91 lines
2.4 KiB
Go
91 lines
2.4 KiB
Go
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package balance
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import (
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"errors"
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"fmt"
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"hash/crc32"
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"hpds_control_center/model"
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"sort"
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"strconv"
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"sync"
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)
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// Hash 1 单调性（唯一） 2平衡性 (数据 目标元素均衡) 3分散性(散列)
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type Hash func(data []byte) uint32
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type UInt32Slice []uint32
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func (s UInt32Slice) Len() int {
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return len(s)
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}
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func (s UInt32Slice) Less(i, j int) bool {
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return s[i] < s[j]
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}
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func (s UInt32Slice) Swap(i, j int) {
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s[i], s[j] = s[j], s[i]
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}
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type ConsistentHashBalance struct {
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mux sync.RWMutex
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hash Hash
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replicas int //复制因子
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keys UInt32Slice //已排序的节点hash切片
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hashMap map[uint32]int64 //节点哈希和key的map, 键是hash值，值是节点key
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}
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func NewConsistentHashBalance(replicas int, fn Hash) *ConsistentHashBalance {
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m := &ConsistentHashBalance{
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replicas: replicas,
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hash: fn,
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hashMap: make(map[uint32]int64),
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}
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if m.hash == nil {
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//最多32位，保证是一个2^32-1环
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m.hash = crc32.ChecksumIEEE
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}
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return m
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}
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func (c *ConsistentHashBalance) IsEmpty() bool {
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return len(c.keys) == 0
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}
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// Add 方法用来添加缓存节点，参数为节点key，比如使用IP
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func (c *ConsistentHashBalance) Add(params model.NodeLastStateItem) error {
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c.mux.Lock()
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defer c.mux.Unlock()
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// 结合复制因子计算所有虚拟节点的hash值，并存入m.keys中，同时在m.hashMap中保存哈希值和key的映射
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for i := 0; i < c.replicas; i++ {
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hash := c.hash([]byte(strconv.Itoa(i) + fmt.Sprintf("%d", params.NodeId)))
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c.keys = append(c.keys, hash)
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c.hashMap[hash] = params.NodeId
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}
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// 对所有虚拟节点的哈希值进行排序，方便之后进行二分查找
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sort.Sort(c.keys)
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return nil
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}
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// Get 方法根据给定的对象获取最靠近它的那个节点
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func (c *ConsistentHashBalance) Get(key int64) (int64, error) {
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if c.IsEmpty() {
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return 0, errors.New("node is empty")
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}
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hash := c.hash([]byte(fmt.Sprintf("%d", key)))
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// 通过二分查找获取最优节点，第一个"服务器hash"值大于"数据hash"值的就是最优"服务器节点"
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idx := sort.Search(len(c.keys), func(i int) bool { return c.keys[i] >= hash })
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// 如果查找结果 大于 服务器节点哈希数组的最大索引，表示此时该对象哈希值位于最后一个节点之后，那么放入第一个节点中
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if idx == len(c.keys) {
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idx = 0
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}
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c.mux.RLock()
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defer c.mux.RUnlock()
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return c.hashMap[c.keys[idx]], nil
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}
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