--- /dev/null
+# Copyright (c) 2018 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generation of Continuous Performance Trending and Analysis.
+"""
+
+import multiprocessing
+import os
+import logging
+import csv
+import prettytable
+import plotly.offline as ploff
+import plotly.graph_objs as plgo
+import plotly.exceptions as plerr
+import pandas as pd
+
+from collections import OrderedDict
+from datetime import datetime
+
+from utils import archive_input_data, execute_command,\
+ classify_anomalies, Worker
+
+
+# Command to build the html format of the report
+HTML_BUILDER = 'sphinx-build -v -c conf_cpta -a ' \
+ '-b html -E ' \
+ '-t html ' \
+ '-D version="{date}" ' \
+ '{working_dir} ' \
+ '{build_dir}/'
+
+# .css file for the html format of the report
+THEME_OVERRIDES = """/* override table width restrictions */
+.wy-nav-content {
+ max-width: 1200px !important;
+}
+"""
+
+COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
+ "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
+ "Violet", "Blue", "Yellow"]
+
+
+def generate_cpta(spec, data):
+ """Generate all formats and versions of the Continuous Performance Trending
+ and Analysis.
+
+ :param spec: Specification read from the specification file.
+ :param data: Full data set.
+ :type spec: Specification
+ :type data: InputData
+ """
+
+ logging.info("Generating the Continuous Performance Trending and Analysis "
+ "...")
+
+ ret_code = _generate_all_charts(spec, data)
+
+ cmd = HTML_BUILDER.format(
+ date=datetime.utcnow().strftime('%m/%d/%Y %H:%M UTC'),
+ working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"],
+ build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"])
+ execute_command(cmd)
+
+ with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE]"], "w") as \
+ css_file:
+ css_file.write(THEME_OVERRIDES)
+
+ with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE2]"], "w") as \
+ css_file:
+ css_file.write(THEME_OVERRIDES)
+
+ archive_input_data(spec)
+
+ logging.info("Done.")
+
+ return ret_code
+
+
+def _generate_trending_traces(in_data, build_info, moving_win_size=10,
+ show_trend_line=True, name="", color=""):
+ """Generate the trending traces:
+ - samples,
+ - trimmed moving median (trending line)
+ - outliers, regress, progress
+
+ :param in_data: Full data set.
+ :param build_info: Information about the builds.
+ :param moving_win_size: Window size.
+ :param show_trend_line: Show moving median (trending plot).
+ :param name: Name of the plot
+ :param color: Name of the color for the plot.
+ :type in_data: OrderedDict
+ :type build_info: dict
+ :type moving_win_size: int
+ :type show_trend_line: bool
+ :type name: str
+ :type color: str
+ :returns: Generated traces (list) and the evaluated result.
+ :rtype: tuple(traces, result)
+ """
+
+ data_x = list(in_data.keys())
+ data_y = list(in_data.values())
+
+ hover_text = list()
+ xaxis = list()
+ for idx in data_x:
+ hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
+ format(build_info[str(idx)][1].rsplit('~', 1)[0],
+ idx))
+ date = build_info[str(idx)][0]
+ xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
+ int(date[9:11]), int(date[12:])))
+
+ data_pd = pd.Series(data_y, index=xaxis)
+
+ anomaly_classification, avgs = classify_anomalies(data_pd)
+
+ anomalies = pd.Series()
+ anomalies_colors = list()
+ anomalies_avgs = list()
+ anomaly_color = {
+ "outlier": 0.0,
+ "regression": 0.33,
+ "normal": 0.66,
+ "progression": 1.0
+ }
+ if anomaly_classification:
+ for idx, item in enumerate(data_pd.items()):
+ if anomaly_classification[idx] in \
+ ("outlier", "regression", "progression"):
+ anomalies = anomalies.append(pd.Series([item[1], ],
+ index=[item[0], ]))
+ anomalies_colors.append(
+ anomaly_color[anomaly_classification[idx]])
+ anomalies_avgs.append(avgs[idx])
+ anomalies_colors.extend([0.0, 0.33, 0.66, 1.0])
+
+ # Create traces
+
+ trace_samples = plgo.Scatter(
+ x=xaxis,
+ y=data_y,
+ mode='markers',
+ line={
+ "width": 1
+ },
+ legendgroup=name,
+ name="{name}-thput".format(name=name),
+ marker={
+ "size": 5,
+ "color": color,
+ "symbol": "circle",
+ },
+ text=hover_text,
+ hoverinfo="x+y+text+name"
+ )
+ traces = [trace_samples, ]
+
+ if show_trend_line:
+ trace_trend = plgo.Scatter(
+ x=xaxis,
+ y=avgs,
+ mode='lines',
+ line={
+ "shape": "linear",
+ "width": 1,
+ "color": color,
+ },
+ legendgroup=name,
+ name='{name}-trend'.format(name=name)
+ )
+ traces.append(trace_trend)
+
+ trace_anomalies = plgo.Scatter(
+ x=anomalies.keys(),
+ y=anomalies_avgs,
+ mode='markers',
+ hoverinfo="none",
+ showlegend=True,
+ legendgroup=name,
+ name="{name}-anomalies".format(name=name),
+ marker={
+ "size": 15,
+ "symbol": "circle-open",
+ "color": anomalies_colors,
+ "colorscale": [[0.00, "grey"],
+ [0.25, "grey"],
+ [0.25, "red"],
+ [0.50, "red"],
+ [0.50, "white"],
+ [0.75, "white"],
+ [0.75, "green"],
+ [1.00, "green"]],
+ "showscale": True,
+ "line": {
+ "width": 2
+ },
+ "colorbar": {
+ "y": 0.5,
+ "len": 0.8,
+ "title": "Circles Marking Data Classification",
+ "titleside": 'right',
+ "titlefont": {
+ "size": 14
+ },
+ "tickmode": 'array',
+ "tickvals": [0.125, 0.375, 0.625, 0.875],
+ "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+ "ticks": "",
+ "ticklen": 0,
+ "tickangle": -90,
+ "thickness": 10
+ }
+ }
+ )
+ traces.append(trace_anomalies)
+
+ return traces, anomaly_classification[-1]
+
+
+def _generate_all_charts(spec, input_data):
+ """Generate all charts specified in the specification file.
+
+ :param spec: Specification.
+ :param input_data: Full data set.
+ :type spec: Specification
+ :type input_data: InputData
+ """
+
+ def _generate_chart(_, data_q, 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 = spec.cpta["data"].keys()[0]
+
+ csv_tbl = list()
+ res = list()
+
+ # Transform the data
+ logs.append(("INFO", " Creating the data set for the {0} '{1}'.".
+ format(graph.get("type", ""), graph.get("title", ""))))
+ data = input_data.filter_data(graph, continue_on_error=True)
+ if data is None:
+ logging.error("No data.")
+ return
+
+ chart_data = dict()
+ for job in data:
+ for index, bld in job.items():
+ for test_name, test in bld.items():
+ if chart_data.get(test_name, None) is None:
+ chart_data[test_name] = OrderedDict()
+ try:
+ chart_data[test_name][int(index)] = \
+ test["result"]["throughput"]
+ except (KeyError, TypeError):
+ pass
+
+ # Add items to the csv table:
+ for tst_name, tst_data in chart_data.items():
+ tst_lst = list()
+ for bld in builds_lst:
+ itm = tst_data.get(int(bld), '')
+ tst_lst.append(str(itm))
+ csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
+ # Generate traces:
+ traces = list()
+ win_size = 14
+ index = 0
+ for test_name, test_data in chart_data.items():
+ if not test_data:
+ logs.append(("WARNING", "No data for the test '{0}'".
+ format(test_name)))
+ continue
+ test_name = test_name.split('.')[-1]
+ trace, rslt = _generate_trending_traces(
+ test_data,
+ build_info=build_info,
+ moving_win_size=win_size,
+ name='-'.join(test_name.split('-')[3:-1]),
+ color=COLORS[index])
+ traces.extend(trace)
+ res.append(rslt)
+ index += 1
+
+ if traces:
+ # Generate the chart:
+ graph["layout"]["xaxis"]["title"] = \
+ graph["layout"]["xaxis"]["title"].format(job=job_name)
+ name_file = "{0}-{1}{2}".format(spec.cpta["output-file"],
+ graph["output-file-name"],
+ spec.cpta["output-file-type"])
+
+ logs.append(("INFO", " Writing the file '{0}' ...".
+ format(name_file)))
+ plpl = plgo.Figure(data=traces, layout=graph["layout"])
+ try:
+ ploff.plot(plpl, show_link=False, auto_open=False,
+ filename=name_file)
+ except plerr.PlotlyEmptyDataError:
+ logs.append(("WARNING", "No data for the plot. Skipped."))
+
+ data_out = {
+ "csv_table": csv_tbl,
+ "results": res,
+ "logs": logs
+ }
+ data_q.put(data_out)
+
+ job_name = spec.cpta["data"].keys()[0]
+
+ builds_lst = list()
+ for build in spec.input["builds"][job_name]:
+ status = build["status"]
+ if status != "failed" and status != "not found":
+ builds_lst.append(str(build["build"]))
+
+ # Get "build ID": "date" dict:
+ build_info = OrderedDict()
+ for build in builds_lst:
+ try:
+ build_info[build] = (
+ input_data.metadata(job_name, build)["generated"][:14],
+ input_data.metadata(job_name, build)["version"]
+ )
+ except KeyError:
+ build_info[build] = ("", "")
+
+ 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()
+
+ # Create the header:
+ csv_table = list()
+ header = "Build Number:," + ",".join(builds_lst) + '\n'
+ csv_table.append(header)
+ build_dates = [x[0] for x in build_info.values()]
+ header = "Build Date:," + ",".join(build_dates) + '\n'
+ csv_table.append(header)
+ vpp_versions = [x[1] for x in build_info.values()]
+ header = "VPP Version:," + ",".join(vpp_versions) + '\n'
+ csv_table.append(header)
+
+ while not data_queue.empty():
+ result = data_queue.get()
+
+ anomaly_classifications.extend(result["results"])
+ csv_table.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()
+
+ # Write the tables:
+ file_name = spec.cpta["output-file"] + "-trending"
+ with open("{0}.csv".format(file_name), 'w') as file_handler:
+ file_handler.writelines(csv_table)
+
+ txt_table = None
+ with open("{0}.csv".format(file_name), 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ line_nr = 0
+ for row in csv_content:
+ if txt_table is None:
+ txt_table = prettytable.PrettyTable(row)
+ else:
+ if line_nr > 1:
+ for idx, item in enumerate(row):
+ try:
+ row[idx] = str(round(float(item) / 1000000, 2))
+ except ValueError:
+ pass
+ try:
+ txt_table.add_row(row)
+ except Exception as err:
+ logging.warning("Error occurred while generating TXT table:"
+ "\n{0}".format(err))
+ line_nr += 1
+ txt_table.align["Build Number:"] = "l"
+ with open("{0}.txt".format(file_name), "w") as txt_file:
+ txt_file.write(str(txt_table))
+
+ # Evaluate result:
+ if anomaly_classifications:
+ result = "PASS"
+ for classification in anomaly_classifications:
+ if classification == "regression" or classification == "outlier":
+ result = "FAIL"
+ break
+ else:
+ result = "FAIL"
+
+ logging.info("Partial results: {0}".format(anomaly_classifications))
+ logging.info("Result: {0}".format(result))
+
+ return result
Code Review / csit.git / blobdiff