Modifying table schemas

This document describes how to modify the schema definitions for existing BigQuery tables. BigQuery natively supports the following schema modifications:

  • Adding columns to a schema definition
  • Relaxing a column's mode from REQUIRED to NULLABLE

It is valid to create a table without defining an initial schema and to add a schema definition to the table at a later time.

All other schema modifications are unsupported and require manual workarounds, including:

  • Changing a column's name
  • Changing a column's data type
  • Changing a column's mode (aside from relaxing REQUIRED columns to NULLABLE)
  • Deleting a column

For information on unsupported schema changes that require workarounds, see Manually changing table schemas.

Adding columns to a table's schema definition

You can add columns to an existing table's schema definition:

  • Manually (creates an empty column)
  • When you use a load or query job to overwrite a table
  • When you append data to a table using a load or query job

Any column you add must adhere to BigQuery's rules for column names. For more information on creating schema components, see Specifying a schema.

Manually adding an empty column

You can add an empty column to an existing table by:

  • Using the GCP Console or the classic BigQuery web UI
  • Using the command-line tool's bq update command
  • Calling the tables.patch API method
  • Using the client libraries

If you add new columns to an existing table schema, the columns must be NULLABLE or REPEATED. You cannot add a REQUIRED column to an existing table schema. If you attempt to add a REQUIRED column to an existing table schema in the CLI or API, the following error is returned: BigQuery error in update operation: Provided Schema does not match Table project_id:dataset.table. Cannot add required columns to an existing schema. REQUIRED columns can be added only when you create a table while loading data, or when you create an empty table with a schema definition.

After adding a new column to your table's schema definition, you can load data into the new column by using a:

To add empty columns to a table's schema definition:

Console

  1. In the Resources pane, select your table.

  2. Below the Query editor, scroll to the bottom of the Schema section and click Edit schema.

Edit table schema

  1. Scroll to the bottom of the panel that opens, and click Add field.

    • For Name, type the column name.
    • For Type, choose the data type.
    • For Mode, choose NULLABLE or REPEATED.

  2. When you are done adding columns, click Save.

Classic UI

  1. In the navigation pane, select your table.

  2. On the Table Details page, click Add New Fields.

  3. In the New Fields section:

    • For Name, type the column name.
    • For Type, choose the data type.

    • For Mode, choose NULLABLE or REPEATED.

      Update table schema

  4. When you are done adding columns, click Add to Table.

CLI

Issue the bq update command and provide a JSON schema file. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

bq update project_id:dataset.table schema

Where:

  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table you're updating.
  • table is the name of the table you're updating.
  • schema is the path to the JSON schema file on your local machine.

When you specify the schema on the command line, you cannot include a RECORD (STRUCT) type, you cannot include a column description, and you cannot specify the column's mode. All modes default to NULLABLE.

If you attempt to add columns using an inline schema definition, you must supply the entire schema definition including the new columns. Because you cannot specify column modes using an inline schema definition, the update will attempt to change any existing REQUIRED column to NULLABLE. This results in the following error: BigQuery error in update operation: Provided Schema does not match Table project_id:dataset.table. Field field has changed mode from REPEATED to NULLABLE.

The preferred method of adding columns to an existing table using the CLI is to supply a JSON schema file.

To add empty columns to a table's schema using a JSON schema file:

  1. First, issue the bq show command with the --schema flag and write the existing table schema to a file. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

    bq show \
--schema \
--format=prettyjson \
project_id:dataset.table > schema_file

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema_file is the schema definition file written to your local machine.

    For example, to write the schema definition of mydataset.mytable to a file, enter the following command. mydataset.mytable is in your default project.

       bq show \
   --schema \
   --format=prettyjson \
   mydataset.mytable > /tmp/myschema.json

  2. Open the schema file in a text editor. The schema should look like the following:

    [
  {
    "mode": "REQUIRED",
    "name": "column1",
    "type": "STRING"
  },
  {
    "mode": "REQUIRED",
    "name": "column2",
    "type": "FLOAT"
  },
  {
    "mode": "REPEATED",
    "name": "column3",
    "type": "STRING"
  }
]

  3. Add the new columns to the end of the schema definition. If you attempt to add new columns elsewhere in the array, the following error is returned: BigQuery error in update operation: Precondition Failed.

    Using a JSON file, you can specify descriptions, NULLABLE or REPEATED modes, and RECORD types for new columns. For example, using the schema definition from the previous step, your new JSON array would look like the following. In this example, a new NULLABLE column is added named column4. column4 includes a description.

      [
    {
      "mode": "REQUIRED",
      "name": "column1",
      "type": "STRING"
    },
    {
      "mode": "REQUIRED",
      "name": "column2",
      "type": "FLOAT"
    },
    {
      "mode": "REPEATED",
      "name": "column3",
      "type": "STRING"
    },
    {
      "description": "my new column",
      "mode": "NULLABLE",
      "name": "column4",
      "type": "STRING"
    }
  ]

    For more information on working with JSON schema files, see Specifying a JSON schema file.

  4. After updating your schema file, issue the following command to update the table's schema. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

    bq update project_id:dataset.table schema

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema is the path to the JSON schema file on your local machine.

    For example, enter the following command to update the schema definition of mydataset.mytable in your default project. The path to the schema file on your local machine is /tmp/myschema.json.

    bq update mydataset.mytable /tmp/myschema.json

API

Call the tables.patch method and use the schema property to add empty columns to your schema definition. Because the tables.update method replaces the entire table resource, the tables.patch method is preferred.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
tableRef := client.Dataset(datasetID).Table(tableID)
meta, err := tableRef.Metadata(ctx)
if err != nil {
	return err
}
newSchema := append(meta.Schema,
	&bigquery.FieldSchema{Name: "phone", Type: bigquery.StringFieldType},
)
update := bigquery.TableMetadataToUpdate{
	Schema: newSchema,
}
if _, err := tableRef.Update(ctx, update, meta.ETag); err != nil {
	return err
}

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 .

Append a new SchemaField object to a copy of the Table.schema and then replace the value of the Table.schema property with the updated schema.

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
# table_id = 'my_table'

table_ref = client.dataset(dataset_id).table(table_id)
table = client.get_table(table_ref)  # API request

original_schema = table.schema
new_schema = original_schema[:]  # creates a copy of the schema
new_schema.append(bigquery.SchemaField("phone", "STRING"))

table.schema = new_schema
table = client.update_table(table, ["schema"])  # API request

assert len(table.schema) == len(original_schema) + 1 == len(new_schema)

Adding a nested column to a RECORD

In addition to adding new columns to a table's schema, you can also add new nested columns to a RECORD. The process for adding a new nested column is very similar to the process for adding a new column.

Console

Adding a new nested field to an exising RECORD column is not currently supported by the GCP Console.

Classic UI

Adding a new nested field to an exising RECORD column is not currently supported by the classic BigQuery web UI.

CLI

Issue the bq update command and provide a JSON schema file that adds the nested field to the existing RECORD column's schema definition. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

bq update project_id:dataset.table schema

Where:

  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table you're updating.
  • table is the name of the table you're updating.
  • schema is the path to the JSON schema file on your local machine.

When you specify the schema on the command line, you cannot include a RECORD (STRUCT) type, you cannot include a column description, and you cannot specify the column's mode. All modes default to NULLABLE. As a result, if you are adding a new nested column to a RECORD, you must supply a JSON schema file.

To add a nested column to a RECORD using a JSON schema file:

  1. First, issue the bq show command with the --schema flag and write the existing table schema to a file. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.table.

    bq show \
--schema \
--format=prettyjson \
project_id:dataset.table > schema_file

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema_file is the schema definition file written to your local machine.

    For example, to write the schema definition of mydataset.mytable to a file, enter the following command. mydataset.mytable is in your default project.

    bq show \
--schema \
--format=prettyjson \
mydataset.mytable > /tmp/myschema.json

  2. Open the schema file in a text editor. The schema should look like the following. In this example, column3 is a nested repeated column. The nested columns are nested1 and nested2. The fields array lists the fields nested within column3.

    [
  {
    "mode": "REQUIRED",
    "name": "column1",
    "type": "STRING"
  },
  {
    "mode": "REQUIRED",
    "name": "column2",
    "type": "FLOAT"
  },
  {
    "fields": [
      {
        "mode": "NULLABLE",
        "name": "nested1",
        "type": "STRING"
      },
      {
        "mode": "NULLABLE",
        "name": "nested2",
        "type": "STRING"
      }
    ],
    "mode": "REPEATED",
    "name": "column3",
    "type": "RECORD"
  }
]

  3. Add the new nested column to the end of the fields array. In this example, nested3 is the new nested column.

      [
    {
      "mode": "REQUIRED",
      "name": "column1",
      "type": "STRING"
    },
    {
      "mode": "REQUIRED",
      "name": "column2",
      "type": "FLOAT"
    },
    {
      "fields": [
        {
          "mode": "NULLABLE",
          "name": "nested1",
          "type": "STRING"
        },
        {
          "mode": "NULLABLE",
          "name": "nested2",
          "type": "STRING"
        },
        {
          "mode": "NULLABLE",
          "name": "nested3",
          "type": "STRING"
        }
      ],
      "mode": "REPEATED",
      "name": "column3",
      "type": "RECORD"
    }
  ]

    For more information on working with JSON schema files, see Specifying a JSON schema file.

  4. After updating your schema file, issue the following command to update the table's schema. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

    bq update project_id:dataset.table schema

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema is the path to the JSON schema file on your local machine.

    For example, enter the following command to update the schema definition of mydataset.mytable in your default project. The path to the schema file on your local machine is /tmp/myschema.json.

    bq update mydataset.mytable /tmp/myschema.json

API

Call the tables.patch method and use the schema property to add the nested columns to your schema definition. Because the tables.update method replaces the entire table resource, the tables.patch method is preferred.

Adding a column when you overwrite or append data

You can add new columns to an existing table when you load data into it and choose to overwrite the existing table. When you overwrite an existing table, the schema of the data you're loading is used to overwrite the existing table's schema. For information on overwriting a table using a load job, see:

You can also add new columns to an existing table when you append data to it using a load or query job.

Adding a column in a load append job

You can add columns to a table while appending data to it in a load job by:

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

Adding a column to an existing table during an append operation is not currently supported by the GCP Console or the classic BigQuery web UI.

When you add columns using an append operation in a load job, the updated schema can be:

  • Automatically detected (for CSV and JSON files)
  • Specified in a JSON schema file (for CSV and JSON files)
  • Retrieved from the self-describing source data for Avro, ORC, Parquet and Cloud Datastore export files

If you specify the schema in a JSON file, the new columns must be defined in it. If the new column definitions are missing, the following error is returned when you attempt to append the data: Error while reading data, error message: parsing error in row starting at position int: No such field: field.

When you add new columns during an append operation, the values in the new columns are set to NULL for existing rows.

To add a new column when you append data to a table during a load job:

Console

You cannot add new columns to an existing table when you load data using the GCP Console.

Classic UI

You cannot add new columns to an existing table when you load data using the classic BigQuery web UI.

CLI

Use the bq load command to load your data and specify the --noreplace flag to indicate that you are appending the data to an existing table.

If the data you're appending is in CSV or newline-delimited JSON format, specify the --autodetect flag to use schema auto-detection or supply the schema in a JSON schema file. The added columns can be automatically inferred from Avro or Cloud Datastore export files.

Set the --schema_update_option flag to ALLOW_FIELD_ADDITION to indicate that the data you're appending contains new columns.

If the table you're appending is in a dataset in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

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

Enter the load command as follows:

bq --location=location load \
--noreplace \
--autodetect \
--schema_update_option=ALLOW_FIELD_ADDITION \
--source_format=format \
project_id:dataset.table \
path_to_source \
schema

Where:

  • location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
  • format is NEWLINE_DELIMITED_JSON, CSV, AVRO, PARQUET, ORC, or DATASTORE_BACKUP.
  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table.
  • table is the name of the table you're appending.
  • path_to_source is a fully-qualified Cloud Storage URI, a comma-separated list of URIs, or the path to a data file on your local machine.
  • schema is the path to a local JSON schema file. A schema file is required only for CSV and JSON files when --autodetect is unspecified. Avro and Cloud Datastore schemas are inferred from the source data.

Examples:

Enter the following command to append a local Avro data file, /tmp/mydata.avro, to mydataset.mytable using a load job. Because schemas can be automatically inferred from Avro data you do not need to use the --autodetect flag. mydataset is in your default project.

bq load \
--noreplace \
--schema_update_option=ALLOW_FIELD_ADDITION \
--source_format=AVRO \
mydataset.mytable \
/tmp/mydata.avro

Enter the following command append a newline-delimited JSON data file in Cloud Storage to mydataset.mytable using a load job. The --autodetect flag is used to detect the new columns. mydataset is in your default project.

bq load \
--noreplace \
--autodetect \
--schema_update_option=ALLOW_FIELD_ADDITION \
--source_format=NEWLINE_DELIMITED_JSON \
mydataset.mytable \
gs://mybucket/mydata.json

Enter the following command append a newline-delimited JSON data file in Cloud Storage to mydataset.mytable using a load job. The schema containing the new columns is specified in a local JSON schema file, /tmp/myschema.json. mydataset is in myotherproject, not your default project.

bq load \
--noreplace \
--schema_update_option=ALLOW_FIELD_ADDITION \
--source_format=NEWLINE_DELIMITED_JSON \
myotherproject:mydataset.mytable \
gs://mybucket/mydata.json \
/tmp/myschema.json

API

Call the jobs.insert method. Configure a load job and set the following properties:

  • Reference your data in Cloud Storage using the sourceUris property.
  • Specify the data format by setting the sourceFormat property.
  • Specify the schema in the schema property.
  • Specify the schema update option using the schemaUpdateOptions property.
  • Set the write disposition of the destination table to WRITE_APPEND using the writeDisposition property.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
sampleSchema := bigquery.Schema{
	{Name: "full_name", Type: bigquery.StringFieldType},
}
meta := &bigquery.TableMetadata{
	Schema: sampleSchema,
}
tableRef := client.Dataset(datasetID).Table(tableID)
if err := tableRef.Create(ctx, meta); err != nil {
	return err
}
// Now, import data from a local file, but specify field additions are allowed.
// Because the data has a second column (age), the schema is amended as part of
// the load.
f, err := os.Open(filename)
if err != nil {
	return err
}
source := bigquery.NewReaderSource(f)
source.AutoDetect = true   // Allow BigQuery to determine schema.
source.SkipLeadingRows = 1 // CSV has a single header line.

loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(source)
loader.SchemaUpdateOptions = []string{"ALLOW_FIELD_ADDITION"}
job, err := loader.Run(ctx)
if err != nil {
	return err
}
status, err := job.Wait(ctx)
if err != nil {
	return err
}
if err := status.Err(); err != nil {
	return err
}

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 . 

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
# filepath = 'path/to/your_file.csv'

# Retrieves the destination table and checks the length of the schema
table_id = "my_table"
table_ref = dataset_ref.table(table_id)
table = client.get_table(table_ref)
print("Table {} contains {} columns.".format(table_id, len(table.schema)))

# Configures the load job to append the data to the destination table,
# allowing field addition
job_config = bigquery.LoadJobConfig()
job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
job_config.schema_update_options = [
    bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION
]
# In this example, the existing table contains only the 'full_name' column.
# 'REQUIRED' fields cannot be added to an existing schema, so the
# additional column must be 'NULLABLE'.
job_config.schema = [
    bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
    bigquery.SchemaField("age", "INTEGER", mode="NULLABLE"),
]
job_config.source_format = bigquery.SourceFormat.CSV
job_config.skip_leading_rows = 1

with open(filepath, "rb") as source_file:
    job = client.load_table_from_file(
        source_file,
        table_ref,
        location="US",  # Must match the destination dataset location.
        job_config=job_config,
    )  # API request

job.result()  # Waits for table load to complete.
print(
    "Loaded {} rows into {}:{}.".format(
        job.output_rows, dataset_id, table_ref.table_id
    )
)

# Checks the updated length of the schema
table = client.get_table(table)
print("Table {} now contains {} columns.".format(table_id, len(table.schema)))

Adding a column in a query append job

You can add columns to a table while appending query results to it by:

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

Adding a column during an append operation is not currently supported by the GCP Console or the classic BigQuery web UI.

When you add columns using an append operation in a query job, the schema of the query results is used to update the schema of the destination table. Note that you cannot query a table in one location and write the results to a table in another location.

To add a new column when you append data to a table during a query job:

Console

You cannot add new columns to an existing table when you append query results using the GCP Console.

Classic UI

You cannot add new columns to an existing table when you append query results using the classic BigQuery web UI.

CLI

Use the bq query command to query your data and specify the --destination_table flag to indicate which table you're appending.

To specify that you are appending query results to an existing destination table, specify the --append_table flag.

Set the --schema_update_option flag to ALLOW_FIELD_ADDITION to indicate that the query results you're appending contain new columns.

Specify the use_legacy_sql=false flag to use standard SQL syntax for the query.

If the table you're appending is in a dataset in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset. Note that the table you're quering and the destination table must be in the same location.

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

bq --location=location query \
--destination_table project_id:dataset.table \
--append_table \
--schema_update_option=ALLOW_FIELD_ADDITION \
--use_legacy_sql=false \
'query'

Where:

  • location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file. Note that you cannot append query results to a table in another location.
  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table you're appending.
  • table is the name of the table you're appending.
  • query is a query in standard SQL syntax.

Examples:

Enter the following command to query mydataset.mytable in your default project and to append the query results to mydataset.mytable2 (also in your default project).

bq query \
--destination_table mydataset.mytable2 \
--append_table \
--schema_update_option=ALLOW_FIELD_ADDITION \
--use_legacy_sql=false \
'SELECT
   column1,column2
 FROM
   mydataset.mytable'

Enter the following command to query mydataset.mytable in your default project and to append the query results to mydataset.mytable2 in myotherproject.

bq query \
--destination_table myotherproject:mydataset.mytable2 \
--append_table \
--schema_update_option=ALLOW_FIELD_ADDITION \
--use_legacy_sql=false \
'SELECT
   column1,column2
 FROM
   mydataset.mytable'

API

Call the jobs.insert method. Configure a query job and set the following properties:

  • Specify the destination table using the destinationTable property.
  • Set the write disposition of the destination table to WRITE_APPEND using the writeDisposition property.
  • Specify the schema update option using the schemaUpdateOptions property.
  • Specify the standard SQL query using the query property.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
sampleSchema := bigquery.Schema{
	{Name: "full_name", Type: bigquery.StringFieldType, Required: true},
	{Name: "age", Type: bigquery.IntegerFieldType, Required: true},
}
original := &bigquery.TableMetadata{
	Schema: sampleSchema,
}
tableRef := client.Dataset(datasetID).Table(tableID)
if err := tableRef.Create(ctx, original); err != nil {
	return err
}
// Our table has two columns.  We'll introduce a new favorite_color column via
// a subsequent query that appends to the table.
q := client.Query("SELECT \"Timmy\" as full_name, 85 as age, \"Blue\" as favorite_color")
q.SchemaUpdateOptions = []string{"ALLOW_FIELD_ADDITION"}
q.QueryConfig.Dst = client.Dataset(datasetID).Table(tableID)
q.WriteDisposition = bigquery.WriteAppend
q.Location = "US"
job, err := q.Run(ctx)
if err != nil {
	return err
}
_, err = job.Wait(ctx)
if err != nil {
	return err
}

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 . 

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')

# Retrieves the destination table and checks the length of the schema
table_id = "my_table"
table_ref = dataset_ref.table(table_id)
table = client.get_table(table_ref)
print("Table {} contains {} columns.".format(table_id, len(table.schema)))

# Configures the query to append the results to a destination table,
# allowing field addition
job_config = bigquery.QueryJobConfig()
job_config.schema_update_options = [
    bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION
]
job_config.destination = table_ref
job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND

query_job = client.query(
    # In this example, the existing table contains only the 'full_name' and
    # 'age' columns, while the results of this query will contain an
    # additional 'favorite_color' column.
    'SELECT "Timmy" as full_name, 85 as age, "Blue" as favorite_color;',
    # Location must match that of the dataset(s) referenced in the query
    # and of the destination table.
    location="US",
    job_config=job_config,
)  # API request - starts the query

query_job.result()  # Waits for the query to finish
print("Query job {} complete.".format(query_job.job_id))

# Checks the updated length of the schema
table = client.get_table(table)
print("Table {} now contains {} columns.".format(table_id, len(table.schema)))

Relaxing a column's mode

Currently, the only supported modification you can make to a column's mode is changing it from REQUIRED to NULLABLE. Changing a column's mode from REQUIRED to NULLABLE is also called column relaxation. You can relax REQUIRED columns:

  • Manually
  • When you overwrite a table using a load or query job
  • When you append data to a table using a query job

Manually changing REQUIRED columns to NULLABLE

You can manually change a column's mode from REQUIRED to NULLABLE by:

  • Using the GCP Console or the classic BigQuery web UI
  • Using the command-line tool's bq update command
  • Calling the tables.patch API method.
  • Using the client libraries

To manually change a column's mode from REQUIRED to NULLABLE:

Console

You cannot currently relax a column's mode using the GCP Console.

Classic UI

  1. Expand your dataset and select your table.

  2. On the Table Details page, click the Schema tab.

  3. Click the down arrow to the right of your required column and choose either:

    • Make NULLABLE — relaxes the individual column's mode

    • All REQUIRED to NULLABLE — changes all REQUIRED columns in the schema definition to NULLABLE

      Relax column mode

  4. In the Confirm Mode Change dialog, click OK to change the mode to NULLABLE. Note that this change cannot be undone.

CLI

  1. First, issue the bq show command with the --schema flag and write the existing table schema to a file. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

    bq show \
--schema \
--format=prettyjson \
project_id:dataset.table > schema_file

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema_file is the schema definition file written to your local machine.

    For example, to write the schema definition of mydataset.mytable to a file, enter the following command. mydataset.mytable is in your default project.

      bq show \
  --schema \
  --format=prettyjson \
  mydataset.mytable > /tmp/myschema.json

  2. Open the schema file in a text editor. The schema should look like the following:

    [
  {
    "mode": "REQUIRED",
    "name": "column1",
    "type": "STRING"
  },
  {
    "mode": "REQUIRED",
    "name": "column2",
    "type": "FLOAT"
  },
  {
    "mode": "REPEATED",
    "name": "column3",
    "type": "STRING"
  }
]

  3. Change an existing column's mode from REQUIRED to NULLABLE. In this example, the mode for column1 is relaxed.

    [
  {
    "mode": "NULLABLE",
    "name": "column1",
    "type": "STRING"
  },
  {
    "mode": "REQUIRED",
    "name": "column2",
    "type": "FLOAT"
  },
  {
    "mode": "REPEATED",
    "name": "column3",
    "type": "STRING"
  }
]

    For more information on working with JSON schema files, see Specifying a JSON schema file.

  4. After updating your schema file, issue the following command to update the table's schema. If the table you're updating is in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

    bq update project_id:dataset.table schema

    Where:

    • project_id is your project ID.
    • dataset is the name of the dataset that contains the table you're updating.
    • table is the name of the table you're updating.
    • schema is the path to the JSON schema file on your local machine.

    For example, enter the following command to update the schema definition of mydataset.mytable in your default project. The path to the schema file on your local machine is /tmp/myschema.json.

      bq update mydataset.mytable /tmp/myschema.json

API

Call tables.patch and use the schema property to change a REQUIRED column to NULLABLE in your schema definition. Because the tables.update method replaces the entire table resource, the tables.patch method is preferred.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
tableRef := client.Dataset(datasetID).Table(tableID)
meta, err := tableRef.Metadata(ctx)
if err != nil {
	return err
}
// Iterate through the schema to set all Required fields to false (nullable).
var relaxed bigquery.Schema
for _, v := range meta.Schema {
	v.Required = false
	relaxed = append(relaxed, v)
}
newMeta := bigquery.TableMetadataToUpdate{
	Schema: relaxed,
}
if _, err := tableRef.Update(ctx, newMeta, meta.ETag); err != nil {
	return err
}

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 .

Overwrite the Table.schema property with a list of SchemaField objects with the mode property set to 'NULLABLE'

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
# table_id = 'my_table'

original_schema = [
    bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
    bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
]
table_ref = client.dataset(dataset_id).table(table_id)
table = bigquery.Table(table_ref, schema=original_schema)
table = client.create_table(table)
assert all(field.mode == "REQUIRED" for field in table.schema)

# SchemaField properties cannot be edited after initialization.
# To make changes, construct new SchemaField objects.
relaxed_schema = [
    bigquery.SchemaField("full_name", "STRING", mode="NULLABLE"),
    bigquery.SchemaField("age", "INTEGER", mode="NULLABLE"),
]
table.schema = relaxed_schema
table = client.update_table(table, ["schema"])

assert all(field.mode == "NULLABLE" for field in table.schema)

Changing REQUIRED to NULLABLE in a load or query job

You can relax REQUIRED columns to NULLABLE in an existing table's schema when you load data into it and choose to overwrite the existing table. When you overwrite an existing table, the schema of the data you're loading is used to overwrite the existing table's schema. For information on overwriting a table using a load job, see:

You can also relax REQUIRED columns to NULLABLE in an existing table's schema when you append data to it using a query job.

Changing REQUIRED to NULLABLE in a load append job

You can relax a column's mode while appending data to a table in a load job by:

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

Changing a column's mode during an append operation is not currently supported by the GCP Console or the classic BigQuery web UI.

When you relax a column's mode using an append operation in a load job, you can:

  • Relax the mode for individual columns by specifying a JSON schema file (when appending data from CSV and JSON files)
  • Relax columns to null in your Avro, ORC, or Parquet schema and allow schema inference to detect the relaxed columns

To relax a column from REQUIRED to NULLABLEwhen you append data to a table during a load job:

Console

You cannot currently relax a column's mode using the GCP Console.

Classic UI

You cannot relax an existing column's mode when you append data to a table in a load job using the classic BigQuery web UI.

CLI

Use the bq load command to load your data and specify the --noreplace flag to indicate that you are appending the data to an existing table.

If the data you're appending is in CSV or newline-delimited JSON format, specify the relaxed columns in a local JSON schema file or use the --autodetect flag to use schema detection to discover relaxed columns in the source data. For information on relaxing column modes using a JSON schema file, see Manually changing REQUIRED columns to NULLABLE.

Relaxed columns can be automatically inferred from Avro, ORC, and Parquet files. Column relaxation does not apply to Cloud Datastore export appends. The columns in tables created by loading Cloud Datastore export files are always NULLABLE.

Set the --schema_update_option flag to ALLOW_FIELD_RELAXATION to indicate that the data you're appending contains relaxed columns.

If the table you're appending is in a dataset in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

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

Enter the load command as follows:

bq --location=location load \
--noreplace \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--source_format=format \
project_id:dataset.table \
path_to_source \
schema

Where:

  • location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
  • format is NEWLINE_DELIMITED_JSON, CSV, PARQUET, ORC, or AVRO. DATASTORE_BACKUP files do not require column relaxation. The columns in tables created from Cloud Datastore export files are always NULLABLE.
  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table.
  • table is the name of the table you're appending.
  • path_to_source is a fully-qualified Cloud Storage URI, a comma-separated list of URIs, or the path to a data file on your local machine.
  • schema is the path to a local JSON schema file. This option is used only for CSV and JSON files. Relaxed columns are automatically inferred from Avro files.

Examples:

Enter the following command to append a local Avro data file, /tmp/mydata.avro, to mydataset.mytable using a load job. Since relaxed columns can be automatically inferred from Avro data you do not need to specify a schema file. mydataset is in your default project.

bq load \
--noreplace \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--source_format=AVRO \
mydataset.mytable \
/tmp/mydata.avro

Enter the following command to append data from a newline-delimited JSON file in Cloud Storage to mydataset.mytable using a load job. The schema containing the relaxed columns is in a local JSON schema file — /tmp/myschema.json. mydataset is in your default project.

bq load \
--noreplace \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--source_format=NEWLINE_DELIMITED_JSON \
mydataset.mytable \
gs://mybucket/mydata.json \
/tmp/myschema.json

Enter the following command to append data in a CSV file on your local machine to mydataset.mytable using a load job. The command uses schema auto-detection to discover relaxed columns in the source data. mydataset is in myotherproject, not your default project.

bq load \
--noreplace \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--source_format=CSV \
--autodetect \
myotherproject:mydataset.mytable \
mydata.csv

API

Call the jobs.insert method. Configure a load job and set the following properties:

  • Reference your data in Cloud Storage using the sourceUris property.
  • Specify the data format by setting the sourceFormat property.
  • Specify the schema in the schema property.
  • Specify the schema update option using the schemaUpdateOptions property.
  • Set the write disposition of the destination table to WRITE_APPEND using the writeDisposition property.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
sampleSchema := bigquery.Schema{
	{Name: "full_name", Type: bigquery.StringFieldType, Required: true},
	{Name: "age", Type: bigquery.IntegerFieldType, Required: true},
}
meta := &bigquery.TableMetadata{
	Schema: sampleSchema,
}
tableRef := client.Dataset(datasetID).Table(tableID)
if err := tableRef.Create(ctx, meta); err != nil {
	return err
}
// Now, import data from a local file, but specify relaxation of required
// fields as a side effect while the data is appended.
f, err := os.Open(filename)
if err != nil {
	return err
}
source := bigquery.NewReaderSource(f)
source.AutoDetect = true   // Allow BigQuery to determine schema.
source.SkipLeadingRows = 1 // CSV has a single header line.

loader := client.Dataset(datasetID).Table(tableID).LoaderFrom(source)
loader.SchemaUpdateOptions = []string{"ALLOW_FIELD_RELAXATION"}
job, err := loader.Run(ctx)
if err != nil {
	return err
}
status, err := job.Wait(ctx)
if err != nil {
	return err
}
if err := status.Err(); err != nil {
	return err
}

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 . 

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
# filepath = 'path/to/your_file.csv'

# Retrieves the destination table and checks the number of required fields
table_id = "my_table"
table_ref = dataset_ref.table(table_id)
table = client.get_table(table_ref)
original_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
# In this example, the existing table has 3 required fields.
print("{} fields in the schema are required.".format(original_required_fields))

# Configures the load job to append the data to a destination table,
# allowing field relaxation
job_config = bigquery.LoadJobConfig()
job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
job_config.schema_update_options = [
    bigquery.SchemaUpdateOption.ALLOW_FIELD_RELAXATION
]
# In this example, the existing table contains three required fields
# ('full_name', 'age', and 'favorite_color'), while the data to load
# contains only the first two fields.
job_config.schema = [
    bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
    bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
]
job_config.source_format = bigquery.SourceFormat.CSV
job_config.skip_leading_rows = 1

with open(filepath, "rb") as source_file:
    job = client.load_table_from_file(
        source_file,
        table_ref,
        location="US",  # Must match the destination dataset location.
        job_config=job_config,
    )  # API request

job.result()  # Waits for table load to complete.
print(
    "Loaded {} rows into {}:{}.".format(
        job.output_rows, dataset_id, table_ref.table_id
    )
)

# Checks the updated number of required fields
table = client.get_table(table)
current_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
print("{} fields in the schema are now required.".format(current_required_fields))

Changing REQUIRED to NULLABLE in a query append job

You can relax all columns in a table while appending query results to it by:

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

Relaxing columns during an append operation is not currently supported by the GCP Console or the classic BigQuery web UI.

When you relax columns using an append operation in a query job, you can relax all required fields in the destination table by setting the --schema_update_option flag to ALLOW_FIELD_RELAXATION. You cannot relax individual columns in a destination table using a query append.

To relax all columns in a destination table when you append data to it during a query job:

Console

You cannot currently relax a column's mode using the GCP Console.

Classic UI

You cannot relax columns in a destination table when you append query results using the BigQuery web UI.

CLI

Use the bq query command to query your data and specify the --destination_table flag to indicate which table you're appending.

To specify that you are appending query results to an existing destination table, specify the --append_table flag.

Set the --schema_update_option flag to ALLOW_FIELD_RELAXATION to indicate that all REQUIRED columns in the table you're appending should be changed to NULLABLE.

Specify the use_legacy_sql=false flag to use standard SQL syntax for the query.

If the table you're appending is in a dataset in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

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

bq --location=location query \
--destination_table project_id:dataset.table \
--append_table \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--use_legacy_sql=false \
'query'

Where:

  • location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
  • project_id is your project ID.
  • dataset is the name of the dataset that contains the table you're appending.
  • table is the name of the table you're appending.
  • query is a query in standard SQL syntax.

Examples:

Enter the following command query mydataset.mytable in your default project and to append the query results to mydataset.mytable2 (also in your default project). The command changes all REQUIRED columns in the destination table to NULLABLE.

bq query \
--destination_table mydataset.mytable2 \
--append_table \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--use_legacy_sql=false \
'SELECT
   column1,column2
 FROM
   mydataset.mytable'

Enter the following command query mydataset.mytable in your default project and to append the query results to mydataset.mytable2 in myotherproject. The command changes all REQUIRED columns in the destination table to NULLABLE.

bq query \
--destination_table myotherproject:mydataset.mytable2 \
--append_table \
--schema_update_option=ALLOW_FIELD_RELAXATION \
--use_legacy_sql=false \
'SELECT
   column1,column2
 FROM
   mydataset.mytable'

API

Call the jobs.insert method. Configure a query job and set the following properties:

  • Specify the destination table using the destinationTable property.
  • Set the write disposition of the destination table to WRITE_APPEND using the writeDisposition property.
  • Specify the schema update option using the schemaUpdateOptions property.
  • Specify the standard SQL query using thequery property.

Go 

// To run this sample, you will need to create (or reuse) a context and
// an instance of the bigquery client.  For example:
// import "cloud.google.com/go/bigquery"
// ctx := context.Background()
// client, err := bigquery.NewClient(ctx, "your-project-id")
sampleSchema := bigquery.Schema{
	{Name: "full_name", Type: bigquery.StringFieldType, Required: true},
	{Name: "age", Type: bigquery.IntegerFieldType, Required: true},
}
meta := &bigquery.TableMetadata{
	Schema: sampleSchema,
}
tableRef := client.Dataset(datasetID).Table(tableID)
if err := tableRef.Create(ctx, meta); err != nil {
	return err
}
// Now, append a query result that includes nulls, but allow the job to relax
// all required columns.
q := client.Query("SELECT \"Beyonce\" as full_name")
q.QueryConfig.Dst = client.Dataset(datasetID).Table(tableID)
q.SchemaUpdateOptions = []string{"ALLOW_FIELD_RELAXATION"}
q.WriteDisposition = bigquery.WriteAppend
q.Location = "US"
job, err := q.Run(ctx)
if err != nil {
	return err
}
_, err = job.Wait(ctx)
if err != nil {
	return err
}

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 . 

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')

# Retrieves the destination table and checks the number of required fields
table_id = "my_table"
table_ref = dataset_ref.table(table_id)
table = client.get_table(table_ref)
original_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
# In this example, the existing table has 2 required fields
print("{} fields in the schema are required.".format(original_required_fields))

# Configures the query to append the results to a destination table,
# allowing field relaxation
job_config = bigquery.QueryJobConfig()
job_config.schema_update_options = [
    bigquery.SchemaUpdateOption.ALLOW_FIELD_RELAXATION
]
job_config.destination = table_ref
job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND

query_job = client.query(
    # In this example, the existing table contains 'full_name' and 'age' as
    # required columns, but the query results will omit the second column.
    'SELECT "Beyonce" as full_name;',
    # Location must match that of the dataset(s) referenced in the query
    # and of the destination table.
    location="US",
    job_config=job_config,
)  # API request - starts the query

query_job.result()  # Waits for the query to finish
print("Query job {} complete.".format(query_job.job_id))

# Checks the updated number of required fields
table = client.get_table(table)
current_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
print("{} fields in the schema are now required.".format(current_required_fields))
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