Loading CSV data from Cloud Storage

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

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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.

When you load CSV files into BigQuery, note the following:

• CSV files don't support nested or repeated data.
• Remove byte order mark (BOM) characters. They might cause unexpected issues.
• If you use gzip compression, BigQuery cannot read the data in parallel. Loading compressed CSV data into BigQuery is slower than loading uncompressed data. See Loading compressed and uncompressed data.
• You cannot include both compressed and uncompressed files in the same load job.
• The maximum size for a gzip file is 4 GB.
• Loading CSV data using schema autodetection does not automatically detect headers if all of the columns are string types. In this case, add a numerical column to the input or declare the schema explicitly.
• When you load CSV or JSON data, values in DATE columns must use the dash (-) separator and the date must be in the following format: YYYY-MM-DD (year-month-day).
• When you load JSON or CSV data, values in TIMESTAMP columns must use a dash (-) or slash (/) separator for the date portion of the timestamp, and the date must be in one of the following formats: YYYY-MM-DD (year-month-day) or YYYY/MM/DD (year/month/day). The hh:mm:ss (hour-minute-second) portion of the timestamp must use a colon (:) separator.
• Your files must meet the CSV file size limits described in the load jobs limits.

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.

CSV compression

You can use the gzip utility to compress CSV files. Note that gzip performs full file compression, unlike the file content compression performed by compression codecs for other file formats, such as Avro. Using gzip to compress your CSV files might have a performance impact; for more information about the trade-offs, see Loading compressed and uncompressed data.

Loading CSV data into a table

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

Console

---

To follow step-by-step guidance for this task directly in the Cloud Shell Editor, click Guide me:

Guide me

---

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 CSV.
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, enter the schema definition. To enable the auto detection of a schema, select Auto detect. You can enter schema information manually by using one of the following methods:
   • Option 1: Click Edit as text and paste the schema in the form of a JSON array. When you use a JSON array, you generate the schema using the same process as creating a JSON schema file. You can view the schema of an existing table in JSON format by entering the following command:

         bq show --format=prettyjson dataset.table
         

   • Option 2: Click Add field and enter the table schema. Specify each field's Name, Type, and Mode.
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.
   • For Number of errors allowed, accept the default value of 0 or enter the maximum number of rows containing errors that can be ignored. If the number of rows with errors exceeds this value, the job will result in an invalid message and fail. This option applies only to CSV and JSON files.
   • If you want to ignore values in a row that are not present in the table's schema, then select Unknown values.
   • For Field delimiter, choose the character that separates the cells in your CSV file: Comma, Tab, Pipe, or Custom. If you choose Custom, enter the delimiter in the Custom field delimiter box. The default value is Comma.
   • For Header rows to skip, enter the number of header rows to skip at the top of the CSV file. The default value is 0.
   • For Quoted newlines, check Allow quoted newlines to allow quoted data sections that contain newline characters in a CSV file. The default value is false.
   • For Jagged rows, check Allow jagged rows to accept rows in CSV files that are missing trailing optional columns. The missing values are treated as nulls. If unchecked, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false.
   • 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 a CSV 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
   (x INT64,y STRING)
   FROM FILES (
     format = 'CSV',
     uris = ['gs://bucket/path/file.csv']);
   

3. Click Run.

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

bq

Use the bq load command, specify CSV 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. Supply the schema inline, in a schema definition file, or use schema auto-detect. If you don't specify a schema, and --autodetect is false, and the destination table exists, then the schema of the destination table is used.

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

Other optional flags include:

• --allow_jagged_rows: When specified, accept rows in CSV files that are missing trailing optional columns. The missing values are treated as nulls. If unchecked, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false.
• --allow_quoted_newlines: When specified, allows quoted data sections that contain newline characters in a CSV file. The default value is false.
• --field_delimiter: The character that indicates the boundary between columns in the data. Both \t and tab are allowed for tab delimiters. The default value is ,.
• --null_marker: An optional custom string that represents a NULL value in CSV data.
• --skip_leading_rows: Specifies the number of header rows to skip at the top of the CSV file. The default value is 0.
• --quote: The quote character to use to enclose records. The default value is ". To indicate no quote character, use an empty string.
• --max_bad_records: An integer that specifies the maximum number of bad records allowed before the entire job fails. The default value is 0. At most, five errors of any type are returned regardless of the --max_bad_records value.
• --ignore_unknown_values: When specified, allows and ignores extra, unrecognized values in CSV or JSON data.
• --autodetect: When specified, enable schema auto-detection for CSV and JSON data.
• --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.

• --column_name_character_map: Defines the scope and handling of characters in column names, with the option of enabling flexible column names. Requires the --autodetect option for CSV files. For more information, see load_option_list.

  For more information on the bq load command, see:

  • Command-line reference

  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 CSV data into BigQuery, enter the following command:

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

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 CSV.
• 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.
• schema is a valid schema. The schema can be a local JSON file, or it can be typed inline as part of the command. You can also use the --autodetect flag instead of supplying a schema definition.

Examples:

The following command loads data from gs://mybucket/mydata.csv into a table named mytable in mydataset. The schema is defined in a local schema file named myschema.json.

    bq load \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    ./myschema.json

The following command loads data from gs://mybucket/mydata.csv into a table named mytable in mydataset. The schema is defined in a local schema file named myschema.json. The CSV file includes two header rows. If --skip_leading_rows is unspecified, the default behavior is to assume the file does not contain headers.

    bq load \
    --source_format=CSV \
    --skip_leading_rows=2
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    ./myschema.json

The following command loads data from gs://mybucket/mydata.csv into an ingestion-time partitioned table named mytable in mydataset. The schema is defined in a local schema file named myschema.json.

    bq load \
    --source_format=CSV \
    --time_partitioning_type=DAY \
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    ./myschema.json

The following command loads data from gs://mybucket/mydata.csv into a new partitioned table named mytable in mydataset. The table is partitioned on the mytimestamp column. The schema is defined in a local schema file named myschema.json.

    bq load \
    --source_format=CSV \
    --time_partitioning_field mytimestamp \
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    ./myschema.json

The following command loads data from gs://mybucket/mydata.csv into a table named mytable in mydataset. The schema is auto detected.

    bq load \
    --autodetect \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata.csv

The following command loads data from gs://mybucket/mydata.csv into a table named mytable in mydataset. The schema is defined inline in the format field:data_type,field:data_type.

    bq load \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    qtr:STRING,sales:FLOAT,year:STRING

Note: When you specify the schema using the bq command-line tool, you cannot include a RECORD (STRUCT) type, you cannot include a field description, and you cannot specify the field mode. All field modes default to NULLABLE. To include field descriptions, modes, and RECORD types, supply a JSON schema file instead.

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. The schema is auto detected.

    bq load \
    --autodetect \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata*.csv

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. The schema is defined in a local schema file named myschema.json.

    bq load \
    --source_format=CSV \
    mydataset.mytable \
    "gs://mybucket/00/*.csv","gs://mybucket/01/*.csv" \
    ./myschema.json

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 CSV data format by setting the sourceFormat property to CSV.

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 nonfatal errors, such as problems importing a few rows. Nonfatal 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.

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.Cloud.BigQuery.V2;
using System;

public class BigQueryLoadTableGcsCsv
{
    public void LoadTableGcsCsv(
        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.csv";
        var dataset = client.GetDataset(datasetId);
        var schema = new TableSchemaBuilder {
            { "name", BigQueryDbType.String },
            { "post_abbr", BigQueryDbType.String }
        }.Build();
        var destinationTableRef = dataset.GetTableReference(
            tableId: "us_states");
        // Create job configuration
        var jobOptions = new CreateLoadJobOptions()
        {
            // The source format defaults to CSV; line below is optional.
            SourceFormat = FileFormat.Csv,
            SkipLeadingRows = 1
        };
        // Create and run job
        var loadJob = client.CreateLoadJob(
            sourceUri: gcsURI, destination: destinationTableRef,
            schema: schema, 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"
)

// importCSVExplicitSchema demonstrates loading CSV data from Cloud Storage into a BigQuery
// table and providing an explicit schema for the data.
func importCSVExplicitSchema(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.csv")
	gcsRef.SkipLeadingRows = 1
	gcsRef.Schema = bigquery.Schema{
		{Name: "name", Type: bigquery.StringFieldType},
		{Name: "post_abbr", Type: bigquery.StringFieldType},
	}
	loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(gcsRef)
	loader.WriteDisposition = bigquery.WriteEmpty

	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.CsvOptions;
import com.google.cloud.bigquery.Field;
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 CSV data from Cloud Storage into a new BigQuery table
public class LoadCsvFromGcs {

  public static void runLoadCsvFromGcs() throws Exception {
    // 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.csv";
    Schema schema =
        Schema.of(
            Field.of("name", StandardSQLTypeName.STRING),
            Field.of("post_abbr", StandardSQLTypeName.STRING));
    loadCsvFromGcs(datasetName, tableName, sourceUri, schema);
  }

  public static void loadCsvFromGcs(
      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();

      // Skip header row in the file.
      CsvOptions csvOptions = CsvOptions.newBuilder().setSkipLeadingRows(1).build();

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

      // Load data from a GCS CSV 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("CSV 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 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.csv';

async function loadCSVFromGCS() {
  // Imports a GCS file into a table with manually defined schema.

  /**
   * 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: 'CSV',
    skipLeadingRows: 1,
    schema: {
      fields: [
        {name: 'name', type: 'STRING'},
        {name: 'post_abbr', type: 'STRING'},
      ],
    },
    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.csv';
$schema = [
    'fields' => [
        ['name' => 'name', 'type' => 'string'],
        ['name' => 'post_abbr', 'type' => 'string']
    ]
];
$loadConfig = $table->loadFromStorage($gcsUri)->schema($schema)->skipLeadingRows(1);
$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.

Use the Client.load_table_from_uri() method to load data from a CSV file in Cloud Storage. Supply an explicit schema definition by setting the LoadJobConfig.schema property to a list of SchemaField objects.

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"),
    ],
    skip_leading_rows=1,
    # The source format defaults to CSV, so the line below is optional.
    source_format=bigquery.SourceFormat.CSV,
)
uri = "gs://cloud-samples-data/bigquery/us-states/us-states.csv"

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)  # Make an API request.
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_csv 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.csv"
  table_id = "us_states"

  load_job = dataset.load_job table_id, gcs_uri, skip_leading: 1 do |schema|
    schema.string "name"
    schema.string "post_abbr"
  end
  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

Loading CSV data into a table that uses column-based time partitioning

To load CSV data from Cloud Storage into a BigQuery table that uses column-based time partitioning:

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"
	"time"

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

// importPartitionedTable demonstrates specifing time partitioning for a BigQuery table when loading
// CSV data from Cloud Storage.
func importPartitionedTable(projectID, destDatasetID, destTableID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydataset"
	// tableID := "mytable"
	ctx := context.Background()
	client, err := bigquery.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("bigquery.NewClient: %v", err)
	}
	defer client.Close()

	gcsRef := bigquery.NewGCSReference("gs://cloud-samples-data/bigquery/us-states/us-states-by-date.csv")
	gcsRef.SkipLeadingRows = 1
	gcsRef.Schema = bigquery.Schema{
		{Name: "name", Type: bigquery.StringFieldType},
		{Name: "post_abbr", Type: bigquery.StringFieldType},
		{Name: "date", Type: bigquery.DateFieldType},
	}
	loader := client.Dataset(destDatasetID).Table(destTableID).LoaderFrom(gcsRef)
	loader.TimePartitioning = &bigquery.TimePartitioning{
		Field:      "date",
		Expiration: 90 * 24 * time.Hour,
	}
	loader.WriteDisposition = bigquery.WriteEmpty

	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.Field;
import com.google.cloud.bigquery.FormatOptions;
import com.google.cloud.bigquery.Job;
import com.google.cloud.bigquery.JobId;
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;
import com.google.cloud.bigquery.TimePartitioning;
import java.time.Duration;
import java.time.temporal.ChronoUnit;
import java.util.UUID;

public class LoadPartitionedTable {

  public static void runLoadPartitionedTable() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    String sourceUri = "/path/to/file.csv";
    loadPartitionedTable(datasetName, tableName, sourceUri);
  }

  public static void loadPartitionedTable(String datasetName, String tableName, String sourceUri)
      throws Exception {
    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);

      Schema schema =
          Schema.of(
              Field.of("name", StandardSQLTypeName.STRING),
              Field.of("post_abbr", StandardSQLTypeName.STRING),
              Field.of("date", StandardSQLTypeName.DATE));

      // Configure time partitioning. For full list of options, see:
      // https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#TimePartitioning
      TimePartitioning partitioning =
          TimePartitioning.newBuilder(TimePartitioning.Type.DAY)
              .setField("date")
              .setExpirationMs(Duration.of(90, ChronoUnit.DAYS).toMillis())
              .build();

      LoadJobConfiguration loadJobConfig =
          LoadJobConfiguration.builder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.csv())
              .setSchema(schema)
              .setTimePartitioning(partitioning)
              .build();

      // Create a job ID so that we can safely retry.
      JobId jobId = JobId.of(UUID.randomUUID().toString());
      Job loadJob = bigquery.create(JobInfo.newBuilder(loadJobConfig).setJobId(jobId).build());

      // Load data from a GCS parquet file into the table
      // Blocks until this load table job completes its execution, either failing or succeeding.
      Job completedJob = loadJob.waitFor();

      // Check for errors
      if (completedJob == null) {
        throw new Exception("Job not executed since it no longer exists.");
      } else if (completedJob.getStatus().getError() != null) {
        // You can also look at queryJob.getStatus().getExecutionErrors() for all
        // errors, not just the latest one.
        throw new Exception(
            "BigQuery was unable to load into the table due to an error: \n"
                + loadJob.getStatus().getError());
      }
      System.out.println("Data successfully loaded into time partitioned table during load job");
    } catch (BigQueryException | InterruptedException e) {
      System.out.println(
          "Data not loaded into time partitioned table during load job \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 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-by-date.csv';

async function loadTablePartitioned() {
  // Load data into a table that uses column-based time partitioning.

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

  // Configure the load job. For full list of options, see:
  // https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationLoad
  const partitionConfig = {
    type: 'DAY',
    expirationMs: '7776000000', // 90 days
    field: 'date',
  };

  const metadata = {
    sourceFormat: 'CSV',
    skipLeadingRows: 1,
    schema: {
      fields: [
        {name: 'name', type: 'STRING'},
        {name: 'post_abbr', type: 'STRING'},
        {name: 'date', type: 'DATE'},
      ],
    },
    location: 'US',
    timePartitioning: partitionConfig,
  };

  // 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.`);
}

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(
    schema=[
        bigquery.SchemaField("name", "STRING"),
        bigquery.SchemaField("post_abbr", "STRING"),
        bigquery.SchemaField("date", "DATE"),
    ],
    skip_leading_rows=1,
    time_partitioning=bigquery.TimePartitioning(
        type_=bigquery.TimePartitioningType.DAY,
        field="date",  # Name of the column to use for partitioning.
        expiration_ms=7776000000,  # 90 days.
    ),
)
uri = "gs://cloud-samples-data/bigquery/us-states/us-states-by-date.csv"

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

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

table = client.get_table(table_id)
print("Loaded {} rows to table {}".format(table.num_rows, table_id))

Appending to or overwriting a table with CSV 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.

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 CSV.
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, enter the schema definition. To enable the auto detection of a schema, select Auto detect. You can enter schema information manually by using one of the following methods:
   • Option 1: Click Edit as text and paste the schema in the form of a JSON array. When you use a JSON array, you generate the schema using the same process as creating a JSON schema file. You can view the schema of an existing table in JSON format by entering the following command:

         bq show --format=prettyjson dataset.table
         

   • Option 2: Click Add field and enter the table schema. Specify each field's Name, Type, and Mode.
   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.
   • For Number of errors allowed, accept the default value of 0 or enter the maximum number of rows containing errors that can be ignored. If the number of rows with errors exceeds this value, the job will result in an invalid message and fail. This option applies only to CSV and JSON files.
   • If you want to ignore values in a row that are not present in the table's schema, then select Unknown values.
   • For Field delimiter, choose the character that separates the cells in your CSV file: Comma, Tab, Pipe, or Custom. If you choose Custom, enter the delimiter in the Custom field delimiter box. The default value is Comma.
   • For Header rows to skip, enter the number of header rows to skip at the top of the CSV file. The default value is 0.
   • For Quoted newlines, check Allow quoted newlines to allow quoted data sections that contain newline characters in a CSV file. The default value is false.
   • For Jagged rows, check Allow jagged rows to accept rows in CSV files that are missing trailing optional columns. The missing values are treated as nulls. If unchecked, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false.
   • 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 a CSV 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 = 'CSV',
     uris = ['gs://bucket/path/file.csv']);
   

3. Click Run.

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

bq

Use the bq load command, specify CSV 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.

Supply the schema inline, in a schema definition file, or use schema auto-detect. If you don't specify a schema, and --autodetect is false, and the destination table exists, then the schema of the destination table is used.

Specify 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.

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:

• --allow_jagged_rows: When specified, accept rows in CSV files that are missing trailing optional columns. The missing values are treated as nulls. If unchecked, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false.
• --allow_quoted_newlines: When specified, allows quoted data sections that contain newline characters in a CSV file. The default value is false.
• --field_delimiter: The character that indicates the boundary between columns in the data. Both \t and tab are allowed for tab delimiters. The default value is ,.
• --null_marker: An optional custom string that represents a NULL value in CSV data.
• --skip_leading_rows: Specifies the number of header rows to skip at the top of the CSV file. The default value is 0.
• --quote: The quote character to use to enclose records. The default value is ". To indicate no quote character, use an empty string.
• --max_bad_records: An integer that specifies the maximum number of bad records allowed before the entire job fails. The default value is 0. At most, five errors of any type are returned regardless of the --max_bad_records value.
• --ignore_unknown_values: When specified, allows and ignores extra, unrecognized values in CSV or JSON data.
• --autodetect: When specified, enable schema auto-detection for CSV and JSON data.
• --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 \
schema

where:

• location is your location. The --location flag is optional. You can set a default value for the location using the .bigqueryrc file.
• format is CSV.
• 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.
• schema is a valid schema. The schema can be a local JSON file, or it can be typed inline as part of the command. You can also use the --autodetect flag instead of supplying a schema definition.

Examples:

The following command loads data from gs://mybucket/mydata.csv and overwrites a table named mytable in mydataset. The schema is defined using schema auto-detection.

    bq load \
    --autodetect \
    --replace \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata.csv

The following command loads data from gs://mybucket/mydata.csv and appends data to a table named mytable in mydataset. The schema is defined using a JSON schema file — myschema.json.

    bq load \
    --noreplace \
    --source_format=CSV \
    mydataset.mytable \
    gs://mybucket/mydata.csv \
    ./myschema.json

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 CSV.

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"
)

// importCSVTruncate demonstrates loading data from CSV data in Cloud Storage and overwriting/truncating
// data in the existing table.
func importCSVTruncate(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.csv")
	gcsRef.SourceFormat = bigquery.CSV
	gcsRef.AutoDetect = true
	gcsRef.SkipLeadingRows = 1
	loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(gcsRef)
	loader.WriteDisposition = bigquery.WriteTruncate

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

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

Java

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

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.JobInfo.WriteDisposition;
import com.google.cloud.bigquery.LoadJobConfiguration;
import com.google.cloud.bigquery.TableId;

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

  public static void runLoadCsvFromGcsTruncate() throws Exception {
    // 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.csv";
    loadCsvFromGcsTruncate(datasetName, tableName, sourceUri);
  }

  public static void loadCsvFromGcsTruncate(String datasetName, String tableName, String sourceUri)
      throws Exception {
    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 configuration =
          LoadJobConfiguration.builder(tableId, sourceUri)
              .setFormatOptions(FormatOptions.csv())
              // Set the write disposition to overwrite existing table data
              .setWriteDisposition(WriteDisposition.WRITE_TRUNCATE)
              .build();

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

      // Load data from a GCS parquet file into the table
      // Blocks until this load table job completes its execution, either failing or succeeding.
      Job completedJob = loadJob.waitFor();

      // Check for errors
      if (completedJob == null) {
        throw new Exception("Job not executed since it no longer exists.");
      } else if (completedJob.getStatus().getError() != null) {
        // You can also look at queryJob.getStatus().getExecutionErrors() for all
        // errors, not just the latest one.
        throw new Exception(
            "BigQuery was unable to load into the table due to an error: \n"
                + loadJob.getStatus().getError());
      }
      System.out.println("Table is successfully overwritten by CSV file loaded from GCS");
    } 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.

To replace the rows in an existing table, set the writeDisposition value in the metadata parameter to 'WRITE_TRUNCATE'.

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

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

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

async function loadCSVFromGCSTruncate() {
  /**
   * 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: 'CSV',
    skipLeadingRows: 1,
    schema: {
      fields: [
        {name: 'name', type: 'STRING'},
        {name: 'post_abbr', type: 'STRING'},
      ],
    },
    // 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;
  }
}

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.csv';
$loadConfig = $table->loadFromStorage($gcsUri)->skipLeadingRows(1)->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 SourceFormat constant WRITE_TRUNCATE.

import six

from google.cloud import bigquery

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

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

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

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

job_config = bigquery.LoadJobConfig(
    write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,
    source_format=bigquery.SourceFormat.CSV,
    skip_leading_rows=1,
)

uri = "gs://cloud-samples-data/bigquery/us-states/us-states.csv"
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 CSV data

BigQuery supports loading hive-partitioned CSV data stored on Cloud Storage and will populate the hive partitioning columns as columns in the destination BigQuery managed table. For more information, see Loading Externally Partitioned Data from Cloud Storage.

Details of loading CSV data

This section describes how BigQuery handles various CSV formatting options.

Encoding

BigQuery expects CSV data to be UTF-8 encoded. If you have CSV files with other supported encoding types, you should explicitly specify the encoding so that BigQuery can properly convert the data to UTF-8.

BigQuery supports the following encoding types for CSV files:

• UTF-8
• ISO-8859-1
• UTF-16BE (UTF-16 Big Endian)
• UTF-16LE (UTF-16 Little Endian)
• UTF-32BE (UTF-32 Big Endian)
• UTF-32LE (UTF-32 Little Endian)

If you don't specify an encoding, or if you specify UTF-8 encoding when the CSV file is not UTF-8 encoded, BigQuery attempts to convert the data to UTF-8. Generally, if the CSV file is ISO-8859-1 encoded, your data will be loaded successfully, but it may not exactly match what you expect. If the CSV file is UTF-16BE, UTF-16LE, UTF-32BE, or UTF-32LE encoded, the load might fail. To avoid unexpected failures, specify the correct encoding by using the --encoding flag.

Note: If the CSV file is UTF-16BE, UTF-16LE, UTF-32BE, or UTF-32LE encoded, and the --allow_quoted_newlines flag is set as true, then the CSV file has a maximum size limit of 1GB.

Note: By default, if the CSV file contains the ASCII 0 (NULL) character, you can't load the data into BigQuery. If you want to allow ASCII 0 and other ASCII control characters, then set --preserve_ascii_control_characters=true to your load jobs.

If BigQuery can't convert a character other than the ASCII 0 character, BigQuery converts the character to the standard Unicode replacement character: �.

Field delimiters

Delimiters in CSV files can be any single-byte character. If the source file uses ISO-8859-1 encoding, any character can be a delimiter. If the source file uses UTF-8 encoding, any character in the decimal range 1-127 (U+0001-U+007F) can be used without modification. You can insert an ISO-8859-1 character outside of this range as a delimiter, and BigQuery will interpret it correctly. However, if you use a multibyte character as a delimiter, some of the bytes will be interpreted incorrectly as part of the field value.

Generally, it's a best practice to use a standard delimiter, such as a tab, pipe, or comma. The default is a comma.

Data types

Boolean. BigQuery can parse any of the following pairs for Boolean data: 1 or 0, true or false, t or f, yes or no, or y or n (all case insensitive). Schema autodetection automatically detects any of these except 0 and 1.

Bytes. Columns with BYTES types must be encoded as Base64.

Date. Columns with DATE types must be in the format YYYY-MM-DD.

Datetime. Columns with DATETIME types must be in the format YYYY-MM-DD HH:MM:SS[.SSSSSS].

Geography. Columns with GEOGRAPHY types must contain strings in one of the following formats:

• Well-known text (WKT)
• Well-known binary (WKB)
• GeoJSON

If you use WKB, the value should be hex encoded.

The following list shows examples of valid data:

• WKT: POINT(1 2)
• GeoJSON: { "type": "Point", "coordinates": [1, 2] }
• Hex encoded WKB: 0101000000feffffffffffef3f0000000000000040

Before loading GEOGRAPHY data, also read Loading geospatial data.

Interval. Columns with INTERVAL types must be in the format Y-M D H:M:S[.F], where:

• Y = Year. Supported range is 0-10,000.
• M = Month. Supported range is 1-12.
• D = Day. Supported range is 1-[last day of the indicated month].
• H = Hour.
• M = Minute.
• S = Second.
• [.F] = Fractions of a second up to six digits, with microsecond precision.

You can indicate a negative value by prepending a dash (-).

The following list shows examples of valid data:

• 10-6 0 0:0:0
• 0-0 -5 0:0:0
• 0-0 0 0:0:1.25

To load INTERVAL data, you must use the bq load command and use the --schema flag to specify a schema. You can't upload INTERVAL data by using the console.

JSON. Quotes are escaped by using the two character sequence "". For more information, see an example of loading JSON data from a CSV file

Time. Columns with TIME types must be in the format HH:MM:SS[.SSSSSS].

Timestamp. BigQuery accepts various timestamp formats. The timestamp must include a date portion and a time portion.

• The date portion can be formatted as YYYY-MM-DD or YYYY/MM/DD.

• The timestamp portion must be formatted as HH:MM[:SS[.SSSSSS]] (seconds and fractions of seconds are optional).

• The date and time must be separated by a space or 'T'.

• Optionally, the date and time can be followed by a UTC offset or the UTC zone designator (Z). For more information, see Time zones.

For example, any of the following are valid timestamp values:

• 2018-08-19 12:11
• 2018-08-19 12:11:35
• 2018-08-19 12:11:35.22
• 2018/08/19 12:11
• 2018-07-05 12:54:00 UTC
• 2018-08-19 07:11:35.220 -05:00
• 2018-08-19T12:11:35.220Z

If you provide a schema, BigQuery also accepts Unix epoch time for timestamp values. However, schema autodetection doesn't detect this case, and treats the value as a numeric or string type instead.

Examples of Unix epoch timestamp values:

• 1534680695
• 1.534680695e11

RANGE. Represented in CSV files in the format [LOWER_BOUND, UPPER_BOUND), where LOWER_BOUND and UPPER_BOUND are valid DATE, DATETIME, or TIMESTAMP strings. NULL and UNBOUNDED represent unbounded start or end values.

Note: Since the range CSV format contains a comma, if the CSV delimiter is a comma, it must be surrounded by double quotes for the CSV file to be valid.

The following are example of CSV values for RANGE<DATE>:

• "[2020-01-01, 2021-01-01)"
• "[UNBOUNDED, 2021-01-01)"
• "[2020-03-01, NULL)"
• "[UNBOUNDED, UNBOUNDED)"

Schema auto-detection

This section describes the behavior of schema auto-detection when loading CSV files.

CSV delimiter

BigQuery detects the following delimiters:

• comma ( , )
• pipe ( | )
• tab ( \t )

CSV header

BigQuery infers headers by comparing the first row of the file with other rows in the file. If the first line contains only strings, and the other lines contain other data types, BigQuery assumes that the first row is a header row. BigQuery assigns column names based on the field names in the header row. The names might be modified to meet the naming rules for columns in BigQuery. For example, spaces will be replaced with underscores.

Otherwise, BigQuery assumes the first row is a data row, and assigns generic column names such as string_field_1. Note that after a table is created, the column names cannot be updated in the schema, although you can change the names manually after the table is created. Another option is to provide an explicit schema instead of using autodetect.

You might have a CSV file with a header row, where all of the data fields are strings. In that case, BigQuery will not automatically detect that the first row is a header. Use the --skip_leading_rows option to skip the header row. Otherwise, the header will be imported as data. Also consider providing an explicit schema in this case, so that you can assign column names.

CSV quoted new lines

BigQuery detects quoted new line characters within a CSV field and does not interpret the quoted new line character as a row boundary.

Troubleshoot parsing errors

If there's a problem parsing your CSV files, then the load job's errors resource is populated with the error details.

Generally, these errors identify the start of the problematic line with a byte offset. For uncompressed files you can use gcloud storage with the --recursive argument to access the relevant line.

For example, you run the bq load command and receive an error:

bq load
    --skip_leading_rows=1 \
    --source_format=CSV \
    mydataset.mytable \
    gs://my-bucket/mytable.csv \
    'Number:INTEGER,Name:STRING,TookOffice:STRING,LeftOffice:STRING,Party:STRING'

The error in the output is similar to the following:

Waiting on bqjob_r5268069f5f49c9bf_0000018632e903d7_1 ... (0s)
Current status: DONE
BigQuery error in load operation: Error processing job
'myproject:bqjob_r5268069f5f49c9bf_0000018632e903d7_1': Error while reading
data, error message: Error detected while parsing row starting at position: 1405.
Error: Data between close quote character (") and field separator.
File: gs://my-bucket/mytable.csv
Failure details:
- gs://my-bucket/mytable.csv: Error while reading data,
error message: Error detected while parsing row starting at
position: 1405. Error: Data between close quote character (") and
field separator. File: gs://my-bucket/mytable.csv
- Error while reading data, error message: CSV processing encountered
too many errors, giving up. Rows: 22; errors: 1; max bad: 0; error
percent: 0

Based on the preceding error, there's a format error in the file. To view the file's content, run the gcloud storage cat command:

gcloud storage cat 1405-1505 gs://my-bucket/mytable.csv --recursive

The output is similar to the following:

16,Abraham Lincoln,"March 4, 1861","April 15, "1865,Republican
18,Ulysses S. Grant,"March 4, 1869",
...

Based on the output of the file, the problem is a misplaced quote in "April 15, "1865.

Compressed CSV files

Debugging parsing errors is more challenging for compressed CSV files, since the reported byte offset refers to the location in the uncompressed file. The following gcloud storage cat command streams the file from Cloud Storage, decompresses the file, identifies the appropriate byte offset, and prints the line with the format error:

gcloud storage cat gs://my-bucket/mytable.csv.gz | gunzip - | tail -c +1406 | head -n 1

The output is similar to the following:

16,Abraham Lincoln,"March 4, 1861","April 15, "1865,Republican

CSV options

To change how BigQuery parses CSV data, specify additional options in the Google Cloud console, the bq command-line tool, or the API.

For more information on the CSV format, see RFC 4180.

CSV option	Console option	bq tool flag	BigQuery API property	Description
Field delimiter	Field delimiter: Comma, Tab, Pipe, Custom	-F or --field_delimiter	fieldDelimiter (Java, Python)	(Optional) The separator for fields in a CSV file. The separator can be any ISO-8859-1 single-byte character. BigQuery converts the string to ISO-8859-1 encoding, and uses the first byte of the encoded string to split the data in its raw, binary state. BigQuery also supports the escape sequence "\t" to specify a tab separator. The default value is a comma (`,`).
Header rows	Header rows to skip	--skip_leading_rows	skipLeadingRows (Java, Python)	(Optional) An integer indicating the number of header rows in the source data.
Number of bad records allowed	Number of errors allowed	--max_bad_records	maxBadRecords (Java, Python)	(Optional) The maximum number of bad records that BigQuery can ignore when running the job. If the number of bad records exceeds this value, an invalid error is returned in the job result. The default value is 0, which requires that all records are valid.
Newline characters	Allow quoted newlines	--allow_quoted_newlines	allowQuotedNewlines (Java, Python)	(Optional) Indicates whether to allow quoted data sections that contain newline characters in a CSV file. The default value is false.
Custom null values	None	--null_marker	nullMarker (Java, Python)	(Optional) Specifies a string that represents a null value in a CSV file. For example, if you specify "\N", BigQuery interprets "\N" as a null value when loading a CSV file. The default value is the empty string. If you set this property to a custom value, BigQuery throws an error if an empty string is present for all data types except for STRING and BYTE. For STRING and BYTE columns, BigQuery interprets the empty string as an empty value.
Trailing optional columns	Allow jagged rows	--allow_jagged_rows	allowJaggedRows (Java, Python)	(Optional) Accept rows that are missing trailing optional columns. The missing values are treated as nulls. If false, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false. Only applicable to CSV, ignored for other formats.
Unknown values	Ignore unknown values	--ignore_unknown_values	ignoreUnknownValues (Java, Python)	(Optional) Indicates if BigQuery should allow extra values that are not represented in the table schema. If true, the extra values are ignored. If false, records with extra columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false. The sourceFormat property determines what BigQuery treats as an extra value: CSV: Trailing columns JSON: Named values that don't match any column names
Quote	Quote character: Double quote, Single quote, None, Custom	--quote	quote (Java, Python)	(Optional) The value that is used to quote data sections in a CSV file. BigQuery converts the string to ISO-8859-1 encoding, and then uses the first byte of the encoded string to split the data in its raw, binary state. The default value is a double-quote ('"'). If your data does not contain quoted sections, set the property value to an empty string. If your data contains quoted newline characters, you must also set the allowQuotedNewlines property to true. To include the specific quote character within a quoted value, precede it with an additional matching quote character. For example, if you want to escape the default character ' " ', use ' "" '.
Encoding	None	-E or --encoding	encoding (Java, Python)	(Optional) The character encoding of the data. The supported values are UTF-8, ISO-8859-1, UTF-16BE, UTF-16LE, UTF-32BE, or UTF-32LE. The default value is UTF-8. BigQuery decodes the data after the raw, binary data has been split using the values of the quote and fieldDelimiter properties.
ASCII control character	None	--preserve_ascii_control_characters	None	(Optional) If you want to allow ASCII 0 and other ASCII control characters, then set --preserve_ascii_control_characters to true to your load jobs.