redis和sqlserver数据同步_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

@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 redisTemplate(RedisConnectionFactory connectionFactory) {

RedisTemplate 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 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

org.springframework.cloud

spring-cloud-starter-stream-kafka

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 msg = new TransMsg(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 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 columns) {

for (Column column : columns) {

System.out.println(column.getName() + " : " + column.getValue() + " update=" + column.getUpdated());

}

}

private static void redisInsert( List 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 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 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