def run_tests(self, csv_queue):
"""
execute the whole benchmark, including initialization, warmup and
benchmark runs
"""
# derive configuration for the CPU count / RAM size
configs = PgBench._configure(cpu_count(), available_ram())
results = {'ro': {}, 'rw': {}} #ro:read only rw:read-write
j = 0
for config in configs:
scale = config['scale']
if scale not in results['ro']:
results['ro'][scale] = {}
if scale not in results['rw']:
results['rw'][scale] = {}
#print(results)
# init for the dataset scale and warmup
self._init(scale)
warmup = self._run('w%d' % j, scale, self._duration, cpu_count(),
cpu_count())
j += 1
# read-only & read-write
for ro in [True, False]:
if ro:
tag = 'ro'
else:
tag = 'rw'
for i in range(self._runs):
log("pgbench: %s run=%d" % (tag, i))
for clients in config['clients']:
if clients not in results[tag][scale]:
results[tag][scale][clients] = {}
results[tag][scale][clients]['results'] = []
r = self._run(i, scale, self._duration, clients,
clients, ro, True, csv_queue)
r.update({'run': i})
results[tag][scale][clients]['results'].append(r)
tps = []
for result in results[tag][scale][clients]['results']:
tps.append(float(result['tps']))
results[tag][scale][clients]['metric'] = mean(tps)
results[tag][scale][clients]['median'] = median(tps)
results[tag][scale][clients]['std'] = std(tps)
self._results['pgbench'] = results
return self._results
def run_tests(self, csv_queue):
"""
execute the whole benchmark, including initialization, warmup and
benchmark runs
"""
# derive configuration for the CPU count / RAM size
configs = PgBench._configure(cpu_count(), available_ram())
info = {}
results = []
result = {}
configuration = {}
j = 0
for config in configs:
info['clients'] = config['clients']
scale = config['scale']
# init for the dataset scale and warmup
self._init(scale)
warmup = self._run('w%d' % j, scale, self._duration, cpu_count(),
cpu_count())
j += 1
# read-only & read-write
for ro in [True, False]:
if ro:
tag = 'ro'
else:
tag = 'rw'
for i in range(self._runs):
log("pgbench: %s run=%d" % (tag, i))
for clients in config['clients']:
if clients not in results:
result['clients'] = clients
r = self._run(i, scale, self._duration, clients,
clients, ro, True, csv_queue)
r.update({'run': i})
results.append(r)
info['runs'] = results
self._results['pgbench'] = info
return self._results
def run_tests(self, csv_queue, benchmark_options=None):
"""
execute the whole benchmark, including initialization, warmup and
benchmark runs
"""
# derive configuration for the CPU count / RAM size
# if there is benchmark setting in settings.py, take it and use,
# otherwise use the default one
configs = []
if benchmark_options:
configs.append(benchmark_options)
else:
configs = PgBench._configure(cpu_count(), available_ram())
results = {'ro': {}, 'rw': {}, 'customeScript': {}}
j = 0
for config in configs:
scale = config['scale']
if scale not in results['ro']:
results['ro'][scale] = {}
if scale not in results['rw']:
results['rw'][scale] = {}
# if scale not in results['customeScript']:
# results['customeScript'][scale] = {}
# init for the dataset scale and warmup
self._init(scale)
warmup = self._run('w%d' % j, scale, self._duration, cpu_count(),
cpu_count())
j += 1
# read-only & read-write
for ro in [True, False]:
if ro:
tag = 'ro'
else:
tag = 'rw'
for i in range(self._runs):
log("pgbench: %s run=%d" % (tag, i))
for clients in config['clients']:
if clients not in results[tag][scale]:
results[tag][scale][clients] = {}
results[tag][scale][clients]['results'] = []
r = self._run(i, scale, self._duration, clients,
clients, ro, True, csv_queue)
r.update({'run': i})
results[tag][scale][clients]['results'].append(r)
tps = []
for result in results[tag][scale][clients]['results']:
tps.append(float(result['tps']))
results[tag][scale][clients]['metric'] = mean(tps)
results[tag][scale][clients]['median'] = median(tps)
results[tag][scale][clients]['std'] = std(tps)
# todo add cmmand
# args = ['pgbench', '-c', str(nclients), '-j', str(njobs), '-T',
# str(duration)]
script = ScriptCollector(scriptdirpath=SCRIPTS_DIR,
env=self._env,
dbname=self._dbname)
if script.hasScript():
start = time.time()
scriptResult = script.run_custem_script()
print('scriptResult ')
print(scriptResult)
end = time.time()
r = PgBench._parse_results(scriptResult[1])
# r.update({'customeScript': read_only})
# results[tag][scale][clients]['results'].append(r)
r.update({'start': start, 'end': end})
results['customeScript']['results'] = []
results['customeScript']['results'].append(r)
tps = []
for result in results['customeScript']['results']:
tps.append(float(result['tps']))
results['customeScript']['metric'] = mean(tps)
results['customeScript']['median'] = median(tps)
results['customeScript']['std'] = std(tps)
results['customeScript']['scriptList'] = script.getScriptListJson()
self._results['pgbench'] = results
return self._results