第一步，在spring boot框架的pom.xml配置文件中引入Maven依赖。
<!-- cache --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-cache</artifactId> </dependency> <!-- redis --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> </dependency>
第二步，在application.yml中配置Redis连接信息。
#redis缓存配置 redis: # 使用的数据库，默认为0 #database: 1 # host主机，默认为localhost host: localhost # 端口号,默认为6379 port: 6379 # 密码，默认为空 password: xxxxxxxxxxxxx
第三步，写一个公共的RedisService方便调用。
package com.allen.blog.service;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.*;
import org.springframework.stereotype.Service;
import java.io.Serializable;
import java.util.List;
import java.util.Set;
import java.util.concurrent.TimeUnit;
@Service
public class RedisService {
@Autowired
private RedisTemplate redisTemplate;
/**
* 写入缓存
* @param key
* @param value
* @return
*/
public boolean set(final String key, Object value) {
boolean result = false;
try {
ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
operations.set(key, value);
result = true;
} catch (Exception e) {
e.printStackTrace();
}
return result;
}
/**
* 写入缓存设置时效时间
* @param key
* @param value
* @return
*/
public boolean set(final String key, Object value, Long expireTime) {
boolean result = false;
try {
ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
operations.set(key, value);
redisTemplate.expire(key, expireTime, TimeUnit.SECONDS);
result = true;
} catch (Exception e) {
e.printStackTrace();
}
return result;
}
/**
* 批量删除对应的value
* @param keys
*/
public void remove(final String... keys) {
for (String key : keys) {
remove(key);
}
}
/**
* 批量删除key
* @param pattern
*/
public void removePattern(final String pattern) {
Set<Serializable> keys = redisTemplate.keys(pattern);
if (keys.size() > 0)
redisTemplate.delete(keys);
}
/**
* 删除对应的value
* @param key
*/
public void remove(final String key) {
if (exists(key)) {
redisTemplate.delete(key);
}
}
/**
* 判断缓存中是否有对应的value
* @param key
* @return
*/
public boolean exists(final String key) {
return redisTemplate.hasKey(key);
}
/**
* 读取缓存
* @param key
* @return
*/
public Object get(final String key) {
Object result = null;
ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
result = operations.get(key);
return result;
}
/**
* 哈希 添加
* @param key
* @param hashKey
* @param value
*/
public void hmSet(String key, Object hashKey, Object value){
HashOperations<String, Object, Object> hash = redisTemplate.opsForHash();
hash.put(key,hashKey,value);
}
/**
* 哈希获取数据
* @param key
* @param hashKey
* @return
*/
public Object hmGet(String key, Object hashKey){
HashOperations<String, Object, Object> hash = redisTemplate.opsForHash();
return hash.get(key,hashKey);
}
/**
* 列表添加
* @param k
* @param v
*/
public void lPush(String k,Object v){
ListOperations<String, Object> list = redisTemplate.opsForList();
list.rightPush(k,v);
}
/**
* 列表获取
* @param k
* @param l
* @param l1
* @return
*/
public List<Object> lRange(String k, long l, long l1){
ListOperations<String, Object> list = redisTemplate.opsForList();
return list.range(k,l,l1);
}
/**
* 集合添加
* @param key
* @param value
*/
public void add(String key,Object value){
SetOperations<String, Object> set = redisTemplate.opsForSet();
set.add(key,value);
}
/**
* 集合获取
* @param key
* @return
*/
public Set<Object> setMembers(String key){
SetOperations<String, Object> set = redisTemplate.opsForSet();
return set.members(key);
}
/**
* 有序集合添加
* @param key
* @param value
* @param scoure
*/
public void zAdd(String key,Object value,double scoure){
ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
zset.add(key,value,scoure);
}
/**
* 有序集合获取
* @param key
* @param scoure
* @param scoure1
* @return
*/
public Set<Object> rangeByScore(String key,double scoure,double scoure1){
ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
return zset.rangeByScore(key, scoure, scoure1);
}
}第四部，测试读写数据。
package com.allen.blog;
import com.allen.blog.service.RedisService;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;
@RunWith(SpringRunner.class)
@SpringBootTest
public class BlogApplicationTests {
@Autowired
private RedisService redisService ;
@Test
public void contextLoads() {
redisService.set("liqinglin","liqinglin0314");
System.out.println(redisService.get("liqinglin"));
}
}结果：
数据已经存了进来，但是数据出现\xAC\xED\x00\x05t\x00\x09这样的乱码，spring-data-redis的RedisTemplate<K, V>模板类在操作redis时默认使用JdkSerializationRedisSerializer来进行序列化，解决方法只需要在RedisService中添加如下代码：
@Autowired
private RedisTemplate redisTemplate;
@Autowired(required = false)
public void setRedisTemplate(RedisTemplate redisTemplate) {
RedisSerializer stringSerializer = new StringRedisSerializer();
redisTemplate.setKeySerializer(stringSerializer);
redisTemplate.setValueSerializer(stringSerializer);
redisTemplate.setHashKeySerializer(stringSerializer);
redisTemplate.setHashValueSerializer(stringSerializer);
this.redisTemplate = redisTemplate;
}
这样乱码问题就解决啦~