Algorithm selection skills for Golang function performance optimization

Algorithm selection skills for Go language function performance optimization

The choice of algorithm directly affects the execution efficiency of the function. In the Go language, it is crucial to choose the appropriate algorithm based on different scenarios and data characteristics. The following are several commonly used algorithms and their implementation in Go language:

Sort algorithm

• Bubble sort: sort.Slice()
• Quick sort: sort.SliceIsSorted()
• ##Merge sort: sort.Merge()
• Heap sort: sort.Sort()

Search algorithm

• Linear search: Manual traversal of slices or arrays
• Binary search: sort.Search( )
• Hash table: map Type

Actual case

Suppose we have a slice of 1 million integers

data and need to sort it.

Algorithm comparison

• Bubble sort: Time complexity O(n²), not recommended for large amounts of data.
• Quick sort: The average time complexity is O(n log n), but there are worst-case performance issues.
• Merge sort: Time complexity O(n log n), stable performance.

Code Example

Using

sort.SliceIsSorted() Use quick sort on data:

package main

import (
    "sort"
)

func main() {
    data := make([]int, 1000000)
    // ...（填充 data 切片）

    sort.SliceIsSorted(data, func(i, j int) bool { return data[i] < data[j] })
}

This code uses quick sort to sort

data.

Selection skills

##Small amount of data ( Bubble sort or linear search Medium amount of data (1000-10000):
• Quick sort or binary search
Large amount of data (>10000):
• Merge sort or hash table
The data is unevenly distributed (there are a large number of duplicate elements):
• Hash table

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