https://github.com/zyallers/consistent_hash

一致性哈希算法实现数据分布, 它支持动态添加、删除节点, 并引入虚拟节点保证数据在节点间的均匀分布
https://github.com/zyallers/consistent_hash

consistent-hash go golang hash murmur3-hashing node

Last synced: about 1 month ago
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一致性哈希算法实现数据分布, 它支持动态添加、删除节点, 并引入虚拟节点保证数据在节点间的均匀分布

• Host: GitHub
• URL: https://github.com/zyallers/consistent_hash
• Owner: ZYallers
• License: mit
• Created: 2023-04-28T05:43:49.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2023-04-28T08:05:31.000Z (about 3 years ago)
• Last Synced: 2025-01-12T00:10:16.650Z (over 1 year ago)
• Topics: consistent-hash, go, golang, hash, murmur3-hashing, node
• Language: Go
• Homepage:
• Size: 4.88 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# consistent_hash

[![Go Report Card](https://goreportcard.com/badge/github.com/ZYallers/consistent_hash)](https://goreportcard.com/report/github.com/ZYallers/consistent_hash)

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一致性哈希算法实现数据分布, 它支持动态添加、删除节点, 并引入虚拟节点保证数据在节点间的均匀分布。

## 特点

1. 动态添加、删除节点

2. 引入虚拟节点保证数据均匀分布

3. 自定义hash函数

## 扩容数据分布

```go

func TestConsistentHash_DataDistribution(t *testing.T) {

	ch := NewConsistentHash(300, nil)

	ch.AddNode("db1", "db2", "db3", "db4")

	dataTotal := 50000

	nodesHash := make(map[string]int)

	for i := 0; i < dataTotal; i++ {

		dataKey := "data" + strconv.Itoa(i)

		node := ch.GetNode(dataKey)

		nodesHash[node]++

	}

	for k, v := range nodesHash {

		t.Logf("%s count: %d (%.2f%%)\n", k, v, float64(v)/float64(dataTotal)*100)

	}

	t.Log("--------------扩容后---------------")

	ch.AddNode("db5", "db6")

	countPrint := func(total, incr int) int {

		t.Logf("--------------数据增长%d---------------\n", incr)

		dataTotal := incr + total

		for i := total; i < dataTotal; i++ {

			dataKey := "data" + strconv.Itoa(i)

			node := ch.GetNode(dataKey)

			nodesHash[node]++

		}

		for k, v := range nodesHash {

			t.Logf("%s count: %d (%.2f%%)\n", k, v, float64(v)/float64(dataTotal)*100)

		}

		return dataTotal

	}

	dataTotal = countPrint(dataTotal, 100000)

	dataTotal = countPrint(dataTotal, 300000)

	dataTotal = countPrint(dataTotal, 500000)

}

```

测试结果：

```bash

db1 count: 12220 (24.44%)

db3 count: 13211 (26.42%)

db4 count: 12153 (24.31%)

db2 count: 12416 (24.83%)

--------------扩容后---------------

--------------数据增长100000---------------

db6 count: 15861 (10.57%)

db5 count: 16160 (10.77%)

db1 count: 29222 (19.48%)

db3 count: 31524 (21.02%)

db4 count: 29421 (19.61%)

db2 count: 27812 (18.54%)

--------------数据增长300000---------------

db6 count: 63630 (14.14%)

db5 count: 64501 (14.33%)

db1 count: 80014 (17.78%)

db3 count: 85481 (19.00%)

db4 count: 81780 (18.17%)

db2 count: 74594 (16.58%)

--------------数据增长500000---------------

db5 count: 145391 (15.30%)

db1 count: 164249 (17.29%)

db3 count: 175432 (18.47%)

db4 count: 169568 (17.85%)

db2 count: 152161 (16.02%)

db6 count: 143199 (15.07%)

```

# License

Released under the [MIT License](https://github.com/ZYallers/consistent_hash/blob/master/LICENSE)