Data definition language (DDL) statements in GoogleSQL
Data definition language (DDL) statements let you create and modify BigQuery resources using GoogleSQL query syntax. You can use DDL commands to create, alter, and delete resources, such as tables, table clones, table snapshots, views, user-defined functions (UDFs), and row-level access policies.
Required permissions
To create a job that runs a DDL statement, you must have the
bigquery.jobs.create
permission for the project where you are running the job.
Each DDL statement also requires specific permissions on the affected resources,
which are documented under each statement.
IAM roles
The predefined IAM roles bigquery.user
,
bigquery.jobUser
, and bigquery.admin
include the required
bigquery.jobs.create
permission.
For more information about IAM roles in BigQuery, see Predefined roles and permissions or the IAM permissions reference.
Run DDL statements
You can run DDL statements by using the Google Cloud console, by using the
bq command-line tool, by calling the
jobs.query
REST API, or
programmatically using the
BigQuery API client libraries.
Console
Go to the BigQuery page in the Google Cloud console.
Click Compose new query.
Enter the DDL statement into the Query editor text area. For example:
CREATE TABLE mydataset.newtable ( x INT64 )
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. OrDROP 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 aCREATE TABLE/VIEW
statement or aDROP TABLE/VIEW
statement, the target table is returned as an object with 3 fields:- "projectId": string
- "datasetId": string
- "tableId": string
Java
Call the
BigQuery.create()
method to start a query job. Call the
Job.waitFor()
method to wait for the DDL query to finish.
Before trying this sample, follow the Java setup instructions in the
BigQuery quickstart using
client libraries.
For more information, see the
BigQuery Java API
reference documentation.
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see
Set up authentication for client libraries.
Node.js
Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Node.js API reference documentation.
To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.
Python
Call the
Client.query()
method to start a query job. Call the
QueryJob.result()
method to wait for the DDL query to finish.
Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.
To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.
On-demand query size calculation
If you use on-demand billing, BigQuery charges for data definition language (DDL) queries based on the number of bytes processed by the query.
DDL statement | Bytes processed |
---|---|
CREATE TABLE |
None. |
CREATE TABLE ... AS SELECT ... |
The sum of bytes processed for all the columns referenced from the tables scanned by the query. |
CREATE VIEW |
None. |
DROP TABLE |
None. |
DROP VIEW |
None. |
For more information about cost estimation, see Estimate and control costs.
CREATE SCHEMA
statement
Creates a new dataset.
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, theCREATE
statement has no effect.DEFAULT COLLATE collate_specification
: When a new table is created in the dataset, the table inherits a default collation specification unless a collation specification is explicitly specified for a column.If you remove or change this collation specification later with the
ALTER SCHEMA
statement, this will not change existing collation specifications in this dataset. If you want to update an existing collation specification in a dataset, you must alter the column that contains the specification.project_name
: The name of the project where you are creating the dataset. Defaults to the project that runs this DDL statement.dataset_name
: The name of the dataset to create.schema_option_list
: A list of options for creating the dataset.
Details
The dataset is created in the location that you specify in the query settings. For more information, see Specifying your location.
For more information about creating a dataset, see Creating datasets. For information about quotas, see Dataset limits.
schema_option_list
The option list specifies options for the dataset. Specify the options in the
following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
default_kms_key_name |
STRING |
Specifies the default Cloud KMS key for encrypting table data in this dataset. You can override this value when you create a table. |
default_partition_expiration_days |
FLOAT64 |
Specifies the default expiration time, in days, for table partitions in this dataset. You can override this value when you create a table. |
default_rounding_mode |
|
Example: This specifies the
|
default_table_expiration_days |
FLOAT64 |
Specifies the default expiration time, in days, for tables in this dataset. You can override this value when you create a table. |
description |
STRING |
The description of the dataset. |
failover_reservation |
STRING |
Associates the dataset to a reservation in the case of a failover scenario. |
friendly_name |
STRING |
A descriptive name for the dataset. |
is_case_insensitive |
BOOL |
TRUE if the dataset and its table names are
case-insensitive, otherwise FALSE . By default, this
is FALSE , which means the dataset and its table names are
case-sensitive.
|
is_primary |
BOOLEAN |
Declares if the dataset is the primary replica. |
labels |
<ARRAY<STRUCT<STRING, STRING>>> |
An array of labels for the dataset, expressed as key-value pairs. |
location |
STRING |
The location in which to create the dataset. If you don't specify this option, the dataset is created in the location where the query runs. If you specify this option and also explicitly set the location for the query job, the two values must match; otherwise the query fails. |
max_time_travel_hours |
SMALLINT |
Specifies the duration in hours of the
time travel window
for the dataset. The max_time_travel_hours value must
be an integer expressed in multiples of 24 (48, 72, 96, 120, 144, 168)
between 48 (2 days) and 168 (7 days). 168 hours is the default
if this option isn't specified.
|
primary_replica |
STRING |
The replica name to set as the primary replica. |
storage_billing_model |
STRING |
Alters the
storage billing model
for the dataset. Set the The When you change a dataset's billing model, it takes 24 hours for the change to take effect. Once you change a dataset's storage billing model, you must wait 14 days before you can change the storage billing model again. |
|
|
Tags attached to a table in a namespaced <key, value> syntax. For more information, see Tags and conditional access. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.create |
The project where you create the dataset. |
Examples
Creating a new dataset
The following example creates a dataset with a default table expiration and a set of labels.
CREATE SCHEMA mydataset OPTIONS( location="us", default_table_expiration_days=3.75, labels=[("label1","value1"),("label2","value2")] )
Creating a case-insensitive dataset
The following example creates a case-insensitive dataset. Both the dataset name and table names inside the dataset are case-insensitive.
CREATE SCHEMA mydataset OPTIONS( is_case_insensitive=TRUE )
Creating a dataset with collation support
The following example creates a dataset with a collation specification.
CREATE SCHEMA mydataset DEFAULT COLLATE 'und:ci'
CREATE TABLE
statement
Creates a new table.
Syntax
CREATE [ OR REPLACE ] [ TEMP | TEMPORARY ] TABLE [ IF NOT EXISTS ] table_name [( column | constraint_definition[, ...] )] [DEFAULT COLLATE collate_specification] [PARTITION BY partition_expression] [CLUSTER BY clustering_column_list] [OPTIONS(table_option_list)] [AS query_statement] column:= column_definition constraint_definition:= [primary_key] | [[CONSTRAINT constraint_name] foreign_key, ...] primary_key := PRIMARY KEY (column_name[, ...]) NOT ENFORCED foreign_key := FOREIGN KEY (column_name[, ...]) foreign_reference foreign_reference := REFERENCES primary_key_table(column_name[, ...]) NOT ENFORCED
Arguments
OR REPLACE
: Replaces any table with the same name if it exists. Cannot appear withIF NOT EXISTS
.TEMP | TEMPORARY
: Creates a temporary table.IF NOT EXISTS
: If any table exists with the same name, theCREATE
statement has no effect. Cannot appear withOR REPLACE
.table_name
: The name of the table to create. See Table path syntax. For temporary tables, do not include the project name or dataset name.column
: The table's schema information.constraint_definition
: An expression that defines a table constraint.collation_specification
: When a new column is added to the table without an explicit collation specification, the column inherits this collation specification forSTRING
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.primary_key
: An expression that defines a primary key table constraint.foreign_key
: An expression that defines a foreign key table constraint.
Details
CREATE TABLE
statements must comply with the following rules:
- Only one
CREATE
statement is allowed. - Either the column list, the
as query_statement
clause, or both must be present. - When both the column list and the
as query_statement
clause are present, BigQuery ignores the names in theas 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 theas query_statement
clause. - Column names must be specified either through the column list,
the
as query_statement
clause or schema of the table in theLIKE
clause. - Duplicate column names are not allowed.
- When both the
LIKE
and theas query_statement
clause are present, the column list in the query statement must match the columns of the table referenced by theLIKE
clause. - Table names are case-sensitive unless the dataset they belong to is not. To create a case-insensitive dataset, see Creating a case-insensitive dataset. To alter a dataset to make it case-insensitive dataset, see Turning on case insensitivity for a dataset.
Limitations:
- It is not possible to create an
ingestion-time partitioned table
from the result of a query. Instead, use a
CREATE TABLE
DDL statement to create the table, and then use anINSERT
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 aCREATE 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 | STRUCT<field_list> | ARRAY<array_element_schema> } [PRIMARY KEY NOT ENFORCED | REFERENCES table_name(column_name) NOT ENFORCED] [DEFAULT default_expression] [NOT NULL] [OPTIONS(column_option_list)] simple_type := { data_type | STRING COLLATE collate_specification } field_list := field_name column_schema [, ...] array_element_schema := { simple_type | STRUCT<field_list> } [NOT NULL]
column_name
is the name of the column. A column name:- Must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_)
- Must start with a letter or underscore
- Can be up to 300 characters
column_schema
: Similar to a data type, but supports an optionalNOT NULL
constraint for types other thanARRAY
.column_schema
also supports options on top-level columns andSTRUCT
fields.column_schema
can be used only in the column definition list ofCREATE TABLE
statements. It cannot be used as a type in expressions.simple_type
: Any supported data type aside fromSTRUCT
andARRAY
.If
simple_type
is aSTRING
, it supports an additional clause for collation, which defines how a resultingSTRING
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 theNOT NULL
constraint is present for a column or field, the column or field is created withREQUIRED
mode. Conversely, when theNOT NULL
constraint is absent, the column or field is created withNULLABLE
mode.Columns and fields of
ARRAY
type do not support theNOT NULL
modifier. For example, acolumn_schema
ofARRAY<INT64> NOT NULL
is invalid, sinceARRAY
columns haveREPEATED
mode and can be empty but cannot beNULL
. An array element in a table can never beNULL
, regardless of whether theNOT NULL
constraint is specified. For example,ARRAY<INT64>
is equivalent toARRAY<INT64 NOT NULL>
.The
NOT NULL
attribute of a table'scolumn_schema
does not propagate through queries over the table. If tableT
contains a column declared asx INT64 NOT NULL
, for example,CREATE TABLE dataset.newtable AS SELECT x FROM T
creates a table nameddataset.newtable
in whichx
isNULLABLE
.
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 theAS query_statement
clause.DATE(_PARTITIONTIME)
. Equivalent to_PARTITIONDATE
. This syntax cannot be used with theAS query_statement
clause.<date_column>
. Partition by aDATE
column with daily partitions.DATE({ <timestamp_column> | <datetime_column> })
. Partition by aTIMESTAMP
orDATETIME
column with daily partitions.DATETIME_TRUNC(<datetime_column>, { DAY | HOUR | MONTH | YEAR })
. Partition by aDATETIME
column with the specified partitioning type.TIMESTAMP_TRUNC(<timestamp_column>, { DAY | HOUR | MONTH | YEAR })
. Partition by aTIMESTAMP
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 theAS query_statement
clause.DATE_TRUNC(<date_column>, { MONTH | YEAR })
. Partition by aDATE
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: This property is equivalent to the expirationTime table resource property. |
partition_expiration_days |
|
Example: 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 |
|
Example: 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
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 |
|
Example: This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
default_rounding_mode |
|
Example: This specifies the default rounding mode
that's used for values written to any new
This property is equivalent to the
|
enable_change_history |
|
In preview. Example: Set this property to |
max_staleness |
|
Example: The maximum interval behind the current time where it's
acceptable to read stale data. For example, with
change data capture,
when this option is set, the table copy operation is denied if data is
more stale than the
|
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
, orUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
If VALUE
evaluates to NULL
, the corresponding option NAME
in the
CREATE TABLE
statement is ignored.
column_option_list
Specify a column option list in the following format:
NAME=VALUE, ...
NAME
and VALUE
must be one of the following combinations:
NAME |
VALUE |
Details |
---|---|---|
description |
|
Example: This property is equivalent to the schema.fields[].description table resource property. |
rounding_mode |
|
Example: This specifies the rounding mode
that's used for values written to a
This property is equivalent to the
|
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
, orUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
Setting the VALUE
replaces the existing value of that option for the column, if
there was one. Setting the VALUE
to NULL
clears the column's value for that
option.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the table. |
In addition, the OR REPLACE
clause requires bigquery.tables.update
and
bigquery.tables.updateData
permissions.
If the OPTIONS
clause includes any expiration options, then the
bigquery.tables.delete
permission is also required.
Examples
Creating a new table
The following example creates a partitioned table named newtable
in
mydataset
:
CREATE TABLE mydataset.newtable ( x INT64 OPTIONS(description="An optional INTEGER field"), y STRUCT< a ARRAY<STRING> OPTIONS(description="A repeated STRING field"), b BOOL > ) PARTITION BY _PARTITIONDATE OPTIONS( expiration_timestamp=TIMESTAMP "2025-01-01 00:00:00 UTC", partition_expiration_days=1, description="a table that expires in 2025, with each partition living for 24 hours", labels=[("org_unit", "development")] )
If you haven't configured a default project, prepend a project ID to the dataset
name in the example SQL, and enclose the name in backticks if project_id
contains special characters:
`project_id.dataset.table`
. So, instead of
mydataset.newtable
, your table qualifier might be
`myproject.mydataset.newtable`
.
If the table name exists in the dataset, the following error is returned:
Already Exists: project_id:dataset.table
The table uses the following partition_expression
to partition the table:
PARTITION BY _PARTITIONDATE
. This expression partitions the table using
the date in the _PARTITIONDATE
pseudocolumn.
The table schema contains two columns:
- x: An integer, with description "An optional INTEGER field"
y: A STRUCT containing two columns:
- a: An array of strings, with description "A repeated STRING field"
- b: A boolean
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
ofSTRUCT
s containing 2 fields:word
(aSTRING
) andword_count
(anINT64
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 (aREQUIRED
boolean), and c (aNULLABLE
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 |
+-----+---|-----+
Load data across clouds
Example 1
Suppose you have a BigLake table named myawsdataset.orders
that
references data from Amazon S3.
You want to transfer data from that table to a
BigQuery table myotherdataset.shipments
in the US multi-region.
First, display information about the myawsdataset.orders
table:
bq show myawsdataset.orders;
The output is similar to the following:
Last modified Schema Type Total URIs Expiration ----------------- -------------------------- ---------- ------------ ----------- 31 Oct 17:40:28 |- l_orderkey: integer EXTERNAL 1 |- l_partkey: integer |- l_suppkey: integer |- l_linenumber: integer |- l_returnflag: string |- l_linestatus: string |- l_commitdate: date
Next, display information about the myotherdataset.shipments
table:
bq show myotherdataset.shipments
The output is similar to the following. Some columns are omitted to simplify the output.
Last modified Schema Total Rows Total Bytes Expiration Time Partitioning Clustered Fields Total Logical ----------------- --------------------------- ------------ ------------- ------------ ------------------- ------------------ --------------- 31 Oct 17:34:31 |- l_orderkey: integer 3086653 210767042 210767042 |- l_partkey: integer |- l_suppkey: integer |- l_commitdate: date |- l_shipdate: date |- l_receiptdate: date |- l_shipinstruct: string |- l_shipmode: string
Now, using the CREATE TABLE AS SELECT
statement you can selectively load data
to the myotherdataset.orders
table in the US multi-region:
CREATE OR REPLACE TABLE myotherdataset.orders PARTITION BY DATE_TRUNC(l_commitdate, YEAR) AS SELECT * FROM myawsdataset.orders WHERE EXTRACT(YEAR FROM l_commitdate) = 1992;
You can then perform a join operation with the newly created table:
SELECT orders.l_orderkey, orders.l_orderkey, orders.l_suppkey, orders.l_commitdate, orders.l_returnflag, shipments.l_shipmode, shipments.l_shipinstruct FROM myotherdataset.shipments JOIN `myotherdataset.orders` as orders ON orders.l_orderkey = shipments.l_orderkey AND orders.l_partkey = shipments.l_partkey AND orders.l_suppkey = shipments.l_suppkey WHERE orders.l_returnflag = 'R'; -- 'R' means refunded.
When new data is available, append the data of the 1993 year to the destination
table using the INSERT INTO SELECT
statement:
INSERT INTO myotherdataset.orders SELECT * FROM myawsdataset.orders WHERE EXTRACT(YEAR FROM l_commitdate) = 1993;
Example 2
The following example inserts data into an ingestion-time partitioned table:
CREATE TABLE mydataset.orders(id String, numeric_id INT) PARTITION BY _PARTITIONDATE;
After creating a partitioned table, you can insert data into the ingestion-time partitioned table:
INSERT INTO mydataset.orders( _PARTITIONTIME, id, numeric_id) SELECT TIMESTAMP("2023-01-01"), id, numeric_id, FROM mydataset.ordersof23 WHERE numeric_id > 4000000;
CREATE TABLE LIKE
statement
Creates a new table with all of the same metadata of another table.
Syntax
CREATE [ OR REPLACE ] TABLE [ IF NOT EXISTS ] table_name LIKE [[project_name.]dataset_name.]source_table_name ... [OPTIONS(table_option_list)]
Details
This statement is a variant of the CREATE TABLE
statement and has the same
limitations.
Other than the use of the LIKE
clause in place of a column list,
the syntax is identical to the CREATE TABLE
syntax.
The CREATE TABLE LIKE
statement copies only the metadata of the source table.
You can use the as query_statement
clause to include data into the new table.
The new table has no relationship to the source table after creation; thus modifications to the source table will not propagate to the new table.
By default, the new table inherits partitioning, clustering, and options
metadata from the source table. You can customize metadata in the new table by
using the optional clauses in the SQL statement. For example, if you want to
specify a different set of options for the new table, then include the OPTIONS
clause with a list of options and values. This behavior matches that of
ALTER TABLE SET OPTIONS
.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the table. |
bigquery.tables.get |
The source table. |
In addition, the OR REPLACE
clause requires bigquery.tables.update
and
bigquery.tables.updateData
permissions.
If the OPTIONS
clause includes any expiration options, then the
bigquery.tables.delete
permission is also required.
Examples
Example 1
The following example creates a new table named newtable
in
mydataset
with the same metadata as sourcetable
:
CREATE TABLE mydataset.newtable LIKE mydataset.sourcetable
Example 2
The following example creates a new table named newtable
in
mydataset
with the same metadata as sourcetable
and the data from the
SELECT
statement:
CREATE TABLE mydataset.newtable LIKE mydataset.sourcetable AS SELECT * FROM mydataset.myothertable
CREATE TABLE COPY
statement
Creates a table that has the same metadata and data as another table. The source table can be a table, a table clone, or a table snapshot.
Syntax
CREATE [ OR REPLACE ] TABLE [ IF NOT EXISTS ] table_name COPY source_table_name ... [OPTIONS(table_option_list)]
Details
This statement is a variant of the CREATE TABLE
statement and has the same
limitations.
Other than the use of the COPY
clause in place of a column list,
the syntax is identical to the CREATE TABLE
syntax.
The CREATE TABLE COPY
statement copies both the metadata and data from the
source table.
The new table inherits partitioning and clustering from the source table. By
default, the table options metadata from the source table are also inherited,
but you can override table options by using the OPTIONS
clause. The behavior
is equivalent to running ALTER TABLE SET OPTIONS
after the table is copied.
The new table has no relationship to the source table after creation; modifications to the source table are not propagated to the new table.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the table 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 the
bigquery.tables.delete
permission is also required.
CREATE SNAPSHOT TABLE
statement
Creates a table snapshot based on a source table. The source table can be a table, a table clone, or a table snapshot.
Syntax
CREATE SNAPSHOT TABLE [ IF NOT EXISTS ] table_snapshot_name CLONE source_table_name [FOR SYSTEM_TIME AS OF time_expression] [OPTIONS(snapshot_option_list)]
Arguments
IF NOT EXISTS
: If a table snapshot or other table resource exists with the same name, theCREATE
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 bytimestamp_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: This property is equivalent to the
|
friendly_name |
|
Example: This property is equivalent to the
|
description |
|
Example: This property is equivalent to the
|
labels |
|
Example: This property is equivalent to the
|
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
, andUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
If VALUE
evaluates to NULL
, the corresponding option NAME
in the
CREATE SNAPSHOT TABLE
statement is ignored.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create
|
The dataset where you create the table snapshot. |
bigquery.tables.createSnapshot |
The source table. |
bigquery.tables.get |
The source table. |
bigquery.tables.getData |
The source table. |
Examples
Create a table snapshot: fail if it already exists
The following example creates a table snapshot of the table
myproject.mydataset.mytable
. The table snapshot is created in the dataset
mydataset
and is named mytablesnapshot
:
CREATE SNAPSHOT TABLE `myproject.mydataset.mytablesnapshot` CLONE `myproject.mydataset.mytable` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="my_table_snapshot", description="A table snapshot that expires in 2 days", labels=[("org_unit", "development")] )
If the table snapshot name already exists in the dataset, then the following error is returned:
Already Exists: myproject.mydataset.mytablesnapshot
The table snapshot option list specifies the following:
- Expiration time: 48 hours after the time the table snapshot is created
- Friendly name:
my_table_snapshot
- Description:
A table snapshot that expires in 2 days
- Label:
org_unit = development
Create a table snapshot: ignore if it already exists
The following example creates a table snapshot of the table
myproject.mydataset.mytable
. The table snapshot is created in the dataset
mydataset
and is named mytablesnapshot
:
CREATE SNAPSHOT TABLE IF NOT EXISTS `myproject.mydataset.mytablesnapshot` CLONE `myproject.mydataset.mytable` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="my_table_snapshot", description="A table snapshot that expires in 2 days" labels=[("org_unit", "development")] )
The table snapshot option list specifies the following:
- Expiration time: 48 hours after the time the table snapshot is created
- Friendly name:
my_table_snapshot
- Description:
A table snapshot that expires in 2 days
- Label:
org_unit = development
If the table snapshot name already exists in the dataset, then no action is taken, and no error is returned.
For information about restoring table snapshots, see
CREATE TABLE CLONE
.
For information about removing table snapshots, see
DROP SNAPSHOT TABLE
.
CREATE TABLE CLONE
statement
Creates a table clone based on a source table. The source table can be a table, a table clone, or a table snapshot.
Syntax
CREATE TABLE [ IF NOT EXISTS ] destination_table_name CLONE source_table_name [FOR SYSTEM_TIME AS OF time_expression] ... [OPTIONS(table_option_list)]
Details
Other than the use of the CLONE
clause in place of a column list, the syntax
is identical to the CREATE TABLE
syntax.
Arguments
IF NOT EXISTS
: If the specified destination table name already exists, theCREATE
statement has no effect.destination_table_name
: The name of the table that you want to create. The table name must be unique per dataset. The table name can contain the following:- Up to 1,024 characters
- Letters (upper or lower case), numbers, and underscores
OPTIONS(table_option_list)
: Lets you specify additional table creation options such as a label and an expiration time.source_table_name
: The name of the source table.
CREATE TABLE CLONE
statements must comply with the following rules:
- Only one
CREATE
statement is allowed. - The table that is being cloned must be a table, a table clone, or a table snapshot.
OPTIONS
CREATE TABLE CLONE
options are the same as
CREATE TABLE
options.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the table clone. |
bigquery.tables.get |
The source table. |
bigquery.tables.getData |
The source table. |
bigquery.tables.restoreSnapshot |
The source table (required only if the source table is a table snapshot). |
If the OPTIONS
clause includes any expiration options, then the
bigquery.tables.delete
permission is also required.
Examples
Restore a table snapshot: fail if destination table already exists
The following example creates the table
myproject.mydataset.mytable
from the table snapshot
myproject.mydataset.mytablesnapshot
:
CREATE TABLE `myproject.mydataset.mytable` CLONE `myproject.mydataset.mytablesnapshot` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 365 DAY), friendly_name="my_table", description="A table that expires in 1 year", labels=[("org_unit", "development")] )
If the table name exists in the dataset, then the following error is returned:
Already Exists: myproject.mydataset.mytable.
The table option list specifies the following:
- Expiration time: 365 days after the time that the table is created
- Friendly name:
my_table
- Description:
A table that expires in 1 year
- Label:
org_unit = development
Create a clone of a table: ignore if the destination table already exists
The following example creates the table clone
myproject.mydataset.mytableclone
based on the table
myproject.mydataset.mytable
:
CREATE TABLE IF NOT EXISTS `myproject.mydataset.mytableclone` CLONE `myproject.mydataset.mytable` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 365 DAY), friendly_name="my_table", description="A table that expires in 1 year", labels=[("org_unit", "development")] )
The table option list specifies the following:
- Expiration time: 365 days after the time the table is created
- Friendly name:
my_table
- Description:
A table that expires in 1 year
- Label:
org_unit = development
If the table name exists in the dataset, then no action is taken, and no error is returned.
For information about creating a copy of a table, see
CREATE TABLE COPY
.
For information about creating a snapshot of a table, see
CREATE SNAPSHOT TABLE
.
CREATE VIEW
statement
Creates a new view.
Syntax
CREATE [ OR REPLACE ] VIEW [ IF NOT EXISTS ] view_name [(view_column_name_list)] [OPTIONS(view_option_list)] AS query_expression view_column_name_list := view_column[, ...] view_column := column_name [OPTIONS(view_column_option_list)]
Arguments
OR REPLACE
: Replaces any view with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If a view or other table resource exists with the same name, theCREATE
statement has no effect. Cannot appear withOR REPLACE
.view_name
: The name of the view you're creating. See Table path syntax.view_column_name_list
: Lets you explicitly specify the column names of the view, which may be aliases to the column names in the underlying SQL query.view_option_list
: Additional view creation options such as a label and an expiration time.query_expression
: The GoogleSQL query expression used to define the view.
Details
CREATE VIEW
statements must comply with the following rules:
- Only one
CREATE
statement is allowed.
view_column_name_list
The view's column name list is optional. The names must be unique but do not have to be the same as the column names of the underlying SQL query. For example, if your view is created with the following statement:
CREATE VIEW mydataset.age_groups(age, count) AS SELECT age, COUNT(*)
FROM mydataset.people
group by age;
Then you can query it with:
SELECT age, count from mydataset.age_groups;
The number of columns in the column name list must match the number of columns in the underlying SQL query. If the columns in the table of the underlying SQL query is added or dropped, the view becomes invalid and must be recreated. For example, if the age
column is dropped from the mydataset.people
table, then the view created in the previous example becomes invalid.
view_column_option_list
The view_column_option_list
lets you specify optional top-level column
options. Column options for a view have the same syntax and requirements as
for a table, but with a different list of NAME
and VALUE
fields:
NAME |
VALUE |
Details |
---|---|---|
description |
|
Example: |
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: This property is equivalent to the expirationTime table resource property. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
privacy_policy |
|
The policies to enforce when anyone queries the view.
To learn more about the policies available for a view, see
the |
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
, orUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
If VALUE
evaluates to NULL
, the corresponding option NAME
in the
CREATE VIEW
statement is ignored.
privacy_policy
The following policies are available in the
privacy_policy
view option
to create analysis rules. A policy represents
a condition that needs to be met before a query can be run.
Policy | Details |
---|---|
|
The aggregation threshold policy to enforce when a view is queried. Syntax: '{ "aggregation_threshold_policy": { "threshold": value, "privacy_unit_columns": value } }' Parameters:
Example:
privacy_policy='{"aggregation_threshold_policy":
{"threshold" : 50,
"privacy_unit_columns": "ID"}}'
|
|
A differential privacy policy for the view. When this parameter is included, only differentially private queries can be run on the view. Syntax: '{ "differential_privacy_policy": { "privacy_unit_column": value, "max_epsilon_per_query": value, "epsilon_budget": value, "delta_per_query": value, "delta_budget": value, "max_groups_contributed": value } }' Parameters:
Example:
privacy_policy='{"differential_privacy_policy": {
"privacy_unit_column": "contributor_id",
"max_epsilon_per_query": 0.01,
"epsilon_budget": 25.6,
"delta_per_query": 0.005,
"delta_budget": 9.6,
"max_groups_contributed": 2}}'
|
|
A join restriction policy for the view. When this parameter is included, only the specified joins can be run on the specified columns in the view. This policy can be used alone or with other policies, such as the aggregation threshold or differential privacy policy. Syntax: '{ "join_restriction_policy": { "join_condition": value, "join_allowed_columns": value } }' Parameters:
Example:
privacy_policy='{"join_restriction_policy": {
"join_condition": 'JOIN_ANY',
"join_allowed_columns": ['col1', 'col2']}}'
|
Default project in view body
If the view is created in the same project used to run the CREATE VIEW
statement, the view body query_expression
can reference entities without
specifying the project; the default project is the project
which owns the view. Consider the sample query below.
CREATE VIEW myProject.myDataset.myView AS SELECT * FROM anotherDataset.myTable;
After running the above CREATE VIEW
query in the project myProject
, you can
run the query SELECT * FROM myProject.myDataset.myView
. Regardless of the project you
choose to run this SELECT
query, the referenced table anotherDataset.myTable
is always resolved against project myProject
.
If the view is not created in the same project used to run the CREATE VIEW
statement, then all references in the view body query_expression
must be
qualified with project IDs. For instance, the preceding sample CREATE VIEW
query
is invalid if it runs in a project different from myProject
.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the view. |
In addition, the OR REPLACE
clause requires bigquery.tables.update
permission.
If the OPTIONS
clause includes an expiration time, then the
bigquery.tables.delete
permission is also required.
Examples
Creating a new view
The following example creates a view named newview
in mydataset
:
CREATE VIEW `myproject.mydataset.newview` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="newview", description="a view that expires in 2 days", labels=[("org_unit", "development")] ) AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
If the view name exists in the dataset, the following error is returned:
Already Exists: project_id:dataset.table
The view is defined using the following GoogleSQL query:
SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The view option list specifies the:
- Expiration time: 48 hours from the time the view is created
- Friendly name:
newview
- Description:
A view that expires in 2 days
- Label:
org_unit = development
Creating a view only if the view doesn't exist
The following example creates a view named newview
in mydataset
only if no
view named newview
exists in mydataset
. If the view name exists in the
dataset, no error is returned, and no action is taken.
CREATE VIEW IF NOT EXISTS `myproject.mydataset.newview` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="newview", description="a view that expires in 2 days", labels=[("org_unit", "development")] ) AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The view is defined using the following GoogleSQL query:
SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The view option list specifies the:
- Expiration time: 48 hours from the time the view is created
- Friendly name:
newview
- Description:
A view that expires in 2 days
- Label:
org_unit = development
Creating or replacing a view
The following example creates a view named newview
in mydataset
, and if
newview
exists in mydataset
, it is overwritten using the specified query
expression.
CREATE OR REPLACE VIEW `myproject.mydataset.newview` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="newview", description="a view that expires in 2 days", labels=[("org_unit", "development")] ) AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The view is defined using the following GoogleSQL query:
SELECT column_1, column_2, column_3 FROM
myproject.mydataset.mytable
The view option list specifies the:
- Expiration time: 48 hours from the time the view is created
- Friendly name:
newview
- Description:
A view that expires in 2 days
- Label:
org_unit = development
Creating a view with column descriptions
The following example creates a view named newview
in mydataset
. This view
definition provides the column description for each column in mytable
.
You can rename columns from the original query.
CREATE VIEW `myproject.mydataset.newview` ( column_1_new_name OPTIONS (DESCRIPTION='Description of the column 1 contents'), column_2_new_name OPTIONS (DESCRIPTION='Description of the column 2 contents'), column_3_new_name OPTIONS (DESCRIPTION='Description of the column 3 contents') ) AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
CREATE MATERIALIZED VIEW
statement
Creates a new materialized view.
Syntax
CREATE [ OR REPLACE ] MATERIALIZED VIEW [ IF NOT EXISTS ] materialized_view_name [PARTITION BY partition_expression] [CLUSTER BY clustering_column_list] [OPTIONS(materialized_view_option_list)] AS query_expression
Arguments
OR REPLACE
: Replaces a materialized view with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If a materialized view or other table resource exists with the same name, theCREATE
statement has no effect. Cannot appear withOR 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 inquery_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 inquery_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 inquery expression
(the base table) is partitioned.clustering_column_list
: A comma-separated list of column references that determine how to cluster the materialized view.materialized_view_option_list
: Allows you to specify additional materialized view options such as a whether refresh is enabled, the refresh interval, a label, and an expiration time.query_expression
: The GoogleSQL query expression used to define the materialized view.
Details
CREATE MATERIALIZED VIEW
statements must comply with the following rules:
- Only one
CREATE
statement is allowed.
Default project in materialized view body
If the materialized view is created in the same project used to run the CREATE MATERIALIZED VIEW
statement, the materialized view body query_expression
can reference entities without
specifying the project; the default project is the project
which owns the materialized view. Consider the sample query below.
CREATE MATERIALIZED VIEW myProject.myDataset.myView AS SELECT * FROM anotherDataset.myTable;
After running the above CREATE MATERIALIZED VIEW
query in the project myProject
, you can
run the query SELECT * FROM myProject.myDataset.myView
. Regardless of the project you
choose to run this SELECT
query, the referenced table anotherDataset.myTable
is always resolved against project myProject
.
If the materialized view is not created in the same project used to run the CREATE VIEW
statement, then all references in the materialized view body query_expression
must be
qualified with project IDs. For instance, the preceding sample CREATE MATERIALIZED VIEW
query
is invalid if it runs in a project different from myProject
.
materialized_view_option_list
The option list allows you to set materialized view options such as a whether refresh is enabled. the refresh interval, a label and an expiration time. You can include multiple options using a comma-separated list.
Specify a materialized view option list in the following format:
NAME=VALUE, ...
NAME
and VALUE
must be one of the following combinations:
NAME |
VALUE |
Details |
---|---|---|
enable_refresh |
BOOLEAN |
Example: |
refresh_interval_minutes |
FLOAT64 |
Example: |
expiration_timestamp |
TIMESTAMP |
Example: This property is equivalent to the
expirationTime
table resource property. |
max_staleness |
INTERVAL |
Example: The
|
allow_non_incremental_definition |
BOOLEAN |
Example: The
|
kms_key_name |
|
Example: This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create
|
The dataset where you create the materialized view. |
In addition, the OR REPLACE
clause requires bigquery.tables.update
permission.
If the OPTIONS
clause includes any expiration options, then the
bigquery.tables.delete
permission is also required.
Examples
Creating a new materialized view
The following example creates a materialized view named new_mv
in mydataset
:
CREATE MATERIALIZED VIEW `myproject.mydataset.new_mv` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="new_mv", description="a materialized view that expires in 2 days", labels=[("org_unit", "development")], enable_refresh=true, refresh_interval_minutes=20 ) AS SELECT column_1, SUM(column_2) AS sum_2, AVG(column_3) AS avg_3 FROM `myproject.mydataset.mytable` GROUP BY column_1
If the materialized view name exists in the dataset, the following error is returned:
Already Exists: project_id:dataset.materialized_view
When you use a DDL statement to create a materialized view, you must specify the
project, dataset, and materialized view in the following format:
`project_id.dataset.materialized_view`
(including the backticks if project_id
contains special characters); for example,
`myproject.mydataset.new_mv`
.
The materialized view is defined using the following GoogleSQL query:
SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The materialized view option list specifies the:
- Expiration time: 48 hours from the time the materialized view is created
- Friendly name:
new_mv
- Description:
A materialized view that expires in 2 days
- Label:
org_unit = development
- Refresh enabled: true
- Refresh interval: 20 minutes
Creating a materialized view only if the materialized view doesn't exist
The following example creates a materialized view named new_mv
in mydataset
only if no materialized view named new_mv
exists in mydataset
. If the
materialized view name exists in the dataset, no error is returned, and no
action is taken.
CREATE MATERIALIZED VIEW IF NOT EXISTS `myproject.mydataset.new_mv` OPTIONS( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR), friendly_name="new_mv", description="a view that expires in 2 days", labels=[("org_unit", "development")], enable_refresh=false ) AS SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The materialized view is defined using the following GoogleSQL query:
SELECT column_1, column_2, column_3 FROM `myproject.mydataset.mytable`
The materialized view option list specifies the:
- Expiration time: 48 hours from the time the view is created
- Friendly name:
new_mv
- Description:
A view that expires in 2 days
- Label:
org_unit = development
- Refresh enabled: false
Creating a materialized view with partitioning and clustering
The following example creates a materialized view named new_mv
in mydataset
,
partitioned by the col_datetime
column and clustered
by the col_int
column:
CREATE MATERIALIZED VIEW `myproject.mydataset.new_mv` PARTITION BY DATE(col_datetime) CLUSTER BY col_int AS SELECT col_int, col_datetime, COUNT(1) as cnt FROM `myproject.mydataset.mv_base_table` GROUP BY col_int, col_datetime
The base table, mv_base_table
, must also be partitioned by the
col_datetime
column. For more information, see
Working with partitioned and clustered tables.
CREATE MATERIALIZED VIEW AS REPLICA OF
statement
Creates a replica of a materialized view. The source materialized view must be over an Amazon Simple Storage Service (Amazon S3) BigLake table. You can use the materialized view replica to make Amazon S3 data available locally for joins.
For more information, see Create materialized view replicas.
Syntax
CREATE MATERIALIZED VIEW replica_name [OPTIONS(materialized_view_replica_option_list)] AS REPLICA OF source_materialized_view_name
Arguments
replica_name
: The name of the materialized view replica you're creating, in table path syntax. If the project name is omitted from the materialized view replica name, the current project is used as the default.The materialized view replica name must be unique for each dataset.
materialized_view_replica_option_list
: Allows you to specify options such as the replication interval.source_materialized_view_name
: The name of the materialized view you are replicating, in table path syntax. The source materialized view must be over an Amazon S3 BigLake table, and must be authorized on the dataset that contains that table.
materialized_view_replica_option_list
The option list lets you set materialized view replica options.
Specify a materialized view replica option list in the following format:
NAME=VALUE, ...
NAME |
VALUE |
Details |
---|---|---|
replication_interval_seconds |
INT64 |
Specifies how often to replicate the data from the source materialized
view to the replica. Must be a value between Example: |
Required permissions
This statement requires the following IAM permissions:
bigquery.tables.create
bigquery.tables.get
bigquery.tables.getData
bigquery.tables.replicateData
bigquery.jobs.create
Example
The following example creates a materialized view replica named mv_replica
in bq_dataset
:
CREATE MATERIALIZED VIEW `myproject.bq_dataset.mv_replica` OPTIONS( replication_interval_seconds=600 ) AS REPLICA OF `myproject.s3_dataset.my_s3_mv`
CREATE EXTERNAL SCHEMA
statement
Creates a new federated dataset.
A federated dataset is a connection between BigQuery and an external data source at the dataset level. For an example, see Create AWS Glue federated datasets.
Syntax
CREATE EXTERNAL SCHEMA [ IF NOT EXISTS ] dataset_name [WITH CONNECTION connection_name] [OPTIONS(external_schema_option_list)]
Arguments
IF NOT EXISTS
: If any dataset exists with the same name, theCREATE
statement has no effect.dataset_name
: The name of the dataset to create.connection_name
: Specifies a connection resource that has credentials for accessing the external data. Specify the connection name in the form PROJECT_ID.LOCATION.CONNECTION_ID. If the project ID or location contains a dash, enclose the connection name in backticks (`
).external_schema_option_list
: A list of options for creating the federated dataset.
Details
The dataset is created in the location that you specify in the query settings. For more information, see Specify locations. The location must support the kind of federated dataset that you are creating, for example, you can only create AWS Glue federated datasets in AWS locations.
For more information about creating a dataset, see Create datasets. For information about quotas, see dataset limits.
external_schema_option_list
The option list specifies options for the federated dataset. Specify the options
in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
description |
STRING |
The description of the dataset. |
friendly_name |
STRING |
A descriptive name for the dataset. |
labels |
<ARRAY<STRUCT<STRING, STRING>>> |
An array of labels for the dataset, expressed as key-value pairs. |
location |
STRING |
The location in which to create the dataset. If you don't specify this option, the dataset is created in the location where the query runs. If you specify this option and also explicitly set the location for the query job, the two values must match; otherwise the query fails. The location must support the kind of federated dataset that you are creating, for example, you can only create AWS Glue federated datasets in AWS locations. |
external_source |
STRING |
The source of the external dataset, such as the
Amazon Resource Name (ARN),
with a prefix identifying the source, such as aws-glue:// .
|
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.create |
The project where you create the federated dataset. |
bigquery.connections.use |
The project where you create the federated dataset. |
bigquery.connections.delegate |
The project where you create the federated dataset. |
Examples
The following example creates an AWS Glue federated dataset:
CREATE EXTERNAL SCHEMA mydataset
WITH CONNECTION myproject.`aws-us-east-1`.myconnection
OPTIONS (
external_source = 'aws-glue://arn:aws:glue:us-east-1:123456789:database/test_database',
location = 'aws-us-east-1');
CREATE EXTERNAL TABLE
statement
Creates a new external table.
External tables let BigQuery query data that is stored outside of BigQuery storage. For more information about external tables, see Introduction to external data sources.
Syntax
CREATE [ OR REPLACE ] EXTERNAL TABLE [ IF NOT EXISTS ] table_name [( column_name column_schema, ... )] [WITH CONNECTION connection_name] [WITH PARTITION COLUMNS [( partition_column_name partition_column_type, ... )] ] OPTIONS ( external_table_option_list, ... );
Arguments
OR REPLACE
: Replaces any external table with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If an external table or other table resource exists with the same name, theCREATE
statement has no effect. Cannot appear withOR 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 thecolumn_schema
definition in theCREATE 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 |
If Applies to CSV data. |
allow_quoted_newlines |
If Applies to CSV data. |
bigtable_options |
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
|
compression |
The compression type of the data source. Supported values include:
Applies to CSV and JSON data. |
decimal_target_types |
Determines how to convert a Example: |
description |
A description of this table. |
enable_list_inference |
If Applies to Parquet data. |
enable_logical_types |
If Applies to Avro data. |
encoding |
The character encoding of the data. Supported values include:
Applies to CSV data. |
enum_as_string |
If Applies to Parquet data. |
expiration_timestamp |
The time when this table expires. If not specified, the table does not expire. Example: |
field_delimiter |
The separator for fields in a CSV file. Applies to CSV data. |
format |
The format of the external data.
Supported values for
Supported values for
The value |
hive_partition_uri_prefix |
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: |
file_set_spec_type |
Specifies how to interpret source URIs for load jobs and external tables. Supported values include:
For example, if you have a source URI of |
ignore_unknown_values |
If Applies to CSV and JSON data. |
json_extension |
For JSON data, indicates a particular JSON interchange format. If not specified, BigQuery reads the data as generic JSON records. Supported values include: |
max_bad_records |
The maximum number of bad records to ignore when reading the data. Applies to: CSV, JSON, and Google Sheets data. |
max_staleness |
Applicable for BigLake tables and object tables. Specifies whether cached metadata is used by operations against the table, and how fresh the cached metadata must be in order for the operation to use it. To disable metadata caching, specify 0. This is the default. To enable metadata caching, specify an
interval literal
value between 30 minutes and 7 days. For example, specify
|
null_marker |
The string that represents Applies to CSV data. |
object_metadata |
Only required when creating an object table. Set the value of this option to |
preserve_ascii_control_characters |
If Applies to CSV data. |
projection_fields |
A list of entity properties to load. Applies to Datastore data. |
quote |
The string used to quote data sections in a CSV file. If your data
contains quoted newline characters, also set the
Applies to CSV data. |
reference_file_schema_uri |
User provided reference file with the table schema. Applies to Parquet/ORC/AVRO data. Example: |
require_hive_partition_filter |
If Applies to Avro, CSV, JSON, Parquet, and ORC data. |
sheet_range |
Range of a Google Sheets spreadsheet to query from. Applies to Google Sheets data. Example: |
skip_leading_rows |
The number of rows at the top of a file to skip when reading the data. Applies to CSV and Google Sheets data. |
uris |
For external tables, including object tables, that aren't Bigtable tables:
An array of fully qualified URIs for the external data locations.
Each URI can contain one
asterisk ( The following examples show valid
For Bigtable tables:
The URI identifying the Bigtable table to use as a data source. You can only specify one Bigtable URI. Example:
For more information on constructing a Bigtable URI, see Retrieving the Bigtable URI. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.create |
The dataset where you create the external table. |
In addition, the OR REPLACE
clause requires bigquery.tables.update
permission.
If the OPTIONS
clause includes an expiration time, then the
bigquery.tables.delete
permission is also required.
Examples
The following example creates a BigLake table and explicitly specifies the schema. It also specifies refreshing metadata cache automatically at a system-defined interval.
CREATE OR REPLACE EXTERNAL TABLE mydataset.newtable (x INT64, y STRING, z BOOL)
WITH CONNECTION myconnection
OPTIONS(
format ="PARQUET",
max_staleness = STALENESS_INTERVAL,
metadata_cache_mode = 'AUTOMATIC');
The following example creates an external table from multiple URIs. The data format is CSV. This example uses schema auto-detection.
CREATE EXTERNAL TABLE dataset.CsvTable OPTIONS (
format = 'CSV',
uris = ['gs://bucket/path1.csv', 'gs://bucket/path2.csv']
);
The following example creates an external table from a CSV file and explicitly
specifies the schema. It also specifies the field delimiter ('|'
) and sets the
maximum number of bad records allowed.
CREATE OR REPLACE EXTERNAL TABLE dataset.CsvTable
(
x INT64,
y STRING
)
OPTIONS (
format = 'CSV',
uris = ['gs://bucket/path1.csv'],
field_delimiter = '|',
max_bad_records = 5
);
The following example creates an externally partitioned table. It uses schema
auto-detection to detect both the file schema and the hive partitioning
layout. If the external path is
gs://bucket/path/field_1=first/field_2=1/data.parquet
, the partition columns
are detected as field_1
(STRING
) and field_2
(INT64
).
CREATE EXTERNAL TABLE dataset.AutoHivePartitionedTable WITH PARTITION COLUMNS OPTIONS ( uris = ['gs://bucket/path/*'], format = 'PARQUET', hive_partition_uri_prefix = 'gs://bucket/path', require_hive_partition_filter = false);
The following example creates an externally partitioned table by explicitly
specifying the partition columns. This example assumes that the external file
path has the pattern gs://bucket/path/field_1=first/field_2=1/data.parquet
.
CREATE EXTERNAL TABLE dataset.CustomHivePartitionedTable WITH PARTITION COLUMNS ( field_1 STRING, -- column order must match the external path field_2 INT64) OPTIONS ( uris = ['gs://bucket/path/*'], format = 'PARQUET', hive_partition_uri_prefix = 'gs://bucket/path', require_hive_partition_filter = false);
CREATE FUNCTION
statement
Creates a new user-defined function (UDF). BigQuery supports UDFs written in either SQL or JavaScript.
Syntax
To create a SQL UDF, use the following syntax:
CREATE [ OR REPLACE ] [ TEMPORARY | TEMP ] FUNCTION [ IF NOT EXISTS ] [[project_name.]dataset_name.]function_name ([named_parameter[, ...]]) ([named_parameter[, ...]]) [RETURNS data_type] AS (sql_expression) [OPTIONS (function_option_list)] named_parameter: param_name param_type
To create a JavaScript UDF, use the following syntax:
CREATE [OR REPLACE] [TEMPORARY | TEMP] FUNCTION [IF NOT EXISTS] [[project_name.]dataset_name.]function_name ([named_parameter[, ...]]) RETURNS data_type [determinism_specifier] LANGUAGE js [OPTIONS (function_option_list)] AS javascript_code named_parameter: param_name param_type determinism_specifier: { DETERMINISTIC | NOT DETERMINISTIC }
To create a remote function, use the following syntax:
CREATE [OR REPLACE] FUNCTION [IF NOT EXISTS] [[project_name.]dataset_name.]function_name ([named_parameter[, ...]]) RETURNS data_type REMOTE WITH CONNECTION connection_path [OPTIONS (function_option_list)] named_parameter: param_name param_type
Routine names must contain only letters, numbers, and underscores, and be at most 256 characters long.
Arguments
OR REPLACE
: Replaces any function with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If any dataset exists with the same name, theCREATE
statement has no effect. Cannot appear withOR REPLACE
.TEMP
orTEMPORARY
: Creates a temporary function. If the clause is not present, the statement creates a persistent UDF. You can reuse persistent UDFs across multiple queries, whereas you can only use temporary UDFs in a single query, script, session, or procedure.project_name
: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Do not include the project name for temporary functions.dataset_name
: For persistent functions, the name of the dataset where you are creating the function. Defaults to thedefaultDataset
in the request. Do not include the dataset name for temporary functions.function_name
: The name of the function.named_parameter
: A comma-separatedparam_name
andparam_type
pair. The value ofparam_type
is a BigQuery data type. For a SQL UDF, the value ofparam_type
can also beANY 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 functionadd_one(i)
always returnsi + 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 functionjadd_random(i)
returnsi + 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 theRETURNS
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 fordata_type
, see Supported JavaScript UDF data types.
- If the function is defined in SQL, then the
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 codereturn "\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.
- Quoted string
connection_name
: Specifies a connection resource that has credentials for accessing the remote endpoint. Specify the connection name in the formproject_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 |
|
A description of the UDF. This option isn't supported when creating a temporary function. |
library |
|
An array of JavaScript libraries to include in the function definition. Applies only to JavaScript UDFs. For more information, see Including JavaScript libraries. Example: |
endpoint |
|
A HTTP endpoint of Cloud Functions. Applies only to remote functions. Example: For more information, see Creating a Remote Function. |
user_defined_context |
|
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: |
max_batching_rows |
|
The maximum number of rows in each HTTP request. If not specified, BigQuery decides how many rows are included in a HTTP request. Applies only to remote functions. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.create |
The dataset where you create the function. |
In addition, the OR REPLACE
clause requires bigquery.routines.update
permission.
To create a remote function, additional IAM permissions are needed:
Permission | Resource |
---|---|
bigquery.connections.delegate
|
The connection which you use to create the remote function. |
Examples
Create a SQL UDF
The following example creates a persistent SQL UDF named multiplyInputs
in
a dataset named mydataset
.
CREATE FUNCTION mydataset.multiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
AS (x * y);
Create a JavaScript UDF
The following example creates a temporary JavaScript UDF named multiplyInputs
and calls it from inside a SELECT
statement.
CREATE TEMP FUNCTION multiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
LANGUAGE js
AS r"""
return x*y;
""";
SELECT multiplyInputs(a, b) FROM (SELECT 3 as a, 2 as b);
Create a remote function
The following example creates a persistent remote function named
remoteMultiplyInputs
in a dataset named mydataset
, assuming mydataset
is
in US
location and there is a connection myconnection
in the same location
and same project.
CREATE FUNCTION mydataset.remoteMultiplyInputs(x FLOAT64, y FLOAT64)
RETURNS FLOAT64
REMOTE WITH CONNECTION us.myconnection
OPTIONS(endpoint="https://us-central1-myproject.cloudfunctions.net/multiply");
CREATE AGGREGATE FUNCTION
statement (SQL)
For support during the preview, email [email protected].
Creates a new SQL user-defined aggregate function (UDAF).
Syntax
To create a SQL UDAF, use the following syntax:
CREATE [ OR REPLACE ] [ { TEMPORARY | TEMP } ] AGGREGATE FUNCTION [ IF NOT EXISTS ] function_path ( [ function_parameter[, ...] ] ) [ RETURNS data_type ] AS ( sql_function_body ) [ OPTIONS ( function_option_list ) ] function_path: [[project_name.]dataset_name.]function_name function_parameter: parameter_name data_type [ NOT AGGREGATE ]
Arguments
-
OR REPLACE
: Replaces any function with the same name if it exists.OR REPLACE
can't appear withIF NOT EXISTS
. -
IF NOT EXISTS
: If any dataset exists with the same name, theCREATE
statement has no effect.IF NOT EXISTS
can't appear withOR REPLACE
. -
TEMP
orTEMPORARY
: The function is temporary; that is, it exists for the lifetime of a single query, script, session, or procedure. A temporary function can't have the same name as a built-in function. If the names match, an error is produced. IfTEMP
orTEMPORARY
is not included, a persistent function is created. You can reuse persistent functions across multiple queries. -
function_path
: The path where the function must be created and the name of the function.-
project_name
: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Don't include the project name for temporary functions. -
dataset_name
: For persistent functions, the name of the dataset where you are creating the function. Defaults todefaultDataset
in the request. Don't include the dataset name for temporary functions. -
function_name
: The name of the function. Function names must contain only letters, numbers, and underscores, and be at most 256 characters long.
-
-
function_parameter
: A parameter for the function.-
parameter_name
: The name of the function parameter. -
parameter_data_type
: The GoogleSQL data type for the function parameter. -
NOT AGGREGATE
: The function parameter is not an aggregate. A non-aggregate function parameter can appear anywhere in the function definition.
-
-
return_data_type
: The GoogleSQL data type that the function should return. GoogleSQL infers the result data type of the function from the function body when theRETURN
clause is omitted. -
function_body
: The SQL expression that defines the function body. -
function_option_list
: A list of options for creating the function. For more information, seefunction_option_list
.
function_option_list
The option list specifies options for creating a SQL UDAF. The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
description |
|
A description of the UDAF. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.create |
The dataset where you create the function. |
In addition, the OR REPLACE
clause requires the bigquery.routines.update
permission.
Examples
Create and call a SQL UDAF
The following example shows a persistent SQL UDAF that includes a
non-aggregate function parameter. Inside the function definition, the
aggregate SUM
method takes the aggregate function parameter dividend,
while the non-aggregate division operator ( /
) takes the
non-aggregate function parameter divisor.
CREATE AGGREGATE FUNCTION myProject.myDataset.ScaledSum( dividend FLOAT64, divisor FLOAT64 NOT AGGREGATE) RETURNS FLOAT64 AS ( SUM(dividend) / divisor ); -- Call the SQL UDAF. SELECT ScaledSum(col1, 2) AS scaled_sum FROM ( SELECT 1 AS col1 UNION ALL SELECT 3 AS col1 UNION ALL SELECT 5 AS col1 ); /*------------* | scaled_sum | +------------+ | 4.5 | *------------*/
CREATE AGGREGATE FUNCTION
statement (JavaScript)
For support during the preview, email [email protected].
Creates a new JavaScript user-defined aggregate function (UDAF).
Syntax
To create a JavaScript UDAF, use the following syntax:
CREATE [ OR REPLACE ] [ { TEMPORARY | TEMP } ] AGGREGATE FUNCTION [ IF NOT EXISTS ] function_path([ function_parameter[, ...] ]) RETURNS return_data_type LANGUAGE js [ OPTIONS ( function_option_list ) ] AS function_body function_path: [[project_name.]dataset_name.]function_name function_parameter: parameter_name parameter_data_type [ NOT AGGREGATE ]
Arguments
-
OR REPLACE
: Replaces any function with the same name if it exists.OR REPLACE
can't appear withIF NOT EXISTS
. -
IF NOT EXISTS
: If any dataset exists with the same name, theCREATE
statement has no effect.IF NOT EXISTS
can't appear withOR REPLACE
. -
TEMP
orTEMPORARY
: The function is temporary; that is, it exists for the lifetime of a single query, script, session, or procedure. A temporary function can't have the same name as a built-in function. If the names match, an error is produced. IfTEMP
orTEMPORARY
is not included, a persistent function is created. You can reuse persistent functions across multiple queries. -
function_path
: The path where the function must be created and the name of the function.-
project_name
: For persistent functions, the name of the project where you are creating the function. Defaults to the project that runs the DDL query. Don't include the project name for temporary functions. -
dataset_name
: For persistent functions, the name of the dataset where you are creating the function. Defaults todefaultDataset
in the request. Don't include the dataset name for temporary functions. -
function_name
: The name of the function. Function names must contain only letters, numbers, and underscores, and be at most 256 characters long.
-
-
function_parameter
: A parameter for the function.-
parameter_name
: The name of the function parameter. -
parameter_data_type
: The GoogleSQL data type for the function parameter. -
NOT AGGREGATE
: The function parameter is not an aggregate. Only one non-aggregate function parameter is allowed per JavaScript UDAF, and it must be the last parameter in the list.
-
-
return_data_type
: The GoogleSQL data type that the function should return. -
function_body
: The JavaScript expression that defines the function body. For more information, seefunction_body
. -
function_option_list
: A list of options for creating the function. For more information, seefunction_option_list
.
function_body
The body of the JavaScript function must be a quoted string literal that represents the JavaScript code. To learn more about the different types of quoted string literals you can use, see Formats for quoted literals.
Only certain type encodings are allowed. To learn more, see SQL type encodings in a JavaScript UDAF.
The JavaScript function body must include four JavaScript functions
that initialize, aggregate, merge, and finalize the results for the
JavaScript UDAF. To learn more about the initialState
, aggregate
, merge
,
and finalize
JavaScript functions, see Required aggregate functions in a JavaScript UDAF.
Only serialized data can be passed into the JavaScript aggregate functions. If you need to serialize data such as functions or symbols to pass them into the aggregate functions, use the JavaScript serialization functions. For more information, see Serialization functions for a JavaScript UDAF.
function_option_list
The option list specifies options for creating a JavaScript UDAF. The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
description |
|
A description of the UDAF. |
library |
|
An array of JavaScript libraries to include in the JavaScript UDAF function body.
Example: |
SQL type encodings in a JavaScript UDAF
In JavaScript UDAFs, GoogleSQL data types represent JavaScript data types in the following manner:
GoogleSQL data type |
JavaScript data type |
Notes |
---|---|---|
ARRAY |
Array |
An array of arrays is not supported. To get around this
limitation, use the
Array<Object<Array>> (JavaScript) and
ARRAY<STRUCT<ARRAY>> (GoogleSQL)
data types.
|
BIGNUMERIC
|
Number or String
|
Same as NUMERIC .
|
BOOL |
Boolean |
|
BYTES |
Uint8Array |
|
DATE |
Date |
|
FLOAT64 |
Number |
|
INT64 |
BigInt |
|
JSON |
Various types |
The GoogleSQL JSON data type can be converted
into a JavaScript Object , Array , or other
GoogleSQL-supported JavaScript data type.
|
NUMERIC
|
Number or String
|
If a NUMERIC value can be represented exactly as an
IEEE 754 floating-point
value (range [-253, 253] ),
and has no fractional part, it is encoded as a Number
data type, otherwise it is encoded as a String data type.
|
STRING |
String |
|
STRUCT |
Object |
Each STRUCT field is a named property in the
Object data type. An unnamed STRUCT field is
not supported.
|
TIMESTAMP |
Date |
Date contains a microsecond field with the
microsecond fraction of TIMESTAMP .
|
Required aggregation functions in a JavaScript UDAF
The JavaScript function body must include the following exportable JavaScript functions:
initialState
function: Sets up the initial aggregation state of the UDAF and then returns the initial aggregation state.Syntax:
export function initialState([nonAggregateParam]){...}
Parameters:
nonAggregateParam
: Replace this parameter with aNOT AGGREGATE
function parameter name.
Examples:
export function initialState(){...}
export function initialState(initialSum){...}
aggregate
function: Aggregates one row of data, updating state to store the result of the aggregation. Doesn't return a value.Syntax:
export function aggregate(state, aggregateParam[, ...][, nonAggregateParam]){...}
Parameters:
state
: The aggregate state, which isinitialState
on the first invocation, and then the return value of the previous call toaggregate
thereafter.aggregateParam
: The name of an aggregation parameter in the JavaScript UDAF. The argument for this parameter will be aggregated.nonAggregateParam
: Replace with aNOT AGGREGATE
function parameter name.
Example:
export function aggregate(currentState, aggX, aggWeight, initialSum)
merge
function: Combines two aggregation states from a prior call to theaggregate
,merge
, orinitialState
function. This function does not return a value.Syntax:
export function merge(state, partialState[, nonAggregateParam]){...}
Parameters:
state
: The state into whichpartialState
is merged.partialState
: The second aggregation state to merge.nonAggregateParam
: Replace with aNOT AGGREGATE
function parameter name.
Details:
Depending on the size and organization of the underlying data being queried, the
merge
function might or might not be called. For example, if a particular set of data is small, or the data is partitioned in a way that results in small sets of data, themerge
function won't be called.Example:
export function merge(currentState, partialState, initialSum)
finalize
function: Computes the final aggregation result and then returns this result for the UDAF.Syntax:
export function finalize(state[, nonAggregateParam]){...}
Parameters:
state
: The final aggregation state.nonAggregateParam
: Replace with aNOT AGGREGATE
function parameter name.
The final aggregation state is returned by the
merge
function (oraggregate
function ifmerge
is never invoked). If the input is empty afterNULL
filtering, the final aggregation state isinitialState
.Example:
export function finalize(finalState, initialSum)
Serialization functions for a JavaScript UDAF
If you want to work with non-serializable aggregation states, the
JavaScript UDAF must provide the serialize
and deserialize
functions:
serialize
function: Converts an aggregation state into a BigQuery-serializable object. An object in JavaScript is BigQuery-serializable if all fields are a JavaScript primitive data type (for example,String
,Number
,null
,undefined
), another BigQuery-serializable object, or a JavaScriptArray
, where all elements are either primitives or BigQuery-serializable objects.Syntax:
export function serialize(state[, nonAggregateParam]){...}
Arguments:
state
: The aggregation state to serialize.nonAggregateParam
: Replace with aNOT AGGREGATE
function parameter name.
Example:
export function serialize(stateToSerialize, initialSum)
deserialize
function: Converts a serialized state into an aggregation state. An aggregated state can be passed into theserialize
,aggregate
,merge
, andfinalize
functions.Syntax:
export function deserialize(serializedState[, nonAggregateParam]){...}
Arguments:
serializedState
: The serialized state to convert into the aggregation state.nonAggregateParam
: Replace with aNOT AGGREGATE
function parameter name.
Example:
export function deserialize(stateToDeserialize, initialSum)
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.create |
The dataset where you create the function. |
In addition, the OR REPLACE
clause requires the bigquery.routines.update
permission.
Examples
Calculate the positive sum of all rows
A JavaScript UDAF is similar to a JavaScript UDF, but defines an aggregate function instead of a scalar function. In the following example, a temporary JavaScript UDAF calculates the sum of all rows that have a positive value. The JavaScript UDAF body is quoted within a raw string:
CREATE TEMP AGGREGATE FUNCTION SumPositive(x FLOAT64) RETURNS FLOAT64 LANGUAGE js AS r''' export function initialState() { return {sum: 0} } export function aggregate(state, x) { if (x > 0) { state.sum += x; } } export function merge(state, partialState) { state.sum += partialState.sum; } export function finalize(state) { return state.sum; } '''; -- Call the JavaScript UDAF. WITH numbers AS ( SELECT * FROM UNNEST([1.0, -1.0, 3.0, -3.0, 5.0, -5.0]) AS x) SELECT SumPositive(x) AS sum FROM numbers; /*-----* | sum | +-----+ | 9.0 | *-----*/
Get the weighted average of all rows
A JavaScript UDAF can have aggregate and non-aggregate parameters.
In the following example, the JavaScript UDAF calculates the weighted average
for x
after starting with an initial sum (initialSum
). x
and weight
are
aggregate parameters, and initialSum
is a non-aggregate parameter:
CREATE OR REPLACE AGGREGATE FUNCTION my_project.my_dataset.WeightedAverage( x INT64, weight FLOAT64, initialSum FLOAT64 NOT AGGREGATE) RETURNS INT64 LANGUAGE js AS ''' export function initialState(initialSum) { return {count: 0, sum: initialSum} } export function aggregate(state, x, weight) { state.count += 1; state.sum += Number(x) * weight; } export function merge(state, partialState) { state.sum += partialState.sum; state.count += partialState.count; } export function finalize(state) { return state.sum / state.count; } '''; SELECT my_project.my_dataset.WeightedAverage(item, weight, 2) AS weighted_average FROM ( SELECT 1 AS item, 2.45 AS weight UNION ALL SELECT 3 AS item, 0.11 AS weight UNION ALL SELECT 5 AS item, 7.02 AS weight ); /*------------------* | weighted_average | +------------------+ | 13 | *------------------*/
CREATE TABLE FUNCTION
statement
Creates a new table function, also called a table-valued function (TVF).
Syntax
CREATE [ OR REPLACE ] TABLE FUNCTION [ IF NOT EXISTS ] [[project_name.]dataset_name.]function_name ( [ function_parameter [, ...] ] ) [RETURNS TABLE < column_declaration [, ...] > ] [OPTIONS (table_function_options_list) ] AS sql_query function_parameter: parameter_name { data_type | ANY TYPE } column_declaration: column_name data_type
Arguments
OR REPLACE
: Replaces any table function with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If any table function exists with the same name, theCREATE
statement has no effect. Cannot appear withOR 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 ofdata_type
is a scalar BigQuery data type orANY 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. IfRETURNS TABLE
is absent, BigQuery infers the output schema from the query statement in the function body. IfRETURNS TABLE
is included, the names in the returned table type must match column names from the SQL query.sql_query
: Specifies the SQL query to run. The SQL query must include names for all columns.
table_function_options_list
The table_function_options_list
lets you specify table function options. Table function
options have the same syntax and requirements as table options but with a
different list of NAME
s and VALUE
s:
NAME |
VALUE |
Details |
---|---|---|
description |
|
The description of the table function. |
Details
BigQuery coerces argument types when possible. For example, if
the parameter type is FLOAT64
and you pass an INT64
value, then
BigQuery coerces it to a FLOAT64
.
If a parameter type is ANY TYPE
, the function accepts an input of any type for
this argument. The type that you pass to the function must be compatible with
the function definition. If you pass an argument with an incompatible type, the
query returns an error. If more than one parameter has type ANY TYPE
,
BigQuery does not enforce any type relationship between them.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.create
|
The dataset where you create the table function. |
In addition, the OR REPLACE
clause requires bigquery.routines.update
permission.
Examples
The following table function takes an INT64
parameter that is used to filter
the results of a query:
CREATE OR REPLACE TABLE FUNCTION mydataset.names_by_year(y INT64) AS SELECT year, name, SUM(number) AS total FROM `bigquery-public-data.usa_names.usa_1910_current` WHERE year = y GROUP BY year, name
The following example specifies the return TABLE
type in the RETURNS
clause:
CREATE OR REPLACE TABLE FUNCTION mydataset.names_by_year(y INT64) RETURNS TABLE<name STRING, year INT64, total INT64> AS SELECT year, name, SUM(number) AS total FROM `bigquery-public-data.usa_names.usa_1910_current` WHERE year = y GROUP BY year, name
CREATE PROCEDURE
statement
Creates a new procedure, which is a block of statements that can be called from other queries. Procedures can call themselves recursively.
Syntax
To create a GoogleSQL stored procedure, use the following syntax:
CREATE [OR REPLACE] PROCEDURE [IF NOT EXISTS] [[project_name.]dataset_name.]procedure_name (procedure_argument[, ...] ) [OPTIONS(procedure_option_list)] BEGIN multi_statement_query END; procedure_argument: [procedure_argument_mode] argument_name argument_type
procedure_argument_mode: IN | OUT | INOUT
To create a stored procedure for Apache Spark, use the following syntax:
CREATE [OR REPLACE] PROCEDURE [IF NOT EXISTS] [[project_name.]dataset_name.]procedure_name (procedure_argument[, ...] ) [EXTERNAL SECURITY external_security] WITH CONNECTION connection_project_id.connection_region.connection_id [OPTIONS(procedure_option_list)] LANGUAGE language [AS pyspark_code] procedure_argument: [procedure_argument_mode] argument_name argument_type
procedure_argument_mode: IN | OUT | INOUT external_security: INVOKER
Arguments
OR REPLACE
: Replaces any procedure with the same name if it exists. Cannot appear withIF NOT EXISTS
.IF NOT EXISTS
: If any procedure exists with the same name, theCREATE
statement has no effect. Cannot appear withOR 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 thedefaultDataset
in the request.procedure_name
: The name of the procedure to create.external_security
: The procedure to be executed with the privileges of the user that calls it.connection_project_id
: the project that contains the connection to run Spark procedures—for example,myproject
.connection_region
: the region that contains the connection to run Spark procedures—for example,us
.connection_id
: the connection ID—for example,myconnection
.When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example
projects/myproject/locations/connection_location/connections/myconnection
.For more information, see Create a stored procedure for Apache Spark.
multi_statement_query
: The multi-statement query to run.language
: The language in which the stored procedure for Apache Spark is written. BigQuery supports stored procedures for Apache Spark that are written in Python, Java, or Scala.pyspark_code
: The PySpark code for the stored procedure for Apache Spark if you want to pass the body of the procedure inline. Cannot appear withmain_file_uri
inprocedure_option_list
.argument_type
: Any valid BigQuery type.procedure_argument_mode
: Specifies whether an argument is an input, an output, or both.
procedure_option_list
The procedure_option_list
lets you specify procedure options. Procedure
options have the same syntax and requirements as table options but with a
different list of NAME
s and VALUE
s:
NAME |
VALUE |
Details |
---|---|---|
strict_mode |
|
It is useful for catching many common types of errors. The errors are not
exhaustive, and successful creation of a procedure with
strict_mode doesn't guarantee that the procedure will
successfully execute at runtime.
If
If Default value is strict_mode=FALSE
|
description |
|
A description of the procedure. Example: description="A procedure that runs a query."
|
engine |
STRING |
The engine type for processing stored procedures for Apache Spark. Must be specified for stored procedures for Spark. Valid value:engine="SPARK"
|
runtime_version |
STRING |
The runtime version of stored procedures for Spark. If not specified, the system default runtime version is used. Stored procedures for Spark support the same list of runtime versions as Dataproc Serverless. However, we recommend to specify a runtime version. For more information, see Dataproc Serverless Spark runtime releases. Example:runtime_version="1.1"
|
container_image |
STRING |
Custom container image for the runtime environment of the stored procedure for Spark. If not specified, the system default container image that includes the default Spark, Java, and Python packages associated with a runtime version is used. You can provide a custom container Docker image that includes your own built Java or Python dependencies. As Spark is mounted into your custom container at runtime, you must omit Spark in your custom container image. For optimized performance, we recommend you to host your image in Artifact Registry. For more information, see Use custom containers with Dataproc Serverless for Spark.
Example:
|
properties |
ARRAY<STRUCT<STRING, STRING>> |
A key-value pair to include properties for stored procedures for Spark.
Stored procedures for Spark support most of the
Spark properties
and a list of
custom
Dataproc Serverless properties. If you specify unsupported Spark properties such as YARN-related
Spark properties, BigQuery fails to create the
stored procedure. You can add Spark properties using the
following format:
bq query --nouse_legacy_sql --dry_run 'CREATE PROCEDURE my_bq_project.my_dataset.spark_proc() WITH CONNECTION `my-project-id.us.my-connection` OPTIONS( engine="SPARK", main_file_uri="gs://my-bucket/my-pyspark-main.py", properties=[ ("spark.executor.instances", "3"), ("spark.yarn.am.memory", "3g") ]) LANGUAGE PYTHON' # Error in query string: Invalid value: \ Invalid properties: \ Attempted to set unsupported properties: \ [spark:spark.yarn.am.memory] at [1:1] |
main_file_uri |
STRING |
The Cloud Storage URI of the main Python, Scala, or Java JAR file of the Spark application. Applies only to stored procedures for Spark.
Alternatively, if you want to add the body of
the stored procedure that's written in Python in the main_file_uri="gs://my-bucket/my-pyspark-main.py"
For Scala and Java languages, this field contains a path to only one JAR file. You can set only one value
for main_file_uri="gs://my-bucket/my-scala-main.jar"
|
main_class |
STRING |
Applies only to stored procedures for Spark written in Java and Scala.
Specify a fully-qualified
class name in a JAR set with the main_class=”com.example.wordcount”
|
py_file_uris |
ARRAY<STRING> |
Python files to be placed on the
Optional. Cloud Storage URIs of Python files to pass to the
PySpark framework. Supported file formats include the following:
py_file_uris=[ "gs://my-bucket/my-pyspark-file1.py",
"gs://my-bucket/my-pyspark-file2.py" ]
|
jar_uris |
ARRAY<STRING> |
Path to the JAR files to include on the driver and executor classpaths. Applies only to stored procedures for Apache Spark. Optional. Cloud Storage URIs of JAR files to add to the classpath of the Spark driver and tasks. Example:jar_uris=["gs://my-bucket/my-lib1.jar",
"gs://my-bucket/my-lib2.jar"]
|
file_uris |
ARRAY<STRING> |
Files to be placed in the working directory of each executor. Applies only to stored procedures for Apache Spark. Optional. Cloud Storage URIs of files to be placed in the working directory of each executor. Example:file_uris=["gs://my-bucket/my-file1",
"gs://my-bucket/my-file2"]
|
archive_uris |
ARRAY<STRING> |
Archive files to be extracted into the working directory of each executor. Applies only to stored procedures for Apache Spark.
Optional. Cloud Storage URIs of archives to be extracted into
the working directory of each executor. Supported file formats include
the following: archive_uris=["gs://my-bucket/my-archive1.zip",
"gs://my-bucket/my-archive2.zip"]
|
Argument mode
IN
indicates that the argument is only an input to the procedure. You can
specify either a variable or a value expression for IN
arguments.
OUT
indicates that the argument is an output of the procedure. An OUT
argument is initialized to NULL
when the procedure starts. You
must specify a variable for OUT
arguments.
INOUT
indicates that the argument is both an input to and an output from
the procedure. You must specify a variable for INOUT
arguments. An INOUT
argument can be referenced in the body of a procedure as a variable and assigned
new values.
If neither IN
, OUT
, nor INOUT
is specified, the argument is treated as an
IN
argument.
Variable scope
If a variable is declared outside a procedure, passed as an INOUT or OUT argument to a procedure, and the procedure assigns a new value to that variable, that new value is visible outside of the procedure.
Variables declared in a procedure are not visible outside of the procedure, and vice versa.
An OUT
or INOUT
argument can be assigned a value using SET
, in which case
the modified value is visible outside of the procedure. If the procedure exits
successfully, then the value of the OUT
or INOUT
argument is the final value
assigned to that INOUT
variable.
Temporary tables exist for the duration of the script, so if a procedure creates a temporary table, the caller of the procedure will be able to reference the temporary table as well.
Default project in procedure body
Procedure bodies can reference entities without specifying the project; the
default project is the project which owns the procedure, not necessarily the
project used to run the CREATE PROCEDURE
statement. Consider the sample query
below.
CREATE PROCEDURE myProject.myDataset.QueryTable()
BEGIN
SELECT * FROM anotherDataset.myTable;
END;
After creating the above procedure, you can run the query
CALL myProject.myDataset.QueryTable()
. Regardless of the project you
choose to run this CALL
query, the referenced table anotherDataset.myTable
is always resolved against project myProject
.
Required permissions
This statement requires the following IAM permission:
Permission | Resource |
---|---|
bigquery.routines.create |
The dataset where you create the procedure. |
To create a stored procedure for Apache Spark, additional IAM permission are needed:
Permission | Resource |
---|---|
bigquery.connections.delegate |
The connection which you use to create the stored procedure for Apache Spark. |
In addition, the OR REPLACE
clause requires bigquery.routines.update
permission.
SQL examples
You can also see examples of stored procedures for Apache Spark.
The following example creates a SQL procedure that both takes x
as an input
argument and returns x
as output; because no argument mode is present for the
argument delta
, it is an input argument. The procedure consists of a block
containing a single statement, which assigns the sum of the two input arguments
to x
.
CREATE PROCEDURE mydataset.AddDelta(INOUT x INT64, delta INT64)
BEGIN
SET x = x + delta;
END;
The following example calls the AddDelta
procedure from the example above,
passing it the variable accumulator
both times; because the changes to x
within AddDelta
are visible outside of AddDelta
, these procedure calls
increment accumulator
by a total of 8.
DECLARE accumulator INT64 DEFAULT 0;
CALL mydataset.AddDelta(accumulator, 5);
CALL mydataset.AddDelta(accumulator, 3);
SELECT accumulator;
This returns the following:
+-------------+
| accumulator |
+-------------+
| 8 |
+-------------+
The following example creates the procedure SelectFromTablesAndAppend
, which
takes target_date
as an input argument and returns rows_added
as an output.
The procedure creates a temporary table DataForTargetDate
from a query; then,
it calculates the number of rows in DataForTargetDate
and assigns the result
to rows_added
. Next, it inserts a new row into TargetTable
, passing the
value of target_date
as one of the column names. Finally, it drops the table
DataForTargetDate
and returns rows_added
.
CREATE PROCEDURE mydataset.SelectFromTablesAndAppend(
target_date DATE, OUT rows_added INT64)
BEGIN
CREATE TEMP TABLE DataForTargetDate AS
SELECT t1.id, t1.x, t2.y
FROM dataset.partitioned_table1 AS t1
JOIN dataset.partitioned_table2 AS t2
ON t1.id = t2.id
WHERE t1.date = target_date
AND t2.date = target_date;
SET rows_added = (SELECT COUNT(*) FROM DataForTargetDate);
SELECT id, x, y, target_date -- note that target_date is a parameter
FROM DataForTargetDate;
DROP TABLE DataForTargetDate;
END;
The following example declares a variable rows_added
, then passes it as an
argument to the SelectFromTablesAndAppend
procedure from the previous example,
along with the value of CURRENT_DATE
; then it returns a message stating how
many rows were added.
DECLARE rows_added INT64;
CALL mydataset.SelectFromTablesAndAppend(CURRENT_DATE(), rows_added);
SELECT FORMAT('Added %d rows', rows_added);
CREATE ROW ACCESS POLICY
statement
Creates or replaces a row-level access policy. Row-level access policies on a table must have unique names.
Syntax
CREATE [ OR REPLACE ] ROW ACCESS POLICY [ IF NOT EXISTS ]
row_access_policy_name ON table_name
[GRANT TO (grantee_list)]
FILTER USING (filter_expression);
Arguments
IF NOT EXISTS
: If any row-level access policy exists with the same name, theCREATE
statement has no effect. Cannot appear withOR 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.grantee_list
is a list ofiam_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
typesuser:{emailid}
An email address that represents a specific Google account.
Example:
user:[email protected]
serviceAccount:{emailid}
An email address that represents a service account.
Example:
serviceAccount:[email protected]
group:{emailid}
An email address that represents a Google group.
Example:
group:[email protected]
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:[email protected]","group:[email protected]","user:[email protected]"
filter_expression
: Defines the subset of table rows to show only to the members of thegrantee_list
. Thefilter_expression
is similar to theWHERE
clause in aSELECT
query.The following are valid filter expressions:
- GoogleSQL scalar functions.
SESSION_USER()
, to restrict access only to rows that belong to the user running the query. If none of the row-level access policies are applicable to the querying user, then the user has no access to the data in the table.TRUE
. Grants the principals in thegrantee_list
field access to all rows of the table.
The filter expression cannot contain the following:
- References to other tables, such as subqueries.
- SQL statements such as
SELECT
,CREATE
, orUPDATE
. - 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.
Syntax
CREATE CAPACITY `project_id.location_id.commitment_id` OPTIONS (capacity_commitment_option_list);
Arguments
project_id
: The project ID of the administration project that will maintain ownership of this commitment.location_id
: The location of the commitment.commitment_id
: The ID of the commitment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.capacity_commitment_option_list
: The options you can set to describe the capacity commitment.
capacity_commitment_option_list
The option list specifies options for the capacity commitment. Specify the options in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
TYPE |
Details |
---|---|---|
plan | String | The commitment plan to
purchase. Supported values include: ANNUAL ,
THREE_YEAR , and TRIAL . For more
information, see Commitment
plans. |
renewal_plan | String | The commitment
renewal plan. Applies only when plan
is ANNUAL , THREE_YEAR , or TRIAL .
For more information, see Renewing
commitments. |
slot_count |
Integer | The number of slots in the commitment. | edition |
String | The edition associated with this reservation. For more information about editions, see Introduction to BigQuery editions. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.capacityCommitments.create
|
The administration project that maintains ownership of the commitments. |
Example
The following example creates a capacity commitment of 100 annual slots that are
located in the region-us
region and managed by a project admin_project
:
CREATE CAPACITY `admin_project.region-us.my-commitment` OPTIONS ( slot_count = 100, plan = 'ANNUAL');
CREATE RESERVATION
statement
Creates a reservation. For more information, see Introduction to Reservations.
Syntax
CREATE RESERVATION `project_id.location_id.reservation_id` OPTIONS (reservation_option_list);
Arguments
project_id
: The project ID of the administration project where the capacity commitment was created.location
: The location of the reservation.reservation_id
: The reservation ID.reservation_option_list
: The options you can set to describe the reservation.
reservation_option_list
The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
TYPE |
Details |
---|---|---|
ignore_idle_slots |
BOOLEAN |
If the value is true , then the reservation uses only the
slots that are provisioned to it. The default value is false .
For more information, see
Idle slots. |
slot_capacity |
INTEGER |
The number of slots to allocate to the reservation. If this reservation was created with an edition, this is equivalent to the amount of baseline slots. |
target_job_concurrency |
INTEGER |
A soft upper bound on the number of jobs that can run concurrently in this reservation. |
edition |
STRING |
The edition associated with this reservation. For more information about editions, see Introduction to BigQuery editions. |
autoscale_max_slots |
INTEGER |
The maximum number of slots that could be added to the reservation by autoscaling. |
secondary_location |
STRING |
The secondary location to use in the case of disaster recovery. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.reservations.create
|
The administration project that maintains ownership of the commitments. |
Example
The following example creates a reservation of 100 slots in the project
admin_project
:
CREATE RESERVATION `admin_project.region-us.prod` OPTIONS ( slot_capacity = 100);
CREATE ASSIGNMENT
statement
Assigns a project, folder, or organization to a reservation.
Syntax
CREATE ASSIGNMENT `project_id.location_id.reservation_id.assignment_id` OPTIONS (assignment_option_list)
Arguments
project_id
: The project ID of the administration project where the reservation was created.location
: The location of the reservation.reservation_id
: The reservation ID.assignment_id
: The ID of the assignment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.assignment_option_list
: The options you can set to describe assignment.
To remove a project from any reservations and use on-demand billing instead, set
reservation_id
to none
.
assignment_option_list
The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
TYPE |
Details |
---|---|---|
assignee |
String | The ID of the project, folder, or organization to assign to the reservation. |
job_type |
String | The type of job to assign to this reservation. Supported values include
QUERY , PIPELINE , 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` OPTIONS ( assignee = 'projects/my_project', job_type = 'QUERY');
The following example assigns an organization to the prod
reservation for
pipeline jobs, such as load and export jobs:
CREATE ASSIGNMENT `admin_project.region-us.prod.my_assignment` OPTIONS ( assignee = 'organizations/1234', job_type = 'PIPELINE');
CREATE SEARCH INDEX
statement
Creates a new search index on one or more columns of a table.
A search index enables efficient queries using the
SEARCH
function.
Syntax
CREATE SEARCH INDEX [ IF NOT EXISTS ] index_name ON table_name({ALL COLUMNS | column_name [, ...]}) [OPTIONS(index_option_list)]
Arguments
IF NOT EXISTS
: If there is already a search index by that name on the table, do nothing. If the table has a search index by a different name, then return an error.index_name
: The name of the search index you're creating. Since the search index is always created in the same project and dataset as the base table, there is no need to specify these in the name.table_name
: The name of the table. See Table path syntax.ALL COLUMNS
: If data types are not specified, creates a search index on every column in the table which contains aSTRING
field. If data types are specified, create a search index on every column in the table which matches any of the data types specified.column_name
: The name of a top-level column in the table which is one of the following supported data types or contains a field with one of the supported data types:Supported data types Notes STRING
Primitive data type. INT64
Primitive data type. For support during the preview, email [email protected]. TIMESTAMP
Primitive data type. For support during the preview, email [email protected]. ARRAY<PRIMITIVE_DATA_TYPE>
Must contain a primitive data type in this list. STRUCT
Must contain at least one nested field that is a primitive data type in this list or ARRAY<PRIMITIVE_DATA_TYPE>
.JSON
Must contain at least one nested field of a type that matches any data types in this list. index_option_list
: The list of options to set on the search index.
Details
You can create only one search index per base table. You cannot create a search
index on a view or materialized view. To modify which columns are
indexed, DROP
the current index and create a new one.
BigQuery returns an error if any column_name
is not a STRING
or does not contain a STRING
field, or if you call CREATE SEARCH INDEX
on
ALL COLUMNS
of a table which contains no STRING
fields.
Creating a search index fails on a table which has column ACLs or row filters; however, these may all be added to the table after creation of the index.
index_option_list
The option list specifies options for the search index. Specify the options in
the following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
analyzer |
STRING |
Example: The text analyzer to use to generate tokens for the search
index. The supported values are |
analyzer_options |
JSON-formatted STRING |
The text analyzer configurations to set when creating a search index. Supported when analyzer is equal to 'LOG_ANALYZER' or 'PATTERN_ANALYZER' . For examples of JSON-formatted strings with different text analyzers, see Work with text analyzers. |
data_types |
ARRAY<STRING> |
Example:
An array of data types to set when creating a search index. Supported
data types are |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.createIndex |
The base table where you create the index. |
Examples
The following example creates a search index called my_index
on all string
columns of my_table
. In this case, the index is only created on column a
.
CREATE TABLE dataset.my_table(a STRING, b INT64); CREATE SEARCH INDEX my_index ON dataset.my_table(ALL COLUMNS);
The following example creates a search index on columns a
,
my_struct.string_field
, and b
that uses the NO_OP_ANALYZER
text analyzer.
CREATE TABLE dataset.complex_table( a STRING, my_struct STRUCT<string_field STRING, int_field INT64>, b ARRAY<STRING> ); CREATE SEARCH INDEX my_index ON dataset.complex_table(a, my_struct, b) OPTIONS (analyzer = 'NO_OP_ANALYZER');
CREATE VECTOR INDEX
statement
To provide feedback or request support for this feature, send email to [email protected].
Creates a new vector index on a column of a table.
A vector index lets you perform a vector search more quickly, with the trade-off of reducing recall and so returning more approximate results.
Syntax
CREATE [ OR REPLACE ] VECTOR INDEX [ IF NOT EXISTS ] index_name ON table_name(column_name) [STORING(stored_column_name [, ...])] OPTIONS(index_option_list);
Arguments
OR REPLACE
: Replaces any vector index with the same name if it exists. Can't appear withIF NOT EXISTS
.IF NOT EXISTS
: If there is already a vector index by that name on the table, do nothing. If the table has a vector index by a different name, then return an error.index_name
: The name of the vector index you're creating. Since the index is always created in the same project and dataset as the base table, there is no need to specify these in the name.table_name
: The name of the table. See Table path syntax.column_name
: The name of a column with a type ofARRAY<FLOAT64>
. The column can't have any child fields. All elements in the array must be non-NULL
, and all values in the column must have the same array dimensions.stored_column_name
: The name of a top-level column in the table to store in the vector index. The column type can't beRANGE
. Stored columns are not used if the table has a row-level access policy or the column has a policy tag. To learn more and enable column storing for your vector indexes, see Store columns and pre-filter.index_option_list
: The list of options to set on the vector index.
Details
You can only create vector indexes on standard tables.
You can create only one vector index per table. You can't create a vector index on a table that already has a search index with the same index name.
To modify which column is indexed, DROP
the current
index and create a new one.
index_option_list
The option list specifies options for the vector index. Specify the options in
the following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
index_type |
STRING |
Required. The algorithm to use to build the vector index. The supported
values are IVF and TREE_AH .
|
distance_type |
STRING |
Specifies the default distance type to use when performing a vector
search using this index. The supported values are
EUCLIDEAN ,
COSINE ,
and
DOT_PRODUCT .
EUCLIDEAN is the default.
The index creation itself always uses If you specify a value for the |
ivf_options |
JSON-formatted STRING |
The options to use with the IVF algorithm. Defaults to
'{}' to denote that all underlying options use their
corresponding default values.
The only supported option is The IVF algorithm divides the whole data space into a number of lists
equal to the You can use If you don't specify a value for The statement fails if |
tree_ah_options |
JSON-formatted STRING |
The options to use with the TREE_AH algorithm. Defaults to
'{}' to denote that all underlying options use their
corresponding default values.
Two options are supported:
For example The statement fails if |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.createIndex |
The table where you create the vector index. |
If you choose to use the OR REPLACE
clause, you must also have the
bigquery.tables.updateIndex
permission.
Examples
The following example creates a vector index on the embedding
column
of my_table
:
CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>); CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding) OPTIONS (index_type = 'IVF');
The following example creates a vector index on the embedding
column
of my_table
, and specifies the distance type to use and the IVF options:
CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>); CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding) OPTIONS ( index_type = 'IVF', distance_type = 'COSINE', ivf_options = '{"num_lists":2500}');
The following example creates a vector index on the embedding
column
of my_table
, and specifies the distance type to use and the TREE_AH options:
CREATE TABLE my_dataset.my_table(id INT64, embedding ARRAY<FLOAT64>); CREATE VECTOR INDEX my_index ON my_dataset.my_table(embedding) OPTIONS ( index_type = 'TREE_AH', distance_type = 'EUCLIDEAN', tree_ah_options = '{"normalization_type": "L2"}');
ALTER SCHEMA SET DEFAULT COLLATE
statement
Sets collation specifications on a dataset.
Syntax
ALTER SCHEMA [IF EXISTS] [project_name.]dataset_name SET DEFAULT COLLATE collate_specification
Arguments
IF EXISTS
: If no dataset exists with that name, the statement has no effect.DEFAULT COLLATE collate_specification
: When a new table is created in the dataset, the table inherits a default collation specification unless a collation specification is explicitly specified for a column.The updated collation specification only applies to tables created afterwards.
project_name
: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.dataset_name
: The name of the dataset.collate_specification
: Specifies the collation specifications to set.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.get |
The dataset to alter. |
bigquery.datasets.update |
The dataset to alter. |
Example
Assume you have an existing table, mytable_a
, in a dataset called mydataset
.
For example:
CREATE SCHEMA mydataset
CREATE TABLE mydataset.mytable_a ( number INT64, word STRING )
+----------------------+
| mydataset.mytable_a |
| number INT64 |
| word STRING |
+----------------------+
At a later time, you decide to add a collation specification to your dataset. For example:
ALTER SCHEMA mydataset SET DEFAULT COLLATE 'und:ci'
If you create a new table for your dataset, it inherits COLLATE 'und:ci'
for
all STRING
columns. For example, collation is added to characters
when you create the mytable_b
table in the mydataset
dataset:
CREATE TABLE mydataset.mytable_b ( amount INT64, characters STRING )
+--------------------------------------+
| mydataset.mytable_b |
| amount INT64 |
| characters STRING COLLATE 'und:ci' |
+--------------------------------------+
However, although you have updated the collation specification for the dataset,
your existing table, mytable_a
, continues to use the previous
collation specification. For example:
+---------------------+
| mydataset.mytable_a |
| number INT64 |
| word STRING |
+---------------------+
ALTER SCHEMA SET OPTIONS
statement
Sets options on a dataset.
The statement runs in the location of the dataset if the dataset exists, unless you specify the location in the query settings. For more information, see Specifying your location.
Syntax
ALTER SCHEMA [IF EXISTS] [project_name.]dataset_name SET OPTIONS(schema_set_options_list)
Arguments
IF EXISTS
: If no dataset exists with that name, the statement has no effect.project_name
: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.dataset_name
: The name of the dataset.schema_set_options_list
: The list of options to set.
schema_set_options_list
The option list specifies options for the dataset. Specify the options in the
following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
default_kms_key_name |
STRING |
Specifies the default Cloud KMS key for encrypting table data in this dataset. You can override this value when you create a table. |
default_partition_expiration_days |
FLOAT64 |
Specifies the default expiration time, in days, for table partitions in this dataset. You can override this value when you create a table. |
default_rounding_mode |
|
Example: This specifies the
|
default_table_expiration_days |
FLOAT64 |
Specifies the default expiration time, in days, for tables in this dataset. You can override this value when you create a table. |
description |
STRING |
The description of the dataset. |
failover_reservation |
STRING |
Associates the dataset to a reservation in the case of a failover scenario. |
friendly_name |
STRING |
A descriptive name for the dataset. |
is_case_insensitive |
BOOL |
TRUE if the dataset and its table names are
case-insensitive, otherwise FALSE . By default, this
is FALSE , which means the dataset and its table names are
case-sensitive.
|
is_primary |
BOOLEAN |
Declares if the dataset is the primary replica. |
labels |
<ARRAY<STRUCT<STRING, STRING>>> |
An array of labels for the dataset, expressed as key-value pairs. |
location |
STRING |
The location in which to create the dataset. If you don't specify this option, the dataset is created in the location where the query runs. If you specify this option and also explicitly set the location for the query job, the two values must match; otherwise the query fails. |
max_time_travel_hours |
SMALLINT |
Specifies the duration in hours of the
time travel window
for the dataset. The max_time_travel_hours value must
be an integer expressed in multiples of 24 (48, 72, 96, 120, 144, 168)
between 48 (2 days) and 168 (7 days). 168 hours is the default
if this option isn't specified.
|
primary_replica |
STRING |
The replica name to set as the primary replica. |
storage_billing_model |
STRING |
Alters the
storage billing model
for the dataset. Set the The When you change a dataset's billing model, it takes 24 hours for the change to take effect. Once you change a dataset's storage billing model, you must wait 14 days before you can change the storage billing model again. |
|
|
Tags attached to a table in a namespaced <key, value> syntax. For more information, see Tags and conditional access. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.get |
The dataset to alter. |
bigquery.datasets.update |
The dataset to alter. |
Examples
Setting the default table expiration for a dataset
The following example sets the default table expiration.
ALTER SCHEMA mydataset SET OPTIONS( default_table_expiration_days=3.75 )
Turning on case insensitivity for a dataset
The following example turns on case insensitivity for the name of a dataset and the table names within that dataset.
ALTER SCHEMA mydataset SET OPTIONS( is_case_insensitive=TRUE )
ALTER SCHEMA ADD REPLICA
statement
Adds a replica to a schema (preview).
Syntax
ALTER SCHEMA [IF EXISTS] [project_name.]dataset_name ADD REPLICA replica_name [OPTIONS(add_replica_options_list)]
Arguments
IF EXISTS
: If no dataset exists with that name, the statement has no effect.dataset_name
: The name of the table to alter. See Table path syntax.replica_name
: The name of the new replica. Conventionally, this is the same as the location you are creating the replica in.add_replica_option_list
: The list of options to set.
add_replica_options_list
The option list specifies options for the dataset. Specify the options in the
following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
location |
STRING |
The location in which to create the replica. |
replica_kms_key |
STRING |
The
Cloud Key Management Service key set in the destination region. replica_kms_key
is used as a substitute encryption key in the destination region for any
keys used in the source region. Any table in the source region that's
encrypted with a Cloud KMS key is encrypted with the
replica_kms_key . This value must be a Cloud KMS key
created in the replica dataset's region, not the source dataset's
region. For more information about setting up a Cloud KMS key, see Grant encryption and decryption permission. |
Required permissions
To get the permissions that you need to manage replicas,
ask your administrator to grant you the
BigQuery Data Editor (roles/bigquery.dataEditor
) IAM role on your schema.
For more information about granting roles, see Manage access.
You might also be able to get the required permissions through custom roles or other predefined roles.
Examples
The following example adds a secondary replica that is named EU
in the EU
multi-region to a schema that is named cross_region_dataset
:
ALTER SCHEMA cross_region_dataset ADD REPLICA `EU` OPTIONS(location=`eu`);
ALTER SCHEMA DROP REPLICA
statement
Drops a replica from a schema (preview).
Syntax
ALTER SCHEMA [IF EXISTS] dataset_name DROP REPLICA replica_name
IF EXISTS
: If no dataset exists with that name, the statement has no effect.dataset_name
: The name of the table to alter. See Table path syntax.replica_name
: The name of the replica to drop.
Required permissions
To get the permissions that you need to manage replicas,
ask your administrator to grant you the
BigQuery Data Editor (roles/bigquery.dataEditor
) IAM role on your schema.
For more information about granting roles, see Manage access.
You might also be able to get the required permissions through custom roles or other predefined roles.
Examples
The following example removes a replica that is located in the us-east4
region from the cross-region-dataset
dataset:
ALTER SCHEMA [IF EXISTS] cross-region-dataset DROP REPLICA `us-east4`
ALTER TABLE SET OPTIONS
statement
Sets the options on a table.
Syntax
ALTER TABLE [IF EXISTS] table_name SET OPTIONS(table_set_options_list)
Arguments
IF EXISTS
: If no table exists with that name, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.table_set_options_list
: The list of options to set.
Details
This statement is not supported for external tables.
table_set_options_list
The option list 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: This property is equivalent to the expirationTime table resource property. |
partition_expiration_days |
|
Example: 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 |
|
Example: 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
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 |
|
Example: This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
default_rounding_mode |
|
Example: This specifies the default rounding mode
that's used for values written to any new
This property is equivalent to the
|
enable_change_history |
|
In preview. Example: Set this property to |
max_staleness |
|
Example: The maximum interval behind the current time where it's
acceptable to read stale data. For example, with
change data capture,
when this option is set, the table copy operation is denied if data is
more stale than the
|
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
, orUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
Setting the value replaces the existing value of that option for the table, if
there was one. Setting the value to NULL
clears the table's value for that
option.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
Setting the expiration timestamp and description on a table
The following example sets the expiration timestamp on a table to seven days
from the execution time of the ALTER TABLE
statement, and sets the description
as well:
ALTER TABLE mydataset.mytable SET OPTIONS ( expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 7 DAY), description="Table that expires seven days from now" )
Setting the require partition filter attribute on a partitioned table
The following example sets the
timePartitioning.requirePartitionFilter
attribute on a partitioned table:
ALTER TABLE mydataset.mypartitionedtable SET OPTIONS (require_partition_filter=true)
Queries that reference this table must use a filter on the partitioning column,
or else BigQuery returns an error. Setting this option to true
can help prevent mistakes in querying more data than intended.
Clearing the expiration timestamp on a table
The following example clears the expiration timestamp on a table so that it will not expire:
ALTER TABLE mydataset.mytable SET OPTIONS (expiration_timestamp=NULL)
ALTER TABLE ADD COLUMN
statement
Adds one or more new columns to an existing table schema.
Syntax
ALTER TABLE table_name
ADD COLUMN [IF NOT EXISTS] column [, ...]
Arguments
table_name
: The name of the table. See Table path syntax.IF NOT EXISTS
: If the column name already exists, the statement has no effect.column
: The column to add. This includes the name of the column and schema to add. The column name and schema use the same syntax used in theCREATE 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 withNULLABLE
mode. This is the default mode.- An empty
ARRAY
if the new column was added withREPEATED
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 typeSTRING
. - Column
B
of typeGEOGRAPHY
. - Column
C
of typeNUMERIC
withREPEATED
mode. - Column
D
of typeDATE
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 typeGEOGRAPHY
. - Column
C
of typeINT64
withREPEATED
mode. - Column
D
of typeINT64
withREQUIRED
mode. - Column
E
of typeTIMESTAMP
with a description.
ALTER TABLE mydataset.mytable
ADD COLUMN A STRUCT<
B GEOGRAPHY,
C ARRAY<INT64>,
D INT64 NOT NULL,
E TIMESTAMP OPTIONS(description="creation time")
>
The query fails if the table already has a column named A
, even if that
column does not contain any of the nested columns that are specified.
The new STRUCT
named A
is nullable, but the nested column D
within A
is
required for any STRUCT
values of A
.
Adding collation support to a column
When you create a new column for your table, you can specifically assign a new collation specification to that column.
ALTER TABLE mydataset.mytable ADD COLUMN word STRING COLLATE 'und:ci'
ALTER TABLE ADD FOREIGN KEY
statement
Adds a foreign key constraint to an existing table.
You can add multiple foreign key constraints by using additional
ADD FOREIGN KEY
statements.
Syntax
ALTER TABLE [[project_name.]dataset_name.]fk_table_name ADD [CONSTRAINT [IF NOT EXISTS] constraint_name] FOREIGN KEY (fk_column_name[, ...]) REFERENCES pk_table_name(pk_column_name[,...]) NOT ENFORCED [ADD...];
Arguments
project_name
: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.dataset_name
: The name of the dataset that contains the table with a primary key. Defaults to the project that runs this DDL statement if undefined.fk_table_name
: The name of the existing table to add a foreign key to.IF NOT EXISTS
: If a constraint of the same name already exists in the defined table, the statement has no effect.constraint_name
: The name of the constraint to add.fk_column_name
: In the foreign key table, the name of the foreign key column. Only top-level columns can be used as foreign key columns.pk_table_name
: The name of the table that contains the primary key.pk_column_name
: In the primary key table, the name of the primary key column. Only top-level columns can be used as primary key columns.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example adds the my_fk_name
foreign key constraint to the
fk_table
table. This example depends on an existing table, pk_table
.
Add a primary key to the
pk_table
table:ALTER TABLE pk_table ADD PRIMARY KEY (x,y) NOT ENFORCED;
Create a table named
fk_table
for the foreign key.CREATE TABLE fk_table(x int64, y int64, i int64, j int64, u int64, v int64);
Add the
my_fk_name
foreign key constraint to thefk_table
.ALTER TABLE fk_table ADD CONSTRAINT my_fk_name FOREIGN KEY (u, v) REFERENCES pk_table(x, y) NOT ENFORCED
The following example adds the fk
and fk2
foreign key constraints to the
fk_table
table in a single statement. This example depends on an existing
table, pk_table
.
Add a primary key to the
pk_table
table:ALTER TABLE pk_table ADD PRIMARY KEY (x,y) NOT ENFORCED;
Create a table named
fk_table
for multiple foreign key constraints.CREATE TABLE fk_table(x int64, y int64, i int64, j int64, u int64, v int64);
Add the
fk
andfk2
constraints tofk_table
in one statement.ALTER TABLE fk_table ADD PRIMARY KEY (x,y) NOT ENFORCED, ADD CONSTRAINT fk FOREIGN KEY (u, v) REFERENCES pk_table(x, y) NOT ENFORCED, ADD CONSTRAINT fk2 FOREIGN KEY (i, j) REFERENCES pk_table(x, y) NOT ENFORCED;
ALTER TABLE ADD PRIMARY KEY
statement
Adds a primary key to an existing table.
Syntax
ALTER TABLE [[project_name.]dataset_name.]table_name ADD PRIMARY KEY(column_list) NOT ENFORCED;
Arguments
project_name
: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.dataset_name
: The name of the dataset that contains the table with a primary key.table_name
: The name of the existing table with a primary key.column_list
: The list of columns to be added as primary keys.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example adds the primary key constraint of x
and y
to the pk_table
table.
ALTER TABLE pk_table ADD PRIMARY KEY (x,y) NOT ENFORCED;
ALTER TABLE RENAME TO
statement
Renames a clone, snapshot or table.
Syntax
ALTER TABLE [IF EXISTS] table_name RENAME TO new_table_name
Arguments
IF EXISTS
: If no table exists with that name, the statement has no effect.table_name
: The name of the table to rename. See Table path syntax.new_table_name
: The new name of the table. The new name cannot be an existing table name.
Details
- If you want to rename a table that has data streaming into it, you must stop the streaming and wait for BigQuery to indicate that streaming is not in use.
- A table can usually be renamed within 72 hours of the last streaming operation, but it might take longer.
- Existing table ACLs and row access policies are preserved, but table ACL and row access policy updates made during the table rename are not preserved.
- You can't concurrently rename a table and run a DML statement on that table.
- Renaming a table removes all Data Catalog tags on the table.
- You can't rename external tables.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
Renaming a table
The following example renames the table mydataset.mytable
to
mydataset.mynewtable
:
ALTER TABLE mydataset.mytable RENAME TO mynewtable
ALTER TABLE RENAME COLUMN
statement
Renames one or more columns in an existing table schema.
Syntax
ALTER TABLE [IF EXISTS] table_name RENAME COLUMN [IF EXISTS] column_to_column[, ...] column_to_column := column_name TO new_column_name
Arguments
(ALTER TABLE) IF EXISTS
: If the specified table does not exist, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top-level column you're altering.new_column_name
: The new name of the column. The new name cannot be an existing column name.
Details
This statement is not supported for external tables.
If the table to be modified has active row-level access policies, the statement returns an error.
Without the IF EXISTS
clause, if the table does not contain a column with that
name, then the statement returns an error. If the IF EXISTS
clause is included
and the column name does not exist, then no error is returned, and no action is
taken.
This statement only renames the column from the table. Any objects that refer to the column, such as views or materialized views, must be updated or recreated separately.
You cannot use this statement to rename the following:
- Subfields, such as nested columns in a
STRUCT
- Partitioning columns
- Clustering columns
- Fields that are part of primary key constraints or foreign key constraints
- Columns in a table that has row access policies
After one or more columns in a table are renamed, you cannot do the following:
- Query the table with legacy SQL.
- Query the table as a wildcard table.
Renaming the columns with their original names removes these restrictions.
Multiple RENAME COLUMN
statements in one ALTER TABLE
statement are
supported. The sequence of renames are interpreted and validated in order.
Each column_name
must refer to a column name that exists after all preceding
renames have been applied. RENAME COLUMN
cannot be used with other ALTER
TABLE
actions in one statement.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
Renaming columns
The following example renames columns from an existing table named mytable
:
- Column
A
->columnA
- Column
B
->columnB
ALTER TABLE mydataset.mytable RENAME COLUMN A TO columnA, RENAME COLUMN IF EXISTS B TO columnB
If column A
does not exist, then the statement fails. If column B
does not
exist, then the statement still succeeds because of the IF EXISTS
clause.
The following example swaps the names of columnA
and columnB
:
ALTER TABLE mydataset.mytable RENAME COLUMN columnA TO temp, RENAME COLUMN columnB TO columnA, RENAME COLUMN temp TO columnB
ALTER TABLE DROP COLUMN
statement
Drops one or more columns from an existing table schema.
Syntax
ALTER TABLE table_name
DROP COLUMN [IF EXISTS] column_name [, ...]
Arguments
table_name
: The name of the table to alter. See Table path syntax. The table must already exist and have a schema.IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the column to drop.
Details
Dropping a column is a metadata-only operation and does not
immediately free up the storage that is associated with the dropped column. The
storage is freed up the next time the table is written to, typically when you
perform a DML operation on it or when a background optimzation job happens.
Since DROP COLUMN
is not a data cleanup operation, there is no guaranteed
time window within which the data will be deleted.
There are two options for immediately reclaiming storage:
- Overwrite a table with a
SELECT * EXCEPT
query. - Export the data to Cloud Storage, delete the unwanted columns, and then load the data into a new table with the correct schema.
You can restore a dropped column in a table using time travel. You cannot use this statement to drop the following:
- Partitioned columns
- Clustered columns
- Fields that are part of primary key constraints or foreign key constraints
- Nested columns inside existing
RECORD
fields - Columns in a table that has row access policies
After one or more columns in a table are dropped you cannot do the following:
- Query the table with legacy SQL.
- Query the table as a wildcard table.
This statement is not supported for external tables.
Without the IF EXISTS
clause, if the table does not contain a column with that
name, then the statement returns an error. If the IF EXISTS
clause is included and
the column name does not exist, then no error is returned, and no action is taken.
This statement only removes the column from the table. Any objects that refer to the column, such as views or materialized views, must be updated or recreated separately.
For more information about schema modifications in BigQuery, see Modifying table schemas.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
Dropping columns
The following example drops the following columns from an existing table named
mytable
:
- Column
A
- Column
B
ALTER TABLE mydataset.mytable
DROP COLUMN A,
DROP COLUMN IF EXISTS B
If the column named A
does not exist, then the statement fails. If column B
does not exist, then the statement still succeeds because of the IF EXISTS
clause.
After one or more columns in a table are dropped, you cannot do the following:
- Query the table with legacy SQL.
- Accelerate queries on the table with BigQuery BI Engine.
- Query the table as a Wildcard Table.
- Copy the table in the Google Cloud console.
- Copy the table using the
bq cp
command.
Recreating the table using CREATE TABLE ... AS SELECT ...
removes these restrictions.
ALTER TABLE DROP CONSTRAINT
statement
Drops a constraint from an existing table. You can use this statement to drop foreign key constraints from a table.
Syntax
ALTER TABLE [[project_name.]dataset_name.]table_name DROP CONSTRAINT [IF EXISTS] constraint_name;
Arguments
project_name
: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.dataset_name
: The name of the dataset that contains the table with a primary key.table_name
: The name of the existing table with a primary key.IF EXISTS
: If no primary key exists in the defined table, the statement has no effect.constraint_name
: The name of the constraint to drop.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example drops the constraint myConstraint
from the existing
table myTable
.
ALTER TABLE mytable DROP CONSTRAINT myConstraint;
ALTER TABLE DROP PRIMARY KEY
statement
Drops a primary key from an existing table.
Syntax
ALTER TABLE [[project_name.]dataset_name.]table_name DROP PRIMARY KEY [IF EXISTS];
Arguments
project_name
: The name of the project containing the table with a primary key. Defaults to the project that runs this DDL statement if undefined.dataset_name
: The name of the dataset that contains the table with a primary key.table_name
: The name of the existing table with a primary key.IF EXISTS
: If no primary key exists in the defined table, the statement has no effect.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example drops all primary keys from the existing table myTable
.
ALTER TABLE myTable DROP PRIMARY KEY;
ALTER TABLE SET DEFAULT COLLATE
statement
Sets collation specifications on a table.
Syntax
ALTER TABLE table_name SET DEFAULT COLLATE collate_specification
Arguments
table_name
: The name of the table to alter. See Table path syntax. The table must already exist and have a schema.SET DEFAULT COLLATE collate_specification
: When a new column is created in the schema, and if the column does not have an explicit collation specification, the column inherits this collation specification forSTRING
types. The updated collation specification only applies to columns added afterwards.If you want to add a collation specification on a new column in an existing table, you can do this when you add the column. If you add a collation specification directly on a column, the collation specification for the column has precedence over a table's default collation specification. You cannot update an existing collation specification on a column.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Example
Assume you have an existing table, mytable
, in a schema called mydataset
.
CREATE TABLE mydataset.mytable ( number INT64, word STRING ) DEFAULT COLLATE 'und:ci'
When you create mytable
, all STRING
columns inherit COLLATE 'und:ci'
.
The resulting table has this structure:
+--------------------------------+
| mydataset.mytable |
| number INT64 |
| word STRING COLLATE 'und:ci' |
+--------------------------------+
At a later time, you decide to change the collation specification for your table.
ALTER TABLE mydataset.mytable SET DEFAULT COLLATE ''
Although you have updated the collation specification, your existing column,
word
, continues to use the previous collation specification.
+--------------------------------+
| mydataset.mytable |
| number INT64 |
| word STRING COLLATE 'und:ci' |
+--------------------------------+
However, if you create a new column for your table, the new column includes the
new collation specification. In the following example a column called name
is added. Because the new collation specification is empty, the default
collation specification is used.
ALTER TABLE mydataset.mytable ADD COLUMN name STRING
+--------------------------------+
| mydataset.mytable |
| number INT64 |
| word STRING COLLATE 'und:ci' |
| name STRING COLLATE |
+--------------------------------+
ALTER COLUMN SET OPTIONS
statement
Sets options, such as the column description, on a column in a table or view in BigQuery.
Syntax
ALTER { TABLE | VIEW } [IF EXISTS] name ALTER COLUMN [IF EXISTS] column_name SET OPTIONS({ column_set_options_list | view_column_set_options_list })
Arguments
(ALTER { TABLE | VIEW }) IF EXISTS
: If no table or view exists with that name, then the statement has no effect.name
: The name of the table or view to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top-level column you're altering. Modifying subfields, such as nested columns in aSTRUCT
, is not supported.column_set_options_list
: The list of options to set on the column of the table. This option must be used withTABLE
.view_column_set_options_list
: The list of options to set on the column of the view. This option must be used withVIEW
.
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 |
|
Example: This property is equivalent to the schema.fields[].description table resource property. |
rounding_mode |
|
Example: This specifies the rounding mode
that's used for values written to a
This property is equivalent to the
|
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
, orUPDATE
- User-defined functions, aggregate functions, or analytic functions
- The following scalar functions:
ARRAY_TO_STRING
REPLACE
REGEXP_REPLACE
RAND
FORMAT
LPAD
RPAD
REPEAT
SESSION_USER
GENERATE_ARRAY
GENERATE_DATE_ARRAY
Setting the VALUE
replaces the existing value of that option for the column, if
there was one. Setting the VALUE
to NULL
clears the column's value for that
option.
view_column_set_options_list
The view_column_option_list
lets you specify optional top-level column
options. Column options for a view have the same syntax and requirements as
for a table, but with a different list of NAME
and VALUE
fields:
NAME |
VALUE |
Details |
---|---|---|
description |
|
Example: |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example sets a new description on a table column called price
:
ALTER TABLE mydataset.mytable ALTER COLUMN price SET OPTIONS (description = 'Price per unit');
The following example sets a new description on a view column called total
:
ALTER VIEW mydataset.myview ALTER COLUMN total SET OPTIONS (description = 'Total sales of the product');
ALTER COLUMN DROP NOT NULL
statement
Removes a NOT NULL
constraint from a column in a table in BigQuery.
Syntax
ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column DROP NOT NULL
Arguments
(ALTER TABLE) IF EXISTS
: If no table exists with that name, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top level column you're altering. Modifying subfields is not supported.
Details
If a column does not have a NOT NULL
constraint the query returns an error.
This statement is not supported for external tables.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example removes the NOT NULL
constraint from a column called mycolumn
:
ALTER TABLE mydataset.mytable ALTER COLUMN mycolumn DROP NOT NULL
ALTER COLUMN SET DATA TYPE
statement
Changes the data type of a column in a table in BigQuery
to a less restrictive data type. For example, a NUMERIC
data type can be changed
to a BIGNUMERIC
type but not the reverse.
Syntax
ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column_name SET DATA TYPE column_schema
Arguments
(ALTER TABLE) IF EXISTS
: If no table exists with that name, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top level column you're altering. Modifying subfields is not supported.column_schema
: The schema that you're converting the column to. This schema uses the same syntax used in theCREATE TABLE
statement.
Details
The following data type conversions are supported: :
INT64
toNUMERIC
,BIGNUMERIC
,FLOAT64
NUMERIC
toBIGNUMERIC
,FLOAT64
You can also convert data types from more restrictive to less restrictive parameterized data types. For example, you can increase the maximum length of a string type or increase the precision or scale of a numeric type.
The following are examples of valid parameterized data type conversions:
NUMERIC(10, 6)
toNUMERIC(12, 8)
NUMERIC
toBIGNUMERIC(40, 20)
STRING(5)
toSTRING(7)
This statement is not supported for external tables.
Without the IF EXISTS
clause, if the table does not contain a column with that
name, the statement returns an error. If the IF EXISTS
clause is included and
the column name does not exist, no error is returned, and no action is taken.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
Changing the data type for a column
The following example changes the data type of column c1
from an INT64
to NUMERIC
:
CREATE TABLE dataset.table(c1 INT64); ALTER TABLE dataset.table ALTER COLUMN c1 SET DATA TYPE NUMERIC;
Changing the data type for a field
The following example changes the data type of one of the fields in the s1
column:
CREATE TABLE dataset.table(s1 STRUCT<a INT64, b STRING>); ALTER TABLE dataset.table ALTER COLUMN s1 SET DATA TYPE STRUCT<a NUMERIC, b STRING>;
Changing precision
The following example changes the precision of a parameterized data type column:
CREATE TABLE dataset.table (pt NUMERIC(7,2)); ALTER TABLE dataset.table ALTER COLUMN pt SET DATA TYPE NUMERIC(8,2);
ALTER COLUMN SET DEFAULT
statement
Sets the default value of a column.
Syntax
ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column_name SET DEFAULT default_expression;
Arguments
(ALTER TABLE) IF EXISTS
: If the specified table does not exist, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top-level column to add a default value to.default_expression
: The default value assigned to the column. The expression must be a literal or one of the following functions:
Details
Setting the default value for a column only affects future inserts to the table. It does not change any existing table data.
The type of the default value must match the type of the column.
A STRUCT
type can only have a default value set for the entire STRUCT
field. You
cannot set the default value for a subset of the fields. You cannot set the
default value of an array to NULL
or set an element within
an array to NULL
.
If the default value is a function, it is evaluated at the time that the value is written to the table, not the time the table is created.
You can't set default values on columns that are primary keys.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example sets the default value of the column mycolumn
to the
current time:
ALTER TABLE mydataset.mytable ALTER COLUMN mycolumn SET DEFAULT CURRENT_TIME();
ALTER COLUMN DROP DEFAULT
statement
Removes the default value assigned to a column.
This is the same as setting the default value to NULL
.
Syntax
ALTER TABLE [IF EXISTS] table_name ALTER COLUMN [IF EXISTS] column_name DROP DEFAULT;
Arguments
(ALTER TABLE) IF EXISTS
: If the specified table does not exist, the statement has no effect.table_name
: The name of the table to alter. See Table path syntax.(ALTER COLUMN) IF EXISTS
: If the specified column does not exist, the statement has no effect.column_name
: The name of the top-level column to remove the default value from. If you drop the default value from a column that does not have a default set, an error is returned.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The table to alter. |
bigquery.tables.update |
The table to alter. |
Examples
The following example removes the default value from the column mycolumn
:
ALTER TABLE mydataset.mytable ALTER COLUMN mycolumn DROP DEFAULT;
ALTER VIEW SET OPTIONS
statement
Sets the options on a view.
Syntax
ALTER VIEW [IF EXISTS] view_name SET OPTIONS(view_set_options_list)
Arguments
IF EXISTS
: If no view exists with that name, the statement has no effect.view_name
: The name of the view to alter. See Table path syntax.view_set_options_list
: The list of options to set.
view_set_options_list
The option list allows you to set view options such as a label and an expiration time. You can include multiple options using a comma-separated list.
Specify a view option list in the following format:
NAME=VALUE, ...
NAME
and VALUE
must be one of the following combinations:
NAME |
VALUE |
Details |
---|---|---|
expiration_timestamp |
TIMESTAMP |
Example: This property is equivalent to the expirationTime table resource property. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
privacy_policy |
|
The policies to enforce when anyone queries the view.
To learn more about the policies available for a view, see
the |
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
, orUPDATE
- 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: |
refresh_interval_minutes |
FLOAT64 |
Example: |
expiration_timestamp |
TIMESTAMP |
Example: This property is equivalent to the
expirationTime
table resource property. |
max_staleness |
INTERVAL |
Example: The
|
allow_non_incremental_definition |
BOOLEAN |
Example: The
|
kms_key_name |
|
Example: This property is equivalent to the encryptionConfiguration.kmsKeyName table resource property. See more details about Protecting data with Cloud KMS keys. |
friendly_name |
|
Example: This property is equivalent to the friendlyName table resource property. |
description |
|
Example: This property is equivalent to the description table resource property. |
labels |
|
Example: This property is equivalent to the labels table resource property. |
Setting the value replaces the existing value of that option for the
materialized view, if there was one. Setting the value to NULL
clears the
materialized view's value for that option.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.get |
The materialized view to alter. |
bigquery.tables.update |
The materialized view to alter. |
Examples
Setting the enable refresh state and refresh interval on a materialized view
The following example enables refresh and sets the refresh interval to 20 minutes on a materialized view:
ALTER MATERIALIZED VIEW mydataset.my_mv SET OPTIONS ( enable_refresh=true, refresh_interval_minutes=20 )
ALTER ORGANIZATION SET OPTIONS
statement
Sets the options on an organization.
Syntax
ALTER ORGANIZATION SET OPTIONS ( organization_set_options_list);
Arguments
organization_set_options_list
: The list of options to set.
organization_set_options_list
The option list specifies options for the organization. Specify the options in the
following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
default_kms_key_name |
STRING |
The default Cloud Key Management Service key for encrypting table data, including temporary or anonymous tables. For more information, see Customer-managed Cloud KMS keys. Example: This property is equivalent to the
|
default_time_zone |
STRING |
The default time zone to use in time zone-dependent SQL functions, when a time zone is not specified as an argument. For more information, see time zones. Example: |
default_query_job_timeout_ms |
INT64 |
The default time after which a query job times out. The timeout period must be between 10 minutes and 6 hours. Example: |
default_interactive_query_queue_timeout_ms |
INT64 |
The default amount of time that an interactive query is queued. If unset, the default is 6 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable interactive query queueing, set the value to -1. Example: |
default_batch_query_queue_timeout_ms |
INT64 |
The default amount of time that a batch query is queued. If unset, the default is 24 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable batch query queueing, set the value to -1. Example: |
default_query_optimizer_options (preview) |
STRING |
The history-based query optimizations. This option can be one of the following:
Example: |
Setting the value replaces the existing value of that option for the
organization, if there is one. Setting the value to NULL
clears the
organization's value for that option.
Required permissions
The ALTER ORGANIZATION SET OPTIONS
statement requires the following
IAM permissions:
Permission | Resource |
---|---|
bigquery.config.update |
The organization to alter. |
Examples
The following example sets the default time zone to America/Chicago and the default query job timeout to one hour for an organization in the US region:
ALTER ORGANIZATION SET OPTIONS ( `region-us.default_time_zone` = "America/Chicago", `region-us.default_job_query_timeout_ms` = 3600000 );
The following example sets the default time zone, the default query job timeout,
the default interactive and batch queue timeouts, and the default
Cloud KMS key to NULL
, clearing the organization level default
settings:
ALTER ORGANIZATION SET OPTIONS ( `region-us.default_time_zone` = NULL, `region-us.default_kms_key_name` = NULL, `region-us.default_query_job_timeout_ms` = NULL, `region-us.default_interactive_query_queue_timeout_ms` = NULL, `region-us.default_batch_query_queue_timeout_ms` = NULL);
ALTER PROJECT SET OPTIONS
statement
Sets the options on a project.
Syntax
ALTER PROJECT project_id SET OPTIONS (project_set_options_list);
Arguments
project_id
: The name of the project you're altering. This argument is optional, and defaults to the project that runs this DDL query.project_set_options_list
: The list of options to set.
project_set_options_list
The option list specifies options for the project. Specify the options in the
following format: NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
default_kms_key_name |
STRING |
The default Cloud Key Management Service key for encrypting table data, including temporary or anonymous tables. For more information, see Customer-managed Cloud KMS keys. Example:kms_key_name="projects/project_id/locations/ location/keyRings/keyring/cryptoKeys/key"
This property is equivalent to the
|
default_time_zone |
STRING |
The default time zone to use in time zone-dependent SQL functions, when a time zone is not specified as an argument. For more information, see time zones. Example: |
default_query_job_timeout_ms |
INT64 |
The default time after which a query job times out. The timeout period must be between 10 minutes and 6 hours. Example: |
default_interactive_query_queue_timeout_ms |
INT64 |
The default amount of time that an interactive query is queued. If unset, the default is 6 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable interactive query queueing, set the value to -1. Example: |
default_batch_query_queue_timeout_ms |
INT64 |
The default amount of time that a batch query is queued. If unset, the default is 24 hours. The minimum value is 1 millisecond. The maximum value is 48 hours. To disable batch query queueing, set the value to -1. Example: |
default_query_optimizer_options (preview) |
STRING |
The history-based query optimizations. This option can be one of the following:
Example: |
Setting the value replaces the existing value of that option for the project, if there was one. Setting the value to NULL
clears the
project's value for that option.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.config.update |
The project to alter. |
Examples
The following example sets the default time zone to America/New_York
and the default query job timeout to 30 minutes for a project in the us
region.
ALTER PROJECT project_id SET OPTIONS ( `region-us.default_time_zone` = "America/New_York", `region-us.default_job_query_timeout_ms` = 1800000 );
The following example sets the default time zone, the default query job timeout, the default Cloud KMS key to NULL
, and the default interactive and batch queue
timeouts, clearing the project level default settings:
ALTER PROJECT project_id SET OPTIONS ( `region-us.default_time_zone` = NULL, `region-us.default_kms_key_name` = NULL, `region-us.default_query_job_timeout_ms` = NULL, `region-us.default_interactive_query_queue_timeout_ms` = NULL, `region-us.default_batch_query_queue_timeout_ms` = NULL);
ALTER BI_CAPACITY SET OPTIONS
statement
Sets the options on BigQuery BI Engine capacity.
Syntax
ALTER BI_CAPACITY `project_id.location_id.default` SET OPTIONS(bi_capacity_options_list)
Arguments
project_id
: Optional project ID of the project that will benefit from BI Engine acceleration. If omitted, the query project ID is used.location_id
: The location where data needs to be cached, prefixed withregion-
. Examples:region-us
,region-us-central1
.bi_capacity_options_list
: The list of options to set.
bi_capacity_options_list
The option list specifies a set of options for BigQuery BI Engine capacity.
Specify a column option list in the following format:
NAME=VALUE, ...
The following options are supported:
NAME |
VALUE |
Details |
---|---|---|
size_gb |
INT64 |
Specifies the size of the reservation in gigabytes. |
preferred_tables |
<ARRAY<STRING>> |
List of tables that acceleration should be applied to. Format:
project.dataset.table or dataset.table . If project is omitted, query project
is used. |
Setting VALUE
replaces the existing value of that option for the BI Engine
capacity, if there is one. Setting VALUE
to NULL
clears the value
for that option.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.bireservations.update |
BI Engine reservation |
Examples
Allocating BI Engine capacity without preferred tables
ALTER BI_CAPACITY `my-project.region-us.default` SET OPTIONS( size_gb = 250 )
Deallocating BI capacity
ALTER BI_CAPACITY `my-project.region-us.default` SET OPTIONS( size_gb = 0 )
Removing a set of preferred tables from reservation
ALTER BI_CAPACITY `my-project.region-us.default` SET OPTIONS( preferred_tables = NULL )
Allocating BI Capacity with preferred tables list
ALTER BI_CAPACITY `my-project.region-us.default` SET OPTIONS( size_gb = 250, preferred_tables = ["data_project1.dataset1.table1", "data_project2.dataset2.table2"] )
Overwriting list of preferred tables without changing the size
ALTER BI_CAPACITY `region-us.default` SET OPTIONS( preferred_tables = ["dataset1.table1", "data_project2.dataset2.table2"] )
ALTER CAPACITY SET OPTIONS
statement
Alters an existing capacity commitment.
Syntax
ALTER CAPACITY `project_id.location_id.commitment_id` SET OPTIONS (alter_capacity_commitment_option_list);
Arguments
project_id
: The project ID of the administration project that maintains ownership of this commitment.location_id
: The location of the commitment.commitment_id
: The ID of the commitment. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.alter_capacity_commitment_option_list
: The options you can set to alter the capacity commitment.
alter_capacity_commitment_option_list
The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
TYPE |
Details |
---|---|---|
plan | String | The commitment plan to
purchase. Supported values include: ANNUAL ,
THREE_YEAR , and TRIAL . For more
information, see Commitment
plans. |
renewal_plan |
String | The plan this capacity commitment is converted to after commitment_end_time passes. Once the plan is changed, the committed period is extended according to the commitment plan. Applicable for ANNUAL, THREE_YEAR, and TRIAL commitments. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.capacityCommitments.update
|
The administration project that maintains ownership of the commitments. |
Example
The following example changes a capacity commitment to a three-year plan that is
located in the region-us
region and managed by a project admin_project
:
ALTER CAPACITY `admin_project.region-us.my-commitment` SET OPTIONS ( plan = 'THREE_YEAR');
ALTER RESERVATION SET OPTIONS
statement
Alters an existing reservation.
Syntax
ALTER RESERVATION `project_id.location_id.reservation_id` SET OPTIONS (alter_reservation_option_list);
Arguments
project_id
: The project ID of the administration project that maintains ownership of this reservation.location_id
: The location of the reservation.reservation_id
: The ID of the reservation. The value must be unique to the project and location. It must start and end with a lowercase letter or a number and contain only lowercase letters, numbers and dashes.alter_reservation_option_list
: The options you can set to alter the reservation.
alter_reservation_option_list
The option list specifies options for the dataset. Specify the options in the following format: NAME=VALUE, ...
The following options are supported:
NAME |
TYPE |
Details |
---|---|---|
ignore_idle_slots |
BOOLEAN |
If the value is true , then the reservation uses only the
slots that are provisioned to it. The default value is false .
For more information, see
Idle slots. |
slot_capacity |
INTEGER |
The number of slots to allocate to the reservation. If this reservation was created with an edition, this is equivalent to the amount of baseline slots. |
target_job_concurrency |
INTEGER |
A soft upper bound on the number of jobs that can run concurrently in this reservation. |
autoscale_max_slots |
INTEGER |
The maximum number of slots that can be added to the reservation by autoscaling. |
secondary_location |
STRING |
The secondary location to use in the case of disaster recovery. |
is_primary |
BOOLEAN |
If the value is true , the reservation is set to be the primary reservation. |
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.reservations.update
|
The administration project that maintains ownership of the commitments. |
Examples
Autoscaling example
The following example changes an autoscaling reservation to 300 baseline slots
and 400 autoscaling slots for a max reservation size of 700. These slots are
located in the region-us
region and managed by a project admin_project
:
ALTER RESERVATION `admin_project.region-us.my-reservation` SET OPTIONS ( slot_capacity = 300, autoscale_max_slots = 400);
DROP SCHEMA
statement
Deletes a dataset.
Syntax
DROP [EXTERNAL] SCHEMA [IF EXISTS]
[project_name.]dataset_name
[ CASCADE | RESTRICT ]
Arguments
EXTERNAL
: Specifies if that dataset is a federated dataset. TheDROP EXTERNAL
statement only removes the external definition from BigQuery. The data stored in the external location is not affected.IF EXISTS
: If no dataset exists with that name, the statement has no effect.project_name
: The name of the project that contains the dataset. Defaults to the project that runs this DDL statement.dataset_name
: The name of the dataset to delete.CASCADE
: Deletes the dataset and all resources within the dataset, such as tables, views, and functions. You must have permission to delete the resources, or else the statement returns an error. For a list of BigQuery permissions, see Predefined roles and permissions.RESTRICT
: Deletes the dataset only if it's empty. Otherwise, returns an error. If you don't specify eitherCASCADE
orRESTRICT
, then the default behavior isRESTRICT
.
Details
The statement runs in the location of the dataset if it exists, unless you specify the location in the query settings. For more information, see Specifying your location.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.delete |
The dataset to delete. |
bigquery.tables.delete
|
The dataset to delete. If the dataset is empty, then this permission is not required. |
Examples
The following example deletes the dataset named mydataset
. If the dataset does
not exist or is not empty, then the statement returns an error.
DROP SCHEMA mydataset
The following example drops the dataset named mydataset
and any resources
in that dataset. If the dataset does not exist, then no error is returned.
DROP SCHEMA IF EXISTS mydataset CASCADE
UNDROP SCHEMA
statement
Undeletes a dataset within your time travel window.
Syntax
UNDROP SCHEMA [IF NOT EXISTS]
[project_name.]dataset_name
Arguments
IF NOT EXISTS
: If a dataset already exists with that name, the statement has no effect.project_name
: The name of the project that contained the deleted dataset. Defaults to the project that runs this DDL statement.dataset_name
: The name of the dataset to undelete.
Details
When you run this statement, you must
specify the location
where the dataset was deleted. If you don't, the US
multi-region is used.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.datasets.create
|
The project where you are undeleting the dataset. |
bigquery.datasets.get |
The dataset that you are undeleting. |
Examples
The following example undeletes the dataset named mydataset
. If the dataset
already exists or has passed the time travel window, then the statement returns
an error.
UNDROP SCHEMA mydataset;
DROP TABLE
statement
Deletes a table or table clone.
Syntax
DROP TABLE [IF EXISTS] table_name
Arguments
IF EXISTS
: If no table exists with that name, the statement has no effect.table_name
: The name of the table to delete. See Table path syntax.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.delete |
The table to delete. |
bigquery.tables.get |
The table to delete. |
Examples
Deleting a table
The following example deletes a table named mytable
in the mydataset
:
DROP TABLE mydataset.mytable
If the table name does not exist in the dataset, the following error is returned:
Error: Not found: Table myproject:mydataset.mytable
Deleting a table only if the table exists
The following example deletes a table named mytable
in mydataset
only if
the table exists. If the table name does not exist in the dataset, no error is
returned, and no action is taken.
DROP TABLE IF EXISTS mydataset.mytable
DROP SNAPSHOT TABLE
statement
Deletes a table snapshot.
Syntax
DROP SNAPSHOT TABLE [IF EXISTS] table_snapshot_name
Arguments
IF EXISTS
: If no table snapshot exists with that name, then the statement has no effect.table_snapshot_name
: The name of the table snapshot to delete. See Table path syntax.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.deleteSnapshot |
The table snapshot to delete. |
Examples
Delete a table snapshot: fail if it doesn't exist
The following example deletes the table snapshot named mytablesnapshot
in the
mydataset
dataset:
DROP SNAPSHOT TABLE mydataset.mytablesnapshot
If the table snapshot does not exist in the dataset, then the following error is returned:
Error: Not found: Table snapshot myproject:mydataset.mytablesnapshot
Delete a table snapshot: ignore if it doesn't exist
The following example deletes the table snapshot named mytablesnapshot
in the
mydataset
dataset.
DROP SNAPSHOT TABLE IF EXISTS mydataset.mytablesnapshot
If the table snapshot doesn't exist in the dataset, then no action is taken, and no error is returned.
For information about creating table snapshots, see CREATE SNAPSHOT TABLE.
For information about restoring table snapshots, see CREATE TABLE CLONE.
DROP EXTERNAL TABLE
statement
Deletes an external table.
Syntax
DROP EXTERNAL TABLE [IF EXISTS] table_name
Arguments
IF EXISTS
: If no external table exists with that name, then the statement has no effect.table_name
: The name of the external table to delete. See Table path syntax.
Details
If table_name
exists but is not an external table, the statement returns the following
error:
Cannot drop table_name which has type TYPE. An
external table was expected.
The DROP EXTERNAL
statement only removes the external table definition from
BigQuery. The data stored in the external location is not
affected.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.delete |
The external table to delete. |
bigquery.tables.get |
The external table to delete. |
Examples
The following example drops the external table named external_table
from the
dataset mydataset
. It returns an error if the external table does not exist.
DROP EXTERNAL TABLE mydataset.external_table
The following example drops the external table named external_table
from the
dataset mydataset
. If the external table does not exist, no error is returned.
DROP EXTERNAL TABLE IF EXISTS mydataset.external_table
DROP VIEW
statement
Deletes a view.
Syntax
DROP VIEW [IF EXISTS] view_name
Arguments
IF EXISTS
: If no view exists with that name, the statement has no effect.view_name
: The name of the view to delete. See Table path syntax.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.delete |
The view to delete. |
bigquery.tables.get |
The view to delete. |
Examples
Deleting a view
The following example deletes a view named myview
in mydataset
:
DROP VIEW mydataset.myview
If the view name does not exist in the dataset, the following error is returned:
Error: Not found: Table myproject:mydataset.myview
Deleting a view only if the view exists
The following example deletes a view named myview
in mydataset
only if
the view exists. If the view name does not exist in the dataset, no error is
returned, and no action is taken.
DROP VIEW IF EXISTS mydataset.myview
DROP MATERIALIZED VIEW
statement
Deletes a materialized view.
Syntax
DROP MATERIALIZED VIEW [IF EXISTS] mv_name
Arguments
IF EXISTS
: If no materialized view exists with that name, the statement has no effect.mv_name
: The name of the materialized view to delete. See Table path syntax.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.delete |
The materialized view to delete. |
bigquery.tables.get |
The materialized view to delete. |
Examples
Deleting a materialized view
The following example deletes a materialized view named my_mv
in mydataset
:
DROP MATERIALIZED VIEW mydataset.my_mv
If the materialized view name does not exist in the dataset, the following error is returned:
Error: Not found: Table myproject:mydataset.my_mv
If you are deleting a materialized view in another project, you must specify the
project, dataset, and materialized view in the following format:
`project_id.dataset.materialized_view`
(including the backticks if project_id
contains special characters); for example,
`myproject.mydataset.my_mv`
.
Deleting a materialized view only if it exists
The following example deletes a materialized view named my_mv
in mydataset
only if the materialized view exists. If the materialized view name does not
exist in the dataset, no error is returned, and no action is taken.
DROP MATERIALIZED VIEW IF EXISTS mydataset.my_mv
If you are deleting a materialized view in another project, you must specify the
project, dataset, and materialized view in the following format:
`project_id.dataset.materialized_view`,
(including the backticks if project_id
contains special characters); for example,
`myproject.mydataset.my_mv`
.
DROP FUNCTION
statement
Deletes a persistent user-defined function (UDF) or user-defined aggregate function (UDAF).
Syntax
DROP FUNCTION [IF EXISTS] [[project_name.]dataset_name.]function_name
Arguments
IF EXISTS
: If no function exists with that name, the statement has no effect.project_name
: The name of the project containing the function to delete. Defaults to the project that runs this DDL query. If the project name contains special characters such as colons, it should be quoted in backticks`
(example:`google.com:my_project`
).dataset_name
: The name of the dataset containing the function to delete. Defaults to thedefaultDataset
in the request.function_name
: The name of the function you're deleting.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.delete |
The function to delete. |
Examples
The following example statement deletes the function parseJsonAsStruct
contained in the dataset mydataset
.
DROP FUNCTION mydataset.parseJsonAsStruct;
The following example statement deletes the function parseJsonAsStruct
from
the dataset sample_dataset
in the project other_project
.
DROP FUNCTION `other_project`.sample_dataset.parseJsonAsStruct;
DROP TABLE FUNCTION
Deletes a table function.
Syntax
DROP TABLE FUNCTION [IF EXISTS] [[project_name.]dataset_name.]function_name
Arguments
IF EXISTS
: If no table function exists with this name, the statement has no effect.project_name
: The name of the project containing the table function to delete. Defaults to the project that runs this DDL query.dataset_name
: The name of the dataset containing the table function to delete.function_name
: The name of the table function to delete.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.delete |
The table function to delete. |
Example
The following example deletes a table function named my_table_function
:
DROP TABLE FUNCTION mydataset.my_table_function;
DROP PROCEDURE
statement
Deletes a stored procedure.
Syntax
DROP PROCEDURE [IF EXISTS] [[project_name.]dataset_name.]procedure_name
Arguments
IF EXISTS
: If no procedure exists with that name, the statement has no effect.project_name
: The name of the project containing the procedure to delete. Defaults to the project that runs this DDL query. If the project name contains special characters such as colons, it should be quoted in backticks`
(example:`google.com:my_project`
).dataset_name
: The name of the dataset containing the procedure to delete. Defaults to thedefaultDataset
in the request.procedure_name
: The name of the procedure you're deleting.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.routines.delete |
The procedure to delete. |
Examples
The following example statement deletes the procedure myprocedure
contained in the dataset mydataset
.
DROP PROCEDURE mydataset.myProcedure;
The following example statement deletes the procedure myProcedure
from
the dataset sample_dataset
in the project other_project
.
DROP PROCEDURE `other-project`.sample_dataset.myprocedure;
DROP ROW ACCESS POLICY
statement
Deletes a row-level access policy.
Syntax
DROP ROW ACCESS POLICY [ IF EXISTS ]
row_access_policy_name ON table_name;
DROP ALL ROW ACCESS POLICIES ON table_name;
Arguments
IF EXISTS
: If no row-level access policy exists with that name, the statement has no effect.row_access_policy_name
: The name of the row-level access policy that you are deleting. Each row-level access policy on a table has a unique name.table_name
: The name of the table with the row-level access policy or policies that you want to delete.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.rowAccessPolicies.delete |
The row-level access policy to delete. |
bigquery.rowAccessPolicies.setIamPolicy |
The row-level access policy to delete. |
bigquery.rowAccessPolicies.list |
The table to delete all row-level access policies on. Only required for DROP ALL statements. |
Examples
Delete a row-level access policy from a table:
DROP ROW ACCESS POLICY my_row_filter ON project.dataset.my_table;
Delete all the row-level access policies from a table:
DROP ALL ROW ACCESS POLICIES ON project.dataset.my_table;
DROP CAPACITY
statement
Deletes a capacity commitment.
Syntax
DROP CAPACITY [IF EXISTS]
project_id.location.capacity-commitment-id
Arguments
IF EXISTS
: If no capacity commitment exists with that ID, the statement has no effect.project_id
: The project ID of the administration project where the reservation was created.location
: The location of the commitment.capacity-commitment-id
: The capacity commitment ID.
To find the capacity commitment ID, query the
INFORMATION_SCHEMA.CAPACITY_COMMITMENTS_BY_PROJECT
table.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.capacityCommitments.delete
|
The administration project that maintains ownership of the commitments. |
Example
The following example deletes the capacity commitment:
DROP CAPACITY `admin_project.region-us.1234`
DROP RESERVATION
statement
Deletes a reservation.
Syntax
DROP RESERVATION [IF EXISTS]
project_id.location.reservation_id
Arguments
IF EXISTS
: If no reservation exists with that ID, the statement has no effect.project_id
: The project ID of the administration project where the reservation was created.location
: The location of the reservation.reservation_id
: The reservation ID.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.reservations.delete
|
The administration project that maintains ownership of the commitments. |
Example
The following example deletes the reservation prod
:
DROP RESERVATION `admin_project.region-us.prod`
DROP ASSIGNMENT
statement
Deletes a reservation assignment.
Syntax
DROP ASSIGNMENT [IF EXISTS]
project_id.location.reservation_id.assignment_id
Arguments
IF EXISTS
: If no assignment exists with that ID, the statement has no effect.project_id
: The project ID of the administration project where the reservation was created.location
: The location of the reservation.reservation_id
: The reservation ID.assignment_id
: The assignment ID.
To find the assignment ID, query the
INFORMATION_SCHEMA.ASSIGNMENTS
view.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.reservationAssignments.delete
|
The administration project and the assignee. |
Example
The following example deletes an assignment from the reservation named prod
:
DROP ASSIGNMENT `admin_project.region-us.prod.1234`
DROP SEARCH INDEX
statement
Deletes a search index on a table.
Syntax
DROP SEARCH INDEX [ IF EXISTS ] index_name ON table_name
Arguments
IF EXISTS
: If no search index exists with that name on the table, the statement has no effect.index_name
: The name of the search index to be deleted.table_name
: The name of the table with the index.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.deleteIndex |
The table with the search index to delete. |
Example
The following example deletes a search index my_index
from my_table
:
DROP SEARCH INDEX my_index ON dataset.my_table;
DROP VECTOR INDEX
statement
To provide feedback or request support for this feature, send email to [email protected].
Deletes a vector index on a table.
Syntax
DROP VECTOR INDEX [ IF EXISTS ] index_name ON table_name
Arguments
IF EXISTS
: If no vector index exists with that name on the table, the statement has no effect.index_name
: The name of the vector index to be deleted.table_name
: The name of the table with the vector index.
Required permissions
This statement requires the following IAM permissions:
Permission | Resource |
---|---|
bigquery.tables.deleteIndex |
The table with the vector index to delete. |
Example
The following example deletes a vector index my_index
from my_table
:
DROP VECTOR INDEX my_index ON dataset.my_table;
Table path syntax
Use the following syntax when specifying the path of a table resource, including standard tables, views, materialized views, external tables, and table snapshots.
table_path :=
[[project_name.]dataset_name.]table_name
project_name
: The name of the project that contains the table resource. Defaults to the project that runs the DDL query. If the project name contains special characters such as colons, quote the name in backticks`
(example:`google.com:my_project`
).dataset_name
: The name of the dataset that contains the table resource. Defaults to thedefaultDataset
in the request.table_name
: The name of the table resource.
When you create a table in BigQuery, the table name must be unique per dataset. The table name can:
- Contain characters with a total of up to 1,024 UTF-8 bytes.
- Contain Unicode characters in category L (letter), M (mark), N (number), Pc (connector, including underscore), Pd (dash), Zs (space). For more information, see General Category.
The following are all examples of valid table names:
table 01
, ग्राहक
, 00_お客様
, étudiant-01
.
Caveats:
- Table names are case-sensitive by default.
mytable
andMyTable
can coexist in the same dataset, unless they are part of a dataset with case-sensitivity turned off. - Some table names and table name prefixes are reserved. If you receive an error saying that your table name or prefix is reserved, then select a different name and try again.
If you include multiple dot operators (
.
) in a sequence, the duplicate operators are implicitly stripped.For example, this:
project_name....dataset_name..table_name
Becomes this:
project_name.dataset_name.table_name