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 |
|
@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