JDBC to BigQuery template
Use the Dataproc Serverless JDBC to BigQuery template to extract data from JDBC databases to BigQuery.
This template supports the following databases as input:
- MySQL
- PostgreSQL
- Microsoft SQL Server
- Oracle
Use the template
Run the template using the gcloud CLI or Dataproc API.
Before using any of the command data below, make the following replacements:
PROJECT_ID : Required. Your Google Cloud project ID listed in the IAM Settings.REGION : Required. Compute Engine region.TEMPLATE_VERSION : Required. Specifylatest
for the latest template version, or the date of a specific version, for example,2023-03-17_v0.1.0-beta
(visit gs://dataproc-templates-binaries or rungcloud storage ls gs://dataproc-templates-binaries
to list available template versions).SUBNET : Optional. If a subnet is not specified, the subnet in the specified REGION in thedefault
network is selected.Example:
projects/PROJECT_ID/regions/REGION/subnetworks/SUBNET_NAME
JDBC_CONNECTOR_CLOUD_STORAGE_PATH : Required. The full Cloud Storage path, including the filename, where the JDBC connector jar is stored. You can use the following commands to download JDBC connectors for uploading to Cloud Storage:- MySQL:
wget http://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java-5.1.30.tar.gz
- Postgres SQL:
wget https://jdbc.postgresql.org/download/postgresql-42.2.6.jar
- Microsoft SQL Server:
wget https://repo1.maven.org/maven2/com/microsoft/sqlserver/mssql-jdbc/6.4.0.jre8/mssql-jdbc-6.4.0.jre8.jar
- Oracle:
wget https://repo1.maven.org/maven2/com/oracle/database/jdbc/ojdbc8/21.7.0.0/ojdbc8-21.7.0.0.jar
- MySQL:
DATASET andTABLE : Required. Destination BigQuery dataset and table.- The following variables are used to construct the required
JDBC_CONNECTION_URL :JDBC_HOST JDBC_PORT JDBC_DATABASE , or, for Oracle,JDBC_SERVICE JDBC_USERNAME JDBC_PASSWORD
Construct the JDBC_CONNECTION_URL using one of the following connector-specific formats:
- MySQL:
jdbc:mysql://
JDBC_HOST :JDBC_PORT /JDBC_DATABASE ?user=JDBC_USERNAME &password=JDBC_PASSWORD - Postgres SQL:
jdbc:postgresql://
JDBC_HOST :JDBC_PORT /JDBC_DATABASE ?user=JDBC_USERNAME &password=JDBC_PASSWORD - Microsoft SQL Server:
jdbc:sqlserver://
JDBC_HOST :JDBC_PORT ;databaseName=JDBC_DATABASE ;user=JDBC_USERNAME ;password=JDBC_PASSWORD - Oracle:
jdbc:oracle:thin:@//
JDBC_HOST :JDBC_PORT /JDBC_SERVICE ?user=JDBC_USERNAME &password=JDBC_PASSWORD
DRIVER : Required. The JDBC driver which will be used for the connection:- MySQL:
com.mysql.cj.jdbc.Driver
- Postgres SQL:
org.postgresql.Driver
- Microsoft SQL Server:
com.microsoft.sqlserver.jdbc.SQLServerDriver
- Oracle:
oracle.jdbc.driver.OracleDriver
- MySQL:
QUERY : Required. SQL Query to extract data from JDBC.MODE : Required. Write mode for BigQuery output. Options:append
,overwrite
,ignore
, orerrorifexists
.TEMP_BUCKET : Required. Cloud Storage bucket name. This bucket is used for BigQuery loading.Example:
gs://dataproc-templates/jdbc_to_cloud_storage_output
INPUT_PARTITION_COLUMN ,LOWERBOUND ,UPPERBOUND ,PARTITIONS : Optional. If used, all of the following parameters must be specified:- INPUT_PARTITION_COLUMN: JDBC input table partition column name.
- LOWERBOUND: JDBC input table partition column lower bound used to determine the partition stride.
- UPPERBOUND: JDBC input table partition column upper bound used to decide the partition stride.
- PARTITIONS: The maximum number of partitions that can be used for parallelism of table reads and writes.
If specified, this value is used for the JDBC input and output connection. Default:
10
.
FETCHSIZE : Optional. How many rows to fetch per round trip. Default: 10.TEMPVIEW andSQL_QUERY : Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into BigQuery. TEMPVIEW is the temporary view name, and SQL_QUERY is the query statement. TEMPVIEW and the table name in SQL_QUERY must match.SERVICE_ACCOUNT : Optional. If not provided, the default Compute Engine service account is used.PROPERTY andPROPERTY_VALUE : Optional. Comma-separated list of Spark property=value
pairs.LABEL andLABEL_VALUE : Optional. Comma-separated list oflabel
=value
pairs.LOG_LEVEL : Optional. Level of logging. Can be one ofALL
,DEBUG
,ERROR
,FATAL
,INFO
,OFF
,TRACE
, orWARN
. Default:INFO
.-
KMS_KEY : Optional. The Cloud Key Management Service key to use for encryption. If a key is not specified, data is encrypted at rest using a Google-owned and Google-managed encryption key.Example:
projects/PROJECT_ID/regions/REGION/keyRings/KEY_RING_NAME/cryptoKeys/KEY_NAME
Execute the following command:
Linux, macOS, or Cloud Shell
gcloud dataproc batches submit spark \ --class=com.google.cloud.dataproc.templates.main.DataProcTemplate \ --version="1.2" \ --project="PROJECT_ID " \ --region="REGION " \ --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION /java/dataproc-templates.jar,JDBC_CONNECTOR_CLOUD_STORAGE_PATH " \ --subnet="SUBNET " \ --kms-key="KMS_KEY " \ --service-account="SERVICE_ACCOUNT " \ --properties="PROPERTY =PROPERTY_VALUE " \ --labels="LABEL =LABEL_VALUE " \ -- --template=JDBCTOBIGQUERY \ --templateProperty log.level="LOG_LEVEL " \ --templateProperty jdbctobq.bigquery.location="DATASET .TABLE " \ --templateProperty jdbctobq.jdbc.url="JDBC_CONNECTION_URL " \ --templateProperty jdbctobq.jdbc.driver.class.name="DRIVER " \ --templateProperty jdbctobq.write.mode="MODE " \ --templateProperty jdbctobq.temp.gcs.bucket="TEMP_BUCKET " \ --templateProperty jdbctobq.sql="QUERY " \ --templateProperty jdbctobq.sql.numPartitions="PARTITIONS " \ --templateProperty jdbctobq.sql.partitionColumn="INPUT_PARTITION_COLUMN " \ --templateProperty jdbctobq.sql.lowerBound="LOWERBOUND " \ --templateProperty jdbctobq.sql.upperBound="UPPERBOUND " \ --templateProperty jdbctobq.jdbc.fetchsize="FETCHSIZE " \ --templateProperty jdbctobq.temp.table="TEMPVIEW " \ --templateProperty jdbctobq.temp.query="SQL_QUERY "
Windows (PowerShell)
gcloud dataproc batches submit spark ` --class=com.google.cloud.dataproc.templates.main.DataProcTemplate ` --version="1.2" ` --project="PROJECT_ID " ` --region="REGION " ` --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION /java/dataproc-templates.jar,JDBC_CONNECTOR_CLOUD_STORAGE_PATH " ` --subnet="SUBNET " ` --kms-key="KMS_KEY " ` --service-account="SERVICE_ACCOUNT " ` --properties="PROPERTY =PROPERTY_VALUE " ` --labels="LABEL =LABEL_VALUE " ` -- --template=JDBCTOBIGQUERY ` --templateProperty log.level="LOG_LEVEL " ` --templateProperty jdbctobq.bigquery.location="DATASET .TABLE " ` --templateProperty jdbctobq.jdbc.url="JDBC_CONNECTION_URL " ` --templateProperty jdbctobq.jdbc.driver.class.name="DRIVER " ` --templateProperty jdbctobq.write.mode="MODE " ` --templateProperty jdbctobq.temp.gcs.bucket="TEMP_BUCKET " ` --templateProperty jdbctobq.sql="QUERY " ` --templateProperty jdbctobq.sql.numPartitions="PARTITIONS " ` --templateProperty jdbctobq.sql.partitionColumn="INPUT_PARTITION_COLUMN " ` --templateProperty jdbctobq.sql.lowerBound="LOWERBOUND " ` --templateProperty jdbctobq.sql.upperBound="UPPERBOUND " ` --templateProperty jdbctobq.jdbc.fetchsize="FETCHSIZE " ` --templateProperty jdbctobq.temp.table="TEMPVIEW " ` --templateProperty jdbctobq.temp.query="SQL_QUERY "
Windows (cmd.exe)
gcloud dataproc batches submit spark ^ --class=com.google.cloud.dataproc.templates.main.DataProcTemplate ^ --version="1.2" ^ --project="PROJECT_ID " ^ --region="REGION " ^ --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION /java/dataproc-templates.jar,JDBC_CONNECTOR_CLOUD_STORAGE_PATH " ^ --subnet="SUBNET " ^ --kms-key="KMS_KEY " ^ --service-account="SERVICE_ACCOUNT " ^ --properties="PROPERTY =PROPERTY_VALUE " ^ --labels="LABEL =LABEL_VALUE " ^ -- --template=JDBCTOBIGQUERY ^ --templateProperty log.level="LOG_LEVEL " ^ --templateProperty jdbctobq.bigquery.location="DATASET .TABLE " ^ --templateProperty jdbctobq.jdbc.url="JDBC_CONNECTION_URL " ^ --templateProperty jdbctobq.jdbc.driver.class.name="DRIVER " ^ --templateProperty jdbctobq.write.mode="MODE " ^ --templateProperty jdbctobq.temp.gcs.bucket="TEMP_BUCKET " ^ --templateProperty jdbctobq.sql="QUERY " ^ --templateProperty jdbctobq.sql.numPartitions="PARTITIONS " ^ --templateProperty jdbctobq.sql.partitionColumn="INPUT_PARTITION_COLUMN " ^ --templateProperty jdbctobq.sql.lowerBound="LOWERBOUND " ^ --templateProperty jdbctobq.sql.upperBound="UPPERBOUND " ^ --templateProperty jdbctobq.jdbc.fetchsize="FETCHSIZE " ^ --templateProperty jdbctobq.temp.table="TEMPVIEW " ^ --templateProperty jdbctobq.temp.query="SQL_QUERY "
Before using any of the request data, make the following replacements:
PROJECT_ID : Required. Your Google Cloud project ID listed in the IAM Settings.REGION : Required. Compute Engine region.TEMPLATE_VERSION : Required. Specifylatest
for the latest template version, or the date of a specific version, for example,2023-03-17_v0.1.0-beta
(visit gs://dataproc-templates-binaries or rungcloud storage ls gs://dataproc-templates-binaries
to list available template versions).SUBNET : Optional. If a subnet is not specified, the subnet in the specified REGION in thedefault
network is selected.Example:
projects/PROJECT_ID/regions/REGION/subnetworks/SUBNET_NAME
JDBC_CONNECTOR_CLOUD_STORAGE_PATH : Required. The full Cloud Storage path, including the filename, where the JDBC connector jar is stored. You can use the following commands to download JDBC connectors for uploading to Cloud Storage:- MySQL:
wget http://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java-5.1.30.tar.gz
- Postgres SQL:
wget https://jdbc.postgresql.org/download/postgresql-42.2.6.jar
- Microsoft SQL Server:
wget https://repo1.maven.org/maven2/com/microsoft/sqlserver/mssql-jdbc/6.4.0.jre8/mssql-jdbc-6.4.0.jre8.jar
- Oracle:
wget https://repo1.maven.org/maven2/com/oracle/database/jdbc/ojdbc8/21.7.0.0/ojdbc8-21.7.0.0.jar
- MySQL:
DATASET andTABLE : Required. Destination BigQuery dataset and table.- The following variables are used to construct the required
JDBC_CONNECTION_URL :JDBC_HOST JDBC_PORT JDBC_DATABASE , or, for Oracle,JDBC_SERVICE JDBC_USERNAME JDBC_PASSWORD
Construct the JDBC_CONNECTION_URL using one of the following connector-specific formats:
- MySQL:
jdbc:mysql://
JDBC_HOST :JDBC_PORT /JDBC_DATABASE ?user=JDBC_USERNAME &password=JDBC_PASSWORD - Postgres SQL:
jdbc:postgresql://
JDBC_HOST :JDBC_PORT /JDBC_DATABASE ?user=JDBC_USERNAME &password=JDBC_PASSWORD - Microsoft SQL Server:
jdbc:sqlserver://
JDBC_HOST :JDBC_PORT ;databaseName=JDBC_DATABASE ;user=JDBC_USERNAME ;password=JDBC_PASSWORD - Oracle:
jdbc:oracle:thin:@//
JDBC_HOST :JDBC_PORT /JDBC_SERVICE ?user=JDBC_USERNAME &password=JDBC_PASSWORD
DRIVER : Required. The JDBC driver which will be used for the connection:- MySQL:
com.mysql.cj.jdbc.Driver
- Postgres SQL:
org.postgresql.Driver
- Microsoft SQL Server:
com.microsoft.sqlserver.jdbc.SQLServerDriver
- Oracle:
oracle.jdbc.driver.OracleDriver
- MySQL:
QUERY : Required. SQL Query to extract data from JDBC.MODE : Required. Write mode for BigQuery output. Options:append
,overwrite
,ignore
, orerrorifexists
.TEMP_BUCKET : Required. Cloud Storage bucket name. This bucket is used for BigQuery loading.Example:
gs://dataproc-templates/jdbc_to_cloud_storage_output
INPUT_PARTITION_COLUMN ,LOWERBOUND ,UPPERBOUND ,PARTITIONS : Optional. If used, all of the following parameters must be specified:- INPUT_PARTITION_COLUMN: JDBC input table partition column name.
- LOWERBOUND: JDBC input table partition column lower bound used to determine the partition stride.
- UPPERBOUND: JDBC input table partition column upper bound used to decide the partition stride.
- PARTITIONS: The maximum number of partitions that can be used for parallelism of table reads and writes.
If specified, this value is used for the JDBC input and output connection. Default:
10
.
FETCHSIZE : Optional. How many rows to fetch per round trip. Default: 10.TEMPVIEW andSQL_QUERY : Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into BigQuery. TEMPVIEW is the temporary view name, and SQL_QUERY is the query statement. TEMPVIEW and the table name in SQL_QUERY must match.SERVICE_ACCOUNT : Optional. If not provided, the default Compute Engine service account is used.PROPERTY andPROPERTY_VALUE : Optional. Comma-separated list of Spark property=value
pairs.LABEL andLABEL_VALUE : Optional. Comma-separated list oflabel
=value
pairs.LOG_LEVEL : Optional. Level of logging. Can be one ofALL
,DEBUG
,ERROR
,FATAL
,INFO
,OFF
,TRACE
, orWARN
. Default:INFO
.-
KMS_KEY : Optional. The Cloud Key Management Service key to use for encryption. If a key is not specified, data is encrypted at rest using a Google-owned and Google-managed encryption key.Example:
projects/PROJECT_ID/regions/REGION/keyRings/KEY_RING_NAME/cryptoKeys/KEY_NAME
HTTP method and URL:
POST https://dataproc.googleapis.com/v1/projects/PROJECT_ID /locations/REGION /batches
Request JSON body:
{ "environmentConfig": { "executionConfig": { "subnetworkUri": "SUBNET ", "kmsKey": "KMS_KEY ", "serviceAccount": "SERVICE_ACCOUNT " } }, "labels": { "LABEL ": "LABEL_VALUE " }, "runtimeConfig": { "version": "1.2", "properties": { "PROPERTY ": "PROPERTY_VALUE " } }, "sparkBatch": { "mainClass": "com.google.cloud.dataproc.templates.main.DataProcTemplate", "args": [ "--template","JDBCTOBIGQUERY", "--templateProperty","log.level=LOG_LEVEL ", "--templateProperty","jdbctobq.bigquery.location=DATASET .TABLE ", "--templateProperty","jdbctobq.jdbc.url=JDBC_CONNECTION_URL ", "--templateProperty","jdbctobq.jdbc.driver.class.name=DRIVER ", "--templateProperty","jdbctobq.sql=QUERY ", "--templateProperty","jdbctobq.write.mode=MODE ", "--templateProperty","jdbctobq.temp.gcs.bucket=TEMP_BUCKET ", "--templateProperty","jdbctobq.sql.partitionColumn=INPUT_PARTITION_COLUMN ", "--templateProperty","jdbctobq.sql.lowerBound=LOWERBOUND ", "--templateProperty","jdbctobq.sql.upperBound=UPPERBOUND ", "--templateProperty","jdbctobq.sql.numPartitions=PARTITIONS ", "--templateProperty","jdbctobq.jdbc.fetchsize=FETCHSIZE " ], "jarFileUris": [ "gs://dataproc-templates-binaries/TEMPLATE_VERSION /java/dataproc-templates.jar","gs://JDBC_CONNECTOR_GCS_PATH " ] } }
To send your request, expand one of these options:
curl (Linux, macOS, or Cloud Shell)
Save the request body in a file named request.json
,
and execute the following command:
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://dataproc.googleapis.com/v1/projects/PROJECT_ID /locations/REGION /batches"
PowerShell (Windows)
Save the request body in a file named request.json
,
and execute the following command:
$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }
Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://dataproc.googleapis.com/v1/projects/PROJECT_ID /locations/REGION /batches" | Select-Object -Expand Content
You should receive a JSON response similar to the following:
{ "name": "projects/PROJECT_ID /regions/REGION /operations/OPERATION_ID ", "metadata": { "@type": "type.googleapis.com/google.cloud.dataproc.v1.BatchOperationMetadata", "batch": "projects/PROJECT_ID /locations/REGION /batches/BATCH_ID ", "batchUuid": "de8af8d4-3599-4a7c-915c-798201ed1583", "createTime": "2023-02-24T03:31:03.440329Z", "operationType": "BATCH", "description": "Batch" } }