Getting information about datasets

This document describes how to get information or metadata about datasets in BigQuery.

You can get dataset information in the following ways:

Using the Cloud Console.
Using the bq show command in the bq command-line tool.
Calling the datasets.get API method.
Querying the INFORMATION_SCHEMA views (beta).
Using the client libraries.

Required permissions

At a minimum, to get information or metadata about a dataset, you must be granted bigquery.datasets.get permissions. The following predefined IAM roles include bigquery.datasets.get permissions:

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

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

Getting dataset information

To get information about datasets in a project:

Console

Click the dataset name in the Resources panel. Below the Query editor you should see dataset's description and details. The tables for a dataset are nested below it in the Resources panel.

View dataset.

By default, anonymous datasets are hidden from the Cloud Console. To show information about anonymous datasets, use the bq command-line tool or the API.

bq

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

To show information about an anonymous dataset, use the bq ls --all command to list all datasets and then use the name of the anonymous dataset in the bq show command.

bq show --format=prettyjson project_id:dataset

Replace the following:

project_id is the name of your project.
dataset is the name of the dataset.

Examples:

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

bq show --format=prettyjson mydataset

Enter the following command to display information about mydataset in myotherproject.

bq show --format=prettyjson myotherproject:mydataset

Enter the following command to display information about an anonymous dataset, _1234abcd56efgh78ijkl1234 in your default project.

bq show --format=prettyjson _1234abcd56efgh78ijkl1234

API

Call the datasets.get API 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"
	"google.golang.org/api/iterator"
)

// printDatasetInfo demonstrates fetching dataset metadata and printing some of it to an io.Writer.
func printDatasetInfo(w io.Writer, projectID, datasetID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydataset"
	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).Metadata(ctx)
	if err != nil {
		return err
	}

	fmt.Fprintf(w, "Dataset ID: %s\n", datasetID)
	fmt.Fprintf(w, "Description: %s\n", meta.Description)
	fmt.Fprintln(w, "Labels:")
	for k, v := range meta.Labels {
		fmt.Fprintf(w, "\t%s: %s", k, v)
	}
	fmt.Fprintln(w, "Tables:")
	it := client.Dataset(datasetID).Tables(ctx)

	cnt := 0
	for {
		t, err := it.Next()
		if err == iterator.Done {
			break
		}
		cnt++
		fmt.Fprintf(w, "\t%s\n", t.TableID)
	}
	if cnt == 0 {
		fmt.Fprintln(w, "\tThis dataset does not contain any tables.")
	}
	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.Dataset;
import com.google.cloud.bigquery.DatasetId;
import com.google.cloud.bigquery.Table;

public class GetDatasetInfo {

  public static void runGetDatasetInfo() {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "MY_PROJECT_ID";
    String datasetName = "MY_DATASET_NAME";
    getDatasetInfo(projectId, datasetName);
  }

  public static void getDatasetInfo(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);
      Dataset dataset = bigquery.getDataset(datasetId);

      // View dataset properties
      String description = dataset.getDescription();
      System.out.println(description);

      // View tables in the dataset
      // For more information on listing tables see:
      // https://javadoc.io/static/com.google.cloud/google-cloud-bigquery/0.22.0-beta/com/google/cloud/bigquery/BigQuery.html
      Page<Table> tables = bigquery.listTables(datasetName, TableListOption.pageSize(100));

      tables.iterateAll().forEach(table -> System.out.print(table.getTableId().getTable() + "\n"));

      System.out.println("Dataset info retrieved successfully.");
    } catch (BigQueryException e) {
      System.out.println("Dataset info 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 getDataset() {
  // Retrieves dataset named "my_dataset".

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

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

  console.log('Dataset:');
  console.log(dataset.metadata.datasetReference);
}
getDataset();

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 to fetch.
# dataset_id = 'your-project.your_dataset'

dataset = client.get_dataset(dataset_id)  # Make an API request.

full_dataset_id = "{}.{}".format(dataset.project, dataset.dataset_id)
friendly_name = dataset.friendly_name
print(
    "Got dataset '{}' with friendly_name '{}'.".format(
        full_dataset_id, friendly_name
    )
)

# View dataset properties.
print("Description: {}".format(dataset.description))
print("Labels:")
labels = dataset.labels
if labels:
    for label, value in labels.items():
        print("\t{}: {}".format(label, value))
else:
    print("\tDataset has no labels defined.")

# View tables in dataset.
print("Tables:")
tables = list(client.list_tables(dataset))  # Make an API request(s).
if tables:
    for table in tables:
        print("\t{}".format(table.table_id))
else:
    print("\tThis dataset does not contain any tables.")

`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.

`SCHEMATA` view

When you query the INFORMATION_SCHEMA.SCHEMATA view, the query results contain one row for each dataset in a project to which the current user has access.

The INFORMATION_SCHEMA.SCHEMATA view has the following schema:

Column name	Data type	Value
`CATALOG_NAME`	`STRING`	The name of the project that contains the dataset
`SCHEMA_NAME`	`STRING`	The dataset's name also referred to as the `datasetId`
`SCHEMA_OWNER`	`STRING`	The value is always `NULL`
`CREATION_TIME`	`TIMESTAMP`	The dataset's creation time
`LAST_MODIFIED_TIME`	`TIMESTAMP`	The dataset's last modified time
`LOCATION`	`STRING`	The dataset's geographic location

Examples

The following example retrieves all columns from the INFORMATION_SCHEMA.SCHEMATA view except for schema_owner which is reserved for future use. The metadata returned is for all datasets in the 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`.INFORMATION_SCHEMA.view for example, `myproject`.INFORMATION_SCHEMA.SCHEMATA.

To run the query:

Console

Open the BigQuery page in the Cloud Console.

Go to the BigQuery page
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(schema_owner)
FROM
 INFORMATION_SCHEMA.SCHEMATA
```
Note: INFORMATION_SCHEMA view names are case-sensitive.
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(schema_owner)
 FROM
   INFORMATION_SCHEMA.SCHEMATA'

The results should look like the following:

  +----------------+---------------+---------------------+---------------------+-----------------+
  |  catalog_name  |  schema_name  |    creation_time    | last_modified_time  |    location     |
  +----------------+---------------+---------------------+---------------------+-----------------+
  | myproject      | mydataset1    | 2018-11-07 19:50:24 | 2018-11-07 19:50:24 | US              |
  | myproject      | mydataset2    | 2018-07-16 04:24:22 | 2018-07-16 04:24:22 | US              |
  | myproject      | mydataset3    | 2018-02-07 21:08:45 | 2018-05-01 23:32:53 | asia-northeast1 |
  +----------------+---------------+---------------------+---------------------+-----------------+

`SCHEMATA_OPTIONS` view

When you query the INFORMATION_SCHEMA.SCHEMATA_OPTIONS view, the query results contain one row for each dataset in a project to which the current user has access.

The INFORMATION_SCHEMA.SCHEMATA_OPTIONS view has the following schema:

Column name	Data type	Value
`CATALOG_NAME`	`STRING`	The name of the project that contains the dataset
`SCHEMA_NAME`	`STRING`	The dataset's name also referred to as the `datasetId`
`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`
`default_table_expiration_days`	`FLOAT64`	The default lifetime, in days, of all tables in the dataset
`friendly_name`	`STRING`	The dataset's descriptive name
`description`	`STRING`	A description of the dataset
`labels`	`ARRAY<STRUCT<STRING, STRING>>`	An array of `STRUCT`'s that represent the labels on the dataset

Examples

Example 1:

The following example retrieves the default table expiration times for all datasets in your default project (myproject) by querying the INFORMATION_SCHEMATA.SCHEMATA_OPTIONS view.

To run the query:

Console

Open the BigQuery page in the Cloud Console.

Go to the BigQuery page
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
 INFORMATION_SCHEMA.SCHEMATA_OPTIONS
WHERE
 option_name="default_table_expiration_days"
```
Note: INFORMATION_SCHEMA view names are case-sensitive.
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
   INFORMATION_SCHEMA.SCHEMATA_OPTIONS
 WHERE
   option_name="default_table_expiration_days"'

The results should look like the following:

  +----------------+---------------+-------------------------------+-------------+---------------------+
  |  catalog_name  |  schema_name  |          option_name          | option_type |    option_value     |
  +----------------+---------------+-------------------------------+-------------+---------------------+
  | myproject      | mydataset3    | default_table_expiration_days | FLOAT64     | 0.08333333333333333 |
  | myproject      | mydataset2    | default_table_expiration_days | FLOAT64     | 90.0                |
  | myproject      | mydataset1    | default_table_expiration_days | FLOAT64     | 30.0                |
  +----------------+---------------+-------------------------------+-------------+---------------------+

Example 2:

The following example retrieves the labels for all datasets in your default project (myproject) by querying the INFORMATION_SCHEMATA.SCHEMATA_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`.INFORMATION_SCHEMA.view; for example, `myproject`.INFORMATION_SCHEMA.SCHEMATA_OPTIONS.

To run the query:

Console

Open the BigQuery page in the Cloud Console.

Go to the BigQuery page
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
 INFORMATION_SCHEMA.SCHEMATA_OPTIONS
WHERE
 option_name="labels"
```
Note: INFORMATION_SCHEMA view names are case-sensitive.
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
   INFORMATION_SCHEMA.SCHEMATA_OPTIONS
 WHERE
   option_name="labels"'

The results should look like the following:

  +----------------+---------------+-------------+---------------------------------+------------------------+
  |  catalog_name  |  schema_name  | option_name |          option_type            |      option_value      |
  +----------------+---------------+-------------+---------------------------------+------------------------+
  | myproject      | mydataset1    | labels      | ARRAY<STRUCT<STRING, STRING>>   | [STRUCT("org", "dev")] |
  | myproject      | mydataset2    | labels      | ARRAY<STRUCT<STRING, STRING>>   | [STRUCT("org", "dev")] |
  +----------------+---------------+-------------+---------------------------------+------------------------+

Next steps

For more information on creating datasets, see Creating datasets.
For more information on assigning access controls to datasets, see Controlling access to datasets.
For more information on listing datasets in a project, see Listing datasets.
For more information on changing dataset properties, see Updating datasets.
For more information on creating and managing labels, see Creating and managing labels.
To see an overview of INFORMATION_SCHEMA, go to Introduction to BigQuery INFORMATION_SCHEMA.

Getting information about datasets

Required permissions

Getting dataset information

Console

bq

API

Go

Java

Node.js

Python

INFORMATION_SCHEMA (beta)

SCHEMATA view

Examples

Console

bq

SCHEMATA_OPTIONS view

Options table

Examples

Example 1:

Console

bq

Example 2:

Console

bq

Next steps

`INFORMATION_SCHEMA` (beta)

`SCHEMATA` view

`SCHEMATA_OPTIONS` view