Source code for airflow.contrib.operators.dataflow_operator

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import re
import uuid
import copy

from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
from airflow.contrib.hooks.gcp_dataflow_hook import DataFlowHook
from airflow.models import BaseOperator
from airflow.version import version
from airflow.utils.decorators import apply_defaults


[docs]class DataFlowJavaOperator(BaseOperator): """ Start a Java Cloud DataFlow batch job. The parameters of the operation will be passed to the job. It's a good practice to define dataflow_* parameters in the default_args of the dag like the project, zone and staging location. .. code-block:: python default_args = { 'dataflow_default_options': { 'project': 'my-gcp-project', 'zone': 'europe-west1-d', 'stagingLocation': 'gs://my-staging-bucket/staging/' } } You need to pass the path to your dataflow as a file reference with the ``jar`` parameter, the jar needs to be a self executing jar (see documentation here: https://beam.apache.org/documentation/runners/dataflow/#self-executing-jar). Use ``options`` to pass on options to your job. .. code-block:: python t1 = DataFlowOperation( task_id='datapflow_example', jar='{{var.value.gcp_dataflow_base}}pipeline/build/libs/pipeline-example-1.0.jar', options={ 'autoscalingAlgorithm': 'BASIC', 'maxNumWorkers': '50', 'start': '{{ds}}', 'partitionType': 'DAY', 'labels': {'foo' : 'bar'} }, gcp_conn_id='gcp-airflow-service-account', dag=my-dag) Both ``jar`` and ``options`` are templated so you can use variables in them. """ template_fields = ['options', 'jar'] ui_color = '#0273d4' @apply_defaults def __init__( self, jar, dataflow_default_options=None, options=None, gcp_conn_id='google_cloud_default', delegate_to=None, poll_sleep=10, job_class=None, *args, **kwargs): """ Create a new DataFlowJavaOperator. Note that both dataflow_default_options and options will be merged to specify pipeline execution parameter, and dataflow_default_options is expected to save high-level options, for instances, project and zone information, which apply to all dataflow operators in the DAG. .. seealso:: For more detail on job submission have a look at the reference: https://cloud.google.com/dataflow/pipelines/specifying-exec-params :param jar: The reference to a self executing DataFlow jar. :type jar: string :param dataflow_default_options: Map of default job options. :type dataflow_default_options: dict :param options: Map of job specific options. :type options: dict :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: string :param delegate_to: The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: string :param poll_sleep: The time in seconds to sleep between polling Google Cloud Platform for the dataflow job status while the job is in the JOB_STATE_RUNNING state. :type poll_sleep: int :param job_class: The name of the dataflow job class to be executued, it is often not the main class configured in the dataflow jar file. :type job_class: string """ super(DataFlowJavaOperator, self).__init__(*args, **kwargs) dataflow_default_options = dataflow_default_options or {} options = options or {} options.setdefault('labels', {}).update( {'airflow-version': 'v' + version.replace('.', '-').replace('+', '-')}) self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.jar = jar self.dataflow_default_options = dataflow_default_options self.options = options self.poll_sleep = poll_sleep self.job_class = job_class def execute(self, context): bucket_helper = GoogleCloudBucketHelper( self.gcp_conn_id, self.delegate_to) self.jar = bucket_helper.google_cloud_to_local(self.jar) hook = DataFlowHook(gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, poll_sleep=self.poll_sleep) dataflow_options = copy.copy(self.dataflow_default_options) dataflow_options.update(self.options) hook.start_java_dataflow(self.task_id, dataflow_options, self.jar, self.job_class)
[docs]class DataflowTemplateOperator(BaseOperator): """ Start a Templated Cloud DataFlow batch job. The parameters of the operation will be passed to the job. It's a good practice to define dataflow_* parameters in the default_args of the dag like the project, zone and staging location. .. seealso:: https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment .. code-block:: python default_args = { 'dataflow_default_options': { 'project': 'my-gcp-project' 'zone': 'europe-west1-d', 'tempLocation': 'gs://my-staging-bucket/staging/' } } } You need to pass the path to your dataflow template as a file reference with the ``template`` parameter. Use ``parameters`` to pass on parameters to your job. Use ``environment`` to pass on runtime environment variables to your job. .. code-block:: python t1 = DataflowTemplateOperator( task_id='datapflow_example', template='{{var.value.gcp_dataflow_base}}', parameters={ 'inputFile': "gs://bucket/input/my_input.txt", 'outputFile': "gs://bucket/output/my_output.txt" }, gcp_conn_id='gcp-airflow-service-account', dag=my-dag) ``template``, ``dataflow_default_options`` and ``parameters`` are templated so you can use variables in them. """ template_fields = ['parameters', 'dataflow_default_options', 'template'] ui_color = '#0273d4' @apply_defaults def __init__( self, template, dataflow_default_options=None, parameters=None, gcp_conn_id='google_cloud_default', delegate_to=None, poll_sleep=10, *args, **kwargs): """ Create a new DataflowTemplateOperator. Note that dataflow_default_options is expected to save high-level options for project information, which apply to all dataflow operators in the DAG. .. seealso:: https://cloud.google.com/dataflow/docs/reference/rest/v1b3 /LaunchTemplateParameters https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment For more detail on job template execution have a look at the reference: https://cloud.google.com/dataflow/docs/templates/executing-templates :param template: The reference to the DataFlow template. :type template: string :param dataflow_default_options: Map of default job environment options. :type dataflow_default_options: dict :param parameters: Map of job specific parameters for the template. :type parameters: dict :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: string :param delegate_to: The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: string :param poll_sleep: The time in seconds to sleep between polling Google Cloud Platform for the dataflow job status while the job is in the JOB_STATE_RUNNING state. :type poll_sleep: int """ super(DataflowTemplateOperator, self).__init__(*args, **kwargs) dataflow_default_options = dataflow_default_options or {} parameters = parameters or {} self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.dataflow_default_options = dataflow_default_options self.poll_sleep = poll_sleep self.template = template self.parameters = parameters def execute(self, context): hook = DataFlowHook(gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, poll_sleep=self.poll_sleep) hook.start_template_dataflow(self.task_id, self.dataflow_default_options, self.parameters, self.template)
[docs]class DataFlowPythonOperator(BaseOperator): template_fields = ['options', 'dataflow_default_options'] @apply_defaults def __init__( self, py_file, py_options=None, dataflow_default_options=None, options=None, gcp_conn_id='google_cloud_default', delegate_to=None, poll_sleep=10, *args, **kwargs): """ Create a new DataFlowPythonOperator. Note that both dataflow_default_options and options will be merged to specify pipeline execution parameter, and dataflow_default_options is expected to save high-level options, for instances, project and zone information, which apply to all dataflow operators in the DAG. .. seealso:: For more detail on job submission have a look at the reference: https://cloud.google.com/dataflow/pipelines/specifying-exec-params :param py_file: Reference to the python dataflow pipleline file.py, e.g., /some/local/file/path/to/your/python/pipeline/file. :type py_file: string :param py_options: Additional python options. :type pyt_options: list of strings, e.g., ["-m", "-v"]. :param dataflow_default_options: Map of default job options. :type dataflow_default_options: dict :param options: Map of job specific options. :type options: dict :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :type gcp_conn_id: string :param delegate_to: The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: string :param poll_sleep: The time in seconds to sleep between polling Google Cloud Platform for the dataflow job status while the job is in the JOB_STATE_RUNNING state. :type poll_sleep: int """ super(DataFlowPythonOperator, self).__init__(*args, **kwargs) self.py_file = py_file self.py_options = py_options or [] self.dataflow_default_options = dataflow_default_options or {} self.options = options or {} self.options.setdefault('labels', {}).update( {'airflow-version': 'v' + version.replace('.', '-').replace('+', '-')}) self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.poll_sleep = poll_sleep
[docs] def execute(self, context): """Execute the python dataflow job.""" bucket_helper = GoogleCloudBucketHelper( self.gcp_conn_id, self.delegate_to) self.py_file = bucket_helper.google_cloud_to_local(self.py_file) hook = DataFlowHook(gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, poll_sleep=self.poll_sleep) dataflow_options = self.dataflow_default_options.copy() dataflow_options.update(self.options) # Convert argument names from lowerCamelCase to snake case. camel_to_snake = lambda name: re.sub( r'[A-Z]', lambda x: '_' + x.group(0).lower(), name) formatted_options = {camel_to_snake(key): dataflow_options[key] for key in dataflow_options} hook.start_python_dataflow( self.task_id, formatted_options, self.py_file, self.py_options)
class GoogleCloudBucketHelper(): """GoogleCloudStorageHook helper class to download GCS object.""" GCS_PREFIX_LENGTH = 5 def __init__(self, gcp_conn_id='google_cloud_default', delegate_to=None): self._gcs_hook = GoogleCloudStorageHook(gcp_conn_id, delegate_to) def google_cloud_to_local(self, file_name): """ Checks whether the file specified by file_name is stored in Google Cloud Storage (GCS), if so, downloads the file and saves it locally. The full path of the saved file will be returned. Otherwise the local file_name will be returned immediately. :param file_name: The full path of input file. :type file_name: string :return: The full path of local file. :type: string """ if not file_name.startswith('gs://'): return file_name # Extracts bucket_id and object_id by first removing 'gs://' prefix and # then split the remaining by path delimiter '/'. path_components = file_name[self.GCS_PREFIX_LENGTH:].split('/') if path_components < 2: raise Exception( 'Invalid Google Cloud Storage (GCS) object path: {}.' .format(file_name)) bucket_id = path_components[0] object_id = '/'.join(path_components[1:]) local_file = '/tmp/dataflow{}-{}'.format(str(uuid.uuid1())[:8], path_components[-1]) file_size = self._gcs_hook.download(bucket_id, object_id, local_file) if file_size > 0: return local_file raise Exception( 'Failed to download Google Cloud Storage GCS object: {}' .format(file_name))