added fio log parser tool.

Signed-off-by: Mark Nelson <mnelson@redhat.com>

diff --git a/tools/fiologpaser.py b/tools/fiologpaser.py
new file mode 100755 (executable)
index 0000000..82fd5eb
--- /dev/null
+++ b/tools/fiologpaser.py
@@ -0,0 +1,152 @@
+#!/bin/python
+#
+# fiologparser.py
+#
+# This tool lets you parse multiple fio log files and look at interaval
+# statistics even when samples are non-uniform.  For instance:
+#
+# fiologparser.py -s *bw*
+#
+# to see per-interval sums for all bandwidth logs or:
+#
+# fiologparser.py -a *clat*
+#
+# to see per-interval average completion latency.
+
+import argparse
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--interval', required=False, type=int, default=1000, help='interval of time in seconds.')
+    parser.add_argument('-d', '--divisor', required=False, type=int, default=1, help='divide the results by this value.')
+    parser.add_argument('-f', '--full', dest='full', action='store_true', default=False, help='print full output.')
+    parser.add_argument('-a', '--average', dest='average', action='store_true', default=False, help='print the average for each interval.')
+    parser.add_argument('-s', '--sum', dest='sum', action='store_true', default=False, help='print the sum for each interval.')
+    parser.add_argument("FILE", help="collectl log output files to parse", nargs="+")
+    args = parser.parse_args()
+
+    return args
+
+def get_ftime(series):
+    ftime = 0
+    for ts in series:
+        if ftime == 0 or ts.last.end < ftime:
+            ftime = ts.last.end
+    return ftime
+
+def print_full(ctx, series):
+    ftime = get_ftime(series)
+    start = 0 
+    end = ctx.interval
+
+    while (start < ftime):
+        end = ftime if ftime < end else end
+        results = [ts.get_value(start, end) for ts in series]
+        print "%s, %s" % (end, ', '.join(["%0.3f" % i for i in results]))
+        start += ctx.interval
+        end += ctx.interval
+
+def print_sums(ctx, series):
+    ftime = get_ftime(series)
+    start = 0
+    end = ctx.interval
+
+    while (start < ftime):
+        end = ftime if ftime < end else end
+        results = [ts.get_value(start, end) for ts in series]
+        print "%s, %0.3f" % (end, sum(results))
+        start += ctx.interval
+        end += ctx.interval
+
+def print_averages(ctx, series):
+    ftime = get_ftime(series)
+    start = 0
+    end = ctx.interval
+
+    while (start < ftime):
+        end = ftime if ftime < end else end
+        results = [ts.get_value(start, end) for ts in series]
+        print "%s, %0.3f" % (end, float(sum(results))/len(results))
+        start += ctx.interval
+        end += ctx.interval
+
+
+def print_default(ctx, series):
+    ftime = get_ftime(series)
+    start = 0
+    end = ctx.interval
+    averages = []
+    weights = []
+
+    while (start < ftime):
+        end = ftime if ftime < end else end
+        results = [ts.get_value(start, end) for ts in series]
+        averages.append(sum(results)) 
+        weights.append(end-start)
+        start += ctx.interval
+        end += ctx.interval
+
+    total = 0
+    for i in xrange(0, len(averages)):
+        total += averages[i]*weights[i]
+    print '%0.3f' % (total/sum(weights))
+ 
+class TimeSeries():
+    def __init__(self, ctx, fn):
+        self.ctx = ctx
+        self.last = None 
+        self.samples = []
+        self.read_data(fn)
+
+    def read_data(self, fn):
+        f = open(fn, 'r')
+        p_time = 0
+        for line in f:
+            (time, value, foo, bar) = line.rstrip('\r\n').rsplit(', ')
+            self.add_sample(p_time, int(time), int(value))
+            p_time = int(time)
+ 
+    def add_sample(self, start, end, value):
+        sample = Sample(ctx, start, end, value)
+        if not self.last or self.last.end < end:
+            self.last = sample
+        self.samples.append(sample)
+
+    def get_value(self, start, end):
+        value = 0
+        for sample in self.samples:
+            value += sample.get_contribution(start, end)
+        return value
+
+class Sample():
+    def __init__(self, ctx, start, end, value):
+       self.ctx = ctx
+       self.start = start
+       self.end = end
+       self.value = value
+
+    def get_contribution(self, start, end):
+       # short circuit if not within the bound
+       if (end < self.start or start > self.end):
+           return 0 
+
+       sbound = self.start if start < self.start else start
+       ebound = self.end if end > self.end else end
+       ratio = float(ebound-sbound) / (end-start) 
+       return self.value*ratio/ctx.divisor
+
+
+if __name__ == '__main__':
+    ctx = parse_args()
+    series = []
+    for fn in ctx.FILE:
+       series.append(TimeSeries(ctx, fn)) 
+    if ctx.sum:
+        print_sums(ctx, series)
+    elif ctx.average:
+        print_averages(ctx, series)
+    elif ctx.full:
+        print_full(ctx, series)
+    else:
+        print_default(ctx, series)
+