$ go version
go version go1.13 linux/amd64
I'm not sure how to deal with this phenomenon when I find that the parallel
performance using go routine is not very good when writing database(olap)
code. I have written the following example to verify this:
```
package main
import (
"fmt"
"hash/crc32"
"time"
)
const (
Loop = 10000
)
func main() {
data := make([]byte, 4<<20)
t := time.Now()
for i := 0; i < Loop; i++ {
crc32.ChecksumIEEE(data)
}
fmt.Printf("process: %v\n", time.Now().Sub(t))
}
```
```
package main
import (
"fmt"
"hash/crc32"
"sync"
"time"
)
const (
Mcpu = 8
Loop = 10000 / Mcpu
)
func main() {
data := make([]byte, 4<<20)
var wg sync.WaitGroup
t := time.Now()
for i := 1; i < Mcpu; i++ {
wg.Add(1)
go func(idx int) {
defer wg.Done()
tt := time.Now()
for j := 0; j < Loop; j++ {
crc32.ChecksumIEEE(data)
}
fmt.Printf("%v's process: %v\n", idx, time.Now().Sub(tt))
}(i)
}
{
tt := time.Now()
for j := 0; j < Loop; j++ {
crc32.ChecksumIEEE(data)
}
fmt.Printf("0's process: %v\n", time.Now().Sub(tt))
}
wg.Wait()
fmt.Printf("process: %v\n", time.Now().Sub(t))
}
```
My machine has exactly 8 cpu's and I found that the runtime does not
decrease linearly when the number of go routines increases.
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