allow_non_incremental_definition=true is disabled by default.
kms_key_name	STRING	Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys.
friendly_name	STRING	Example: friendly_name="my_mv" This property is equivalent to the friendlyName table resource property.
description	STRING	Example: description="a materialized view that expires in 2025" This property is equivalent to the description table resource property.
labels	ARRAY<STRUCT<STRING, STRING>>	Example: labels=[("org_unit", "development")] This property is equivalent to the labels table resource property.

Setting the value replaces the existing value of that option for the materialized view, if there was one. Setting the value to NULL clears the materialized view's value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.get	The materialized view to alter.
bigquery.tables.update	The materialized view to alter.

Examples

Setting the enable refresh state and refresh interval on a materialized view

The following example enables refresh and sets the refresh interval to 20 minutes on a materialized view:

ALTER MATERIALIZED VIEW mydataset.my_mv
SET OPTIONS (
  enable_refresh=true,
  refresh_interval_minutes=20
)

ALTER ORGANIZATION SET OPTIONS statement

Sets the options on an organization.

Syntax

ALTER ORGANIZATION
SET OPTIONS (
  organization_set_options_list);

Arguments

• organization_set_options_list: The list of options to set.

organization_set_options_list

The option list specifies options for the organization. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
default_kms_key_name	STRING	The default Cloud Key Management Service key for encrypting table data, including temporary or anonymous tables. For more information, see Customer-managed Cloud KMS keys. Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property.
default_time_zone	STRING	The default time zone to use in time zone-dependent SQL functions, when a time zone is not specified as an argument. For more information, see time zones. Example: `region-us.default_time_zone` = "America/Los_Angeles". Sets the default time zone to America/Los_Angeles in the us region.
default_query_job_timeout_ms	INT64	The default time after which a query job times out. The timeout period must be between 10 minutes and 6 hours. Example: `region-us.default_query_job_timeout_ms` = 1800000. Sets the default query job timeout time to 30 minutes for all jobs in the us region.
default_interactive_query_queue_timeout_ms	INT64	The default amount of time that an interactive query is queued. If unset, the default is 6 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable interactive query queueing, set the value to -1. Example: `region-us.default_interactive_query_queue_timeout_ms` = 1800000. Sets the default queue timeout for interactive queries in the us region to 30 minutes.
default_batch_query_queue_timeout_ms	INT64	The default amount of time that a batch query is queued. If unset, the default is 24 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable batch query queueing, set the value to -1. Example: `region-us.default_batch_query_queue_timeout_ms` = 1800000. Sets the default queue timeout for batch queries in the us region to 30 minutes.
default_query_optimizer_options	STRING	The history-based query optimizations. This option can be one of the following: 'adaptive=on': Use history-based query optimizations. 'adaptive=off': Don't use history-based query optimizations. NULL (default): Use the default history-based query optimizations setting, which is equivalent to 'adaptive=off'. Example: `region-us.default_query_optimizer_options` = 'adaptive=on'

Setting the value replaces the existing value of that option for the organization, if there is one. Setting the value to NULL clears the organization's value for that option.

Required permissions

The ALTER ORGANIZATION SET OPTIONS statement requires the following IAM permissions:

Permission	Resource
bigquery.config.update	The organization to alter.

Examples

The following example sets the default time zone to America/Chicago and the default query job timeout to one hour for an organization in the US region:

ALTER ORGANIZATION
SET OPTIONS (
  `region-us.default_time_zone` = "America/Chicago",
  `region-us.default_job_query_timeout_ms` = 3600000
);

The following example sets the default time zone, the default query job timeout, the default interactive and batch queue timeouts, and the default Cloud KMS key to NULL, clearing the organization level default settings:

ALTER ORGANIZATION
SET OPTIONS (
  `region-us.default_time_zone` = NULL,
  `region-us.default_kms_key_name` = NULL,
  `region-us.default_query_job_timeout_ms` = NULL,
  `region-us.default_interactive_query_queue_timeout_ms` = NULL,
  `region-us.default_batch_query_queue_timeout_ms` = NULL);

ALTER PROJECT SET OPTIONS statement

Sets the options on a project.

Syntax

ALTER PROJECT project_id
SET OPTIONS (project_set_options_list);

Arguments

• project_id: The name of the project you're altering. This argument is optional, and defaults to the project that runs this DDL query.
• project_set_options_list: The list of options to set.

project_set_options_list

The option list specifies options for the project. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
default_kms_key_name	STRING	The default Cloud Key Management Service key for encrypting table data, including temporary or anonymous tables. For more information, see Customer-managed Cloud KMS keys. Example: kms_key_name="projects/project_id/locations/location/keyRings/keyring/cryptoKeys/key" This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property.
default_time_zone	STRING	The default time zone to use in time zone-dependent SQL functions, when a time zone is not specified as an argument. For more information, see time zones. Example: `region-us.default_time_zone` = "America/Los_Angeles". Sets the default time zone to America/Los_Angeles in the us region.
default_query_job_timeout_ms	INT64	The default time after which a query job times out. The timeout period must be between 10 minutes and 6 hours. Example: `region-us.default_query_job_timeout_ms` = 1800000. Sets the default query job timeout time to 30 minutes for jobs run in the us region.
default_interactive_query_queue_timeout_ms	INT64	The default amount of time that an interactive query is queued. If unset, the default is 6 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable interactive query queueing, set the value to -1. Example: `region-us.default_interactive_query_queue_timeout_ms` = 1800000. Sets the default queue timeout for interactive queries in the us region to 30 minutes.
default_batch_query_queue_timeout_ms	INT64	The default amount of time that a batch query is queued. If unset, the default is 24 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable batch query queueing, set the value to -1. Example: `region-us.default_batch_query_queue_timeout_ms` = 1800000. Sets the default queue timeout for batch queries in the us region to 30 minutes.
default_query_optimizer_options	STRING	The history-based query optimizations. This option can be one of the following: 'adaptive=on': Use history-based query optimizations. 'adaptive=off': Don't use history-based query optimizations. NULL (default): Use the default history-based query optimizations setting, which is equivalent to 'adaptive=off'. Example: `region-us.default_query_optimizer_options` = 'adaptive=on'

Setting the value replaces the existing value of that option for the project, if there was one. Setting the value to NULL clears the project's value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.config.update	The project to alter.

Examples

The following example sets the default time zone to America/New_York and the default query job timeout to 30 minutes for a project in the us region.

ALTER PROJECT project_id
SET OPTIONS (
  `region-us.default_time_zone` = "America/New_York",
  `region-us.default_job_query_timeout_ms` = 1800000
);

The following example sets the default time zone, the default query job timeout, the default Cloud KMS key to NULL, and the default interactive and batch queue timeouts, clearing the project level default settings:

ALTER PROJECT project_id
SET OPTIONS (
  `region-us.default_time_zone` = NULL,
  `region-us.default_kms_key_name` = NULL,
  `region-us.default_query_job_timeout_ms` = NULL,
  `region-us.default_interactive_query_queue_timeout_ms` = NULL,
  `region-us.default_batch_query_queue_timeout_ms` = NULL);

ALTER BI_CAPACITY SET OPTIONS statement

Sets the options on BigQuery BI Engine capacity.

Syntax

ALTER BI_CAPACITY `project_id.location_id.default`
SET OPTIONS(bi_capacity_options_list)

Arguments

• project_id: Optional project ID of the project that will benefit from BI Engine acceleration. If omitted, the query project ID is used.

• location_id: The location where data needs to be cached, prefixed with region-. Examples: region-us, region-us-central1.

• bi_capacity_options_list: The list of options to set.

bi_capacity_options_list

The option list specifies a set of options for BigQuery BI Engine capacity.

Specify a column option list in the following format:

NAME=VALUE, ...

The following options are supported:

NAME	VALUE	Details
size_gb	INT64	Specifies the size of the reservation in gigabytes.
preferred_tables	<ARRAY<STRING>>	List of tables that acceleration should be applied to. Format: project.dataset.table or dataset.table. If project is omitted, query project is used.

Setting VALUE replaces the existing value of that option for the BI Engine capacity, if there is one. Setting VALUE to NULL clears the value for that option.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.bireservations.update	BI Engine reservation

Examples

Allocating BI Engine capacity without preferred tables

ALTER BI_CAPACITY `my-project.region-us.default`
SET OPTIONS(
  size_gb = 250
)

Deallocating BI capacity

ALTER BI_CAPACITY `my-project.region-us.default`
SET OPTIONS(
  size_gb = 0
)

Removing a set of preferred tables from reservation

ALTER BI_CAPACITY `my-project.region-us.default`
SET OPTIONS(
  preferred_tables = NULL
)

Allocating BI Capacity with preferred tables list

ALTER BI_CAPACITY `my-project.region-us.default`
SET OPTIONS(
  size_gb = 250,
  preferred_tables = ["data_project1.dataset1.table1",
                      "data_project2.dataset2.table2"]
)

Overwriting list of preferred tables without changing the size

ALTER BI_CAPACITY `region-us.default`
SET OPTIONS(
  preferred_tables = ["dataset1.table1",
                      "data_project2.dataset2.table2"]
)

ALTER CAPACITY SET OPTIONS statement

Alters an existing capacity commitment.

Syntax

ALTER CAPACITY `project_id.location_id.commitment_id`
SET OPTIONS (alter_capacity_commitment_option_list);

Arguments

• project_id: The project ID of the administration project that maintains ownership of this commitment.
• location_id: The location of the commitment.
• commitment_id: The ID of the commitment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.
• alter_capacity_commitment_option_list: The options you can set to alter the capacity commitment.

alter_capacity_commitment_option_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	TYPE	Details
plan	String	The commitment plan to purchase. Supported values include: ANNUAL, THREE_YEAR, and TRIAL. For more information, see Commitment plans.
renewal_plan	String	The plan this capacity commitment is converted to after commitment_end_time passes. Once the plan is changed, the committed period is extended according to the commitment plan. Applicable for ANNUAL, THREE_YEAR, and TRIAL commitments.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.capacityCommitments.update	The administration project that maintains ownership of the commitments.

Example

The following example changes a capacity commitment to a three-year plan that is located in the region-us region and managed by a project admin_project:

ALTER CAPACITY `admin_project.region-us.my-commitment`
SET OPTIONS (
  plan = 'THREE_YEAR');

ALTER RESERVATION SET OPTIONS statement

Alters an existing reservation.

Syntax

ALTER RESERVATION `project_id.location_id.reservation_id`
SET OPTIONS (alter_reservation_option_list);

Arguments

• project_id: The project ID of the administration project that maintains ownership of this reservation.
• location_id: The location of the reservation.
• reservation_id: The ID of the reservation. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.
• alter_reservation_option_list: The options you can set to alter the reservation.

alter_reservation_option_list

The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...

The following options are supported:

NAME	TYPE	Details
ignore_idle_slots	BOOLEAN	If the value is true, then the reservation uses only the slots that are provisioned to it. The default value is false. For more information, see Idle slots.
slot_capacity	INTEGER	The number of slots to allocate to the reservation. If this reservation was created with an edition, this is equivalent to the amount of baseline slots.
target_job_concurrency	INTEGER	A soft upper bound on the number of jobs that can run concurrently in this reservation.
autoscale_max_slots	INTEGER	The maximum number of slots that can be added to the reservation by autoscaling.
secondary_location	STRING	The secondary location to use in the case of disaster recovery.
is_primary	BOOLEAN	If the value is true, the reservation is set to be the primary reservation.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.reservations.update	The administration project that maintains ownership of the commitments.

Examples

Autoscaling example

The following example changes an autoscaling reservation to 300 baseline slots and 400 autoscaling slots for a max reservation size of 700. These slots are located in the region-us region and managed by a project admin_project:

ALTER RESERVATION `admin_project.region-us.my-reservation`
SET OPTIONS (
  slot_capacity = 300,
  autoscale_max_slots = 400);

DROP SCHEMA statement

Deletes a dataset.

Syntax

DROP [EXTERNAL] SCHEMA [IF EXISTS]
[project_name.]dataset_name
[ CASCADE | RESTRICT ]

Arguments

• EXTERNAL: Specifies if that dataset is a federated dataset. The DROP EXTERNAL statement only removes the external definition from BigQuery. The data stored in the external location is not affected.

• IF EXISTS: If no dataset exists with that name, the statement has no effect.

• project_name: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.

• dataset_name: The name of the dataset to delete.

• CASCADE: Deletes the dataset and all resources within the dataset, such as tables, views, and functions. You must have permission to delete the resources, or else the statement returns an error. For a list of BigQuery permissions, see Predefined roles and permissions.

• RESTRICT: Deletes the dataset only if it's empty. Otherwise, returns an error. If you don't specify either CASCADE or RESTRICT, then the default behavior is RESTRICT.

Details

The statement runs in the location of the dataset if it exists, unless you specify the location in the query settings. For more information, see Specifying your location.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.delete	The dataset to delete.
bigquery.tables.delete	The dataset to delete. If the dataset is empty, then this permission is not required.

Examples

The following example deletes the dataset named mydataset. If the dataset does not exist or is not empty, then the statement returns an error.

DROP SCHEMA mydataset

The following example drops the dataset named mydataset and any resources in that dataset. If the dataset does not exist, then no error is returned.

DROP SCHEMA IF EXISTS mydataset CASCADE

UNDROP SCHEMA statement

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Undeletes a dataset within your time travel window.

Syntax

UNDROP SCHEMA [IF NOT EXISTS]
[project_name.]dataset_name

Arguments

• IF NOT EXISTS: If a dataset already exists with that name, the statement has no effect.

• project_name: The name of the project that contained the deleted dataset. Defaults to the project that runs this DDL statement.

• dataset_name: The name of the dataset to undelete.

Details

When you run this statement, you must specify the location where the dataset was deleted. If you don't, the US multi-region is used.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.datasets.create	The project where you are undeleting the dataset.
bigquery.datasets.get	The dataset that you are undeleting.

Examples

The following example undeletes the dataset named mydataset. If the dataset already exists or has passed the time travel window, then the statement returns an error.

UNDROP SCHEMA mydataset;

DROP TABLE statement

Deletes a table or table clone.

Syntax

DROP TABLE [IF EXISTS] table_name

Arguments

• IF EXISTS: If no table exists with that name, the statement has no effect.

• table_name: The name of the table to delete. See Table path syntax.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.delete	The table to delete.
bigquery.tables.get	The table to delete.

Examples

Deleting a table

The following example deletes a table named mytable in the mydataset:

DROP TABLE mydataset.mytable

If the table name does not exist in the dataset, the following error is returned:

Error: Not found: Table myproject:mydataset.mytable

Deleting a table only if the table exists

The following example deletes a table named mytable in mydataset only if the table exists. If the table name does not exist in the dataset, no error is returned, and no action is taken.

DROP TABLE IF EXISTS mydataset.mytable

DROP SNAPSHOT TABLE statement

Deletes a table snapshot.

Syntax

DROP SNAPSHOT TABLE [IF EXISTS] table_snapshot_name

Arguments

• IF EXISTS: If no table snapshot exists with that name, then the statement has no effect.

• table_snapshot_name: The name of the table snapshot to delete. See Table path syntax.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.deleteSnapshot	The table snapshot to delete.

Examples

Delete a table snapshot: fail if it doesn't exist

The following example deletes the table snapshot named mytablesnapshot in the mydataset dataset:

DROP SNAPSHOT TABLE mydataset.mytablesnapshot

If the table snapshot does not exist in the dataset, then the following error is returned:

Error: Not found: Table snapshot myproject:mydataset.mytablesnapshot

Delete a table snapshot: ignore if it doesn't exist

The following example deletes the table snapshot named mytablesnapshot in the mydataset dataset.

DROP SNAPSHOT TABLE IF EXISTS mydataset.mytablesnapshot

If the table snapshot doesn't exist in the dataset, then no action is taken, and no error is returned.

For information about creating table snapshots, see CREATE SNAPSHOT TABLE.

For information about restoring table snapshots, see CREATE TABLE CLONE.

DROP EXTERNAL TABLE statement

Deletes an external table.

Syntax

DROP EXTERNAL TABLE [IF EXISTS] table_name

Arguments

• IF EXISTS: If no external table exists with that name, then the statement has no effect.

• table_name: The name of the external table to delete. See Table path syntax.

Details

If table_name exists but is not an external table, the statement returns the following error:

Cannot drop table_name which has type TYPE. An external table was expected.

The DROP EXTERNAL statement only removes the external table definition from BigQuery. The data stored in the external location is not affected.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.delete	The external table to delete.
bigquery.tables.get	The external table to delete.

Examples

The following example drops the external table named external_table from the dataset mydataset. It returns an error if the external table does not exist.

DROP EXTERNAL TABLE mydataset.external_table

The following example drops the external table named external_table from the dataset mydataset. If the external table does not exist, no error is returned.

DROP EXTERNAL TABLE IF EXISTS mydataset.external_table

DROP VIEW statement

Deletes a view.

Syntax

DROP VIEW [IF EXISTS] view_name

Arguments

• IF EXISTS: If no view exists with that name, the statement has no effect.

• view_name: The name of the view to delete. See Table path syntax.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.delete	The view to delete.
bigquery.tables.get	The view to delete.

Examples

Deleting a view

The following example deletes a view named myview in mydataset:

DROP VIEW mydataset.myview

If the view name does not exist in the dataset, the following error is returned:

Error: Not found: Table myproject:mydataset.myview

Deleting a view only if the view exists

The following example deletes a view named myview in mydataset only if the view exists. If the view name does not exist in the dataset, no error is returned, and no action is taken.

DROP VIEW IF EXISTS mydataset.myview

DROP MATERIALIZED VIEW statement

Deletes a materialized view.

Syntax

DROP MATERIALIZED VIEW [IF EXISTS] mv_name

Arguments

• IF EXISTS: If no materialized view exists with that name, the statement has no effect.

• mv_name: The name of the materialized view to delete. See Table path syntax.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.delete	The materialized view to delete.
bigquery.tables.get	The materialized view to delete.

Examples

Deleting a materialized view

The following example deletes a materialized view named my_mv in mydataset:

DROP MATERIALIZED VIEW mydataset.my_mv

If the materialized view name does not exist in the dataset, the following error is returned:

Error: Not found: Table myproject:mydataset.my_mv

If you are deleting a materialized view in another project, you must specify the project, dataset, and materialized view in the following format: `project_id.dataset.materialized_view` (including the backticks if project_id contains special characters); for example, `myproject.mydataset.my_mv`.

Deleting a materialized view only if it exists

The following example deletes a materialized view named my_mv in mydataset only if the materialized view exists. If the materialized view name does not exist in the dataset, no error is returned, and no action is taken.

DROP MATERIALIZED VIEW IF EXISTS mydataset.my_mv

If you are deleting a materialized view in another project, you must specify the project, dataset, and materialized view in the following format: `project_id.dataset.materialized_view`, (including the backticks if project_id contains special characters); for example, `myproject.mydataset.my_mv`.

DROP FUNCTION statement

Deletes a persistent user-defined function (UDF) or user-defined aggregate function (UDAF).

Syntax

DROP FUNCTION [IF EXISTS] [[project_name.]dataset_name.]function_name

Arguments

• IF EXISTS: If no function exists with that name, the statement has no effect.

• project_name: The name of the project containing the function to delete. Defaults to the project that runs this DDL query. If the project name contains special characters such as colons, it should be quoted in backticks ` (example: `google.com:my_project`).

• dataset_name: The name of the dataset containing the function to delete. Defaults to the defaultDataset in the request.

• function_name: The name of the function you're deleting.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.delete	The function to delete.

Examples

The following example statement deletes the function parseJsonAsStruct contained in the dataset mydataset.

DROP FUNCTION mydataset.parseJsonAsStruct;

The following example statement deletes the function parseJsonAsStruct from the dataset sample_dataset in the project other_project.

DROP FUNCTION `other_project`.sample_dataset.parseJsonAsStruct;

DROP TABLE FUNCTION

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Deletes a table function.

Syntax

DROP TABLE FUNCTION [IF EXISTS] [[project_name.]dataset_name.]function_name

Arguments

• IF EXISTS: If no table function exists with this name, the statement has no effect.

• project_name: The name of the project containing the table function to delete. Defaults to the project that runs this DDL query.

• dataset_name: The name of the dataset containing the table function to delete.

• function_name: The name of the table function to delete.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.delete	The table function to delete.

Example

The following example deletes a table function named my_table_function:

DROP TABLE FUNCTION mydataset.my_table_function;

DROP PROCEDURE statement

Deletes a stored procedure.

Syntax

DROP PROCEDURE [IF EXISTS] [[project_name.]dataset_name.]procedure_name

Arguments

• IF EXISTS: If no procedure exists with that name, the statement has no effect.

• project_name: The name of the project containing the procedure to delete. Defaults to the project that runs this DDL query. If the project name contains special characters such as colons, it should be quoted in backticks ` (example: `google.com:my_project`).

• dataset_name: The name of the dataset containing the procedure to delete. Defaults to the defaultDataset in the request.

• procedure_name: The name of the procedure you're deleting.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.routines.delete	The procedure to delete.

Examples

The following example statement deletes the procedure myprocedure contained in the dataset mydataset.

DROP PROCEDURE mydataset.myProcedure;

The following example statement deletes the procedure myProcedure from the dataset sample_dataset in the project other_project.

DROP PROCEDURE `other-project`.sample_dataset.myprocedure;

DROP ROW ACCESS POLICY statement

Deletes a row-level access policy.

Important: You cannot delete the last row-level access policy from a table using DROP ROW ACCESS POLICY. Attempting to do so results in an error. To delete the last row-level access policy on table, you must use DROP ALL ROW ACCESS POLICIES instead.

Syntax

DROP ROW ACCESS POLICY [ IF EXISTS ]
row_access_policy_name ON table_name;

DROP ALL ROW ACCESS POLICIES ON table_name;

Arguments

• IF EXISTS: If no row-level access policy exists with that name, the statement has no effect.

• row_access_policy_name: The name of the row-level access policy that you are deleting. Each row-level access policy on a table has a unique name.

• table_name: The name of the table with the row-level access policy or policies that you want to delete.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.rowAccessPolicies.delete	The row-level access policy to delete.
bigquery.rowAccessPolicies.setIamPolicy	The row-level access policy to delete.
bigquery.rowAccessPolicies.list	The table to delete all row-level access policies on. Only required for DROP ALL statements.

Examples

Delete a row-level access policy from a table:

DROP ROW ACCESS POLICY my_row_filter ON project.dataset.my_table;

Delete all the row-level access policies from a table:

DROP ALL ROW ACCESS POLICIES ON project.dataset.my_table;

DROP CAPACITY statement

Deletes a capacity commitment.

Syntax

DROP CAPACITY [IF EXISTS]
project_id.location.capacity-commitment-id

Arguments

• IF EXISTS: If no capacity commitment exists with that ID, the statement has no effect.
• project_id: The project ID of the administration project where the reservation was created.
• location: The location of the commitment.
• capacity-commitment-id: The capacity commitment ID.

To find the capacity commitment ID, query the INFORMATION_SCHEMA.CAPACITY_COMMITMENTS_BY_PROJECT table.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.capacityCommitments.delete	The administration project that maintains ownership of the commitments.

Example

The following example deletes the capacity commitment:

DROP CAPACITY `admin_project.region-us.1234`

DROP RESERVATION statement

Deletes a reservation.

Syntax

DROP RESERVATION [IF EXISTS]
project_id.location.reservation_id

Arguments

• IF EXISTS: If no reservation exists with that ID, the statement has no effect.
• project_id: The project ID of the administration project where the reservation was created.
• location: The location of the reservation.
• reservation_id: The reservation ID.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.reservations.delete	The administration project that maintains ownership of the commitments.

Example

The following example deletes the reservation prod:

DROP RESERVATION `admin_project.region-us.prod`

DROP ASSIGNMENT statement

Deletes a reservation assignment.

Syntax

DROP ASSIGNMENT [IF EXISTS]
project_id.location.reservation_id.assignment_id

Arguments

• IF EXISTS: If no assignment exists with that ID, the statement has no effect.
• project_id: The project ID of the administration project where the reservation was created.
• location: The location of the reservation.
• reservation_id: The reservation ID.
• assignment_id: The assignment ID.

To find the assignment ID, query the INFORMATION_SCHEMA.ASSIGNMENTS view.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.reservationAssignments.delete	The administration project and the assignee.

Example

The following example deletes an assignment from the reservation named prod:

DROP ASSIGNMENT `admin_project.region-us.prod.1234`

DROP SEARCH INDEX statement

Deletes a search index on a table.

Syntax

DROP SEARCH INDEX [ IF EXISTS ] index_name ON table_name

Arguments

• IF EXISTS: If no search index exists with that name on the table, the statement has no effect.
• index_name: The name of the search index to be deleted.
• table_name: The name of the table with the index.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.deleteIndex	The table with the search index to delete.

Example

The following example deletes a search index my_index from my_table:

DROP SEARCH INDEX my_index ON dataset.my_table;

DROP VECTOR INDEX statement

Deletes a vector index on a table.

Syntax

DROP VECTOR INDEX [ IF EXISTS ] index_name ON table_name

Arguments

• IF EXISTS: If no vector index exists with that name on the table, the statement has no effect.
• index_name: The name of the vector index to be deleted.
• table_name: The name of the table with the vector index.

Required permissions

This statement requires the following IAM permissions:

Permission	Resource
bigquery.tables.deleteIndex	The table with the vector index to delete.

Example

The following example deletes a vector index my_index from my_table:

DROP VECTOR INDEX my_index ON dataset.my_table;

Table path syntax

Use the following syntax when specifying the path of a table resource, including standard tables, views, materialized views, external tables, and table snapshots.

table_path :=
  [[project_name.]dataset_name.]table_name

• project_name: The name of the project that contains the table resource. Defaults to the project that runs the DDL query. If the project name contains special characters such as colons, quote the name in backticks ` (example: `google.com:my_project`).

• dataset_name: The name of the dataset that contains the table resource. Defaults to the defaultDataset in the request.

• table_name: The name of the table resource.

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