2 posts tagged with "performance"

In today’s world of fast-moving software delivery, it’s not just about shipping features quickly — it’s about doing it reliably, sustainably, and with confidence. That’s where DORA Metrics come in.

No, we’re not talking about Dora the Explorer — though this set of metrics does help you explore your team’s DevOps efficiency pretty effectively. Originally developed through research from the DevOps Research and Assessment (DORA) team (acquired by Google Cloud), these metrics have become an industry standard for evaluating software delivery performance.

So let’s break them down — with a developer’s eye and a practical mindset.

What Are DORA Metrics?#

The DORA team identified four key metrics (a fifth one is often included now) that high-performing software teams use to measure their effectiveness:

1. Deployment Frequency (DF)
2. Lead Time for Changes (LTC)
3. Change Failure Rate (CFR)
4. Mean Time to Recovery (MTTR)
5. Reliability

These metrics are backed by years of research and correlate directly with business performance. You can read the original research via Google Cloud’s DevOps Research.

Let’s unpack each of these with real-world context.

1. Deployment Frequency (DF)#

What it means:
How often your team deploys code to production.

Why it matters:
The more frequently you deploy, the faster you can deliver value, fix bugs, and iterate.

Dev perspective:
If you’re deploying once a sprint, that’s okay. If you're deploying multiple times a day without breaking stuff — that’s elite. Tools like GitHub Actions, ArgoCD, and Spinnaker help teams streamline CI/CD.

🛠️ Tool to try: GitHub Deployments API

2. Lead Time for Changes (LTC)#

What it means:
Time from a commit to that change running in production.

Why it matters:
Shorter lead times = faster feedback loops and more agile teams.

Dev perspective:
If your PR sits in review for 3 days, you’ve already got a bottleneck. Optimize review processes, CI speeds, and test execution.

Visualization tip: Use git log, JIRA, or DORA dashboards in tools like Datadog to see patterns.

3. Change Failure Rate (CFR)#

What it means:
What percentage of deployments lead to failures (bugs, outages, rollbacks)?

Why it matters:
Deploying fast is good. Deploying fast without breaking stuff is better.

Dev perspective:
Don’t ignore test coverage and observability. Tools like Sentry, New Relic, or Honeycomb can alert you to regressions before users scream.

Metric hack: Count production issues tied to deployments using bug/incident labels in GitHub Issues or ServiceNow.

4. Mean Time to Recovery (MTTR)#

What it means:
How long it takes to restore service when a production incident occurs.

Why it matters:
Downtime costs money, trust, and developer morale.

Dev perspective:
Can you rollback with confidence? Do you have runbooks, alerts, and dashboards? Fast recovery starts with great incident response playbooks and observability.

Tools to use: PagerDuty, Grafana, AWS CloudWatch

Bonus: Reliability#

This fifth “unofficial” metric is often included in newer DORA implementations. It reflects system uptime, SLIs/SLOs, and general confidence in your platform. It’s especially crucial in SRE-heavy teams.

How to Collect These Metrics?#

You don’t need a giant analytics stack to start.

Start small: You can collect these via scripts that hit the GitHub API.

Use ready-made tools like:

• DORA Metrics GitHub Project – lightweight, deployable API.

• Harness, OpsLevel, LinearB

  Self-hosting: Log these to Prometheus/Grafana for visualization.

  Want a head start? You can build a Dockerized DORA metrics API using Node.js and Serverless framework in under 30 minutes.

Why Should You Care?#

Here’s the thing: DORA metrics aren’t just vanity numbers. They directly correlate with high-performing engineering cultures. In fact, companies that excel in DORA metrics:

• Ship features faster
• Respond to incidents quicker
• Break production less
• Have higher developer satisfaction

But they’re not about punishing teams — they’re about surfacing bottlenecks, improving workflows, and celebrating improvements.

Final Thoughts#

DORA metrics provide an honest mirror into your DevOps practices. They’re not the full picture — context is always king — but they’re damn good indicators.

So whether you’re on a two-person startup team or managing 40 microservices at an enterprise scale, DORA metrics give you the pulse of your software delivery health.

Nife.io's release management capabilities let you automate deployments, promote workloads across environments, and track release history with built-in rollback options — all while maintaining governance and compliance.

Nife’s Manage Continuous Deployments solution brings consistency, control, and speed to your release cycles. From microservices to multi-cluster rollouts, it helps teams ship faster, recover quickly, and reduce risk

System performance matters—whether you're running a heavy-duty backend server on Linux, multitasking on Windows, or pushing Xcode to its limits on macOS. You don’t want your laptop sounding like a jet engine or your EC2 instance crashing from an out-of-memory error.

This guide walks you through how to check and analyze CPU and memory usage, interpret the data, and take practical actions across Linux, Windows, and macOS. Let’s dive in.

Linux Performance Monitoring with htop, vmstat & swap tuning#

Check CPU and Memory Usage#

Linux gives you surgical control via CLI tools. Start with:

• top or htop: Real-time usage metrics

  top

  sudo apt install htop

  htop

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• ps aux --sort=-%mem: Sorts by memory usage

  ps aux --sort=-%mem | head -n 10

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• free -h: View memory in a human-readable format

  free -h

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• vmstat: Shows memory, swap, and CPU context switching

  vmstat 1 5

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Learn more: Linux Memory Explained

Optimization Tips#

• Enable swap (if disabled) – Many VMs (like EC2) don’t enable swap by default:

  sudo fallocate -l 4G /swapfile

  sudo chmod 600 /swapfile

  sudo mkswap /swapfile

  sudo swapon /swapfile

  echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

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• Tune Java apps (JVM-based) — Limit memory usage:

  -Xmx512M -Xms512M

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Windows: Task Manager, Resource Monitor & PowerShell Tricks#

Check Resource Usage#

• Task Manager (Ctrl + Shift + Esc):

  • View CPU usage per core
  • Check memory consumption
  • Review app/resource breakdowns

• Resource Monitor:

  • From Task Manager > Performance > Open Resource Monitor
  • Monitor by process, network, disk, and more

• PowerShell:

  Get-Process | Sort-Object CPU -Descending | Select-Object -First 10

  Get-Process | Sort-Object WS -Descending | Select-Object -First 10

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Learn more: Windows Performance Tuning

Optimization Tips#

• Disable startup apps — Uncheck unnecessary ones in the Startup tab
• Enable paging file (virtual memory)
• Remove bloatware — Pre-installed apps often hog memory

macOS: Activity Monitor, Terminal Tools & Optimization#

Check Resource Usage#

• Activity Monitor:

  • Open via Spotlight (Cmd + Space > “Activity Monitor”)
  • Tabs: CPU, Memory, Energy, Disk, Network

• Terminal Tools:

  top

  vm_stat

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  • Get free memory in MB:
    pagesize=$(pagesize)

    vm_stat | awk -v page_size=$pagesize '/Pages free/ {print $3 * page_size / 1024 / 1024 " MB"}'

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• ps + sort:

  ps aux | sort -nrk 3 | head -n 10 # Top CPU

  ps aux | sort -nrk 4 | head -n 10 # Top Memory

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Learn more: Apple Developer Performance Tips

Optimization Tips#

• Close idle Chrome tabs — Each one is a separate process
• Purge caches (dev use only):
  sudo purge

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• Reindex Spotlight (if mds is hogging CPU):
  sudo mdutil -E /

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Must-Know CPU & Memory Metrics Explained#

Best Cross-Platform Tools for Monitoring#

• Glances: All-in-one CLI monitor → pip install glances
  Glances GitHub
• Netdata: Live dashboards → Netdata Cloud
• Grafana + Prometheus: For full observability
  Grafana Docs
• Process Explorer: Windows power tool → Microsoft Sysinternals

Common Symptoms & Quick Fixes#

Final Thoughts#

System performance isn’t just about uptime — it’s about user experience, developer productivity, and infrastructure cost. The key is to observe patterns, know what “normal” looks like, and take action before things go south.

Whether you're debugging a dev laptop or running a multi-node Kubernetes cluster, these tools and tips will help you stay fast and lean.

Nife.io makes multi-cloud infrastructure and application orchestration simple. It provides enterprises with a unified platform to automate, scale, and manage workloads effortlessly.

Discover how Nife streamlines Application Lifecycle Management.