Python SparkSubmitHook.on_kill方法代码示例

# 需要导入模块: from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook [as 别名]
# 或者: from airflow.contrib.hooks.spark_submit_hook.SparkSubmitHook import on_kill [as 别名]
    def test_yarn_process_on_kill(self, mock_popen):
        # Given
        mock_popen.return_value.stdout = six.StringIO('stdout')
        mock_popen.return_value.stderr = six.StringIO('stderr')
        mock_popen.return_value.poll.return_value = None
        mock_popen.return_value.wait.return_value = 0
        log_lines = [
            'SPARK_MAJOR_VERSION is set to 2, using Spark2',
            'WARN NativeCodeLoader: Unable to load native-hadoop library for your ' +
            'platform... using builtin-java classes where applicable',
            'WARN DomainSocketFactory: The short-circuit local reads feature cannot ' +
            'be used because libhadoop cannot be loaded.',
            'INFO Client: Requesting a new application from cluster with 10 ' +
            'NodeManagerapplication_1486558679801_1820s',
            'INFO Client: Submitting application application_1486558679801_1820 ' +
            'to ResourceManager'
        ]
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')
        hook._process_spark_submit_log(log_lines)
        hook.submit()

        # When
        hook.on_kill()

        # Then
        self.assertIn(call(['yarn', 'application', '-kill',
                            'application_1486558679801_1820'],
                           stderr=-1, stdout=-1),
                      mock_popen.mock_calls)

# 需要导入模块: from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook [as 别名]
# 或者: from airflow.contrib.hooks.spark_submit_hook.SparkSubmitHook import on_kill [as 别名]
    def test_k8s_process_on_kill(self, mock_popen, mock_client_method):
        # Given
        mock_popen.return_value.stdout = six.StringIO('stdout')
        mock_popen.return_value.stderr = six.StringIO('stderr')
        mock_popen.return_value.poll.return_value = None
        mock_popen.return_value.wait.return_value = 0
        client = mock_client_method.return_value
        hook = SparkSubmitHook(conn_id='spark_k8s_cluster')
        log_lines = [
            'INFO  LoggingPodStatusWatcherImpl:54 - State changed, new state:' +
            'pod name: spark-pi-edf2ace37be7353a958b38733a12f8e6-driver' +
            'namespace: default' +
            'labels: spark-app-selector -> spark-465b868ada474bda82ccb84ab2747fcd,' +
            'spark-role -> driver' +
            'pod uid: ba9c61f6-205f-11e8-b65f-d48564c88e42' +
            'creation time: 2018-03-05T10:26:55Z' +
            'service account name: spark' +
            'volumes: spark-init-properties, download-jars-volume,' +
            'download-files-volume, spark-token-2vmlm' +
            'node name: N/A' +
            'start time: N/A' +
            'container images: N/A' +
            'phase: Pending' +
            'status: []' +
            '2018-03-05 11:26:56 INFO  LoggingPodStatusWatcherImpl:54 - State changed,' +
            ' new state:' +
            'pod name: spark-pi-edf2ace37be7353a958b38733a12f8e6-driver' +
            'namespace: default' +
            'Exit code: 0'
        ]
        hook._process_spark_submit_log(log_lines)
        hook.submit()

        # When
        hook.on_kill()

        # Then
        import kubernetes
        kwargs = {'pretty': True, 'body': kubernetes.client.V1DeleteOptions()}
        client.delete_namespaced_pod.assert_called_once_with(
            'spark-pi-edf2ace37be7353a958b38733a12f8e6-driver',
            'mynamespace', **kwargs)

# 需要导入模块: from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook [as 别名]
# 或者: from airflow.contrib.hooks.spark_submit_hook.SparkSubmitHook import on_kill [as 别名]

#.........这里部分代码省略.........
    :type executor_cores: int
    :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G)
    :type executor_memory: str
    :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)
    :type driver_memory: str
    :param keytab: Full path to the file that contains the keytab
    :type keytab: str
    :param principal: The name of the kerberos principal used for keytab
    :type principal: str
    :param name: Name of the job (default airflow-spark)
    :type name: str
    :param num_executors: Number of executors to launch
    :type num_executors: int
    :param application_args: Arguments for the application being submitted
    :type application_args: list
    :param verbose: Whether to pass the verbose flag to spark-submit process for debugging
    :type verbose: bool
    """
    template_fields = ('_name', '_application_args','_packages')
    ui_color = WEB_COLORS['LIGHTORANGE']

    @apply_defaults
    def __init__(self,
                 application='',
                 conf=None,
                 conn_id='spark_default',
                 files=None,
                 py_files=None,
                 driver_classpath=None,
                 jars=None,
                 java_class=None,
                 packages=None,
                 exclude_packages=None,
                 repositories=None,
                 total_executor_cores=None,
                 executor_cores=None,
                 executor_memory=None,
                 driver_memory=None,
                 keytab=None,
                 principal=None,
                 name='airflow-spark',
                 num_executors=None,
                 application_args=None,
                 verbose=False,
                 *args,
                 **kwargs):
        super(SparkSubmitOperator, self).__init__(*args, **kwargs)
        self._application = application
        self._conf = conf
        self._files = files
        self._py_files = py_files
        self._driver_classpath = driver_classpath
        self._jars = jars
        self._java_class = java_class
        self._packages = packages
        self._exclude_packages = exclude_packages
        self._repositories = repositories
        self._total_executor_cores = total_executor_cores
        self._executor_cores = executor_cores
        self._executor_memory = executor_memory
        self._driver_memory = driver_memory
        self._keytab = keytab
        self._principal = principal
        self._name = name
        self._num_executors = num_executors
        self._application_args = application_args
        self._verbose = verbose
        self._hook = None
        self._conn_id = conn_id

    def execute(self, context):
        """
        Call the SparkSubmitHook to run the provided spark job
        """
        self._hook = SparkSubmitHook(
            conf=self._conf,
            conn_id=self._conn_id,
            files=self._files,
            py_files=self._py_files,
            driver_classpath=self._driver_classpath,
            jars=self._jars,
            java_class=self._java_class,
            packages=self._packages,
            exclude_packages=self._exclude_packages,
            repositories=self._repositories,
            total_executor_cores=self._total_executor_cores,
            executor_cores=self._executor_cores,
            executor_memory=self._executor_memory,
            driver_memory=self._driver_memory,
            keytab=self._keytab,
            principal=self._principal,
            name=self._name,
            num_executors=self._num_executors,
            application_args=self._application_args,
            verbose=self._verbose
        )
        self._hook.submit(self._application)

    def on_kill(self):
        self._hook.on_kill()