redis缓存和mysql数据库同步

转载自：https://www.cnblogs.com/lanbo203/p/7494587.html

解决方案

一、对强一致要求比较高的，应采用实时同步方案，即查询缓存查询不到再从DB查询，保存到缓存；更新缓存时，先更新数据库，再将缓存的设置过期(建议不要去更新缓存内容，直接设置缓存过期)。

二、对于并发程度较高的，可采用异步队列的方式同步，可采用kafka等消息中间件处理消息生产和消费。

三、使用阿里的同步工具canal，canal实现方式是模拟mysql slave和master的同步机制，监控DB bitlog的日志更新来触发缓存的更新，此种方法可以解放程序员双手，减少工作量，但在使用时有些局限性。

四、采用UDF自定义函数的方式，面对mysql的API进行编程，利用触发器进行缓存同步，但UDF主要是c/c++语言实现，学习成本高。

实时同步

spring3+提供了注解的方式进行缓存编程

@Cacheable(key = "caches[0].name + T(String).valueOf(#userId)",unless = "#result eq null")

@CachePut(key = "caches[0].name + T(String).valueOf(#user.userId)")

@CacheEvict(key = "caches[0].name + T(String).valueOf(#userId)" )

@Caching(evict = {@CacheEvict(key = "caches[0].name + T(String).valueOf(#userId)" ),
                  @CacheEvict(key = "caches[0].name + #result.name" )})

@Cacheable：查询时使用，注意Long类型需转换为Sting类型，否则会抛异常

@CachePut：更新时使用，使用此注解，一定会从DB上查询数据

@CacheEvict：删除时使用；

@Caching：组合用法      具体注解的使用可参考官网

注意：注解方式虽然能使我们的代码简洁，但是注解方式有局限性：对key的获取，以及嵌套使用时注解无效，如下所示

public class User {
    private Long userId;
    private String name;
    private Integer age;
    private String sex;
    private String addr;
　　//get set .....
}

service接口

1 2 3 4 5 6 7

public interface UserService {     User getUser(Long userId);     User updateUser(User user);     User getUserByName(String name);     int insertUser(User user);     User  delete (Long userId); }<br>//实现类<br>//假设有需求是由name查询user的，一般我们是先由name->id,再由id->user，这样会减少redis缓存的冗余信息

@Service(value = "userSerivceImpl")
@CacheConfig(cacheNames = "user")
public class UserServiceImpl implements UserService {
    private static Logger log = LoggerFactory.getLogger(UserServiceImpl.class);
    @Autowired
    UserMapper userMapper;

    @Cacheable(key = "caches[0].name + T(String).valueOf(#userId)",unless = "#result eq null")
    public User getUser(Long userId) {
        User user = userMapper.selectByPrimaryKey(userId);
        return user;
    }
    @Cacheable(key = "caches[0].name + #name")
    public String getIdByName(String name){
        Long userId = userMapper.getIdByName(name);
        return  String.valueOf(userId);
    }

//使用getUserByName方式调用getIdByName 和getUser方法来实现查询，但是如果用此方式在controller中直接调用

//getUserByName方法，缓存效果是不起作用的，必须是直接调用getIdByName和getUser方法才能起作用

    public User getUserByName(String name) {
        //通过name 查询到主键  再由主键查询实体
        return getUser(Long.valueOf(getIdByName(name)));
    }

非注解方式实现

1.先定义一个RedisCacheConfig类用于生成RedisTemplate和对CacheManager的管理

@Configuration
public class RedisCacheConfig  extends CachingConfigurerSupport {

    /*定义缓存数据 key 生成策略的bean
     *包名+类名+方法名+所有参数
    */
    @Bean
    public KeyGenerator keyGenerator() {
        return new KeyGenerator() {
            @Override
            public Object generate(Object target, Method method, Object... params) {
                StringBuilder sb = new StringBuilder();
                sb.append(target.getClass().getName());
                sb.append(method.getName());
                for (Object obj : params) {
                    sb.append(obj.toString());
                }
                return sb.toString();
            }
        };
    }

     //@Bean
     public CacheManager cacheManager(
             @SuppressWarnings("rawtypes") RedisTemplate redisTemplate) {
         //RedisCacheManager cacheManager = new RedisCacheManager(redisTemplate);
           //cacheManager.setDefaultExpiration(60);//设置缓存保留时间（seconds）
         return cacheManager;
     }

    //1.项目启动时此方法先被注册成bean被spring管理
    @Bean
    public StringRedisTemplate stringRedisTemplate(RedisConnectionFactory factory) {

        StringRedisTemplate template = new StringRedisTemplate(factory);
        Jackson2JsonRedisSerializer jackson2JsonRedisSerializer = new Jackson2JsonRedisSerializer(Object.class);
        ObjectMapper om = new ObjectMapper();
        om.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);
        om.enableDefaultTyping(ObjectMapper.DefaultTyping.NON_FINAL);
        jackson2JsonRedisSerializer.setObjectMapper(om);
        template.setValueSerializer(jackson2JsonRedisSerializer);
        template.afterPropertiesSet();
        return template;
    }

    @Bean
    public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory connectionFactory) {
        RedisTemplate<String, Object> template = new RedisTemplate<>();
        template.setConnectionFactory(connectionFactory);

        //使用Jackson2JsonRedisSerializer来序列化和反序列化redis的value值
        Jackson2JsonRedisSerializer serializer = new Jackson2JsonRedisSerializer(Object.class);

        System.out.println("==============obj:"+Object.class.getName());
        ObjectMapper mapper = new ObjectMapper();
        mapper.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);
        mapper.enableDefaultTyping(ObjectMapper.DefaultTyping.NON_FINAL);
        serializer.setObjectMapper(mapper);

        template.setValueSerializer(serializer);
        //使用StringRedisSerializer来序列化和反序列化redis的key值
        template.setKeySerializer(new StringRedisSerializer());
        template.afterPropertiesSet();
        return template;
    }
}

2.定义一个redisUtil类用于存取缓存值

@Component
public class RedisCacheUtil {

    @Autowired
    private StringRedisTemplate stringRedisTemplate;
    @Autowired
    private RedisTemplate<String, Object> redisTemplate;

    /**
     * 存储字符串
     * @param key string类型的key
     * @param value String类型的value
     */
    public void set(String key, String value) {
        stringRedisTemplate.opsForValue().set(key, value);
    }

    /**
     * 存储对象
     * @param key String类型的key
     * @param value Object类型的value
     */
    public void set(String key, Object value) {
        redisTemplate.opsForValue().set(key, value);
    }

    /**
     * 存储对象
     * @param key String类型的key
     * @param value Object类型的value
     */
    public void set(String key, Object value,Long timeOut) {
        redisTemplate.opsForValue().set(key, value,timeOut, TimeUnit.SECONDS);
    }

    /**
     * 根据key获取字符串数据
     * @param key
     * @return
     */
    public String getValue(String key) {
        return stringRedisTemplate.opsForValue().get(key);
    }

//    public Object getValue(String key) {
//        return redisTemplate.opsForValue().get(key);
//    }
    /**
     * 根据key获取对象
     * @param key
     * @return
     */
    public Object getValueOfObject(String key) {
        return redisTemplate.opsForValue().get(key);
    }
    /**
     * 根据key删除缓存信息
     * @param key
     */
    public void delete(String key) {
        redisTemplate.delete(key);
    }
    /**
     * 查询key是否存在
     * @param key
     * @return
     */
    @SuppressWarnings("unchecked")
    public boolean exists(String key) {
        return redisTemplate.hasKey(key);
    }
}

3.实现类

/**
 * Created by yexin on 2017/9/8.
 *
 * 在Impl基础上+ 防止缓存雪崩和缓存穿透功能
 */
@Service(value = "userServiceImpl4")
public class UserServiceImpl4 implements UserService {

    @Autowired
    UserMapper userMapper;

    @Autowired
    RedisCacheUtil redisCacheUtil;

    @Value("${timeOut}")
    private long timeOut;

    @Override
    public User getUser(Long userId) {

        String key = "user" + userId;
        User user = (User) redisCacheUtil.getValueOfObject(key);
        String keySign = key + "_sign";
        String valueSign = redisCacheUtil.getValue(keySign);
        if(user == null){//防止第一次查询时返回时空结果
            //防止缓存穿透
            if(redisCacheUtil.exists(key)){
                return  null;
            }
            user = userMapper.selectByPrimaryKey(userId);

            redisCacheUtil.set(key,user);
            redisCacheUtil.set(keySign,"1",timeOut *(new Random().nextInt(10) + 1));
//            redisCacheUtil.set(keySign,"1",0L);  //过期时间不能设置为0，必须比0大的数
            return user;
        }

        if(valueSign != null){
            return user;
        }else {
            //设置标记的实效时间
            Long tt = timeOut * (new Random().nextInt(10) + 1);
            System.out.println("tt:"+tt);
            redisCacheUtil.set(keySign,"1",tt);
            //异步处理缓存更新  应对与高并发的情况，会产生脏读的情况
            ThreadPoolUtil.getExecutorService().execute(new Runnable(){
                public void run() { //
                    System.out.println("-----执行异步操作-----");
                    User user1 = userMapper.selectByPrimaryKey(userId);
                    redisCacheUtil.set(key,user1);
                }
            });

//            new Thread(){
//                public void run() { //应对与高并发的情况，会产生脏读的情况
//                    System.out.println("-----执行异步操作-----");
//                    User user1 = userMapper.selectByPrimaryKey(userId);
//                    redisCacheUtil.set(key,user1);
//                }
//            }.start();
        }
        return user;
    }
}

异步实现

异步实现通过kafka作为消息队列实现，异步只针对更新操作，查询无需异步，实现类如下

1.pom文件需依赖

<dependency>
     <groupId>org.springframework.cloud</groupId>
     <artifactId>spring-cloud-starter-stream-kafka</artifactId>
 </dependency>

2.生产着代码

@EnableBinding(Source.class)
public class SendService {
    @Autowired
    private Source source;
    public void sendMessage(String msg) {
        try{
            source.output().send(MessageBuilder.withPayload(msg).build());
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
//接受的是一个实体类，具体配置在application.yml
    public void sendMessage(TransMsg msg) {
        try {
            //MessageBuilder.withPayload(msg).setHeader(KafkaHeaders.TOPIC,"111111").build();
            source.output().send(MessageBuilder.withPayload(msg).build());
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

3.消费者代码

@EnableBinding(Sink.class)
public class MsgSink {
    @Resource(name = "userSerivceImpl3")
    UserService userService;
    @StreamListener(Sink.INPUT)
    public void process(TransMsg<?> msg) throws NoSuchMethodException, InvocationTargetException, IllegalAccessException, ClassNotFoundException {
        System.out.println("sink......"+msg);
        System.out.println("opt db strat ----");
        userService.updateUser((User) msg.getParams());
        System.out.println("执行db结束------");
    }
}

4.application.yml配置

spring:
  application:
    name: demo-provider
  redis:
    database: 0
    host: 192.168.252.128
    #host: localhost
    port: 6379
    password:
    pool:
      max-active: 50
      max-wait: -1
      max-idle: 50
    timeout: 0
#kafka
  cloud:
      stream:
        kafka:
          binder:
            brokers: 192.168.252.128:9092
            zk-nodes: 192.168.252.128:2181
            minPartitionCount: 1
            autoCreateTopics: true
            autoAddPartitions: true
        bindings:
          input:
            destination: topic-02
#            content-type: application/json
            content-type: application/x-java-object   #此种类型配置在消费端接受到的为一个实体类
            group: t1
            consumer:
              concurrency: 1
              partitioned: false
          output:
            destination: topic-02
            content-type: application/x-java-object             
            producer:
              partitionCount: 1
        instance-count: 1
        instance-index: 0

5.实现类

@Service(value = "userServiceImpl2")
public class UserServiceImpl2  implements UserService{
    @Autowired
    UserMapper userMapper;
    @Autowired
    RedisCacheUtil redisCacheUtil;
    private static Logger log = LoggerFactory.getLogger(UserServiceImpl.class);
    @Autowired
    SendService sendService;
    public User updateUser(User user) {
        System.out.println("   impl2   active   ");
        String key = "user"+ user.getUserId();
        System.out.println("key:"+key);
        //是否存在key
        if(!redisCacheUtil.exists(key)){
         return userMapper.updateByPrimaryKeySelective(user) == 1 ? user : null;
        }
        /*  更新key对应的value
            更新队列
         */
        User user1 = (User)redisCacheUtil.getValueOfObject(key);
        try {
            redisCacheUtil.set(key,user);
            TransMsg<User> msg = new TransMsg<User>(key,user,this.getClass().getName(),"updateUser",user);
            sendService.sendMessage(msg);

        }catch (Exception e){
            redisCacheUtil.set(key,user1);
        }
        return user;
    }
}

注意:kafka与zookeeper的配置在此不介绍

canal实现方式

先要安装canal，配置canal的example文件等，配置暂不介绍

package org.example.canal;

import com.alibaba.fastjson.JSONObject;
import com.alibaba.otter.canal.client.CanalConnector;
import com.alibaba.otter.canal.client.CanalConnectors;
import com.alibaba.otter.canal.common.utils.AddressUtils;
import com.alibaba.otter.canal.protocol.Message;
import com.alibaba.otter.canal.protocol.CanalEntry.Column;
import com.alibaba.otter.canal.protocol.CanalEntry.Entry;
import com.alibaba.otter.canal.protocol.CanalEntry.EntryType;
import com.alibaba.otter.canal.protocol.CanalEntry.EventType;
import com.alibaba.otter.canal.protocol.CanalEntry.RowChange;
import com.alibaba.otter.canal.protocol.CanalEntry.RowData;
import org.example.canal.util.RedisUtil;

import java.net.InetSocketAddress;
import java.util.List;

public class CanalClient {

    public static void main(String[] args) {
        // 创建链接
        CanalConnector connector = CanalConnectors.newSingleConnector(new InetSocketAddress(AddressUtils.getHostIp(),
                11111), "example", "", "");
        int batchSize = 1000;

        try {
            connector.connect();
            connector.subscribe(".*\..*");
            connector.rollback();
            while (true) {
                Message message = connector.getWithoutAck(batchSize); // 获取指定数量的数据
                long batchId = message.getId();
                int size = message.getEntries().size();
                if (batchId == -1 || size == 0) {
                    try {
                        Thread.sleep(1000);
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                } else {
                    printEntry(message.getEntries());
                }
                connector.ack(batchId); // 提交确认
                // connector.rollback(batchId); // 处理失败, 回滚数据
            }
        } finally {
            connector.disconnect();
        }
    }

    private static void printEntry( List<Entry> entrys) {
        for (Entry entry : entrys) {
            if (entry.getEntryType() == EntryType.TRANSACTIONBEGIN || entry.getEntryType() == EntryType.TRANSACTIONEND) {
                continue;
            }
            RowChange rowChage = null;
            try {
                System.out.println("tablename:"+entry.getHeaderOrBuilder().getTableName());
                rowChage = RowChange.parseFrom(entry.getStoreValue());
            } catch (Exception e) {
                throw new RuntimeException("ERROR ## parser of eromanga-event has an error , data:" + entry.toString(),
                        e);
            }
            EventType eventType = rowChage.getEventType();
            System.out.println(String.format("================> binlog[%s:%s] , name[%s,%s] , eventType : %s",
                    entry.getHeader().getLogfileName(), entry.getHeader().getLogfileOffset(),
                    entry.getHeader().getSchemaName(), entry.getHeader().getTableName(),
                    eventType));

            for (RowData rowData : rowChage.getRowDatasList()) {
                if (eventType == EventType.DELETE) {
                    redisDelete(rowData.getBeforeColumnsList());
                } else if (eventType == EventType.INSERT) {
                    redisInsert(rowData.getAfterColumnsList());
                } else {
                    System.out.println("-------> before");
                    printColumn(rowData.getBeforeColumnsList());
                    System.out.println("-------> after");
                    redisUpdate(rowData.getAfterColumnsList());
                }
            }
        }
    }

    private static void printColumn( List<Column> columns) {
        for (Column column : columns) {
            System.out.println(column.getName() + " : " + column.getValue() + "    update=" + column.getUpdated());
        }
    }

    private static void redisInsert( List<Column> columns){
        JSONObject json=new JSONObject();
        for (Column column : columns) {
            json.put(column.getName(), column.getValue());
        }
        if(columns.size()>0){
            RedisUtil.stringSet("user:"+ columns.get(0).getValue(),json.toJSONString());
        }
    }

    private static  void redisUpdate( List<Column> columns){
        JSONObject json=new JSONObject();
        for (Column column : columns) {
            json.put(column.getName(), column.getValue());
        }
        if(columns.size()>0){
            RedisUtil.stringSet("user:"+ columns.get(0).getValue(),json.toJSONString());
        }
    }

    private static  void redisDelete( List<Column> columns){
        JSONObject json=new JSONObject();
        for (Column column : columns) {
            json.put(column.getName(), column.getValue());
        }
        if(columns.size()>0){
            RedisUtil.delKey("user:"+ columns.get(0).getValue());
        }
    }

}

package org.example.canal.util;
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisPoolConfig;
public class RedisUtil {

    // Redis服务器IP
    private static String ADDR = "192.168.252.128";
    // Redis的端口号
    private static int PORT = 6379;
    // 访问密码
    //private static String AUTH = "admin";
    // 可用连接实例的最大数目，默认值为8；
    // 如果赋值为-1，则表示不限制；如果pool已经分配了maxActive个jedis实例，则此时pool的状态为exhausted(耗尽)。
    private static int MAX_ACTIVE = 1024;
    // 控制一个pool最多有多少个状态为idle(空闲的)的jedis实例，默认值也是8。
    private static int MAX_IDLE = 200;
    // 等待可用连接的最大时间，单位毫秒，默认值为-1，表示永不超时。如果超过等待时间，则直接抛出JedisConnectionException；
    private static int MAX_WAIT = 10000;
    // 过期时间
    protected static int  expireTime = 60 * 60 *24;
    // 连接池
    protected static JedisPool pool;

    static {
        JedisPoolConfig config = new JedisPoolConfig();
        //最大连接数
        config.setMaxTotal(MAX_ACTIVE);
        //最多空闲实例
        config.setMaxIdle(MAX_IDLE);
        //超时时间
        config.setMaxWaitMillis(MAX_WAIT);
        //
        config.setTestOnBorrow(false);
        pool = new JedisPool(config, ADDR, PORT, 1000);
    }
    /**
     * 获取jedis实例
     */
    protected static synchronized Jedis getJedis() {
        Jedis jedis = null;
        try {
            jedis = pool.getResource();
        } catch (Exception e) {
            e.printStackTrace();
            if (jedis != null) {
                pool.returnBrokenResource(jedis);
            }
        }
        return jedis;
    }

    /**
     * 释放jedis资源
     * @param jedis
     * @param isBroken
     */
    protected static void closeResource(Jedis jedis, boolean isBroken) {
        try {
            if (isBroken) {
                pool.returnBrokenResource(jedis);
            } else {
                pool.returnResource(jedis);
            }
        } catch (Exception e) {

        }
    }

    /**
     * 是否存在key
     * @param key
     */
    public static boolean existKey(String key) {
        Jedis jedis = null;
        boolean isBroken = false;
        try {
            jedis = getJedis();
            jedis.select(0);
            return jedis.exists(key);
        } catch (Exception e) {
            isBroken = true;
        } finally {
            closeResource(jedis, isBroken);
        }
        return false;
    }

    /**
     * 删除key
     * @param key
     */
    public static void delKey(String key) {
        Jedis jedis = null;
        boolean isBroken = false;
        try {
            jedis = getJedis();
            jedis.select(0);
            jedis.del(key);
        } catch (Exception e) {
            isBroken = true;
        } finally {
            closeResource(jedis, isBroken);
        }
    }

    /**
     * 取得key的值
     * @param key
     */
    public static String stringGet(String key) {
        Jedis jedis = null;
        boolean isBroken = false;
        String lastVal = null;
        try {
            jedis = getJedis();
            jedis.select(0);
            lastVal = jedis.get(key);
            jedis.expire(key, expireTime);
        } catch (Exception e) {
            isBroken = true;
        } finally {
            closeResource(jedis, isBroken);
        }
        return lastVal;
    }

    /**
     * 添加string数据
     * @param key
     * @param value
     */
    public static String stringSet(String key, String value) {
        Jedis jedis = null;
        boolean isBroken = false;
        String lastVal = null;
        try {
            jedis = getJedis();
            jedis.select(0);
            lastVal = jedis.set(key, value);
            jedis.expire(key, expireTime);
        } catch (Exception e) {
            e.printStackTrace();
            isBroken = true;
        } finally {
            closeResource(jedis, isBroken);
        }
        return lastVal;
    }

    /**
     *  添加hash数据
     * @param key
     * @param field
     * @param value
     */
    public static void hashSet(String key, String field, String value) {
        boolean isBroken = false;
        Jedis jedis = null;
        try {
            jedis = getJedis();
            if (jedis != null) {
                jedis.select(0);
                jedis.hset(key, field, value);
                jedis.expire(key, expireTime);
            }
        } catch (Exception e) {
            isBroken = true;
        } finally {
            closeResource(jedis, isBroken);
        }
    }

}

附redis关于缓存雪崩和缓存穿透，热点key

穿透

穿透：频繁查询一个不存在的数据，由于缓存不命中，每次都要查询持久层。从而失去缓存的意义。

解决办法： 持久层查询不到就缓存空结果，查询时先判断缓存中是否exists(key) ,如果有直接返回空，没有则查询后返回，

                  注意insert时需清除查询的key，否则即便DB中有值也查询不到(当然也可以设置空缓存的过期时间）

雪崩

雪崩：缓存大量失效的时候，引发大量查询数据库。
解决办法：①用锁/分布式锁或者队列串行访问

                  ②缓存失效时间均匀分布

热点key

热点key:某个key访问非常频繁，当key失效的时候有打量线程来构建缓存，导致负载增加，系统崩溃。

解决办法：

①使用锁，单机用synchronized,lock等，分布式用分布式锁。

②缓存过期时间不设置，而是设置在key对应的value里。如果检测到存的时间超过过期时间则异步更新缓存。

③在value设置一个比过期时间t0小的过期时间值t1，当t1过期的时候，延长t1并做更新缓存操作。

4设置标签缓存，标签缓存设置过期时间，标签缓存过期后，需异步地更新实际缓存  具体参照userServiceImpl4的处理方式

总结

一、查询redis缓存时，一般查询如果以非id方式查询，建议先由条件查询到id,再由id查询pojo

二、异步kafka在消费端接受信息后，该怎么识别处理那张表，调用哪个方法，此问题暂时还没解决

三、比较简单的redis缓存，推荐使用canal

参考文档

http://blog.csdn.net/fly_time2012/article/details/50751316

http://blog.csdn.net/kkgbn/article/details/60576477

http://www.cnblogs.com/fidelQuan/p/4543387.html