Re: [Autotest] [PATCH 1/2] IOzone test: Introduce postprocessing module

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

 



I'm slightly surprised this isn't called from postprocess
in the test? Any downside to doing that?

On Fri, Apr 30, 2010 at 2:20 PM, Lucas Meneghel Rodrigues
<lmr@xxxxxxxxxx> wrote:
> This module contains code to postprocess IOzone data
> in a convenient way so we can generate performance graphs
> and condensed data. The graph generation part depends
> on gnuplot, but if the utility is not present,
> functionality will gracefully degrade.
>
> The reason why this was created as a separate module is:
>  * It doesn't pollute the main test class.
>  * Allows us to use the postprocess module as a stand alone program,
>   that can even do performance comparison between 2 IOzone runs.
>
> Signed-off-by: Lucas Meneghel Rodrigues <lmr@xxxxxxxxxx>
> ---
>  client/tests/iozone/postprocessing.py |  487 +++++++++++++++++++++++++++++++++
>  1 files changed, 487 insertions(+), 0 deletions(-)
>  create mode 100755 client/tests/iozone/postprocessing.py
>
> diff --git a/client/tests/iozone/postprocessing.py b/client/tests/iozone/postprocessing.py
> new file mode 100755
> index 0000000..b495502
> --- /dev/null
> +++ b/client/tests/iozone/postprocessing.py
> @@ -0,0 +1,487 @@
> +#!/usr/bin/python
> +"""
> +Postprocessing module for IOzone. It is capable to pick results from an
> +IOzone run, calculate the geometric mean for all throughput results for
> +a given file size or record size, and then generate a series of 2D and 3D
> +graphs. The graph generation functionality depends on gnuplot, and if it
> +is not present, functionality degrates gracefully.
> +
> +@copyright: Red Hat 2010
> +"""
> +import os, sys, optparse, logging, math, time
> +import common
> +from autotest_lib.client.common_lib import logging_config, logging_manager
> +from autotest_lib.client.common_lib import error
> +from autotest_lib.client.bin import utils, os_dep
> +
> +
> +_LABELS = ('file_size', 'record_size', 'write', 'rewrite', 'read', 'reread',
> +           'randread', 'randwrite', 'bkwdread', 'recordrewrite', 'strideread',
> +           'fwrite', 'frewrite', 'fread', 'freread')
> +
> +
> +def unique(list):
> +    """
> +    Return a list of the elements in list, but without duplicates.
> +
> +    @param list: List with values.
> +    @return: List with non duplicate elements.
> +    """
> +    n = len(list)
> +    if n == 0:
> +        return []
> +    u = {}
> +    try:
> +        for x in list:
> +            u[x] = 1
> +    except TypeError:
> +        return None
> +    else:
> +        return u.keys()
> +
> +
> +def geometric_mean(values):
> +    """
> +    Evaluates the geometric mean for a list of numeric values.
> +
> +    @param values: List with values.
> +    @return: Single value representing the geometric mean for the list values.
> +    @see: http://en.wikipedia.org/wiki/Geometric_mean
> +    """
> +    try:
> +        values = [int(value) for value in values]
> +    except ValueError:
> +        return None
> +    product = 1
> +    n = len(values)
> +    if n == 0:
> +        return None
> +    return math.exp(sum([math.log(x) for x in values])/n)
> +
> +
> +def compare_matrices(matrix1, matrix2, treshold=0.05):
> +    """
> +    Compare 2 matrices nxm and return a matrix nxm with comparison data
> +
> +    @param matrix1: Reference Matrix with numeric data
> +    @param matrix2: Matrix that will be compared
> +    @param treshold: Any difference bigger than this percent treshold will be
> +            reported.
> +    """
> +    improvements = 0
> +    regressions = 0
> +    same = 0
> +    comparison_matrix = []
> +
> +    new_matrix = []
> +    for line1, line2 in zip(matrix1, matrix2):
> +        new_line = []
> +        for element1, element2 in zip(line1, line2):
> +            ratio = float(element2) / float(element1)
> +            if ratio < (1 - treshold):
> +                regressions += 1
> +                new_line.append((100 * ratio - 1) - 100)
> +            elif ratio > (1 + treshold):
> +                improvements += 1
> +                new_line.append("+" + str((100 * ratio - 1) - 100))
> +            else:
> +                same + 1
> +                if line1.index(element1) == 0:
> +                    new_line.append(element1)
> +                else:
> +                    new_line.append(".")
> +        new_matrix.append(new_line)
> +
> +    total = improvements + regressions + same
> +
> +    return (new_matrix, improvements, regressions, total)
> +
> +
> +class IOzoneAnalyzer(object):
> +    """
> +    Analyze an unprocessed IOzone file, and generate the following types of
> +    report:
> +
> +    * Summary of throughput for all file and record sizes combined
> +    * Summary of throughput for all file sizes
> +    * Summary of throughput for all record sizes
> +
> +    If more than one file is provided to the analyzer object, a comparison
> +    between the two runs is made, searching for regressions in performance.
> +    """
> +    def __init__(self, list_files, output_dir):
> +        self.list_files = list_files
> +        if not os.path.isdir(output_dir):
> +            os.makedirs(output_dir)
> +        self.output_dir = output_dir
> +        logging.info("Results will be stored in %s", output_dir)
> +
> +
> +    def average_performance(self, results, size=None):
> +        """
> +        Flattens a list containing performance results.
> +
> +        @param results: List of n lists containing data from performance runs.
> +        @param size: Numerical value of a size (say, file_size) that was used
> +                to filter the original results list.
> +        @return: List with 1 list containing average data from the performance
> +                run.
> +        """
> +        average_line = []
> +        if size is not None:
> +            average_line.append(size)
> +        for i in range(2, 15):
> +            average = geometric_mean([line[i] for line in results]) / 1024.0
> +            average = int(average)
> +            average_line.append(average)
> +        return average_line
> +
> +
> +    def process_results(self, results, label=None):
> +        """
> +        Process a list of IOzone results according to label.
> +
> +        @label: IOzone column label that we'll use to filter and compute
> +                geometric mean results, in practical term either 'file_size'
> +                or 'record_size'.
> +        @result: A list of n x m columns with original iozone results.
> +        @return: A list of n-? x (m-1) columns with geometric averages for
> +                values of each label (ex, average for all file_sizes).
> +        """
> +        performance = []
> +        if label is not None:
> +            index = _LABELS.index(label)
> +            sizes = unique([line[index] for line in results])
> +            sizes.sort()
> +            for size in sizes:
> +                r_results = [line for line in results if line[index] == size]
> +                performance.append(self.average_performance(r_results, size))
> +        else:
> +            performance.append(self.average_performance(results))
> +
> +        return performance
> +
> +
> +    def parse_file(self, file):
> +        """
> +        Parse an IOzone results file.
> +
> +        @param file: File object that will be parsed.
> +        @return: Matrix containing IOzone results extracted from the file.
> +        """
> +        lines = []
> +        for line in file.readlines():
> +            fields = line.split()
> +            if len(fields) != 15:
> +                continue
> +            try:
> +                lines.append([int(i) for i in fields])
> +            except ValueError:
> +                continue
> +        return lines
> +
> +
> +    def report(self, overall_results, record_size_results, file_size_results):
> +        """
> +        Generates analysis data for IOZone run.
> +
> +        Generates a report to both logs (where it goes with nice headers) and
> +        output files for further processing (graph generation).
> +
> +        @param overall_results: 1x15 Matrix containing IOzone results for all
> +                file sizes
> +        @param record_size_results: nx15 Matrix containing IOzone results for
> +                each record size tested.
> +        @param file_size_results: nx15 Matrix containing file size results
> +                for each file size tested.
> +        """
> +        # Here we'll use the logging system to put the output of our analysis
> +        # to files
> +        logger = logging.getLogger()
> +        formatter = logging.Formatter("")
> +
> +        logging.info("")
> +        logging.info("TABLE:  SUMMARY of ALL FILE and RECORD SIZES                        Results in MB/sec")
> +        logging.info("")
> +        logging.info("FILE & RECORD  INIT    RE              RE    RANDOM  RANDOM  BACKWD   RECRE  STRIDE    F       FRE     F       FRE")
> +        logging.info("SIZES (KB)     WRITE   WRITE   READ    READ    READ   WRITE    READ   WRITE    READ    WRITE   WRITE   READ    READ")
> +        logging.info("-------------------------------------------------------------------------------------------------------------------")
> +        for result_line in overall_results:
> +            logging.info("ALL            %-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s" % tuple(result_line))
> +        logging.info("")
> +
> +        logging.info("DRILLED DATA:")
> +
> +        logging.info("")
> +        logging.info("TABLE:  RECORD Size against all FILE Sizes                          Results in MB/sec")
> +        logging.info("")
> +        logging.info("RECORD    INIT    RE              RE    RANDOM  RANDOM  BACKWD   RECRE  STRIDE    F       FRE     F       FRE ")
> +        logging.info("SIZE (KB) WRITE   WRITE   READ    READ    READ   WRITE    READ   WRITE    READ    WRITE   WRITE   READ    READ")
> +        logging.info("--------------------------------------------------------------------------------------------------------------")
> +
> +        foutput_path = os.path.join(self.output_dir, '2d-datasource-file')
> +        if os.path.isfile(foutput_path):
> +            os.unlink(foutput_path)
> +        foutput = logging.FileHandler(foutput_path)
> +        foutput.setFormatter(formatter)
> +        logger.addHandler(foutput)
> +        for result_line in record_size_results:
> +            logging.info("%-10s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s" % tuple(result_line))
> +        logger.removeHandler(foutput)
> +
> +        logging.info("")
> +
> +        logging.info("")
> +        logging.info("TABLE:  FILE Size against all RECORD Sizes                          Results in MB/sec")
> +        logging.info("")
> +        logging.info("RECORD    INIT    RE              RE    RANDOM  RANDOM  BACKWD   RECRE  STRIDE    F       FRE     F       FRE ")
> +        logging.info("SIZE (KB) WRITE   WRITE   READ    READ    READ   WRITE    READ   WRITE    READ    WRITE   WRITE   READ    READ")
> +        logging.info("--------------------------------------------------------------------------------------------------------------")
> +
> +        routput_path = os.path.join(self.output_dir, '2d-datasource-record')
> +        if os.path.isfile(routput_path):
> +            os.unlink(routput_path)
> +        routput = logging.FileHandler(routput_path)
> +        routput.setFormatter(formatter)
> +        logger.addHandler(routput)
> +        for result_line in file_size_results:
> +            logging.info("%-10s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s%-8s" % tuple(result_line))
> +        logger.removeHandler(routput)
> +
> +        logging.info("")
> +
> +
> +    def report_comparison(self, record, file):
> +        """
> +        Generates comparison data for 2 IOZone runs.
> +
> +        It compares 2 sets of nxm results and outputs a table with differences.
> +        If a difference higher or smaller than 5% is found, a warning is
> +        triggered.
> +
> +        @param record: Tuple with 4 elements containing results for record size.
> +        @param file: Tuple with 4 elements containing results for file size.
> +        """
> +        (record_size, record_improvements, record_regressions,
> +         record_total) = record
> +        (file_size, file_improvements, file_regressions,
> +         file_total) = file
> +        logging.info("ANALYSIS of DRILLED DATA:")
> +
> +        logging.info("")
> +        logging.info("TABLE:  RECsize Difference between runs                            Results are % DIFF")
> +        logging.info("")
> +        logging.info("RECORD    INIT    RE              RE    RANDOM  RANDOM  BACKWD   RECRE  STRIDE    F       FRE     F       FRE ")
> +        logging.info("SIZE (KB) WRITE   WRITE   READ    READ    READ   WRITE    READ   WRITE    READ    WRITE   WRITE   READ    READ")
> +        logging.info("--------------------------------------------------------------------------------------------------------------")
> +        for result_line in record_size:
> +            logging.info("%-10s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s" % tuple(result_line))
> +        logging.info("REGRESSIONS: %d (%.2f%%)    Improvements: %d (%.2f%%)",
> +                     record_regressions,
> +                     (100 * record_regressions/float(record_total)),
> +                     record_improvements,
> +                     (100 * record_improvements/float(record_total)))
> +        logging.info("")
> +
> +        logging.info("")
> +        logging.info("TABLE:  FILEsize Difference between runs                           Results are % DIFF")
> +        logging.info("")
> +        logging.info("RECORD    INIT    RE              RE    RANDOM  RANDOM  BACKWD   RECRE  STRIDE    F       FRE     F       FRE ")
> +        logging.info("SIZE (KB) WRITE   WRITE   READ    READ    READ   WRITE    READ   WRITE    READ    WRITE   WRITE   READ    READ")
> +        logging.info("--------------------------------------------------------------------------------------------------------------")
> +        for result_line in file_size:
> +            logging.info("%-10s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s%-8.6s" % tuple(result_line))
> +        logging.info("REGRESSIONS: %d (%.2f%%)    Improvements: %d (%.2f%%)",
> +                     file_regressions,
> +                     (100 * file_regressions/float(file_total)),
> +                     file_improvements,
> +                     (100 * file_improvements/float(file_total)))
> +        logging.info("")
> +
> +
> +    def analyze(self):
> +        """
> +        Analyzes and eventually compares sets of IOzone data.
> +        """
> +        overall = []
> +        record_size = []
> +        file_size = []
> +        for path in self.list_files:
> +            file = open(path, 'r')
> +            logging.info('FILE: %s', path)
> +
> +            results = self.parse_file(file)
> +
> +            overall_results = self.process_results(results)
> +            record_size_results = self.process_results(results, 'record_size')
> +            file_size_results = self.process_results(results, 'file_size')
> +            self.report(overall_results, record_size_results, file_size_results)
> +
> +            if len(self.list_files) == 2:
> +                overall.append(overall_results)
> +                record_size.append(record_size_results)
> +                file_size.append(file_size_results)
> +
> +        if len(self.list_files) == 2:
> +            record_comparison = compare_matrices(*record_size)
> +            file_comparison = compare_matrices(*file_size)
> +            self.report_comparison(record_comparison, file_comparison)
> +
> +
> +class IOzonePlotter(object):
> +    """
> +    Plots graphs based on the results of an IOzone run.
> +
> +    Plots graphs based on the results of an IOzone run. Uses gnuplot to
> +    generate the graphs.
> +    """
> +    def __init__(self, results_file, output_dir):
> +        self.active = True
> +        try:
> +            self.gnuplot = os_dep.command("gnuplot")
> +        except:
> +            logging.error("Command gnuplot not found, disabling graph "
> +                          "generation")
> +            self.active = False
> +
> +        if not os.path.isdir(output_dir):
> +            os.makedirs(output_dir)
> +        self.output_dir = output_dir
> +
> +        if not os.path.isfile(results_file):
> +            logging.error("Invalid file %s provided, disabling graph "
> +                          "generation", results_file)
> +            self.active = False
> +            self.results_file = None
> +        else:
> +            self.results_file = results_file
> +            self.generate_data_source()
> +
> +
> +    def generate_data_source(self):
> +        """
> +        Creates data file without headers for gnuplot consumption.
> +        """
> +        results_file = open(self.results_file, 'r')
> +        self.datasource = os.path.join(self.output_dir, '3d-datasource')
> +        datasource = open(self.datasource, 'w')
> +        for line in results_file.readlines():
> +            fields = line.split()
> +            if len(fields) != 15:
> +                continue
> +            try:
> +                values = [int(i) for i in fields]
> +                datasource.write(line)
> +            except ValueError:
> +                continue
> +        datasource.close()
> +
> +
> +    def plot_2d_graphs(self):
> +        """
> +        For each one of the throughput parameters, generate a set of gnuplot
> +        commands that will create a parametric surface with file size vs.
> +        record size vs. throughput.
> +        """
> +        datasource_2d = os.path.join(self.output_dir, '2d-datasource-file')
> +        for index, label in zip(range(1, 14), _LABELS[2:]):
> +            commands_path = os.path.join(self.output_dir, '2d-%s.do' % label)
> +            commands = ""
> +            commands += "set title 'Iozone performance: %s'\n" % label
> +            commands += "set logscale x\n"
> +            commands += "set xlabel 'File size (KB)'\n"
> +            commands += "set ylabel 'Througput (MB/s)'\n"
> +            commands += "set terminal png small size 450 350\n"
> +            commands += "set output '%s'\n" % os.path.join(self.output_dir,
> +                                                           '2d-%s.png' % label)
> +            commands += ("plot '%s' using 1:%s title '%s' with lines \n" %
> +                         (datasource_2d, index, label))
> +            commands_file = open(commands_path, 'w')
> +            commands_file.write(commands)
> +            commands_file.close()
> +            try:
> +                utils.run("%s %s" % (self.gnuplot, commands_path))
> +            except error.CmdError, e:
> +                logging.error("Problem plotting from commands file %s: %s",
> +                              commands_file, str(e))
> +
> +
> +    def plot_3d_graphs(self):
> +        """
> +        For each one of the throughput parameters, generate a set of gnuplot
> +        commands that will create a parametric surface with file size vs.
> +        record size vs. throughput.
> +        """
> +        for index, label in zip(range(1, 14), _LABELS[2:]):
> +            commands_path = os.path.join(self.output_dir, '%s.do' % label)
> +            commands = ""
> +            commands += "set title 'Iozone performance: %s'\n" % label
> +            commands += "set grid lt 2 lw 1\n"
> +            commands += "set surface\n"
> +            commands += "set parametric\n"
> +            commands += "set xtics\n"
> +            commands += "set ytics\n"
> +            commands += "set logscale x 2\n"
> +            commands += "set logscale y 2\n"
> +            commands += "set logscale z\n"
> +            commands += "set xrange [2.**5:2.**24]\n"
> +            commands += "set xlabel 'File size (KB)'\n"
> +            commands += "set ylabel 'Record size (KB)'\n"
> +            commands += "set zlabel 'Througput (KB/s)'\n"
> +            commands += "set data style lines\n"
> +            commands += "set dgrid3d 80,80, 3\n"
> +            commands += "set terminal png small size 900 700\n"
> +            commands += "set output '%s'\n" % os.path.join(self.output_dir,
> +                                                           '%s.png' % label)
> +            commands += ("splot '%s' using 1:2:%s title '%s'\n" %
> +                         (self.datasource, index, label))
> +            commands_file = open(commands_path, 'w')
> +            commands_file.write(commands)
> +            commands_file.close()
> +            try:
> +                utils.run("%s %s" % (self.gnuplot, commands_path))
> +            except error.CmdError, e:
> +                logging.error("Problem plotting from commands file %s: %s",
> +                              commands_file, str(e))
> +
> +
> +    def plot_all(self):
> +        """
> +        Plot all graphs that are to be plotted, provided that we have gnuplot.
> +        """
> +        if self.active:
> +            self.plot_2d_graphs()
> +            self.plot_3d_graphs()
> +
> +
> +class AnalyzerLoggingConfig(logging_config.LoggingConfig):
> +    def configure_logging(self, results_dir=None, verbose=False):
> +        super(AnalyzerLoggingConfig, self).configure_logging(use_console=True,
> +                                                        verbose=verbose)
> +
> +
> +if __name__ == "__main__":
> +    parser = optparse.OptionParser("usage: %prog [options] [filenames]")
> +    options, args = parser.parse_args()
> +
> +    logging_manager.configure_logging(AnalyzerLoggingConfig())
> +
> +    if args:
> +        filenames = args
> +    else:
> +        parser.print_help()
> +        sys.exit(1)
> +
> +    if len(args) > 2:
> +        parser.print_help()
> +        sys.exit(1)
> +
> +    o = os.path.join(os.getcwd(),
> +                     "iozone-graphs-%s" % time.strftime('%Y-%m-%d-%H.%M.%S'))
> +    if not os.path.isdir(o):
> +        os.makedirs(o)
> +
> +    a = IOzoneAnalyzer(list_files=filenames, output_dir=o)
> +    a.analyze()
> +    p = IOzonePlotter(results_file=filenames[0], output_dir=o)
> +    p.plot_all()
> --
> 1.7.0.1
>
> _______________________________________________
> Autotest mailing list
> Autotest@xxxxxxxxxxxxxxx
> http://test.kernel.org/cgi-bin/mailman/listinfo/autotest
>
--
To unsubscribe from this list: send the line "unsubscribe kvm" in
the body of a message to majordomo@xxxxxxxxxxxxxxx
More majordomo info at  http://vger.kernel.org/majordomo-info.html

[Index of Archives]     [KVM ARM]     [KVM ia64]     [KVM ppc]     [Virtualization Tools]     [Spice Development]     [Libvirt]     [Libvirt Users]     [Linux USB Devel]     [Linux Audio Users]     [Yosemite Questions]     [Linux Kernel]     [Linux SCSI]     [XFree86]
  Powered by Linux