Harnessing DORA Metrics for Enhanced Software Delivery: A Practical Guide with Jenkins, Prometheus, and Observe
Introduction
In the dynamic world of DevOps, the adoption of DORA (DevOps Research and Assessment) metrics provides a structured approach to evaluate and enhance software delivery processes. This article explores the integration of DORA metrics with Jenkins, Prometheus, and Observe, detailing how these tools can be employed to not only track but also supercharge your software delivery efficiency.
What Are DORA Metrics?
DORA metrics comprise four critical performance indicators crucial for assessing the effectiveness of DevOps practices within an organization:
- Deployment Frequency (DF): Measures the frequency of code deployments to production.
- Lead Time for Changes (LT): Tracks the duration from code commit to production deployment.
- Change Failure Rate (CFR): Calculates the percentage of changes that fail in production.
- Mean Time to Restore (MTTR): Gauges the average time required to recover from a failure in production.
These metrics offer actionable insights by highlighting the operational aspects of software development that correlate with high performance and reliability.
Tracking DORA Metrics in Jenkins
Jenkins, a widely adopted automation server for continuous integration and delivery (CI/CD), can effectively track DORA metrics. Below is an outline of using a Jenkins pipeline to log deployment frequency, calculate lead times, monitor change failure rates, and determine mean time to restore:
pipeline {
agent any
environment {
DEPLOY_LOG = 'deploy.log'
FAIL_LOG = 'fail.log'
}
//Build Application
stages {
stage('Build') {
steps {
echo 'Building the application...'
// Run required build commands
sh 'make build'
}
}
// Test Application
stage('Test') {
steps {
echo 'Running tests...'
// run required test commands
sh 'make test'
}
}
// Deploy application
stage('Deploy') {
steps {
echo 'Deploying the application...'
// run the deployment steps
sh 'make deploy'
// Log the deployment into log file to compute deployment frequency
sh "echo $(date '+%F_%T') >> ${DEPLOY_LOG}"
}
}
}
post {
always {
script {
// Computing deployment frequency (DF)
def deploymentCount = sh(script: "wc -l < ${DEPLOY_LOG}", returnStdout: true).trim()
echo "# of Deployments: ${deploymentCount}"
// Writing build failures into log for computing CFR
if (currentBuild.result == 'FAILURE') {
sh "echo $(date '+%F_%T') >> ${FAIL_LOG}"
}
// Computing Change Failure Rate (CFR)
def failureCount = sh(script: "wc -l < ${FAIL_LOG}", returnStdout: true).trim()
def CFR = (failureCount.toInteger() * 100) / deploymentCount.toInteger()
echo "Change Failure Rate: ${CFR}%"
// Computing Lead Time for Changes(LTC) using last commit and deploy times
def commitTime = sh(script: "git log -1 --pretty=format:'%ct'", returnStdout: true).trim()
def currentTime = sh(script: "date +%s", returnStdout: true).trim()
def leadTime = (currentTime.toLong() - commitTime.toLong()) / 3600
echo "Lead Time for Changes: ${leadTime} hours"
}
}
//End if pipeline
success {
echo 'Deployment Successful!'
}
failure {
echo 'Deployment failed!'
// Failure handling
}
}
} Monitoring DORA Metrics With Prometheus and Observe
Prometheus, an open-source monitoring toolkit, and Observe, a modern observability platform, can be combined to visualize and monitor DORA metrics in real-time. Here’s how you can integrate these tools:
- Install Prometheus: Set up Prometheus to scrape metrics from Jenkins.Configure Prometheus: Set up the prometheus.yml configuration file
#setting time interval at which metrics are collected global: scrape_interval: 30s #Configuring Prometheus to collect metics from Jenkins on specific port scrape_configs: - job_name: 'jenkins' static_configs: - targets: ['<JENKINS_SERVER>:<PORT>']
- Expose Metrics in Jenkins: Use the Prometheus plugin for Jenkins or custom scripts to make the metrics available.Example Python script:
from prometheus_client import start_http_server, Gauge
import random
import time
# Creating Prometheus metrics gauges for the four DORA KPIs
DF = Gauge('Deployment Frequency', 'No. of deployments in a day')
LT = Gauge('Lead Time For Changes', 'Average lead time for changes in hours')
CFR = Gauge('Change Failure Rate', 'Percentage of changes failures in production')
MTTR = Gauge('Mean Time To Restore', 'Mean time to restore service after failure in minutes')
#Start server
start_http_server(8000)
#Sending random values to generate sample metrics to test
while True:
DF.set(random.randint(1, 9))
LT.set(random.uniform(1, 18))
CFR.set(random.uniform(0, 27))
MTTR.set(random.uniform(1, 45))
#Sleep for 30s
time.sleep(30)- Set up Observe: Add Prometheus as a data source in Observe and create dashboards to display these metrics.
Conclusion
Implementing DORA metrics through Jenkins, monitored via Prometheus, and visualized through Observe enables organizations to achieve a comprehensive overview of their DevOps health. This robust setup not only aids in identifying bottlenecks but also fosters a culture of continuous improvement, critical for staying competitive in today’s fast-paced software development environments.
Call to Action
How do you utilize DORA metrics in your projects? Are there particular challenges or successes you’ve encountered? Share your experiences and let’s discuss how we can further leverage these insights to streamline software delivery processes.
References:
https://linearb.io/blog/dora-metrics
https://docs.gitlab.com/ee/user/analytics/dora_metrics.html
https://dzone.com/articles/dora-metrics-tracking-and-observability-with-jenki
My LinkedIn post
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