WfInstances: Workflow Instances¶

Workflow execution instances are widely used to profile and characterize workflow executions, and to build distributions of workflow execution behaviors, which are used to evaluate methods and techniques in simulation or in real conditions.

The WfCommons project targets the analysis of actual workflow execution instances (i.e., the workflow execution profile data and characterizations) in order to build Workflow Recipes of workflow applications. These recipes contain the necessary information for generating synthetic, yet realistic, workflow instances that resemble the structure and distribution of the original workflow executions.

With the tools on this page you can:

Load any workflow instance in WfFormat and inspect its task graph programmatically.
Parse execution logs from production workflow systems into WfFormat instances.
Analyze collections of instances to obtain statistical characterizations of task runtimes and data sizes.

A list of workflow execution instances that are compatible with WfFormat is kept constantly updated in our project GitHub — you can start from those instead of running workflows yourself.

Loading Workflow Instances¶

A workflow execution instance represents an actual execution of a scientific workflow on a distributed platform (e.g., clouds, grids, HPC). In the WfCommons project, an instance is represented in a JSON file following the schema described in WfFormat. This Python package provides an instance loader tool for importing workflow execution instances for analysis. The code snippet below shows how an instance can be loaded using the Instance class:

import pathlib
from wfcommons import Instance

input_instance = pathlib.Path('/path/to/instance/file.json')
instance = Instance(input_instance=input_instance)

The Instance class provides a number of methods for interacting with the workflow instance, including:

draw(): produces an image or a pdf file representing the instance.
leaves(): gets the leaves of the workflow (i.e., the tasks without any successors).
roots(): gets the roots of the workflow (i.e., the tasks without any predecessors).
write_dot(): writes a dot file of the instance.

Note

Although the analysis methods are inherently used by WfCommons (specifically WfChef) for WfChef: Workflow Recipes, they can also be used in a standalone manner.

Parsing Workflow Execution Logs¶

The most common way of obtaining workflow 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. The parsers provided in this Python package automatically scan execution logs to produce instances using WfFormat.

Each parser class is derived from the abstract LogsParser class, and provides a build_workflow() method that returns a Workflow object, which can then be written to a WfFormat JSON file with write_json().

Supported log parsers¶

System	Parser class	Main input
Makeflow	`MakeflowLogsParser`	Execution directory + Resource Monitor logs
Nextflow	`NextflowLogsParser`	Execution directory with a trace file
Pegasus	`PegasusLogsParser`	Submit directory
RO-Crate	`ROCrateLogsParser`	RO-Crate directory
Snakemake	`SnakemakeLogsParser`	Execution directory + snkmt SQLite database
StreamFlow	`StreamflowLogsParser`	RO-Crate directory
TaskVine	`TaskVineLogsParser`	`vine-logs` directory

Below we give basic examples for using the log parsers. Review the API documentation of each log parser for additional parameters that configure its behavior.

Makeflow¶

Makeflow is a workflow system for executing large complex workflows on clusters, clouds, and grids. The Makeflow language is similar to traditional “Make”, and a workflow can be just a few commands chained together or a complex application consisting of thousands of tasks. The following example shows the analysis of Makeflow execution logs, stored in a local folder (execution_dir), using the MakeflowLogsParser class:

import pathlib
from wfcommons.wfinstances import MakeflowLogsParser

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

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

# writing the workflow instance to a JSON file
workflow.write_json(pathlib.Path('./makeflow-workflow.json'))

Note

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

Nextflow¶

Nextflow is a reactive workflow framework and a programming DSL that eases the writing of data-intensive computational pipelines. The following example shows the analysis of Nextflow execution logs, stored in a local folder (execution_dir), using the NextflowLogsParser class:

import pathlib
from wfcommons.wfinstances import NextflowLogsParser

# creating the parser for the Nextflow workflow
execution_dir = pathlib.Path('/path/to/nextflow/execution/dir/')
parser = NextflowLogsParser(execution_dir=execution_dir,
                            nextflow_version='24.10.0')

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

# writing the workflow instance to a JSON file
workflow.write_json(pathlib.Path('./nextflow-workflow.json'))

Note

The NextflowLogsParser class expects the execution directory to contain a Nextflow trace file (by default, any file matching *trace*.txt, as produced by running Nextflow with the -with-trace option or with tracing enabled in the configuration).

Pegasus¶

Pegasus is used in production to execute workflows for dozens of high-profile applications in a wide range of scientific domains. It provides the necessary abstractions for scientists to create workflows and allows for transparent execution of these workflows on a range of compute platforms, with execution managed by HTCondor DAGMan. The following example shows the analysis of Pegasus execution logs, stored in a local submit directory, using the PegasusLogsParser class:

import pathlib
from wfcommons.wfinstances import PegasusLogsParser

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

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

# writing the workflow instance to a JSON file
workflow.write_json(pathlib.Path('./pegasus-workflow.json'))

RO-Crate¶

RO-Crate is a format for packaging research data so as to promote open and reproducible science. The RO-Crate logs parser processes RO-Crate artifacts created by executing workflows (e.g., with the StreamFlow workflow management system). It takes as input the path to the RO-Crate directory, which contains the ro-crate-metadata.json file, and the name of the workflow system that produced the crate:

import pathlib
from wfcommons.wfinstances import ROCrateLogsParser

crate_dir = pathlib.Path('/path/to/ro-crate/dir/')
parser = ROCrateLogsParser(crate_dir=crate_dir, wms_name='streamflow')
workflow = parser.build_workflow('ro-crate-workflow-test')
workflow.write_json(pathlib.Path('./ro-crate-workflow.json'))

Snakemake¶

Snakemake is a popular and easy-to-use workflow system. The Snakemake logs parser processes execution logs generated by the Snakemake snkmt plugin. It takes as input the path of the directory where all workflow data files reside (input and output of workflow tasks), and the path of the SQLite database created by the snkmt plugin:

import pathlib
from wfcommons.wfinstances import SnakemakeLogsParser

execution_dir = pathlib.Path('/path/to/snakemake/execution/dir/')
snkmt_db = pathlib.Path('/path/to/snkmt/sqlite/database/file')
parser = SnakemakeLogsParser(execution_dir=execution_dir, snkmt_db=snkmt_db)
workflow = parser.build_workflow('snakemake-workflow-test')
workflow.write_json(pathlib.Path('./snakemake-workflow.json'))

StreamFlow¶

StreamFlow is a container-native workflow management system. The StreamFlow logs parser processes the RO-Crate archive produced by a StreamFlow execution:

import pathlib
from wfcommons.wfinstances import StreamflowLogsParser

crate_dir = pathlib.Path('/path/to/streamflow/ro-crate/dir/')
parser = StreamflowLogsParser(crate_dir=crate_dir,
                              streamflow_version='0.2.0')
workflow = parser.build_workflow('streamflow-workflow-test')
workflow.write_json(pathlib.Path('./streamflow-workflow.json'))

TaskVine¶

TaskVine is a task scheduler for data-intensive dynamic workflows. The TaskVine logs parser translates the logs found in a TaskVine vine-logs directory into workflow instances compatible with WfFormat:

import pathlib
from wfcommons.wfinstances import TaskVineLogsParser

vine_logs_dir = pathlib.Path('/path/to/taskvine/vine-logs/dir/')
parser = TaskVineLogsParser(vine_logs_dir=vine_logs_dir)
workflow = parser.build_workflow('taskvine-workflow-test')
workflow.write_json(pathlib.Path('./taskvine-workflow.json'))

The Instance Analyzer¶

The InstanceAnalyzer class provides a number of tools for analyzing collections of workflow execution instances. Its goal is to analyze one or multiple workflow execution instances and build summaries of the analysis per workflow task type prefix. These summaries are what allows WfChef recipes — and thus WfGen’s synthetic workflows — to be realistic rather than merely random.

Warning

Although any workflow execution instance represented as an Instance object (i.e., compatible with WfFormat) can be appended to the InstanceAnalyzer, we strongly recommend that only instances of a single workflow application type be appended to an analyzer object. You may, though, create several analyzer objects per workflow application.

The append_instance() method allows you to include instances for analysis. The build_summary() method processes all appended instances. It applies probability distribution fitting to a series of data to find the best probability distribution representing the analyzed data (i.e., the one that minimizes the mean square error). The method returns a summary of the analysis of instances in the form of a Python dictionary object, in which keys are task prefixes (provided when invoking the method) and values describe the best probability distribution fit for tasks’ runtime and input and output data file sizes. The code excerpt below shows an example of an analysis summary showing the best-fit probability distribution for the runtime of the individuals tasks (1000Genome workflow):

"individuals": {
    "runtime": {
        "min": 48.846,
        "max": 192.232,
        "distribution": {
            "name": "skewnorm",
            "params": [
                11115267.652937062,
                -2.9628504044929433e-05,
                56.03957070238482
            ]
        }
    },
    ...
}

Workflow analysis summaries are used by WfChef to develop Workflow Recipes, which in turn are used to generate realistic synthetic workflow instances.

Probability distribution fits can also be plotted by using the generate_fit_plots() or generate_all_fit_plots() methods — plots will be saved as png files.

Example: analyzing a set of Seismology instances¶

The following example shows the analysis of a set of instances, stored in a local folder, of a Seismology workflow. In this example, we seek the best probability distribution fitting for task prefixes of the Seismology workflow (sG1IterDecon and wrapper_siftSTFByMisfit), and generate all fit plots (runtime, and input and output files) into the fits folder using seismology as a prefix for each generated plot:

import pathlib
from wfcommons import Instance, InstanceAnalyzer

# obtaining list of instance files in the folder
INSTANCES_PATH = pathlib.Path('/path/to/some/instance/folder/')
instance_files = [f for f in INSTANCES_PATH.glob('*') if INSTANCES_PATH.joinpath(f).is_file()]

# creating the instance analyzer object
analyzer = InstanceAnalyzer()

# appending instance files to the instance analyzer
for instance_file in instance_files:
    instance = Instance(input_instance=INSTANCES_PATH.joinpath(instance_file))
    analyzer.append_instance(instance)

# list of workflow task name prefixes to be analyzed in each instance
workflow_tasks = ['sG1IterDecon', 'wrapper_siftSTFByMisfit']

# building the instance summary
instances_summary = analyzer.build_summary(workflow_tasks, include_raw_data=True)

# generating all fit plots (runtime, and input and output files)
analyzer.generate_all_fit_plots(outfile_prefix='fits/seismology')