Profiling with Go

Table of Contents #

This post contains notes from a hands-on talk during the Go Bangalore April meetup. Various profiling techniques were explored to improve the performance of a sample application.

Introduction #

The Go standard library has several built-in mechanisms for profiling:

  1. Using net/http/pprof as a blank import to profile a running application (such as a backend service)
  2. Using various profiling flags present in the go test command
  3. Utilising the runtime/pprof library to profile a certain block of code

Using the net/http/pprof package as a blank import #

On performing a blank import of net/http/pprof, Go automatically attaches a debug/pprof endpoint to the application. This endpoint contains an interactive web console which can be used to analyse the live profile data.

Using various profiling flags present in the go test command #

The go test command has several flags to profile an application. During the talk, the following two were covered:


go test -bench . -benchmem -cpuprofile pprof.cpu

The cpuprofile flag generates a binary and a file that can analysed using pprof.

go tool pprof app.test pprof.cpu

Walkthrough #

The demo used for the talk was a simple web application that returned the user agent and latency of the endpoint. During the load test of the initial version using wrk, it handled an average of 33,000 requests per second. While impressive, this number could be improved.

Initial investigation #

The speaker first performed a benchmark using net/http/pprof to identify possible bottlenecks.

go tool pprof -seconds <duration> -http <profiling_url> <debug/pprof/profile_url_endpoint>

The profile of the application can be generated using the above command.

The data can be visualised in different forms, such as a graph or a flame graph.

During the session, the regular graph was used. It displayed the total runtime of each function call, the number of times it was invoked and other useful details.

A lot of time was taken by os.Hostname() present in the handler function. The os.Hostname() function retrieves the hostname of the machine, which is a syscall. By moving the call outside of the handler and invoking it just once, the total runtime of the function reduced from 2s to 0.2s.

Image of the profile graph generated by pprof

Analysing line-by-line performance #

The next step was to use the profiling flags present in the go test command to understand the time taken during different operations. By using thecpuprofile file flag, the profile will be written to a file for further analysis.

Image of the generation of the cpuprofile file

As seen above, the memory allocation for the benchmark is printed to stdout. Here, the application performed 12 allocations for a total of 447 bytes per operation.

go tool pprof <binary_name> <profile_file>

This command starts an interactive application. Commands such as web and disasm can be used to understand different parts of the profile.

The list <function_name> command displays the time time taken by certain operations of the particular function.

Image of the time taken by various operations, analysed using pprof

The disasm <function> command shows the assembly code of the function along with the time taken by some operations.

Image of the assembly of the handler function

Utilising string buffers and sync.Pool #

The analysis of the results revealed that string slice appends were consuming most of the time. These operations involve repeated memory allocations to increase the slice capacity.

As the string slice was returned at the end of the function, a string buffer could be utilised to store and append the contents of the response. By creating a bytes.Buffer and using WriteString() to add contents to it, the number of allocations and time taken could be reduced.

Lastly, the number of allocation of bytes.Buffer objects could be reduced by using a sync.Pool. A sync.Pool is a type that holds a set of temporary objects. These objects can be reused later, leading to fewer allocations. The pros of using a sync.Pool outweigh the cons as a bytes.Buffer object created during one request could be reused later.

After all of these optimisations, each operation took about 520ns, a huge improvement from the 716ns reported by the initial benchmark.