Esempio n. 1
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    def pre_run(self):
        if 'FILESIZE' not in self.config_params:
            # need to detect file size
            pass

        self.fio_configs = fio_cfg_compile(self.raw_cfg,
                                           self.config_fname,
                                           self.config_params)
        self.fio_configs = list(self.fio_configs)

        files = {}
        for section in self.fio_configs:
            sz = ssize2b(section.vals['size'])
            msz = sz / (1024 ** 2)

            if sz % (1024 ** 2) != 0:
                msz += 1

            fname = section.vals['filename']

            # if already has other test with the same file name
            # take largest size
            files[fname] = max(files.get(fname, 0), msz)

        with ThreadPoolExecutor(len(self.config.nodes)) as pool:
            fc = functools.partial(self.pre_run_th,
                                   files=files,
                                   force=self.force_prefill)
            list(pool.map(fc, self.config.nodes))
Esempio n. 2
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    def pre_run(self):
        if 'FILESIZE' not in self.config_params:
            # need to detect file size
            pass

        self.fio_configs = fio_cfg_compile(self.raw_cfg, self.config_fname,
                                           self.config_params)
        self.fio_configs = list(self.fio_configs)

        files = {}
        for section in self.fio_configs:
            sz = ssize2b(section.vals['size'])
            msz = sz / (1024**2)

            if sz % (1024**2) != 0:
                msz += 1

            fname = section.vals['filename']

            # if already has other test with the same file name
            # take largest size
            files[fname] = max(files.get(fname, 0), msz)

        with ThreadPoolExecutor(len(self.config.nodes)) as pool:
            fc = functools.partial(self.pre_run_th,
                                   files=files,
                                   force=self.force_prefill)
            list(pool.map(fc, self.config.nodes))
Esempio n. 3
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 def key_func(data):
     tpl = data.summary_tpl()
     return (data.name,
             tpl.oper,
             tpl.mode,
             ssize2b(tpl.bsize),
             int(tpl.th_count) * int(tpl.vm_count))
Esempio n. 4
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def make_plots(processed_results, plots):
    """
    processed_results: [PerfInfo]
    plots = [(test_name_prefix:str, fname:str, description:str)]
    """
    files = {}
    for name_pref, fname, desc in plots:
        chart_data = []

        for res in processed_results:
            summ = res.name + "_" + res.summary
            if summ.startswith(name_pref):
                chart_data.append(res)

        if len(chart_data) == 0:
            raise ValueError("Can't found any date for " + name_pref)

        use_bw = ssize2b(chart_data[0].p.blocksize) > 16 * 1024

        chart_data.sort(key=lambda x: x.params['vals']['numjobs'])

        lat = None
        lat_min = None
        lat_max = None

        lat_50 = [x.lat_50 for x in chart_data]
        lat_95 = [x.lat_95 for x in chart_data]

        lat_diff_max = max(x.lat_95 / x.lat_50 for x in chart_data)
        lat_log_scale = (lat_diff_max > 10)

        testnodes_count = x.testnodes_count
        concurence = [(testnodes_count, x.concurence) for x in chart_data]

        if use_bw:
            data = [x.bw.average / 1000 for x in chart_data]
            data_conf = [x.bw.confidence / 1000 for x in chart_data]
            data_dev = [x.bw.deviation * 2.5 / 1000 for x in chart_data]
            name = "BW"
        else:
            data = [x.iops.average for x in chart_data]
            data_conf = [x.iops.confidence for x in chart_data]
            data_dev = [x.iops.deviation * 2 for x in chart_data]
            name = "IOPS"

        fc = io_chart(title=desc,
                      concurence=concurence,
                      latv=lat,
                      latv_min=lat_min,
                      latv_max=lat_max,
                      iops_or_bw=data,
                      iops_or_bw_err=data_conf,
                      legend=name,
                      log_lat=lat_log_scale,
                      latv_50=lat_50,
                      latv_95=lat_95,
                      error2=data_dev)
        files[fname] = fc

    return files
def finall_process(sec, counter=[0]):
    sec = sec.copy()

    if sec.vals.get('numjobs', '1') != 1:
        msg = "Group reporting should be set if numjobs != 1"
        assert 'group_reporting' in sec.vals, msg

    sec.vals['unified_rw_reporting'] = '1'

    if isinstance(sec.vals['size'], Var):
        raise ValueError("Variable {0} isn't provided".format(
            sec.vals['size'].name))

    sz = ssize2b(sec.vals['size'])
    offset = sz * ((MAGIC_OFFSET * counter[0]) % 1.0)
    offset = int(offset) // 1024 ** 2
    new_vars = {'UNIQ_OFFSET': str(offset) + "m"}

    for name, val in sec.vals.items():
        if isinstance(val, Var):
            if val.name in new_vars:
                sec.vals[name] = new_vars[val.name]

    for vl in sec.vals.values():
        if isinstance(vl, Var):
            raise ValueError("Variable {0} isn't provided".format(vl.name))

    params = sec.vals.copy()
    params['UNIQ'] = 'UN{0}'.format(counter[0])
    params['COUNTER'] = str(counter[0])
    params['TEST_SUMM'] = get_test_summary(sec)
    sec.name = sec.name.format(**params)
    counter[0] += 1

    return sec
Esempio n. 6
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def finall_process(sec, counter=[0]):
    sec = sec.copy()

    if sec.vals.get('numjobs', '1') != 1:
        msg = "Group reporting should be set if numjobs != 1"
        assert 'group_reporting' in sec.vals, msg

    sec.vals['unified_rw_reporting'] = '1'

    if isinstance(sec.vals['size'], Var):
        raise ValueError("Variable {0} isn't provided".format(
            sec.vals['size'].name))

    sz = ssize2b(sec.vals['size'])
    offset = sz * ((MAGIC_OFFSET * counter[0]) % 1.0)
    offset = int(offset) // 1024**2
    new_vars = {'UNIQ_OFFSET': str(offset) + "m"}

    for name, val in sec.vals.items():
        if isinstance(val, Var):
            if val.name in new_vars:
                sec.vals[name] = new_vars[val.name]

    for vl in sec.vals.values():
        if isinstance(vl, Var):
            raise ValueError("Variable {0} isn't provided".format(vl.name))

    params = sec.vals.copy()
    params['UNIQ'] = 'UN{0}'.format(counter[0])
    params['COUNTER'] = str(counter[0])
    params['TEST_SUMM'] = get_test_summary(sec)
    sec.name = sec.name.format(**params)
    counter[0] += 1

    return sec
Esempio n. 7
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    def pre_run(self):
        files = {}
        for section in self.fio_configs:
            sz = ssize2b(section.vals['size'])
            msz = sz / (1024 ** 2)

            if sz % (1024 ** 2) != 0:
                msz += 1

            fname = section.vals['filename']

            # if already has other test with the same file name
            # take largest size
            files[fname] = max(files.get(fname, 0), msz)

        with ThreadPoolExecutor(len(self.config.nodes)) as pool:
            fc = functools.partial(self.pre_run_th,
                                   files=files,
                                   force=self.force_prefill)
            list(pool.map(fc, self.config.nodes))
Esempio n. 8
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def make_plots(processed_results, plots):
    """
    processed_results: [PerfInfo]
    plots = [(test_name_prefix:str, fname:str, description:str)]
    """
    files = {}
    for name_pref, fname, desc in plots:
        chart_data = []

        for res in processed_results:
            summ = res.name + "_" + res.summary
            if summ.startswith(name_pref):
                chart_data.append(res)

        if len(chart_data) == 0:
            raise ValueError("Can't found any date for " + name_pref)

        use_bw = ssize2b(chart_data[0].p.blocksize) > 16 * 1024

        chart_data.sort(key=lambda x: x.params['vals']['numjobs'])

        lat = None
        lat_min = None
        lat_max = None

        lat_50 = [x.lat_50 for x in chart_data]
        lat_95 = [x.lat_95 for x in chart_data]

        lat_diff_max = max(x.lat_95 / x.lat_50 for x in chart_data)
        lat_log_scale = (lat_diff_max > 10)

        testnodes_count = x.testnodes_count
        concurence = [(testnodes_count, x.concurence)
                      for x in chart_data]

        if use_bw:
            data = [x.bw.average / 1000 for x in chart_data]
            data_conf = [x.bw.confidence / 1000 for x in chart_data]
            data_dev = [x.bw.deviation * 2.5 / 1000 for x in chart_data]
            name = "BW"
        else:
            data = [x.iops.average for x in chart_data]
            data_conf = [x.iops.confidence for x in chart_data]
            data_dev = [x.iops.deviation * 2 for x in chart_data]
            name = "IOPS"

        fc = io_chart(title=desc,
                      concurence=concurence,

                      latv=lat,
                      latv_min=lat_min,
                      latv_max=lat_max,

                      iops_or_bw=data,
                      iops_or_bw_err=data_conf,

                      legend=name,
                      log_lat=lat_log_scale,

                      latv_50=lat_50,
                      latv_95=lat_95,

                      error2=data_dev)
        files[fname] = fc

    return files
Esempio n. 9
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def linearity_report(processed_results, lab_info, comment):
    labels_and_data_mp = collections.defaultdict(lambda: [])
    vls = {}

    # plot io_time = func(bsize)
    for res in processed_results.values():
        if res.name.startswith('linearity_test'):
            iotimes = [1000. / val for val in res.iops.raw]

            op_summ = get_test_summary(res.params)[:3]

            labels_and_data_mp[op_summ].append(
                [res.p.blocksize, res.iops.raw, iotimes])

            cvls = res.params.vals.copy()
            del cvls['blocksize']
            del cvls['rw']

            cvls.pop('sync', None)
            cvls.pop('direct', None)
            cvls.pop('buffered', None)

            if op_summ not in vls:
                vls[op_summ] = cvls
            else:
                assert cvls == vls[op_summ]

    all_labels = None
    _, ax1 = plt.subplots()
    for name, labels_and_data in labels_and_data_mp.items():
        labels_and_data.sort(key=lambda x: ssize2b(x[0]))

        labels, _, iotimes = zip(*labels_and_data)

        if all_labels is None:
            all_labels = labels
        else:
            assert all_labels == labels

        plt.boxplot(iotimes)
        if len(labels_and_data) > 2 and \
           ssize2b(labels_and_data[-2][0]) >= 4096:

            xt = range(1, len(labels) + 1)

            def io_time(sz, bw, initial_lat):
                return sz / bw + initial_lat

            x = numpy.array(map(ssize2b, labels))
            y = numpy.array([sum(dt) / len(dt) for dt in iotimes])
            popt, _ = scipy.optimize.curve_fit(io_time, x, y, p0=(100., 1.))

            y1 = io_time(x, *popt)
            plt.plot(xt, y1, linestyle='--',
                     label=name + ' LS linear approx')

            for idx, (sz, _, _) in enumerate(labels_and_data):
                if ssize2b(sz) >= 4096:
                    break

            bw = (x[-1] - x[idx]) / (y[-1] - y[idx])
            lat = y[-1] - x[-1] / bw
            y2 = io_time(x, bw, lat)
            plt.plot(xt, y2, linestyle='--',
                     label=abbv_name_to_full(name) +
                     ' (4k & max) linear approx')

    plt.setp(ax1, xticklabels=labels)

    plt.xlabel("Block size")
    plt.ylabel("IO time, ms")

    plt.subplots_adjust(top=0.85)
    plt.legend(bbox_to_anchor=(0.5, 1.15),
               loc='upper center',
               prop={'size': 10}, ncol=2)
    plt.grid()
    iotime_plot = get_emb_data_svg(plt)
    plt.clf()

    # plot IOPS = func(bsize)
    _, ax1 = plt.subplots()

    for name, labels_and_data in labels_and_data_mp.items():
        labels_and_data.sort(key=lambda x: ssize2b(x[0]))
        _, data, _ = zip(*labels_and_data)
        plt.boxplot(data)
        avg = [float(sum(arr)) / len(arr) for arr in data]
        xt = range(1, len(data) + 1)
        plt.plot(xt, avg, linestyle='--',
                 label=abbv_name_to_full(name) + " avg")

    plt.setp(ax1, xticklabels=labels)
    plt.xlabel("Block size")
    plt.ylabel("IOPS")
    plt.legend(bbox_to_anchor=(0.5, 1.15),
               loc='upper center',
               prop={'size': 10}, ncol=2)
    plt.grid()
    plt.subplots_adjust(top=0.85)

    iops_plot = get_emb_data_svg(plt)

    res = set(get_test_lcheck_params(res) for res in processed_results.values())
    ncount = list(set(res.testnodes_count for res in processed_results.values()))
    conc = list(set(res.concurence for res in processed_results.values()))

    assert len(conc) == 1
    assert len(ncount) == 1

    descr = {
        'vm_count': ncount[0],
        'concurence': conc[0],
        'oper_descr': ", ".join(res).capitalize()
    }

    params_map = {'iotime_vs_size': iotime_plot,
                  'iops_vs_size': iops_plot,
                  'descr': descr}

    return get_template('report_linearity.html').format(**params_map)
Esempio n. 10
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 def key_func(data):
     tpl = data.summary_tpl()
     return (data.name, tpl.oper, tpl.mode, ssize2b(tpl.bsize),
             int(tpl.th_count) * int(tpl.vm_count))
Esempio n. 11
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def linearity_report(processed_results, lab_info, comment):
    labels_and_data_mp = collections.defaultdict(lambda: [])
    vls = {}

    # plot io_time = func(bsize)
    for res in processed_results.values():
        if res.name.startswith('linearity_test'):
            iotimes = [1000. / val for val in res.iops.raw]

            op_summ = get_test_summary(res.params)[:3]

            labels_and_data_mp[op_summ].append(
                [res.p.blocksize, res.iops.raw, iotimes])

            cvls = res.params.vals.copy()
            del cvls['blocksize']
            del cvls['rw']

            cvls.pop('sync', None)
            cvls.pop('direct', None)
            cvls.pop('buffered', None)

            if op_summ not in vls:
                vls[op_summ] = cvls
            else:
                assert cvls == vls[op_summ]

    all_labels = None
    _, ax1 = plt.subplots()
    for name, labels_and_data in labels_and_data_mp.items():
        labels_and_data.sort(key=lambda x: ssize2b(x[0]))

        labels, _, iotimes = zip(*labels_and_data)

        if all_labels is None:
            all_labels = labels
        else:
            assert all_labels == labels

        plt.boxplot(iotimes)
        if len(labels_and_data) > 2 and \
           ssize2b(labels_and_data[-2][0]) >= 4096:

            xt = range(1, len(labels) + 1)

            def io_time(sz, bw, initial_lat):
                return sz / bw + initial_lat

            x = numpy.array(map(ssize2b, labels))
            y = numpy.array([sum(dt) / len(dt) for dt in iotimes])
            popt, _ = scipy.optimize.curve_fit(io_time, x, y, p0=(100., 1.))

            y1 = io_time(x, *popt)
            plt.plot(xt, y1, linestyle='--', label=name + ' LS linear approx')

            for idx, (sz, _, _) in enumerate(labels_and_data):
                if ssize2b(sz) >= 4096:
                    break

            bw = (x[-1] - x[idx]) / (y[-1] - y[idx])
            lat = y[-1] - x[-1] / bw
            y2 = io_time(x, bw, lat)
            plt.plot(xt,
                     y2,
                     linestyle='--',
                     label=abbv_name_to_full(name) +
                     ' (4k & max) linear approx')

    plt.setp(ax1, xticklabels=labels)

    plt.xlabel("Block size")
    plt.ylabel("IO time, ms")

    plt.subplots_adjust(top=0.85)
    plt.legend(bbox_to_anchor=(0.5, 1.15),
               loc='upper center',
               prop={'size': 10},
               ncol=2)
    plt.grid()
    iotime_plot = get_emb_data_svg(plt)
    plt.clf()

    # plot IOPS = func(bsize)
    _, ax1 = plt.subplots()

    for name, labels_and_data in labels_and_data_mp.items():
        labels_and_data.sort(key=lambda x: ssize2b(x[0]))
        _, data, _ = zip(*labels_and_data)
        plt.boxplot(data)
        avg = [float(sum(arr)) / len(arr) for arr in data]
        xt = range(1, len(data) + 1)
        plt.plot(xt,
                 avg,
                 linestyle='--',
                 label=abbv_name_to_full(name) + " avg")

    plt.setp(ax1, xticklabels=labels)
    plt.xlabel("Block size")
    plt.ylabel("IOPS")
    plt.legend(bbox_to_anchor=(0.5, 1.15),
               loc='upper center',
               prop={'size': 10},
               ncol=2)
    plt.grid()
    plt.subplots_adjust(top=0.85)

    iops_plot = get_emb_data_svg(plt)

    res = set(
        get_test_lcheck_params(res) for res in processed_results.values())
    ncount = list(
        set(res.testnodes_count for res in processed_results.values()))
    conc = list(set(res.concurence for res in processed_results.values()))

    assert len(conc) == 1
    assert len(ncount) == 1

    descr = {
        'vm_count': ncount[0],
        'concurence': conc[0],
        'oper_descr': ", ".join(res).capitalize()
    }

    params_map = {
        'iotime_vs_size': iotime_plot,
        'iops_vs_size': iops_plot,
        'descr': descr
    }

    return get_template('report_linearity.html').format(**params_map)