In Java development, caching technology is extremely important, especially in high-concurrency scenarios. Reasonable cache design can significantly improve system performance and save server resources. For the data in the cache, especially in a multi-threaded environment, the correctness and reliability of the cached data are particularly critical. Therefore, this article will introduce a common caching technology: cache data locking.
1. Why do you need to lock cached data?
In applications, caching is an important part of maintaining and improving system performance. However, when multiple threads concurrently access cached data, you need to consider how to ensure the correctness and reliability of the cached data.
For example, there is such a scenario: in an application, there is a keyword search function. After the user enters a keyword, the corresponding data will be searched from the cache and the query results will be returned to the user. . Assume that in a high concurrency situation, multiple threads request the same keyword data at the same time, and the data is not hit in the cache, then multiple threads will request the database at the same time, which may lead to repeated database query operations and waste of servers. resources, and the returned query results may also be different, ultimately leading to business logic errors.
In order to avoid the above situation, we need to lock the data in the cache to ensure that only one thread can perform read and write operations at the same time to avoid data inconsistency caused by concurrent access by multiple threads.
2. How to implement cached data locking
In Java, shared variables can be added through the synchronized keyword Lock. In caching technology, synchronized can be used to lock the cache to ensure that only one thread can read and write to the cache.
The sample code is as follows:
public class Cache { private static Map<String, Object> cacheData = new HashMap<>(); // 缓存数据加锁 public static synchronized void put(String key, Object value) { cacheData.put(key, value); } // 缓存数据加锁 public static synchronized Object get(String key) { return cacheData.get(key); } }
In the above code, we use synchronized to lock the put and get methods to ensure that only one thread can perform read and write operations at the same time.
In addition to using the synchronized keyword for locking, you can also use the ReentrantReadWriteLock read-write lock to lock cached read and write operations. Compared with using synchronized, ReentrantReadWriteLock can provide more flexible control of read and write operations. For example, we can allow multiple threads to read data in the cache at the same time to improve the concurrent processing capabilities of the system.
The sample code is as follows:
public class Cache { private static Map<String, Object> cacheData = new HashMap<>(); private static ReentrantReadWriteLock lock = new ReentrantReadWriteLock(); // 缓存数据加写锁 public static void put(String key, Object value) { lock.writeLock().lock(); try { cacheData.put(key, value); } finally { lock.writeLock().unlock(); } } // 缓存数据加读锁 public static Object get(String key) { lock.readLock().lock(); try { return cacheData.get(key); } finally { lock.readLock().unlock(); } } }
In the above code, we use ReentrantReadWriteLock to lock the put and get methods. During the write operation, we need to acquire the write lock, while the read operation only needs to acquire Just read the lock.
3. Precautions for locking cached data
In addition to implementing the above method of locking cached data, we also need to pay attention to the following points:
4. Summary
Cache data locking is an important measure in Java cache technology, which can ensure the correctness and reliability of multi-threaded concurrent access to cached data. This article introduces two implementation methods of cache data locking: synchronized keyword and ReentrantReadWriteLock read-write lock, and introduces several issues that need to be paid attention to when locking. In practical applications, we need to choose the appropriate locking method and granularity based on specific business needs to improve system performance and response speed.
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