Golang load balancing algorithm implementation example

Load balancing algorithm

In Go language, load balancing algorithms are usually implemented by proxy, reverse proxy, or application-layer load balancer. Among these implementations, there are some classic load balancing algorithms:

Round Robin:The simplest load balancing algorithm is to assign requests to the backend server in sequence. Each request is assigned to different servers in a prior agreed order, and is repeated over and over again.
Random method:Randomly select a server to make the request. The advantage of this algorithm is that it is simple and easy to understand, and is suitable for situations where the request is relatively uniform.
Least Connections:Select the server with the least number of connections to make the request. This can make the load relatively balanced and avoid overloading of a certain server.
Weighted Round Robin:Based on the polling method, the weights assigned by different servers are different. High weighted servers can handle more requests.
Weighted Random:Based on the random method, different servers have different weights. Depending on the size of the weight, the probability that the server is randomly selected is different.
IP Hash method:Use the client's IP address for hashing operations, and assign the request to a specific server based on the hash value. This ensures that requests for the same client IP address will be assigned to the same server, which is suitable for scenarios where session consistency is required.

You can also use some third-party libraries to achieve load balancing, such as gobalancer, ghosty, etc. These libraries provide implementations of a variety of load balancing algorithms and can be easily integrated into Go applications.

Here are simple example codes for these classic load balancing algorithms:

Round Robin

package main

import (
    "fmt"
    "sync"
)

type RoundRobin struct {
    servers []string
    index   int
    lock    
}

func NewRoundRobin(servers []string) *RoundRobin {
    return &amp;RoundRobin{
        servers: servers,
        index:   0,
    }
}

func (rr *RoundRobin) GetNextServer() string {
    ()
    defer ()

    server := []
     = ( + 1) % len()
    return server
}

func main() {
    servers := []string{"Server1", "Server2", "Server3"}
    rr := NewRoundRobin(servers)

    for i := 0; i &lt; 10; i++ {
        ("Request sent to:", ())
    }
}

Random method

package main

import (
    "fmt"
    "math/rand"
    "time"
)

type Random struct {
    servers []string
}

func NewRandom(servers []string) *Random {
    return &amp;Random{
        servers: servers,
    }
}

func (r *Random) GetRandomServer() string {
    (().UnixNano())
    index := (len())
    return [index]
}

func main() {
    servers := []string{"Server1", "Server2", "Server3"}
    random := NewRandom(servers)

    for i := 0; i &lt; 10; i++ {
        ("Request sent to:", ())
    }
}

Least Connections

This algorithm needs to be implemented in an actual load balancer, involving statistics and dynamic adjustment of the number of connections.

Weighted Round Robin

package main

import (
    "fmt"
    "sync"
)

type WeightedRoundRobin struct {
    servers    []string
    weights    []int
    currentIdx int
    lock       
}

func NewWeightedRoundRobin(servers []string, weights []int) *WeightedRoundRobin {
    return &amp;amp;WeightedRoundRobin{
        servers:    servers,
        weights:    weights,
        currentIdx: 0,
    }
}

func (wrr *WeightedRoundRobin) GetNextServer() string {
    ()
    defer ()

    server := []
     = ( + 1) % len()
    return server
}

func main() {
    servers := []string{"Server1", "Server2", "Server3"}
    weights := []int{2, 1, 3} // Server1 weight is 2, Server2 weight is 1, Server3 weight is 3
    wrr := NewWeightedRoundRobin(servers, weights)

    for i := 0; i &amp;lt; 10; i++ {
        ("Request sent to:", ())
    }
}

Weighted Random

package main
import (
    "fmt"
    "math/rand"
    "time"
)
type WeightedRandom struct {
    servers []string
    weights []int
}
func NewWeightedRandom(servers []string, weights []int) *WeightedRandom {
    return &amp;WeightedRandom{
        servers: servers,
        weights: weights,
    }
}
func (wr *WeightedRandom) GetWeightedRandomServer() string {
    (().UnixNano())
    totalWeight := 0
    for _, weight := range  {
        totalWeight += weight
    }
    randWeight := (totalWeight)
    for i, weight := range  {
        if randWeight &lt; weight {
            return [i]
        }
        randWeight -= weight
    }
    return [len()-1]
}
func main() {
    servers := []string{"Server1", "Server2", "Server3"}
    weights := []int{2, 1, 3} // Server1 weight is 2, Server2 weight is 1, Server3 weight is 3    wr := NewWeightedRandom(servers, weights)
    for i := 0; i &lt; 10; i++ {
        ("Request sent to:", ())
    }
}

IP Hash method

package main

import (
    "fmt"
    "hash/fnv"
    "strconv"
)

type IPHash struct {
    servers []string
}

func NewIPHash(servers []string) *IPHash {
    return &amp;IPHash{
        servers: servers,
    }
}

func (ih *IPHash) GetServerByIP(ip string) string {
    h := fnv.New32a()
    ([]byte(ip))
    index := int(h.Sum32()) % len()
    return [index]
}

func main() {
    servers := []string{"Server1", "Server2", "Server3"}
    ih := NewIPHash(servers)

    ips := []string{"192.168.1.1", "192.168.1.2", "192.168.1.3"}

    for _, ip := range ips {
        ("Request from IP %s sent to: %s\n", ip, (ip))
    }
}

Please note that these sample codes are intended to demonstrate the basic principles of the algorithm, and require more complex implementations in actual applications, involving connection management, health checks, etc. In actual projects, it is recommended to use an off-the-shelf load balancing library or a reverse proxy server.

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