On Fri, Apr 30, 2010 at 2:37 PM, Lucas Meneghel Rodrigues
<lmr@xxxxxxxxxx> wrote:
> On Fri, 2010-04-30 at 14:23 -0700, Martin Bligh wrote:
>> I'm slightly surprised this isn't called from postprocess
>> in the test? Any downside to doing that?
>
> In the second patch I do the change to make the test to use the
> postprocessing module.
Ah, OK, missed that. Will go look. This one looks good.
>
>> 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
>> >
>
>
>
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