Google Cloud Dataproc Operators¶
Dataproc is a managed Apache Spark and Apache Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don’t need them.
For more information about the service visit Dataproc production documentation <Product documentation
Create a Cluster¶
Before you create a dataproc cluster you need to define the cluster. It describes the identifying information, config, and status of a cluster of Compute Engine instances. For more information about the available fields to pass when creating a cluster, visit Dataproc create cluster API.
A cluster configuration can look as followed:
CLUSTER_CONFIG = {
"master_config": {
"num_instances": 1,
"machine_type_uri": "n1-standard-4",
"disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 1024},
},
"worker_config": {
"num_instances": 2,
"machine_type_uri": "n1-standard-4",
"disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 1024},
},
}
With this configuration we can create the cluster:
DataprocCreateClusterOperator
create_cluster = DataprocCreateClusterOperator(
task_id="create_cluster",
project_id=PROJECT_ID,
cluster_config=CLUSTER_CONFIG,
region=REGION,
cluster_name=CLUSTER_NAME,
)
Update a cluster¶
You can scale the cluster up or down by providing a cluster config and a updateMask. In the updateMask argument you specifies the path, relative to Cluster, of the field to update. For more information on updateMask and other parameters take a look at Dataproc update cluster API.
An example of a new cluster config and the updateMask:
CLUSTER_UPDATE = {
"config": {"worker_config": {"num_instances": 3}, "secondary_worker_config": {"num_instances": 3}}
}
UPDATE_MASK = {
"paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
}
To update a cluster you can use:
DataprocUpdateClusterOperator
scale_cluster = DataprocUpdateClusterOperator(
task_id="scale_cluster",
cluster_name=CLUSTER_NAME,
cluster=CLUSTER_UPDATE,
update_mask=UPDATE_MASK,
graceful_decommission_timeout=TIMEOUT,
project_id=PROJECT_ID,
location=REGION,
)
Deleting a cluster¶
To delete a cluster you can use:
DataprocDeleteClusterOperator.
delete_cluster = DataprocDeleteClusterOperator(
task_id="delete_cluster", project_id=PROJECT_ID, cluster_name=CLUSTER_NAME, region=REGION
)
Submit a job to a cluster¶
Dataproc supports submitting jobs of different big data components. The list currently includes Spark, Hadoop, Pig and Hive. For more information on versions and images take a look at Cloud Dataproc Image version list
To submit a job to the cluster you need a provide a job source file. The job source file can be on GCS, the cluster or on your local file system. You can specify a file:/// path to refer to a local file on a cluster’s master node.
The job configuration can be submitted by using:
DataprocSubmitJobOperator.
pyspark_task = DataprocSubmitJobOperator(
task_id="pyspark_task", job=PYSPARK_JOB, location=REGION, project_id=PROJECT_ID
)
Examples of job configurations to submit¶
We have provided an example for every framework below. There are more arguments to provide in the jobs than the examples show. For the complete list of arguments take a look at DataProc Job arguments
Example of the configuration for a PySpark Job:
PYSPARK_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"pyspark_job": {"main_python_file_uri": PYSPARK_URI},
}
Example of the configuration for a SparkSQl Job:
SPARK_SQL_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"spark_sql_job": {"query_list": {"queries": ["SHOW DATABASES;"]}},
}
Example of the configuration for a Spark Job:
SPARK_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"spark_job": {
"jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
"main_class": "org.apache.spark.examples.SparkPi",
},
}
Example of the configuration for a Hive Job:
HIVE_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"hive_job": {"query_list": {"queries": ["SHOW DATABASES;"]}},
}
Example of the configuration for a Hadoop Job:
HADOOP_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"hadoop_job": {
"main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
"args": ["wordcount", "gs://pub/shakespeare/rose.txt", OUTPUT_PATH],
},
}
Example of the configuration for a Pig Job:
PIG_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"pig_job": {"query_list": {"queries": ["define sin HiveUDF('sin');"]}},
}
Example of the configuration for a SparkR:
SPARKR_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"spark_r_job": {"main_r_file_uri": SPARKR_URI},
}
Working with workflows templates¶
Dataproc supports creating workflow templates that can be triggered later on.
A workflow template can be created using:
DataprocCreateWorkflowTemplateOperator.
create_workflow_template = DataprocCreateWorkflowTemplateOperator(
task_id="create_workflow_template",
template=WORKFLOW_TEMPLATE,
project_id=PROJECT_ID,
location=REGION,
)
Once a workflow is created users can trigger it using
DataprocInstantiateWorkflowTemplateOperator:
trigger_workflow = DataprocInstantiateWorkflowTemplateOperator(
task_id="trigger_workflow", region=REGION, project_id=PROJECT_ID, template_id=WORKFLOW_NAME
)
References¶
For further information, take a look at: