def main(): hr = HadoopRuntime() settings = hr.settings print(settings) hr.clean_working_dir() output_dir = hr.get_hdfs_working_dir("dump_dir") sqoop = MySqoop(settings.Param.Sqoop2Server_Host, int(settings.Param.Sqoop2Server_Port)) # First, Create an connection conn_name = "import_m_job%s_blk%s" % (settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) conn_ret = sqoop.create_connection( conn_name=conn_name, conn_str=settings.Param.connection_string, username=settings.Param.connection_username, password=settings.Param.connection_password) # Then, Run sqoop import job fw_ps = { "output.storageType": "HDFS", "output.outputFormat": "TEXT_FILE", "output.outputDirectory": output_dir } if settings.Param.where_clause and settings.Param.where_clause != None and str( settings.Param.where_clause).strip(" ") != "": table_sql = "select %s from %s where ${CONDITIONS} and %s " % ( settings.Param.input_columns, settings.Param.table_name, settings.Param.where_clause) else: table_sql = "select %s from %s where ${CONDITIONS}" % ( settings.Param.input_columns, settings.Param.table_name) partition_column = settings.Param.partition_column print settings.Param.where_clause print table_sql job_ps = { "table.sql": table_sql, "table.partitionColumn": partition_column } job_name = "import job :: username(%s) job %s, block %s" % ( settings.GlobalParam["userName"], settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) r = sqoop.create_import_job(job_name=job_name, connection_id=conn_ret["id"], framework_params=fw_ps, job_params=job_ps) pp(r) sqoop.run_job(r['id']) sqoop.wait_job(r['id']) sqoop.delete_job(r['id']) # Finally, Delete connection we created sqoop.delete_connection_by_id(conn_ret["id"]) settings.Output.output_dir.val = output_dir print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) # allocate output_path, and clean it output_path = get_s3_working_dir(settings, "output_path") s3_delete(output_path, settings) # Prepare working directory hr.hdfs_clean_working_dir() temp_path = hr.get_hdfs_working_dir("temp") # build parameters for hadoop job jar_file = "mahout-core-1.0-SNAPSHOT-job.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params_str = " ".join( ["%s=%s" % (k, v) for k, v in hadoop_params.items()]) jar_defs = {} jar_defs[ "fs.s3n.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs[ "fs.s3n.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs[ "fs.s3.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs[ "fs.s3.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs["mapreduce.framework.name"] = "yarn" jar_defs[ "yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs[ "yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs["mapreduce.output.fileoutputformat.compress"] = "false" jar_defs_str = " ".join(["-D %s=%s" % (k, v) for k, v in jar_defs.items()]) other_args = OrderedDict() other_args["similarityClassname"] = "SIMILARITY_EUCLIDEAN_DISTANCE" other_args["input"] = settings.Input.ratings.as_datasource['URL'] other_args["usersFile"] = settings.Input.usersFile.as_datasource['URL'] other_args["output"] = output_path other_args["tempDir"] = temp_path other_args_str = " ".join( ["--%s %s" % (k, v) for k, v in other_args.items()]) cmd_str = '%s hadoop jar %s org.apache.mahout.cf.taste.hadoop.item.RecommenderJob %s %s' % \ (hadoop_params_str, jar_file, jar_defs_str, other_args_str) print("Executing:") print(cmd_str) ret = cmd(cmd_str) if ret != 0: print("Job failed") sys.exit(ret) settings.Output.output_path.val = output_path print("Done")
def main(): hr = HadoopRuntime() settings = hr.settings print(settings) hr.clean_working_dir() output_dir = hr.get_hdfs_working_dir("dump_dir") sqoop = MySqoop(settings.Param.Sqoop2Server_Host, int(settings.Param.Sqoop2Server_Port)) # First, Create an connection conn_name = "import_m_job%s_blk%s" % ( settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) conn_ret = sqoop.create_connection(conn_name=conn_name, conn_str=settings.Param.connection_string, username=settings.Param.connection_username, password=settings.Param.connection_password) # Then, Run sqoop import job fw_ps = { "output.storageType": "HDFS", "output.outputFormat": "TEXT_FILE", "output.outputDirectory": output_dir } if settings.Param.where_clause and settings.Param.where_clause != None and str(settings.Param.where_clause).strip(" ") != "": table_sql = "select %s from %s where ${CONDITIONS} and %s " %(settings.Param.input_columns,settings.Param.table_name,settings.Param.where_clause) else: table_sql = "select %s from %s where ${CONDITIONS}" %(settings.Param.input_columns,settings.Param.table_name) partition_column = settings.Param.partition_column print settings.Param.where_clause print table_sql job_ps = { "table.sql": table_sql, "table.partitionColumn": partition_column } job_name = "import job :: username(%s) job %s, block %s" % ( settings.GlobalParam["userName"], settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) r = sqoop.create_import_job(job_name=job_name, connection_id=conn_ret["id"], framework_params=fw_ps, job_params=job_ps) pp(r) sqoop.run_job(r['id']) sqoop.wait_job(r['id']) sqoop.delete_job(r['id']) # Finally, Delete connection we created sqoop.delete_connection_by_id(conn_ret["id"]) settings.Output.output_dir.val = output_dir print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) # allocate output_path, and clean it output_path = get_s3_working_dir(settings, "output_path") s3_delete(output_path, settings) # Prepare working directory hr.hdfs_clean_working_dir() temp_path = hr.get_hdfs_working_dir("temp") # build parameters for hadoop job jar_file = "mahout-core-1.0-SNAPSHOT-job.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params_str = " ".join(["%s=%s" % (k, v) for k, v in hadoop_params.items()]) jar_defs = {} jar_defs["fs.s3n.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs["fs.s3n.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs["fs.s3.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs["fs.s3.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs["mapreduce.framework.name"] = "yarn" jar_defs["yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs["yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs["mapreduce.output.fileoutputformat.compress"] = "false" jar_defs_str = " ".join(["-D %s=%s" % (k, v) for k, v in jar_defs.items()]) other_args = OrderedDict() other_args["similarityClassname"] = "SIMILARITY_EUCLIDEAN_DISTANCE" other_args["input"] = settings.Input.ratings.as_datasource["URL"] other_args["usersFile"] = settings.Input.usersFile.as_datasource["URL"] other_args["output"] = output_path other_args["tempDir"] = temp_path other_args_str = " ".join(["--%s %s" % (k, v) for k, v in other_args.items()]) cmd_str = "%s hadoop jar %s org.apache.mahout.cf.taste.hadoop.item.RecommenderJob %s %s" % ( hadoop_params_str, jar_file, jar_defs_str, other_args_str, ) print("Executing:") print(cmd_str) ret = cmd(cmd_str) if ret != 0: print("Job failed") sys.exit(ret) settings.Output.output_path.val = output_path print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) # Prepare working directory hr.hdfs_clean_working_dir() # allocate temp_path temp_path = hr.get_hdfs_working_dir("temp") # allocate output_path output_path = hr.get_hdfs_working_dir("output_path") # build parameters for hadoop job jar_file = "./mahout-core-1.0-SNAPSHOT-job.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params_str = " ".join(["%s=%s" % (k,v) for k,v in hadoop_params.items()]) jar_defs = {} jar_defs["mapreduce.framework.name"] = "yarn" jar_defs["yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs["yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs["mapreduce.output.fileoutputformat.compress"] = "false" jar_defs_str = " ".join(["-D %s=%s" % (k,v) for k,v in jar_defs.items()]) other_args = OrderedDict() other_args["similarityClassname"] = "SIMILARITY_EUCLIDEAN_DISTANCE" other_args["input"] = settings.Input.ratings.val other_args["usersFile"] = settings.Input.usersFile.val other_args["output"] = output_path other_args["tempDir"] = temp_path other_args_str = " ".join(["--%s %s" % (k,v) for k,v in other_args.items()]) line_num =get_the_line_of_transaction(settings.Input.ratings.val) if line_num >0: cmd_str = '%s hadoop jar %s org.apache.mahout.cf.taste.hadoop.item.RecommenderJob %s %s' % \ (hadoop_params_str, jar_file, jar_defs_str, other_args_str) print("Executing:") print(cmd_str) ret = cmd(cmd_str) if ret != 0: print("Job failed") sys.exit(ret) else: print "Collaborative Input Transaction Matrix is empty. Skip the calcuating." settings.Output.cl_result.val = output_path print("Done")
def main(): hr = HadoopRuntime() settings = hr.settings print(settings) hr.clean_working_dir() output_dir = hr.get_hdfs_working_dir("message_dir") sqoop = MySqoop(settings.Param.Sqoop2Server_Host, int(settings.Param.Sqoop2Server_Port)) # First, Create an connection conn_name = "import_m_job%s_blk%s" % (settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) conn_ret = sqoop.create_connection( conn_name=conn_name, conn_str=settings.Param.connection_string, username=settings.Param.connection_username, password=settings.Param.connection_password) # Then, Run sqoop import job fw_ps = { "output.storageType": "HDFS", "output.outputFormat": "TEXT_FILE", "output.outputDirectory": output_dir } job_ps = { "table.sql": "select UserId,Description,RefreshDate from Message where ${CONDITIONS}", "table.partitionColumn": "UserId" } job_name = "import job :: username(%s) job %s, block %s" % ( settings.GlobalParam["userName"], settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) r = sqoop.create_import_job(job_name=job_name, connection_id=conn_ret["id"], framework_params=fw_ps, job_params=job_ps) pp(r) sqoop.run_job(r['id']) sqoop.wait_job(r['id']) sqoop.delete_job(r['id']) # Finally, Delete connection we created sqoop.delete_connection_by_id(conn_ret["id"]) settings.Output.message_dir.val = output_dir print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) ds = json.load(open(settings.Input.DS)) if ds['Type'] != "AWS_S3": raise ValueError("Invalid data_source type: '%s'" % ds['Type']) # Prepare working directory hr.hdfs_clean_working_dir() output_dir = hr.get_hdfs_working_dir("sentiment_result") settings.Output.sentiment_result.val = output_dir AWS_ACCESS_KEY_ID = ds['Meta']['key'] AWS_SECRET_ACCESS_KEY = ds['Meta']['token'] # Execute "hadoop jar" jar_file = "HelloAvro-1.1-jar-with-dependencies.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params["AWS_ACCESS_KEY_ID"] = ds['Meta']['key'] hadoop_params["AWS_SECRET_ACCESS_KEY"] = ds['Meta']['token'] hadoop_params_str = " ".join( ["%s=%s" % (k, v) for k, v in hadoop_params.items()]) jar_defs = {} jar_defs["fs.s3n.awsAccessKeyId"] = '"%s"' % AWS_ACCESS_KEY_ID jar_defs["fs.s3n.awsSecretAccessKey"] = '"%s"' % AWS_SECRET_ACCESS_KEY jar_defs["fs.s3.awsAccessKeyId"] = '"%s"' % AWS_ACCESS_KEY_ID jar_defs["fs.s3.awsSecretAccessKey"] = '"%s"' % AWS_SECRET_ACCESS_KEY jar_defs["mapreduce.framework.name"] = "yarn" jar_defs[ "yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs[ "yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs_str = " ".join(["-D %s=%s" % (k, v) for k, v in jar_defs.items()]) cmd_str = '%s hadoop jar %s %s %s %s' % ( hadoop_params_str, jar_file, jar_defs_str, ds['URL'], output_dir) print("Executing:") print(cmd_str) ret = cmd(cmd_str) print("exit code = %d" % ret) sys.exit(ret)
def main(): hr = HadoopRuntime() settings = hr.settings print(settings) hr.clean_working_dir() output_dir = hr.get_hdfs_working_dir("message_dir") sqoop = MySqoop(settings.Param.Sqoop2Server_Host, int(settings.Param.Sqoop2Server_Port)) # First, Create an connection conn_name = "import_m_job%s_blk%s" % ( settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) conn_ret = sqoop.create_connection(conn_name=conn_name, conn_str=settings.Param.connection_string, username=settings.Param.connection_username, password=settings.Param.connection_password) # Then, Run sqoop import job fw_ps = { "output.storageType": "HDFS", "output.outputFormat": "TEXT_FILE", "output.outputDirectory": output_dir } job_ps = { "table.sql": "select UserId,Description,RefreshDate from Message where ${CONDITIONS}", "table.partitionColumn": "UserId" } job_name = "import job :: username(%s) job %s, block %s" % ( settings.GlobalParam["userName"], settings.GlobalParam["jobId"], settings.GlobalParam["blockId"]) r = sqoop.create_import_job(job_name=job_name, connection_id=conn_ret["id"], framework_params=fw_ps, job_params=job_ps) pp(r) sqoop.run_job(r['id']) sqoop.wait_job(r['id']) sqoop.delete_job(r['id']) # Finally, Delete connection we created sqoop.delete_connection_by_id(conn_ret["id"]) settings.Output.message_dir.val = output_dir print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) # allocate output_path, and clean it output_path = get_s3_working_dir(settings, "output_path") s3_delete(output_path, settings) # build parameters for hadoop job jar_file = "avro_tools/hadoop-streaming-2.0.0-mr1-cdh4.6.0.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params_str = " ".join( ["%s=%s" % (k, v) for k, v in hadoop_params.items()]) jar_defs = {} jar_defs["mapred.job.name"] = "avro-streaming" jar_defs["mapred.reduce.tasks"] = "0" jar_defs["mapred.output.compress"] = "false" jar_defs[ "fs.s3n.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs[ "fs.s3n.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs[ "fs.s3.awsAccessKeyId"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_ID jar_defs[ "fs.s3.awsSecretAccessKey"] = '"%s"' % settings.Param.AWS_ACCESS_KEY_SECRET jar_defs["mapreduce.framework.name"] = "yarn" jar_defs[ "yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs[ "yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs_str = " ".join(["-D %s=%s" % (k, v) for k, v in jar_defs.items()]) other_args = OrderedDict() other_args[ "files"] = "avro_tools/avro-1.7.4-cdh4.5.0.2.jar,avro_tools/avro-mapred-1.7.4-cdh4.5.0.2-hadoop2.jar" other_args[ "libjars"] = "avro_tools/avro-1.7.4-cdh4.5.0.2.jar,avro_tools/avro-mapred-1.7.4-cdh4.5.0.2-hadoop2.jar" other_args["mapper"] = "org.apache.hadoop.mapred.lib.IdentityMapper" other_args["inputformat"] = "org.apache.avro.mapred.AvroAsTextInputFormat" other_args["input"] = settings.Input.avro_path.val other_args["output"] = output_path other_args_str = " ".join( ["-%s %s" % (k, v) for k, v in other_args.items()]) cmd_str = '%s hadoop jar %s %s %s' % (hadoop_params_str, jar_file, jar_defs_str, other_args_str) print("Executing:") print(cmd_str) ret = cmd(cmd_str) if ret != 0: print("Job failed") sys.exit(ret) settings.Output.output_path.val = output_path print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) ds = json.load(open(settings.Input.DS)) if ds['Type'] != "AWS_S3": raise ValueError("Invalid data_source type: '%s'" % ds['Type']) # Prepare working directory hr.hdfs_clean_working_dir() output_dir = hr.get_hdfs_working_dir("sentiment_result") settings.Output.sentiment_result.val = output_dir AWS_ACCESS_KEY_ID = ds['Meta']['key'] AWS_SECRET_ACCESS_KEY = ds['Meta']['token'] # Execute "hadoop jar" jar_file = "HelloAvro-1.1-jar-with-dependencies.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params["AWS_ACCESS_KEY_ID"] = ds['Meta']['key'] hadoop_params["AWS_SECRET_ACCESS_KEY"] = ds['Meta']['token'] hadoop_params_str = " ".join(["%s=%s" % (k,v) for k,v in hadoop_params.items()]) jar_defs = {} jar_defs["fs.s3n.awsAccessKeyId"] = '"%s"' % AWS_ACCESS_KEY_ID jar_defs["fs.s3n.awsSecretAccessKey"] = '"%s"' % AWS_SECRET_ACCESS_KEY jar_defs["fs.s3.awsAccessKeyId"] = '"%s"' % AWS_ACCESS_KEY_ID jar_defs["fs.s3.awsSecretAccessKey"] = '"%s"' % AWS_SECRET_ACCESS_KEY jar_defs["mapreduce.framework.name"] = "yarn" jar_defs["yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs["yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs_str = " ".join(["-D %s=%s" % (k,v) for k,v in jar_defs.items()]) cmd_str = '%s hadoop jar %s %s %s %s' % (hadoop_params_str, jar_file, jar_defs_str, ds['URL'], output_dir) print("Executing:") print(cmd_str) ret = cmd(cmd_str) print("exit code = %d" % ret) sys.exit(ret)
def main(): hr = HadoopRuntime() settings = hr.settings match_result_output_dir = hr.get_hdfs_working_dir("match_result") settings.Output.match_result.val = match_result_output_dir match_analysis_output_dir = hr.get_hdfs_working_dir("match_analysis") settings.Output.match_analysis.val = match_analysis_output_dir #SPARK_HOME=/home/run/spark-1.1.0-bin-cdh4 #/home/run/spark_word_segement.jar # os.system("SPARK_HOME=/home/ansibler/work/spark/spark-1.1.0-bin-cdh4") os.system( '''SPARK_HOME=/home/run/spark-1.1.0-bin-cdh4 \ && $SPARK_HOME/bin/spark-submit --class \"com.zetdata.hero.trial.SimpleApp\" \ --master %s \ --num-executors 3 --driver-memory 1024m --executor-memory 1024m --executor-cores 1 \ --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:MaxPermSize=1024m" \ /home/run/spark_word_segement.jar \ %s %s %s %s %s ''' % (settings.Param.spark_host, settings.Input.jd_dir.val, settings.Input.rs_dir.val, settings.Output.match_result.val, settings.Output.match_analysis.val, settings.Input.white_dict.val)) print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) s3_path = settings.Input.s3_path.val content_type = settings.Param.Metadata_Type s3_set_metadata(s3_path, settings, {"Content-Type": content_type}) list_html_link = s3_make_list(s3_path, settings) s3_set_acl(s3_path, settings, {"public-read": None}) settings.Output.list_html.val = list_html_link print("Done")
def main(): hr = HadoopRuntime("spec.json") settings = hr.settings print(settings) # Prepare working directory hr.hdfs_clean_working_dir() # allocate temp_path temp_path = hr.get_hdfs_working_dir("temp") # allocate output_path output_path = hr.get_hdfs_working_dir("output_path") # build parameters for hadoop job jar_file = "./mahout-core-1.0-SNAPSHOT-job.jar" hadoop_params = {} hadoop_params["HADOOP_MAPRED_HOME"] = "/usr/lib/hadoop-mapreduce" hadoop_params_str = " ".join( ["%s=%s" % (k, v) for k, v in hadoop_params.items()]) jar_defs = {} jar_defs["mapreduce.framework.name"] = "yarn" jar_defs[ "yarn.resourcemanager.address"] = settings.Param.yarn_resourcemanager jar_defs[ "yarn.resourcemanager.scheduler.address"] = settings.Param.yarn_resourcemanager_scheduler jar_defs["fs.defaultFS"] = settings.Param.hdfs_root jar_defs["mapreduce.output.fileoutputformat.compress"] = "false" jar_defs_str = " ".join(["-D %s=%s" % (k, v) for k, v in jar_defs.items()]) other_args = OrderedDict() other_args["similarityClassname"] = "SIMILARITY_EUCLIDEAN_DISTANCE" other_args["input"] = settings.Input.ratings.val other_args["usersFile"] = settings.Input.usersFile.val other_args["output"] = output_path other_args["tempDir"] = temp_path other_args_str = " ".join( ["--%s %s" % (k, v) for k, v in other_args.items()]) line_num = get_the_line_of_transaction(settings.Input.ratings.val) if line_num > 0: cmd_str = '%s hadoop jar %s org.apache.mahout.cf.taste.hadoop.item.RecommenderJob %s %s' % \ (hadoop_params_str, jar_file, jar_defs_str, other_args_str) print("Executing:") print(cmd_str) ret = cmd(cmd_str) if ret != 0: print("Job failed") sys.exit(ret) else: print "Collaborative Input Transaction Matrix is empty. Skip the calcuating." settings.Output.cl_result.val = output_path print("Done")
def main(): hr = HadoopRuntime() settings = hr.settings settings.Output.hdfs_path.val = settings.Param.data_path print("Done")