Create and use clustered tables
This document describes how to create and use clustered tables in BigQuery. For an overview of clustered table support in BigQuery, see Introduction to clustered tables.
Create clustered tables
You can create a clustered table by using the following methods:
Create a table from a query result:
- Run a DDL
CREATE TABLE AS SELECT
statement. - Run a query that creates a clustered destination table.
- Run a DDL
Use a DDL
CREATE TABLE
statement with aCLUSTER BY
clause containing aclustering_column_list
.Run the bq command-line tool
bq mk
command.Make calls to the
tables.insert
API method.Load data into BigQuery.
Use the client libraries.
Table naming
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
Required permissions
To create a table, you need the following IAM permissions:
bigquery.tables.create
bigquery.tables.updateData
bigquery.jobs.create
Additionally, you might require the bigquery.tables.getData
permission to
access the data that you write to the table.
Each of the following predefined IAM roles includes the permissions that you need in order to create a table:
roles/bigquery.dataEditor
roles/bigquery.dataOwner
roles/bigquery.admin
(includes thebigquery.jobs.create
permission)roles/bigquery.user
(includes thebigquery.jobs.create
permission)roles/bigquery.jobUser
(includes thebigquery.jobs.create
permission)
Additionally, if you have the bigquery.datasets.create
permission, you can
create and update tables in the datasets that you create.
For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.
Create an empty clustered table with a schema definition
You specify clustering columns when you create a table in BigQuery. After the table is created, you can modify the clustering columns; see Modifying clustering specification for details.
Clustering columns must be top-level, non-repeated columns, and they must be one of the following simple data types:
DATE
BOOLEAN
GEOGRAPHY
INTEGER
NUMERIC
BIGNUMERIC
STRING
TIMESTAMP
RANGE
You can specify up to four clustering columns. When you specify multiple columns, the order of the columns determines how the data is sorted. For example, if the table is clustered by columns a, b and c, the data is sorted in the same order: first by column a, then by column b, and then by column c. As a best practice, place the most frequently filtered or aggregated column first.
The order of your clustering columns also affects query performance and pricing. For more information about query best practices for clustered tables, see Querying clustered tables.
To create an empty clustered table with a schema definition:
Console
In the Google Cloud console, go to the BigQuery page.
- In the Explorer pane, expand your project, and then select a dataset.
- In the Dataset info section, click Create table.
- In the Create table panel, specify the following details:
- In the Source section, select Empty table in the Create table from list.
- In the Destination section, specify the following details:
- For Dataset, select the dataset in which you want to create the table.
- In the Table field, enter the name of the table that you want to create.
- Verify that the Table type field is set to Native table.
- In the Schema section, enter the schema
definition.
You can enter schema information manually by using one of
the following methods:
- Option 1: Click Edit as text and paste the schema in the form of a
JSON array. When you use a JSON array, you generate the schema using the
same process as creating a JSON schema file.
You can view the schema of an existing table in JSON format by entering the following
command:
bq show --format=prettyjson dataset.table
- Option 2: Click Type, and Mode. Add field and enter the table schema. Specify each field's Name,
- Option 1: Click Edit as text and paste the schema in the form of a
JSON array. When you use a JSON array, you generate the schema using the
same process as creating a JSON schema file.
You can view the schema of an existing table in JSON format by entering the following
command:
- For Clustering order, enter between one and four comma-separated column names.
- Optional: In the Advanced options section, if you want to use a customer-managed encryption key, then select the Use a customer-managed encryption key (CMEK) option. By default, BigQuery encrypts customer content stored at rest by using a Google-owned and Google-managed key.
- Click Create table.
SQL
Use the CREATE TABLE
DDL statement
command with the CLUSTER BY
option. The following example creates a
clustered table named myclusteredtable
in mydataset
:
In the Google Cloud console, go to the BigQuery page.
In the query editor, enter the following statement:
CREATE TABLE mydataset.myclusteredtable ( customer_id STRING, transaction_amount NUMERIC ) CLUSTER BY customer_id OPTIONS ( description = 'a table clustered by customer_id');
Click
Run.
For more information about how to run queries, see Run an interactive query.
bq
Use the bq mk
command
with the following flags:
--table
(or the-t
shortcut).--schema
. You can supply the table's schema definition inline or use a JSON schema file.--clustering_fields
. You can specify up to four clustering columns.
Optional parameters include --expiration
, --description
,
--time_partitioning_type
, --time_partitioning_field
,
--time_partitioning_expiration
, --destination_kms_key
, and --label
.
If you are creating a table in a project other than your default project,
add the project ID to the dataset in the following format:
project_id:dataset
.
--destination_kms_key
is not demonstrated here. For information about
using --destination_kms_key
, see
customer-managed encryption keys.
Enter the following command to create an empty clustered table with a schema definition:
bq mk \ --table \ --expiration INTEGER1 \ --schema SCHEMA \ --clustering_fields CLUSTER_COLUMNS \ --description "DESCRIPTION" \ --label KEY:VALUE,KEY:VALUE \ PROJECT_ID:DATASET.TABLE
Replace the following:
INTEGER1
: the default lifetime, in seconds, for the table. The minimum value is 3,600 seconds (one hour). The expiration time evaluates to the current UTC time plus the integer value. If you set the table's expiration time when you create a table, the dataset's default table expiration setting is ignored. Setting this value deletes the table after the specified time.SCHEMA
: an inline schema definition in the formatCOLUMN:DATA_TYPE,COLUMN:DATA_TYPE
or the path to the JSON schema file on your local machine.CLUSTER_COLUMNS
: a comma-separated list of up to four clustering columns. The list cannot contain any spaces.DESCRIPTION
: a description of the table, in quotes.KEY:VALUE
: the key-value pair that represents a label. You can enter multiple labels using a comma-separated list.PROJECT_ID
: your project ID.DATASET
: a dataset in your project.TABLE
: the name of the table you're creating.
When you specify the schema on the command line, you cannot include a
RECORD
(STRUCT
)
type, you cannot include a column description, and you
cannot specify the column's mode. All modes default to NULLABLE
. To
include descriptions, modes, and RECORD
types, supply a JSON schema
file instead.
Examples:
Enter the following command to create a clustered table
named myclusteredtable
in mydataset
in your default project. The table's
expiration is set to 2,592,000 (1 30-day month), the description is set to
This is my clustered table
, and the label is set to
organization:development
. The command uses the -t
shortcut instead of
--table
.
The schema is specified inline as:
timestamp:timestamp,customer_id:string,transaction_amount:float
. The
specified clustering field customer_id
is used to cluster the table.
bq mk \
-t \
--expiration 2592000 \
--schema 'timestamp:timestamp,customer_id:string,transaction_amount:float' \
--clustering_fields customer_id \
--description "This is my clustered table" \
--label org:dev \
mydataset.myclusteredtable
Enter the following command to create a clustered table named
myclusteredtable
in myotherproject
, not your default project. The
description is set to This is my clustered table
, and the label is set
to organization:development
. The command uses the -t
shortcut instead of
--table
. This command does not specify a table expiration. If the dataset
has a default table expiration, it is applied. If the dataset has no default
table expiration, the table never expires.
The schema is specified in a local JSON file: /tmp/myschema.json
. The
customer_id
field is used to cluster the table.
bq mk \
-t \
--expiration 2592000 \
--schema /tmp/myschema.json \
--clustering_fields=customer_id \
--description "This is my clustered table" \
--label org:dev \
myotherproject:mydataset.myclusteredtable
After the table is created, you can update the table's description and labels.
Terraform
Use the
google_bigquery_table
resource.
To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.
The following example creates a table named mytable
that is clustered
on the ID
and Created
columns:
To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.
Prepare Cloud Shell
- Launch Cloud Shell.
-
Set the default Google Cloud project where you want to apply your Terraform configurations.
You only need to run this command once per project, and you can run it in any directory.
export GOOGLE_CLOUD_PROJECT=PROJECT_ID
Environment variables are overridden if you set explicit values in the Terraform configuration file.
Prepare the directory
Each Terraform configuration file must have its own directory (also called a root module).
-
In Cloud Shell, create a directory and a new
file within that directory. The filename must have the
.tf
extension—for examplemain.tf
. In this tutorial, the file is referred to asmain.tf
.mkdir DIRECTORY && cd DIRECTORY && touch main.tf
-
If you are following a tutorial, you can copy the sample code in each section or step.
Copy the sample code into the newly created
main.tf
.Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.
- Review and modify the sample parameters to apply to your environment.
- Save your changes.
-
Initialize Terraform. You only need to do this once per directory.
terraform init
Optionally, to use the latest Google provider version, include the
-upgrade
option:terraform init -upgrade
Apply the changes
-
Review the configuration and verify that the resources that Terraform is going to create or
update match your expectations:
terraform plan
Make corrections to the configuration as necessary.
-
Apply the Terraform configuration by running the following command and entering
yes
at the prompt:terraform apply
Wait until Terraform displays the "Apply complete!" message.
- Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.
API
Call the tables.insert
method with a defined table resource
that specifies the clustering.fields
property and the schema
property.
Python
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.
Go
Before trying this sample, follow the Go setup instructions in the
BigQuery quickstart using
client libraries.
For more information, see the
BigQuery Go API
reference documentation.
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see
Set up authentication for client libraries.
Java
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.
Create a clustered table from a query result
There are two ways to create a clustered table from a query result:
- Write the results to a new destination table and specify the clustering columns.
- By using a DDL
CREATE TABLE AS SELECT
statement. For more information about this method, see Creating a clustered table from the result of a query on the Using data definition language statements page.
You can create a clustered table by querying either a partitioned table or a non-partitioned table. You cannot change an existing table to a clustered table by using query results.
When you create a clustered table from a query result, you must use standard SQL. Currently, legacy SQL is not supported for querying clustered tables or for writing query results to clustered tables.
SQL
To create a clustered table from a query result, use the
CREATE TABLE
DDL statement
with the CLUSTER BY
option. The following example creates a new
table clustered by customer_id
by querying an existing unclustered table:
In the Google Cloud console, go to the BigQuery page.
In the query editor, enter the following statement:
CREATE TABLE mydataset.clustered_table ( customer_id STRING, transaction_amount NUMERIC ) CLUSTER BY customer_id AS ( SELECT * FROM mydataset.unclustered_table );
Click
Run.
For more information about how to run queries, see Run an interactive query.
bq
Enter the following command to create a new, clustered destination table from a query result:
bq --location=LOCATION query \ --use_legacy_sql=false 'QUERY'
Replace the following:
LOCATION
: the name of your location. The--location
flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value toasia-northeast1
. You can set a default value for the location using the .bigqueryrc file.QUERY
: a query in GoogleSQL syntax. Currently, you cannot use legacy SQL to query clustered tables or to write query results to clustered tables. The query can contain aCREATE TABLE
DDL statement that specifies the options for creating your clustered table. You can use DDL rather than specifying the individual command-line flags.
Examples:
Enter the following command to write query results to a clustered
destination table named myclusteredtable
in mydataset
. mydataset
is in
your default project. The query retrieves data from a non-partitioned table:
mytable. The table's customer_id
column is used to cluster the
table. The table's timestamp
column is used to create a partitioned table.
bq query --use_legacy_sql=false \
'CREATE TABLE
mydataset.myclusteredtable
PARTITION BY
DATE(timestamp)
CLUSTER BY
customer_id
AS (
SELECT
*
FROM
`mydataset.mytable`
);'
API
To save query results to a clustered table,
call the jobs.insert
method,
configure a
query
job,
and include a CREATE TABLE
DDL
statement that creates your clustered table.
Specify your location in the location
property in the
jobReference
section of the
job resource.
Create a clustered table when you load data
You can create a clustered table by specifying clustering columns when you load data into a new table. You do not need to create an empty table before loading data into it. You can create the clustered table and load your data at the same time.
For more information about loading data, see Introduction to loading data into BigQuery.
To define clustering when defining a load job:
SQL
Use the LOAD DATA
statement.
The following example loads AVRO data to create a table that is partitioned
by the transaction_date
field and clustered by the customer_id
field.
It also configures the partitions to expire after three days.
In the Google Cloud console, go to the BigQuery page.
In the query editor, enter the following statement:
LOAD DATA INTO mydataset.mytable PARTITION BY transaction_date CLUSTER BY customer_id OPTIONS ( partition_expiration_days = 3) FROM FILES( format = 'AVRO', uris = ['gs://bucket/path/file.avro']);
Click
Run.
For more information about how to run queries, see Run an interactive query.
API
To define a clustering configuration when creating a table through a
load job, you can populate the
Clustering
properties for the table.
Go
Before trying this sample, follow the Go setup instructions in the
BigQuery quickstart using
client libraries.
For more information, see the
BigQuery Go API
reference documentation.
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see
Set up authentication for client libraries.
Java
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.
Python
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.
Control access to clustered tables
To configure access to tables and views, you can grant an IAM role to an entity at the following levels, listed in order of range of resources allowed (largest to smallest):
- a high level in the Google Cloud resource hierarchy such as the project, folder, or organization level
- the dataset level
- the table or view level
You can also restrict data access within tables, by using the following methods:
Access with any resource protected by IAM is additive. For example, if an entity does not have access at the high level such as a project, you could grant the entity access at the dataset level, and then the entity will have access to the tables and views in the dataset. Similarly, if the entity does not have access at the high level or the dataset level, you could grant the entity access at the table or view level.
Granting IAM roles at a higher level in the Google Cloud resource hierarchy such as the project, folder, or organization level gives the entity access to a broad set of resources. For example, granting a role to an entity at the project level gives that entity permissions that apply to all datasets throughout the project.
Granting a role at the dataset level specifies the operations an entity is allowed to perform on tables and views in that specific dataset, even if the entity does not have access at a higher level. For information on configuring dataset-level access controls, see Controlling access to datasets.
Granting a role at the table or view level specifies the operations an entity is allowed to perform on specific tables and views, even if the entity does not have access at a higher level. For information on configuring table-level access controls, see Controlling access to tables and views.
You can also create IAM custom roles. If you create a custom role, the permissions you grant depend on the specific operations you want the entity to be able to perform.
You can't set a "deny" permission on any resource protected by IAM.
For more information about roles and permissions, see Understanding roles in the IAM documentation and the BigQuery IAM roles and permissions.
Use clustered tables
Get information about clustered tables
You can get information about tables in the following ways:
- Using the Google Cloud console.
- Using the bq command-line tool's
bq show
command. - Calling the
tables.get
API method. - Querying
INFORMATION_SCHEMA
views.
Required permissions
At a minimum, to get information about tables, you must be granted
bigquery.tables.get
permissions. The following predefined IAM
roles include bigquery.tables.get
permissions:
bigquery.metadataViewer
bigquery.dataViewer
bigquery.dataOwner
bigquery.dataEditor
bigquery.admin
In addition, if a user has bigquery.datasets.create
permissions, when that
user creates a dataset, they are granted bigquery.dataOwner
access to it.
bigquery.dataOwner
access gives the user the ability to get information about
tables in a dataset.
For more information about IAM roles and permissions in BigQuery, see Predefined roles and permissions.
Get clustered table information
To view information about a clustered table:
Console
In the Google Cloud console, go to the Resources pane. Click your dataset name to expand it, and then click the table name you want to view.
Click Details. This page displays the table's details including the clustering columns.
SQL
For clustered tables, you can query the CLUSTERING_ORDINAL_POSITION
column
in the INFORMATION_SCHEMA.COLUMNS
view
to find the 1-indexed offset of the column within the table's clustering
columns:
In the Google Cloud console, go to the BigQuery page.
In the query editor, enter the following statement:
CREATE TABLE mydataset.data (column1 INT64, column2 INT64) CLUSTER BY column1, column2; SELECT column_name, clustering_ordinal_position FROM mydataset.INFORMATION_SCHEMA.COLUMNS;
Click
Run.
For more information about how to run queries, see Run an interactive query.
The clustering ordinal position is 1 for column1
and 2 for column2
.
More table metadata is available through the TABLES
, TABLE_OPTIONS
,
COLUMNS
, and COLUMN_FIELD_PATH
views in INFORMATION_SCHEMA
.
bq
Issue the bq show
command to display all table information. Use the
--schema
flag to display only table schema information. The --format
flag can be used to control the output.
If you are getting information about a table in a project other than
your default project, add the project ID to the dataset in the following
format: project_id:dataset
.
bq show \ --schema \ --format=prettyjson \ PROJECT_ID:DATASET.TABLE
Replace the following:
PROJECT_ID
: your project IDDATASET
: the name of the datasetTABLE
: the name of the table
Examples:
Enter the following command to display all information about
myclusteredtable
in mydataset
. mydataset
in your default project.
bq show --format=prettyjson mydataset.myclusteredtable
The output should look like the following:
{ "clustering": { "fields": [ "customer_id" ] }, ... }
API
Call the bigquery.tables.get
method and provide any relevant parameters.
List clustered tables in a dataset
You can list clustered tables in datasets in the following ways:
- Using the Google Cloud console.
- Using the bq command-line tool's
bq ls
command. - Calling the
tables.list
API method. - Using the client libraries.
- Querying the
CLUSTERING_ORDINAL_POSITION
column in theINFORMATION_SCHEMA.COLUMNS
view.
The permissions required to list clustered tables and the steps to list them are the same as for standard tables. For more information about listing tables, see Listing tables in a dataset.
Modify clustering specification
You can change or remove a table's clustering specifications, or change the set of clustered columns in a clustered table. This method of updating the clustering column set is useful for tables that use continuous streaming inserts because those tables cannot be easily swapped by other methods.
Follow these steps to apply a new clustering specification to unpartitioned or partitioned tables.
In the bq tool, update the clustering specification of your table to match the new clustering:
bq update --clustering_fields=CLUSTER_COLUMN DATASET.ORIGINAL_TABLE
Replace the following:
CLUSTER_COLUMN
: the column you are clustering on—for example,mycolumn
DATASET
: the name of the dataset containing the table—for example,mydataset
ORIGINAL_TABLE
: the name of your original table—for example,mytable
You can also call the
tables.update
ortables.patch
API method to modify the clustering specification.To cluster all rows according to the new clustering specification, run the following
UPDATE
statement:UPDATE DATASET.ORIGINAL_TABLE SET CLUSTER_COLUMN=CLUSTER_COLUMN WHERE true
Table security
To control access to tables in BigQuery, see Introduction to table access controls.
What's next
- For information about querying clustered tables, see Querying clustered tables.
- For an overview of partitioned table support in BigQuery, see Introduction to partitioned tables.
- To learn how to create partitioned tables, see Creating partitioned tables.
- To see an overview of
INFORMATION_SCHEMA
, go to Introduction to BigQueryINFORMATION_SCHEMA
.