Data definition language (DDL) statements in Google Standard SQL

Data definition language (DDL) statements let you create and modify BigQuery resources using Google Standard SQL 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.

Running 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.

samples/snippets/src/main/java/com/example/bigquery/DDLCreateView.java

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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

samples/ddlCreateView.js

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// 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.

docs/snippets.py

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# 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,
    )
)

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. Cannot appear with OR REPLACE.

• 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.

  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_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.
friendly_name	STRING	A descriptive name for the dataset.
is_case_insensitive	BOOL	In preview. Available for CREATE SCHEMA but not ALTER SCHEMA. TRUE if dataset and its table names are case-insensitive, otherwise FALSE. If not previously set, the dataset and its table names are case-sensitive.
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	In preview. Specifies the duration in hours of the time travel window for the dataset. The max_time_travel_hours value must be an integer between 48 (2 days) and 168 (7 days). 168 hours is the default if this option isn't specified. For more information on the time travel window, see Configuring the time travel window.

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 schema

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

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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 schema with collation support

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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[, ...]
)]
[DEFAULT COLLATE collate_specification]
[PARTITION BY partition_expression]
[CLUSTER BY clustering_column_list]
[OPTIONS(table_option_list)]
[AS query_statement]

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.

• 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.

• 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. {: #query_statement } If a collation specification is used on this table, collation passes through this query statement.

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.

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.

column :=
  column_name column_schema

column_schema :=
   {
     simple_type [NOT NULL]
     | STRUCT<field_list> [NOT NULL]
     | ARRAY<array_element_schema>
   }
   [DEFAULT default_expression]
   [OPTIONS(column_option_list)]

field_list :=
  field_name column_schema [, ...]

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

simple_type :=
  { data_type | STRING COLLATE collate_specification }

• 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 allows you to 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 do not 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.

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

The column_option_list in column_schema lets you specify optional column or field options. Column options have the same syntax and requirements as table options but with a different list of NAMEs and VALUEs:

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 parameterized 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, 2.5 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 roundingMode table resource property.

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 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 pseudo column.

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

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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),
    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 and maximum scale of 2), 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 |
+-----+---|-----+

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 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 snapshot.
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 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 [ OR REPLACE ] 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

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

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

destination_table_name is 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 is 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).

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

If the OPTIONS clause includes any expiration options, then 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

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 Google Standard SQL 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_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.

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.

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 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 Google Standard SQL 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 Google Standard SQL 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 Google Standard SQL 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

CREATE MATERIALIZED VIEW statement

Creates a new materialized view.

Syntax

CREATE 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

• 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 Google Standard SQL 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
refresh_interval_minutes	FLOAT64	Example: refresh_interval_minutes=20
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_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 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 Google Standard SQL 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 Google Standard SQL 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 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.
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.
description	STRING A description of this table.
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.
enum_as_string	BOOL If true, infer Parquet ENUM logical type as STRING instead of BYTES by default. Applies to Parquet data.
enable_list_inference	BOOL If true, use schema inference specifically for Parquet LIST logical type. Applies to Parquet 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.
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, CSV, DATASTORE_BACKUP, GOOGLE_SHEETS, NEWLINE_DELIMITED_JSON (or JSON), ORC, PARQUET, CLOUD_BIGTABLE. Supported values for LOAD DATA include: AVRO, CSV, NEWLINE_DELIMITED_JSON (or JSON), ORC, PARQUET. The value JSON is equivalent to NEWLINE_DELIMITED_JSON.
decimal_target_types	ARRAY<STRING> Determines how to convert a Decimal type. Equivalent to ExternalDataConfiguration.decimal_target_types Example: ["NUMERIC", "BIGNUMERIC"].
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.
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".
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.
max_bad_records	INT64 The maximum number of bad records to ignore when reading the data. Applies to: CSV, JSON, and Sheets data.
null_marker	STRING The string that represents NULL values in a CSV file. Applies to CSV data.
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 Sheets spreadsheet to query from. Applies to 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 Sheets data.
uris	For external tables that aren't Cloud Bigtable tables: ARRAY<STRING> An array of fully qualified URIs for the external data locations. Example: ["gs://bucket/path1/*", "gs://bucket/path2/*"]. 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.
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.

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 bigquery.tables.delete permission is also required.

Examples

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 delimeter ('|') 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:

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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, 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.
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 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 [, ...] > ]
  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.

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

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

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.

• multi_statement_query: The multi-statement query to run.

• 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	Example: strict_mode=FALSE If strict_mode is TRUE, the procedure body will undergo additional checks for errors such as non-existent tables or columns. The CREATE PROCEDURE statement will fail if the body fails any of these checks. While strict_mode is useful for catching many common types of errors, it is not exhaustive, and successful creation of a procedure with strict_mode does not guarantee that the procedure will successfully execute at runtime. 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.
description	STRING	Example: description="A procedure that runs a query." A description of the procedure.

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 permissions:

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

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

Examples

The following example creates a 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:

  • Google Standard SQL scalar functions, aggregate functions, analytic 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:

  • A reference to a table.
  • Subqueries, or 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
AS JSON
capacity_json_object

Arguments

• project_id: The project ID of the administration project that will maintain ownership of this commitment.
• location_idThe location of the project.
• commitment_id: Tthe 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_json_object: A JSON string that describes the capacity commitment.

capacity_json_object

Specifies a JSON object that contains the following fields:

NAME	TYPE	Details
plan	String	The commitment plan to purchase. Supported values include: FLEX, MONTHLY, ANNUAL. For more information, see Commitment plans.
renewal_plan	String	The commitment renewal plan. Applies only when plan is ANNUAL. For more information, see Renewing commitments.
slot_count	Integer	The number of slots in the commitment.

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 Flex slots that are located in the region-us region and managed by a project admin_project:

CREATE CAPACITY `admin_project.region-us.my-commitment`
AS JSON """{
 "slot_count": 100,
 "plan": "FLEX"
}"""

CREATE RESERVATION statement

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

Syntax

CREATE RESERVATION
project_id.location_id.reservation_id
AS JSON
reservation_json_object

Arguments

• project_id: The project ID of the administration project where the capacity commitment was created.
• location_id: The location of the project.
• reservation_id: The reservation ID.
• reservation_json_object: A JSON string that describes the reservation.

reservation_json_object

Specifies a JSON object that contains the following fields:

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.

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`
AS JSON """{
 "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
AS JSON
assignment_json_object

Arguments

• project_idThe project ID of the administration project where the reservation was created.
• location_id: The location of the project.
• 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_json_object: A JSON string that describes the assignment.

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

assignment_json_object

Specifies a JSON object that contains the following fields:

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, and ML_EXTERNAL. 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`
AS JSON """{
 "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`
AS JSON """{
 "assignee": "organizations/1234",
 "job_type": "PIPELINE"
}"""

CREATE SEARCH INDEX statement

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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 an index by that name on the table, do nothing. If the table has an index by a different name, then return an error.

• index_name: The name of the 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.

• ALL COLUMNS: Creates an index on every column in the table which contains a STRING field.

• column_name: The name of a top-level column in the table which is a STRING or contains a STRING field. The column must be one of the following types:

  • STRING
  • ARRAY<STRING>
  • STRUCT containing at least one nested field of type STRING or ARRAY<STRING>
  • JSON

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

Details

You can create only one index per base table. You cannot create an 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 an index will fail 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 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' and 'NO_OP_ANALYZER'.

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 an 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 an 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');

ALTER SCHEMA SET DEFAULT COLLATE statement

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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 schema, 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. If you want to update an existing collation specification, you must alter the column that contains the specification.

• 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 schema 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 schema. For example:

ALTER SCHEMA mydataset
SET DEFAULT COLLATE 'und:ci'

If you create a new table for your schema, 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 schema:

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 schema, 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_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.
friendly_name	STRING	A descriptive name for the dataset.
is_case_insensitive	BOOL	In preview. Available for CREATE SCHEMA but not ALTER SCHEMA. TRUE if dataset and its table names are case-insensitive, otherwise FALSE. If not previously set, the dataset and its table names are case-sensitive.
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	In preview. Specifies the duration in hours of the time travel window for the dataset. The max_time_travel_hours value must be an integer between 48 (2 days) and 168 (7 days). 168 hours is the default if this option isn't specified. For more information on the time travel window, see Configuring the time travel window.

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
  )

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 allows you to 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 do not 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.

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 include the name of the column and schema to add. This column name and schema use the same syntax used in 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

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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 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

• This statement is not supported for external tables.
• If you change table policies or row-level access policies when you rename the table, then those changes might not be effective.
• If you want to rename a table that has data streaming into it, you must stop the streaming and wait for BigQuery to indicate that streaming is not in use.

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

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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

After one or more columns in a table are renamed, you may not 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.

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

Beta

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Terms of Service. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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

The statement does not immediately free up the storage that's associated with the dropped column. Storage is claimed in the background over the period of 7 days from the day that a column is dropped.

For information about immediately reclaiming storage, see Deleting a column from a table schema.

You cannot use this statement to drop the following:

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

Note: BigQuery does not support dropping columns from CMEK tables or from tables greater than 1 TB in size during Preview.

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.

Note: A successful column drop would reserve the column name A and B within the target table for the duration of 7 days. Other tables can still leverage these names.

ALTER TABLE SET DEFAULT COLLATE statement

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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 colloation specification for STRING types. The updated collation specification only applies to columns added afterwards.

  If you want to update an existing collation specification, you must alter the column that contains the specification. 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.

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 in BigQuery.

Syntax

ALTER TABLE [IF EXISTS] table_name
ALTER COLUMN [IF EXISTS] column_name SET OPTIONS(column_set_options_list)

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, such as nested columns in a STRUCT, is not supported.

• column_set_options_list: The list of options to set on the column.

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 table that expires in 2025"

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

Examples

The following example sets a new description on a column called price:

ALTER TABLE mydataset.mytable
ALTER COLUMN price
SET OPTIONS (
  description="Price per unit"
)

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

For a table of valid data type coercions, compare the "From Type" column to the "Coercion To" column in the Conversion rules in Google Standard SQL page.

The following are examples of valid data type coercions:

• INT64 to NUMERIC, BIGNUMERIC, FLOAT64
• NUMERIC to BIGNUMERIC, FLOAT64

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.

You can also coerce 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 changes:

• NUMERIC(6,10) to NUMERIC(8,12)
• NUMERIC to BIGNUMERIC(40, 20)
• STRING(5) to STRING(7)

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

Preview

This product or feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

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.

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.

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
refresh_interval_minutes	FLOAT64	Example: refresh_interval_minutes=20
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_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.