Practical application of Redis in Java
With the rapid development of the Internet and information technology, a large amount of data and applications need to be stored, processed and accessed. In this context, Redis, as a high-performance, high-reliability, distributed memory database, has gradually become one of the necessary skills for Java developers. This article will introduce the actual application of Redis in Java, including the use of data structures, implementation of connection pools, cluster construction, and application scenario cases.
1. Use of data structures
Redis has a very rich data structure, including String, List, Set, Sorted Set, Hash and HyperLogLog, etc. The following describes how to use it in Java.
String
String is the most basic data type of Redis. You can set a Key and the corresponding Value.
Jedis jedis = new Jedis("localhost", 6379);
jedis.set("name", "Tom");
String name = jedis.get ("name");
#List is an ordered collection that stores multiple elements and can be added, deleted and queried based on the index value.
jedis.lpush("list", "a", "b", "c");
jedis.rpush("list", "d", "e", " f");
List
Set is An unordered collection that does not allow duplicate elements.
jedis.sadd("set", "a", "b", "c", "d");
jedis.srem("set", "a");
Set
Sorted Set is an ordered set. Each element has a score and can be sorted based on the score.
jedis.zadd("sortedset", 5, "a");
jedis.zadd("sortedset", 10, "b");
jedis. zrem("sortedset", "a");
Set
Hash is a key-value pair storage structure that can store multiple attributes and corresponding values.
jedis.hset("hash", "name", "Tom");
jedis.hset("hash", "age", "20");
String name = jedis.hget("hash", "name");
HyperLogLog is a radix algorithm used to count the number of elements. This can be done without recording the original value.
jedis.pfadd("hll", "a", "b", "c");
long count = jedis.pfcount("hll");
2. Implementation of connection pool
In order to ensure high concurrency and high performance, Redis Java clients generally use connection pools to manage connections. Here we take Jedis as an example to introduce the implementation method of connection pool.
JedisPoolConfig poolConfig = new JedisPoolConfig();
poolConfig.setMaxIdle(10);
poolConfig.setMaxTotal(20);
poolConfig.setMaxWaitMillis( 1000);
JedisPool jedisPool = new JedisPool(poolConfig, "localhost", 6379);
Jedis jedis = null;
try {
jedis = jedisPool.getResource(); ...
} finally {
if (jedis != null) { jedis.close(); } jedisPool.close();
}
3. Cluster construction
When the amount of data reaches a certain scale, a single Redis instance can no longer meet the demand, and a Redis cluster needs to be built. . Redis officially provides Cluster mode for cluster construction. Multiple Redis instances are started to form a cluster to achieve high data availability and load balancing. Here is an introduction to how to build Cluster mode.
redis-cli --cluster create node1:6379 node2:6379 node3:6379
Start 3 Redis instances respectively, the port numbers are 6379, use the redis-cli command to combine them a cluster.
4. Application scenario cases
Redis can be used as a cache to improve access speed. Storing some frequently accessed data in Redis can reduce the access pressure on the database and improve system performance.
Redis can implement distributed locks to avoid problems caused by multiple processes accessing the same resource at the same time and improve the stability and reliability of the system. .
Redis can be used as a counter. The value of the counter can be incremented or decremented, and concurrent operations are supported.
Redis can be used as a queue, supports producer and consumer modes, and provides multiple queue implementation methods.
Summary:
This article introduces the actual application of Redis in Java, including the use of data structures, implementation of connection pools, cluster construction, and application scenario cases. With the powerful functions of Redis and the rich library functions of Java, we can quickly build a high-performance, high-reliability distributed application system and improve the efficiency and scalability of the system.
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