Query a clustered table

Query a table that has a clustering specification.

Explore further

For detailed documentation that includes this code sample, see the following:

• Querying clustered tables

Code sample

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

import (
	"context"
	"fmt"
	"io"

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

// queryClusteredTable demonstrates querying a table that has a clustering specification.
func queryClusteredTable(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: %w", err)
	}
	defer client.Close()

	q := client.Query(fmt.Sprintf(`
	SELECT
	  COUNT(1) as transactions,
	  SUM(amount) as total_paid,
	  COUNT(DISTINCT destination) as distinct_recipients
    FROM
	  `+"`%s.%s`"+`
	 WHERE
	    timestamp > TIMESTAMP('2015-01-01')
		AND origin = @wallet`, datasetID, tableID))
	q.Parameters = []bigquery.QueryParameter{
		{
			Name:  "wallet",
			Value: "wallet00001866cb7e0f09a890",
		},
	}
	// Run the query and process the returned row iterator.
	it, err := q.Read(ctx)
	if err != nil {
		return fmt.Errorf("query.Read(): %w", err)
	}
	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 authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

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

public class QueryClusteredTable {

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

  public static void queryClusteredTable(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();

      String sourceTable = "`" + projectId + "." + datasetName + "." + tableName + "`";
      String query =
          "SELECT word, word_count\n"
              + "FROM "
              + sourceTable
              + "\n"
              // Optimize query performance by filtering the clustered columns in sort order
              + "WHERE corpus = 'romeoandjuliet'\n"
              + "AND word_count >= 1";

      QueryJobConfiguration queryConfig = QueryJobConfiguration.newBuilder(query).build();

      TableResult results = bigquery.query(queryConfig);

      results
          .iterateAll()
          .forEach(row -> row.forEach(val -> System.out.printf("%s,", val.toString())));

      System.out.println("Query clustered table performed successfully.");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Query not performed \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.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

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

async function queryClusteredTable() {
  // Queries a table that has a clustering specification.

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

  const query = 'SELECT * FROM `bigquery-public-data.samples.shakespeare`';
  const fields = ['corpus'];

  // 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.
    location: 'US',
    destination: destinationTableId,
    clusterFields: fields,
  };

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

  console.log(`Job ${job.id} started.`);

  // Print the status and statistics
  console.log('Status:');
  console.log(job.metadata.status);
  console.log('\nJob Statistics:');
  console.log(job.metadata.statistics);
}

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

from google.cloud import bigquery

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

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

sql = "SELECT * FROM `bigquery-public-data.samples.shakespeare`"
cluster_fields = ["corpus"]

job_config = bigquery.QueryJobConfig(
    clustering_fields=cluster_fields, destination=table_id
)

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

table = client.get_table(table_id)  # Make an API request.
if table.clustering_fields == cluster_fields:
    print(
        "The destination table is written using the cluster_fields configuration."
    )

What's next

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