xfy
f145a8770e
Apply modern Go patterns across the codebase:
- Replace `interface{}` with `any` (Go 1.18+)
- Use `for range n` instead of `for i := 0; i < n; i++` (Go 1.22+)
- Replace `sort.Slice` with `slices.Sort` from slices package
- Simplify sync.WaitGroup patterns with errgroup where appropriate
- Add Makefile targets for modernize analyzer
Total: 84 files updated, net reduction of 79 lines
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
349 lines
8.6 KiB
Go
349 lines
8.6 KiB
Go
// Package loadbalance 提供负载均衡算法的基准测试。
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//
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// 该文件测试各负载均衡算法在不同规模下的性能表现，包括：
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// - 轮询算法（RoundRobin）在不同目标数量下的吞吐量
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// - 加权轮询算法（WeightedRoundRobin）在等权重/变权重场景下的性能
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// - 一致性哈希算法（ConsistentHash）在不同虚拟节点数下的选择和重建开销
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// - 一致性哈希排除选择（SelectExcludingByKey）的性能
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// - 最少连接（LeastConnections）和 IP 哈希（IPHash）的基准性能
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// - 所有算法的横向对比测试
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//
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// 主要用途：
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//
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// 用于评估负载均衡算法在不同规模下的性能特征，指导算法选型和参数调优。
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//
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// 注意事项：
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// - 基准测试使用并行模式（RunParallel），结果受 CPU 核心数影响
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// - 测试前需确保目标均为健康状态
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//
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// 作者：xfy
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package loadbalance
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import (
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"fmt"
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"sync/atomic"
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"testing"
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)
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// generateTargets 生成指定数量的健康目标，用于基准测试。
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//
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// 参数：
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// - count: 目标数量
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//
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// 返回值：
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// - 包含 count 个健康目标的切片，权重均为 1
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func generateTargets(count int) []*Target {
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targets := make([]*Target, count)
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for i := range count {
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targets[i] = &Target{
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URL: fmt.Sprintf("http://backend%d:8080", i),
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Weight: 1,
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}
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targets[i].Healthy.Store(true)
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}
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return targets
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}
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// generateWeightedTargets 生成带指定权重的健康目标，用于基准测试。
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//
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// 参数：
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// - count: 目标数量
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// - weights: 权重列表，长度不足时默认权重为 1
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//
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// 返回值：
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// - 包含 count 个健康目标的切片，按 weights 分配权重
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func generateWeightedTargets(count int, weights []int) []*Target {
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targets := make([]*Target, count)
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for i := range count {
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weight := 1
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if i < len(weights) {
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weight = weights[i]
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}
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targets[i] = &Target{
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URL: fmt.Sprintf("http://backend%d:8080", i),
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Weight: weight,
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}
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targets[i].Healthy.Store(true)
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}
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return targets
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}
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// BenchmarkRoundRobinSelect 基准测试轮询算法在不同目标数量下的性能。
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//
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// 测试用例：
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// - 3 个目标：小规模场景
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// - 50 个目标：中等规模场景
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// - 200 个目标：大规模场景
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func BenchmarkRoundRobinSelect(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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}{
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{"3targets", 3},
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{"50targets", 50},
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{"200targets", 200},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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rr := NewRoundRobin()
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b.ResetTimer()
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b.RunParallel(func(pb *testing.PB) {
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for pb.Next() {
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rr.Select(targets)
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}
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})
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})
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}
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}
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// BenchmarkWeightedRoundRobin 基准测试加权轮询算法在等权重和变权重场景下的性能。
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//
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// 测试用例：
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// - 等权重：所有目标权重相同（1:1:1）
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// - 变权重：目标权重差异较大（1:5:10）
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// - 不同规模：3/50/200 个目标
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func BenchmarkWeightedRoundRobin(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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weights []int
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}{
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{"3targets_equal", 3, []int{1, 1, 1}},
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{"3targets_weighted", 3, []int{1, 5, 10}},
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{"50targets_equal", 50, nil},
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{"50targets_weighted", 50, []int{1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}},
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{"200targets_equal", 200, nil},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateWeightedTargets(tc.targets, tc.weights)
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wrr := NewWeightedRoundRobin()
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b.ResetTimer()
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b.RunParallel(func(pb *testing.PB) {
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for pb.Next() {
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wrr.Select(targets)
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}
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})
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})
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}
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}
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// BenchmarkConsistentHashSelect 基准测试一致性哈希算法在不同虚拟节点数下的选择性能。
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//
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// 测试用例：
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// - 10 个目标 + 50/150/200 个虚拟节点
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// - 50/100 个目标 + 150 个虚拟节点
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//
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// 验证虚拟节点数量对哈希环遍历性能的影响。
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func BenchmarkConsistentHashSelect(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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virtualNodes int
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}{
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{"10targets_50vnodes", 10, 50},
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{"10targets_150vnodes", 10, 150},
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{"10targets_200vnodes", 10, 200},
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{"50targets_150vnodes", 50, 150},
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{"100targets_150vnodes", 100, 150},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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ch := NewConsistentHash(tc.virtualNodes, "ip")
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ch.Rebuild(targets)
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keys := make([]string, 100)
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for i := range 100 {
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keys[i] = fmt.Sprintf("192.168.1.%d", i)
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}
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b.ResetTimer()
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b.RunParallel(func(pb *testing.PB) {
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i := uint64(0)
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for pb.Next() {
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keyIdx := atomic.AddUint64(&i, 1) % uint64(len(keys))
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ch.SelectByKey(targets, keys[keyIdx])
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}
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})
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})
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}
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}
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// BenchmarkConsistentHashRebuild 基准测试一致性哈希环重建性能。
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func BenchmarkConsistentHashRebuild(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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virtualNodes int
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}{
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{"10targets_150vnodes", 10, 150},
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{"50targets_150vnodes", 50, 150},
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{"100targets_150vnodes", 100, 150},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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ch := NewConsistentHash(tc.virtualNodes, "ip")
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b.ResetTimer()
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for b.Loop() {
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ch.Rebuild(targets)
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}
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})
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}
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}
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// BenchmarkConsistentHashSelectExcluding 基准测试一致性哈希排除选择算法。
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func BenchmarkConsistentHashSelectExcluding(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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virtualNodes int
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excludeCount int
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}{
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{"50targets_150vnodes_exclude5", 50, 150, 5},
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{"50targets_150vnodes_exclude10", 50, 150, 10},
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{"100targets_150vnodes_exclude5", 100, 150, 5},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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ch := NewConsistentHash(tc.virtualNodes, "ip")
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// 预计算所有目标的虚拟节点哈希
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ch.PrecomputeHashes(targets, tc.virtualNodes)
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ch.Rebuild(targets)
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excluded := targets[:tc.excludeCount]
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key := "test-request-key"
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b.ResetTimer()
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for b.Loop() {
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ch.SelectExcludingByKey(targets, excluded, key)
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}
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})
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}
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}
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// BenchmarkLeastConnSelect 基准测试最少连接算法在不同目标数量下的性能。
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//
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// 测试用例：
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// - 3/50/200 个目标，连接数按递增方式分配（模拟负载差异）
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func BenchmarkLeastConnSelect(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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}{
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{"3targets", 3},
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{"50targets", 50},
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{"200targets", 200},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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// 设置不同的连接数以模拟真实场景
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for i, t := range targets {
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t.Connections = int64(i * 10)
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}
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lc := NewLeastConnections()
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b.ResetTimer()
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b.RunParallel(func(pb *testing.PB) {
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for pb.Next() {
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lc.Select(targets)
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}
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})
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})
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}
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}
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// BenchmarkIPHashSelect 基准测试 IP 哈希算法。
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func BenchmarkIPHashSelect(b *testing.B) {
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testCases := []struct {
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name string
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targets int
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}{
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{"3targets", 3},
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{"50targets", 50},
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{"200targets", 200},
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}
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for _, tc := range testCases {
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b.Run(tc.name, func(b *testing.B) {
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targets := generateTargets(tc.targets)
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iph := NewIPHash()
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ips := make([]string, 100)
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for i := range 100 {
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ips[i] = fmt.Sprintf("192.168.1.%d", i)
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}
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b.ResetTimer()
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b.RunParallel(func(pb *testing.PB) {
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i := uint64(0)
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for pb.Next() {
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ipIdx := atomic.AddUint64(&i, 1) % uint64(len(ips))
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iph.SelectByIP(targets, ips[ipIdx])
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}
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})
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})
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}
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}
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// BenchmarkAllBalancers 对比所有负载均衡算法的性能。
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func BenchmarkAllBalancers(b *testing.B) {
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targets := generateTargets(50)
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weightedTargets := generateWeightedTargets(50, nil)
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b.Run("RoundRobin", func(b *testing.B) {
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rr := NewRoundRobin()
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b.ResetTimer()
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for b.Loop() {
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rr.Select(targets)
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}
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})
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b.Run("WeightedRoundRobin", func(b *testing.B) {
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wrr := NewWeightedRoundRobin()
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b.ResetTimer()
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for b.Loop() {
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wrr.Select(weightedTargets)
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}
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})
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b.Run("LeastConnections", func(b *testing.B) {
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lc := NewLeastConnections()
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b.ResetTimer()
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for b.Loop() {
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lc.Select(targets)
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}
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})
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b.Run("IPHash", func(b *testing.B) {
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iph := NewIPHash()
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b.ResetTimer()
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for b.Loop() {
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iph.SelectByIP(targets, "192.168.1.100")
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}
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})
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b.Run("ConsistentHash", func(b *testing.B) {
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ch := NewConsistentHash(150, "ip")
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ch.Rebuild(targets)
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b.ResetTimer()
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for b.Loop() {
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ch.SelectByKey(targets, "192.168.1.100")
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}
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})
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}
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