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:

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 and MyTable can coexist in the same dataset, unless they are part of a dataset with case-sensitivity turned off.
  • Some table names and table name prefixes are reserved. If you receive an error saying that your table name or prefix is reserved, then select a different name and try again.

  • If you include multiple dot operators (.) in a sequence, the duplicate operators are implicitly stripped.

    For example, this: project_name....dataset_name..table_name

    Becomes this: project_name.dataset_name.table_name

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 the bigquery.jobs.create permission)
  • roles/bigquery.user (includes the bigquery.jobs.create permission)
  • roles/bigquery.jobUser (includes the bigquery.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 data types:

  • BIGNUMERIC
  • BOOL
  • DATE
  • DATETIME
  • GEOGRAPHY
  • INT64
  • NUMERIC
  • RANGE
  • STRING
  • TIMESTAMP

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

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, expand your project, and then select a dataset.
  3. In the Dataset info section, click Create table.
  4. In the Create table panel, specify the following details:
    1. In the Source section, select Empty table in the Create table from list.
    2. In the Destination section, specify the following details:
      1. For Dataset, select the dataset in which you want to create the table.
      2. In the Table field, enter the name of the table that you want to create.
      3. Verify that the Table type field is set to Native table.
    3. 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 Add field and enter the table schema. Specify each field's Name, Type, and Mode.
    4. For Clustering order, enter between one and four comma-separated column names.
    5. 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.
    6. 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:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. 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');

  3. 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 format COLUMN: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:

resource "google_bigquery_dataset" "default" {
  dataset_id                      = "mydataset"
  default_partition_expiration_ms = 2592000000  # 30 days
  default_table_expiration_ms     = 31536000000 # 365 days
  description                     = "dataset description"
  location                        = "US"
  max_time_travel_hours           = 96 # 4 days

  labels = {
    billing_group = "accounting",
    pii           = "sensitive"
  }
}

resource "google_bigquery_table" "default" {
  dataset_id          = google_bigquery_dataset.default.dataset_id
  table_id            = "mytable"
  deletion_protection = false # set to "true" in production

  clustering = ["ID", "Created"]

  schema = <<EOF
[
  {
    "name": "ID",
    "type": "INT64",
    "description": "Item ID"
  },
  {
    "name": "Item",
    "type": "STRING",
    "mode": "NULLABLE"
  },
 {
   "name": "Created",
   "type": "TIMESTAMP"
 }
]
EOF

}

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell

  1. Launch Cloud Shell.

  2. 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).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. 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.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. 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

  1. 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.

  2. 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.

  3. 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. 

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to create.
# table_id = "your-project.your_dataset.your_table_name"

schema = [
    bigquery.SchemaField("full_name", "STRING"),
    bigquery.SchemaField("city", "STRING"),
    bigquery.SchemaField("zipcode", "INTEGER"),
]

table = bigquery.Table(table_id, schema=schema)
table.clustering_fields = ["city", "zipcode"]
table = client.create_table(table)  # Make an API request.
print(
    "Created clustered table {}.{}.{}".format(
        table.project, table.dataset_id, table.table_id
    )
)

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. 

import (
	"context"
	"fmt"
	"time"

	"cloud.google.com/go/bigquery"
)

// createTableClustered demonstrates creating a BigQuery table with advanced properties like
// partitioning and clustering features.
func createTableClustered(projectID, datasetID, tableID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydatasetid"
	// tableID := "mytableid"
	ctx := context.Background()

	client, err := bigquery.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("bigquery.NewClient: %v", err)
	}
	defer client.Close()

	sampleSchema := bigquery.Schema{
		{Name: "timestamp", Type: bigquery.TimestampFieldType},
		{Name: "origin", Type: bigquery.StringFieldType},
		{Name: "destination", Type: bigquery.StringFieldType},
		{Name: "amount", Type: bigquery.NumericFieldType},
	}
	metaData := &bigquery.TableMetadata{
		Schema: sampleSchema,
		TimePartitioning: &bigquery.TimePartitioning{
			Field:      "timestamp",
			Expiration: 90 * 24 * time.Hour,
		},
		Clustering: &bigquery.Clustering{
			Fields: []string{"origin", "destination"},
		},
	}
	tableRef := client.Dataset(datasetID).Table(tableID)
	if err := tableRef.Create(ctx, metaData); err != nil {
		return err
	}
	return nil
}

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. 

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Clustering;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.StandardTableDefinition;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;
import com.google.cloud.bigquery.TimePartitioning;
import com.google.common.collect.ImmutableList;

public class CreateClusteredTable {
  public static void runCreateClusteredTable() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    createClusteredTable(datasetName, tableName);
  }

  public static void createClusteredTable(String datasetName, String tableName) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      TableId tableId = TableId.of(datasetName, tableName);

      TimePartitioning partitioning = TimePartitioning.of(TimePartitioning.Type.DAY);

      Schema schema =
          Schema.of(
              Field.of("name", StandardSQLTypeName.STRING),
              Field.of("post_abbr", StandardSQLTypeName.STRING),
              Field.of("date", StandardSQLTypeName.DATE));

      Clustering clustering =
          Clustering.newBuilder().setFields(ImmutableList.of("name", "post_abbr")).build();

      StandardTableDefinition tableDefinition =
          StandardTableDefinition.newBuilder()
              .setSchema(schema)
              .setTimePartitioning(partitioning)
              .setClustering(clustering)
              .build();
      TableInfo tableInfo = TableInfo.newBuilder(tableId, tableDefinition).build();

      bigquery.create(tableInfo);
      System.out.println("Clustered table created successfully");
    } catch (BigQueryException e) {
      System.out.println("Clustered table was not created. \n" + e.toString());
    }
  }
}

Create a clustered table from a query result

There are two ways to create a clustered table from a query result:

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:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. 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
);

  3. 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 to asia-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 a CREATE 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.

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. 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']);

  3. 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. 

import (
	"context"
	"fmt"

	"cloud.google.com/go/bigquery"
)

// importClusteredTable demonstrates creating a table from a load job and defining partitioning and clustering
// properties.
func importClusteredTable(projectID, destDatasetID, destTableID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydataset"
	// tableID := "mytable"
	ctx := context.Background()
	client, err := bigquery.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("bigquery.NewClient: %v", err)
	}
	defer client.Close()

	gcsRef := bigquery.NewGCSReference("gs://cloud-samples-data/bigquery/sample-transactions/transactions.csv")
	gcsRef.SkipLeadingRows = 1
	gcsRef.Schema = bigquery.Schema{
		{Name: "timestamp", Type: bigquery.TimestampFieldType},
		{Name: "origin", Type: bigquery.StringFieldType},
		{Name: "destination", Type: bigquery.StringFieldType},
		{Name: "amount", Type: bigquery.NumericFieldType},
	}
	loader := client.Dataset(destDatasetID).Table(destTableID).LoaderFrom(gcsRef)
	loader.TimePartitioning = &bigquery.TimePartitioning{
		Field: "timestamp",
	}
	loader.Clustering = &bigquery.Clustering{
		Fields: []string{"origin", "destination"},
	}
	loader.WriteDisposition = bigquery.WriteEmpty

	job, err := loader.Run(ctx)
	if err != nil {
		return err
	}
	status, err := job.Wait(ctx)
	if err != nil {
		return err
	}

	if status.Err() != nil {
		return fmt.Errorf("job completed with error: %v", status.Err())
	}
	return nil
}

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. 

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Clustering;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.FormatOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobInfo;
import com.google.cloud.bigquery.LoadJobConfiguration;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TimePartitioning;
import com.google.common.collect.ImmutableList;

public class LoadTableClustered {

  public static void runLoadTableClustered() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    String sourceUri = "/path/to/file.csv";
    loadTableClustered(datasetName, tableName, sourceUri);
  }

  public static void loadTableClustered(String datasetName, String tableName, String sourceUri)
      throws Exception {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      TableId tableId = TableId.of(datasetName, tableName);

      Schema schema =
          Schema.of(
              Field.of("name", StandardSQLTypeName.STRING),
              Field.of("post_abbr", StandardSQLTypeName.STRING),
              Field.of("date", StandardSQLTypeName.DATE));

      TimePartitioning partitioning = TimePartitioning.of(TimePartitioning.Type.DAY);

      Clustering clustering =
          Clustering.newBuilder().setFields(ImmutableList.of("name", "post_abbr")).build();

      LoadJobConfiguration loadJobConfig =
          LoadJobConfiguration.builder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.csv())
              .setSchema(schema)
              .setTimePartitioning(partitioning)
              .setClustering(clustering)
              .build();

      Job loadJob = bigquery.create(JobInfo.newBuilder(loadJobConfig).build());

      // Load data from a GCS parquet file into the table
      // Blocks until this load table job completes its execution, either failing or succeeding.
      Job completedJob = loadJob.waitFor();

      // Check for errors
      if (completedJob == null) {
        throw new Exception("Job not executed since it no longer exists.");
      } else if (completedJob.getStatus().getError() != null) {
        // You can also look at queryJob.getStatus().getExecutionErrors() for all
        // errors, not just the latest one.
        throw new Exception(
            "BigQuery was unable to load into the table due to an error: \n"
                + loadJob.getStatus().getError());
      }
      System.out.println("Data successfully loaded into clustered table during load job");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Data not loaded into clustered table during load job \n" + e.toString());
    }
  }
}

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. 

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to create.
# table_id = "your-project.your_dataset.your_table_name"

job_config = bigquery.LoadJobConfig(
    skip_leading_rows=1,
    source_format=bigquery.SourceFormat.CSV,
    schema=[
        bigquery.SchemaField("timestamp", bigquery.SqlTypeNames.TIMESTAMP),
        bigquery.SchemaField("origin", bigquery.SqlTypeNames.STRING),
        bigquery.SchemaField("destination", bigquery.SqlTypeNames.STRING),
        bigquery.SchemaField("amount", bigquery.SqlTypeNames.NUMERIC),
    ],
    time_partitioning=bigquery.TimePartitioning(field="timestamp"),
    clustering_fields=["origin", "destination"],
)

job = client.load_table_from_uri(
    ["gs://cloud-samples-data/bigquery/sample-transactions/transactions.csv"],
    table_id,
    job_config=job_config,
)

job.result()  # Waits for the job to complete.

table = client.get_table(table_id)  # Make an API request.
print(
    "Loaded {} rows and {} columns to {}".format(
        table.num_rows, len(table.schema), table_id
    )
)

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):

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

  1. 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.

  2. Click Details. This page displays the table's details including the clustering columns.

    Table details.

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:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. 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;

  3. 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 ID
  • DATASET: the name of the dataset
  • TABLE: 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 the INFORMATION_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.

  1. 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 or tables.patch API method to modify the clustering specification.

  2. 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.

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