aboutsummaryrefslogtreecommitdiffstats
' href='#n483'>483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 
# 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

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;
}
.rst-content blockquote {
    margin-left: 0px;
    line-height: 18px;
    margin-bottom: 0px;
}
.wy-menu-vertical a {
    display: inline-block;
    line-height: 18px;
    padding: 0 2em;
    display: block;
    position: relative;
    font-size: 90%;
    color: #d9d9d9
}
.wy-menu-vertical li.current a {
    color: gray;
    border-right: solid 1px #c9c9c9;
    padding: 0 3em;
}
.wy-menu-vertical li.toctree-l2.current > a {
    background: #c9c9c9;
    padding: 0 3em;
}
.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a {
    display: block;
    background: #c9c9c9;
    padding: 0 4em;
}
.wy-menu-vertical li.toctree-l3.current li.toctree-l4 > a {
    display: block;
    background: #bdbdbd;
    padding: 0 5em;
}
.wy-menu-vertical li.on a, .wy-menu-vertical li.current > a {
    color: #404040;
    padding: 0 2em;
    font-weight: bold;
    position: relative;
    background: #fcfcfc;
    border: none;
        border-top-width: medium;
        border-bottom-width: medium;
        border-top-style: none;
        border-bottom-style: none;
        border-top-color: currentcolor;
        border-bottom-color: currentcolor;
    padding-left: 2em -4px;
}
"""

COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
          "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
          "Violet", "Blue", "Yellow", "BurlyWood", "CadetBlue", "Crimson",
          "DarkBlue", "DarkCyan", "DarkGreen", "Green", "GoldenRod",
          "LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
          "MediumSeaGreen", "SeaGreen", "LightSlateGrey",
          "SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
          "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
          "Violet", "Blue", "Yellow", "BurlyWood", "CadetBlue", "Crimson",
          "DarkBlue", "DarkCyan", "DarkGreen", "Green", "GoldenRod",
          "LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
          "MediumSeaGreen", "SeaGreen", "LightSlateGrey"
          ]


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('%Y-%m-%d %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, job_name, build_info,
                              show_trend_line=True, name="", color=""):
    """Generate the trending traces:
     - samples,
     - outliers, regress, progress
     - average of normal samples (trending line)

    :param in_data: Full data set.
    :param job_name: The name of job which generated the data.
    :param build_info: Information about the builds.
    :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 job_name: str
    :type build_info: dict
    :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:
        date = build_info[job_name][str(idx)][0]
        hover_str = ("date: {0}<br>"
                     "value: {1:,}<br>"
                     "{2}-ref: {3}<br>"
                     "csit-ref: mrr-{4}-build-{5}")
        if "dpdk" in job_name:
            hover_text.append(hover_str.format(
                date,
                int(in_data[idx].avg),
                "dpdk",
                build_info[job_name][str(idx)][1].
                rsplit('~', 1)[0],
                "weekly",
                idx))
        elif "vpp" in job_name:
            hover_text.append(hover_str.format(
                date,
                int(in_data[idx].avg),
                "vpp",
                build_info[job_name][str(idx)][1].
                rsplit('~', 1)[0],
                "daily",
                idx))

        xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
                              int(date[9:11]), int(date[12:])))

    data_pd = OrderedDict()
    for key, value in zip(xaxis, data_y):
        data_pd[key] = value

    anomaly_classification, avgs = classify_anomalies(data_pd)

    anomalies = OrderedDict()
    anomalies_colors = list()
    anomalies_avgs = list()
    anomaly_color = {
        "regression": 0.0,
        "normal": 0.5,
        "progression": 1.0
    }
    if anomaly_classification:
        for idx, (key, value) in enumerate(data_pd.iteritems()):
            if anomaly_classification[idx] in \
                    ("outlier", "regression", "progression"):
                anomalies[key] = value
                anomalies_colors.append(
                    anomaly_color[anomaly_classification[idx]])
                anomalies_avgs.append(avgs[idx])
        anomalies_colors.extend([0.0, 0.5, 1.0])

    # Create traces

    trace_samples = plgo.Scatter(
        x=xaxis,
        y=[y.avg for y in data_y],
        mode='markers',
        line={
            "width": 1
        },
        showlegend=True,
        legendgroup=name,
        name="{name}".format(name=name),
        marker={
            "size": 5,
            "color": color,
            "symbol": "circle",
        },
        text=hover_text,
        hoverinfo="text"
    )
    traces = [trace_samples, ]

    if show_trend_line:
        trace_trend = plgo.Scatter(
            x=xaxis,
            y=avgs,
            mode='lines',
            line={
                "shape": "linear",
                "width": 1,
                "color": color,
            },
            showlegend=False,
            legendgroup=name,
            name='{name}'.format(name=name),
            text=["trend: {0:,}".format(int(avg)) for avg in avgs],
            hoverinfo="text+name"
        )
        traces.append(trace_trend)

    trace_anomalies = plgo.Scatter(
        x=anomalies.keys(),
        y=anomalies_avgs,
        mode='markers',
        hoverinfo="none",
        showlegend=False,
        legendgroup=name,
        name="{name}-anomalies".format(name=name),
        marker={
            "size": 15,
            "symbol": "circle-open",
            "color": anomalies_colors,
            "colorscale": [[0.00, "red"],
                           [0.33, "red"],
                           [0.33, "white"],
                           [0.66, "white"],
                           [0.66, "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": [0.167, 0.500, 0.833],
                "ticktext": ["Regression", "Normal", "Progression"],
                "ticks": "",
                "ticklen": 0,
                "tickangle": -90,
                "thickness": 10
            }
        }
    )
    traces.append(trace_anomalies)

    if anomaly_classification:
        return traces, anomaly_classification[-1]
    else:
        return traces, None


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 = graph["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, job_data in data.iteritems():
            if job != job_name:
                continue
            for index, bld in job_data.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"]["receive-rate"]
                    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_dict[job_name]:
                itm = tst_data.get(int(bld), '')
                if not isinstance(itm, str):
                    itm = itm.avg
                tst_lst.append(str(itm))
            csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
        # Generate traces:
        traces = list()
        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
            message = "index: {index}, test: {test}".format(
                index=index, test=test_name)
            test_name = test_name.split('.')[-1]
            try:
                trace, rslt = _generate_trending_traces(
                    test_data,
                    job_name=job_name,
                    build_info=build_info,
                    name='-'.join(test_name.split('-')[2:-1]),
                    color=COLORS[index])
            except IndexError:
                message = "Out of colors: {}".format(message)
                logs.append(("ERROR", message))
                logging.error(message)
                index += 1
                continue
            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 = {
            "job_name": job_name,
            "csv_table": csv_tbl,
            "results": res,
            "logs": logs
        }
        data_q.put(data_out)

    builds_dict = dict()
    for job in spec.input["builds"].keys():
        if builds_dict.get(job, None) is None:
            builds_dict[job] = list()
        for build in spec.input["builds"][job]:
            status = build["status"]
            if status != "failed" and status != "not found":
                builds_dict[job].append(str(build["build"]))

    # Create "build ID": "date" dict:
    build_info = dict()
    for job_name, job_data in builds_dict.items():
        if build_info.get(job_name, None) is None:
            build_info[job_name] = OrderedDict()
        for build in job_data:
            build_info[job_name][build] = (
                input_data.metadata(job_name, build).get("generated", ""),
                input_data.metadata(job_name, build).get("version", "")
            )

    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_tables = dict()
    for job_name in builds_dict.keys():
        if csv_tables.get(job_name, None) is None:
            csv_tables[job_name] = list()
        header = "Build Number:," + ",".join(builds_dict[job_name]) + '\n'
        csv_tables[job_name].append(header)
        build_dates = [x[0] for x in build_info[job_name].values()]
        header = "Build Date:," + ",".join(build_dates) + '\n'
        csv_tables[job_name].append(header)
        versions = [x[1] for x in build_info[job_name].values()]
        header = "Version:," + ",".join(versions) + '\n'
        csv_tables[job_name].append(header)

    while not data_queue.empty():
        result = data_queue.get()

        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()

    # Write the tables:
    for job_name, csv_table in csv_tables.items():
        file_name = spec.cpta["output-file"] + "-" + job_name + "-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