Cloud Profiler: Qwik Start

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Cloud Profiler: Qwik Start

45 minutes Free


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Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications. It attributes that information to the application's source code, helping you identify the parts of the application consuming the most resources, and otherwise illuminating the performance characteristics of the code.

In this lab you will learn how to set up and use Cloud Profiler. First you'll download a sample Go program and run it with profiling enabled. Then you'll use the Cloud Profiler interface to explore the captured data.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

  1. Click Activate Cloud Shell Activate Cloud Shell icon at the top of the Google Cloud console.

When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

  1. (Optional) You can list the active account name with this command:

gcloud auth list
  1. Click Authorize.

  2. Your output should now look like this:


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

Example output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Enable the Stackdriver Profiler API

To enable the Strackriver Profiler API:

  1. In the Navigation menu Navigation menu icon, click APIs & services > Library.

  2. Type "Stackdriver Profiler" in the search box.

  3. Click the Stackdriver Profiler API, and then click Enable if it is not Enabled.

Task 2. Get a program to profile

The sample program, profiler_quickstart, is in the golang-samples repository on GitHub.

  • To get it, retrieve the package of Go samples:
go install

Task 3. Profile the code

The profiler_quickstart program creates a CPU-intensive workload to provide data to the profiler.

Your previous command imported the profiler_quickstart program as a Go module and you are now able to directly run the program.

  • Run the following to start the program. You can leave it running:


This program is designed to load the CPU as it runs, and configured to use Cloud Profiler. Cloud Profiler collects profiling data from the program as it runs and periodically saves it. Progress is indicated with a pair of messages:

2018/06/19 20:38:18 profiler has started 2018/06/19 20:39:00 successfully created profile CPU 2018/06/19 20:39:11 start uploading profile 2018/06/19 20:40:23 successfully created profile CPU 2018/06/19 20:40:33 start uploading profile 2018/06/19 20:41:15 successfully created profile CPU 2018/06/19 20:41:25 start uploading profile 2018/06/19 20:41:45 successfully created profile CPU ...

The program will continue to emit these messages while it runs.

Let 2-3 profiles get created, then continue with the lab.

Task 4. Start the Profiler interface

  1. In the Console, go to Profiler.
  2. From the Navigation menu, in the Operations section, click on Profiler.

The Profiler interface opens:

The Profiler page, which includes its specifications and metrics.

The interface is divided into two general areas:

  • A control area for selecting the data to visualize.

  • A flame-graph representation of the selected data.

Selecting profiles

The interface offers an array of controls for exploring the profiling data. At the top of the interface, there are time controls, so you can examine data for the time range you choose.

Below that are options choosing the set of profile data to use:

  • Service is for selecting the origin of the profiled data, useful if you are profiling several different applications.
  • Profile type lets you choose the kind of profile data to display.
  • Zone name and Version let you restrict display to data from Compute Engine zones or versions of the application.

Just below the selectors for Service, Profile, etc. is the filter selector. Filters allow you to refine how the graph displays data. In the screenshot above, the CPU time filter is on, so all the CPU time data is displayed.

Exploring the data

Below the selection controls, the selected data is displayed as a flame graph. This type of chart shows you the call stacks in the program. Each function is represented by a frame in the graph, and its relative size shows the proportion of resource consumption that function is responsible for.

The top frame represents the entire program. This frame always shows 100% of the resource consumption, and it indicates how many profiles are averaged together in this graph.

The sample program does not appear to have a complicated set of call stacks; in the preceding screenshot, you see 5 frames:

Five color-coded frames within the stack.

  • The gray frame represents the entire executable, which accounts for 100% of the resources being consumed.
  • The green main frame is the Go runtime.main.
  • The orange main frame is the main routine of the sample program.
  • The orange busyloop frame is a routine called from the sample's main.
  • The orange main.load frame is a routine called from the sample's main.

The filter selector lets you do things like filter out functions. For example, if there is a standard library of utility functions, you can remove them from the graph. You can also remove call stacks originating at a certain method, and simplify the graph in other ways.

The profiler_quickstart application is very simple, so there's not much to filter out, but in a complex application, being able to remove elements from the graph is very useful.

To practice exploring the data, use a filter to hide the call stack from the main routine to let you see what's happening outside main.

This extra work accounts for a tiny 0.29% of the resource consumption, but it makes a much more interesting flame graph.

  1. Click next to the CPU time filter to see other available filters.

  2. Select Hide stacks then type in "main" as the value. Your flame graph will look something like this:

The updated stack, wherein all frames are green.

The more profiles that get generated, the more interesting your flame graph gets. In a few minutes refresh the Stackdriver Profiler console to see the graph develop. Wait a few more minutes and do it again.


You learned how to use Cloud Profiler on Google Cloud.

Finish your quest

This self-paced lab is part of the Cloud Development quest. A quest is a series of related labs that form a learning path. Completing this quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in this quest or any quest that contains this lab and get immediate completion credit. See the Google Cloud Skills Boost catalog to see all available quests.

Next steps / learn more

This lab is also part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Search for "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

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Manual last updated November 30, 2022

Lab last tested November 30, 2022

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