def sub():
#
# Parse/aggregate.
#
tputs = collections.defaultdict(list)
lats = collections.defaultdict(list)
for fname in glob.glob('sync-*-count-*-npar-*-rep-*.out'):
m = re.match(r'sync-(\d+)-count-(\d+)-npar-(\d+)-rep-(\d+)\.out', fname)
sync, count, npar, rep = map(int, m.groups())
with file(fname) as f:
m = re.search(r'finished subs, tput = ([\d\.]+) ops/s, avg latency = (\d+)', f.readlines()[-2])
tput, lat = map(float, m.groups())
tputs[sync, count, npar].append(tput)
lats[sync, count, npar].append(lat)
for d in tputs, lats:
for k in d:
d[k] = array(d[k]).mean(), array(d[k]).std()
print k, d[k]
#
# Plot.
#
for title, ylabel, fname, d in [ ('Subscription throughput over three trials', 'Subscriptions per second', 'tput', tputs),
('Subscription latency over three trials', 'Round-trip time in ms', 'lat', lats) ]:
means = dict((k, v[0]) for k,v in d.iteritems())
sdevs = dict((k, v[1]) for k,v in d.iteritems())
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 500, 400)
syncs, counts, npars = [ sorted(set(x[i] for x in means))
for i in xrange(3) ]
print syncs, counts, npars
dataset = [ ( '%d topics, %s' %
( count, 'synchronous' if sync else 'asynchronous' ),
[ ( npar/10, means[sync, count, npar], sdevs[sync, count, npar] )
for npar in npars ] )
for count in counts
for sync in syncs ]
options = {'legend.position':
{'top': None, 'left': None, 'bottom': 100, 'right': 20},
'axis.x.ticks': [{'v': x, 'label': max(1,10*x)}
for i,(x,y,e) in enumerate(dataset[0][1])],
'axis.x.label': 'Number of outstanding subscription requests',
'axis.y.label': ylabel,
'padding.left': 50,
'title': title,
'background.color': '#f0f0f0'}
chart = VerticalBarChart(surface, structs.sparse_dict(options))
chart.addDataset(dataset)
chart.render()
surface.write_to_png(fname + '.png')
def subhelper(keyname, pat, xlabel):
#
# Parse/aggregate.
#
print 'Analyzing', keyname, 'for', 'publishers' if do_pubs else 'receivers'
print '========================'
print
tputs = collections.defaultdict(list)
lats = collections.defaultdict(list)
fnames = [ ( fname, tuple(map(int, m.groups())) )
for fname, m in filter( lambda m: m[1] is not None,
( ( fname, re.match(pat, fname) )
for fname in os.listdir('.') ) ) ]
tup2fname = dict( (tup, fname) for fname, tup in fnames )
keys, reps, nodes = map(lambda xs: sorted(set(xs)),
zip(*(tup for fname, tup in fnames)))
raw_table = []
print '== raw data =='
raw_table.append( [ keyname, 'rep', 'node', 'tput' ] + ( ['lat'] if do_pubs else [] ) + ['sum/mean tput', 'mean lat'] )
for key in keys:
for rep in reps:
tmptputs = []
tmplats = []
for node in nodes:
if (key, rep, node) in tup2fname:
with file(tup2fname[key, rep, node]) as f:
try:
if do_pubs:
m = re.search(r'finished acked pubs, tput = ([\d\.]+) ops/s, avg latency = (\d+)', f.readlines()[-2])
tput, lat = map(float, m.groups())
else:
m = re.search(r'finished recvs, tput = ([\d\.]+) ops/s', f.read())
[tput] = map(float, m.groups())
except AttributeError:
print >> sys.stderr, "While processing", tup2fname[key, rep, node]
raise
raw_table.append( [ key, rep, node, tput ] + ( [lat] if do_pubs else [] ) + ['',''] )
tmptputs.append(tput)
if do_pubs: tmplats.append(lat)
if keyname == 'npubs': tputs[key].append(sum(tmptputs))
else: tputs[key].append(array(tmptputs).mean())
if do_pubs: lats[key].append(array(tmplats).mean())
if len(nodes) > 1:
raw_table.append( [''] * (len(raw_table[0]) - 2) + [tputs[key][-1]] + ( [lats[key][-1]] if do_pubs else [] ) )
print strs.show_table_by_rows(raw_table)
print
print '== aggregated over reps =='
agg_table = []
agg_table.append( ( keyname, 'mean', 'sd' ) )
for d in tputs, lats:
for k in d:
d[k] = array(d[k]).mean(), array(d[k]).std()
agg_table.append( ( k, d[k][0], d[k][1] ) )
print strs.show_table_by_rows(agg_table)
print
#
# Plot.
#
if do_pubs:
plots = [ ('Publishing throughput over three trials', 'Publishes per second', '%s-pub-tput' % keyname, tputs),
('Publishing latency over three trials', 'Round-trip time in ms', '%s-pub-lat' % keyname, lats) ]
else:
plots = [ ('Receiving throughput over three trials', 'Receives per second', '%s-recv-tput' % keyname, tputs) ]
for title, ylabel, fname, d in plots:
means = dict((k, v[0]) for k,v in d.iteritems())
sdevs = dict((k, v[1]) for k,v in d.iteritems())
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 500, 400)
dataset = [ ( 'main',
[ ( i, means[key], sdevs[key] )
for i, key in enumerate(keys) ] ) ]
options = {'legend.position':
{'top': None, 'left': None, 'bottom': 100, 'right': 20},
'axis.x.ticks': [{'v': x, 'label': k}
for k,(x,y,e) in zip(keys, dataset[0][1])],
'axis.x.label': xlabel,
'axis.y.label': ylabel,
'padding.left': 50,
'title': title,
'background.color': '#f0f0f0'}
chart = VerticalBarChart(surface, structs.sparse_dict(options))
chart.addDataset(dataset)
chart.render()
surface.write_to_png(fname + '.png')
print
print
print
