"""Generation of Continuous Performance Trending and Analysis.
"""
-import multiprocessing
-import os
import logging
import csv
import prettytable
from datetime import datetime
from copy import deepcopy
-from utils import archive_input_data, execute_command, \
- classify_anomalies, Worker
+from utils import archive_input_data, execute_command, classify_anomalies
# Command to build the html format of the report
css_file:
css_file.write(THEME_OVERRIDES)
- archive_input_data(spec)
+ if spec.configuration.get("archive-inputs", True):
+ archive_input_data(spec)
logging.info("Done.")
if "dpdk" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="dpdk",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="weekly",
elif "vpp" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="vpp",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="daily",
trace_samples = plgo.Scatter(
x=xaxis,
- y=[y.avg for y in data_y],
+ y=[y for y in data_y], # Was: y.avg
mode='markers',
line={
"width": 1
:type input_data: InputData
"""
- def _generate_chart(_, data_q, graph):
+ def _generate_chart(graph):
"""Generates the chart.
"""
logs = list()
- logging.info(" Generating the chart '{0}' ...".
- format(graph.get("title", "")))
logs.append(("INFO", " Generating the chart '{0}' ...".
format(graph.get("title", ""))))
job_name = graph["data"].keys()[0]
csv_tbl = list()
- res = list()
+ res = dict()
# Transform the data
logs.append(("INFO", " Creating the data set for the {0} '{1}'.".
tst_lst = list()
for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), '')
- if not isinstance(itm, str):
- itm = itm.avg
+ # CSIT-1180: Itm will be list, compute stats.
tst_lst.append(str(itm))
csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
for group in groups:
visible = list()
for tag in group:
- for test_name, test_data in chart_data.items():
+ for tst_name, test_data in chart_data.items():
if not test_data:
logs.append(("WARNING",
"No data for the test '{0}'".
- format(test_name)))
+ format(tst_name)))
continue
- if tag in chart_tags[test_name]:
+ if tag in chart_tags[tst_name]:
message = "index: {index}, test: {test}".format(
- index=index, test=test_name)
- test_name = test_name.split('.')[-1]
+ index=index, test=tst_name)
try:
trace, rslt = _generate_trending_traces(
test_data,
job_name=job_name,
build_info=build_info,
- name='-'.join(test_name.split('-')[2:-1]),
+ name='-'.join(tst_name.split('.')[-1].
+ split('-')[2:-1]),
color=COLORS[index])
except IndexError:
message = "Out of colors: {}".format(message)
continue
traces.extend(trace)
visible.extend([True for _ in range(len(trace))])
- res.append(rslt)
+ res[tst_name] = rslt
index += 1
break
visibility.append(visible)
else:
- for test_name, test_data in chart_data.items():
+ for tst_name, test_data in chart_data.items():
if not test_data:
logs.append(("WARNING", "No data for the test '{0}'".
- format(test_name)))
+ format(tst_name)))
continue
message = "index: {index}, test: {test}".format(
- index=index, test=test_name)
- test_name = test_name.split('.')[-1]
+ index=index, test=tst_name)
try:
trace, rslt = _generate_trending_traces(
test_data,
job_name=job_name,
build_info=build_info,
- name='-'.join(test_name.split('-')[2:-1]),
+ name='-'.join(tst_name.split('.')[-1].split('-')[2:-1]),
color=COLORS[index])
except IndexError:
message = "Out of colors: {}".format(message)
index += 1
continue
traces.extend(trace)
- res.append(rslt)
+ res[tst_name] = rslt
index += 1
if traces:
except plerr.PlotlyEmptyDataError:
logs.append(("WARNING", "No data for the plot. Skipped."))
- data_out = {
- "job_name": job_name,
- "csv_table": csv_tbl,
- "results": res,
- "logs": logs
- }
- data_q.put(data_out)
+ for level, line in logs:
+ if level == "INFO":
+ logging.info(line)
+ elif level == "ERROR":
+ logging.error(line)
+ elif level == "DEBUG":
+ logging.debug(line)
+ elif level == "CRITICAL":
+ logging.critical(line)
+ elif level == "WARNING":
+ logging.warning(line)
+
+ return {"job_name": job_name, "csv_table": csv_tbl, "results": res}
builds_dict = dict()
for job in spec.input["builds"].keys():
testbed
)
- work_queue = multiprocessing.JoinableQueue()
- manager = multiprocessing.Manager()
- data_queue = manager.Queue()
- cpus = multiprocessing.cpu_count()
-
- workers = list()
- for cpu in range(cpus):
- worker = Worker(work_queue,
- data_queue,
- _generate_chart)
- worker.daemon = True
- worker.start()
- workers.append(worker)
- os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
- format(cpu, worker.pid))
-
- for chart in spec.cpta["plots"]:
- work_queue.put((chart, ))
- work_queue.join()
-
- anomaly_classifications = list()
+ anomaly_classifications = dict()
# Create the header:
csv_tables = dict()
header = "Version:," + ",".join(versions) + '\n'
csv_tables[job_name].append(header)
- while not data_queue.empty():
- result = data_queue.get()
+ for chart in spec.cpta["plots"]:
+ result = _generate_chart(chart)
- anomaly_classifications.extend(result["results"])
csv_tables[result["job_name"]].extend(result["csv_table"])
- for item in result["logs"]:
- if item[0] == "INFO":
- logging.info(item[1])
- elif item[0] == "ERROR":
- logging.error(item[1])
- elif item[0] == "DEBUG":
- logging.debug(item[1])
- elif item[0] == "CRITICAL":
- logging.critical(item[1])
- elif item[0] == "WARNING":
- logging.warning(item[1])
-
- del data_queue
-
- # Terminate all workers
- for worker in workers:
- worker.terminate()
- worker.join()
+ if anomaly_classifications.get(result["job_name"], None) is None:
+ anomaly_classifications[result["job_name"]] = dict()
+ anomaly_classifications[result["job_name"]].update(result["results"])
# Write the tables:
for job_name, csv_table in csv_tables.items():
# Evaluate result:
if anomaly_classifications:
result = "PASS"
- for classification in anomaly_classifications:
- if classification == "regression" or classification == "outlier":
- result = "FAIL"
- break
+ for job_name, job_data in anomaly_classifications.iteritems():
+ file_name = "{0}-regressions-{1}.txt".\
+ format(spec.cpta["output-file"], job_name)
+ with open(file_name, 'w') as txt_file:
+ for test_name, classification in job_data.iteritems():
+ if classification == "regression":
+ txt_file.write(test_name + '\n')
+ if classification == "regression" or \
+ classification == "outlier":
+ result = "FAIL"
+ file_name = "{0}-progressions-{1}.txt".\
+ format(spec.cpta["output-file"], job_name)
+ with open(file_name, 'w') as txt_file:
+ for test_name, classification in job_data.iteritems():
+ if classification == "progression":
+ txt_file.write(test_name + '\n')
else:
result = "FAIL"