Continuous Profiling for Golang applications

This post was originally published on pyroscope.io. Grafana Labs acquired Pyroscope in 2023.

Note: For documentation on Pyroscope’s Golang integration, visit our website for Golang push mode or Golang pull mode.

Background

In this example we show a simplified, basic use case of Pyroscope. We simulate a “ride share” company which has three endpoints found in main.go:

• /bike: calls the OrderBike(search_radius) function to order a bike
• /car: calls the OrderCar(search_radius) function to order a car
• /scooter: calls the OrderScooter(search_radius) function to order a scooter

We also simulate running 3 distinct servers in 3 different regions (via docker-compose.yml)

• us-east-1
• us-west-1
• eu-west-1

One of the most useful capabilities of Pyroscope is the ability to tag your data in a way that is meaningful to you. In this case, we have two natural divisions, and so we “tag” our data to represent those:

• region: statically tags the region of the server running the code
• vehicle: dynamically tags the endpoint

Tagging static region

Tagging something static, like the region, can be done in the initialization code in the main() function:

Copy

    pyroscope.Start(pyroscope.Config{
        ApplicationName: "ride-sharing-app",
        ServerAddress:   serverAddress,
        Logger:          pyroscope.StandardLogger,
        Tags:            map[string]string{"region": os.Getenv("REGION")},
    })

Tagging dynamically within functions

Tagging something more dynamically, like we do for the vehicle tag can be done inside our utility FindNearestVehicle() function using a with pyroscope.tag_wrapper() block.

Copy

func FindNearestVehicle(search_radius int64, vehicle string) {
    pyroscope.TagWrapper(context.Background(), pyroscope.Labels("vehicle", vehicle), func(ctx context.Context) {
       
        // Mock "doing work" to find a vehicle
        var i int64 = 0
        start_time := time.Now().Unix()
        for (time.Now().Unix() - start_time) < search_radius {
            i++
        }
    })
}

What this block does, is:

1. Add the label pyroscope.Labels("vehicle", vehicle)
2. Execute the FindNearestVehicle() function
3. Before the block ends it will (behind the scenes) remove the pyroscope.Labels("vehicle", vehicle) from the application since that block is complete

Resulting flame graph / performance results from the example

To run the example run the following commands:

Copy

# Pull latest pyroscope image:
docker pull pyroscope/pyroscope:latest

# Run the example project:
docker-compose up --build

# Reset the database (if needed):
# docker-compose down

What this example will do is run all the code mentioned above and also send some mock-load to the 3 servers as well as their respective 3 endpoints. If you select our application: ride-sharing-app.cpu from the dropdown, you should see a flame graph that looks like this (below). After we give 20-30 seconds for the flame graph to update and then click the refresh button we see our 3 functions at the bottom of the flame graph taking CPU resources proportional to the size of their respective search_radius parameters.

Where’s the performance bottleneck?

The first step when analyzing a profile outputted from your application, is to take note of the largest node which is where your application is spending the most resources. In this case, it happens to be the OrderCar function.

The benefit of using the Pyroscope package, is that now that we can investigate further as to why the OrderCar() function is problematic. Tagging both region and vehicle allows us to test two good hypotheses:

• Something is wrong with the /car endpoint code
• Something is wrong with one of our regions

To analyze this we can select one or more tags from the “Select Tag” dropdown:

Narrowing in on the issue using tags

Knowing there is an issue with the OrderCar() function we automatically select that tag. Then, after inspecting multiple region tags, it becomes clear by looking at the timeline that there is an issue with the us-west-1 region, where it alternates between high-cpu times and low-cpu times.

We can also see that the mutexLock() function is consuming almost 70% of CPU resources during this time period.

Comparing two time periods

Using Pyroscope’s “comparison view” we can actually select two different time ranges from the timeline to compare the resulting flame graphs. The pink section on the left timeline results in the left flame graph, and the blue section on the right represents the right flame graph.

When we select a period of low-CPU utilization and a period of high-CPU utilization we can see that there is clearly different behavior in the mutexLock() function where it takes 33% of CPU during low-CPU times and 71% of CPU during high-CPU times.

More use cases

We have been beta testing this feature with several different companies and some of the ways that we’ve seen companies tag their performance data:

• Tagging Kubernetes attributes
• Tagging controllers
• Tagging regions
• Tagging jobs from a queue
• Tagging commits
• Tagging staging / production environments
• Tagging different parts of their testing suites

Future roadmap

We would love for you to try out this example and see what ways you can adapt this to your Golang application. While this example focused on CPU debugging, Golang also provides memory profiling as well. Continuous profiling has become an increasingly popular tool for the monitoring and debugging of performance issues (arguably the fourth pillar of observability).

We’d love to continue to improve our Golang integrations and so we would love to hear what features you would like to see.