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import pandas as pd
from flask import current_app as app
from flask import render_template
import parsl.monitoring.queries.pandas as queries
from parsl.monitoring.visualization.models import Status, Task, Workflow, db
from parsl.monitoring.visualization.plots.default.task_plots import (
time_series_memory_per_task_plot,
)
from parsl.monitoring.visualization.plots.default.workflow_plots import (
task_gantt_plot,
task_per_app_plot,
workflow_dag_plot,
)
from parsl.monitoring.visualization.plots.default.workflow_resource_plots import (
resource_distribution_plot,
resource_efficiency,
worker_efficiency,
)
dummy = True
import datetime
def format_time(value):
if value is None:
return value
elif isinstance(value, float):
return str(datetime.timedelta(seconds=round(value)))
elif isinstance(value, datetime.datetime):
return value.replace(microsecond=0)
elif isinstance(value, datetime.timedelta):
rounded_timedelta = datetime.timedelta(days=value.days, seconds=value.seconds)
return rounded_timedelta
else:
return "Incorrect time format found (neither float nor datetime.datetime object)"
def format_duration(value):
(start, end) = value
if start and end:
return format_time(end - start)
else:
return "-"
app.jinja_env.filters['timeformat'] = format_time
app.jinja_env.filters['durationformat'] = format_duration
@app.route('/')
def index():
workflows = Workflow.query.all()
for workflow in workflows:
workflow.status = 'Running'
if workflow.time_completed is not None:
workflow.status = 'Completed'
return render_template('workflows_summary.html', workflows=workflows)
@app.route('/workflow/<workflow_id>/')
def workflow(workflow_id):
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
if workflow_details is None:
return render_template('error.html', message="Workflow %s could not be found" % workflow_id)
df_status = queries.status_for_workflow(workflow_id, db.engine)
df_task = queries.completion_times_for_workflow(workflow_id, db.engine)
df_task_tries = queries.tries_for_workflow(workflow_id, db.engine)
task_summary = queries.app_counts_for_workflow(workflow_id, db.engine)
return render_template('workflow.html',
workflow_details=workflow_details,
task_summary=task_summary,
task_gantt=task_gantt_plot(df_task, df_status, time_completed=workflow_details.time_completed),
task_per_app=task_per_app_plot(df_task_tries, df_status, time_completed=workflow_details.time_completed))
@app.route('/workflow/<workflow_id>/app/<app_name>')
def parsl_app(workflow_id, app_name):
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
if workflow_details is None:
return render_template('error.html', message="Workflow %s could not be found" % workflow_id)
task_summary = Task.query.filter_by(
run_id=workflow_id, task_func_name=app_name)
return render_template('app.html',
app_name=app_name,
workflow_details=workflow_details,
task_summary=task_summary)
@app.route('/workflow/<workflow_id>/task/')
def parsl_apps(workflow_id):
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
if workflow_details is None:
return render_template('error.html', message="Workflow %s could not be found" % workflow_id)
task_summary = Task.query.filter_by(run_id=workflow_id)
return render_template('app.html',
app_name="All Apps",
workflow_details=workflow_details,
task_summary=task_summary)
@app.route('/workflow/<workflow_id>/task/<task_id>')
def task(workflow_id, task_id):
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
if workflow_details is None:
return render_template('error.html', message="Workflow %s could not be found" % workflow_id)
task_details = Task.query.filter_by(
run_id=workflow_id, task_id=task_id).first()
task_status = Status.query.filter_by(
run_id=workflow_id, task_id=task_id).order_by(Status.timestamp)
df_resources = queries.resources_for_task(workflow_id, task_id, db.engine)
return render_template('task.html',
workflow_details=workflow_details,
task_details=task_details,
task_status=task_status,
time_series_memory_resident=time_series_memory_per_task_plot(
df_resources, 'psutil_process_memory_resident', 'Memory Usage'),
)
@app.route('/workflow/<workflow_id>/dag_<path>')
def workflow_dag_details(workflow_id, path):
assert path == "group_by_apps" or path == "group_by_states"
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
query = """SELECT task.task_id, task.task_func_name, task.task_depends, status.task_status_name
FROM task LEFT JOIN status
ON task.task_id = status.task_id
AND task.run_id = status.run_id
AND status.timestamp = (SELECT MAX(status.timestamp)
FROM status
WHERE status.task_id = task.task_id and status.run_id = task.run_id
)
WHERE task.run_id='%s'""" % (workflow_id)
df_tasks = pd.read_sql_query(query, db.engine)
group_by_apps = (path == "group_by_apps")
return render_template('dag.html',
workflow_details=workflow_details,
group_by_apps=group_by_apps,
workflow_dag_plot=workflow_dag_plot(df_tasks, group_by_apps=group_by_apps))
@app.route('/workflow/<workflow_id>/resource_usage')
def workflow_resources(workflow_id):
workflow_details = Workflow.query.filter_by(run_id=workflow_id).first()
if workflow_details is None:
return render_template('error.html', message="Workflow %s could not be found" % workflow_id)
df_resources = queries.resources_for_workflow(workflow_id, db.engine)
if df_resources.empty:
return render_template('error.html',
message="Workflow %s does not have any resource usage records." % workflow_id)
df_task = queries.tasks_for_workflow(workflow_id, db.engine)
df_task_tries = pd.read_sql_query("""SELECT task.task_id, task_func_name,
task_try_time_launched, task_try_time_running, task_try_time_returned from task, try
WHERE task.task_id = try.task_id AND task.run_id='%s' and try.run_id='%s'"""
% (workflow_id, workflow_id), db.engine)
df_node = queries.nodes_for_workflow(workflow_id, db.engine)
return render_template('resource_usage.html', workflow_details=workflow_details,
user_time_distribution_max_plot=resource_distribution_plot(
df_resources, df_task, type='psutil_process_time_user', label='CPU Time Distribution', option='max'),
memory_usage_distribution_avg_plot=resource_distribution_plot(
df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='avg'),
memory_usage_distribution_max_plot=resource_distribution_plot(
df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='max'),
cpu_efficiency=resource_efficiency(df_resources, df_node, label='CPU'),
memory_efficiency=resource_efficiency(df_resources, df_node, label='mem'),
worker_efficiency=worker_efficiency(df_task_tries, df_node),
)
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