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    BigQuery batch source

    This page provides guidance about configuring the BigQuery batch source plugin in Cloud Data Fusion.

    The BigQuery source plugin lets you connect and load data from BigQuery tables. Data from a BigQuery table is exported into a temporary location in Cloud Storage, and then gets read into the pipeline from there.

    Before you begin

    Cloud Data Fusion typically has two service accounts:

    Before using the BigQuery batch source plugin, grant the following roles or permissions to each service account.

    This service account already has all the required permissions and you don't need to add additional permissions. For reference, it has the following permissions:

    • bigquery.datasets.get
    • bigquery.tables.create
    • bigquery.tables.get
    • bigquery.tables.updateData
    • bigquery.tables.update
    • bigquery.tables.export

    If you're using a namespace service account in addition to the default design-time service account, add the permissions from the preceding list to it.

    In your Google Cloud project, grant the following IAM roles or permissions to the Compute Engine Service Account:

    • BigQuery Job User ( roles/bigquery.jobUser ). This predefined role contains the required bigquery.jobs.create permission.

    • BigQuery Data Editor ( roles/bigquery.dataEditor ). This predefined role contains the following required permissions:

      • bigquery.datasets.get
      • bigquery.tables.create
      • bigquery.tables.get
      • bigquery.tables.updateData
      • bigquery.tables.update
      • bigquery.tables.export

    These roles and permissions can also be assigned on the BigQuery dataset or table, depending on your use case.

    • Storage Legacy Bucket Writer ( roles/storage.legacyBucketWriter ). This predefined role contains the following required permissions:

      • storage.buckets.get
      • storage.objects.get
      • storage.objects.list

    This role and these permissions can also be assigned on the Cloud Storage bucket, depending on your use case.

    Configure the plugin

    1. Go to the Cloud Data Fusion web interface and click Studio.
    2. Check that Data Pipeline - Batchis selected (not Realtime).
    3. In the Sourcemenu, click BigQuery. The BigQuery node appears in your pipeline.
    4. To configure the source, go to the BigQuery node and click Properties.

    5. Enter the following properties. For a complete list, see Properties .

      1. Enter a Labelfor the BigQuery node—for example, BigQuery tables .

      2. Enter the connection details. You can set up a new, one-time connection, or an existing, reusable connection.

        New connection

        To add a one-time connection to BigQuery, follow these steps:

        1. In the Project IDfield, leave the value as auto-detect.
        2. If the BigQuery dataset is in a different project, in the Dataset Project IDfield, enter the ID.

        3. In the Service account typefield, choose one of the following and enter the content in the next field:

          • File path
          • JSON

        Reusable connection

        To reuse an existing connection, follow these steps:

        1. Turn on Use connection.
        2. Click Browse connections.

        3. Click the connection name—for example, BigQuery Default.

        4. Optional: If a connection doesn't exist and you want to create a new reusable connection, click Add connectionand refer to the steps in the New connection tab on this page.

      3. In the Reference namefield, enter a name to use for lineage.

      4. Optional: If your dataset is already available in your instance, click Browseand select the data to read.

      5. In the Datasetfield, enter the name of the dataset that contains the table.

      6. In the Tablefield, enter the name of the table.

      7. To test connectivity, click Get schema.

      8. Optional: In the Partition start datefield, enter the inclusive start date string—for example, 2021-01-11 .

      9. Optional: In the Partition end datefield, enter the inclusive end date string—for example, 2024-01-11 .

      10. Optional: In the Filterfield, enter a BigQuery WHERE clause .

      11. Optional: In the Temporary bucket namefield, enter a name for the Cloud Storage bucket.

      12. Optional: In the Encryption Key Namefield, enter the Cloud Key Management Service (Cloud KMS) encryption key name. For more information, see Get the resource name for the key .

      13. Optional: Turn on Enable querying views. If you enable them, do the following:

        • In the Temporary table creation projectfield, enter the project name where the temporary table is created.
        • In the Temporary table creation datasetfield, enter the dataset name where the temporary table is created.

      14. Optional: Click Validateand address any errors found.

      15. Click Close. Properties are saved and you can continue to build your data pipeline in the Cloud Data Fusion web interface.

    Properties

    Property
    Macro enabled
    Required property
    Description
    Label
    No
    Yes
    The name of the node in your data pipeline.
    Use connection
    No
    No
    Browse for a reusable connection to the source. For more information about adding, importing, and editing the connections that appear when you browse connections, see Manage connections .
    Connection
    Yes
    Yes
    If Use connectionis turned on, the name of the reusable connection you select appears in this field.
    Project ID
    Yes
    No
    Used only when Use connectionis turned off. A globally unique identifier for the project where the BigQuery job runs.
    Default is auto-detect .
    Dataset project ID
    Yes
    No
    Used only when Use connectionis turned off. If the dataset is in a different project than the one where the BigQuery job runs, this value is the globally unique identifier for the project with the BigQuery dataset. If no value is given, the field defaults to the Project IDvalue. The BigQuery Data Viewer role must be granted to the specified service account to read BigQuery data on the project.
    Service account type
    Yes
    No
    Select one of the following options:
    • File path: the path where the service account is located.
    • JSON: JSON content of the service account.
    Service account file path
    Yes
    No
    Used only when the Service account type value is File path. The path on the local file system of the service account key used for authorization. If jobs run on Dataproc clusters, set the value to auto-detect. If jobs run on other types of clusters, the file must be present on every node in the cluster.
    Default is auto-detect .
    Service account JSON
    Yes
    No
    Used only when the Service account type value is JSON. The JSON file content of the service account.
    Reference name
    No
    Yes
    Name that uniquely identifies this source for other services, such as lineage and annotating metadata.
    Dataset
    Yes
    Yes
    The dataset the table belongs to. A dataset is contained within a specific project. Datasets are top-level containers that organize and control access to tables and views.
    Table
    Yes
    Yes
    The table to read from. A table contains individual records organized in rows. Each record is composed of columns (also called fields). Every table is defined by a schema that describes the column names, data types, and other information.
    Partition start date
    Yes
    No
    Inclusive partition start date, specified as yyyy-mm-dd . If no value is given, all partitions up to the partition end date will be read.
    Partition end date
    Yes
    No
    Exclusive partition end date, specified as yyyy-mm-dd . If no value is given, all partitions up from the partition start date are read.
    Filter
    Yes
    No
    A SQL query that filters for the given conditions. For example, this query returns all rows from the Roster table where the SchoolID column has the value SchoolID > 52 :
    SELECT * FROM Roster WHERE SchoolID > 52; . This is the same as the WHERE clause in BigQuery.
    Temporary bucket name
    Yes
    No
    The Cloud Storage bucket for temporary data storage. It is automatically created if it does not exist. Temporary data is deleted after it has been read. If a name isn't provided, a unique bucket is created and then deleted after the run finishes.
    Encryption key name
    Yes
    No
    Key that encrypts data written to any bucket created by the plugin. If the bucket exists, this value is ignored. For more information, see CMEK .
    Enable querying views
    Yes
    No
    Whether to allow BigQuery Logical and Materialized views. Since BigQuery views aren't enabled by default, querying them may have performance overhead.
    Default is No.
    Temporary table creation project
    Yes
    No
    Used only when Enable querying viewsis turned on. The project name where the temporary table should be created. Defaults to the same project the table is in.
    Temporary table creation dataset
    Yes
    No
    The dataset in the specified project where the temporary table should be created. Defaults to the same dataset the table is in.
    Output schema
    Yes
    Yes
    Schema of the table to read. You can fetch it by clicking Get schema.

    Data type mappings

    The following table is a list of BigQuery data types with corresponding CDAP types.

    BigQuery data type CDAP schema data type
    BOOL boolean
    BYTES bytes
    DATE date
    DATETIME datetime, string
    FLOAT64 double
    GEO unsupported
    INT64 long
    NUMERIC decimal
    BIGNUMERIC decimal
    REPEATED array
    STRING string , datetime (ISO 8601 format)
    STRUCT record
    TIME time (microseconds)
    TIMESTAMP timestamp (microseconds)
    JSON unsupported

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