docs-archive/apache-airflow-providers-yandex/3.0.0/_sources/_api/airflow/providers/yandex/operators/yandexcloud_dataproc/index.rst.txt

:py:mod:`airflow.providers.yandex.operators.yandexcloud_dataproc`
=================================================================

.. py:module:: airflow.providers.yandex.operators.yandexcloud_dataproc


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocCreateClusterOperator
   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocDeleteClusterOperator
   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocCreateHiveJobOperator
   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocCreateMapReduceJobOperator
   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocCreateSparkJobOperator
   airflow.providers.yandex.operators.yandexcloud_dataproc.DataprocCreatePysparkJobOperator




.. py:class:: DataprocCreateClusterOperator(*, folder_id = None, cluster_name = None, cluster_description = '', cluster_image_version = None, ssh_public_keys = None, subnet_id = None, services = ('HDFS', 'YARN', 'MAPREDUCE', 'HIVE', 'SPARK'), s3_bucket = None, zone = 'ru-central1-b', service_account_id = None, masternode_resource_preset = None, masternode_disk_size = None, masternode_disk_type = None, datanode_resource_preset = None, datanode_disk_size = None, datanode_disk_type = None, datanode_count = 1, computenode_resource_preset = None, computenode_disk_size = None, computenode_disk_type = None, computenode_count = 0, computenode_max_hosts_count = None, computenode_measurement_duration = None, computenode_warmup_duration = None, computenode_stabilization_duration = None, computenode_preemptible = False, computenode_cpu_utilization_target = None, computenode_decommission_timeout = None, connection_id = None, log_group_id = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Creates Yandex.Cloud Data Proc cluster.

   :param folder_id: ID of the folder in which cluster should be created.
   :param cluster_name: Cluster name. Must be unique inside the folder.
   :param cluster_description: Cluster description.
   :param cluster_image_version: Cluster image version. Use default.
   :param ssh_public_keys: List of SSH public keys that will be deployed to created compute instances.
   :param subnet_id: ID of the subnetwork. All Data Proc cluster nodes will use one subnetwork.
   :param services: List of services that will be installed to the cluster. Possible options:
       HDFS, YARN, MAPREDUCE, HIVE, TEZ, ZOOKEEPER, HBASE, SQOOP, FLUME, SPARK, SPARK, ZEPPELIN, OOZIE
   :param s3_bucket: Yandex.Cloud S3 bucket to store cluster logs.
                     Jobs will not work if the bucket is not specified.
   :param zone: Availability zone to create cluster in.
                Currently there are ru-central1-a, ru-central1-b and ru-central1-c.
   :param service_account_id: Service account id for the cluster.
                              Service account can be created inside the folder.
   :param masternode_resource_preset: Resources preset (CPU+RAM configuration)
                                      for the primary node of the cluster.
   :param masternode_disk_size: Masternode storage size in GiB.
   :param masternode_disk_type: Masternode storage type. Possible options: network-ssd, network-hdd.
   :param datanode_resource_preset: Resources preset (CPU+RAM configuration)
                                    for the data nodes of the cluster.
   :param datanode_disk_size: Datanodes storage size in GiB.
   :param datanode_disk_type: Datanodes storage type. Possible options: network-ssd, network-hdd.
   :param computenode_resource_preset: Resources preset (CPU+RAM configuration)
                                       for the compute nodes of the cluster.
   :param computenode_disk_size: Computenodes storage size in GiB.
   :param computenode_disk_type: Computenodes storage type. Possible options: network-ssd, network-hdd.
   :param connection_id: ID of the Yandex.Cloud Airflow connection.
   :param computenode_max_count: Maximum number of nodes of compute autoscaling subcluster.
   :param computenode_warmup_duration: The warmup time of the instance in seconds. During this time,
                                       traffic is sent to the instance,
                                       but instance metrics are not collected. In seconds.
   :param computenode_stabilization_duration: Minimum amount of time in seconds for monitoring before
                                  Instance Groups can reduce the number of instances in the group.
                                  During this time, the group size doesn't decrease,
                                  even if the new metric values indicate that it should. In seconds.
   :param computenode_preemptible: Preemptible instances are stopped at least once every 24 hours,
                       and can be stopped at any time if their resources are needed by Compute.
   :param computenode_cpu_utilization_target: Defines an autoscaling rule
                                  based on the average CPU utilization of the instance group.
                                  in percents. 10-100.
                                  By default is not set and default autoscaling strategy is used.
   :param computenode_decommission_timeout: Timeout to gracefully decommission nodes during downscaling.
                                            In seconds.
   :param log_group_id: Id of log group to write logs. By default logs will be sent to default log group.
                   To disable cloud log sending set cluster property dataproc:disable_cloud_logging = true

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: DataprocDeleteClusterOperator(*, connection_id = None, cluster_id = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Deletes Yandex.Cloud Data Proc cluster.

   :param connection_id: ID of the Yandex.Cloud Airflow connection.
   :param cluster_id: ID of the cluster to remove. (templated)

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['cluster_id']

      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: DataprocCreateHiveJobOperator(*, query = None, query_file_uri = None, script_variables = None, continue_on_failure = False, properties = None, name = 'Hive job', cluster_id = None, connection_id = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Runs Hive job in Data Proc cluster.

   :param query: Hive query.
   :param query_file_uri: URI of the script that contains Hive queries. Can be placed in HDFS or S3.
   :param properties: A mapping of property names to values, used to configure Hive.
   :param script_variables: Mapping of query variable names to values.
   :param continue_on_failure: Whether to continue executing queries if a query fails.
   :param name: Name of the job. Used for labeling.
   :param cluster_id: ID of the cluster to run job in.
                      Will try to take the ID from Dataproc Hook object if it's specified. (templated)
   :param connection_id: ID of the Yandex.Cloud Airflow connection.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['cluster_id']

      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: DataprocCreateMapReduceJobOperator(*, main_class = None, main_jar_file_uri = None, jar_file_uris = None, archive_uris = None, file_uris = None, args = None, properties = None, name = 'Mapreduce job', cluster_id = None, connection_id = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Runs Mapreduce job in Data Proc cluster.

   :param main_jar_file_uri: URI of jar file with job.
                             Can be placed in HDFS or S3. Can be specified instead of main_class.
   :param main_class: Name of the main class of the job. Can be specified instead of main_jar_file_uri.
   :param file_uris: URIs of files used in the job. Can be placed in HDFS or S3.
   :param archive_uris: URIs of archive files used in the job. Can be placed in HDFS or S3.
   :param jar_file_uris: URIs of JAR files used in the job. Can be placed in HDFS or S3.
   :param properties: Properties for the job.
   :param args: Arguments to be passed to the job.
   :param name: Name of the job. Used for labeling.
   :param cluster_id: ID of the cluster to run job in.
                      Will try to take the ID from Dataproc Hook object if it's specified. (templated)
   :param connection_id: ID of the Yandex.Cloud Airflow connection.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['cluster_id']

      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: DataprocCreateSparkJobOperator(*, main_class = None, main_jar_file_uri = None, jar_file_uris = None, archive_uris = None, file_uris = None, args = None, properties = None, name = 'Spark job', cluster_id = None, connection_id = None, packages = None, repositories = None, exclude_packages = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Runs Spark job in Data Proc cluster.

   :param main_jar_file_uri: URI of jar file with job. Can be placed in HDFS or S3.
   :param main_class: Name of the main class of the job.
   :param file_uris: URIs of files used in the job. Can be placed in HDFS or S3.
   :param archive_uris: URIs of archive files used in the job. Can be placed in HDFS or S3.
   :param jar_file_uris: URIs of JAR files used in the job. Can be placed in HDFS or S3.
   :param properties: Properties for the job.
   :param args: Arguments to be passed to the job.
   :param name: Name of the job. Used for labeling.
   :param cluster_id: ID of the cluster to run job in.
                      Will try to take the ID from Dataproc Hook object if it's specified. (templated)
   :param connection_id: ID of the Yandex.Cloud Airflow connection.
   :param packages: List of maven coordinates of jars to include on the driver and executor classpaths.
   :param repositories: List of additional remote repositories to search for the maven coordinates
                       given with --packages.
   :param exclude_packages: List of groupId:artifactId, to exclude while resolving the dependencies
                       provided in --packages to avoid dependency conflicts.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['cluster_id']

      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: DataprocCreatePysparkJobOperator(*, main_python_file_uri = None, python_file_uris = None, jar_file_uris = None, archive_uris = None, file_uris = None, args = None, properties = None, name = 'Pyspark job', cluster_id = None, connection_id = None, packages = None, repositories = None, exclude_packages = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Runs Pyspark job in Data Proc cluster.

   :param main_python_file_uri: URI of python file with job. Can be placed in HDFS or S3.
   :param python_file_uris: URIs of python files used in the job. Can be placed in HDFS or S3.
   :param file_uris: URIs of files used in the job. Can be placed in HDFS or S3.
   :param archive_uris: URIs of archive files used in the job. Can be placed in HDFS or S3.
   :param jar_file_uris: URIs of JAR files used in the job. Can be placed in HDFS or S3.
   :param properties: Properties for the job.
   :param args: Arguments to be passed to the job.
   :param name: Name of the job. Used for labeling.
   :param cluster_id: ID of the cluster to run job in.
                      Will try to take the ID from Dataproc Hook object if it's specified. (templated)
   :param connection_id: ID of the Yandex.Cloud Airflow connection.
   :param packages: List of maven coordinates of jars to include on the driver and executor classpaths.
   :param repositories: List of additional remote repositories to search for the maven coordinates
                        given with --packages.
   :param exclude_packages: List of groupId:artifactId, to exclude while resolving the dependencies
                        provided in --packages to avoid dependency conflicts.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['cluster_id']

      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.