How Can I Optimize Slice Chunking in Go for Efficient Large Data Processing?

Slice Chunking in Go: An Optimized Approach

For efficient processing of large slices, chunking them into smaller, manageable slices can be valuable. In Go, achieving balanced slice chunking requires a slightly different approach than the one you've attempted.

In your code, you correctly calculate the chunk size using NumCPU and the slice length. However, instead of creating new slices, you can simply append slices of logs to the divided slice. This optimization ensures that memory is not wasted on unnecessary data copying and reduces the overall memory footprint.

Here is a revised demonstration:

import "fmt"

var logs = make([]string, 2100000) // Simulate a slice with 2.1 million strings

func main() {
    numCPU := runtime.NumCPU()
    chunkSize := (len(logs) + numCPU - 1) / numCPU

    var divided [][]string

    for i := 0; i < len(logs); i += chunkSize {
        end := i + chunkSize

        if end > len(logs) {
            end = len(logs)
        }

        divided = append(divided, logs[i:end])
    }

    fmt.Printf("%#v\n", divided)
}

This optimized approach calculates the chunk size dynamically based on the number of CPUs and the slice length. It iterates over the logs slice, appending slices of logs to the divided slice as needed. By avoiding unnecessary slice creation, this solution significantly improves performance and memory usage.

The provided code sample can be tested using the Go playground: http://play.golang.org/p/vyihJZlDVy

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