Loading Avro data from Cloud Storage

Avro is an open source data format that bundles serialized data with the data's schema in the same file.

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

Note: BigQuery doesn't support loading standalone Avro schema (.avsc) files. To import a schema into BigQuery, Specify a JSON schema file.

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.

Input file requirements

To avoid resourcesExceeded errors when loading Avro files into BigQuery, follow these guidelines:

• Keep row sizes to 50 MB or less.
• If the row contains many array fields, or any very long array fields, break the array values into separate fields.

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 and table 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 and table

To store your data, you must create a BigQuery dataset, and then create a BigQuery table within that dataset.

Advantages of Avro

Avro is the preferred format for loading data into BigQuery. Loading Avro files has the following advantages over CSV and JSON (newline delimited):

• The Avro binary format:
  • Is faster to load. The data can be read in parallel, even if the data blocks are compressed.
  • Doesn't require typing or serialization.
  • Is easier to parse because there are no encoding issues found in other formats such as ASCII.
• When you load Avro files into BigQuery, the table schema is automatically retrieved from the self-describing source data.

Avro schemas

When you load Avro files into a new BigQuery table, the table schema is automatically retrieved using the source data. When BigQuery retrieves the schema from the source data, the alphabetically last file is used.

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

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

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.avro:

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

When importing multiple Avro files with different Avro schemas, all schemas must be compatible with Avro's schema resolution.

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

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.avro".

Avro compression

BigQuery supports the following compression codecs for Avro file contents:

• Snappy
• DEFLATE
• ZSTD

Loading Avro data into a new table

To load Avro data from Cloud Storage into a new BigQuery table, select one of the following options:

Console

1. In the Google Cloud console, open the BigQuery page.

   Go to BigQuery

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

3. Expand the Actions option and click Open.

4. In the details panel, click Create table .

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

   • For Create table from, select Google Cloud Storage.

   • In the source field, browse to or enter the Cloud Storage URI. Note that 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're creating.

     

   • For File format, select Avro.

6. 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.

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

8. (Optional) To partition the table, choose your options in the Partition and cluster settings. For more information, see Creating partitioned tables.

9. (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 may reduce cost and improve performance. For more information, see Require a partition filter in queries. This option is unavailable if No partitioning is selected.

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

11. (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 Unknown values, leave Ignore unknown values cleared. 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.

12. 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 Avro 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 = 'avro',
     uris = ['gs://bucket/path/file.avro']);

3. Click Run.

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

bq

Use the bq load command, specify AVRO 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. 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 Require a partition filter in queries.
• --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 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 Avro data into BigQuery, enter the following command:

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

Replace the following:

• 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 AVRO.
• 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.avro into a table named mytable in mydataset.

    bq load \
    --source_format=AVRO \
    mydataset.mytable \
    gs://mybucket/mydata.avro

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

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

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

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

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=AVRO \
    mydataset.mytable \
    gs://mybucket/mydata*.avro

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=AVRO \
    mydataset.mytable \
    "gs://mybucket/00/*.avro","gs://mybucket/01/*.avro"

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 Avro data format by setting the sourceFormat property to AVRO.

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 will include 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.

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"
)

// importAvro demonstrates loading Apache Avro data from Cloud Storage into a table.
func importAvro(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.avro")
	gcsRef.SourceFormat = bigquery.Avro
	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.

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

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.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 load Avro data from Cloud Storage into a new BigQuery table
public class LoadAvroFromGCS {

  public static void runLoadAvroFromGCS() {
    // 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.avro";
    loadAvroFromGCS(datasetName, tableName, sourceUri);
  }

  public static void loadAvroFromGCS(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.of(tableId, sourceUri, FormatOptions.avro());

      // Load data from a GCS Avro 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()) {
        System.out.println("Avro from GCS successfully loaded in a table");
      } 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 Avro file at
 * https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.avro
 *
 * TODO(developer): Replace the following lines with the path to your file.
 */
const bucketName = 'cloud-samples-data';
const filename = 'bigquery/us-states/us-states.avro';

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

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

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

  // 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), jobConfigurationLoad);

  // 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;
  }
}

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

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))

Extract JSON data from Avro data

There are two ways to ensure that Avro data is loaded into BigQuery as JSON data:

1. Annotate your Avro schema with sqlType set to JSON. For example, if you load data with the following Avro schema, then the json_field column is read as a JSON type:

   {
       "type": {"type": "string", "sqlType": "JSON"},
       "name": "json_field"
   }

2. Specify the BigQuery destination table schema explicitly and set the column type to JSON. For more information, see Specifying a schema.

If you do not specify JSON as the type in either the Avro schema or the BigQuery table schema, then the data will be read as a STRING.

Appending to or overwriting a table with Avro 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.

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 Avro data:

Console

1. In the Google Cloud console, open the BigQuery page.

   Go to BigQuery

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

3. Expand the Actions option and click Open.

4. In the details panel, click Create table .

5. 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 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're appending or overwriting.

     

   • For File format, select Avro.

6. 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.

7. In the Schema section, no action is necessary. The schema is self-described in Avro 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.

8. 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 Google Cloud console does not support appending to or overwriting partitioned or clustered tables in a load job.

9. Click Advanced options.

   • For Write preference, choose Append to table or Overwrite table.
   • For Unknown values, leave Ignore unknown values cleared. 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.

10. Click Create table.

SQL

Use the LOAD DATA DDL statement. The following example appends an Avro 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 = 'avro',
     uris = ['gs://bucket/path/file.avro']);

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 AVRO. Because Avro 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 is your location. The --location flag is optional. You can set a default value for the location by using the .bigqueryrc file.
• format is AVRO.
• 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.avro and overwrites a table named mytable in mydataset.

    bq load \
    --replace \
    --source_format=AVRO \
    mydataset.mytable \
    gs://mybucket/mydata.avro

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

    bq load \
    --noreplace \
    --source_format=AVRO \
    mydataset.mytable \
    gs://mybucket/mydata.avro

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 AVRO.

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.

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"
)

// importAvroTruncate demonstrates loading Apache Avro data from Cloud Storage into a table
// and overwriting/truncating existing data in the table.
func importAvroTruncate(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.avro")
	gcsRef.SourceFormat = bigquery.Avro
	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

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

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

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.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 AVRO file from GCS
public class LoadAvroFromGCSTruncate {

  public static void runLoadAvroFromGCSTruncate() {
    // 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.avro";
    loadAvroFromGCSTruncate(datasetName, tableName, sourceUri);
  }

  public static void loadAvroFromGCSTruncate(
      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.avro())
              // Set the write disposition to overwrite existing table data
              .setWriteDisposition(JobInfo.WriteDisposition.WRITE_TRUNCATE)
              .build();

      // Load data from a GCS Avro 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()) {
        System.out.println("Table is successfully overwritten by AVRO 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 clients
const bigquery = new BigQuery();
const storage = new Storage();

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

async function loadTableGCSAvroTruncate() {
  /**
   * 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 = 'us_states';

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

  // 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), jobConfigurationLoad);

  // 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;
  }
}

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.

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.AVRO,
)

uri = "gs://cloud-samples-data/bigquery/us-states/us-states.avro"
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 Avro data

BigQuery supports loading hive partitioned Avro 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.

Avro conversions

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

Primitive types

Avro data type without logicalType attribute	BigQuery data type	Notes
null	BigQuery ignores these values
boolean	BOOLEAN
int	INTEGER
long	INTEGER
float	FLOAT
double	FLOAT
bytes	BYTES
string	STRING	UTF-8 only

Logical types

By default, BigQuery ignores the logicalType attribute for most of the types and uses the underlying Avro type instead. To convert Avro logical types to their corresponding BigQuery data types, set the --use_avro_logical_types flag to true using the bq command-line tool, or set the useAvroLogicalTypes property in the job resource when you call the jobs.insert method to create a load job.

The table below shows the conversion of Avro logical types to BigQuery data types.

Avro logical type	BigQuery data type: Logical type disabled	BigQuery data type: Logical type enabled
date	INTEGER	DATE
time-millis	INTEGER	TIME
time-micros	INTEGER (converted from LONG)	TIME
timestamp-millis	INTEGER (converted from LONG)	TIMESTAMP
timestamp-micros	INTEGER (converted from LONG)	TIMESTAMP
local-timestamp-millis	INTEGER (converted from LONG)	DATETIME
local-timestamp-micros	INTEGER (converted from LONG)	DATETIME
duration	BYTES (converted from fixed type of size 12)	BYTES (converted from fixed type of size 12)
decimal	NUMERIC, BIGNUMERIC, or STRING (see Decimal logical type)	NUMERIC, BIGNUMERIC, or STRING (see Decimal logical type)

For more information on Avro data types, see the Apache Avro™ 1.8.2 Specification.

Note: When exporting to Avro from BigQuery, DATETIME is exported as a STRING with a custom logical time that is not recognized as a DATETIME upon importing back into BigQuery.

Date logical type

In any Avro file you intend to load, you must specify date logical types in the following format:

{
       "type": {"logicalType": "date", "type": "int"},
       "name": "date_field"
}

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.

For backward compatibility, if the decimal target types are not specified, you can load an Avro file containing a bytes column with the decimal logical type into a BYTES column of an existing table. In this case, the decimal logical type on the column in the Avro file is ignored. This conversion mode is deprecated and might be removed in the future.

For more information on the Avro decimal logical type, see the Apache Avro™ 1.8.2 Specification.

Time logical type

In any Avro file you intend to load, you must specify time logical types in one of the following formats.

For millisecond precision:

{
       "type": {"logicalType": "time-millis", "type": "int"},
       "name": "time_millis_field"
}

For microsecond precision:

{
       "type": {"logicalType": "time-micros", "type": "int"},
       "name": "time_micros_field"
}

Timestamp logical type

In any Avro file you intend to load, you must specify timestamp logical types in one of the following formats.

For millisecond precision:

{
       "type": {"logicalType": "timestamp-millis", "type": "long"},
       "name": "timestamp_millis_field"
}

For microsecond precision:

{
       "type": {"logicalType": "timestamp-micros", "type": "long"},
       "name": "timestamp_micros_field"
}

Local-Timestamp logical type

In any Avro file you intend to load, you must specify a local-timestamp logical type in one of the following formats.

For millisecond precision:

{
       "type": {"logicalType": "local-timestamp-millis", "type": "long"},
       "name": "local_timestamp_millis_field"
}

For microsecond precision:

{
       "type": {"logicalType": "local-timestamp-micros", "type": "long"},
       "name": "local_timestamp_micros_field"
}

Complex types

Avro data type	BigQuery data type	Notes
record	RECORD	Aliases are ignored Doc is converted into a field description Default values are set at read time Order is ignored Recursive fields are dropped — Only the first level of nesting is maintained for recursive fields
enum	STRING	The string is the symbolic value of the enum Aliases are ignored Doc is converted into a field description
array	repeated fields	Arrays of arrays are not supported. Arrays containing only NULL types are ignored.
map<T>	RECORD	BigQuery converts an Avro map<T> field to a repeated RECORD that contains two fields: a key and a value. BigQuery stores the key as a STRING, and converts the value to its corresponding data type in BigQuery.
union	Nullable field RECORD with a list of nullable fields	When union only has one non-null type, it converts to a nullable field. Otherwise it converts to a RECORD with a list of nullable fields. Only one of these fields will be set at read time.
fixed	BYTES	Aliases are ignored Size is ignored

Limitations

• Nested array formatting is not supported in BigQuery. Avro files using this format must be converted before importing.
• In an Avro file, names and namespaces for a fullname can only contain alphanumeric characters and the underscore character _. The following regular expression shows the allowed characters: [A-Za-z_][A-Za-z0-9_]*

For more information, see BigQuery load jobs limits.