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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.

    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 
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 
// 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 
# 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.

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.

storage_billing_model STRING

In preview.

Alters the storage billing model for the dataset to use physical bytes instead of logical bytes when calculating storage changes. To make this change, set the storage_billing_model value to physical.

When you change a dataset's billing model, it takes 24 hours for the change to take effect.

If you change a dataset's storage billing model to use physical bytes, you can't change it back to using logical bytes.

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

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

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. 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:

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
     | STRUCT<field_list>
     | ARRAY<array_element_schema>
   }
   [DEFAULT default_expression]
   [NOT NULL]
   [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.

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

In preview.

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

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)

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)

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)

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

Creating a table with collation support

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

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

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

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

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

Creating a table with parameterized data types

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

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

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

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

Already Exists: project_id:dataset.table

The table schema contains 3 columns:

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

Creating a partitioned table

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

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

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

The table schema contains 2 columns:

  • transaction_id: An integer
  • transaction_date: A date

The table option list specifies the:

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

Creating a partitioned table from the result of a query

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

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

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

The table schema contains 2 columns:

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

The table option list specifies the:

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

Creating a clustered table

Example 1

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

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

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

The table schema contains 3 columns:

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

The table option list specifies the:

  • Partition expiration: 3 days
  • Description: A table clustered by customer_id
Example 2

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

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

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

The table schema contains 2 columns:

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

The table option list specifies the:

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

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

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

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

The table schema contains 2 columns:

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

The table option list specifies the:

  • Description: A table clustered by customer_id

Creating a clustered table from the result of a query

Example 1

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

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

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

The table schema contains 3 columns:

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

The table option list specifies the:

  • Partition expiration: 3 days
  • Description: A table clustered by customer_id
Example 2

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

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

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

The table schema contains 2 columns:

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

The table option list specifies the:

  • Description: A table clustered by customer_id

Creating a temporary table

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

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

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

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

SELECT *
FROM Example;

This script returns the following output:

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

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
    • <