How Redis implements data sharding expansion function-Redis-php.cn

How Redis implements data sharding expansion function

#Redis is a widely used open source Key-Value database. It is favored by developers for its advantages such as high performance, low latency, and high concurrency. However, as the amount of data continues to increase, single-node Redis can no longer meet business needs. In order to solve this problem, Redis introduced the data sharding function to achieve horizontal expansion of data and improve the overall performance of Redis.

This article will introduce how Redis implements the data sharding extension function and provide specific code examples.

1. The principle of Redis data sharding

Redis data sharding refers to storing a data set (such as Key-Value) in multiple Redis instances, that is to say, a The Redis cluster is divided into multiple nodes responsible for different data. The specific implementation method is as follows:

Use consistent hashing algorithm

The consistent hashing algorithm can evenly distribute data across multiple nodes, and each node is responsible for There will not be too much or too little data. For the addition of new nodes, only a small amount of data migration is required to complete the data balance.

Add virtual nodes

In order to prevent node load imbalance and single point of failure, you can add multiple virtual nodes to each physical node and map these virtual nodes into the data collection so that the data is more evenly distributed on various physical nodes.

2. Implementation of Redis data sharding

The following are the specific steps for Redis to implement the data sharding function:

Create a Redis cluster

You can use the Redis cluster tool to create a Redis cluster easily and quickly, so I won’t go into details here.

Use consistent hashing algorithm

Redis provides a hash slot allocator, which can allocate data to different nodes according to the consistent hashing algorithm. The example is as follows ：

hash_slot_cnt = 16384 # hash槽数量 def get_slot(s): return crc16(s) % hash_slot_cnt # 根据字符串s计算其hash槽 class RedisCluster: def __init__(self, nodes): self.nodes = nodes # 节点列表 self.slot2node = {} for node in self.nodes: for slot in node['slots']: self.slot2node[slot] = node def get_node(self, key): slot = get_slot(key) return self.slot2node[slot] # 根据key获取节点

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Add virtual node

In order to prevent a single node from crashing or overloading, we can use virtual nodes, the example is as follows:

virtual_node_num = 10 # 每个实际节点添加10个虚拟节点 class RedisCluster: def __init__(self, nodes): self.nodes = nodes self.slot2node = {} for node in self.nodes: for i in range(virtual_node_num): virtual_slot = crc16(node['host'] + str(i)) % hash_slot_cnt self.slot2node[virtual_slot] = node def get_node(self, key): slot = get_slot(key) return self.slot2node[slot]

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Data migration

When a new node joins or an old node leaves the cluster, data migration needs to be performed. Redistribute the data originally allocated to the old node to the new node. The example is as follows:

def migrate_slot(from_node, to_node, slot): if from_node == to_node: # 节点相同，不需要进行迁移 return data = from_node['client'].cluster('getkeysinslot', slot, 10) print('migrate %d keys to node %s' % (len(data), to_node['host'])) if data: to_node['client'].migrate(to_node['host'], hash_slot_cnt, '', 0, 1000, keys=data)

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3. Complete code example

The following is a complete code example for Redis to implement the data sharding extension function:

import redis hash_slot_cnt = 16384 # hash槽数量 virtual_node_num = 10 # 每个实际节点添加10个虚拟节点 def get_slot(s): return crc16(s) % hash_slot_cnt def migrate_slot(from_node, to_node, slot): if from_node == to_node: return data = from_node['client'].cluster('getkeysinslot', slot, 10) print('migrate %d keys to node %s' % (len(data), to_node['host'])) if data: to_node['client'].migrate(to_node['host'], hash_slot_cnt, '', 0, 1000, keys=data) class RedisCluster: def __init__(self, nodes): self.nodes = nodes self.slot2node = {} for node in self.nodes: for i in range(virtual_node_num): virtual_slot = crc16(node['host'] + str(i)) % hash_slot_cnt self.slot2node[virtual_slot] = node def get_node(self, key): slot = get_slot(key) return self.slot2node[slot] def add_node(self, node): self.nodes.append(node) for i in range(virtual_node_num): virtual_slot = crc16(node['host'] + str(i)) % hash_slot_cnt self.slot2node[virtual_slot] = node for slot in range(hash_slot_cnt): if self.slot2node[slot]['host'] == node['host']: migrate_slot(self.slot2node[slot], node, slot) def remove_node(self, node): self.nodes.remove(node) for i in range(virtual_node_num): virtual_slot = crc16(node['host'] + str(i)) % hash_slot_cnt del self.slot2node[virtual_slot] for slot in range(hash_slot_cnt): if self.slot2node[slot]['host'] == node['host']: new_node = None for i in range(len(self.nodes)): if self.nodes[i]['host'] != node['host'] and self.nodes[i]['slots']: new_node = self.nodes[i] break if new_node: migrate_slot(node, new_node, slot) else: print('no new node for slot %d' % slot) if __name__ == '__main__': nodes = [ {'host': '127.0.0.1', 'port': 7000, 'slots': [0, 1, 2]}, {'host': '127.0.0.1', 'port': 7001, 'slots': [3, 4, 5]}, {'host': '127.0.0.1', 'port': 7002, 'slots': [6, 7, 8]}, {'host': '127.0.0.1', 'port': 7003, 'slots': []}, {'host': '127.0.0.1', 'port': 7004, 'slots': []}, {'host': '127.0.0.1', 'port': 7005, 'slots': []}, {'host': '127.0.0.1', 'port': 7006, 'slots': []}, {'host': '127.0.0.1', 'port': 7007, 'slots': []}, {'host': '127.0.0.1', 'port': 7008, 'slots': []}, {'host': '127.0.0.1', 'port': 7009, 'slots': []}, ] clients = [] for node in nodes: client = redis.Redis(host=node['host'], port=node['port']) node['client'] = client clients.append(client) cluster = RedisCluster(nodes) for key in range(100): node = cluster.get_node(str(key)) node['client'].set('key_%d' % key, key) cluster.add_node({'host': '127.0.0.1', 'port': 7010, 'slots': []}) for key in range(100, 200): node = cluster.get_node(str(key)) node['client'].set('key_%d' % key, key) cluster.remove_node(nodes[-1])

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The above code creates a Redis cluster. New nodes are added and old nodes are deleted, demonstrating the balanced dispersion of data and data migration.

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