StorageReadObjects function optimization

[aliciMehmet]

It will be more efficient in terms of performance to access the elements of the slice and put the values instead of appending it after creating the slice.

[CLAassistant]

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[zyro]

The allocation was already being done once, so resizing wasn't needed during the loop iterations if that's the concern. Do you have a little benchmark test that compares the two approaches?

[aliciMehmet]

I don't know it is the appropriate test but here is my example benchmark code and results:

import (
	"testing"
)

var simpleObjs = []simpleObj{{
	Collection: "Collection-1",
	Key:        "0",
	UserId:     "309adcaf-9d5e-44b6-89f2-7b48e1f9ccab",
},
	{
		Collection: "Collection-2",
		Key:        "0",
		UserId:     "309adcaf-9d5e-44b6-89f2-7b48e1f9ccab",
	},
	{
		Collection: "Collection-3",
		Key:        "0",
		UserId:     "309adcaf-9d5e-44b6-89f2-7b48e1f9ccab",
	}}

func BenchmarkOriginalApproach(b *testing.B) {
	for i := 0; i < b.N; i++ {
		OriginalApproach(simpleObjs)
	}
}

func BenchmarkMyApproach(b *testing.B) {
	for i := 0; i < b.N; i++ {
		MyApproach(simpleObjs)
	}
}

func OriginalApproach(objs []simpleObj) {
	collectionParam := make([]string, 0, len(objs))
	keyParam := make([]string, 0, len(objs))
	userIdParam := make([]string, 0, len(objs))

	for _, s := range objs {
		collectionParam = append(collectionParam, s.Collection)
		keyParam = append(keyParam, s.Key)
		userIdParam = append(userIdParam, s.UserId)
	}
}

func MyApproach(objs []simpleObj) {
	collectionParam := make([]string, len(objs))
	keyParam := make([]string, len(objs))
	userIdParam := make([]string, len(objs))

	for index, s := range objs {
		collectionParam[index] = s.Collection
		keyParam[index] = s.Key
		userIdParam[index] = s.UserId
	}
}

type simpleObj struct {
	Collection string
	Key        string
	UserId     string
}

I don't know if it is worth changing the code, after testing I couldn't decide if it had much of an effect.

BenchmarkOriginalApproach-20 12865542 92.40 ns/op
BenchmarkOriginalApproach-20 12672062 93.03 ns/op
BenchmarkOriginalApproach-20 12060676 92.77 ns/op
BenchmarkOriginalApproach-20 12616186 91.90 ns/op
BenchmarkOriginalApproach-20 12636925 93.12 ns/op

BenchmarkMyApproach-20 13567692 88.95 ns/op
BenchmarkMyApproach-20 12824156 88.11 ns/op
BenchmarkMyApproach-20 13065788 89.47 ns/op
BenchmarkMyApproach-20 13172916 90.31 ns/op
BenchmarkMyApproach-20 13523168 90.43 ns/op