Extracting Metrics From Jenkins Job Output

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When working with Jenkins, you may be running jobs that provide some kind of metrics. For example, on a website, you may be monitoring the page load time at every hour, the median / 90th percentile CPU load time, etc. If you are running this as a Jenkins job, the output may be stored as a flat file, JSON or some such format. Typically, this is dumped into the archive folder. In this blog post, I will show how to extract the data and get some meaningful metrics.

Initial Setup

In this case study, I am using a website's performance information. Specifically, I am pulling the median page load time for this blog. The data is pulled at every hour and stored in a flat file called summary.json. The format of the JSON file is as follows:

{
    "p98": "2611", 
    "median": "2611", 
    "p95": "2611", 
    "moe": "0.0", 
    "n": "1"
}

For this workflow, we only care about the "median" metric. We could as well swap the metric and compute our data for other percentiles as well.

I am actually using the Query API for mPulse. I’ll have a follow-up post on the exact workflow.

This Jenkins job runs every hour. Jenkins then moves this JSON file to a folder like $JENKINS_PATH/jobs/$JENKINS_PIPELINE/builds/$BUILD_NUMBER/archives/summary.json.

Computing Variance

The next step is to use this summary.json that is generated every hour to run a check: Is the current performance any different from the past performance?

In this use case, past performance is determined by three parameters:

If you’d like to brush up the stats, please refer to this MathIsFun page on Standard Deviation, Variance and more.

Here’s my algorithm to compute the moving performance benchmark:

Code Snippets

The first part I described earlier is to read the summary.json and extract the median metric. We can do this with this kind of code.

jobs_path = $JENKINS_PATH+$JENKINS_PIPELINE+'mPulse/builds'
directories = os.listdir(jobs_path)
directories.sort(key=natural_keys)
page_load_times = []
for each_directory in directories:
each_job = jobs_path + '/' + each_directory
if os.path.isdir(each_job)==True and os.path.islink(each_job)==False:
each_summary_file = each_job + '/archive/summary.json'
if os.path.isfile(each_summary_file):
with open(each_summary_file) as f:
data = json.loads(f.read())
if 'median' in data and data['median']!=None:
page_load_times.append(int(data['median']))

The next step is to get the last 20 values, without the latest run.

last_20_values = page_load_times[-21:-1]

Next up is to compute the stats.

median = statistics.median(last_20_values)
stddev = statistics.stdev(last_20_values)
variance = statistics.variance(last_20_values)

Finally, compare and find the anomaly.

if (last_median < (median - stddev)) or (last_median > (median + stddev)):
print("***** ALERT ********")
print("Performance anamoly detected: " + str(last_median) )
#force a build failure on jenkins
sys.exit(1)

In this case, I am failing the build when the current median is more than 1 standard deviation away. This will give me an easy visual indication of error.

By doing this, you now have an easy to use “anamoly detection” code! If you want to make it more interesting, we could replace the simple 1 standard deviation rule by more involved computation like the Nelson’s Rule.

Hope this has been fun! I’ll have a follow-up blog that explains the entire Jenkins pipeline and the demo of mPulse API that was used to extract the raw information.

(Note: I had originally published this blog here: https://akshayranganath.github.io/Python-Stats-From-Jenkins-Job-Output/)

 

 

 

 

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