Creating and using tables

This document describes how to create and use standard or "native" tables in BigQuery. For information on creating other table types, see:

• Creating and using ingestion-time partitioned tables
• Creating and using tables partitioned by a DATE or TIMESTAMP column
• Creating and using tables partitioned by an integer column
• Creating and using clustered tables

After creating a table, you can:

• Control access to your table data
• Get information about your tables
• List the tables in a dataset
• Get table metadata

For more information on managing tables including updating table properties, copying a table, and deleting a table, see Managing tables.

Before you begin

Before creating a table in BigQuery, first:

• Setup a project by following a BigQuery getting started guide.
• Create a BigQuery dataset.
• Optionally, read Introduction to tables to understand table limitations, quotas, and pricing.

Table naming

When you create a table in BigQuery, the table name must be unique per dataset. The table name can:

• Contain up to 1,024 characters
• Contain letters (upper or lower case), numbers, and underscores

Creating a table

You can create a table in BigQuery:

• Manually using the Cloud Console, the classic BigQuery web UI, or the bq command-line tool's bq mk command
• Programmatically by calling the tables.insert API method
• By using the client libraries
• From query results
• By defining a table that references an external data source
• When you load data
• By using a CREATE TABLE DDL statement

Required permissions

At a minimum, to create a table, you must be granted the following permissions:

• bigquery.tables.create permissions to create the table
• bigquery.tables.updateData to write data to the table by using a load job, a query job, or a copy job
• bigquery.jobs.create to run a query job, load job, or copy job that writes data to the table

Additional permissions such as bigquery.tables.getData might be required to access the data you're writing to the table.

The following predefined IAM roles include both bigquery.tables.create and bigquery.tables.updateData permissions:

• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.admin

The following predefined IAM roles include bigquery.jobs.create permissions:

• bigquery.user
• bigquery.jobUser
• 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 create and update tables in the dataset.

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Creating an empty table with a schema definition

When you create an empty table with a schema definition, you can:

• Enter the schema using the Cloud Console or the classic BigQuery web UI
• Provide the schema inline using the bq command-line tool
• Submit a JSON schema file using the bq command-line tool
• Provide the schema in a table resource when calling the API's tables.insert method

For more information on specifying a table schema, see Specifying a schema.

After the table is created, you can load data into it or populate it by writing query results to it.

To create an empty table with a schema definition:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. In the navigation panel, in the Resources section, expand your project and select a dataset.

3. On the right side of the window, in the details panel, click Create table.

   

4. On the Create table page, in the Source section, select Empty table.

5. On the Create table page, in the Destination section:

   • For Dataset name, choose the appropriate dataset.

     

   • In the Table name field, enter the name of the table you're creating in BigQuery.

   • Verify that Table type is set to Native table.

6. In the Schema section, enter the schema definition.

   • Enter schema information manually by:

     • Enabling Edit as text and entering the table schema as a JSON array.

       Note: You can view the schema of an existing table in JSON format by entering the following command: bq show --format=prettyjson dataset.table.

     • Using Add field to manually input the schema.

7. For Partition and cluster settings leave the default value — No partitioning.

8. In the Advanced options section, for Encryption leave the default value: Google-managed key. By default, BigQuery encrypts customer content stored at rest.

9. Click Create table.

Note: When you create an empty table using the Cloud Console, you cannot add a label, description, or expiration time. You can add these optional properties when you create a table using the bq command-line tool or API. After you create a table in the Cloud Console, you can add an expiration, description, and labels.

DDL

Data definition language (DDL) statements allow you to create and modify tables and views using standard SQL query syntax.

See more on Using Data Definition Language statements.

To create a table in the Cloud Console by using a DDL statement:

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Click Compose new query.

3. Type your CREATE TABLE DDL statement into the Query editor text area.

   The following query creates a table named newtable that expires on January 1, 2023. The table description is "a table that expires in 2023", and the table's label is org_unit:development.

    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
      >
    )
    OPTIONS(
      expiration_timestamp=TIMESTAMP "2023-01-01 00:00:00 UTC",
      description="a table that expires in 2023",
      labels=[("org_unit", "development")]
    )

4. (Optional) Click More and select Query settings.

5. (Optional) For Processing location, click Auto-select and choose your data's location. If you leave processing location set to unspecified, the processing location is automatically detected.

6. Click Run. When the query completes, the table will appear in the Resources pane.

Classic UI

1. Click the down arrow icon next to your dataset name in the navigation and click Create new table.

2. On the Create Table page, in the Source Data section, click Create empty table.

3. On the Create Table page, in the Destination Table section:

   • For Table name, choose the appropriate dataset, and in the table name field, enter the name of the table you're creating.
   • Verify that Table type is set to Native table.

4. In the Schema section, manually enter the schema definition.

   • You can enter schema information manually by:

     • Clicking Edit as text and entering the table schema as a JSON array:

       

       Note: You can view the schema of an existing table in JSON format by entering the following command: bq show --schema --format=prettyjson project_id:dataset.table.

     • Using Add Field to input the schema:

       

5. In the Options section, leave the default values.

   • Partitioning Type: None
   • Partitioning Field: — Should be unavailable
   • Require partition filter: — Should be unavailable
   • Clustering Fields: — Should be unavailable
   • Destination Encryption: Default By default, BigQuery encrypts customer content stored at rest using a Google-managed key.

6. Click Create Table.

Note: When you create an empty table using the web UI, you cannot add a label, description, or expiration time. You can add these optional properties when you create a table using the API or the bq command-line tool. After you create a table using the BigQuery web UI, you can add an expiration, description, and labels.

bq

Use the mk command with the --table or -t flag. You can supply table schema information inline or via a JSON schema file. Optional parameters include:

• --expiration
• --description
• --time_partitioning_type
• --destination_kms_key
• --label.

--time_partitioning_type and --destination_kms_key are not demonstrated here. For more information on --time_partitioning_type, see ingestion-time partitioned tables or partitioned tables. For more information on --destination_kms_key, see customer-managed encryption keys.

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.

To create an empty table in an existing dataset with a schema definition, enter:

bq mk \
--table \
--expiration integer \
--description description \
--label key:value, key:value \
project_id:dataset.table \
schema

Where:

• integer is the default lifetime (in seconds) for the table. The minimum value is 3600 seconds (one hour). The expiration time evaluates to the current UTC time plus the integer value. If you set the expiration time when you create a table, the dataset's default table expiration setting is ignored.
• description is a description of the table in quotes.
• key:value is the key:value pair that represents a label. You can enter multiple labels using a comma-separated list.
• project_id is your project ID.
• dataset is a dataset in your project.
• table is the name of the table you're creating.
• schema is an inline schema definition in the format field:data_type,field:data_type or the path to the JSON schema file on your local machine.

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 table using an inline schema definition. This command creates a table named mytable in mydataset in your default project. The table expiration is set to 3600 seconds (1 hour), the description is set to This is my table, and the label is set to organization:development. The command uses the -t shortcut instead of --table. The schema is specified inline as: qtr:STRING,sales:FLOAT,year:STRING.

bq mk \
-t \
--expiration 3600 \
--description "This is my table" \
--label organization:development \
mydataset.mytable \
qtr:STRING,sales:FLOAT,year:STRING

Enter the following command to create a table using a JSON schema file. This command creates a table named mytable in mydataset in your default project. The table expiration is set to 3600 seconds (1 hour), the description is set to This is my table, and the label is set to organization:development. The path to the schema file is /tmp/myschema.json.

bq mk \
--table \
--expiration 3600 \
--description "This is my table" \
--label organization:development \
mydataset.mytable \
/tmp/myschema.json

Enter the following command to create a table using an JSON schema file. This command creates a table named mytable in mydataset in myotherproject. The table expiration is set to 3600 seconds (1 hour), the description is set to This is my table, and the label is set to organization:development. The path to the schema file is /tmp/myschema.json.

bq mk \
--table \
--expiration 3600 \
--description "This is my table" \
--label organization:development \
myotherproject:mydataset.mytable \
/tmp/myschema.json

After the table is created, you can update the table's expiration, description, and labels. You can also modify the schema definition.

API

Call the tables.insert method with a defined table resource.

C#

Before trying this sample, follow the C# setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery C# API reference documentation.

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using Google.Cloud.BigQuery.V2;
using System;

public class BigQueryCreateTable
{
    public BigQueryTable CreateTable(
        string projectId = "your-project-id",
        string datasetId = "your_dataset_id"
    )
    {
        BigQueryClient client = BigQueryClient.Create(projectId);
        var dataset = client.GetDataset(datasetId);
        // Create schema for new table.
        var schema = new TableSchemaBuilder
        {
            { "full_name", BigQueryDbType.String },
            { "age", BigQueryDbType.Int64 }
        }.Build();
        // Create the table
        return dataset.CreateTable(tableId: "your_table_id", schema: schema);
    }
}

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.

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import (
	"context"
	"fmt"
	"time"

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

// createTableExplicitSchema demonstrates creating a new BigQuery table and specifying a schema.
func createTableExplicitSchema(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: "full_name", Type: bigquery.StringFieldType},
		{Name: "age", Type: bigquery.IntegerFieldType},
	}

	metaData := &bigquery.TableMetadata{
		Schema:         sampleSchema,
		ExpirationTime: time.Now().AddDate(1, 0, 0), // Table will be automatically deleted in 1 year.
	}
	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.

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import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.StandardTableDefinition;
import com.google.cloud.bigquery.TableDefinition;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;

public class CreateTable {

  public static void runCreateTable() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    Schema schema =
        Schema.of(
            Field.of("stringField", StandardSQLTypeName.STRING),
            Field.of("booleanField", StandardSQLTypeName.BOOL));
    createTable(datasetName, tableName, schema);
  }

  public static void createTable(String datasetName, String tableName, Schema schema) {
    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);
      TableDefinition tableDefinition = StandardTableDefinition.of(schema);
      TableInfo tableInfo = TableInfo.newBuilder(tableId, tableDefinition).build();

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

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.

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// Import the Google Cloud client library and create a client
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function createTable() {
  // Creates a new table named "my_table" in "my_dataset".

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = "my_dataset";
  // const tableId = "my_table";
  // const schema = 'Name:string, Age:integer, Weight:float, IsMagic:boolean';

  // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
  const options = {
    schema: schema,
    location: 'US',
  };

  // Create a new table in the dataset
  const [table] = await bigquery
    .dataset(datasetId)
    .createTable(tableId, options);

  console.log(`Table ${table.id} created.`);
}

PHP

Before trying this sample, follow the PHP setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery PHP API reference documentation.

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use Google\Cloud\BigQuery\BigQueryClient;

/** Uncomment and populate these variables in your code */
// $projectId = 'The Google project ID';
// $datasetId = 'The BigQuery dataset ID';
// $tableId = 'The BigQuery table ID';
// $fields = [
//    [
//        'name' => 'field1',
//        'type' => 'string',
//        'mode' => 'required'
//    ],
//    [
//        'name' => 'field2',
//        'type' => 'integer'
//    ],
//];

$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
]);
$dataset = $bigQuery->dataset($datasetId);
$schema = ['fields' => $fields];
$table = $dataset->createTable($tableId, ['schema' => $schema]);
printf('Created table %s' . PHP_EOL, $tableId);

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.

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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", mode="REQUIRED"),
    bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
]

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

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery Ruby API reference documentation.

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require "google/cloud/bigquery"

def create_table dataset_id = "my_dataset"
  bigquery = Google::Cloud::Bigquery.new
  dataset  = bigquery.dataset dataset_id
  table_id = "my_table"

  table = dataset.create_table table_id do |updater|
    updater.string  "full_name", mode: :required
    updater.integer "age",       mode: :required
  end

  puts "Created table: #{table_id}"
end

Creating an empty table without a schema definition

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.

View on GitHub Feedback

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardTableDefinition;
import com.google.cloud.bigquery.TableDefinition;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;

// Sample to create a table without schema
public class CreateTableWithoutSchema {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    createTableWithoutSchema(datasetName, tableName);
  }

  public static void createTableWithoutSchema(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);
      TableDefinition tableDefinition = StandardTableDefinition.of(Schema.of());
      TableInfo tableInfo = TableInfo.newBuilder(tableId, tableDefinition).build();

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

Creating a table from a query result

To create a table from a query result, write the results to a destination table.

Console

1. Open the BigQuery page in the Cloud Console.
   

   Go to the BigQuery page

2. In the navigation panel, in the Resources section, expand your project and select a dataset.

3. If the query editor is hidden, click Show editor at the top right of the window.

4. Enter a valid SQL query in the Query editor text area.

5. Click More below the editor, then select Query settings.

   

6. Check the box to Set a destination table for query results.

   

7. In the Destination section, select the appropriate Project name and Dataset name where the table will be created, and choose a Table name.

8. In the Destination table write preference section, choose one of the following:

   • Write if empty — Writes the query results to the table only if the table is empty.
   • Append to table — Appends the query results to an existing table.
   • Overwrite table — Overwrites an existing table with the same name using the query results.

9. (Optional) For Processing location, click Auto-select and choose your location.

10. Click Run query. This creates a query job that writes the query results to the table you specified.

Alternatively, if you forget to specify a destination table before running your query, you can copy the cached results table to a permanent table by clicking the Save Results button below the editor.

DDL

Data definition language (DDL) statements allow you to create and modify tables using standard SQL query syntax.

For more information, see the CREATE TABLE statement page and the CREATE TABLE example: Creating a new table from an existing table.

Classic UI

1. Go to the classic BigQuery web UI.
   Go to the classic BigQuery web UI

2. Click the Compose query button.

3. Enter a valid SQL query in the New Query text area.

4. Click Show Options.

5. In the Destination Table section, click Select Table.

6. In the Select Destination Table dialog:

   1. For Project, choose a project to store the dataset and table.

   2. For Dataset, choose the dataset to store the table.

   3. In the Table ID field, enter a table name. The name must be unique in the destination dataset. The table name can be up to 1024 characters long and can contain only a-z, A-Z, 0-9, or _ (the underscore character).

   4. Click OK.

7. In the Destination Table section, for Write Preference, choose one of the following:

   • Write if empty — Writes the query results to the table only if the table is empty.
   • Append to table — Appends the query results to an existing table.
   • Overwrite table — Overwrites an existing table with the same name using the query results.

8. (Optional) For Processing Location, click Unspecified and choose your data's location.

9. Click Run query. This creates a query job that writes the query results to the table you specified.

Alternately, if you forget to specify a destination table before running your query, you can copy the temporary table to a permanent table by clicking the Save as Table button in the results window.

bq

Enter the bq query command and specify the --destination_table flag to create a permanent table based on the query results. Specify the use_legacy_sql=false flag to use standard SQL syntax. To write the query results to a table that is not in your default project, add the project ID to the dataset name in the following format: project_id:dataset.

(Optional) Supply the --location flag and set the value to your location.

To control the write disposition for an existing destination table, specify one of the following optional flags:

• --append_table — If the destination table exists, the query results are appended to it.
• --replace — If the destination table exists, it is overwritten with the query results.

bq --location=location query \
--destination_table project_id:dataset.table \
--use_legacy_sql=false 'query'

Where:

• location is the name of the location used to process the query. 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 by using the .bigqueryrc file.
• project_id is your project ID.
• dataset is the name of the dataset that contains the table to whichn you are writing the query results.
• table is the name of the table to which you're writing the query results.
• query is a query in standard SQL syntax.

If no write disposition flag is specified, the default behavior is to write the results to the table only if it is empty. If the table exists and it is not empty, the following error is returned: `BigQuery error in query operation: Error processing job project_id:bqjob_123abc456789_00000e1234f_1': Already Exists: Table project_id:dataset.table.

Examples:

Note: These examples query a US-based public dataset. Because the public dataset is stored in the US multi-region location, the dataset that contains your destination table must also be in the US. You cannot query a dataset in one location and write the results to a destination table in another location.

Enter the following command to write query results to a destination table named mytable in mydataset. The dataset is in your default project. Since no write disposition flag is specified in the command, the table must be new or empty. Otherwise, an Already exists error is returned. The query retrieves data from the USA Name Data public dataset.

bq query \
--destination_table mydataset.mytable \
--use_legacy_sql=false \
'SELECT
  name,
  number
FROM
  `bigquery-public-data`.usa_names.usa_1910_current
WHERE
  gender = "M"
ORDER BY
  number DESC'

Enter the following command to use query results to overwrite a destination table named mytable in mydataset. The dataset is in your default project. The command uses the --replace flag to overwrite the destination table.

bq query \
--destination_table mydataset.mytable \
--replace \
--use_legacy_sql=false \
'SELECT
   name,
   number
 FROM
   `bigquery-public-data`.usa_names.usa_1910_current
 WHERE
   gender = "M"
 ORDER BY
   number DESC'
 

Enter the following command to append query results to a destination table named mytable in mydataset. The dataset is in my-other-project, not your default project. The command uses the --append_table flag to append the query results to the destination table.

bq query \
--append_table \
--use_legacy_sql=false \
--destination_table my-other-project:mydataset.mytable \
'SELECT
   name,
   number
 FROM
   `bigquery-public-data`.usa_names.usa_1910_current
 WHERE
   gender = "M"
 ORDER BY
   number DESC'
 

The output for each of these examples looks like the following. For readability, some output is truncated.

Waiting on bqjob_r123abc456_000001234567_1 ... (2s) Current status: DONE
+---------+--------+
|  name   | number |
+---------+--------+
| Robert  |  10021 |
| John    |   9636 |
| Robert  |   9297 |
| ...              |
+---------+--------+

API

To save query results to a permanent table, call the jobs.insert method, configure a query job, and include a value for the destinationTable property. To control the write disposition for an existing destination table, configure the writeDisposition property.

To control the processing location for the query job, specify the location property in the jobReference section of the job resource.

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.

bigquery/snippets/querying/bigquery_query_destination_table.go

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import (
	"context"
	"fmt"
	"io"

	"cloud.google.com/go/bigquery"
	"google.golang.org/api/iterator"
)

// queryWithDestination demonstrates saving the results of a query to a specific table by setting the destination
// via the API properties.
func queryWithDestination(w io.Writer, 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()

	q := client.Query("SELECT 17 as my_col")
	q.Location = "US" // Location must match the dataset(s) referenced in query.
	q.QueryConfig.Dst = client.Dataset(destDatasetID).Table(destTableID)
	// Run the query and print results when the query job is completed.
	job, err := q.Run(ctx)
	if err != nil {
		return err
	}
	status, err := job.Wait(ctx)
	if err != nil {
		return err
	}
	if err := status.Err(); err != nil {
		return err
	}
	it, err := job.Read(ctx)
	for {
		var row []bigquery.Value
		err := it.Next(&row)
		if err == iterator.Done {
			break
		}
		if err != nil {
			return err
		}
		fmt.Fprintln(w, row)
	}
	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 save query results to a permanent table, set the destination table to the desired TableId in a QueryJobConfiguration.

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import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.QueryJobConfiguration;
import com.google.cloud.bigquery.TableId;

public class SaveQueryToTable {

  public static void runSaveQueryToTable() {
    // TODO(developer): Replace these variables before running the sample.
    String query = "SELECT corpus FROM `bigquery-public-data.samples.shakespeare` GROUP BY corpus;";
    String destinationTable = "MY_TABLE";
    String destinationDataset = "MY_DATASET";

    saveQueryToTable(destinationDataset, destinationTable, query);
  }

  public static void saveQueryToTable(
      String destinationDataset, String destinationTableId, String query) {
    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();

      // Identify the destination table
      TableId destinationTable = TableId.of(destinationDataset, destinationTableId);

      // Build the query job
      QueryJobConfiguration queryConfig =
          QueryJobConfiguration.newBuilder(query).setDestinationTable(destinationTable).build();

      // Execute the query.
      bigquery.query(queryConfig);

      // The results are now saved in the destination table.

      System.out.println("Saved query ran successfully");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Saved query did not run \n" + e.toString());
    }
  }
}

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.

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// Import the Google Cloud client library
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function queryDestinationTable() {
  // Queries the U.S. given names dataset for the state of Texas
  // and saves results to permanent table.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = 'my_dataset';
  // const tableId = 'my_table';

  // Create destination table reference
  const dataset = bigquery.dataset(datasetId);
  const destinationTable = dataset.table(tableId);

  const query = `SELECT name
    FROM \`bigquery-public-data.usa_names.usa_1910_2013\`
    WHERE state = 'TX'
    LIMIT 100`;

  // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
  const options = {
    query: query,
    // Location must match that of the dataset(s) referenced in the query.
    location: 'US',
    destination: destinationTable,
  };

  // Run the query as a job
  const [job] = await bigquery.createQueryJob(options);

  console.log(`Job ${job.id} started.`);
  console.log(`Query results loaded to table ${destinationTable.id}`);
}

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 save query results to a permanent table, create a QueryJobConfig and set the destination to the desired TableReference. Pass the job configuration to the query method.

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from google.cloud import bigquery

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

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

job_config = bigquery.QueryJobConfig(destination=table_id)

sql = """
    SELECT corpus
    FROM `bigquery-public-data.samples.shakespeare`
    GROUP BY corpus;
"""

# Start the query, passing in the extra configuration.
query_job = client.query(sql, job_config=job_config)  # Make an API request.
query_job.result()  # Wait for the job to complete.

print("Query results loaded to the table {}".format(table_id))

Creating a table that references an external data source

An external data source (also known as a federated data source) is a data source that you can query directly even though the data is not stored in BigQuery. Instead of loading or streaming the data, you create a table that references the external data source.

BigQuery offers support for querying data directly from:

• Cloud Bigtable
• Cloud Storage
• Google Drive
• Cloud SQL

Supported formats are:

• Avro
• CSV
• JSON (newline delimited only)
• ORC
• Parquet

You can query data in a supported external data source by creating a temporary or permanent table that references data stored in the external data source. For more information on working with external data sources, see:

• Querying Cloud Bigtable data
• Querying Cloud Storage data
• Querying Drive data

Creating a table when you load data

When you load data into BigQuery, you can load data into a new table or partition, you can append data to an existing table or partition, or you can overwrite a table or partition. You do not need to create an empty table before loading data into it. You can create the new table and load your data at the same time.

When you load data into BigQuery, you can supply the table or partition schema, or for supported data formats, you can use schema auto-detection.

For more information on loading data, see Introduction to loading data into BigQuery.

Controlling access to 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/view level

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 of 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 on roles and permissions, see:

• Understanding roles in the IAM documentation
• BigQuery Predefined roles and permissions
• Controlling access to datasets
• Controlling access to tables and views
• Restricting access with BigQuery Column-level security

Using tables

Getting information about tables

You can get information or metadata about tables by:

• Using the Cloud Console or the classic BigQuery web UI
• Using the bq command-line tool's bq show command
• Calling the tables.get API method
• Using the client libraries
• Querying the INFORMATION_SCHEMA views (beta)

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 retrieve table metadata.

For more information on IAM roles and permissions in BigQuery, see Access control.

Getting table information

To get information about tables:

Console

1. In the navigation panel, in the Resources section, expand your project and select a dataset. Click the dataset name to expand it. This displays the tables and views in the dataset.

2. Click the table name.

3. Below the editor, click Details. This page displays the table's description and table information.

4. Click the Schema tab to view the table's schema definition.

Classic UI

1. In the navigation pane, click the down arrow icon to the left of your dataset to expand it, or double-click the dataset name. This displays the tables and views in the dataset.

2. Click the table name.

3. Click Details. The Table Details page displays the table's description and table information.

   

4. Click the Schema tab to view the table's schema definition.

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

Where:

• project_id is your project ID.
• dataset is the name of the dataset.
• table is the name of the table.

Examples:

Enter the following command to display all information about mytable in mydataset. mydataset is in your default project.

bq show --format=prettyjson mydataset.mytable

Enter the following command to display all information about mytable in mydataset. mydataset is in myotherproject, not your default project.

bq show --format=prettyjson myotherproject:mydataset.mytable

Enter the following command to display only schema information about mytable in mydataset. mydataset is in myotherproject, not your default project.

bq show --schema --format=prettyjson myotherproject:mydataset.mytable

API

Call the tables.get method and provide any relevant parameters.

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.

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import (
	"context"
	"fmt"
	"io"

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

// printTableInfo demonstrates fetching metadata from a table and printing some basic information
// to an io.Writer.
func printTableInfo(w io.Writer, projectID, datasetID, tableID 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()

	meta, err := client.Dataset(datasetID).Table(tableID).Metadata(ctx)
	if err != nil {
		return err
	}
	// Print basic information about the table.
	fmt.Fprintf(w, "Schema has %d top-level fields\n", len(meta.Schema))
	fmt.Fprintf(w, "Description: %s\n", meta.Description)
	fmt.Fprintf(w, "Rows in managed storage: %d\n", meta.NumRows)
	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.

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import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Table;
import com.google.cloud.bigquery.TableId;

public class GetTable {

  public static void runGetTable() {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "bigquery_public_data";
    String datasetName = "samples";
    String tableName = "shakespeare";
    getTable(projectId, datasetName, tableName);
  }

  public static void getTable(String projectId, 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(projectId, datasetName, tableName);
      Table table = bigquery.getTable(tableId);
      System.out.println("Table info: " + table.getDescription());
    } catch (BigQueryException e) {
      System.out.println("Table not retrieved. \n" + e.toString());
    }
  }
}

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.

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// Import the Google Cloud client library
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function getTable() {
  // Retrieves table named "my_table" in "my_dataset".

  /**
   * TODO(developer): Uncomment the following lines before running the sample
   */
  // const datasetId = "my_dataset";
  // const tableId = "my_table";

  // Retrieve table reference
  const dataset = bigquery.dataset(datasetId);
  const [table] = await dataset.table(tableId).get();

  console.log('Table:');
  console.log(table.metadata.tableReference);
}
getTable();

PHP

Before trying this sample, follow the PHP setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery PHP API reference documentation.

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use Google\Cloud\BigQuery\BigQueryClient;

/** Uncomment and populate these variables in your code */
//$projectId = 'The Google project ID';
//$datasetId = 'The BigQuery dataset ID';
//$tableId   = 'The BigQuery table ID';

$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
]);
$dataset = $bigQuery->dataset($datasetId);
$table = $dataset->table($tableId);

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.

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from google.cloud import bigquery

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

# TODO(developer): Set table_id to the ID of the model to fetch.
# table_id = 'your-project.your_dataset.your_table'

table = client.get_table(table_id)  # Make an API request.

# View table properties
print(
    "Got table '{}.{}.{}'.".format(table.project, table.dataset_id, table.table_id)
)
print("Table schema: {}".format(table.schema))
print("Table description: {}".format(table.description))
print("Table has {} rows".format(table.num_rows))

Getting table information using INFORMATION_SCHEMA (beta)

INFORMATION_SCHEMA is a series of views that provide access to metadata about datasets, routines, tables, views, jobs, reservations, and streaming data.

You can query the INFORMATION_SCHEMA.TABLES and INFORMATION_SCHEMA.TABLE_OPTIONS views to retrieve metadata about tables and views in a project. You can also query the INFORMATION_SCHEMA.COLUMNS and INFORMATION_SCHEMA.COLUMN_FIELD_PATHS views to retrieve metadata about the columns (fields) in a table.

The TABLES and TABLE_OPTIONS views also contain high-level information about views. For detailed information, query the INFORMATION_SCHEMA.VIEWS view instead.

TABLES view

When you query the INFORMATION_SCHEMA.TABLES view, the query results contain one row for each table or view in a dataset.

The INFORMATION_SCHEMA.TABLES view has the following schema:

Column name	Data type	Value
TABLE_CATALOG	STRING	The name of the project that contains the dataset
TABLE_SCHEMA	STRING	The name of the dataset that contains the table or view also referred to as the datasetId
TABLE_NAME	STRING	The name of the table or view also referred to as the tableId
TABLE_TYPE	STRING	The table type: BASE TABLE: A BigQuery table VIEW: A BigQuery view EXTERNAL: A table that references an external data source
IS_INSERTABLE_INTO	STRING	YES or NO depending on whether the table supports DML INSERT statements
IS_TYPED	STRING	The value is always NO
CREATION_TIME	TIMESTAMP	The table's creation time

Examples

Example 1:

The following example retrieves all columns from the INFORMATION_SCHEMA.TABLES view except for is_typed which is reserved for future use. The metadata returned is for all tables in mydataset in your default project — myproject.

mydataset contains the following tables:

• mytable1: a standard BigQuery table
• myview1: a BigQuery view

To run the query against a project other than your default project, add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `myproject`.mydataset.INFORMATION_SCHEMA.TABLES.

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    * EXCEPT(is_typed)
   FROM
    mydataset.INFORMATION_SCHEMA.TABLES
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   * EXCEPT(is_typed)
 FROM
   mydataset.INFORMATION_SCHEMA.TABLES'

The results should look like the following:

  +----------------+---------------+----------------+------------+--------------------+---------------------+
  | table_catalog  | table_schema  |   table_name   | table_type | is_insertable_into |    creation_time    |
  +----------------+---------------+----------------+------------+--------------------+---------------------+
  | myproject      | mydataset     | mytable1       | BASE TABLE | YES                | 2018-10-29 20:34:44 |
  | myproject      | mydataset     | myview1        | VIEW       | NO                 | 2018-12-29 00:19:20 |
  +----------------+---------------+----------------+------------+--------------------+---------------------+
  

Example 2:

The following example retrieves all tables of type BASE TABLE from the INFORMATION_SCHEMA.TABLES view. The is_typed column is excluded. The metadata returned is for tables in mydataset in your default project — myproject.

To run the query against a project other than your default project, add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `myproject`.mydataset.INFORMATION_SCHEMA.TABLES.

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    * EXCEPT(is_typed)
   FROM
    mydataset.INFORMATION_SCHEMA.TABLES
   WHERE
    table_type="BASE TABLE"
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   * EXCEPT(is_typed)
 FROM
   mydataset.INFORMATION_SCHEMA.TABLES
 WHERE
   table_type="BASE TABLE"'

The results should look like the following:

  +----------------+---------------+----------------+------------+--------------------+---------------------+
  | table_catalog  | table_schema  |   table_name   | table_type | is_insertable_into |    creation_time    |
  +----------------+---------------+----------------+------------+--------------------+---------------------+
  | myproject      | mydataset     | mytable1       | BASE TABLE | NO                 | 2018-10-31 22:40:05 |
  +----------------+---------------+----------------+------------+--------------------+---------------------+
  

TABLE_OPTIONS view

When you query the INFORMATION_SCHEMA.TABLE_OPTIONS view, the query results contain one row for each table or view in a dataset.

The INFORMATION_SCHEMA.TABLE_OPTIONS view has the following schema:

Column name	Data type	Value
TABLE_CATALOG	STRING	The name of the project that contains the dataset
TABLE_SCHEMA	STRING	The name of the dataset that contains the table or view also referred to as the datasetId
TABLE_NAME	STRING	The name of the table or view also referred to as the tableId
OPTION_NAME	STRING	One of the name values in the options table
OPTION_TYPE	STRING	One of the data type values in the options table
OPTION_VALUE	STRING	One of the value options in the options table

Options table

OPTION_NAME	OPTION_TYPE	OPTION_VALUE
partition_expiration_days	FLOAT64	The default lifetime, in days, of all partitions in a partitioned table
expiration_timestamp	FLOAT64	The time when this table expires
kms_key_name	STRING	The name of the Cloud KMS key used to encrypt the table
friendly_name	STRING	The table's descriptive name
description	STRING	A description of the table
labels	ARRAY<STRUCT<STRING, STRING>>	An array of STRUCT's that represent the labels on the table
require_partition_filter	BOOL	Whether queries over the table require a partition filter

Examples

Example 1:

The following example retrieves the default table expiration times for all tables in mydataset in your default project (myproject) by querying the INFORMATION_SCHEMA.TABLE_OPTIONS view.

To run the query against a project other than your default project, add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `myproject`.mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS.

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    *
   FROM
    mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS
   WHERE
    option_name="expiration_timestamp"
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   *
 FROM
   mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS
 WHERE
   option_name="expiration_timestamp"'

The results should look like the following:

  +----------------+---------------+------------+----------------------+-------------+--------------------------------------+
  | table_catalog  | table_schema  | table_name |     option_name      | option_type |             option_value             |
  +----------------+---------------+------------+----------------------+-------------+--------------------------------------+
  | myproject      | mydataset     | mytable1   | expiration_timestamp | TIMESTAMP   | TIMESTAMP "2020-01-16T21:12:28.000Z" |
  | myproject      | mydataset     | mytable2   | expiration_timestamp | TIMESTAMP   | TIMESTAMP "2021-01-01T21:12:28.000Z" |
  +----------------+---------------+------------+----------------------+-------------+--------------------------------------+
  

Note: Tables without an expiration time are excluded from the query results.

Example 2:

The following example retrieves metadata about all tables in mydataset that contain test data. The query uses the values in the description option to find tables that contain "test" anywhere in the description. mydataset is in your default project — myproject.

To run the query against a project other than your default project, add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `myproject`.mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS.

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    *
   FROM
    mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS
   WHERE
    option_name="description" AND option_value LIKE "%test%"
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   *
 FROM
   mydataset.INFORMATION_SCHEMA.TABLE_OPTIONS
 WHERE
   option_name="description" AND option_value LIKE "%test%"'

The results should look like the following:

  +----------------+---------------+------------+-------------+-------------+--------------+
  | table_catalog  | table_schema  | table_name | option_name | option_type | option_value |
  +----------------+---------------+------------+-------------+-------------+--------------+
  | myproject      | mydataset     | mytable1   | description | STRING      | "test data"  |
  | myproject      | mydataset     | mytable2   | description | STRING      | "test data"  |
  +----------------+---------------+------------+-------------+-------------+--------------+
  

COLUMNS view

When you query the INFORMATION_SCHEMA.COLUMNS view, the query results contain one row for each column (field) in a table.

The INFORMATION_SCHEMA.COLUMNS view has the following schema:

Column name	Data type	Value
TABLE_CATALOG	STRING	The name of the project that contains the dataset
TABLE_SCHEMA	STRING	The name of the dataset that contains the table also referred to as the datasetId
TABLE_NAME	STRING	The name of the table or view also referred to as the tableId
COLUMN_NAME	STRING	The name of the column
ORDINAL_POSITION	INT64	The 1-indexed offset of the column within the table; if it's a pseudo column such as _PARTITIONTIME or _PARTITIONDATE, the value is NULL
IS_NULLABLE	STRING	YES or NO depending on whether the column's mode allows NULL values
DATA_TYPE	STRING	The column's standard SQL data type
IS_GENERATED	STRING	The value is always NEVER
GENERATION_EXPRESSION	STRING	The value is always NULL
IS_STORED	STRING	The value is always NULL
IS_HIDDEN	STRING	YES or NO depending on whether the column is a pseudo column such as _PARTITIONTIME or _PARTITIONDATE
IS_UPDATABLE	STRING	The value is always NULL
IS_SYSTEM_DEFINED	STRING	YES or NO depending on whether the column is a pseudo column such as _PARTITIONTIME or _PARTITIONDATE
IS_PARTITIONING_COLUMN	STRING	YES or NO depending on whether the column is a partitioning column
CLUSTERING_ORDINAL_POSITION	INT64	The 1-indexed offset of the column within the table's clustering columns; the value is NULL if the table is not a clustered table

Examples

The following example retrieves metadata from the INFORMATION_SCHEMA.COLUMNS view for the population_by_zip_2010 table in the census_bureau_usa dataset. This dataset is part of the BigQuery public dataset program.

Because the table you're querying is in another project, the bigquery-public-data project, you add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `bigquery-public-data`.census_bureau_usa.INFORMATION_SCHEMA.TABLES.

The following columns are excluded from the query results because they are currently reserved for future use:

• IS_GENERATED
• GENERATION_EXPRESSION
• IS_STORED
• IS_UPDATABLE

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    * EXCEPT(is_generated, generation_expression, is_stored, is_updatable)
   FROM
    `bigquery-public-data`.census_bureau_usa.INFORMATION_SCHEMA.COLUMNS
   WHERE
    table_name="population_by_zip_2010"
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   * EXCEPT(is_generated, generation_expression, is_stored, is_updatable)
 FROM
   `bigquery-public-data`.census_bureau_usa.INFORMATION_SCHEMA.COLUMNS
 WHERE
   table_name="population_by_zip_2010"'

The results should look like the following. For readability, table_catalog and table_schema are excluded from the results:

+------------------------+-------------+------------------+-------------+-----------+-----------+-------------------+------------------------+-----------------------------+
|       table_name       | column_name | ordinal_position | is_nullable | data_type | is_hidden | is_system_defined | is_partitioning_column | clustering_ordinal_position |
+------------------------+-------------+------------------+-------------+-----------+-----------+-------------------+------------------------+-----------------------------+
| population_by_zip_2010 | zipcode     |                1 | NO          | STRING    | NO        | NO                | NO                     |                        NULL |
| population_by_zip_2010 | geo_id      |                2 | YES         | STRING    | NO        | NO                | NO                     |                        NULL |
| population_by_zip_2010 | minimum_age |                3 | YES         | INT64     | NO        | NO                | NO                     |                        NULL |
| population_by_zip_2010 | maximum_age |                4 | YES         | INT64     | NO        | NO                | NO                     |                        NULL |
| population_by_zip_2010 | gender      |                5 | YES         | STRING    | NO        | NO                | NO                     |                        NULL |
| population_by_zip_2010 | population  |                6 | YES         | INT64     | NO        | NO                | NO                     |                        NULL |
+------------------------+-------------+------------------+-------------+-----------+-----------+-------------------+------------------------+-----------------------------+
  

COLUMN_FIELD_PATHS view

When you query the INFORMATION_SCHEMA.COLUMN_FIELD_PATHS view, the query results contain one row for each column nested within a RECORD (or STRUCT) column.

The INFORMATION_SCHEMA.COLUMN_FIELD_PATHS view has the following schema:

Column name	Data type	Value
TABLE_CATALOG	STRING	The name of the project that contains the dataset
TABLE_SCHEMA	STRING	The name of the dataset that contains the table also referred to as the datasetId
TABLE_NAME	STRING	The name of the table or view also referred to as the tableId
COLUMN_NAME	STRING	The name of the column
FIELD_PATH	STRING	The path to a column nested within a `RECORD` or `STRUCT` column
DATA_TYPE	STRING	The column's standard SQL data type
DESCRIPTION	STRING	The column's description

Examples

The following example retrieves metadata from the INFORMATION_SCHEMA.COLUMN_FIELD_PATHS view for the commits table in the github_repos dataset. This dataset is part of the BigQuery public dataset program.

Because the table you're querying is in another project, the bigquery-public-data project, you add the project ID to the dataset in the following format: `project_id`.dataset.INFORMATION_SCHEMA.view; for example, `bigquery-public-data`.github_repos.INFORMATION_SCHEMA.COLUMN_FIELD_PATHS.

The commits table contains the following nested and nested and repeated columns:

• author: nested RECORD column
• committer: nested RECORD column
• trailer: nested and repeated RECORD column
• difference: nested and repeated RECORD column

Your query will retrieve metadata about the author and difference columns.

To run the query:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Enter the following standard SQL query in the Query editor box. INFORMATION_SCHEMA requires standard SQL syntax. Standard SQL is the default syntax in the Cloud Console.

   SELECT
    *
   FROM
    `bigquery-public-data`.github_repos.INFORMATION_SCHEMA.COLUMN_FIELD_PATHS
   WHERE
    table_name="commits"
    AND column_name="author"
    OR column_name="difference"
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

bq

Use the query command and specify standard SQL syntax by using the --nouse_legacy_sql or --use_legacy_sql=false flag. Standard SQL syntax is required for INFORMATION_SCHEMA queries.

To run the query, enter:

bq query --nouse_legacy_sql \
'SELECT
   *
 FROM
   `bigquery-public-data`.github_repos.INFORMATION_SCHEMA.COLUMN_FIELD_PATHS
 WHERE
   table_name="commits"
   AND column_name="author"
   OR column_name="difference"'

The results should look like the following. For readability, table_catalog and table_schema are excluded from the results.

  +------------+-------------+---------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+-------------+
  | table_name | column_name |     field_path      |                                                                      data_type                                                                      | description |
  +------------+-------------+---------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+-------------+
  | commits    | author      | author              | STRUCT<name STRING, email STRING, time_sec INT64, tz_offset INT64, date TIMESTAMP>                                                                  | NULL        |
  | commits    | author      | author.name         | STRING                                                                                                                                              | NULL        |
  | commits    | author      | author.email        | STRING                                                                                                                                              | NULL        |
  | commits    | author      | author.time_sec     | INT64                                                                                                                                               | NULL        |
  | commits    | author      | author.tz_offset    | INT64                                                                                                                                               | NULL        |
  | commits    | author      | author.date         | TIMESTAMP                                                                                                                                           | NULL        |
  | commits    | difference  | difference          | ARRAY<STRUCT<old_mode INT64, new_mode INT64, old_path STRING, new_path STRING, old_sha1 STRING, new_sha1 STRING, old_repo STRING, new_repo STRING>> | NULL        |
  | commits    | difference  | difference.old_mode | INT64                                                                                                                                               | NULL        |
  | commits    | difference  | difference.new_mode | INT64                                                                                                                                               | NULL        |
  | commits    | difference  | difference.old_path | STRING                                                                                                                                              | NULL        |
  | commits    | difference  | difference.new_path | STRING                                                                                                                                              | NULL        |
  | commits    | difference  | difference.old_sha1 | STRING                                                                                                                                              | NULL        |
  | commits    | difference  | difference.new_sha1 | STRING                                                                                                                                              | NULL        |
  | commits    | difference  | difference.old_repo | STRING                                                                                                                                              | NULL        |
  | commits    | difference  | difference.new_repo | STRING                                                                                                                                              | NULL        |
  +------------+-------------+---------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+-------------+
  

Listing tables in a dataset

You can list tables in datasets by:

• Using the Cloud Console or the classic BigQuery web UI
• Using the bq command-line tool's bq ls command
• Calling the tables.list API method
• Using the client libraries

Required permissions

At a minimum, to list tables in a dataset, you must be granted bigquery.tables.list permissions. The following predefined IAM roles include bigquery.tables.list permissions:

• bigquery.user
• bigquery.metadataViewer
• bigquery.dataViewer
• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.admin

For more information on IAM roles and permissions in BigQuery, see Access control.

Listing tables

To list the tables in a dataset:

Console

1. In the Cloud Console, in the navigation pane, click your dataset to expand it. This displays the tables and views in the dataset.

2. Scroll through the list to see the tables in the dataset. Tables and views are identified by different icons.

Classic UI

1. In the web UI, in the navigation pane, click the down arrow icon to the left of your dataset to expand it, or double-click the dataset name. This displays the tables and views in the dataset.

2. Scroll through the list to see the tables in the dataset. Tables and views are identified by different icons.

   

bq

Issue the bq ls command. The --format flag can be used to control the output. If you are listing tables in a project other than your default project, add the project ID to the dataset in the following format: project_id:dataset.

Additional flags include:

• --max_results or -n: An integer indicating the maximum number of results. The default value is 50.

bq ls \
--format=pretty \
--max_results integer \
project_id:dataset

Where:

• integer is an integer representing the number of tables to list.
• project_id is your project ID.
• dataset is the name of the dataset.

When you run the command, the Type field displays either TABLE or VIEW. For example:

+-------------------------+-------+----------------------+-------------------+
|         tableId         | Type  |        Labels        | Time Partitioning |
+-------------------------+-------+----------------------+-------------------+
| mytable                 | TABLE | department:shipping  |                   |
| myview                  | VIEW  |                      |                   |
+-------------------------+-------+----------------------+-------------------+

Examples:

Enter the following command to list tables in dataset mydataset in your default project.

bq ls --format=pretty mydataset

Enter the following command to return more than the default output of 50 tables from mydataset. mydataset is in your default project.

bq ls --format=pretty --max_results 60 mydataset

Enter the following command to list tables in dataset mydataset in myotherproject.

bq ls --format=pretty myotherproject:mydataset

API

To list tables using the API, call the tables.list method.

C#

Before trying this sample, follow the C# setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery C# API reference documentation.

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using Google.Cloud.BigQuery.V2;
using System;
using System.Collections.Generic;
using System.Linq;

public class BigQueryListTables
{
    public void ListTables(
        string projectId = "your-project-id",
        string datasetId = "your_dataset_id"
    )
    {
        BigQueryClient client = BigQueryClient.Create(projectId);
        // Retrieve list of tables in the dataset
        List<BigQueryTable> tables = client.ListTables(datasetId).ToList();
        // Display the results
        if (tables.Count > 0)
        {
            Console.WriteLine($"Tables in dataset {datasetId}:");
            foreach (var table in tables)
            {
                Console.WriteLine($"\t{table.Reference.TableId}");
            }
        }
        else
        {
            Console.WriteLine($"{datasetId} does not contain any tables.");
        }
    }
}

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.

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import (
	"context"
	"fmt"
	"io"

	"cloud.google.com/go/bigquery"
	"google.golang.org/api/iterator"
)

// listTables demonstrates iterating through the collection of tables in a given dataset.
func listTables(w io.Writer, projectID, datasetID 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()

	ts := client.Dataset(datasetID).Tables(ctx)
	for {
		t, err := ts.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return err
		}
		fmt.Fprintf(w, "Table: %q\n", t.TableID)
	}
	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.

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import com.google.api.gax.paging.Page;
import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQuery.TableListOption;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.DatasetId;
import com.google.cloud.bigquery.Table;

public class ListTables {

  public static void runListTables() {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "bigquery-public-data";
    String datasetName = "samples";
    listTables(projectId, datasetName);
  }

  public static void listTables(String projectId, String datasetName) {
    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();

      DatasetId datasetId = DatasetId.of(projectId, datasetName);
      Page<Table> tables = bigquery.listTables(datasetId, TableListOption.pageSize(100));
      tables.iterateAll().forEach(table -> System.out.print(table.getTableId().getTable() + "\n"));

      System.out.println("Tables listed successfully.");
    } catch (BigQueryException e) {
      System.out.println("Tables were not listed. Error occurred: " + e.toString());
    }
  }
}

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.

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// Import the Google Cloud client library
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function listTables() {
  // Lists tables in 'my_dataset'.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = 'my_dataset';

  // List all tables in the dataset
  const [tables] = await bigquery.dataset(datasetId).getTables();

  console.log('Tables:');
  tables.forEach(table => console.log(table.id));
}

PHP

Before trying this sample, follow the PHP setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery PHP API reference documentation.

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use Google\Cloud\BigQuery\BigQueryClient;

/** Uncomment and populate these variables in your code */
// $projectId  = 'The Google project ID';
// $datasetId  = 'The BigQuery dataset ID';

$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
]);
$dataset = $bigQuery->dataset($datasetId);
$tables = $dataset->tables();
foreach ($tables as $table) {
    print($table->id() . PHP_EOL);
}

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.

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from google.cloud import bigquery

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

# TODO(developer): Set dataset_id to the ID of the dataset that contains
#                  the tables you are listing.
# dataset_id = 'your-project.your_dataset'

tables = client.list_tables(dataset_id)  # Make an API request.

print("Tables contained in '{}':".format(dataset_id))
for table in tables:
    print("{}.{}.{}".format(table.project, table.dataset_id, table.table_id))

Ruby

Before trying this sample, follow the Ruby setup instructions in the BigQuery Quickstart Using Client Libraries. For more information, see the BigQuery Ruby API reference documentation.

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require "google/cloud/bigquery"

def list_tables dataset_id = "your_dataset_id"
  bigquery = Google::Cloud::Bigquery.new
  dataset  = bigquery.dataset dataset_id

  puts "Tables in dataset #{dataset_id}:"
  dataset.tables.each do |table|
    puts "\t#{table.table_id}"
  end
end

Next steps

• For more information on datasets, see Introduction to datasets.
• For more information on handling table data, see Managing table data.
• For more information on specifying table schemas, see Specifying a schema.
• For more information on modifying table schemas, see Modifying table schemas.
• For more information on managing tables, see Managing tables.
• To see an overview of INFORMATION_SCHEMA, go to Introduction to BigQuery INFORMATION_SCHEMA.