The JDBC to BigQuery template is a batch pipeline that copies data from a relational database table into an existing BigQuery table. This pipeline uses JDBC to connect to the relational database. You can use this template to copy data from any relational database with available JDBC drivers into BigQuery. For an extra layer of protection, you can also pass in a Cloud KMS key along with a Base64-encoded username, password, and connection string parameters encrypted with the Cloud KMS key. See the Cloud KMS API encryption endpoint for additional details on encrypting your username, password, and connection string parameters.
Pipeline requirements
- The JDBC drivers for the relational database must be available.
- The BigQuery table must exist before pipeline execution.
- The BigQuery table must have a compatible schema.
- The relational database must be accessible from the subnet where Dataflow runs.
Template parameters
Parameter | Description |
---|---|
driverJars |
The comma-separated list of driver JAR files. For example, gs://<my-bucket>/driver_jar1.jar,gs://<my-bucket>/driver_jar2.jar . |
driverClassName |
The JDBC driver class name. For example, com.mysql.jdbc.Driver . |
connectionURL |
The JDBC connection URL string. For example, jdbc:mysql://some-host:3306/sampledb .
Can be passed in as a string that's Base64-encoded and then encrypted with a Cloud KMS key.
Note the difference between an Oracle non-RAC database connection string
(jdbc:oracle:thin:@some-host:<port>:<sid> )
and an Oracle RAC database connection string
(jdbc:oracle:thin:@//some-host[:<port>]/<service_name> ). |
query |
The query to be run on the source to extract the data. For example, select * from sampledb.sample_table . |
outputTable |
The BigQuery output table location, in the format of <my-project>:<my-dataset>.<my-table> . |
bigQueryLoadingTemporaryDirectory |
The temporary directory for the BigQuery loading process.
For example, gs://<my-bucket>/my-files/temp_dir . |
connectionProperties |
(Optional) Properties string to use for the JDBC connection. Format of the string must be [propertyName=property;]* . For example, unicode=true;characterEncoding=UTF-8 . |
username |
(Optional) The username to be used for the JDBC connection. Can be passed in as a Base64-encoded string encrypted with a Cloud KMS key. |
password |
(Optional) The password to be used for the JDBC connection. Can be passed in as a Base64-encoded string encrypted with a Cloud KMS key. |
KMSEncryptionKey |
(Optional) Cloud KMS Encryption Key to decrypt the username, password, and connection string. If Cloud KMS key is passed in, the username, password, and connection string must all be passed in encrypted. |
disabledAlgorithms |
(Optional) Comma separated algorithms to disable. If this value is set to none then no algorithm is disabled. Use with care, as the algorithms disabled by default are known to have either vulnerabilities or performance issues. For example: SSLv3, RC4. |
extraFilesToStage |
Comma separated Cloud Storage paths or Secret Manager secrets for files to stage in the worker. These files will be saved under the /extra_files directory in each worker. For example, gs://<my-bucket>/file.txt,projects/<project-id>/secrets/<secret-id>/versions/<version-id> . |
Run the template
Console
- Go to the Dataflow Create job from template page. Go to Create job from template
- In the Job name field, enter a unique job name.
- Optional: For Regional endpoint, select a value from the drop-down menu. The default
regional endpoint is
us-central1
.For a list of regions where you can run a Dataflow job, see Dataflow locations.
- From the Dataflow template drop-down menu, select the JDBC to BigQuery template.
- In the provided parameter fields, enter your parameter values.
- Click Run job.
gcloud
In your shell or terminal, run the template:
gcloud dataflow jobs run JOB_NAME \ --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/Jdbc_to_BigQuery \ --region REGION_NAME \ --parameters \ driverJars=DRIVER_PATHS,\ driverClassName=DRIVER_CLASS_NAME,\ connectionURL=JDBC_CONNECTION_URL,\ query=SOURCE_SQL_QUERY,\ outputTable=PROJECT_ID:DATASET.TABLE_NAME, bigQueryLoadingTemporaryDirectory=PATH_TO_TEMP_DIR_ON_GCS,\ connectionProperties=CONNECTION_PROPERTIES,\ username=CONNECTION_USERNAME,\ password=CONNECTION_PASSWORD,\ KMSEncryptionKey=KMS_ENCRYPTION_KEY
Replace the following:
JOB_NAME
: a unique job name of your choiceVERSION
: the version of the template that you want to useYou can use the following values:
latest
to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/- the version name, like
2023-04-18-00_RC00
, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
REGION_NAME
: the regional endpoint where you want to deploy your Dataflow job—for example,us-central1
DRIVER_PATHS
: the comma-separated Cloud Storage path(s) of the JDBC driver(s)DRIVER_CLASS_NAME
: the drive class nameJDBC_CONNECTION_URL
: the JDBC connection URLSOURCE_SQL_QUERY
: the SQL query to be run on the source databaseDATASET
: your BigQuery dataset, and replaceTABLE_NAME
: your BigQuery table namePATH_TO_TEMP_DIR_ON_GCS
: your Cloud Storage path to the temp directoryCONNECTION_PROPERTIES
: the JDBC connection properties, if necessaryCONNECTION_USERNAME
: the JDBC connection usernameCONNECTION_PASSWORD
: the JDBC connection passwordKMS_ENCRYPTION_KEY
: the Cloud KMS Encryption Key
API
To run the template using the REST API, send an HTTP POST request. For more information on the
API and its authorization scopes, see
projects.templates.launch
.
POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/Jdbc_to_BigQuery { "jobName": "JOB_NAME", "parameters": { "driverJars": "DRIVER_PATHS", "driverClassName": "DRIVER_CLASS_NAME", "connectionURL": "JDBC_CONNECTION_URL", "query": "SOURCE_SQL_QUERY", "outputTable": "PROJECT_ID:DATASET.TABLE_NAME", "bigQueryLoadingTemporaryDirectory": "PATH_TO_TEMP_DIR_ON_GCS", "connectionProperties": "CONNECTION_PROPERTIES", "username": "CONNECTION_USERNAME", "password": "CONNECTION_PASSWORD", "KMSEncryptionKey":"KMS_ENCRYPTION_KEY" }, "environment": { "zone": "us-central1-f" } }
Replace the following:
PROJECT_ID
: the Google Cloud project ID where you want to run the Dataflow jobJOB_NAME
: a unique job name of your choiceVERSION
: the version of the template that you want to useYou can use the following values:
latest
to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/- the version name, like
2023-04-18-00_RC00
, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
LOCATION
: the regional endpoint where you want to deploy your Dataflow job—for example,us-central1
DRIVER_PATHS
: the comma-separated Cloud Storage path(s) of the JDBC driver(s)DRIVER_CLASS_NAME
: the drive class nameJDBC_CONNECTION_URL
: the JDBC connection URLSOURCE_SQL_QUERY
: the SQL query to be run on the source databaseDATASET
: your BigQuery dataset, and replaceTABLE_NAME
: your BigQuery table namePATH_TO_TEMP_DIR_ON_GCS
: your Cloud Storage path to the temp directoryCONNECTION_PROPERTIES
: the JDBC connection properties, if necessaryCONNECTION_USERNAME
: the JDBC connection usernameCONNECTION_PASSWORD
: the JDBC connection passwordKMS_ENCRYPTION_KEY
: the Cloud KMS Encryption Key