Loading Parquet data from Cloud Storage

This page provides an overview of loading Parquet data from Cloud Storage into BigQuery.

Parquet is an open source column-oriented data format that is widely used in the Apache Hadoop ecosystem.

When you load Parquet data from Cloud Storage, you can load the data into a new table or partition, or you can append to or overwrite an existing table or partition. When your data is loaded into BigQuery, it is converted into columnar format for Capacitor (BigQuery's storage format).

When you load data from Cloud Storage into a BigQuery table, the dataset that contains the table must be in the same regional or multi- regional location as the Cloud Storage bucket.

For information about loading Parquet data from a local file, see Loading data from local files.

Parquet schemas

When you load Parquet files into BigQuery, the table schema is automatically retrieved from the self-describing source data. When BigQuery retrieves the schema from the source data, the alphabetically last file is used.

For example, you have the following Parquet files in Cloud Storage:

gs://mybucket/00/
  a.parquet
  z.parquet
gs://mybucket/01/
  b.parquet

Running this command in the bq command-line tool loads all of the files (as a comma-separated list), and the schema is derived from mybucket/01/b.parquet:

bq load \
--source_format=PARQUET \
dataset.table \
"gs://mybucket/00/*.parquet","gs://mybucket/01/*.parquet"

When you load multiple Parquet files that have different schemas, identical columns specified in multiple schemas must have the same mode in each schema definition.

When BigQuery detects the schema, some Parquet data types are converted to BigQuery data types to make them compatible with BigQuery SQL syntax. For more information, see Parquet conversions.

Parquet compression

BigQuery supports the following compression codecs for data blocks in Parquet files:

• GZip
• LZO_1C and LZO_1X
• Snappy
• ZSTD

Required permissions

When you load data into BigQuery, you need permissions to run a load job and permissions that let you load data into new or existing BigQuery tables and partitions. If you are loading data from Cloud Storage, you also need permissions to access to the bucket that contains your data.

BigQuery permissions

At a minimum, the following permissions are required to load data into BigQuery. These permissions are required if you are loading data into a new table or partition, or if you are appending or overwriting a table or partition.

• bigquery.tables.create
• bigquery.tables.updateData
• bigquery.jobs.create

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 lets the user create and update tables in the dataset by using a load job.

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

Cloud Storage permissions

To load data from a Cloud Storage bucket, you must be granted storage.objects.get permissions. If you are using a URI wildcard, you must also have storage.objects.list permissions.

The predefined IAM role storage.objectViewer can be granted to provide both storage.objects.get and storage.objects.list permissions.

Loading Parquet data into a new table

You can load Parquet data into a new table by using one of the following:

• The Cloud Console
• The bq command-line tool's bq load command
• The jobs.insert API method and configuring a load job
• The client libraries

To load Parquet data from Cloud Storage into a new BigQuery table:

Console

1. In the Cloud Console, open the BigQuery page.

   Go to BigQuery

2. In the Explorer panel, expand your project and select a dataset.

   Note: The default experience is the Preview Cloud Console. If you clicked Hide preview features to go to the Generally Available Cloud Console, then perform the following step instead: In the navigation panel, in the Resources section, expand your project and select a dataset.

3. In the details panel, click Create table.

4. On the Create table page, in the Source section:

   • For Create table from, select Cloud Storage.

   • In the source field, browse to or enter the Cloud Storage URI. Note that you cannot include multiple URIs in the Cloud Console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you're creating.

     

   • For File format, select Parquet.

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

   • For Dataset name, choose the appropriate dataset.

     

   • Verify that Table type is set to Native table.

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

6. In the Schema section, no action is necessary. The schema is self-described in Parquet files.

7. (Optional) To partition the table, choose your options in the Partition and cluster settings:

   • To create a partitioned table, click No partitioning, select Partition by field and choose a DATE or TIMESTAMP column. This option is unavailable if your schema does not include a DATE or TIMESTAMP column.
   • To create an ingestion-time partitioned table, click No partitioning and select Partition by ingestion time.

8. (Optional) For Partitioning filter, click the Require partition filter box to require users to include a WHERE clause that specifies the partitions to query. Requiring a partition filter can reduce cost and improve performance. For more information, see Querying partitioned tables. This option is unavailable if No partitioning is selected.

9. (Optional) To cluster the table, in the Clustering order box, enter between one and four field names.

10. (Optional) Click Advanced options.

    • For Write preference, leave Write if empty selected. This option creates a new table and loads your data into it.
    • For Number of errors allowed, accept the default value of 0 or enter the maximum number of rows containing errors that can be ignored. If the number of rows with errors exceeds this value, the job will result in an invalid message and fail.
    • For Unknown values, leave Ignore unknown values unchecked. This option applies only to CSV and JSON files.
    • For Encryption, click Customer-managed key to use a Cloud Key Management Service key. If you leave the Google-managed key setting, BigQuery encrypts the data at rest.

11. Click Create table.

Note: When you load data into an empty table by using the Cloud Console, you cannot add a label, description, table expiration, or partition expiration.

After the table is created, you can update the table's expiration, description, and labels, but you cannot add a partition expiration after a table is created using the Cloud Console. For more information, see Managing tables.

bq

Use the bq load command, specify PARQUET using the --source_format flag, and include a Cloud Storage URI. You can include a single URI, a comma-separated list of URIs, or a URI containing a wildcard.

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

Other optional flags include:

• --time_partitioning_type: Enables time-based partitioning on a table and sets the partition type. Possible values are HOUR, DAY, MONTH, and YEAR. This flag is optional when you create a table partitioned on a DATE, DATETIME, or TIMESTAMP column. The default partition type for time-based partitioning is DAY.
• --time_partitioning_expiration: An integer that specifies (in seconds) when a time-based partition should be deleted. The expiration time evaluates to the partition's UTC date plus the integer value.
• --time_partitioning_field: The DATE or TIMESTAMP column used to create a partitioned table. If time-based partitioning is enabled without this value, an ingestion-time partitioned table is created.
• --require_partition_filter: When enabled, this option requires users to include a WHERE clause that specifies the partitions to query. Requiring a partition filter can reduce cost and improve performance. For more information, see Querying partitioned tables.
• --clustering_fields: A comma-separated list of up to four column names used to create a clustered table.

• --destination_kms_key: The Cloud KMS key for encryption of the table data.

  For more information on partitioned tables, see:

  • Creating and using partitioned tables
  • Creating and using ingestion-time partitioned tables

  For more information on clustered tables, see:

  • Creating and using clustered tables

  For more information on table encryption, see:

  • Protecting data with Cloud KMS keys

To load Parquet data into BigQuery, enter the following command:

bq --location=LOCATION load \
--source_format=FORMAT \
DATASET.TABLE \
PATH_TO_SOURCE

Replace the following:

• LOCATION: your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• FORMAT: PARQUET.
• DATASET: an existing dataset.
• TABLE: the name of the table into which you're loading data.
• PATH_TO_SOURCE: a fully qualified Cloud Storage URI or a comma-separated list of URIs. Wildcards are also supported.

Examples:

The following command loads data from gs://mybucket/mydata.parquet into a table named mytable in mydataset.

    bq load \
    --source_format=PARQUET \
    mydataset.mytable \
    gs://mybucket/mydata.parquet

The following command loads data from gs://mybucket/mydata.parquet into an ingestion-time partitioned table named mytable in mydataset.

    bq load \
    --source_format=PARQUET \
    --time_partitioning_type=DAY \
    mydataset.mytable \
    gs://mybucket/mydata.parquet

The following command loads data from gs://mybucket/mydata.parquet into a partitioned table named mytable in mydataset. The table is partitioned on the mytimestamp column.

    bq load \
    --source_format=PARQUET \
    --time_partitioning_field mytimestamp \
    mydataset.mytable \
    gs://mybucket/mydata.parquet

The following command loads data from multiple files in gs://mybucket/ into a table named mytable in mydataset. The Cloud Storage URI uses a wildcard.

    bq load \
    --source_format=PARQUET \
    mydataset.mytable \
    gs://mybucket/mydata*.parquet

The following command loads data from multiple files in gs://mybucket/ into a table named mytable in mydataset. The command includes a comma- separated list of Cloud Storage URIs with wildcards.

    bq load \
    --source_format=PARQUET \
    mydataset.mytable \
    "gs://mybucket/00/*.parquet","gs://mybucket/01/*.parquet"

API

1. Create a load job that points to the source data in Cloud Storage.

2. (Optional) Specify your location in the location property in the jobReference section of the job resource.

3. The source URIs property must be fully qualified, in the format gs://BUCKET/OBJECT. Each URI can contain one '*' wildcard character.

4. Specify the Parquet data format by setting the sourceFormat property to PARQUET.

5. To check the job status, call jobs.get(JOB_ID*), replacing JOB_ID with the ID of the job returned by the initial request.

   • If status.state = DONE, the job completed successfully.
   • If the status.errorResult property is present, the request failed, and that object includes information describing what went wrong. When a request fails, no table is created and no data is loaded.
   • If status.errorResult is absent, the job finished successfully; although, there might have been some non-fatal errors, such as problems importing a few rows. Non-fatal errors are listed in the returned job object's status.errors property.

API notes:

• Load jobs are atomic and consistent: if a load job fails, none of the data is available, and if a load job succeeds, all of the data is available.

• As a best practice, generate a unique ID and pass it as jobReference.jobId when calling jobs.insert to create a load job. This approach is more robust to network failure because the client can poll or retry on the known job ID.

• Calling jobs.insert on a given job ID is idempotent. You can retry as many times as you like on the same job ID, and at most one of those operations will succeed.

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"

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

// importParquet demonstrates loading Apache Parquet data from Cloud Storage into a table.
func importParquet(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()

	gcsRef := bigquery.NewGCSReference("gs://cloud-samples-data/bigquery/us-states/us-states.parquet")
	gcsRef.SourceFormat = bigquery.Parquet
	gcsRef.AutoDetect = true
	loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(gcsRef)

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

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

Java

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

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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.FormatOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobInfo;
import com.google.cloud.bigquery.LoadJobConfiguration;
import com.google.cloud.bigquery.TableId;
import java.math.BigInteger;

public class LoadParquet {

  public static void runLoadParquet() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    loadParquet(datasetName);
  }

  public static void loadParquet(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();

      String sourceUri = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet";
      TableId tableId = TableId.of(datasetName, "us_states");

      LoadJobConfiguration configuration =
          LoadJobConfiguration.builder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.parquet())
              .build();

      // For more information on Job see:
      // https://googleapis.dev/java/google-cloud-clients/latest/index.html?com/google/cloud/bigquery/package-summary.html
      // Load the table
      Job job = bigquery.create(JobInfo.of(configuration));

      // Blocks until this load table job completes its execution, either failing or succeeding.
      Job completedJob = job.waitFor();
      if (completedJob == null) {
        System.out.println("Job not executed since it no longer exists.");
        return;
      } else if (completedJob.getStatus().getError() != null) {
        System.out.println(
            "BigQuery was unable to load the table due to an error: \n"
                + job.getStatus().getError());
        return;
      }

      // Check number of rows loaded into the table
      BigInteger numRows = bigquery.getTable(tableId).getNumRows();
      System.out.printf("Loaded %d rows. \n", numRows);

      System.out.println("GCS parquet loaded successfully.");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("GCS Parquet was not loaded. \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 libraries
const {BigQuery} = require('@google-cloud/bigquery');
const {Storage} = require('@google-cloud/storage');

// Instantiate clients
const bigquery = new BigQuery();
const storage = new Storage();

/**
 * This sample loads the Parquet file at
 * https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.parquet
 *
 * TODO(developer): Replace the following lines with the path to your file.
 */
const bucketName = 'cloud-samples-data';
const filename = 'bigquery/us-states/us-states.parquet';

async function loadTableGCSParquet() {
  // Imports a GCS file into a table with Parquet source format.

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

  // Configure the load job. For full list of options, see:
  // https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationLoad
  const metadata = {
    sourceFormat: 'PARQUET',
    location: 'US',
  };

  // Load data from a Google Cloud Storage file into the table
  const [job] = await bigquery
    .dataset(datasetId)
    .table(tableId)
    .load(storage.bucket(bucketName).file(filename), metadata);

  // load() waits for the job to finish
  console.log(`Job ${job.id} completed.`);

  // Check the job's status for errors
  const errors = job.status.errors;
  if (errors && errors.length > 0) {
    throw errors;
  }
}

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;
use Google\Cloud\Core\ExponentialBackoff;

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

// instantiate the bigquery table service
$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
]);
$dataset = $bigQuery->dataset($datasetId);
$table = $dataset->table('us_states');

// create the import job
$gcsUri = 'gs://cloud-samples-data/bigquery/us-states/us-states.parquet';
$loadConfig = $table->loadFromStorage($gcsUri)->sourceFormat('PARQUET');
$job = $table->runJob($loadConfig);
// poll the job until it is complete
$backoff = new ExponentialBackoff(10);
$backoff->execute(function () use ($job) {
    print('Waiting for job to complete' . PHP_EOL);
    $job->reload();
    if (!$job->isComplete()) {
        throw new Exception('Job has not yet completed', 500);
    }
});
// check if the job has errors
if (isset($job->info()['status']['errorResult'])) {
    $error = $job->info()['status']['errorResult']['message'];
    printf('Error running job: %s' . PHP_EOL, $error);
} else {
    print('Data imported successfully' . 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.

Use the Client.load_table_from_uri() method to start a load job from Cloud Storage. To use Parquet, set the LoadJobConfig.source_format property to the SourceFormat constant PARQUET and pass the job config as the job_config argument to the load_table_from_uri() method.

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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"

job_config = bigquery.LoadJobConfig(source_format=bigquery.SourceFormat.PARQUET,)
uri = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet"

load_job = client.load_table_from_uri(
    uri, table_id, job_config=job_config
)  # Make an API request.

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

destination_table = client.get_table(table_id)
print("Loaded {} rows.".format(destination_table.num_rows))

Appending to or overwriting a table with Parquet data

You can load additional data into a table either from source files or by appending query results.

In the Cloud Console, use the Write preference option to specify what action to take when you load data from a source file or from a query result.

You have the following options when you load additional data into a table:

Console option	bq tool flag	BigQuery API property	Description
Write if empty	None	WRITE_EMPTY	Writes the data only if the table is empty.
Append to table	--noreplace or --replace=false; if --[no]replace is unspecified, the default is append	WRITE_APPEND	(Default) Appends the data to the end of the table.
Overwrite table	--replace or --replace=true	WRITE_TRUNCATE	Erases all existing data in a table before writing the new data. This action also deletes the table schema and removes any Cloud KMS key.

If you load data into an existing table, the load job can append the data or overwrite the table.

You can append or overwrite a table by using one of the following:

• The Cloud Console
• The bq command-line tool's bq load command
• The jobs.insert API method and configuring a load job
• The client libraries

Note: This page does not cover appending or overwriting partitioned tables. For information on appending and overwriting partitioned tables, see: Appending to and overwriting partitioned table data.

To append or overwrite a table with Parquet data:

Console

1. In the Cloud Console, open the BigQuery page.

   Go to BigQuery

2. In the Explorer panel, expand your project and select a dataset.

   Note: The default experience is the Preview Cloud Console. If you clicked Hide preview features to go to the Generally Available Cloud Console, then perform the following step instead: In the navigation panel, in the Resources section, expand your project and select a dataset.

3. In the details panel, click Create table.

4. On the Create table page, in the Source section:

   • For Create table from, select Cloud Storage.

   • In the source field, browse to or enter the Cloud Storage URI. Note that you cannot include multiple URIs in the Cloud Console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you're appending or overwriting.

     

   • For File format, select Parquet.

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 appending or overwriting in BigQuery.

   • Verify that Table type is set to Native table.

6. In the Schema section, no action is necessary. The schema is self-described in Parquet files.

   Note: It is possible to modify the table's schema when you append or overwrite it. For more information on supported schema changes during a load operation, see Modifying table schemas.

7. For Partition and cluster settings, leave the default values. You cannot convert a table to a partitioned or clustered table by appending or overwriting it, and the Cloud Console does not support appending to or overwriting partitioned or clustered tables in a load job.

8. Click Advanced options.

   • For Write preference, choose Append to table or Overwrite table.
   • For Number of errors allowed, accept the default value of 0 or enter the maximum number of rows containing errors that can be ignored. If the number of rows with errors exceeds this value, the job will result in an invalid message and fail.
   • For Unknown values, leave Ignore unknown values unchecked. This option applies only to CSV and JSON files.

   • For Encryption, click Customer-managed key to use a Cloud Key Management Service key. If you leave the Google-managed key setting, BigQuery encrypts the data at rest.

     

9. Click Create table.

bq

Enter the bq load command with the --replace flag to overwrite the table. Use the --noreplace flag to append data to the table. If no flag is specified, the default is to append data. Supply the --source_format flag and set it to PARQUET. Because Parquet schemas are automatically retrieved from the self-describing source data, you do not need to provide a schema definition.

Note: It is possible to modify the table's schema when you append or overwrite it. For more information on supported schema changes during a load operation, see Modifying table schemas.

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

Other optional flags include:

• --destination_kms_key: The Cloud KMS key for encryption of the table data.

bq --location=LOCATION load \
--[no]replace \
--source_format=FORMAT \
DATASET.TABLE \
PATH_TO_SOURCE

Replace the following:

• location: your location. The --location flag is optional. You can set a default value for the location by using the .bigqueryrc file.
• format: PARQUET.
• dataset: an existing dataset.
• table: the name of the table into which you're loading data.
• path_to_source: a fully qualified Cloud Storage URI or a comma-separated list of URIs. Wildcards are also supported.

Examples:

The following command loads data from gs://mybucket/mydata.parquet and overwrites a table named mytable in mydataset.

    bq load \
    --replace \
    --source_format=PARQUET \
    mydataset.mytable \
    gs://mybucket/mydata.parquet

The following command loads data from gs://mybucket/mydata.parquet and appends data to a table named mytable in mydataset.

    bq load \
    --noreplace \
    --source_format=PARQUET \
    mydataset.mytable \
    gs://mybucket/mydata.parquet

For information on appending and overwriting partitioned tables using the bq command-line tool, see Appending to and overwriting partitioned table data.

API

1. Create a load job that points to the source data in Cloud Storage.

2. (Optional) Specify your location in the location property in the jobReference section of the job resource.

3. The source URIs property must be fully qualified, in the format gs://BUCKET/OBJECT. You can include multiple URIs as a comma-separated list. Note that wildcards are also supported.

4. Specify the data format by setting the configuration.load.sourceFormat property to PARQUET.

5. Specify the write preference by setting the configuration.load.writeDisposition property to WRITE_TRUNCATE or WRITE_APPEND.

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"

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

// importParquetTruncate demonstrates loading Apache Parquet data from Cloud Storage into a table
// and overwriting/truncating existing data in the table.
func importParquetTruncate(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()

	gcsRef := bigquery.NewGCSReference("gs://cloud-samples-data/bigquery/us-states/us-states.parquet")
	gcsRef.SourceFormat = bigquery.Parquet
	gcsRef.AutoDetect = true
	loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(gcsRef)
	loader.WriteDisposition = bigquery.WriteTruncate

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

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

Java

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

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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.FormatOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobInfo;
import com.google.cloud.bigquery.JobInfo.WriteDisposition;
import com.google.cloud.bigquery.LoadJobConfiguration;
import com.google.cloud.bigquery.TableId;
import java.math.BigInteger;

public class LoadParquetReplaceTable {

  public static void runLoadParquetReplaceTable() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    loadParquetReplaceTable(datasetName);
  }

  public static void loadParquetReplaceTable(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();

      // Imports a GCS file into a table and overwrites table data if table already exists.
      // This sample loads CSV file at:
      // https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.csv
      String sourceUri = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet";
      TableId tableId = TableId.of(datasetName, "us_states");

      // For more information on LoadJobConfiguration see:
      // https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigquery/LoadJobConfiguration.Builder.html
      LoadJobConfiguration configuration =
          LoadJobConfiguration.builder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.parquet())
              // Set the write disposition to overwrite existing table data.
              .setWriteDisposition(WriteDisposition.WRITE_TRUNCATE)
              .build();

      // For more information on Job see:
      // https://googleapis.dev/java/google-cloud-clients/latest/index.html?com/google/cloud/bigquery/package-summary.html
      // Load the table
      Job job = bigquery.create(JobInfo.of(configuration));

      // Load data from a GCS parquet file into the table
      // Blocks until this load table job completes its execution, either failing or succeeding.
      Job completedJob = job.waitFor();
      if (completedJob == null) {
        System.out.println("Job not executed since it no longer exists.");
        return;
      } else if (completedJob.getStatus().getError() != null) {
        System.out.println(
            "BigQuery was unable to load into the table due to an error: \n"
                + job.getStatus().getError());
        return;
      }

      // Check number of rows loaded into the table
      BigInteger numRows = bigquery.getTable(tableId).getNumRows();
      System.out.printf("Loaded %d rows. \n", numRows);

      System.out.println("GCS parquet overwrote existing table successfully.");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Table extraction job was interrupted. \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 libraries
const {BigQuery} = require('@google-cloud/bigquery');
const {Storage} = require('@google-cloud/storage');

// Instantiate clients
const bigquery = new BigQuery();
const storage = new Storage();

/**
 * This sample loads the CSV file at
 * https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.csv
 *
 * TODO(developer): Replace the following lines with the path to your file.
 */
const bucketName = 'cloud-samples-data';
const filename = 'bigquery/us-states/us-states.parquet';

async function loadParquetFromGCSTruncate() {
  /**
   * Imports a GCS file into a table and overwrites
   * table data if table already exists.
   */

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

  // Configure the load job. For full list of options, see:
  // https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationLoad
  const metadata = {
    sourceFormat: 'PARQUET',
    // Set the write disposition to overwrite existing table data.
    writeDisposition: 'WRITE_TRUNCATE',
    location: 'US',
  };

  // Load data from a Google Cloud Storage file into the table
  const [job] = await bigquery
    .dataset(datasetId)
    .table(tableId)
    .load(storage.bucket(bucketName).file(filename), metadata);
  // load() waits for the job to finish
  console.log(`Job ${job.id} completed.`);

  // Check the job's status for errors
  const errors = job.status.errors;
  if (errors && errors.length > 0) {
    throw errors;
  }
}

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;
use Google\Cloud\Core\ExponentialBackoff;

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

// instantiate the bigquery table service
$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
]);
$table = $bigQuery->dataset($datasetId)->table($tableId);

// create the import job
$gcsUri = 'gs://cloud-samples-data/bigquery/us-states/us-states.parquet';
$loadConfig = $table->loadFromStorage($gcsUri)->sourceFormat('PARQUET')->writeDisposition('WRITE_TRUNCATE');
$job = $table->runJob($loadConfig);

// poll the job until it is complete
$backoff = new ExponentialBackoff(10);
$backoff->execute(function () use ($job) {
    print('Waiting for job to complete' . PHP_EOL);
    $job->reload();
    if (!$job->isComplete()) {
        throw new Exception('Job has not yet completed', 500);
    }
});

// check if the job has errors
if (isset($job->info()['status']['errorResult'])) {
    $error = $job->info()['status']['errorResult']['message'];
    printf('Error running job: %s' . PHP_EOL, $error);
} else {
    print('Data imported successfully' . 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.

To replace the rows in an existing table, set the LoadJobConfig.write_disposition property to the WriteDisposition constant WRITE_TRUNCATE.

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import six

from google.cloud import bigquery

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

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

job_config = bigquery.LoadJobConfig(
    schema=[
        bigquery.SchemaField("name", "STRING"),
        bigquery.SchemaField("post_abbr", "STRING"),
    ],
)

body = six.BytesIO(b"Washington,WA")
client.load_table_from_file(body, table_id, job_config=job_config).result()
previous_rows = client.get_table(table_id).num_rows
assert previous_rows > 0

job_config = bigquery.LoadJobConfig(
    write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,
    source_format=bigquery.SourceFormat.PARQUET,
)

uri = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet"
load_job = client.load_table_from_uri(
    uri, table_id, job_config=job_config
)  # Make an API request.

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

destination_table = client.get_table(table_id)
print("Loaded {} rows.".format(destination_table.num_rows))

Loading hive-partitioned Parquet data

BigQuery supports loading hive partitioned Parquet data stored on Cloud Storage and populates the hive partitioning columns as columns in the destination BigQuery managed table. For more information, see Loading externally partitioned data.

Parquet conversions

BigQuery converts Parquet data types to the following BigQuery data types:

Type conversions

Parquet type	Parquet logical type(s)	BigQuery data type
BOOLEAN	None	BOOLEAN
INT32	None, INTEGER (UINT_8, UINT_16, UINT_32, INT_8, INT_16, INT_32)	INTEGER
INT32	DECIMAL	NUMERIC, BIGNUMERIC, or STRING
INT32	DATE	DATE
INT64	None, INTEGER (UINT_64, INT_64)	INTEGER
INT64	DECIMAL	NUMERIC, BIGNUMERIC, or STRING
INT64	TIMESTAMP, precision=MILLIS (TIMESTAMP_MILLIS)	TIMESTAMP
INT64	TIMESTAMP, precision=MICROS (TIMESTAMP_MICROS)	TIMESTAMP
INT96	None	TIMESTAMP
FLOAT	None	FLOAT
DOUBLE	None	FLOAT
BYTE_ARRAY	None	BYTES
BYTE_ARRAY	STRING (UTF8)	STRING
FIXED_LEN_BYTE_ARRAY	DECIMAL	NUMERIC, BIGNUMERIC, or STRING
FIXED_LEN_BYTE_ARRAY	None	BYTES

Nested groups are converted into STRUCT types. Other combinations of Parquet types and converted types are not supported.

Decimal logical type

Decimal logical types can be converted to NUMERIC, BIGNUMERIC , or STRING types. The converted type depends on the precision and scale parameters of the decimal logical type and the specified decimal target types. Specify the decimal target type as follows:

• For a load job using the jobs.insert API: use the JobConfigurationLoad.decimalTargetTypes field.
• For a load job using the bq load command in the bq command-line tool: use the --decimal_target_types flag.
• For a query against a table with external sources: use the ExternalDataConfiguration.decimalTargetTypes field.
• For a persistent external table created with DDL: use the decimal_target_types option.

Enum logical type

Enum logical types can be converted to STRING or BYTES. Specify the converted target type as follows:

• For a load job using the jobs.insert API: use the JobConfigurationLoad.parquetOptions field.
• For a load job using the bq load command in the bq command-line tool: use the --parquet_enum_as_string flag.
• For a persistent external table created with bq mk: use the --parquet_enum_as_string flag.

List logical type

You can enable schema inference for Parquet LIST logical types. BigQuery checks whether the LIST node is in the standard form:

<optional | required> group <name> (LIST) {
  repeated group list {
    <optional | required> <element-type> element;
  }
}

If yes, the corresponding field for the LIST node in the converted schema is treated as if the node has the following schema:

repeated <element-type> <name>

The nodes "list" and "element" are omitted.

• For a load job using the jobs.insert API: use the JobConfigurationLoad.parquetOptions field.
• For a load job using the bq load command in the bq command-line tool: use the --parquet_enable_list_inference flag.
• For a persistent external table created with bq mk: use the flag --parquet_enable_list_inference.

Column name conversions

A column name must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_), and it must start with a letter or underscore. The maximum column name length is 300 characters. A column name cannot use any of the following prefixes:

• _TABLE_
• _FILE_
• _PARTITION

Duplicate column names are not allowed even if the case differs. For example, a column named Column1 is considered identical to a column named column1.

You cannot load Parquet files containing columns that have a period (.) in the column name.

If a Parquet column name contains other characters (aside from a period), the characters are replaced with underscores. You can add trailing underscores to column names to avoid collisions. For example, if a Parquet file contains 2 columns Column1 and column1, the columns are loaded as Column1 and column1_ respectively.