Loading ORC data from Cloud Storage

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

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

When you load ORC 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 ORC data from a local file, see Loading data into BigQuery from a local data source.

Limitations

You are subject to the following limitations when you load data into BigQuery from a Cloud Storage bucket:

• If your dataset's location is set to a value other than the US multi-region, then the Cloud Storage bucket must be in the same region or contained in the same multi-region as the dataset.
• BigQuery does not guarantee data consistency for external data sources. Changes to the underlying data while a query is running can result in unexpected behavior.
• BigQuery does not support Cloud Storage object versioning. If you include a generation number in the Cloud Storage URI, then the load job fails.

Before you begin

Grant Identity and Access Management (IAM) roles that give users the necessary permissions to perform each task in this document, and create a dataset to store your data.

Required permissions

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

Permissions to load data into BigQuery

To load data into a new BigQuery table or partition or to append or overwrite an existing table or partition, you need the following IAM permissions:

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

Each of the following predefined IAM roles includes the permissions that you need in order to load data into a BigQuery table or partition:

• roles/bigquery.dataEditor
• roles/bigquery.dataOwner
• roles/bigquery.admin (includes the bigquery.jobs.create permission)
• bigquery.user (includes the bigquery.jobs.create permission)
• bigquery.jobUser (includes the bigquery.jobs.create permission)

Additionally, if you have the bigquery.datasets.create permission, you can create and update tables using a load job in the datasets that you create.

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

Permissions to load data from Cloud Storage

To get the permissions that you need to load data from a Cloud Storage bucket, ask your administrator to grant you the Storage Admin (roles/storage.admin) IAM role on the bucket. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to load data from a Cloud Storage bucket. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to load data from a Cloud Storage bucket:

• storage.buckets.get
• storage.objects.get
• storage.objects.list (required if you are using a URI wildcard)

You might also be able to get these permissions with custom roles or other predefined roles.

Create a dataset

Create a BigQuery dataset to store your data.

ORC schemas

When you load ORC 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 ORC files in Cloud Storage:

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

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.orc:

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

When BigQuery detects the schema, some ORC data types are converted to BigQuery data types to make them compatible with GoogleSQL syntax. All fields in the detected schema are NULLABLE. For more information, see ORC conversions.

When you load multiple ORC files that have different schemas, identical fields (with the same name and same nested level) specified in multiple schemas must map to the same converted BigQuery data type in each schema definition.

To provide a table schema for creating external tables, set the referenceFileSchemaUri property in BigQuery API or
--reference_file_schema_uri parameter in bq command-line tool to the URL of the reference file.

For example, --reference_file_schema_uri="gs://mybucket/schema.orc".

ORC compression

BigQuery supports the following compression codecs for ORC file contents:

• Zlib
• Snappy
• LZO
• LZ4

Data in ORC files doesn't remain compressed after it is uploaded to BigQuery. Data storage is reported in logical bytes or physical bytes, depending on the dataset storage billing model. To get information on storage usage, query the INFORMATION_SCHEMA.TABLE_STORAGE view.

Loading ORC data into a new table

You can load ORC data into a new table by:

• Using the Google Cloud console
• Using the bq command-line tool's bq load command
• Calling the jobs.insert API method and configuring a load job
• Using the client libraries

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

Console

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, expand your project, and then select a dataset.
3. In the Dataset info section, click Create table.
4. In the Create table panel, specify the following details:
1. In the Source section, select Google Cloud Storage in the Create table from list. Then, do the following:
   1. Select a file from the Cloud Storage bucket, or enter the Cloud Storage URI. You cannot include multiple URIs in the Google Cloud console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you want to create, append, or overwrite.
   2. For File format, select ORC.
2. In the Destination section, specify the following details:
   1. For Dataset, select the dataset in which you want to create the table.
   2. In the Table field, enter the name of the table that you want to create.
   3. Verify that the Table type field is set to Native table.
3. In the Schema section, no action is necessary. The schema is self-described in ORC files.
4. Optional: Specify Partition and cluster settings. For more information, see Creating partitioned tables and Creating and using clustered tables.
5. Click Advanced options and do the following:
   • For Write preference, leave Write if empty selected. This option creates a new table and loads your data into it.
   • If you want to ignore values in a row that are not present in the table's schema, then select Unknown values.
   • 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.
6. Click Create table.

Note: When you load data into an empty table by using the Google 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 Google Cloud console. For more information, see Managing tables.

SQL

Use the LOAD DATA DDL statement. The following example loads an ORC file into the new table mytable:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   LOAD DATA OVERWRITE mydataset.mytable
   FROM FILES (
     format = 'ORC',
     uris = ['gs://bucket/path/file.orc']);

3. Click Run.

For more information about how to run queries, see Run an interactive query.

bq

Use the bq load command, specify ORC as the source_format, 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. You cannot change the partitioning specification on an existing table.
• --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 may 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 about partitioned tables, see:

  • Creating partitioned tables

  For more information about clustered tables, see:

  • Creating and using clustered tables

  For more information about table encryption, see:

  • Protecting data with Cloud KMS keys

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

bq --location=location load \
--source_format=format \
dataset.table \
path_to_source

Where:

• location is 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 is ORC.
• dataset is an existing dataset.
• table is the name of the table into which you're loading data.
• path_to_source is 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.orc into a table named mytable in mydataset.

    bq load \
    --source_format=ORC \
    mydataset.mytable \
    gs://mybucket/mydata.orc

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

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

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

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

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=ORC \
    mydataset.mytable \
    gs://mybucket/mydata*.orc

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 --autodetect \
    --source_format=ORC \
    mydataset.mytable \
    "gs://mybucket/00/*.orc","gs://mybucket/01/*.orc"

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 ORC data format by setting the sourceFormat property to ORC.

5. To check the job status, call jobs.get(job_id*), where job_id is 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 succeeds.

C#

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

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

using Google.Apis.Bigquery.v2.Data;
using Google.Cloud.BigQuery.V2;
using System;

public class BigQueryLoadTableGcsOrc
{
    public void LoadTableGcsOrc(
        string projectId = "your-project-id",
        string datasetId = "your_dataset_id"
    )
    {
        BigQueryClient client = BigQueryClient.Create(projectId);
        var gcsURI = "gs://cloud-samples-data/bigquery/us-states/us-states.orc";
        var dataset = client.GetDataset(datasetId);
        TableReference destinationTableRef = dataset.GetTableReference(
            tableId: "us_states");
        // Create job configuration
        var jobOptions = new CreateLoadJobOptions()
        {
            SourceFormat = FileFormat.Orc
        };
        // Create and run job
        var loadJob = client.CreateLoadJob(
            sourceUri: gcsURI,
            destination: destinationTableRef,
            // Pass null as the schema because the schema is inferred when
            // loading Orc data
            schema: null,
            options: jobOptions
        );
        loadJob = loadJob.PollUntilCompleted().ThrowOnAnyError();  // Waits for the job to complete.
        // Display the number of rows uploaded
        BigQueryTable table = client.GetTable(destinationTableRef);
        Console.WriteLine(
            $"Loaded {table.Resource.NumRows} rows to {table.FullyQualifiedId}");
    }
}

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"

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

// importORCTruncate demonstrates loading Apache ORC data from Cloud Storage into a table.
func importORC(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.orc")
	gcsRef.SourceFormat = bigquery.ORC
	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

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.FormatOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobInfo;
import com.google.cloud.bigquery.LoadJobConfiguration;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.TableId;

// Sample to load ORC data from Cloud Storage into a new BigQuery table
public class LoadOrcFromGCS {

  public static void runLoadOrcFromGCS() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    String sourceUri = "gs://cloud-samples-data/bigquery/us-states/us-states.orc";
    Schema schema =
        Schema.of(
            Field.of("name", StandardSQLTypeName.STRING),
            Field.of("post_abbr", StandardSQLTypeName.STRING));
    loadOrcFromGCS(datasetName, tableName, sourceUri, schema);
  }

  public static void loadOrcFromGCS(
      String datasetName, String tableName, String sourceUri, Schema schema) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      TableId tableId = TableId.of(datasetName, tableName);
      LoadJobConfiguration loadConfig =
          LoadJobConfiguration.newBuilder(tableId, sourceUri, FormatOptions.orc())
              .setSchema(schema)
              .build();

      // Load data from a GCS ORC file into the table
      Job job = bigquery.create(JobInfo.of(loadConfig));
      // Blocks until this load table job completes its execution, either failing or succeeding.
      job = job.waitFor();
      if (job.isDone() && job.getStatus().getError() == null) {
        System.out.println("ORC from GCS successfully added during load append job");
      } else {
        System.out.println(
            "BigQuery was unable to load into the table due to an error:"
                + job.getStatus().getError());
      }
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Column not added during load append \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 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 ORC file at
 * https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.orc
 *
 * TODO(developer): Replace the following lines with the path to your file.
 */
const bucketName = 'cloud-samples-data';
const filename = 'bigquery/us-states/us-states.orc';

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

  /**
   * TODO(developer): Uncomment the following line 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: 'ORC',
    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.

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

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.orc';
$loadConfig = $table->loadFromStorage($gcsUri)->sourceFormat('ORC');
$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 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 table to create.
# table_id = "your-project.your_dataset.your_table_name

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

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))

Ruby

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

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

require "google/cloud/bigquery"

def load_table_gcs_orc dataset_id = "your_dataset_id"
  bigquery = Google::Cloud::Bigquery.new
  dataset  = bigquery.dataset dataset_id
  gcs_uri  = "gs://cloud-samples-data/bigquery/us-states/us-states.orc"
  table_id = "us_states"

  load_job = dataset.load_job table_id, gcs_uri, format: "orc"
  puts "Starting job #{load_job.job_id}"

  load_job.wait_until_done! # Waits for table load to complete.
  puts "Job finished."

  table = dataset.table table_id
  puts "Loaded #{table.rows_count} rows to table #{table.id}"
end

Append to or overwrite a table with ORC data

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

In the Google 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	Not supported	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, row level security, 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 the Google Cloud console
• Using the bq command-line tool's bq load command
• Calling the jobs.insert API method and configuring a load job
• Using 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 ORC data:

Console

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, expand your project, and then select a dataset.
3. In the Dataset info section, click Create table.
4. In the Create table panel, specify the following details:
1. In the Source section, select Google Cloud Storage in the Create table from list. Then, do the following:
   1. Select a file from the Cloud Storage bucket, or enter the Cloud Storage URI. You cannot include multiple URIs in the Google Cloud console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you want to create, append, or overwrite.
   2. For File format, select ORC.
Note: It is possible to modify the table's schema when you append or overwrite it. For more information about supported schema changes during a load operation, see Modifying table schemas.
2. In the Destination section, specify the following details:
   1. For Dataset, select the dataset in which you want to create the table.
   2. In the Table field, enter the name of the table that you want to create.
   3. Verify that the Table type field is set to Native table.
3. In the Schema section, no action is necessary. The schema is self-described in ORC files.
Note: It is possible to modify the table's schema when you append or overwrite it. For more information about supported schema changes during a load operation, see Modifying table schemas.
4. Optional: Specify Partition and cluster settings. For more information, see Creating partitioned tables and Creating and using clustered tables. You cannot convert a table to a partitioned or clustered table by appending or overwriting it. The Google Cloud console does not support appending to or overwriting partitioned or clustered tables in a load job.
5. Click Advanced options and do the following:
   • For Write preference, choose Append to table or Overwrite table.
   • If you want to ignore values in a row that are not present in the table's schema, then select Unknown values.
   • 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.
6. Click Create table.

SQL

Use the LOAD DATA DDL statement. The following example appends an ORC file to the table mytable:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   LOAD DATA INTO mydataset.mytable
   FROM FILES (
     format = 'ORC',
     uris = ['gs://bucket/path/file.orc']);

3. Click Run.

For more information about how to run queries, see Run an interactive query.

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 ORC. Because ORC schemas are automatically retrieved from the self-describing source data, you don't 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 about 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

Where:

• location is your location. The --location flag is optional. You can set a default value for the location by using the .bigqueryrc file.
• format is ORC.
• dataset is an existing dataset.
• table is the name of the table into which you're loading data.
• path_to_source is 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.orc and overwrites a table named mytable in mydataset.

    bq load \
    --replace \
    --source_format=ORC \
    mydataset.mytable \
    gs://mybucket/mydata.orc

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

    bq load \
    --noreplace \
    --source_format=ORC \
    mydataset.mytable \
    gs://mybucket/mydata.orc

For information about 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 ORC.

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

C#

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

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

using Google.Apis.Bigquery.v2.Data;
using Google.Cloud.BigQuery.V2;
using System;

public class BigQueryLoadTableGcsOrcTruncate
{
    public void LoadTableGcsOrcTruncate(
        string projectId = "your-project-id",
        string datasetId = "your_dataset_id",
        string tableId = "your_table_id"
    )
    {
        BigQueryClient client = BigQueryClient.Create(projectId);
        var gcsURI = "gs://cloud-samples-data/bigquery/us-states/us-states.orc";
        var dataset = client.GetDataset(datasetId);
        TableReference destinationTableRef = dataset.GetTableReference(
            tableId: "us_states");
        // Create job configuration
        var jobOptions = new CreateLoadJobOptions()
        {
            SourceFormat = FileFormat.Orc,
            WriteDisposition = WriteDisposition.WriteTruncate
        };
        // Create and run job
        var loadJob = client.CreateLoadJob(
            sourceUri: gcsURI,
            destination: destinationTableRef,
            // Pass null as the schema because the schema is inferred when
            // loading Orc data
            schema: null, options: jobOptions);
        loadJob = loadJob.PollUntilCompleted().ThrowOnAnyError();  // Waits for the job to complete.
        // Display the number of rows uploaded
        BigQueryTable table = client.GetTable(destinationTableRef);
        Console.WriteLine(
            $"Loaded {table.Resource.NumRows} rows to {table.FullyQualifiedId}");
    }
}

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"

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

// importORCTruncate demonstrates loading Apache ORC data from Cloud Storage into a table
// and overwriting/truncating existing data in the table.
func importORCTruncate(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.orc")
	gcsRef.SourceFormat = bigquery.ORC
	loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(gcsRef)
	// Default for import jobs is to append data to a table.  WriteTruncate
	// specifies that existing data should instead be replaced/overwritten.
	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

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;

// Sample to overwrite the BigQuery table data by loading a ORC file from GCS
public class LoadOrcFromGcsTruncate {

  public static void runLoadOrcFromGcsTruncate() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    String sourceUri = "gs://cloud-samples-data/bigquery/us-states/us-states.orc";
    loadOrcFromGcsTruncate(datasetName, tableName, sourceUri);
  }

  public static void loadOrcFromGcsTruncate(
      String datasetName, String tableName, String sourceUri) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      TableId tableId = TableId.of(datasetName, tableName);
      LoadJobConfiguration loadConfig =
          LoadJobConfiguration.newBuilder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.orc())
              // Set the write disposition to overwrite existing table data
              .setWriteDisposition(JobInfo.WriteDisposition.WRITE_TRUNCATE)
              .build();

      // Load data from a GCS ORC file into the table
      Job job = bigquery.create(JobInfo.of(loadConfig));
      // Blocks until this load table job completes its execution, either failing or succeeding.
      job = job.waitFor();
      if (job.isDone() && job.getStatus().getError() == null) {
        System.out.println("Table is successfully overwritten by ORC file loaded from GCS");
      } else {
        System.out.println(
            "BigQuery was unable to load into the table due to an error:"
                + job.getStatus().getError());
      }
    } catch (BigQueryException | InterruptedException e) {
      System.out.println("Column not added during load append \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 libraries
const {BigQuery} = require('@google-cloud/bigquery');
const {Storage} = require('@google-cloud/storage');

// Instantiate the 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.orc';

async function loadORCFromGCSTruncate() {
  /**
   * 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: 'ORC',
    // 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.

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

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.orc';
$loadConfig = $table->loadFromStorage($gcsUri)->sourceFormat('ORC')->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 authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

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

import io

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 = io.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.ORC,
)

uri = "gs://cloud-samples-data/bigquery/us-states/us-states.orc"
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))

Ruby

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

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

require "google/cloud/bigquery"

def load_table_gcs_orc_truncate dataset_id = "your_dataset_id",
                                table_id   = "your_table_id"

  bigquery = Google::Cloud::Bigquery.new
  dataset  = bigquery.dataset dataset_id
  gcs_uri  = "gs://cloud-samples-data/bigquery/us-states/us-states.orc"

  load_job = dataset.load_job table_id,
                              gcs_uri,
                              format: "orc",
                              write:  "truncate"
  puts "Starting job #{load_job.job_id}"

  load_job.wait_until_done! # Waits for table load to complete.
  puts "Job finished."

  table = dataset.table table_id
  puts "Loaded #{table.rows_count} rows to table #{table.id}"
end

Load hive-partitioned ORC data

BigQuery supports loading hive partitioned ORC 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 from Cloud Storage.

ORC conversions

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

Primitive types

ORC data type	BigQuery data type	Notes
boolean	BOOLEAN
byte	INTEGER
short	INTEGER
int	INTEGER
long	INTEGER
float	FLOAT
double	FLOAT
string	STRING	UTF-8 only
varchar	STRING	UTF-8 only
char	STRING	UTF-8 only
binary	BYTES
date	DATE	An attempt to convert any value in the ORC data that is less than -719162 days or greater than 2932896 days returns an invalid date value error. If this affects you, contact Support to have unsupported values converted to the BigQuery minimum value of 0001-01-01 or maximum value of 9999-12-31, as appropriate.
timestamp	TIMESTAMP	ORC supports nanosecond precision, but BigQuery converts sub-microsecond values to microseconds when the data is read. An attempt to convert any value in the ORC data that is less than -719162 days or greater than 2932896 days returns an invalid date value error. If this affects you, contact Support to have unsupported values converted to the BigQuery minimum value of 0001-01-01 or maximum value of 9999-12-31, as appropriate.
decimal	NUMERIC, BIGNUMERIC, or STRING	See Decimal type.

Decimal 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.

Complex types

ORC data type	BigQuery data type	Notes
struct	RECORD	All fields are NULLABLE. Order of fields is ignored. Name of a field must be a valid column name.
map<K,V>	RECORD	An ORC map<K,V> field is converted to a repeated RECORD that contains two fields: a key of the same data type as K, and a value of the same data type as V. Both fields are NULLABLE.
list	repeated fields	Nested lists and lists of maps are not supported.
union	RECORD	When union only has one variant, it's converted to a NULLABLE field. Otherwise a union is converted to a RECORD with a list of NULLABLE fields. The NULLABLE fields have suffixes such as field_0, field_1, and so on. Only one of these fields is assigned a value when the data is read.

Column names

A column name can contain letters (a-z, A-Z), numbers (0-9), or underscores (_), and it must start with a letter or underscore. If you use flexible column names, BigQuery supports starting a column name with a number. Exercise caution when starting columns with a number, since using flexible column names with the BigQuery Storage Read API or BigQuery Storage Write API requires special handling. For more information about flexible column name support, see flexible column names.

Column names have a maximum length of 300 characters. Column names can't use any of the following prefixes:

• _TABLE_
• _FILE_
• _PARTITION
• _ROW_TIMESTAMP
• __ROOT__
• _COLIDENTIFIER

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. To learn more about column naming rules, see Column names in the GoogleSQL reference.

If a table name (for example, test) is the same as one of its column names (for example, test), the SELECT expression interprets the test column as a STRUCT containing all other table columns. To avoid this collision, use one of the following methods:

• Avoid using the same name for a table and its columns.

• Assign the table a different alias. For example, the following query assigns a table alias t to the table project1.dataset.test:

  SELECT test FROM project1.dataset.test AS t;
  

• Include the table name when referencing a column. For example:

  SELECT test.test FROM project1.dataset.test;
  

Flexible column names

You have more flexibility in what you name columns, including expanded access to characters in languages other than English as well as additional symbols.

Flexible column names support the following characters:

• Any letter in any language, as represented by the Unicode regular expression \p{L}.
• Any numeric character in any language as represented by the Unicode regular expression \p{N}.
• Any connector punctuation character, including underscores, as represented by the Unicode regular expression \p{Pc}.
• A hyphen or dash as represented by the Unicode regular expression \p{Pd}.
• Any mark intended to accompany another character as represented by the Unicode regular expression \p{M}. For example, accents, umlauts, or enclosing boxes.
• The following special characters:
  • An ampersand (&) as represented by the Unicode regular expression \u0026.
  • A percent sign (%) as represented by the Unicode regular expression \u0025.
  • An equals sign (=) as represented by the Unicode regular expression \u003D.
  • A plus sign (+) as represented by the Unicode regular expression \u002B.
  • A colon (:) as represented by the Unicode regular expression \u003A.
  • An apostrophe (') as represented by the Unicode regular expression \u0027.
  • A less-than sign (<) as represented by the Unicode regular expression \u003C.
  • A greater-than sign (>) as represented by the Unicode regular expression \u003E.
  • A number sign (#) as represented by the Unicode regular expression \u0023.
  • A vertical line (|) as represented by the Unicode regular expression \u007c.
  • Whitespace.

Flexible column names don't support the following special characters:

• An exclamation mark (!) as represented by the Unicode regular expression \u0021.
• A quotation mark (") as represented by the Unicode regular expression \u0022.
• A dollar sign ($) as represented by the Unicode regular expression \u0024.
• A left parenthesis (() as represented by the Unicode regular expression \u0028.
• A right parenthesis ()) as represented by the Unicode regular expression \u0029.
• An asterisk (*) as represented by the Unicode regular expression \u002A.
• A comma (,) as represented by the Unicode regular expression \u002C.
• A period (.) as represented by the Unicode regular expression \u002E.
• A slash (/) as represented by the Unicode regular expression \u002F.
• A semicolon (;) as represented by the Unicode regular expression \u003B.
• A question mark (?) as represented by the Unicode regular expression \u003F.
• An at sign (@) as represented by the Unicode regular expression \u0040.
• A left square bracket ([) as represented by the Unicode regular expression \u005B.
• A backslash (\) as represented by the Unicode regular expression \u005C.
• A right square bracket (]) as represented by the Unicode regular expression \u005D.
• A circumflex accent (^) as represented by the Unicode regular expression \u005E.
• A grave accent (`) as represented by the Unicode regular expression \u0060.
• A left curly bracket {{) as represented by the Unicode regular expression \u007B.
• A right curly bracket (}) as represented by the Unicode regular expression \u007D.
• A tilde (~) as represented by the Unicode regular expression \u007E.

For additional guidelines, see Column names.

The expanded column characters are supported by both the BigQuery Storage Read API and the BigQuery Storage Write API. To use the expanded list of Unicode characters with the BigQuery Storage Read API, you must set a flag. You can use the displayName attribute to retrieve the column name. The following example shows how to set a flag with the Python client:

from google.cloud.bigquery_storage import types
requested_session = types.ReadSession()

#set avro serialization options for flexible column.
options = types.AvroSerializationOptions()
options.enable_display_name_attribute = True
requested_session.read_options.avro_serialization_options = options

To use the expanded list of Unicode characters with the BigQuery Storage Write API, you must provide the schema with column_name notation, unless you are using the JsonStreamWriter writer object. The following example shows how to provide the schema:

syntax = "proto2";
package mypackage;
// Source protos located in github.com/googleapis/googleapis
import "google/cloud/bigquery/storage/v1/annotations.proto";

message FlexibleSchema {
  optional string item_name_column = 1
  [(.google.cloud.bigquery.storage.v1.column_name) = "name-列"];
  optional string item_description_column = 2
  [(.google.cloud.bigquery.storage.v1.column_name) = "description-列"];
}

In this example, item_name_column and item_description_column are placeholder names which need to be compliant with the protocol buffer naming convention. Note that column_name annotations always take precedence over placeholder names.

Limitations

Flexible column names are not supported with external tables.

NULL values

Note that for load jobs, BigQuery ignores NULL elements for the list compound type, since otherwise they would be translated to NULL ARRAY elements which cannot persist to a table (see Data Types for details).

For more information on ORC data types, see the Apache ORC™ Specification v1.