Table of contents
During the use of Redis, various abnormal situations may be encountered, such as: connection failure, database full, cache avalanche, cache breakdown, and cache penetration. These abnormal conditions may cause the system to crash, thereby triggering the crash of the entire system. Therefore, in practical applications, we need to adopt corresponding solutions according to the specific situation to ensure the correctness and stability of Redis.
Abnormal situations and solutions:
1. Connection failed
(1) Symptom: When connecting to the Redis server, the connection fails.
(2) Reason: network abnormality, Redis server not started, Redis server address or port modification, etc.
(3) Treatment plan:
- Check network connectivity to make sure the Redis server is reachable.
- To confirm whether the Redis server is started, you can use tools such as ping and telnet to check.
- Confirm whether the address and port of the Redis server are correct, you can use netstat, ps and other tools to check.
- If it is a cluster environment, you need to deal with the node that failed to connect according to the actual situation.
Sample code:
import java.io.IOException;
import java.util.concurrent.ExecutionException;
public class RedisConnectionFailure {
public static void main(String[] args) {
try {
// 连接 Redis 服务器
Jedis jedis = new Jedis("127.0.0.1", 6379);
// 设置键值对
jedis.set("key", "value");
// 获取键值对
String value = jedis.get("key");
System.out.println("Value: " + value);
// 关闭连接
jedis.close();
} catch (IOException e) {
System.err.println("Redis connection failed: " + e.getMessage());
}
}
}
2. The database is full
(1) Symptom: The Redis database reaches the maximum capacity and cannot store new data.
(2) Reason: The amount of cached data is too large, or the cache expiration time is not set properly.
(3) Treatment plan:
- Check the amount of cached data, and take measures to delete the cached data when the amount of cached data reaches a certain threshold.
- Check the cache expiration time. If the cache expiration time is not set properly, take measures to adjust the cache expiration time.
- Set cache data elimination strategy, such as LRU strategy or LFU strategy.
Sample code:
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
public class RedisDatabaseFull {
public static void main(String[] args) {
JedisPool jedisPool = new JedisPool("127.0.0.1", 6379);
try {
// 设置缓存过期时间
jedisPool.set("key", "value", 1000);
// 获取缓存数据大小
long dbSize = jedisPool.dbSize();
System.out.println("Database size: " + dbSize);
// 删除缓存数据
jedisPool.del("key");
} catch (Exception e) {
System.err.println("Redis database full: " + e.getMessage());
}
}
}
3. Cache Avalanche
When the cache set expires or fails, a large number of requests will be sent to the database layer at the same time, resulting in a surge in pressure on the database layer, which affects the processing of other normal business requests of the database.
Solution:
- Reasonably set the cache expiration time to avoid setting too much data to expire at the same time.
- Check the amount of cached data, and take measures to delete the cached data when the amount of cached data is too large.
- Set cache breakdown protection to prevent a large number of requests from being sent to the database layer when the cache fails centrally.
Sample code:
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
public class RedisCacheSnowy {
public static void main(String[] args) {
JedisPool jedisPool = new JedisPool("127.0.0.1", 6379);
try {
// 设置缓存过期时间
jedisPool.set("key", "value", 1000);
// 获取缓存数据大小
long dbSize = jedisPool.dbSize();
System.out.println("Database size: " + dbSize);
// 模拟缓存集中过期
for (int i = 0; i < 100; i++) {
jedisPool.expire("key", 1000);
}
// 等待缓存过期
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
// 获取缓存数据大小
dbSize = jedisPool.dbSize();
System.out.println("Database size after expiration: " + dbSize);
} catch (Exception e) {
e.printStackTrace();
} finally {
jedisPool.close();
}
}
}
4. Cache breakdown:
When the cache fails centrally, a large number of requests will be sent to the database layer at the same time, causing the pressure on the database layer to surge.
Solution:
- Reasonably set the cache expiration time to avoid setting too much data to expire at the same time.
- Check the amount of cached data, and take measures to delete the cached data when the amount of cached data is too large.
- Set cache breakdown protection to prevent a large number of requests from being sent to the database layer when the cache fails centrally.
Sample code:
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
public class RedisCacheMiss {
public static void main(String[] args) {
JedisPool jedisPool = new JedisPool("127.0.0.1", 6379);
try {
// 设置缓存过期时间
jedisPool.set("key", "value", 1000);
// 获取缓存数据大小
long dbSize = jedisPool.dbSize();
System.out.println("Database size: " + dbSize);
// 模拟缓存集中失效
for (int i = 0; i < 100; i++) {
jedisPool.expire("key", 1000);
}
// 等待缓存失效
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
// 获取缓存数据大小
dbSize = jedisPool.dbSize();
System.out.println("Database size after expiration: " + dbSize);
} catch (Exception e) {
e.printStackTrace();
} finally {
jedisPool.close();
}
}
}
5. Cache penetration:
When there is no data in the cache, a large number of requests will be sent to the database layer at the same time, causing the pressure on the database layer to surge.
Solution:
- Reasonably set the cache expiration time to avoid setting too much data to expire at the same time.
- Check the amount of cached data, and take measures to delete the cached data when the amount of cached data is too large.
- Set cache penetration protection to prevent a large number of requests from being sent to the database layer when the cache fails centrally.
Sample code:
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
public class RedisCachePenetration {
public static void main(String[] args) {
JedisPool jedisPool = new JedisPool("127.0.0.1", 6379);
try {
// 设置缓存过期时间
jedisPool.set("key", "value", 1000);
// 获取缓存数据大小
long dbSize = jedisPool.dbSize();
System.out.println("Database size: " + dbSize);
// 模拟缓存集中失效
for (int i = 0; i < 100; i++) {
jedisPool.expire("key", 1000);
}
// 等待缓存失效
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
// 获取缓存数据大小
dbSize = jedisPool.dbSize();
System.out.println("Database size after expiration: " + dbSize);
} catch (Exception e) {
e.printStackTrace();
} finally {
jedisPool.close();
}
}
}