• Creating a Go benchmark
• Running Go benchmarks

What is benchmarking?

Performance optimization is a critical part of software development. Once your application has been released and is being used by real users, you may need to optimize its performance. One way to do this is to benchmark your code to identify bottlenecks and improve its performance. Benchmarking provides you with data to make informed decisions about what parts of your code can be sped up and by how much.

Benchmarking is the process of measuring your code’s performance. It involves running your code multiple times and measuring how long it takes to execute. By running your code multiple times, you can get an average execution time, which is more reliable than a one-off report.

Identifying the bottlenecks

In our application, we deserialize and process large amounts of JSON data once every hour. We noticed that this process was taking a long time for some of our users. First, we used Go pprof to enable profiling and generated a flame graph to identify the bottlenecks in our code.

The flame graph showed us that the JSON decoding process took the most time. We benchmarked different serialization libraries to find the fastest one for our use case.

Creating a Go benchmark

In Go, you can write benchmarks using the built-in testing package. Benchmarks are written similarly to unit tests but with the Benchmark prefix instead of the Test prefix.

Before creating and running the benchmark, we generated 1000 test JSON files in the testdata directory.

To benchmark JSON decoding, we created the following benchmark.

package main

import (
    "encoding/json"
    "fmt"
    "os"
    "testing"
)

const files = 1000
const itemsPerFile = 1000

func BenchmarkJSONImport(b *testing.B) {
    for i := 0; i < b.N; i++ {

       // Read in the file (do not time)
       b.StopTimer()
       fileNumber := i % files
       data, err := os.ReadFile(fmt.Sprintf("testdata/sample_%d.json", fileNumber))
       if err != nil {
          b.Fatal(err)
       }
       b.StartTimer()

       var samples []Sample
       dec := json.NewDecoder(bytes.NewReader(data))
       if err := dec.Decode(&samples); err != nil {
          b.Fatal(err)
       }
       if len(samples) != itemsPerFile {
          b.Fatalf("expected %d samples, got %d", itemsPerFile, len(samples))
       }
    }
}

Starting the function name with Benchmark indicates to go test that this is a benchmark.

The testing package adjusts the number of iterations through the for i := 0; i < b.N; i++ loop until the function lasts long enough to be timed reliably.

The b.StopTimer() and b.StartTimer() calls exclude part of the code from the benchmark.

Running Go benchmarks

To run all benchmarks, add -bench=. flag to go test:

go test -bench=.

The result will look like this:

goos: darwin
goarch: arm64
pkg: serializer
cpu: Apple M2 Pro
BenchmarkJSONImport-12           357      3324997 ns/op
PASS
ok      serializer 1.868s

It tells us that unmarshalling a single file with json.Decode takes an average of 3.3 milliseconds. The benchmark ran the loop 357 times.

Benchmarking encoding/gob

Next, we will benchmark the built-in encoding/gob library.

func BenchmarkGobImport(b *testing.B) {
    for i := 0; i < b.N; i++ {

       // Read in the file (do not time)
       b.StopTimer()
       fileNumber := i % files
       data, err := os.ReadFile(fmt.Sprintf("testdata/sample_%d.bin", fileNumber))
       if err != nil {
          b.Fatal(err)
       }
       b.StartTimer()

       // decode gob
       var samples []Sample
       dec := gob.NewDecoder(bytes.NewReader(data))
       if err := dec.Decode(&samples); err != nil {
          b.Fatal(err)
       }
       if len(samples) != itemsPerFile {
          b.Fatalf("expected %d samples, got %d", itemsPerFile, len(samples))
       }
    }
}

Running the two benchmarks gives us:

BenchmarkJSONImport-12           360      3279579 ns/op
BenchmarkGobImport-12           2262       475469 ns/op

The benchmark data shows that decoding with encoding/gob takes almost 7 times faster than using encoding/json. This gives sufficient data to present to our management and argue for switching from JSON. In addition, we can benchmark other serialization libraries to see if any of them are even faster.

For additional data, we included reading the file in our benchmark numbers for a complete picture of the expected speedup:

BenchmarkJSONImport-12               360      3374064 ns/op
BenchmarkGobImport-12               2710       481935 ns/op
BenchmarkJSONImportFile-12           306      3746758 ns/op
BenchmarkGobImportFile-12           2176       554254 ns/op

Go benchmark code on GitHub

The complete code is available on GitHub at: https://github.com/getvictor/go-benchmark-serializers

Further reading

• Recently, we wrote about accurately measuring Go test execution time.
• Also, see our previous article on creating fuzz tests in Go.

Watch how to benchmark Go serializers

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