Parsing Workflow Execution Logs

The most common way for obtaining instances from actual workflow executions is to parse execution logs. As part of the WfCommons project, we are constantly developing parsers for commonly used workflow management systems.

Each parser class is derived from the abstract LogsParser class. Thus, each parser provides a build_workflow() method.

Makeflow

Makeflow is a workflow system for executing large complex workflows on clusters, clouds, and grids. The Makeflow language is similar to traditional Make, so if you can write a Makefile, then you can write a Makeflow. A workflow can be just a few commands chained together, or it can be a complex application consisting of thousands of tasks. It can have an arbitrary DAG structure and is not limited to specific patterns. Makeflow is used on a daily basis to execute complex scientific applications in fields such as data mining, high energy physics, image processing, and bioinformatics. It has run on campus clusters, the Open Science Grid, NSF XSEDE machines, NCSA Blue Waters, and Amazon Web Services. Makeflow logs provide time-stamped event instances from these executions. The following example shows the analysis of Makeflow execution logs, stored in a local folder (execution dir), for a workflow execution using the MakeflowLogsParser class:

from wfcommons.trace import MakeflowLogsParser

# creating the parser for the Makeflow workflow
parser = MakeflowLogsParser(execution_dir='/path/to/makeflow/execution/dir/blast/chameleon-small-001/'
                            resource_monitor_logs_dir='/path/to/makeflow/resource/monitor/logs/dir')

# generating the workflow instance object
workflow = parser.build_workflow('workflow-test')

# writing the workflow instance to a JSON file
workflow.write_json('workflow.json')

Note

The MakeflowLogsParser class requires that Makeflow workflows to run with the Resource Monitor tool (e.g., execute the workflow using the --monitor=logs).

Pegasus WMS

Pegasus is being used in production to execute workflows for dozens of high-profile applications in a wide range of scientific domains. Pegasus provides the necessary abstractions for scientists to create workflows and allows for transparent execution of these workflows on a range of compute platforms including clusters, clouds, and national cyberinfrastructures. Workflow execution with Pegasus includes data management, monitoring, and failure handling, and is managed by HTCondor DAGMan. Individual workflow tasks are managed by a workload management framework, HTCondor, which supervises task executions on local and remote resources. Pegasus logs provide time-stamped event instances from these executions. The following example shows the analysis of Pegasus execution logs, stored in a local folder (submit dir), for a workflow execution using the PegasusLogsParser class:

from wfcommons.trace import PegasusLogsParser

# creating the parser for the Pegasus workflow
parser = PegasusLogsParser(submit_dir='/path/to/pegasus/submit/dir/seismology/chameleon-100p-001/')

# generating the workflow instance object
workflow = parser.build_workflow('workflow-test')

# writing the workflow instance to a JSON file
workflow.write_json('workflow.json')

Warning

By default, the PegasusLogsParser class assumes that the submit dir is from a Pegasus execution with version 5.0 or later. To enable parsing of Pegasus execution logs from version 4.9 or earlier, the option legacy=True should be used.