This page shows you how to set up and use Stackdriver Profiler. You will download a sample Go program, run it with profiling enabled, and then use the Profiler interface to explore the captured data.
Before you begin
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
In the GCP Console, go to the Manage resources page and select or create a new project.
- Enable the Stackdriver Profiler API.
Start Cloud Shell
At the top of the GCP Console page for your project, click the Activate Google Cloud Shell button:
A Cloud Shell session opens inside a new frame at the bottom of the console and displays a command-line prompt. It can take a few seconds for the shell session to be initialized.
Get a program to profile
The sample program,
main.go, is in the
golang-samples repository on
GitHub. To get it, retrieve the package of Go samples:
go get -u github.com/GoogleCloudPlatform/golang-samples/profiler/...
Profile the code
Go to the directory of sample code for Stackdriver Profiler in the retrieved package:
main.go program creates a CPU-intensive workload to provide data to
the profiler. Start the program and leave it running:
go run main.go
This program is designed to load the CPU as it runs. It is configured to use Stackdriver Profiler, which collects profiling data from the program as it runs and periodically saves it. As the program runs, it indicates its progress with a pair of messages:
successfully created profile CPU start uploading profile
The program continues to emit these messages as long as it runs.
Start the Profiler interface
In the Google Cloud Platform Console dashboard, go to Profiler:
This takes you to the Profiler interface:
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.
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 Google Compute Engine zones or versions of the application.
Just below the selectors for Service, Profile type and others is the filter selector. This allows you to refine how the graph displays data. In the screenshot above, there are no filters beyond the data type (CPU time), so all the CPU time data will be 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:
- The gray frame represents the entire executable, which accounts for 100% of the resources being consumed.
- The green
mainframe is the Go
- The orange
mainframe is the
mainroutine of the sample program.
- The orange
busyloopframe is a routine called from the sample's
- The orange
main.loadframe is a routine called from the sample's
The filter selector lets you do things like filter out functions that match
some name. For example, if there is a standard library of utility functions,
you can remove them from the graph. You can also remove call stacks
orginating at a certain method, simplify the graph in other ways.
main.go 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.
In the profiling screenshot for the sample code, the gray frame is slightly
larger than the first
main frame under it. Why? Is there something else
going on that's not immediately apparent because the
main call stack
consumes such an overwhelming percentage of the resources?
You can use a filter to hide the call stack from the
and 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:
See Using the Profiler Interface for much more information on filtering and other ways to explore the profiling data.
Need more general information?
- For an overview of Stackdriver Profiler, see About Stackdriver Profiler.
- For an introduction to profiling, see Profiling Concepts.
Ready to profile your own code? Choose your language: