Writing query results

This document describes how to write or save query results.

Temporary and permanent tables

BigQuery saves all query results to a table, which can be either permanent or temporary.

• BigQuery uses temporary tables to cache query results that aren't written to a permanent table. The tables are created in a special dataset and named randomly. You can also create temporary tables for your own use. For further information, see Temporary tables.

  After a query finishes, the temporary table exists for up to 24 hours. To view table structure and data, go to the BigQuery console, click Query history, and choose the query that created the temporary table. Then, in the Destination table row, click Temporary table.

  You cannot query or share temporary tables, and they are not visible using any of the standard list or other table manipulation methods. You are not charged for storing temporary tables.

• A permanent table can be a new or existing table in any dataset to which you have access. If you write query results to a new table, you are charged for storing the data. When you write query results to a permanent table, the tables you're querying must be in the same location as the dataset that contains the destination table.

Required permissions

At a minimum, to write query results to a table, you must be granted the following permissions:

• bigquery.tables.create permissions to create a new table
• bigquery.tables.updateData to write data to a new table, overwrite a table, or append data to a table
• bigquery.jobs.create to run a query job

Additional permissions such as bigquery.tables.getData may be required to access the data you're querying.

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

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

The following predefined Cloud 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 Cloud IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Writing query results to a permanent table

When you write query results to a permanent table, you can create a new table, append the results to an existing table, or overwrite an existing table. You can write query results to a permanent table by:

• Using the Cloud Console or the classic BigQuery web UI
• Using the command-line tool's bq query command
• Calling the jobs.insert API method and configuring a query job
• Using the client libraries

Writing query results

Use the following procedure to write your query results to a permanent table. To help control costs, you can preview data before running the query.

Console

1. Open the BigQuery web UI in the Cloud Console.
   

   Go to the Cloud Console

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.

View on GitHub

// BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();
// String destinationDataset = 'my_destination_dataset';
// String destinationTable = 'my_destination_table';
String query = "SELECT corpus FROM `bigquery-public-data.samples.shakespeare` GROUP BY corpus;";
QueryJobConfiguration queryConfig =
    // Note that setUseLegacySql is set to false by default
    QueryJobConfiguration.newBuilder(query)
        // Save the results of the query to a permanent table.
        .setDestinationTable(TableId.of(destinationDataset, destinationTable))
        .build();

// Print the results.
for (FieldValueList row : bigquery.query(queryConfig).iterateAll()) {
  for (FieldValue val : row) {
    System.out.printf("%s,", val.toString());
  }
  System.out.printf("\n");
}

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))

Writing large query results

Normally, queries have a maximum response size. If you plan to run a query that might return larger results, you can:

• In standard SQL, specify a destination table for the query results.
• In legacy SQL, specify a destination table and set the allowLargeResults option.

When you specify a destination table for large query results, you are charged for storing the data.

Limitations

In legacy SQL, writing large results is subject to these limitations:

• You must specify a destination table.
• You cannot specify a top-level ORDER BY, TOP or LIMIT clause. Doing so negates the benefit of using allowLargeResults, because the query output can no longer be computed in parallel.
• Window functions can return large query results only if used in conjunction with a PARTITION BY clause.

Writing large results using legacy SQL

To write large result sets using legacy SQL:

Console

1. Open the BigQuery web UI in the Cloud Console.
   Go to the Cloud Console

2. Click Compose new query.

3. Enter a valid SQL query in the Query editor text area. Use the #legacySQL prefix or be sure you have Use Legacy SQL checked in the query settings.

4. Click More then select Query settings.

   

5. For Destination, check Set a destination table for query results.

   

6. For Project name, choose the project where the destination table will be created.

7. For Dataset name, choose the dataset that will store the table.

8. In the Table name field, enter a table name.

9. If you are writing a large results set to an existing table, you can use the Destination table write preference options to control the write disposition of the destination table:

   • 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.

   

10. For Results Size, check Allow Large Results (no size limit).

    

11. (Optional) For Processing Location, click Auto-select and choose your data's location.

    

12. Click Save to update the query settings.

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

Classic UI

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

2. Click the Compose query button.

3. Enter a valid BigQuery SQL query in the New Query text area. Use the #legacySQL prefix or be sure you have Use Legacy SQL checked in the query options.

4. Click Show Options.

5. For Destination Table, click Select Table.

6. In the Select Destination Table dialog:

   1. For Project, choose the project where the destination table will be created.

   2. For Dataset, choose the dataset that will store the table.

   3. In the Table ID field, enter a table name.

   4. Click OK.

7. If you are writing a large results set to an existing table, you can use the Write Preference option to control the write disposition of the destination table:

   • 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. For Results Size, check Allow Large Results.

   

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

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

bq

Use the --allow_large_results flag with the --destination_table flag to create a destination table to hold the large results set. Because the -- allow_large_results option only applies to legacy SQL, you must also specify the --use_legacy_sql=true flag. 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]. 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=true \
--allow_large_results "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 using the .bigqueryrc file.
• project_id is your project ID.
• dataset is the name of the dataset that contains the table to which 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 legacy SQL syntax.

Examples:

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

Enter the following command to write large 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. This query is used for example purposes only. The results set returned does not exceed the maximum response size.

bq query \
--destination_table mydataset.mytable \
--use_legacy_sql=true \
--allow_large_results \
"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 large query results to overwrite a destination table named mytable in mydataset. The dataset is in myotherproject, not your default project. The command uses the --replace flag to overwrite the destination table.

bq query \
--destination_table mydataset.mytable \
--replace \
--use_legacy_sql=true \
--allow_large_results \
"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 large query results to a destination table named mytable in mydataset. The dataset is in myotherproject, not your default project. The command uses the --append_table flag to append the query results to the destination table.

bq query \
--destination_table myotherproject:mydataset.mytable \
--append_table \
--use_legacy_sql=true \
--allow_large_results \
"SELECT
  name,
  number
FROM
  [bigquery-public-data:usa_names.usa_1910_current]
WHERE
  gender = 'M'
ORDER BY
  number DESC"

API

To write large results to a destination table, call the jobs.insert method, configure a query job, and set the allowLargeResults property to true. Specify the destination table using the destinationTable property. To control the write disposition for an existing destination table, configure the writeDisposition property.

Specify your location in 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.

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import (
	"context"
	"fmt"
	"io"

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

// queryLegacyLargeResults demonstrates issuing a legacy SQL query and writing a large result set
// into a destination table.
func queryLegacyLargeResults(w io.Writer, projectID, datasetID, tableID string) error {
	// projectID := "my-project-id"
	// datasetID := "destinationdataset"
	// tableID := "destinationtable"
	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 corpus FROM [bigquery-public-data:samples.shakespeare] GROUP BY corpus;")
	q.UseLegacySQL = true
	q.AllowLargeResults = true
	q.QueryConfig.Dst = client.Dataset(datasetID).Table(tableID)
	// 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

To enable large results, set allow large results to true and set the destination table to the desired TableId in a QueryJobConfiguration.

View on GitHub

// BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();
// String destinationDataset = 'my_destination_dataset';
// String destinationTable = 'my_destination_table';
String query = "SELECT corpus FROM [bigquery-public-data:samples.shakespeare] GROUP BY corpus;";
QueryJobConfiguration queryConfig =
    // To use legacy SQL syntax, set useLegacySql to true.
    QueryJobConfiguration.newBuilder(query)
        .setUseLegacySql(true)
        // Save the results of the query to a permanent table.
        .setDestinationTable(TableId.of(destinationDataset, destinationTable))
        // Allow results larger than the maximum response size.
        // If true, a destination table must be set.
        .setAllowLargeResults(true)
        .build();

// Print the results.
for (FieldValueList row : bigquery.query(queryConfig).iterateAll()) {
  for (FieldValue val : row) {
    System.out.printf("%s,", val.toString());
  }
  System.out.printf("\n");
}

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 queryLegacyLargeResults() {
  // Query enables large result sets.

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

  const query = `SELECT word FROM [bigquery-public-data:samples.shakespeare] LIMIT 10;`;

  // For all options, see https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/query
  const options = {
    query: query,
    // Location must match that of the dataset(s) referenced
    // in the query and of the destination table.
    useLegacySql: true,
    allowLargeResult: true,
    destinationTable: {
      projectId: projectId,
      datasetId: datasetId,
      tableId: tableId,
    },
  };

  const [job] = await bigquery.createQueryJob(options);
  console.log(`Job ${job.id} started.`);

  // Wait for the query to finish
  const [rows] = await job.getQueryResults();

  // Print the results
  console.log('Rows:');
  rows.forEach(row => console.log(row));
}

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 destination table.
# table_id = "your-project.your_dataset.your_table_name"

# Set the destination table and use_legacy_sql to True to use
# legacy SQL syntax.
job_config = bigquery.QueryJobConfig(
    allow_large_results=True, destination=table_id, use_legacy_sql=True
)

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))

Downloading and saving query results from a UI

After you run a SQL query by using either classic BigQuery web UI or the Cloud Console, you can save the results to another location. You can download query results to a local file or to Sheets by using either Cloud Console or the classic BigQuery web UI, or you can download the results to Drive by using the Cloud Console. Saving results to a local file, Sheets, or Drive is not supported by the command-line tool or the API.

Limitations

Downloading and saving query results are subject to the following limitations:

• To download query results using the classic BigQuery web UI, the results set must contain fewer than 16,000 rows, and it must be 10 MB or less. If your results are larger than 10 MB or 16,000 rows you can save them to a table instead.
• You can download query results locally only in CSV or newline-delimited JSON format.
• You cannot download query results containing nested and repeated data in CSV format.
• You cannot save query results containing nested and repeated data to Sheets.
• When you save query results to Sheets using the classic BigQuery web UI, the results set must contain fewer than 16,000 rows, and it must be 10 MB or less. If your results are larger than 10 MB or 16,000 rows you can save them to a table instead.
• To save query results to Drive using the Cloud Console, the results set must be 1 GB or less. If your results are larger than 1 GB, you can save them to a table instead.
• You can save query results to Drive only in CSV or newline-delimited JSON format.

Downloading query results to a local file

Downloading query results to a local file is not supported by the command-line tool or the API.

To download query results as a CSV or newline-delimited JSON file by using the web UI:

Console

1. Open the BigQuery web UI in the Cloud Console.
   Go to the Cloud Console

2. Click Compose new query.

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

4. (Optional) To change the processing location, click More and select Query settings. Under Processing location, click Auto-select and choose your data's location.

5. Click Run.

6. When the results are returned, click the Save results and select the format/location where you want to save the results.

   The file is downloaded to your browser's default download location.

Classic UI

1. Go to the BigQuery web UI.
   Go to the 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. (Optional) For Processing Location, click Unspecified and choose your data's location.

6. Click Run Query.

7. When the results are returned, click the Download as CSV or Download as JSON button above the query results.

   

   The file is downloaded to your browser's default download location.

Saving query results to Drive

Beta

This feature is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA features may have limited support, and changes to pre-GA features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

Saving query results to Drive is not supported by the command-line tool, the API, or the classic BigQuery web UI.

To save query results to Drive by using the Cloud Console:

Console

1. Open the BigQuery web UI in the Cloud Console.

   Go to the Cloud Console

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

3. Click Run.

4. When the results are returned, click Save Results.

   

5. Select CSV (Google Drive) or JSON (Google Drive). When you save results to Drive, you cannot choose the location. Results are always saved to the root "My Drive" location.

6. It may take a few minutes to save the results to Drive. When the results are saved, you receive a pop-up message that includes the filename — bq-results-[TIMESTAMP]-[RANDOM_CHARACTERS].[CSV or JSON].

   

7. In the pop-up message, click Open to open the file, or navigate to Drive and click My Drive.

Saving query results to Sheets

Saving query results to Sheets is not supported by the command-line tool or the API.

To save query results to Sheets by using the web UI:

Console

1. Open the BigQuery web UI in the Cloud Console.
   Go to the BigQuery web UI

2. Click Compose new query.

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

4. (Optional) To change the processing location, click More and select Query settings. Under Processing location, click Auto-select and choose your data's location.

5. Click Run.

6. When the results are returned, click the Save results and select Google Sheets.

7. If necessary, follow the prompts to log into your Google Account and click Allow to give BigQuery permission to write the data to your Drive MY Drive folder.

   After following the prompts, you should receive an email with the subject "BigQuery Client Tools connected to your Google Account". The email contains information on the permissions you granted along with steps to remove the permissions.

8. When the results are saved, a message similar to the following appears below the query results in the console BigQuery web UI: Saved to Sheets as "results-20190225-103531. Open. Click the link in the message to view your results in Sheets, or navigate to your My Drive folder and open the file manually.

   When you save query results to Sheets, the filename begins with results-[DATE] where [DATE] is today's date in the format YYYYMMDD.

Note: Saving results to Sheets is not supported by the command-line tool or the API.

Classic UI

1. Go to the BigQuery web UI.
   Go to the 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. (Optional) For Processing Location, click Unspecified and choose your data's location.

6. Click Run Query.

7. When the results are returned, click the Save to Google Sheets button above the query results.

   

8. If necessary, follow the prompts to log into your Google Account and click Allow to give BigQuery permission to write the data to your Drive MY Drive folder.

   After following the prompts, you should receive an email with the subject "BigQuery Client Tools connected to your Google Account". The email contains information on the permissions you granted along with steps to remove the permissions.

9. When the results are saved, a message like the following appears above the query results in the classic BigQuery web UI: Results saved to Google Sheets. Click to view. Click the link in the message to view your results in Google Sheets, or navigate to your MY Drive folder and open the file manually.

   When you save query results to Sheets, the filename begins with results-[DATE] where [DATE] is today's date in the format YYYYMMDD.

Note: Saving results to Sheets is not supported by the command-line tool or the API.