研究微信红包分配算法之Golang版
斌哥_ 人气:0今天来看一下红包的分配，参考几年前流传的微信红包分配算法，今天用Golang实现一版，并测试验证结果。
微信红包的随机算法是怎样实现的？https://www.zhihu.com/question/22625187
红包核心算法
分配：红包里的金额怎么算？为什么出现各个红包金额相差很大？
答：随机，额度在0.01和(剩余平均值*2)之间每次拆红包，额度范围在【0.01 ~ 剩余平均值*2】之间，这是很妙的一个设计。
比如发100元，共发10个红包，那么平均值10元，第一个拆出来的红包的额度在0.01元～20元之间波动，可以确保不会一个人把红包全领了的情况，因为最大就是剩余平均值的2倍。
比如发0.1元，共发10个红包，每个0.01元，这种就不用随机算法了，直接平均分配吧。
No bb, show your code!
设计红包结构体
//reward.go
//红包
type Reward struct {
Count int //个数
Money int //总金额(分)
RemainCount int //剩余个数
RemainMoney int //剩余金额(分)
BestMoney int //手气最佳金额
BestMoneyIndex int //手气最佳序号
MoneyList []int //拆分列表
}- 我这里用int整型做金额计算，可以避免浮点数精度问题，展示的时候除100，就是元单位了。
核心红包随机分配算法
//reward.go
// 抢红包
func GrabReward(reward *Reward) int {
if reward.RemainCount <= 0 {
panic("RemainCount <= 0")
}
//最后一个
if reward.RemainCount - 1 == 0 {
money := reward.RemainMoney
reward.RemainCount = 0
reward.RemainMoney = 0
return money
}`
//是否可以直接0.01
if (reward.RemainMoney / reward.RemainCount) == 1 {
money := 1
reward.RemainMoney -= money
reward.RemainCount--
return money
}
//红包算法参考 https://www.zhihu.com/question/22625187
//最大可领金额 = 剩余金额的平均值x2 = (剩余金额 / 剩余数量) * 2
//领取金额范围 = 0.01 ~ 最大可领金额
maxMoney := int(reward.RemainMoney / reward.RemainCount) * 2
rand.Seed(time.Now().UnixNano())
money := rand.Intn(maxMoney)
for money == 0 {
//防止零
money = rand.Intn(maxMoney)
}
reward.RemainMoney -= money
//防止剩余金额负数
if reward.RemainMoney < 0 {
money += reward.RemainMoney
reward.RemainMoney = 0
reward.RemainCount = 0
} else {
reward.RemainCount--
}
return money
}分配算法完成后，验证一下，用单元测试的办法验证
//reward_test.go
func TestGrabReward2(t *testing.T) {
chanReward := make(chan Reward)
rand.Seed(time.Now().UnixNano())
go func(){
//随机生成1000个红包
for i:=0; i < 1000; i++ {
//随机红包个数 1~50
count := rand.Intn(50) + 1
//随机红包总金额 1~100元
money := rand.Intn(10000) + 100
avg := money / count
for avg == 0 {
//保证金额足够分配
count = rand.Intn(50) + 1
money = rand.Intn(10000) + 100
avg = money / count
}
reward := Reward{Count: count, Money: money,
RemainCount: count, RemainMoney: money}
chanReward <- reward
}
close(chanReward)
}()
//打印拆包列表，带手气最佳
for reward := range chanReward {
for i := 0; reward.RemainCount > 0; i++ {
money := GrabReward(&reward)
if money > reward.BestMoney {
reward.BestMoneyIndex, reward.BestMoney = i, money
}
reward.MoneyList = append(reward.MoneyList, money)
}
t.Logf("总个数:%d, 总金额:%.2f", reward.Count, float32(reward.Money)/100)
for i := range reward.MoneyList {
money := reward.MoneyList[i]
isBest := ""
if reward.BestMoneyIndex == i {
isBest = " ** 手气最佳"
}
t.Logf("money_%d : (%.2f)%s\n", i+1, float32(money)/100, isBest)
}
t.Log("-------")
}
}运行结果
reward_test.go:106: 总个数:7, 总金额:86.59
reward_test.go:113: money_1 : (16.29)
reward_test.go:113: money_2 : (4.93)
reward_test.go:113: money_3 : (22.89) ** 手气最佳
reward_test.go:113: money_4 : (3.17)
reward_test.go:113: money_5 : (20.51)
reward_test.go:113: money_6 : (0.12)
reward_test.go:113: money_7 : (18.68)
reward_test.go:115: -------
reward_test.go:106: 总个数:10, 总金额:53.79
reward_test.go:113: money_1 : (3.56)
reward_test.go:113: money_2 : (6.39)
reward_test.go:113: money_3 : (0.36)
reward_test.go:113: money_4 : (2.60)
reward_test.go:113: money_5 : (10.11)
reward_test.go:113: money_6 : (5.76)
reward_test.go:113: money_7 : (2.84)
reward_test.go:113: money_8 : (14.04) ** 手气最佳
reward_test.go:113: money_9 : (1.95)
reward_test.go:113: money_10 : (6.18)
reward_test.go:115: -------性能测试
//性能测试
func BenchmarkGrabReward(b *testing.B) {
chanReward := make(chan *Reward, b.N)
rand.Seed(time.Now().UnixNano())
go func(){
//随机生成红包
for i:=0; i < b.N; i++ {
//随机红包个数 1~50
count := rand.Intn(50) + 1
//随机红包总金额 1~100元
money := rand.Intn(10000) + 100
avg := money / count
for avg == 0 {
//保证金额足够分配
count = rand.Intn(50) + 1
money = rand.Intn(10000) + 100
avg = money / count
}
reward := Reward{Count: count, Money: money,
RemainCount: count, RemainMoney: money}
chanReward <- &reward
}
close(chanReward)
}()
//打印拆包列表，带手气最佳
for reward := range chanReward {
for i := 0; reward.RemainCount > 0; i++ {
money := GrabReward(reward)
if money > reward.BestMoney {
reward.BestMoneyIndex, reward.BestMoney = i, money
}
reward.MoneyList = append(reward.MoneyList, money)
}
_ = fmt.Sprintf("总个数:%d, 总金额:%.2f", reward.Count, float32(reward.Money)/100)
for i := range reward.MoneyList {
money := reward.MoneyList[i]
isBest := ""
if reward.BestMoneyIndex == i {
isBest = " ** 手气最佳"
}
_ = fmt.Sprintf("money_%d : (%.2f)%s\n", i+1, float32(money)/100, isBest)
}
}
}性能测试结果
BenchmarkGrabReward-8 4461 244842 ns/op
//4核8线的CPU运运行4461次，平均每次244842纳秒=0.244842毫秒性能可以说是很优秀的，这是因为这个测试是纯内存计算，没有网络IO，没有存储写盘，纯粹是为了验证算法，所以性能是很高的。
完成！
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