Job types¶
[1]:
from aapi import *
JobCommand¶
Job that executes an Operating System command
Required arguments:
object_name : The name of the job
command: The command to execute
[2]:
job = JobCommand('JobCommandSample', command='echo Hello')
[3]:
job = JobCommand('JobCommandSample',
command='mycommand',
run_as='user',
host='myhost.com',
pre_command='echo Precommand',
post_command='echo Finished')
JobScript¶
Job that executes a script
Required arguments:
object_name : The name of the job
file_name : The name of the script
file_path : The path of the script
Optional arguments:
arguments: An array of strings that are passed as argument to the script
[4]:
job = JobScript('JobScriptSample', file_name='task.sh', file_path='/home/scripts')
[5]:
job = JobScript('JobScriptSame',
file_name='task.sh',
file_path='%%path',
run_as='user',
arguments=['arg1', 'arg2'],
variables=[{'path': '/home/scripts'}])
JobEmbeddedScript¶
Job that executes a script written in the job itself. Note that control characters need to be escaped
Required arguments:
object_name : The name of the job
script : The written script. (note: all control characters need to be escaped!)
file_name : The name of the script file.
[6]:
job = JobEmbeddedScript('JobEmbeddedScriptSample',
file_name='filename.sh',
script=r'#!/bin/bash\necho "Hello"\necho "Bye"'),
JobFileTransfer¶
Job that executes a list of file transfers
Required arguments:
object_name: The name of the job
file_transfers: A list of
FileTransferobject defining the transfers to be executed by the job
In addition, a Connection Profile will be required to run the job. For a JobFileTransfer, you can either define a Dual Endpoint connection profile connection_profile_dual_endpoint or two connection profiles for source and destination (connection_profile_src and connection_profile_dest)
Optional arguments:
number_of_retries: Number of connection attempts after a connection failure. Range of values: 0–99 as string (“0” to “99”) or “Default” (to inherit the default). Default: 5 attempts. Note: the value should be passed as string. To define 10, it should be passed “10”
s3_bucket_name: For file transfers between a local filesystem and an Amazon S3 or S3-compatible storage service: The name of the S3 bucket
s3_bucket_name_src: For file transfers between two Amazon S3 or S3-compatible storage services: The name of the S3 bucket at the source
s3_bucket_name_dest: For file transfers between two Amazon S3 or S3-compatible storage services: The name of the S3 bucket at the destination
[7]:
job = JobFileTransfer('JobFileTransferSample',
connection_profile_src='CP1', connection_profile_dest='CP2',
number_of_retries='7',
file_transfers=[
FileTransfer(src='/home/ctm/file1', dest='/home/cmt/file2',
transfer_type=FileTransfer.TransferType.Binary,
transfer_option=FileTransfer.TransferOption.SrcToDest),
FileTransfer(src='/home/ctm/file1', dest='/home/cmt/file2',
transfer_type=FileTransfer.TransferType.Binary,
transfer_option=FileTransfer.TransferOption.SrcToDest),
])
[8]:
# An example of a file transer from a S3 storage to a local filesystem:
job = JobFileTransfer('TransferS3ToLocal',
connection_profile_src='amazonCP', connection_profile_dest='localcp',
s3_bucket_name='bucket')
# Note that file_transfers is by default initialized as an empty list
job.file_transfers.append(FileTransfer(src='folder/file1', dest='/home/file1'))
JobFileWatcherCreate¶
Job that detects the successful completion of a file transfer activity that creates a file
[9]:
job = JobFileWatcherCreate('JobFileWatcherCreateSample',
path='C:\\path*.txt',
search_interval='45',
time_limit='22',
start_time='201705041535',
stop_time='201805041535',
minimum_size='10B',
wildcard=False,
minimal_age='1Y',
maximal_age='1D2H4MIN'
)
JobFileWatcherDelete¶
Job that detects the successful completion of a file transfer activity that deletes a file
[10]:
job = JobFileWatcherDelete('JobFileWatcherDeleteSample',
path='C:\\path*.txt',
search_interval='45',
time_limit='22',
start_time='201705041535',
stop_time='201805041535',
)
JobDatabaseEmbeddedQuery¶
Job that runs an embedded query
Required arguments:
object_name : The name of the job
query : The embedded SQL query that you want to run. The SQL query can contain auto edit variables. During job run, these variables are replaced by the values that you specify in Variables parameter (next row). For long queries, you can specify delimiters using \n (new line) and \t (tab).
Optional arguments:
autocommit: Commits statements to the database that completes successfully
output_execution_log: Shows the execution log in the job output
output_sql_output: Shows the SQL sysout in the job output
sql_output_format: Defines the output format as either Text, XML, CSV, or HTML
[11]:
job = JobDatabaseEmbeddedQuery(
'JobEmbeddedQuerySample',
query='SELECT %%firstParamName AS VAR1 \\n FROM DUMMY \\n ORDER BY \\t VAR1 DESC',
connection_profile='CPDB',
variables=[{'firstParamName': 'value'}],
autocommit='N',
output_execution_log='y',
output_sql_output='y',
sql_output_format='XML'
)
JobDatabaseSQLScript¶
Job that runs a SQL script from a file system
Required arguments:
object_name : The name of the job
sql_script : The path of the script
Optional arguments:
parameters: An array of dictionary with name and value. Every name that appears in the SQL script will be replaced by its value pair.
[12]:
job = JobDatabaseSQLScript('JobDatabaseSQLScriptSample',
connection_profile='CPDB',
sql_script='/home/script.sql',
parameters=[{'param1': 'val1'}, {'param2': 'val2'}])
JobDatabaseStoredProcedure¶
Job that runs a program that is stored on the database
Required Arguments:
object_name : The name of the job
stored_procedure : The name of the job
schema : The database schema where the stored procedure resides
Optional arguments:
parameters: A comma-separated list of values and variables for all parameters in the procedure, in the order of their appearence in the procedure
return_value: A variable for the Return parameter (if the procedure contains such a parameter)
package: (Oracle only) Name of a package in the database where the stored procedure resides. The default is “*”, that is, any package in the database.
[13]:
job = JobDatabaseStoredProcedure('JobDatabaseStoredProcedureSample',
connection_profile='CPDB',
stored_procedure='procedure',
schema='public',
return_value='RV')
JobDatabaseMSSQLAgentJob¶
Job that manages a SQL server defined job
Note : Only available with Automation API >= 9.0.19.210
Required arguments:
object_name : The name of the job
job_name : The name of the job defined in the SQL server
Optional arguments:
category: The category of the job, as defined in the SQL server
[14]:
job = JobDatabaseMSSQLAgentJob('JobDatabaseMSSQLAgentJobSample',
job_name='get_version',
connection_profile='CPDB',
category='Data Collector')
JobDatabaseMSSQL_SSIS¶
Job that executes of SQL Server Integration Services (SSIS) packages:
Note : Only available with Automation API >= 9.0.19.220
Required arguments:
object_name : The name of the job
package_source : The source of the SSIS package
package_name : The name of the SSIS package
Optional arguments:
catalog_env: The category of the job, as defined in the SQL server
config_files: Names of configuration files that contain specific data that you want to apply to the SSIS package
properties: Pairs of names and values for properties defined in the SSIS package. Each property name is replaced by its defined value during SSIS package execution.
[15]:
job = JobDatabaseMSSQL_SSIS('JobDatabaseMSSQL_SSISSample',
connection_profile='CPDB',
package_source=JobDatabaseMSSQL_SSIS.PackageSource.SSIS_Catalog,
package_name='\\Data Collector\\SqlTraceCollect',
catalog_env='ENV_NAME',
config_files=[
'C:\\Users\\dbauser\\Desktop\\test.dtsConfig',
'C:\\Users\\dbauser\\Desktop\\test2.dtsConfig'
],
properties=[{
'PropertyName': 'PropertyValue'
},
{
'PropertyName2': 'PropertyValue2'
}]
)
JobHadoopSparkPython¶
Job that runs a Spark Python program
Required arguments:
object_name : The name of the job
spark_script : The name of the Spark script
package_name : The name of the SSIS package
Optional arguments:
spark_options: The options to be passed to the script
[16]:
job = JobHadoopSparkPython('JobHadoopSparkPythonSample',
connection_profile='CP',
spark_script='/home/process.py')
[17]:
job = JobHadoopSparkPython('JobHadoopSparkPythonSample',
connection_profile='CP',
spark_script='/home/process.py',
arguments=[
'1000',
'120'
],
spark_options=[{'--master': 'yarn'},
{'--num': '-executors 50'}]
)
JobHadoopSparkScalaJava¶
[18]:
job = JobHadoopSparkScalaJava('JobHadoopSparkScalaJavaSample',
connection_profile='CP',
program_jar='/home/user/ScalaProgram.jar',
main_class='com.mycomp.sparkScalaProgramName.mainClassName'
)
JobHadoopPig¶
[19]:
job = JobHadoopPig('JobHadoopPigSample',
connection_profile='CP',
pig_script='/home/user/script.pg'
)
JobHadoopSqoop¶
[20]:
job = JobHadoopSqoop('JobHadoopSqoopSample',
connection_profile='CP',
sqoop_command='import --table foo --target-dir /dest_dir',
sqoop_options=[
{"--warehouse-dir": "/shared"},
{"--default-character-set": "latin1"}
]
)
JobHadoopHive¶
[21]:
job = JobHadoopHive('JobHadoopHiveSample',
connection_profile='CP',
hive_script='/home/user1/hive.script',
parameters=[{'amount': '1000'}, {'topic': 'food'}],
hive_options={'hive.root.logger': 'INFO, console'})
JobHadoopDistCp¶
[22]:
job = JobHadoopDistCp('JobHadoopDistCpSample',
connection_profile='CP',
target_path='hdfs://nns2:8020/foo/bar',
source_paths=['hdfs://nns1:8020/foo/a'],
distcp_options=[{'-m': '3'}, {'-filelimit': '100'}]
)
JobHadoopHDFSCommands¶
[23]:
job = JobHadoopHDFSCommands('JobHadoopHDFSCommandsSample',
connection_profile='CP',
commands=[{"get": "hdfs://nn.example.com/user/hadoop/file localfile"},
{"rm": "hdfs://nn.example.com/file /user/hadoop/emptydir"}]
)
JobHadoopHDFSFileWatcher¶
[24]:
job = JobHadoopHDFSFileWatcher('JobHadoopHDFSFileWatcherSample',
connection_profile='CP',
hdfs_file_path='/input/filename',
min_detected_size='1',
max_wait_time='2'
)
JobHadoopOozie¶
[25]:
job = JobHadoopOozie('JobHadoopOozieSample',
connection_profile='CP',
job_properties_file='/home/user/job.properties',
oozie_options=[{"inputDir": "/usr/tucu/inputdir"},
{"outputDir": "/usr/tucu/outputdir"}]
)
JobHadoopMapReduce¶
[26]:
job = JobHadoopMapReduce('JobHadoopMapReduceSample',
connection_profile='CP',
program_jar='/home/user1/hadoop-jobs/hadoop-mapreduce-examples.jar',
main_class='com.mycomp.mainClassName',
arguments=[
'arg1',
'arg2'
])
JobHadoopMapredStreaming¶
[27]:
job = JobHadoopMapredStreaming('JobHadoopMapredStreamingSample',
connection_profile='CP',
input_path='/user/input/',
output_path='/tmp/output',
mapper_command='mapper.py',
reducer_command='reducer.py',
general_options=[
{"-D": "fs.permissions.umask-mode=000"},
{"-files": "/home/user/hadoop-streaming/mapper.py,/home/user/hadoop-streaming/reducer.py"}
]
)
JobHadoopTajoInputFile¶
[28]:
job = JobHadoopTajoInputFile('JobHadoopTajoInputFileSample',
connection_profile='CP',
full_file_path='/home/user/tajo_command.sh',
tajo_options=[
{"amount": "1000"},
{"volume": "120"}
]
)
JobHadoopTajoQuery¶
[29]:
job = JobHadoopTajoQuery('JobHadoopTajoQuerySample',
connection_profile='CP',
open_query='SELECT %%firstParamName AS VAR1 \\n FROM DUMMY \\n ORDER BY \\t VAR1 DESC')
JobPeopleSoft¶
Job that manages PeopleSoft jobs and processes
Required arguments:
object_name : The name of the job
Optional arguments:
user : A PeopleSoft user ID that exists in the PeopleSoft Environment
control_id : Run Control ID for access to run controls at runtime
process_type: A PeopleSoft process type that the user is authorized to perform
process_name: The name of the PeopleSoft process to run
append_to_output: Whether to include PeopleSoft job output in the Control-M job output, either true or false
bind_variables: Values of up to 20 USERDEF variables for sharing data between Control-M and the PeopleSoft job or process
[30]:
job = JobPeopleSoft('JobPeopleSoftSample',
connection_profile='CP_PS',
user='PS_User',
control_id='controlid',
server_name='server',
process_type='ptype',
process_name='pname',
append_to_output=False,
bind_variables=['val1', 'val2'])
JobInformatica¶
[AutomationAPI Documentation]((https://documents.bmc.com/supportu/API/Monthly/en-US/Documentation/API_CodeRef_JobTypes_DataIntegration.htm#Job:Informatica)
Job that manages Informatica workflows
Required arguments:
repository_folder : The Repository folder that contains the workflow that you want to run
workflow : The workflow that you want to run in Control-M for Informatica
Optional arguments:
instance_name: The specific instance of the workflow that you want to run
os_profile: The operating system profile in Informatica
workflow_execution_mode: The mode for executing the workflow
workflow_restart_mode: The operation to execute when the workflow is in a suspended satus
restart_from_task: The task from which to start running the workflow. This parameter is required only if you set WorkflowExecutionMode to StartFromTask.
run_single_task: The workflow task that you want to run. This parameter is required only if you set WorkflowExecutionMode to RunSingleTask.
workflow_parameters_file: The path and name of the workflow parameters file. This enables you to use the same workflow for different actions.
[31]:
job = JobInformatica('JobInformaticaSample',
connection_profile='CP_INF',
repository_folder='POC',
workflow='WF_TEST',
instance_name='MyInstance',
os_profile='OSPROFILE',
workflow_execution_mode=JobInformatica.WorkflowExecutionMode.RunSingleTask,
workflow_restart_mode=JobInformatica.WorkflowRestartMode.ForceRestartFromSpecificTask,
restart_from_task='s_MapTest_Success',
workflow_parameters_file='/opt/wf1.prop')
JobAWSLambda¶
Job that executes an AWS Lambda service on an AWS server
Required arguments:
object_name : The name of the job
function_name : The Lambda function to execute
Optional arguments:
instance_name: The specific instance of the workflow that you want to run
version: The Lambda function version. The default is $LATEST (the latest version).
payload: The Lambda function payload, in JSON string format
append_log: Whether to add the log to the job’s output, either true (the default) or false
[32]:
job = JobAWSLambda('JobAWSLambdaSample',
connection_profile='CPAWS',
function_name='fname',
version='1',
payload='{"myVar":"value1", "othervar":"value2"}',
append_log=True
)
JobAWSStepFunction¶
Job that executes an AWS Step Function service on an AWS server
Required arguments:
object_name : The name of the job
state_machine : The State Machine to use
execution_name: A name for the execution
Optional arguments:
input: The Step Function input in JSON string format
append_log: Whether to add the log to the job’s output, either true (the default) or false
[33]:
job = JobAWSStepFunction('JobAWSStepFunctionSample',
connection_profile='CPAWS',
state_machine='smach1',
execution_name='exec1',
input='{"var":"value", "othervar":"val2"}',
append_log=False)
JobAWSBatch¶
Job that executes an AWS Batch service on an AWS server
Required arguments:
object_name : The name of the job
job_name : The name of the batch job
job_definition: The job definition to use
job_definition_revision: The job definition revision
job_queue: The queue to which the job is submitted
Optional arguments:
aws_job_type: The type of job, either Array or Single
array_size: (For a job of type Array) The size of the array (that is, the number of items in the array)
depends_on: Parameters that determine a job dependency
command: A command to send to the container that overrides the default command from the Docker image or the job definition
memory: The number of megabytes of memory reserved for the job
v_cpus: The number of vCPUs to reserve for the container
job_attempts: The number of retry attempts passed as string
execution_timeout: The timeout duration in seconds
append_log: Whether to add the log to the job’s output, either true (the default) or false
[34]:
job = JobAWSBatch('JobAWSBatchSample',
connection_profile='CPAWS',
job_name='batchjob',
job_definition='jobdef',
job_definition_revision='3',
job_queue='queue1',
aws_job_type=JobAWSBatch.AwsJobType.Array,
array_size='100',
depends_on=JobAWSBatch.DependsOn(
dependency_type=JobAWSBatch.DependsOn.DependencyType.Standard,
job_depends_on='job5'),
command='ls',
memory='10',
v_cpus='2',
job_attempts='5',
execution_timeout='60',
append_log=False)
JobAzureFunction¶
Job that executes an Azure function service.
Required arguments:
object_name : The name of the job
function : The name of the Azure function to execute
function_app: The name of the Azure function app
Optional arguments:
parameters: Function parameters defined as pairs of name and value
append_log: Whether to add the log to the job’s output, either true (the default) or false
[35]:
job = JobAzureFunction('JobAzureFunctionSample',
connection_profile='CPAZURE',
function='AzureFunction',
function_app='funcapp',
append_log=False,
parameters=[
{"firstParamName": "firstParamValue"},
{"secondParamName": "secondParamValue"}
])
JobAzureLogicApps¶
Job that executes an Azure Logic App service
Required arguments:
object_name : The name of the job
logic_app_name : The name of the Azure Logic App
Optional arguments:
request_body: The JSON for the expected payload
append_log: Whether to add the log to the job’s output, either true (the default) or false
[36]:
job = JobAzureLogicApps('JobAzureLogicAppsSample',
connection_profile='CPAZURE',
workflow='workflow_app',
parameters='{"name":"value"}'
)
JobAzureBatchAccount¶
Job that executes an Azure Batch Accounts service
Required arguments:
object_name : The name of the job
job_id : The ID of the batch job
command_line : A command line that the batch job runs
Optional arguments:
wallclock: Maximum limit for the job’s run time
max_tries: If you do not include this parameter, the default is none (no retries).
retention: If you do not include this parameter, the default is none (no retries).
append_log: File retention period for the batch job. If you do not include this parameter, the default is an unlimited retention period.
[37]:
job = JobAzureBatchAccount('JobAzureBatchAccountSample',
connection_profile='CPAZURE',
job_id='azurejob',
command_line='echo "Hello"',
append_log=True,
wallclock=JobAzureBatchAccount.Wallclock(
time='70', unit=JobAzureBatchAccount.Wallclock.Unit.Minutes),
max_tries=JobAzureBatchAccount.MaxTries(
count='6', option=JobAzureBatchAccount.MaxTries.Option.Custom),
retention=JobAzureBatchAccount.Retention(
time='1', unit=JobAzureBatchAccount.Retention.Unit.Hours)
)
JobWebServices¶
Note : Only available with Automation API >= 9.0.20.220
Job that executes standard web services, servlets, or RESTful web services. To manage Web Services jobs, you must have the Control-M for Web Services, Java, and Messaging (Control-M for WJM) plug-in installed in your Control-M environment
[38]:
job = JobWebServices('JobWebServicesSample',
connection_profile='CP_WS',
location='http://www.dneonline.com/calculator.asmx?WSDL',
soap_header_file='c:\\myheader.txt',
service='Calculator(Port:CalculatorSoap)',
operation='Add',
request_type=JobWebServices.RequestType.Parameter,
override_url_endpoint='http://myoverridehost.com',
override_content_type='*/*',
http_connection_timeout='2345',
preemptive_http_authentication='abc@bmc.com',
include_title_in_output=True,
exclude_job_output=False,
output_parameters=[{
"Element": "AddResponse.AddResult",
"HttpCode": "*",
"Destination": "testResultAdd",
"Type": "string"
}],
input_parameters=[
{
"Name": "intA",
"Value": "97",
"Type": "string"
},
{
"Name": "intB",
"Value": "345",
"Type": "string"
},
{
"Name": "accept-encoding",
"Value": "*/*",
"Type": "header"
}
],
soap_request=[
'''<soapenv:Envelope xmlns:soapenv=http://schemas.xmlsoap.org/soap/envelope/ xmlns:tem=http://tempuri.org/>
<soapenv:Header/>
<soapenv:Body>
<tem:Add>
<tem:intA>98978</tem:intA>
<tem:intB>75675</tem:intB>
</tem:Add>
</soapenv:Body>
</soapenv:Envelope>'''
],
input_file='/home/usr/soap.xml')
JobSLAManagement¶
[39]:
job = JobSLAManagement('JobSLAManagementSample',
service_name='SLA-service',
service_priority='1',
job_runs_deviations_tolerance='1',
complete_in=JobSLAManagement.CompleteIn(time='00:01'),
complete_by=JobSLAManagement.CompleteBy(
time='21:40', days='3'),
average_run_time_tolerance=JobSLAManagement.AverageRunTimeTolerance(
average_run_time='10',
units=JobSLAManagement.AverageRunTimeTolerance.Units.Minutes
))
JobZOS¶
[40]:
job = JobZOSMember('JobZOSMemberSample',
member_library='IOAQ.AAPI#AU.JCL',
archiving=JobZOS.Archiving(archive_sys_data=True),
must_end=JobZOS.MustEnd(hours='1', minutes='15', days=1),
run_on_all_agents_in_group=True
)
[41]:
job = JobZOSInStreamJCL('JobZOInStreamJCLSample',
member_library='IOAQ.AAPI#AU.JCL',
jcl="//AUTEST0 JOB ,ASM,CLASS=A,REGION=0M\\n// JCLLIB ORDER=IOAQ.AAPI#AU.PROCLIB\\n// INCLUDE MEMBER=IOASET\\n//S1 EXEC IOATEST,PARM='TERM=C0000'",
archiving=JobZOS.Archiving(archive_sys_data=True),
run_on_all_agents_in_group=True,
created_by='workbench',
run_as='workbench'
)