Changes in benchmark/plot.py [76f5e9f:8fca132]

File:

• benchmark/plot.py (modified) (9 diffs)

benchmark/plot.py

@@ -14 +14 @@
 import math
 import numpy
+import os
 import re
 import statistics
 import sys
-
+import time
+
+import matplotlib
 import matplotlib.pyplot as plt
-from matplotlib.ticker import EngFormatter
+from matplotlib.ticker import EngFormatter, ScalarFormatter
+
+def fmtDur( duration ):
+        if duration :
+                hours, rem = divmod(duration, 3600)
+                minutes, rem = divmod(rem, 60)
+                seconds, millis = divmod(rem, 1)
+                return "%2d:%02d.%03d" % (minutes, seconds, millis * 1000)
+        return " n/a"
 
 class Field:
-        def __init__(self, unit, _min, _log, _name=None):
+        def __init__(self, unit, _min, _log, _name=None, _factor=1.0):
                 self.unit = unit
                 self.min  = _min
                 self.log  = _log
                 self.name = _name
+                self.factor = _factor
 
 field_names = {
         "ns per ops"            : Field('ns'    , 0, False),
-        "Number of processors"  : Field(''      , 1, False),
+        "Number of processors"  : Field(''      , 1, "exact"),
         "Ops per procs"         : Field('Ops'   , 0, False),
         "Ops per threads"       : Field('Ops'   , 0, False),
-        "ns per ops/procs"      : Field(''    , 0, False, _name = "Latency (ns $/$ (Processor $\\times$ Operation))" ),
+        "ns per ops/procs"      : Field(''      , 0, False, _name = "ns $\\times$ (Processor $/$ Total Ops)" ),
         "Number of threads"     : Field(''      , 1, False),
         "Total Operations(ops)" : Field('Ops'   , 0, False),
         "Ops/sec/procs"         : Field('Ops'   , 0, False),
         "Total blocks"          : Field('Blocks', 0, False),
-        "Ops per second"        : Field(''   , 0, False),
+        "Ops per second"        : Field(''      , 0, False),
         "Cycle size (# thrds)"  : Field('thrd'  , 1, False),
         "Duration (ms)"         : Field('ms'    , 0, False),
         "Target QPS"            : Field(''      , 0, False),
         "Actual QPS"            : Field(''      , 0, False),
-        "Average Read Latency"  : Field('us'    , 0, True),
-        "Median Read Latency"   : Field('us'    , 0, True),
-        "Tail Read Latency"     : Field('us'    , 0, True),
-        "Average Update Latency": Field('us'    , 0, True),
-        "Median Update Latency" : Field('us'    , 0, True),
-        "Tail Update Latency"   : Field('us'    , 0, True),
-        "Update Ratio"          : Field('\%'    , 0, False),
+        "Average Read Latency"  : Field('s'     , 0, False, _factor = 0.000001),
+        "Median Read Latency"   : Field('s'     , 0, True, _factor = 0.000001),
+        "Tail Read Latency"     : Field('s'     , 0, True, _factor = 0.000001),
+        "Average Update Latency": Field('s'     , 0, True, _factor = 0.000001),
+        "Median Update Latency" : Field('s'     , 0, True, _factor = 0.000001),
+        "Tail Update Latency"   : Field('s'     , 0, True, _factor = 0.000001),
+        "Update Ratio"          : Field('%'   , 0, False),
+        "Request Rate"          : Field('req/s' , 0, False),
+        "Data Rate"             : Field('b/s'   , 0, False, _factor = 1000 * 1000, _name = "Response Throughput"),
+        "Errors"                : Field('%'   , 0, False),
 }
 
-def plot(in_data, x, y, options):
+def plot(in_data, x, y, options, prefix):
         fig, ax = plt.subplots()
-        colors = itertools.cycle(['#0095e3','#006cb4','#69df00','#0aa000','#fb0300','#e30002','#fd8f00','#ff7f00','#8f00d6','#4b009a','#ffff00','#b13f00'])
-        series = {} # scatter data for each individual data point
-        groups = {} # data points for x value
+        colors  = itertools.cycle(['#006cb4','#0aa000','#ff6600','#8510a1','#0095e3','#fd8f00','#e30002','#8f00d6','#4b009a','#ffff00','#69df00','#fb0300','#b13f00'])
+        markers = itertools.cycle(['x', '+', '1', '2', '3', '4'])
+        series  = {} # scatter data for each individual data point
+        groups  = {} # data points for x value
 
         print("Preparing Data")
@@ -62 +78 @@
         for entry in in_data:
                 name = entry[0]
+                if options.filter and not name.startswith(options.filter):
+                        continue
+
                 if not name in series:
                         series[name] = {'x':[], 'y':[]}
@@ -70 +89 @@
                 if x in entry[2] and y in entry[2]:
                         xval = entry[2][x]
-                        yval = entry[2][y]
+                        yval = entry[2][y] * field_names[y].factor
                         series[name]['x'].append(xval)
                         series[name]['y'].append(yval)
@@ -98 +117 @@
         for name, data in sorted(series.items()):
                 _col = next(colors)
-                plt.scatter(data['x'], data['y'], color=_col, label=name, marker='x')
-                plt.plot(lines[name]['x'], lines[name]['min'], '--', color=_col)
+                _mrk = next(markers)
+                plt.scatter(data['x'], data['y'], color=_col, label=name[len(prefix):], marker=_mrk)
+                plt.plot(lines[name]['x'], lines[name]['min'], ':', color=_col)
                 plt.plot(lines[name]['x'], lines[name]['max'], '--', color=_col)
                 plt.plot(lines[name]['x'], lines[name]['med'], '-', color=_col)
@@ -119 +139 @@
         elif field_names[x].log:
                 ax.set_xscale('log')
+                if field_names[x].log == "exact":
+                        xvals = set()
+                        for s in series.values():
+                                xvals |= set(s['x'])
+                        ax.set_xticks(sorted(xvals))
+                        ax.get_xaxis().set_major_formatter(ScalarFormatter())
+                        plt.xticks(rotation = 45)
         else:
                 plt.xlim(field_names[x].min, mx + 0.25)
 
-        ax.yaxis.set_major_formatter( EngFormatter(unit=field_names[y].unit) )
         if options.logy:
                 ax.set_yscale('log')
@@ -130 +156 @@
                 plt.ylim(field_names[y].min, options.MaxY if options.MaxY else my*1.2)
 
+        ax.yaxis.set_major_formatter( EngFormatter(unit=field_names[y].unit) )
+
         plt.legend(loc='upper left')
 
         print("Results Ready")
+        start = time.time()
         if options.out:
                 plt.savefig(options.out, bbox_inches='tight')
         else:
                 plt.show()
+        end = time.time()
+        print("Took {}".format(fmtDur(end - start)))
 
 
@@ -150 +181 @@
         parser.add_argument('--logy', action='store_true', help="if set, makes the y-axis logscale")
         parser.add_argument('--MaxY', nargs='?', type=int, help="maximum value of the y-axis")
+        parser.add_argument('--filter', nargs='?', type=str, default="", help="if not empty, only print series that start with specified filter")
 
         options =  parser.parse_args()
+
+        # if not options.out:
+        #       matplotlib.use('SVG')
 
         # ================================================================================
@@ -173 +208 @@
                         fields.add(label)
 
+        # filter out the series if needed
+        if options.filter:
+                series = set(filter(lambda elem: elem.startswith(options.filter), series))
+
+        # find the common prefix on series for removal (only if no filter)
+        prefix = os.path.commonprefix(list(series))
+
         if not options.out :
                 print(series)
@@ -193 +235 @@
 
 
-        plot(data, wantx, wanty, options)
+        plot(data, wantx, wanty, options, prefix)