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Debug latency in the TinyHat admin

15 min


This procedure is part of a lab that teaches you how to monitor your Kubernetes cluster with Pixie.

Each procedure in the lab builds upon the last, so make sure you've completed the last procedure, Debug missing images in your service, before starting this one.

After fixing your last TinyHat.me bug, you and your team are feeling confident. It's time to push your next release to production.

Change to the scenario-2 directory and set up your environment:

cd ../scenario-2
Please wait while we update your lab environment.
deployment.apps/admin-service configured
deployment.apps/fetch-service configured
deployment.apps/frontend-service configured
deployment.apps/simulator configured


If you're a windows user, run the PowerShell setup script:


Now that you've released your new code, it's time to check Twitter for some user feedback:

A Twitter thread showing the admin is slow

Oh no! The admin page seems to be slow.

Reproduce the issue

You've been notified by your users that the TinyHat admin page is running slowly. Reproduce the issue for yourself.

Step 1 of 2

Look up your frontend's external IP address:

kubectl get services
add-service ClusterIP <none> 80/TCP 20m
admin-service ClusterIP <none> 80/TCP 20m
fetch-service ClusterIP <none> 80/TCP 20m
frontend-service LoadBalancer 80:32161/TCP 20m
gateway-service LoadBalancer 80:32469/TCP 20m
kubernetes ClusterIP <none> 443/TCP 20m
manipulation-service ClusterIP <none> 80/TCP 20m
moderate-service ClusterIP <none> 80/TCP 20m
mysql ClusterIP <none> 3306/TCP 20m
upload-service ClusterIP <none> 80/TCP 20m
Step 2 of 2

Go to the admin page of your site:



The page should load in between 5 and 10 seconds:

Browser console showing slow response times

Once again, your users were right. Why is your admin site taking so long to load? Use Pixie to find out!

Hunt for the slowness with Pixie

Five seconds is too long for your admin page to load. You can use Pixie to discover the source of the delay.

Step 1 of 12

From the New Relic homepage, go to Kubernetes:

New Relic site with an arrow pointing to Kubernetes

Step 2 of 12

Choose your tiny-hat cluster:

Arrow pointing to the tiny-hat cluster

Step 3 of 12

Then click Live Debugging with Pixie:

Arrow pointing to the live-debugger

Step 4 of 12

Once again, the default script is px/cluster:

Pixie's cluster script

Step 5 of 12

Scroll down, and filter to the default namespace:

Filter to the default namespace

This shows the same information as the cluster script, but only for the default namespace.

Step 6 of 12

Observe the service map for this namespace:

Service map for the default namespace

Here, you see the first exernal request comes to the frontend service. The frontend service makes an internal call to the gateway service, which then calls the admin service. Use your knowledge of this traffic flow to debug your high latency issue.

Step 7 of 12

Scroll down to the Service List and notice some of the high latency in your services:

High service latency

Step 8 of 12

Because you know the first service to receive requests is the frontend service, click on its name in the table:

Frontend service latency

Here, you see spikes in latency over time:

Frontend latency over time

But this only shows you the symptom of an underlying problem. To figure out why your frontend service is slow, you need to dig a little deeper.

Step 9 of 12

Scroll down to see the Sample Of Slow Inbound Requests table:

Sample Of Slow Inbound Requests table

Here, you see that the frontend's requests to /api/admin are slow. That's a promising lead!

Now that you know that the admin service is causing your app latency, it's time to investigate that service. One good place to look when you want to know what's causing slowness in your application is a performance flamegraph for that service.

Pixie's performance flamegraph shows the frequency that every function appears in stack traces across your CPUs. Pixie collects this data with a feature called continuous application profiling where it samples stack traces, aggregates those samples, and then groups the aggregates in the flamegraph.

Step 10 of 12

Change to the px/perf_flamegraph script to see it in action:

Flamegraph script

This graph is complex and, by default, shows more information than you need.

Step 11 of 12

Filter down to the default namespace:

Filter your flamegraph to the default namespace

That's better! The graph is now filtered to primarily show two specific services:

  • manipulation-service
  • admin-service

Flamegraph services

Focus on the admin service because that's what receives requests from the frontend.

Step 12 of 12

Notice two function cells from your admin service's main module:

Main module functions in your flamegraph

Remember, the width of the cell represents the frequency that the cell's function appears in Pixie's samples. Effectively, this represents how long the application spends in that function.

In this graph, you can see that main.createSampleImage is wider than main.setupSQLQuery, implying that the admin service spends more time creating sample images than it does setting up its SQL query. You can also infer from the name of the function and the width of the manipulation-service in the graph that main.createSampleImage makes a call to that service.

Based on your findings, you hypothesize that every time the frontend service calls /api/admin, the main.createSampleImage function gets executed, which sends a request to the manipulation service.

In a real world situation, you could confirm this hypothesis by looking at your code. You could then solve the problem by caching sample images so that they're not created on every request.


To recap, you observed latency in your application's administrative backend and used Pixie in New Relic to:

  1. Understand your services' relationships
  2. Narrow down the sources of high latency
  3. Discover the function that's causing long response times

Throughout this lab, you saw just a few ways that Pixie can help you monitor your software and debug issues when they come up.

Tear down

Now that you're done with this lab, tear down your cluster:

minikube delete -p minikube-pixie-lab


Well done! Now that you've gotten your feet wet using Pixie and New Relic to monitor your Kubernetes clusters, here are some things you can do on your own as you prepare to apply these learnings in real life:

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