This article brings you relevant knowledge about Redis, which mainly introduces the relevant content from high-availability architecture construction to principle analysis. Let’s take a look at it together. I hope it will be helpful to everyone. .

Recommended learning: Redis video tutorial

Due to the company’s recent system optimization, the large table was divided into tables some time ago. , now it’s time to do redis again. Regarding redis, one of the requirements is to migrate the redis service from Alibaba Cloud to the company's own server (due to the nature of the company). I just took this opportunity to review the high-availability cluster architecture of redis. There are three redis cluster modes, namely master-slave replication mode, sentinel mode and Cluster cluster mode. Generally, Sentinel and Cluster clusters are used more frequently. Let’s briefly understand these three modes.

Persistence mechanism

Before understanding the cluster architecture, we must first introduce the persistence mechanism of redis, because persistence will be involved in the subsequent cluster. Redis persistence is to store the data cached in the memory according to some rules to prevent data recovery or master-slave node data synchronization in the cluster architecture when the redis service goes down. There are two ways of redis persistence: RDB and AOF. After version 4.0, a new hybrid persistence mode was introduced.

RDB

RDB is the persistence mechanism enabled by redis by default. Its persistence method is based on the rules configured by the user"At least Y changes have occurred within X seconds" , generate a snapshot and save it to the dump.rdb binary file. By default, redis is configured with three configurations: at least one cache key change has occurred within 900 seconds, at least 10 cache key changes have occurred within 300 seconds, and at least 10,000 changes have occurred within 60 seconds.

In addition to redis automatic snapshot persistence data, there are two commands that can help us manually snapshot memory data. These two commands are saveandbgsave.

• save: Perform data snapshots in a synchronous manner. When the amount of cached data is large, it will block the execution of other commands. low efficiency.

• bgsave: Perform data snapshots in an asynchronous manner. The redis main thread forks out a child process to perform data snapshots, which will not block the execution of other commands. Higher efficiency. Since an asynchronous snapshot is used, it is possible that other commands may modify the data during the snapshot process. In order to avoid this problem, reids adopts the copy-on-write (Cpoy-On-Write) method. Because the process taking the snapshot at this time is forked by the main thread, it enjoys the resources of the main thread. When data changes occur during the snapshot process, , then the data will be copied and the copy data will be generated, and the child process will write the modified copy data to the dump.rdb file.

RDB snapshots are stored in binary, so data recovery will be faster, but there is a risk of data loss. If the snapshot rule is set so that at least 100 data changes occur within 60 seconds, then at 50 seconds, the redis service suddenly goes down for some reason, and all data within these 50 seconds will be lost.

AOF

AOF is another persistence method of Redis. Unlike RDB, AOF records every command that changes data and saves it to the appendonly.aof file on the disk. , when the redis service is restarted, the file will be loaded and the commands saved in the file will be executed again to achieve the effect of data recovery. By default, AOF is turned off and can be turned on by modifying the conf configuration file.

 # appendonly no  关闭AOF持久化
 appendonly yes   # 开启AOF持久化
 # The name of the append only file (default: "appendonly.aof")
 appendfilename "appendonly.aof" # 持久化文件名

AOF provides three ways to save commands to disk. By default, AOF uses appendfsync everysec for command persistence.

appendfsync always #每次有新的改写命令时，都会追加到磁盘的aof文件中。数据安全性最高，但效率最慢。
appendfsync everysec # 每一秒，都会将改写命令追加到磁盘中的aof文件中。如果发生宕机，也只会丢失1秒的数据。
appendfsync no #不会主动进行命令落盘，而是由操作系统决定什么时候写入到磁盘。数据安全性不高。

After opening AOF, you need to restart the redis service. When the relevant rewrite command is executed again, the operation command will be recorded in the aof file.

Compared with RDB, although AOF data security is higher, as the service continues to run, the files of aof will also It gets bigger and bigger, and the next time you restore data, the speed will get slower and slower. If both RDB and AOF are enabled, redis will give priority to AOF when restoring data. After all, AOF loses less data.

混合模式

由于RDB持久化方式容易造成数据丢失，AOF持久化方式数据恢复较慢，所以在redis4.0版本后，新出来混合持久化模式。混合持久化将RDB和AOF的优点进行了集成，并而且依赖于AOF,所以在使用混合持久化前，需要开启AOF。在开启混合持久化后，当发生AOF重写时，会将内存中的数据以RDB的数据格式保存到aof文件中，在下一次的重写之前，混合持久化会追加保存每条改写命令到aof文件中。当需要恢复数据时，会加载保存的rdb内容数据，然后再继续同步aof指令。

  # AOF重写配置，当aof文件达到60MB并且比上次重写后的体量多100%时自动触发AOF重写  auto-aof-rewrite-percentage 100
  auto-aof-rewrite-min-size 64mb
  
  aof-use-rdb-preamble yes # 开启混合持久化# aof-use-rdb-preamble no # 关闭混合持久化

AOF重写是指当aof文件越来越大时，redis会自动优化aof文件中无用的命令，从而减少文件体积。比如在处理文章阅读量时，每查看一次文章就会执行一次Incr命令，但是随着阅读量的不断增加,aof文件中的incr命令也会积累的越来越多。在AOF重写后，将会删除这些没用的Incr命令，将这些命令直接替换为set key value命令。除了redis自动重写AOF，如果需要，也可以通过bgrewriteaof命令手动触发。

主从复制

在生产环境中，一般不会直接配置单节点的redis服务，这样压力太大。为了缓解redis服务压力，可以搭建主从复制，做读写分离。redis主从复制，是有一个主节点Master和多个从节点Slave组成。主从节点间的数据同步只能是单向传输的，只能由Master节点传输到Slave节点。

环境配置

准备三台linux服务器，其中一台作为redis的主节点，两台作为reids的从节点。如果没有足够的机器可以在同一台机器上面将redis文件多复制两份并更改端口号，这样可以搭建一个伪集群。

1. 配置从节点36.130，36.131机器中reids.conf

修改redis.conf文件中的replicaof，配置主节点的ip和端口号，并且开启从节点只读。

2. 启动主节点36.128机器中reids服务

 ./src/redis-server redis.conf

3.  依次启动从节点36.130，36.131机器中的redis服务

 ./src/redis-server redis.conf

启动成功后可以看到日志中显示已经与Master节点建立的连接。如果出现与Master节点的连接被拒，那么先检查Master节点的服务器是否开启防火墙,如果开启，可以开放6379端口或者关闭防火墙。如果防火墙被关闭但连接仍然被拒，那么可以修改Master节点服务中的redis.conf文件。将bing 127.0.0.1修改为本机对外的网卡ip或者直接注释掉即可，然后重启服务器即可。

4. 查看状态

全部节点启动成功后，Master节点可以查看从节点的连接状态，offset偏移量等信息。

 info replication # 主节点查看连接信息

数据同步流程

• 全量数据同步主从节点之间的数据同步是通过建立socket长连接来进行传输的。当Slave节点启动时，会与Master节点建立长连接，并且发送psync同步数据命令。当Master节点收到psync命令时，会执行pgsave进行rdb内存数据快照(这里的rdb快照与conf文件中是否开启rdb无关),如果在快照过程中有新的改写命令，那么Master节点会将这些命令保存到repl buffer缓冲区中。当快照结束后，会将rdb传输给Slave节点。Slave节点在接收到rdb后，如果存在旧数据，那么会将这些旧数据清除并加载rdb。加载完成后会接受master缓存在repl buffer中的新命令。在这些步骤全部执行完成后，主从节点已经算连接成功了，后续Master节点的命令会不断的发送到Slave节点。如果在高并发的情况下，可能会存在数据延迟的情况。

• 部分数据同步

部分数据同步发生在Slave节点发生宕机，并且在短时间内进行了服务恢复。短时间内主从节点之间的数据差额不会太大，如果执行全量数据同步将会比较耗时。部分数据同步时，Slave会向Master节点建立socket长连接并发送带有一个offset偏移量的数据同步请求，这个offset可以理解数据同步的位置。Master节点在收到数据同步请求后，会根据offset结合buffer缓冲区内新的改写命令进行位置确定。如果确定了offset的位置，那么就会将这个位置往后的所有改写命令发送到Slave节点。如果没有确定offset的位置，那么会再次执行全量数据同步。比如，在Slave节点没有宕机之前命令已经同步到了offset=11这个位置，当该节点重启后，向Master节点发送该offset，Master根据offset在缓冲区中进行定位，在定位到11这个位置后，将该位置往后的所有命令发送给Slave。在数据同步完成后，后续Master节点的命令会不断的发送到该Slave节点

优缺点

• 优点

  1. 可以实现一主多从，读写分离，减轻Master节点读操作压力
  2. 是哨兵，集群架构的基础

• 缺点

  1. Does not have automatic master-slave switching function. When the Master node goes down, you need to manually switch the master node.
  2. It is easy to have data inconsistency. When the Master node goes down, if there is data that is not synchronized, It will cause data loss

Sentinel mode

Sentinel mode further optimizes the master-slave replication and separates a separate sentinel process for monitoring the master-slave Regarding the server status in the architecture, once a downtime occurs, Sentinel will elect a new Master node within a short period of time and perform master-slave switching. Not only that, under a multi-sentinel node, each sentinel will monitor each other and monitor whether the sentinel node is down.

Environment Configuration

主从复制是哨兵模式的基础，所以在搭建哨兵前需要完成主从复制的配置。在搭建完主从后，哨兵的搭建就容易很多。 找到安装目录下的sentinel.conf文件并进行修改。主要修改两个地方，分别为哨兵端口port和监控的主节点ip地址和端口号。

在配置完成后，可以使用命令启动各机器的哨兵服务。启动成功后，可查看redis服务和哨兵服务的进行信息。

搭建成功后，就来通过代码演示主节点宕机的情况下，哨兵是否会帮助系统自动进行主备切换。在springboot项目中引入对应的pom,并配置对应的redis哨兵信息。

<dependency>
    <groupid>org.springframework.boot</groupid>
    <artifactid>spring-boot-starter-data-redis</artifactid>
    <version>2.2.2.RELEASE</version></dependency><dependency>
    <groupid>org.apache.commons</groupid>
    <artifactid>commons-pool2</artifactid>
    <version>2.4.2</version></dependency>

server:
  port: 8081spring:
  redis:
    sentinel:
      master: mymaster # 主服务节点
      nodes: 192.168.36.128:26379,192.168.36.130:26379,192.168.36.131:26379 #哨兵节点
    timeout: 3000 #连接超时时间

@Slf4j
@RestController
public class RedisTest {

    @Resource
    private StringRedisTemplate stringRedisTemplate;
    
    /*
    * 每秒钟向redis中写入数据，中途kill掉主节点进程，模拟宕机
    */
    @GetMapping("/redis/testSet")
   public void test(@RequestParam(name = "key") String key,
                    @RequestParam(name = "value") String value) throws InterruptedException {
        int idx=0;
        for(;;){
            try {
                idx++;
                stringRedisTemplate.opsForValue().set(key+idx, value);
                log.info("=====存储成功:{},{}=====",key+idx,value);
            }catch (Exception e){
                log.error("====连接redis服务器失败:{}====",e.getMessage());
            }
            Thread.sleep(1000);
        }
   }
}

当启动服务后，通过节后向后端传递数据，可以看到输出的日志，表示redis哨兵集群已经可以正常运行了。那么这个时候kill掉36.128机器上的主节点，模拟服务宕机。通过日志可以知道，服务出现异常了，在过十几秒发现哨兵已经自动帮系统进行了主从切换，并且服务也可以正常访问了。

2022-11-14 22:20:23.134  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test14,123=====
2022-11-14 22:20:24.142  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test15,123=====
2022-11-14 22:20:24.844  INFO 8764 --- [xecutorLoop-1-1] i.l.core.protocol.ConnectionWatchdog     : Reconnecting, last destination was /192.168.36.128:6379
2022-11-14 22:20:26.909  WARN 8764 --- [ioEventLoop-4-4] i.l.core.protocol.ConnectionWatchdog     : Cannot reconnect to [192.168.36.128:6379]: Connection refused: no further information: /192.168.36.128:6379
2022-11-14 22:20:28.165 ERROR 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : ====连接redis服务器失败:Redis command timed out; nested exception is io.lettuce.core.RedisCommandTimeoutException: Command timed out after 3 second(s)====
2022-11-14 22:20:31.199  INFO 8764 --- [xecutorLoop-1-1] i.l.core.protocol.ConnectionWatchdog     : Reconnecting, last destination was 192.168.36.128:6379

2022-11-14 22:20:52.189 ERROR 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : ====连接redis服务器失败:Redis command timed out; nested exception is io.lettuce.core.RedisCommandTimeoutException: Command timed out after 3 second(s)====
2022-11-14 22:20:53.819  WARN 8764 --- [ioEventLoop-4-2] i.l.core.protocol.ConnectionWatchdog     : Cannot reconnect to [192.168.36.128:6379]: Connection refused: no further information: /192.168.36.128:6379
2022-11-14 22:20:56.194 ERROR 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : ====连接redis服务器失败:Redis command timed out; nested exception is io.lettuce.core.RedisCommandTimeoutException: Command timed out after 3 second(s)====
2022-11-14 22:20:57.999  INFO 8764 --- [xecutorLoop-1-2] i.l.core.protocol.ConnectionWatchdog     : Reconnecting, last destination was 192.168.36.128:6379
2022-11-14 22:20:58.032  INFO 8764 --- [ioEventLoop-4-4] i.l.core.protocol.ReconnectionHandler    : Reconnected to 192.168.36.131:6379
2022-11-14 22:20:58.040  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test24,123=====
2022-11-14 22:20:59.051  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test25,123=====
2022-11-14 22:21:00.057  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test26,123=====
2022-11-14 22:21:01.065  INFO 8764 --- [nio-8081-exec-2] com.gz.redis.RedisTest                   : =====存储成功:test27,123=====

故障转移

在多个哨兵的模式下，每个哨兵都会向redis节点发送心跳包来检测节点的运行状态。如果某个哨兵发现主节点连接超时了，没有收到心跳，那么系统并不会立刻进行故障转移，这种情况叫做主观下线。如果后续的哨兵节点发现，与主节点的心跳也失败了并且哨兵数量超过2个，那么这个时候就会认为主节点客观下线，并且会进行故障转移，这个客观下线的数值可以在哨兵的配置文件中进行配置。

sentinel monitor master 192.168.36.128 6378 2

在故障转移前，需要选举出一个哨兵leader来进行Master节点的重新选举。哨兵的选举过程大致可以分为三步：

• 当某个的哨兵确定主节点已经下线时，会像其他哨兵发送is-master-down-by-addr命令，要求将自己设为leader，并处理故障转移工作。

• 其他哨兵在收到命令后，进行投票选举

• 如果票数过半时，那么发送命令的哨兵节点将成为主节点，并进行故障转移。

当选举出主哨兵后，那么这个主哨兵就会过滤掉宕机的redis节点，重新选举出Master节点。首先会根据redis节点的优先级进行选举(slave-priority)，数值越大的从节点将会被选举为主节点。如果这个优先级相同，那么主哨兵节点就会选择数据最全的从节点作为新的主节点。如果还是选举失败，那么就会选举出进程id最小的从节点作为主节点。

脑裂

在集群环境下会由于网络等原因出现脑裂的情况，所谓的脑裂就是由于主节点和从节点和哨兵处于不同的网络分区，由于网络波动等原因，使得哨兵没有能够即使接收到主节点的心跳，所以通过选举的方式选举了一个从节点为新的主节点，这样就存在了两个主节点，就像一个人有两个大脑一样，这样会导致客户端还在像老的主节点那里写入数据，新节点无法同步数据，当网络恢复后，哨兵会将老的主节点降为从节点，这时再从新主节点同步数据，这会导致大量数据丢失。如果需要避免脑裂的问题，可以配置下面两行信息。

 min-replicas-to-write 3 # 最少从节点为3
 min-replicas-max-lag 10 # 表示数据复制和同步的延迟不能超过10秒

优缺点

优点：除了拥有主从复制的优点外，还可以进行故障转移，主从切换，系统更加可靠。

缺点：故障转移需要花费一定的时间，在高并发场景下容易出现数据丢失。不容易实现在线扩容。

Cluster Mode

In sentry mode, although master-slave switching can be performed when the master node is down, the switching process takes more than ten seconds or more, which may cause some problems. Loss of data. If the amount of concurrency is not high, you can use this cluster mode, but in the case of high concurrency, these ten seconds may cause serious consequences. Therefore, many Internet companies use the Cluster cluster architecture. The Cluster cluster is composed of multiple redis nodes. Each redis service node has a Master node and multiple Slave nodes. When storing data, redis will perform a hash operation on the key of the data and assign it to different slots based on the operation results. Bit. Under normal circumstances, the Cluster cluster architecture requires 6 nodes (three masters and three slaves).

Environment setup

Since there are only three virtual machines, two redis services need to be built on each server, with ports 6379 and 6380 respectively. This can build just 6 nodes.

./src/redis-cli --cluster create 192.168.36.128:6379 192.168.36.128:6380 192.168.36.130:6379 192.168.36.130:6380 192.168.36.131:6379 192.168.36.131:6380 --cluster-replicas 1复制代码

spring:
  redis:
    cluster:
      nodes: 192.168.36.128:6379,192.168.36.128:6380,192.168.36.130:6379,192.168.36.130:6380,192.168.36.131:6379,192.168.36.131:6380#    sentinel:#      master: mymaster#      nodes: 192.168.36.128:26379,192.168.36.130:26379,192.168.36.131:26379
    timeout: 3000
    lettuce:
      pool:
        max-active: 80
        min-idle: 50

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