def _parse_job_samples(self, job: Job) ->\
Tuple[ParallelPerfResult, ParallelPerfResult]:
"""
each perfinterval is samples.csv line #2 (l2) - line #1 (l1) to get # transactions and duration
timestamp is time of l1, but we need to convert it from CLOCK_MONOTONIC time to unix time.
samples.csv looks like this:
```
tid,flow_id,time,transactions,utime,stime,maxrss,minflt,majflt,nvcsw,nivcsw,latency_min,latency_mean,latency_max,latency_stddev
0,0,1898645.747723502,1,0.000371,0.000000,1144,39,0,2,0,0.000000,0.000000,0.000000,-nan
0,0,1898647.747733162,59322,0.185458,0.241758,1144,43,0,59320,0,0.000000,0.000000,0.000000,0.000000
0,0,1898648.747757407,89210,0.281500,0.354934,1144,43,0,89207,0,0.000000,0.000000,0.000000,0.000000
0,0,1898649.747737156,118790,0.281500,0.354934,1144,43,0,89207,0,0.000000,0.000000,0.000000,0.000000
```
:param job:
:type job:
:return:
:rtype:
"""
results = SequentialPerfResult()
cpu_results = SequentialPerfResult()
if not job.passed:
results.append(PerfInterval(0, 0, "transactions", time.time()))
cpu_results.append(PerfInterval(0, 0, "cpu_percent", time.time()))
elif job.what.is_crr_server():
# Neper doesn't support server stats for tcp_crr due to memory issues.
# Use perf_interval of 0.
# For duration neper doesn't have time_start/time_end for tcp_crr
# So just use the value of test length
d = float(job.what.params.test_length)
results.append(PerfInterval(0, d, "transactions", time.time()))
cpu_results.append(PerfInterval(0, d, "cpu_percent", time.time()))
else:
job_start = job.result['start_time']
samples = job.result['samples']
if samples is not None:
neper_start_time = float(samples[0]['time'])
for s_start, s_end in pairwise(samples):
flow, cpu = get_interval(s_start, s_end,
job_start, neper_start_time)
results.append(flow)
cpu_results.append(cpu)
#Wrap in ParallelPerfResult for now for easier graphing
#TODO When we add support for multiple flows and threads
#We want to update this accordingly.
p_results = ParallelPerfResult()
p_results.append(results)
p_cpu_results = ParallelPerfResult()
p_cpu_results.append(cpu_results)
return p_results, p_cpu_results
def _parse_job_streams(self, job):
result = ParallelPerfResult()
if not job.passed:
result.append(PerfInterval(0, 0, "bits"))
else:
for i in job.result["data"]["end"]["streams"]:
result.append(SequentialPerfResult())
for interval in job.result["data"]["intervals"]:
for i, stream in enumerate(interval["streams"]):
result[i].append(
PerfInterval(stream["bytes"] * 8, stream["seconds"],
"bits"))
return result
def _parse_job_streams(self, job):
result = ParallelPerfResult()
if not job.passed:
result.append(PerfInterval(0, 0, "bits", time.time()))
else:
for i in job.result["data"]["end"]["streams"]:
result.append(SequentialPerfResult())
job_start = job.result["data"]["start"]["timestamp"]["timesecs"]
for interval in job.result["data"]["intervals"]:
interval_start = interval["sum"]["start"]
for i, stream in enumerate(interval["streams"]):
result[i].append(PerfInterval(stream["bytes"] * 8,
stream["seconds"],
"bits", job_start + interval_start))
return result