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Loading JSON data from Cloud Storage

You can load newline delimited JSON data from Cloud Storage into a new table or partition, or 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.

The newline delimited JSON format is the same format as the JSON Lines format.

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

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 location 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 JSON files into BigQuery, note the following:

  • JSON data must be newline delimited. Each JSON object must be on a separate line in the file.
  • If you use gzip compression, BigQuery cannot read the data in parallel. Loading compressed JSON data into BigQuery is slower than loading 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.

  • BigQuery supports the JSON type even if schema information is not known at the time of ingestion. A field that is declared as JSON type is loaded with the raw JSON values.

  • If you use the BigQuery API to load an integer outside the range of [-253+1, 253-1] (usually this means larger than 9,007,199,254,740,991), into an integer (INT64) column, pass it as a string to avoid data corruption. This issue is caused by a limitation on integer size in JSON/ECMAScript. For more information, see the Numbers section of RFC 7159.

  • 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 (-) separator for the date portion of the timestamp, and the date must be in the following format: YYYY-MM-DD (year-month-day). The hh:mm:ss (hour-minute-second) portion of the timestamp must use a colon (:) separator.

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 load data from a Cloud Storage bucket, you need the following IAM permissions:

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

Create a dataset

Create a BigQuery dataset to store your data.

Loading JSON data into a new table

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

Console

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

    Go to BigQuery

  2. In the Explorer pane, expand your project, and then select a dataset.
  3. In the Dataset info section, click Create table.
  4. In the Create table panel, specify the following details:
    1. In the Source section, select Google Cloud Storage in the Create table from list. Then, do the following:
      1. Select a file from the Cloud Storage bucket, or enter the Cloud Storage URI. You cannot include multiple URIs in the Google Cloud console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you want to create, append, or overwrite. select source file to create a BigQuery table
      2. For File format, select JSONL (Newline delimited JSON).
    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 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 loads a JSON file into the new table mytable:

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

    Go to BigQuery

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

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

  3. Click Run.

For more information about how to run queries, see Running interactive queries.

bq

Use the bq load command, specify NEWLINE_DELIMITED_JSON 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.

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

Other optional flags include:

  • --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 can reduce cost and improve performance. For more information, see Querying partitioned tables.
  • --clustering_fields: A comma-separated list of up to four column names used to create a clustered table.

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

    For more information on partitioned tables, see:

    • Creating 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 JSON data into BigQuery, enter the following command:

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

Replace the following:

  • LOCATION : your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
  • FORMAT : NEWLINE_DELIMITED_JSON.
  • DATASET : an existing dataset.
  • TABLE : the name of the table into which you're loading data.
  • PATH_TO_SOURCE : a fully qualified Cloud Storage URI or a comma-separated list of URIs. Wildcards are also supported.
  • SCHEMA : a valid schema. The schema can be a local JSON file, or it can be typed inline as part of the command. If you use a schema file, do not give it an extension. You can also use the --autodetect flag instead of supplying a schema definition.

Examples:

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

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

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

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

The following command loads data from gs://mybucket/mydata.json into a 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.

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

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

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

The following command loads data from gs://mybucket/mydata.json 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=NEWLINE_DELIMITED_JSON \     mydataset.mytable \     gs://mybucket/mydata.json \     qtr:STRING,sales:FLOAT,year:STRING

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=NEWLINE_DELIMITED_JSON \     mydataset.mytable \     gs://mybucket/mydata*.json

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.

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

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 JSON data format by setting the sourceFormat property to NEWLINE_DELIMITED_JSON.

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

    • If status.state = DONE, the job completed successfully.
    • If the status.errorResult property is present, the request failed, and that object includes information describing what went wrong. When a request fails, no table is created and no data is loaded.
    • If status.errorResult is absent, the job finished successfully; although, there might have been some 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 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.

Use the BigQueryClient.CreateLoadJob() method to start a load job from Cloud Storage. To use newline-delimited JSON, create a CreateLoadJobOptions object and set its SourceFormat property to FileFormat.NewlineDelimitedJson.

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.

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.

Use the LoadJobConfiguration.builder(tableId, sourceUri) method to start a load job from Cloud Storage. To use newline-delimited JSON, use the LoadJobConfiguration.setFormatOptions(FormatOptions.json()).

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.

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.

Python

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

Use the Client.load_table_from_uri() method to start a load job from Cloud Storage. To use newline-delimited JSON, set the LoadJobConfig.source_format property to the string NEWLINE_DELIMITED_JSON and pass the job config as the job_config argument to the load_table_from_uri() method.

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.

Use the Dataset.load_job() method to start a load job from Cloud Storage. To use newline-delimited JSON, set the format parameter to "json".

Loading nested and repeated JSON data

BigQuery supports loading nested and repeated data from source formats that support object-based schemas, such as JSON, Avro, ORC, Parquet, Firestore, and Datastore.

One JSON object, including any nested/repeated fields, must appear on each line.

The following example shows sample nested/repeated data. This table contains information about people. It consists of the following fields:

  • id
  • first_name
  • last_name
  • dob (date of birth)
  • addresses (a nested and repeated field)
    • addresses.status (current or previous)
    • addresses.address
    • addresses.city
    • addresses.state
    • addresses.zip
    • addresses.numberOfYears (years at the address)

The JSON data file would look like the following. Notice that the address field contains an array of values (indicated by [ ]). 

{"id":"1","first_name":"John","last_name":"Doe","dob":"1968-01-22","addresses":[{"status":"current","address":"123 First Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456 Main Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]} {"id":"2","first_name":"Jane","last_name":"Doe","dob":"1980-10-16","addresses":[{"status":"current","address":"789 Any Avenue","city":"New York","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321 Main Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}

The schema for this table would look like the following: 

[     {         "name": "id",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "first_name",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "last_name",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "dob",         "type": "DATE",         "mode": "NULLABLE"     },     {         "name": "addresses",         "type": "RECORD",         "mode": "REPEATED",         "fields": [             {                 "name": "status",                 "type": "STRING",                 "mode": "NULLABLE"             },             {                 "name": "address",                 "type": "STRING",                 "mode": "NULLABLE"             },             {                 "name": "city",                 "type": "STRING",                 "mode": "NULLABLE"             },             {                 "name": "state",                 "type": "STRING",                 "mode": "NULLABLE"             },             {                 "name": "zip",                 "type": "STRING",                 "mode": "NULLABLE"             },             {                 "name": "numberOfYears",                 "type": "STRING",                 "mode": "NULLABLE"             }         ]     } ]

For information on specifying a nested and repeated schema, see Specifying nested and repeated fields.

Loading semi-structured JSON data

BigQuery supports loading semi-structured data, in which a field can take values of different types. The following example shows data similar to the preceding nested and repeated JSON data example, except that the address field can be a STRING, a STRUCT, or an ARRAY:

{"id":"1","first_name":"John","last_name":"Doe","dob":"1968-01-22","address":"123 First Avenue, Seattle WA 11111"}  {"id":"2","first_name":"Jane","last_name":"Doe","dob":"1980-10-16","address":{"status":"current","address":"789 Any Avenue","city":"New York","state":"NY","zip":"33333","numberOfYears":"2"}}  {"id":"3","first_name":"Bob","last_name":"Doe","dob":"1982-01-10","address":[{"status":"current","address":"789 Any Avenue","city":"New York","state":"NY","zip":"33333","numberOfYears":"2"}, "321 Main Street Hoboken NJ 44444"]}

You can load this data into BigQuery by using the following schema:

[     {         "name": "id",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "first_name",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "last_name",         "type": "STRING",         "mode": "NULLABLE"     },     {         "name": "dob",         "type": "DATE",         "mode": "NULLABLE"     },     {         "name": "address",         "type": "JSON",         "mode": "NULLABLE"     } ]

The address field is loaded into a column with type JSON that allows it to hold the mixed types in the example. You can ingest data as JSON whether it contains mixed types or not. For example, you could specify JSON instead of STRING as the type for the first_name field. For more information, see Working with JSON data in Google Standard SQL.

Appending to or overwriting a table with JSON 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 and removes any Cloud KMS key.

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

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

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

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. select source file to create a BigQuery table
      2. For File format, select JSONL (Newline delimited JSON).
    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. 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 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 JSON 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 = 'JSON',   uris = ['gs://bucket/path/file.json']);

  3. Click Run.

For more information about how to run queries, see Running interactive queries.

bq

Use the bq load command, specify NEWLINE_DELIMITED_JSON 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.

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:

  • --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

Replace the following:

  • LOCATION : your location. The --location flag is optional. You can set a default value for the location using the .bigqueryrc file.
  • FORMAT : NEWLINE_DELIMITED_JSON.
  • DATASET : an existing dataset.
  • TABLE : the name of the table into which you're loading data.
  • PATH_TO_SOURCE : a fully qualified Cloud Storage URI or a comma-separated list of URIs. Wildcards are also supported.
  • SCHEMA : 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.json and overwrites a table named mytable in mydataset. The schema is defined using schema auto-detection.

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

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

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

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. The wildcards are also supported.

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

  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.

Java

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.

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.

Python

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

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.

Ruby

To replace the rows in an existing table, set the write parameter of Table.load_job() to "WRITE_TRUNCATE".

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.

Loading hive-partitioned JSON data

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

Details of loading JSON data

This section describes how BigQuery parses various data types when loading JSON data.

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 ISO 8601 format PYMDTHMS, where:

  • P = Designator that indicates that the value represents a duration. You must always include this.
  • Y = Year
  • M = Month
  • D = Day
  • T = Designator that denotes the time portion of the duration. You must always include this.
  • H = Hour
  • M = Minute
  • S = Second. Seconds can be denoted as a whole value or as a fractional value of up to six digits, at microsecond precision.

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

The following list shows examples of valid data:

  • P-10000Y0M-3660000DT-87840000H0M0S
  • P0Y0M0DT0H0M0.000001S
  • P10000Y0M3660000DT87840000H0M0S

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.

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

Array (repeated field). The value must be a JSON array or null. JSON null is converted to SQL NULL. The array itself cannot contain null values.

Schema auto-detection

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

JSON nested and repeated fields

BigQuery infers nested and repeated fields in JSON files. If a field value is a JSON object, then BigQuery loads the column as a RECORD type. If a field value is an array, then BigQuery loads the column as a repeated column. For an example of JSON data with nested and repeated data, see Loading nested and repeated JSON data.

String conversion

If you enable schema auto-detection, then BigQuery converts strings into Boolean, numeric, or date/time types when possible. For example, using the following JSON data, schema auto-detection converts the id field to an INTEGER column:

                    { "name":"Alice","id":"12"} { "name":"Bob","id":"34"} { "name":"Charles","id":"45"}

JSON options

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

JSON option Console option bq tool flag BigQuery API property Description
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.
Unknown values Ignore unknown values --ignore_unknown_values ignoreUnknownValues (Java, Python) (Optional) Indicates whether 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.