def test_process_spark_submit_log_k8s(self):
        # Given
        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: 999'
        ]

        # When
        hook._process_spark_submit_log(log_lines)

        # Then
        self.assertEqual(hook._kubernetes_driver_pod,
                         'spark-pi-edf2ace37be7353a958b38733a12f8e6-driver')
        self.assertEqual(hook._spark_exit_code, 999)
        def env_vars_exception_in_standalone_cluster_mode():
            # Given
            hook = SparkSubmitHook(conn_id='spark_standalone_cluster',
                                   env_vars={"bar": "foo"})

            # When
            hook._build_spark_submit_command(self._spark_job_file)
    def test_build_command(self):
        # Given
        hook = SparkSubmitHook(**self._config)

        # When
        cmd = hook._build_command(self._spark_job_file)

        # Then
        expected_build_cmd = [
            'spark-submit',
            '--master', 'yarn',
            '--conf', 'parquet.compression=SNAPPY',
            '--files', 'hive-site.xml',
            '--py-files', 'sample_library.py',
            '--jars', 'parquet.jar',
            '--num-executors', '10',
            '--total-executor-cores', '4',
            '--executor-cores', '4',
            '--executor-memory', '22g',
            '--driver-memory', '3g',
            '--keytab', 'privileged_user.keytab',
            '--principal', 'user/[email protected]',
            '--name', 'spark-job',
            '--class', 'com.foo.bar.AppMain',
            '--verbose',
            'test_application.py',
            '-f', 'foo',
            '--bar', 'bar',
            'baz'
        ]
        self.assertEquals(expected_build_cmd, cmd)
    def test_build_spark_submit_command(self):
        # Given
        hook = SparkSubmitHook(**self._config)

        # When
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_build_cmd = [
            'spark-submit',
            '--master', 'yarn',
            '--conf', 'parquet.compression=SNAPPY',
            '--files', 'hive-site.xml',
            '--py-files', 'sample_library.py',
            '--jars', 'parquet.jar',
            '--packages', 'com.databricks:spark-avro_2.11:3.2.0',
            '--exclude-packages', 'org.bad.dependency:1.0.0',
            '--repositories', 'http://myrepo.org',
            '--num-executors', '10',
            '--total-executor-cores', '4',
            '--executor-cores', '4',
            '--executor-memory', '22g',
            '--driver-memory', '3g',
            '--keytab', 'privileged_user.keytab',
            '--principal', 'user/[email protected]',
            '--name', 'spark-job',
            '--class', 'com.foo.bar.AppMain',
            '--verbose',
            'test_application.py',
            '-f', 'foo',
            '--bar', 'bar',
            '--with-spaces', 'args should keep embdedded spaces',
            'baz'
        ]
        self.assertEquals(expected_build_cmd, cmd)
    def test_resolve_spark_submit_env_vars_k8s(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_k8s_cluster',
                               env_vars={"bar": "foo"})

        # When
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        self.assertEqual(cmd[4], "spark.kubernetes.driverEnv.bar=foo")
 def test_submit(self, mock_process):
     # We don't have spark-submit available, and this is hard to mock, so let's
     # just use this simple mock.
     mock_Popen = mock_process.Popen.return_value
     mock_Popen.stdout = StringIO(u'stdout')
     mock_Popen.stderr = StringIO(u'stderr')
     mock_Popen.returncode = None
     mock_Popen.communicate.return_value = ['extra stdout', 'extra stderr']
     hook = SparkSubmitHook()
     hook.submit(self._spark_job_file)
    def test_resolve_spark_submit_env_vars_standalone_client_mode(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster_client_mode',
                               env_vars={"bar": "foo"})

        # When
        hook._build_spark_submit_command(self._spark_job_file)

        # Then
        self.assertEqual(hook._env, {"bar": "foo"})
    def test_spark_process_runcmd(self, mock_popen):
        # Given
        mock_popen.return_value.stdout = StringIO(u'stdout')
        mock_popen.return_value.stderr = StringIO(u'stderr')
        mock_popen.return_value.wait.return_value = 0

        # When
        hook = SparkSubmitHook(conn_id='')
        hook.submit()

        # Then
        self.assertEqual(mock_popen.mock_calls[0], call(['spark-submit', '--master', 'yarn', '--name', 'default-name', ''], stdout=-1, stderr=-2))
    def test_process_log(self):
        # Must select yarn connection
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')

        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 NodeManagers',
            'INFO Client: Submitting application application_1486558679801_1820 to ResourceManager'
        ]

        hook._process_log(log_lines)

        assert hook._yarn_application_id == 'application_1486558679801_1820'
 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 test_resolve_connection_spark_binary_set_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_binary_set')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "yarn",
                                     "spark_binary": "custom-spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": None}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], 'custom-spark-submit')
    def test_resolve_connection_spark_standalone_cluster_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "spark://spark-standalone-master:6066",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": "cluster",
                                     "queue": None,
                                     "spark_home": "/path/to/spark_home"}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], '/path/to/spark_home/bin/spark-submit')
    def test_process_spark_submit_log_standalone_cluster(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        log_lines = [
            'Running Spark using the REST application submission protocol.',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submitting a request '
            'to launch an application in spark://spark-standalone-master:6066',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submission successfully ' +
            'created as driver-20171128111415-0001. Polling submission state...'
        ]
        # When
        hook._process_spark_submit_log(log_lines)

        # Then

        self.assertEqual(hook._driver_id, 'driver-20171128111415-0001')
    def test_process_spark_submit_log_standalone_cluster(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        log_lines = [
            'Running Spark using the REST application submission protocol.',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submitting a request '
            'to launch an application in spark://spark-standalone-master:6066',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submission successfully ' +
            'created as driver-20171128111415-0001. Polling submission state...'
        ]
        # When
        hook._process_spark_submit_log(log_lines)

        # Then

        self.assertEqual(hook._driver_id, 'driver-20171128111415-0001')
Example #15
0
    def test_resolve_connection_spark_binary_and_home_set_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_binary_and_home_set')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "yarn",
                                     "spark_binary": "custom-spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": "/path/to/spark_home",
                                     "namespace": None}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], '/path/to/spark_home/bin/custom-spark-submit')
Example #16
0
    def test_process_log(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')
        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 NodeManagers',
            'INFO Client: Submitting application application_1486558679801_1820 to ResourceManager'
        ]
        # When
        hook._process_log(log_lines)

        # Then

        self.assertEqual(hook._yarn_application_id,
                         'application_1486558679801_1820')
Example #17
0
    def test_resolve_connection_spark_binary_default_value(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_default')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "yarn",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": None,
                                     "queue": 'root.default',
                                     "spark_home": None,
                                     "namespace": 'default'}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], 'spark-submit')
Example #18
0
    def test_resolve_connection_spark_standalone_cluster_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "spark://spark-standalone-master:6066",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": "cluster",
                                     "queue": None,
                                     "spark_home": "/path/to/spark_home",
                                     "namespace": 'default'}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], '/path/to/spark_home/bin/spark-submit')
    def test_resolve_connection_spark_home_set_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_home_set')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {"master": "yarn://yarn-master",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": "/opt/myspark",
                                     "namespace": 'default'}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], '/opt/myspark/bin/spark-submit')
    def test_resolve_connection_mesos_default_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_default_mesos')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"master": "mesos://host:5050",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": None}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "mesos://host:5050")
    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)
    def test_resolve_connection_spark_home_not_set_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_home_not_set')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_spark_connection = {
            "master": "yarn://yarn-master",
            "spark_binary": "spark-submit",
            "deploy_mode": None,
            "queue": None,
            "spark_home": None
        }
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(cmd[0], 'spark-submit')
    def test_resolve_connection_yarn_default(self):
        # Given
        hook = SparkSubmitHook(conn_id='')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"master": "yarn",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": None,
                                     "namespace": 'default'}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "yarn")
Example #24
0
    def test_resolve_connection_yarn_default(self):
        # Given
        hook = SparkSubmitHook(conn_id='')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"master": "yarn",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": None,
                                     "queue": None,
                                     "spark_home": None,
                                     "namespace": 'default'}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "yarn")
    def test_resolve_should_track_driver_status(self):
        # Given
        hook_default = SparkSubmitHook(conn_id='')
        hook_spark_yarn_cluster = SparkSubmitHook(conn_id='spark_yarn_cluster')
        hook_spark_k8s_cluster = SparkSubmitHook(conn_id='spark_k8s_cluster')
        hook_spark_default_mesos = SparkSubmitHook(conn_id='spark_default_mesos')
        hook_spark_home_set = SparkSubmitHook(conn_id='spark_home_set')
        hook_spark_home_not_set = SparkSubmitHook(conn_id='spark_home_not_set')
        hook_spark_binary_set = SparkSubmitHook(conn_id='spark_binary_set')
        hook_spark_binary_and_home_set = SparkSubmitHook(
            conn_id='spark_binary_and_home_set')
        hook_spark_standalone_cluster = SparkSubmitHook(
            conn_id='spark_standalone_cluster')

        # When
        should_track_driver_status_default = hook_default \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_yarn_cluster = hook_spark_yarn_cluster \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_k8s_cluster = hook_spark_k8s_cluster \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_default_mesos = hook_spark_default_mesos \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_home_set = hook_spark_home_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_home_not_set = hook_spark_home_not_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_binary_set = hook_spark_binary_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_binary_and_home_set = \
            hook_spark_binary_and_home_set._resolve_should_track_driver_status()
        should_track_driver_status_spark_standalone_cluster = \
            hook_spark_standalone_cluster._resolve_should_track_driver_status()

        # Then
        self.assertEqual(should_track_driver_status_default, False)
        self.assertEqual(should_track_driver_status_spark_yarn_cluster, False)
        self.assertEqual(should_track_driver_status_spark_k8s_cluster, False)
        self.assertEqual(should_track_driver_status_spark_default_mesos, False)
        self.assertEqual(should_track_driver_status_spark_home_set, False)
        self.assertEqual(should_track_driver_status_spark_home_not_set, False)
        self.assertEqual(should_track_driver_status_spark_binary_set, False)
        self.assertEqual(should_track_driver_status_spark_binary_and_home_set, False)
        self.assertEqual(should_track_driver_status_spark_standalone_cluster, True)
    def test_build_command(self):
        hook = SparkSubmitHook(**self._config)

        # The subprocess requires an array but we build the cmd by joining on a space
        cmd = ' '.join(hook._build_command(self._spark_job_file))

        # Check if the URL gets build properly and everything exists.
        assert self._spark_job_file in cmd

        # Check all the parameters
        assert "--files {}".format(self._config['files']) in cmd
        assert "--py-files {}".format(self._config['py_files']) in cmd
        assert "--jars {}".format(self._config['jars']) in cmd
        assert "--total-executor-cores {}".format(
            self._config['total_executor_cores']) in cmd
        assert "--executor-cores {}".format(
            self._config['executor_cores']) in cmd
        assert "--executor-memory {}".format(
            self._config['executor_memory']) in cmd
        assert "--keytab {}".format(self._config['keytab']) in cmd
        assert "--principal {}".format(self._config['principal']) in cmd
        assert "--name {}".format(self._config['name']) in cmd
        assert "--num-executors {}".format(
            self._config['num_executors']) in cmd
        assert "--class {}".format(self._config['java_class']) in cmd
        assert "--driver-memory {}".format(
            self._config['driver_memory']) in cmd

        # Check if all config settings are there
        for k in self._config['conf']:
            assert "--conf {0}={1}".format(k, self._config['conf'][k]) in cmd

        # Check the application arguments are there
        for a in self._config['application_args']:
            assert a in cmd

        # Check if application arguments are after the application
        application_idx = cmd.find(self._spark_job_file)
        for a in self._config['application_args']:
            assert cmd.find(a) > application_idx

        if self._config['verbose']:
            assert "--verbose" in cmd
    def test_resolve_connection_spark_k8s_cluster_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_k8s_cluster')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"spark_home": "/opt/spark",
                                     "queue": None,
                                     "spark_binary": "spark-submit",
                                     "master": "k8s://https://k8s-master",
                                     "deploy_mode": "cluster",
                                     "namespace": "mynamespace"}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "k8s://https://k8s-master")
        self.assertEqual(dict_cmd["--deploy-mode"], "cluster")
Example #28
0
 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,
         jars=self._jars,
         executor_cores=self._executor_cores,
         executor_memory=self._executor_memory,
         keytab=self._keytab,
         principal=self._principal,
         name=self._name,
         num_executors=self._num_executors,
         verbose=self._verbose
     )
     self._hook.submit(self._application)
Example #29
0
    def test_resolve_connection_spark_k8s_cluster_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_k8s_cluster')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"spark_home": "/opt/spark",
                                     "queue": None,
                                     "spark_binary": "spark-submit",
                                     "master": "k8s://https://k8s-master",
                                     "deploy_mode": "cluster",
                                     "namespace": "mynamespace"}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "k8s://https://k8s-master")
        self.assertEqual(dict_cmd["--deploy-mode"], "cluster")
    def test_resolve_connection_spark_yarn_cluster_connection(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"master": "yarn://yarn-master",
                                     "spark_binary": "spark-submit",
                                     "deploy_mode": "cluster",
                                     "queue": "root.etl",
                                     "spark_home": None}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "yarn://yarn-master")
        self.assertEqual(dict_cmd["--queue"], "root.etl")
        self.assertEqual(dict_cmd["--deploy-mode"], "cluster")
Example #31
0
    def _run_spark_submit(self, file, jars):
        from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook

        _config = self.config
        deploy = self.deploy
        spark = SparkSubmitHook(
            conf=_config.conf,
            conn_id=self.emr_config.conn_id,
            name=self.job.job_id,
            application_args=list_of_strings(self.task.application_args()),
            java_class=self.task.main_class,
            files=deploy.arg_files(_config.files),
            py_files=deploy.arg_files(_config.py_files),
            driver_class_path=_config.driver_class_path,
            jars=deploy.arg_files(jars),
            packages=_config.packages,
            exclude_packages=_config.exclude_packages,
            repositories=_config.repositories,
            total_executor_cores=_config.total_executor_cores,
            executor_cores=_config.executor_cores,
            executor_memory=_config.executor_memory,
            driver_memory=_config.driver_memory,
            keytab=_config.keytab,
            principal=_config.principal,
            num_executors=_config.num_executors,
            env_vars=self._get_env_vars(),
            verbose=_config.verbose,
        )

        step_id = self.emr_cluster.run_spark_submit_step(
            name=self.job.job_id,
            spark_submit_command=spark._build_spark_submit_command(
                application=deploy.sync(file)
            ),
        )
        self.task_run.set_external_resource_urls(
            self.emr_cluster.get_emr_logs_dict(self.spark_application_logs)
        )
        self.emr_cluster.wait_for_step_completion(
            step_id, status_reporter=self._report_step_status
        )
        pass
Example #32
0
    def test_build_command(self):
        # Given
        hook = SparkSubmitHook(**self._config)

        # When
        cmd = hook._build_command(self._spark_job_file)

        # Then
        expected_build_cmd = [
            'spark-submit', '--master', 'yarn', '--conf',
            'parquet.compression=SNAPPY', '--files', 'hive-site.xml',
            '--py-files', 'sample_library.py', '--jars', 'parquet.jar',
            '--num-executors', '10', '--total-executor-cores', '4',
            '--executor-cores', '4', '--executor-memory', '22g',
            '--driver-memory', '3g', '--keytab', 'privileged_user.keytab',
            '--principal', 'user/[email protected]', '--name', 'spark-job',
            '--class', 'com.foo.bar.AppMain', '--verbose',
            'test_application.py', '-f', 'foo', '--bar', 'bar', 'baz'
        ]
        self.assertEquals(expected_build_cmd, cmd)
    def test_process_spark_driver_status_log(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        log_lines = [
            'Submitting a request for the status of submission ' +
            'driver-20171128111415-0001 in spark://spark-standalone-master:6066',
            '17/11/28 11:15:37 INFO RestSubmissionClient: Server responded with '
            + 'SubmissionStatusResponse:', '{',
            '"action" : "SubmissionStatusResponse",',
            '"driverState" : "RUNNING",', '"serverSparkVersion" : "1.6.0",',
            '"submissionId" : "driver-20171128111415-0001",',
            '"success" : true,', '"workerHostPort" : "172.18.0.7:38561",',
            '"workerId" : "worker-20171128110741-172.18.0.7-38561"', '}'
        ]
        # When
        hook._process_spark_status_log(log_lines)

        # Then

        self.assertEqual(hook._driver_status, 'RUNNING')
    def test_spark_process_runcmd(self, mock_popen):
        # Given
        mock_popen.return_value.stdout = six.StringIO('stdout')
        mock_popen.return_value.stderr = six.StringIO('stderr')
        mock_popen.return_value.wait.return_value = 0

        # When
        hook = SparkSubmitHook(conn_id='')
        hook.submit()

        # Then
        self.assertEqual(
            mock_popen.mock_calls[0],
            call([
                'spark-submit', '--master', 'yarn', '--name', 'default-name',
                ''
            ],
                 stderr=-2,
                 stdout=-1,
                 universal_newlines=True,
                 bufsize=-1))
Example #35
0
    def test_standalone_cluster_process_on_kill(self):
        # Given
        log_lines = [
            'Running Spark using the REST application submission protocol.',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submitting a request ' +
            'to launch an application in spark://spark-standalone-master:6066',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submission successfully ' +
            'created as driver-20171128111415-0001. Polling submission state...'
        ]
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        hook._process_spark_submit_log(log_lines)

        # When
        kill_cmd = hook._build_spark_driver_kill_command()

        # Then
        self.assertEqual(kill_cmd[0], '/path/to/spark_home/bin/spark-submit')
        self.assertEqual(kill_cmd[1], '--master')
        self.assertEqual(kill_cmd[2], 'spark://spark-standalone-master:6066')
        self.assertEqual(kill_cmd[3], '--kill')
        self.assertEqual(kill_cmd[4], 'driver-20171128111415-0001')
    def test_standalone_cluster_process_on_kill(self):
        # Given
        log_lines = [
            'Running Spark using the REST application submission protocol.',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submitting a request ' +
            'to launch an application in spark://spark-standalone-master:6066',
            '17/11/28 11:14:15 INFO RestSubmissionClient: Submission successfully ' +
            'created as driver-20171128111415-0001. Polling submission state...'
        ]
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        hook._process_spark_submit_log(log_lines)

        # When
        kill_cmd = hook._build_spark_driver_kill_command()

        # Then
        self.assertEqual(kill_cmd[0], '/path/to/spark_home/bin/spark-submit')
        self.assertEqual(kill_cmd[1], '--master')
        self.assertEqual(kill_cmd[2], 'spark://spark-standalone-master:6066')
        self.assertEqual(kill_cmd[3], '--kill')
        self.assertEqual(kill_cmd[4], 'driver-20171128111415-0001')
Example #37
0
    def test_resolve_connection_mesos_cluster_env_connection(self):
        # Given
        conn_name = self.gen_conn_name(10)
        os.environ["AIRFLOW_CONN_SPARK_{}".format(
            conn_name.upper())] = "mesos://mesos-master:5050"
        hook = SparkSubmitHook(conn_id='spark_{}'.format(conn_name))

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {
            "master": "mesos://mesos-master:5050",
            "spark_binary": "spark-submit",
            "deploy_mode": None,
            "queue": None,
            "spark_home": None
        }
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "mesos://mesos-master:5050")
    def test_build_command(self):
        hook = SparkSubmitHook(**self._config)

        # The subprocess requires an array but we build the cmd by joining on a space
        cmd = ' '.join(hook._build_command(self._spark_job_file))

        # Check if the URL gets build properly and everything exists.
        assert self._spark_job_file in cmd

        # Check all the parameters
        assert "--files {}".format(self._config['files']) in cmd
        assert "--py-files {}".format(self._config['py_files']) in cmd
        assert "--jars {}".format(self._config['jars']) in cmd
        assert "--total-executor-cores {}".format(self._config['total_executor_cores']) in cmd
        assert "--executor-cores {}".format(self._config['executor_cores']) in cmd
        assert "--executor-memory {}".format(self._config['executor_memory']) in cmd
        assert "--keytab {}".format(self._config['keytab']) in cmd
        assert "--principal {}".format(self._config['principal']) in cmd
        assert "--name {}".format(self._config['name']) in cmd
        assert "--num-executors {}".format(self._config['num_executors']) in cmd
        assert "--class {}".format(self._config['java_class']) in cmd
        assert "--driver-memory {}".format(self._config['driver_memory']) in cmd

        # Check if all config settings are there
        for k in self._config['conf']:
            assert "--conf {0}={1}".format(k, self._config['conf'][k]) in cmd

        # Check the application arguments are there
        for a in self._config['application_args']:
            assert a in cmd

        # Check if application arguments are after the application
        application_idx = cmd.find(self._spark_job_file)
        for a in self._config['application_args']:
            assert cmd.find(a) > application_idx

        if self._config['verbose']:
            assert "--verbose" in cmd
    def test_build_spark_submit_command(self):
        # Given
        hook = SparkSubmitHook(**self._config)

        # When
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        expected_build_cmd = [
            'spark-submit', '--master', 'yarn', '--conf',
            'parquet.compression=SNAPPY', '--files', 'hive-site.xml',
            '--py-files', 'sample_library.py', '--jars', 'parquet.jar',
            '--packages', 'com.databricks:spark-avro_2.11:3.2.0',
            '--exclude-packages', 'org.bad.dependency:1.0.0', '--repositories',
            'http://myrepo.org', '--num-executors', '10',
            '--total-executor-cores', '4', '--executor-cores', '4',
            '--executor-memory', '22g', '--driver-memory', '3g', '--keytab',
            'privileged_user.keytab', '--principal', 'user/[email protected]',
            '--name', 'spark-job', '--class', 'com.foo.bar.AppMain',
            '--verbose', 'test_application.py', '-f', 'foo', '--bar', 'bar',
            '--with-spaces', 'args should keep embdedded spaces', 'baz'
        ]
        self.assertEquals(expected_build_cmd, cmd)
Example #40
0
    def test_SparkProcess_runcmd(self, mock_popen):
        # Given
        mock_popen.return_value.stdout = StringIO(u'stdout')
        mock_popen.return_value.stderr = StringIO(u'stderr')
        mock_popen.return_value.returncode = 0
        mock_popen.return_value.communicate.return_value = [
            StringIO(u'stdout\nstdout'),
            StringIO(u'stderr\nstderr')
        ]

        # When
        hook = SparkSubmitHook(conn_id='')
        hook.submit()

        # Then
        self.assertEqual(
            mock_popen.mock_calls[0],
            call([
                'spark-submit', '--master', 'yarn', '--name', 'default-name',
                ''
            ],
                 stderr=-1,
                 stdout=-1))
Example #41
0
    def test_resolve_connection_spark_k8s_cluster_ns_conf(self):
        # Given we specify the config option directly
        conf = {
            'spark.kubernetes.namespace': 'airflow',
        }
        hook = SparkSubmitHook(conn_id='spark_k8s_cluster', conf=conf)

        # When
        connection = hook._resolve_connection()
        cmd = hook._build_spark_submit_command(self._spark_job_file)

        # Then
        dict_cmd = self.cmd_args_to_dict(cmd)
        expected_spark_connection = {"spark_home": "/opt/spark",
                                     "queue": None,
                                     "spark_binary": "spark-submit",
                                     "master": "k8s://https://k8s-master",
                                     "deploy_mode": "cluster",
                                     "namespace": "airflow"}
        self.assertEqual(connection, expected_spark_connection)
        self.assertEqual(dict_cmd["--master"], "k8s://https://k8s-master")
        self.assertEqual(dict_cmd["--deploy-mode"], "cluster")
        self.assertEqual(dict_cmd["--conf"], "spark.kubernetes.namespace=airflow")
    def test_process_spark_driver_status_log(self):
        # Given
        hook = SparkSubmitHook(conn_id='spark_standalone_cluster')
        log_lines = [
            'Submitting a request for the status of submission ' +
            'driver-20171128111415-0001 in spark://spark-standalone-master:6066',
            '17/11/28 11:15:37 INFO RestSubmissionClient: Server responded with ' +
            'SubmissionStatusResponse:',
            '{',
            '"action" : "SubmissionStatusResponse",',
            '"driverState" : "RUNNING",',
            '"serverSparkVersion" : "1.6.0",',
            '"submissionId" : "driver-20171128111415-0001",',
            '"success" : true,',
            '"workerHostPort" : "172.18.0.7:38561",',
            '"workerId" : "worker-20171128110741-172.18.0.7-38561"',
            '}'
        ]
        # When
        hook._process_spark_status_log(log_lines)

        # Then

        self.assertEqual(hook._driver_status, 'RUNNING')
Example #43
0
    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)
    def test_spark_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_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)
    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)
Example #46
0
class SparkSubmitOperator(BaseOperator):
    """
    This hook is a wrapper around the spark-submit binary to kick off a spark-submit job.
    It requires that the "spark-submit" binary is in the PATH.
    :param application: The application that submitted as a job, either jar or py file.
    :type application: str
    :param conf: Arbitrary Spark configuration properties
    :type conf: dict
    :param conn_id: The connection id as configured in Airflow administration. When an
                    invalid connection_id is supplied, it will default to yarn.
    :type conn_id: str
    :param files: Upload additional files to the container running the job, separated by a
                  comma. For example hive-site.xml.
    :type files: str
    :param py_files: Additional python files used by the job, can be .zip, .egg or .py.
    :type py_files: str
    :param jars: Submit additional jars to upload and place them in executor classpath.
    :type jars: str
    :param executor_cores: Number of cores per executor (Default: 2)
    :type executor_cores: int
    :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G)
    :type executor_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 verbose: Whether to pass the verbose flag to spark-submit process for debugging
    :type verbose: bool
    """

    @apply_defaults
    def __init__(self,
                 application='',
                 conf=None,
                 conn_id='spark_default',
                 files=None,
                 py_files=None,
                 jars=None,
                 executor_cores=None,
                 executor_memory=None,
                 keytab=None,
                 principal=None,
                 name='airflow-spark',
                 num_executors=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._jars = jars
        self._executor_cores = executor_cores
        self._executor_memory = executor_memory
        self._keytab = keytab
        self._principal = principal
        self._name = name
        self._num_executors = num_executors
        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,
            jars=self._jars,
            executor_cores=self._executor_cores,
            executor_memory=self._executor_memory,
            keytab=self._keytab,
            principal=self._principal,
            name=self._name,
            num_executors=self._num_executors,
            verbose=self._verbose
        )
        self._hook.submit(self._application)

    def on_kill(self):
        self._hook.on_kill()
Example #47
0
    def test_resolve_connection(self):

        # Default to the standard yarn connection because conn_id does not exists
        hook = SparkSubmitHook(conn_id='')
        self.assertEqual(hook._resolve_connection(),
                         ('yarn', None, None, None))
        assert "--master yarn" in ' '.join(
            hook._build_command(self._spark_job_file))

        # Default to the standard yarn connection
        hook = SparkSubmitHook(conn_id='spark_default')
        self.assertEqual(hook._resolve_connection(),
                         ('yarn', 'root.default', None, None))
        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master yarn" in cmd
        assert "--queue root.default" in cmd

        # Connect to a mesos master
        hook = SparkSubmitHook(conn_id='spark_default_mesos')
        self.assertEqual(hook._resolve_connection(),
                         ('mesos://host:5050', None, None, None))

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master mesos://host:5050" in cmd

        # Set specific queue and deploy mode
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')
        self.assertEqual(hook._resolve_connection(),
                         ('yarn://yarn-master', 'root.etl', 'cluster', None))

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master yarn://yarn-master" in cmd
        assert "--queue root.etl" in cmd
        assert "--deploy-mode cluster" in cmd

        # Set the spark home
        hook = SparkSubmitHook(conn_id='spark_home_set')
        self.assertEqual(hook._resolve_connection(),
                         ('yarn://yarn-master', None, None, '/opt/myspark'))

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert cmd.startswith('/opt/myspark/bin/spark-submit')

        # Spark home not set
        hook = SparkSubmitHook(conn_id='spark_home_not_set')
        self.assertEqual(hook._resolve_connection(),
                         ('yarn://yarn-master', None, None, None))

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert cmd.startswith('spark-submit')
    def test_resolve_should_track_driver_status(self):
        # Given
        hook_default = SparkSubmitHook(conn_id='')
        hook_spark_yarn_cluster = SparkSubmitHook(conn_id='spark_yarn_cluster')
        hook_spark_default_mesos = SparkSubmitHook(conn_id='spark_default_mesos')
        hook_spark_home_set = SparkSubmitHook(conn_id='spark_home_set')
        hook_spark_home_not_set = SparkSubmitHook(conn_id='spark_home_not_set')
        hook_spark_binary_set = SparkSubmitHook(conn_id='spark_binary_set')
        hook_spark_binary_and_home_set = SparkSubmitHook(
            conn_id='spark_binary_and_home_set')
        hook_spark_standalone_cluster = SparkSubmitHook(
            conn_id='spark_standalone_cluster')

        # When
        should_track_driver_status_default = hook_default \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_yarn_cluster = hook_spark_yarn_cluster \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_default_mesos = hook_spark_default_mesos \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_home_set = hook_spark_home_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_home_not_set = hook_spark_home_not_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_binary_set = hook_spark_binary_set \
            ._resolve_should_track_driver_status()
        should_track_driver_status_spark_binary_and_home_set = \
            hook_spark_binary_and_home_set._resolve_should_track_driver_status()
        should_track_driver_status_spark_standalone_cluster = \
            hook_spark_standalone_cluster._resolve_should_track_driver_status()

        # Then
        self.assertEqual(should_track_driver_status_default, False)
        self.assertEqual(should_track_driver_status_spark_yarn_cluster, False)
        self.assertEqual(should_track_driver_status_spark_default_mesos, False)
        self.assertEqual(should_track_driver_status_spark_home_set, False)
        self.assertEqual(should_track_driver_status_spark_home_not_set, False)
        self.assertEqual(should_track_driver_status_spark_binary_set, False)
        self.assertEqual(should_track_driver_status_spark_binary_and_home_set, False)
        self.assertEqual(should_track_driver_status_spark_standalone_cluster, True)
    def test_resolve_connection(self):

        # Default to the standard yarn connection because conn_id does not exists
        hook = SparkSubmitHook(conn_id='')
        self.assertEqual(hook._resolve_connection(), ('yarn', None, None, None))
        assert "--master yarn" in ' '.join(hook._build_command(self._spark_job_file))

        # Default to the standard yarn connection
        hook = SparkSubmitHook(conn_id='spark_default')
        self.assertEqual(
            hook._resolve_connection(),
            ('yarn', 'root.default', None, None)
        )
        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master yarn" in cmd
        assert "--queue root.default" in cmd

        # Connect to a mesos master
        hook = SparkSubmitHook(conn_id='spark_default_mesos')
        self.assertEqual(
            hook._resolve_connection(),
            ('mesos://host:5050', None, None, None)
        )

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master mesos://host:5050" in cmd

        # Set specific queue and deploy mode
        hook = SparkSubmitHook(conn_id='spark_yarn_cluster')
        self.assertEqual(
            hook._resolve_connection(),
            ('yarn://yarn-master', 'root.etl', 'cluster', None)
        )

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert "--master yarn://yarn-master" in cmd
        assert "--queue root.etl" in cmd
        assert "--deploy-mode cluster" in cmd

        # Set the spark home
        hook = SparkSubmitHook(conn_id='spark_home_set')
        self.assertEqual(
            hook._resolve_connection(),
            ('yarn://yarn-master', None, None, '/opt/myspark')
        )

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert cmd.startswith('/opt/myspark/bin/spark-submit')

        # Spark home not set
        hook = SparkSubmitHook(conn_id='spark_home_not_set')
        self.assertEqual(
            hook._resolve_connection(),
            ('yarn://yarn-master', None, None, None)
        )

        cmd = ' '.join(hook._build_command(self._spark_job_file))
        assert cmd.startswith('spark-submit')
Example #50
0
class SparkSubmitOperator(BaseOperator):
    """
    This hook is a wrapper around the spark-submit binary to kick off a spark-submit job.
    It requires that the "spark-submit" binary is in the PATH or the spark-home is set
    in the extra on the connection.

    :param application: The application that submitted as a job, either jar or py file.
    :type application: str
    :param conf: Arbitrary Spark configuration properties
    :type conf: dict
    :param conn_id: The connection id as configured in Airflow administration. When an
                    invalid connection_id is supplied, it will default to yarn.
    :type conn_id: str
    :param files: Upload additional files to the executor running the job, separated by a
                  comma. Files will be placed in the working directory of each executor.
                  For example, serialized objects.
    :type files: str
    :param py_files: Additional python files used by the job, can be .zip, .egg or .py.
    :type py_files: str
    :param jars: Submit additional jars to upload and place them in executor classpath.
    :param driver_classpath: Additional, driver-specific, classpath settings.
    :type driver_classpath: str
    :type jars: str
    :param java_class: the main class of the Java application
    :type java_class: str
    :param packages: Comma-separated list of maven coordinates of jars to include on the
                     driver and executor classpaths
    :type packages: str
    :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude
                             while resolving the dependencies provided in 'packages'
    :type exclude_packages: str
    :param repositories: Comma-separated list of additional remote repositories to search
                         for the maven coordinates given with 'packages'
    :type repositories: str
    :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors
                                 (Default: all the available cores on the worker)
    :type total_executor_cores: int
    :param executor_cores: (Standalone & YARN only) Number of cores per executor
                           (Default: 2)
    :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()
class SparkSubmitOperator(BaseOperator):
    """
    This hook is a wrapper around the spark-submit binary to kick off a spark-submit job.
    It requires that the "spark-submit" binary is in the PATH or the spark-home is set
    in the extra on the connection.

    :param application: The application that submitted as a job, either jar or py file.
    :type application: str
    :param conf: Arbitrary Spark configuration properties
    :type conf: dict
    :param conn_id: The connection id as configured in Airflow administration. When an
                    invalid connection_id is supplied, it will default to yarn.
    :type conn_id: str
    :param files: Upload additional files to the container running the job, separated by a
                  comma. For example hive-site.xml.
    :type files: str
    :param py_files: Additional python files used by the job, can be .zip, .egg or .py.
    :type py_files: str
    :param jars: Submit additional jars to upload and place them in executor classpath.
    :param driver_classpath: Additional, driver-specific, classpath settings.
    :type driver_classpath: str
    :type jars: str
    :param java_class: the main class of the Java application
    :type java_class: str
    :param packages: Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths
    :type packages: str
    :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in 'packages'
    :type exclude_packages: str
    :param repositories: Comma-separated list of additional remote repositories to search for the maven coordinates given with 'packages'
    :type repositories: str
    :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker)
    :type total_executor_cores: int
    :param executor_cores: (Standalone & YARN only) Number of cores per executor (Default: 2)
    :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()
Example #52
0
    def _run_spark_submit(self, application, jars):
        # task_env = get_cloud_config(Clouds.local)
        spark_local_config = SparkLocalEngineConfig()
        _config = self.config
        deploy = self.deploy

        AIRFLOW_ON = is_airflow_enabled()

        if AIRFLOW_ON:
            from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook
            from airflow.exceptions import AirflowException as SparkException
        else:
            from dbnd_spark._vendor.airflow.spark_hook import (
                SparkException,
                SparkSubmitHook,
            )

        spark = SparkSubmitHook(
            conf=_config.conf,
            conn_id=spark_local_config.conn_id,
            name=self.job.job_id,
            application_args=list_of_strings(self.task.application_args()),
            java_class=self.task.main_class,
            files=deploy.arg_files(_config.files),
            py_files=deploy.arg_files(self.task.get_py_files()),
            driver_class_path=_config.driver_class_path,
            jars=deploy.arg_files(jars),
            packages=_config.packages,
            exclude_packages=_config.exclude_packages,
            repositories=_config.repositories,
            total_executor_cores=_config.total_executor_cores,
            executor_cores=_config.executor_cores,
            executor_memory=_config.executor_memory,
            driver_memory=_config.driver_memory,
            keytab=_config.keytab,
            principal=_config.principal,
            num_executors=_config.num_executors,
            env_vars=self._get_env_vars(),
            verbose=_config.verbose,
        )
        if not AIRFLOW_ON:
            # If there's no Airflow then there's no Connection so we
            # take conn information from spark config
            spark.set_connection(spark_local_config.conn_uri)

        log_buffer = StringIO()
        with log_buffer as lb:
            dbnd_log_handler = self._capture_submit_log(spark, lb)
            try:
                # sync the application file to remote if needed
                spark.submit(application=deploy.sync(application))
            except SparkException as ex:
                return_code = self._get_spark_return_code_from_exception(ex)
                if return_code != "0":
                    error_snippets = parse_spark_log_safe(
                        log_buffer.getvalue().split(os.linesep))
                    raise failed_to_run_spark_script(
                        self,
                        spark._build_spark_submit_command(
                            application=application),
                        application,
                        return_code,
                        error_snippets,
                    )
                else:
                    raise failed_spark_status(ex)
            finally:
                spark.log.handlers = [
                    h for h in spark.log.handlers if not dbnd_log_handler
                ]