LRU cache framework in multi -threaded environment

In a multi -threaded environment, LRU (recently used) cache frames need to be carried out concurrently to ensure the correctness and performance of the cache operation.This article will discuss how to achieve concurrent processing in a multi -threaded environment, while providing examples of Java code. 1. The needs of concurrent treatment When multiple threads access the LRU cache at the same time, the following needs may occur: the needs of concurrency processing: 1. Concurrent reading: Read the data in the cache at the same time. 2. Concurrent writing: Multiple threads are written into the data in the cache at the same time. 3. Reading and writing conflict: One thread is reading data in the cache, and the other thread is writing new data to the cache. 4. Cache elimination: When the cache space is insufficient, it is necessary to eliminate the latest data used recently, which may involve concurrent treatment. Second, concurrent reading processing Multi -threaded concurrent reading is relatively easy to handle, because there is no data competition between reading operations.In the LRU cache framework, you need to ensure that the read operation will not change the cache state. The following is an example of the LRU cache implementation, which demonstrates how to achieve concurrent reading: import java.util.HashMap; import java.util.LinkedHashMap; import java.util.Map; public class LRUCache<K, V> { private final int capacity; private final Map<K, V> cache; public LRUCache(int capacity) { this.capacity = capacity; this.cache = new LinkedHashMap<K, V>(capacity, 0.75f, true) { @Override protected boolean removeEldestEntry(Map.Entry<K, V> eldest) { return size() > capacity; } }; } public synchronized V get(K key) { return cache.get(key); } public synchronized void put(K key, V value) { cache.put(key, value); } } In the above example, both the GET and PUT methods use the synchronized keyword to ensure the security of the thread.In this way, when multiple threads access the cache at the same time, only one thread can perform reading or writing operations, and other threads will be blocked until the lock is obtained. Third, concurrent writing processing Concurrent writing is a relatively complicated problem, because multiple threads may compete for the cache state at the same time.In the LRU cache framework, the atomicity and consistency of writing operations need to be guaranteed. The following is an example of the LRU cache implementation, which demonstrates how to achieve concurrent writing: import java.util.HashMap; import java.util.LinkedHashMap; import java.util.Map; import java.util.concurrent.locks.ReadWriteLock; import java.util.concurrent.locks.ReentrantReadWriteLock; public class LRUCache<K, V> { private final int capacity; private final Map<K, V> cache; private final ReadWriteLock lock; public LRUCache(int capacity) { this.capacity = capacity; this.cache = new LinkedHashMap<K, V>(capacity, 0.75f, true) { @Override protected boolean removeEldestEntry(Map.Entry<K, V> eldest) { return size() > capacity; } }; this.lock = new ReentrantReadWriteLock(); } public V get(K key) { lock.readLock().lock(); try { return cache.get(key); } finally { lock.readLock().unlock(); } } public void put(K key, V value) { lock.writeLock().lock(); try { cache.put(key, value); } finally { lock.writeLock().unlock(); } } } In the above example, we use ReadWriteLock to implement and write concurrently.For reading operations, multiple threads can get read locks at the same time and read data from cache.For writing operations, only one thread can get writing locks to ensure the atomicity and consistency of writing. Fourth, read and write conflict treatment Reading and writing conflict means that one thread is reading the cache, and the other thread is writing new data to the cache.In order to solve read and write conflicts, we can use the read and write lock to achieve it. The following is the LRU cache implementation of the example, which demonstrates how to deal with read and write conflicts: import java.util.HashMap; import java.util.LinkedHashMap; import java.util.Map; import java.util.concurrent.locks.ReadWriteLock; import java.util.concurrent.locks.ReentrantReadWriteLock; public class LRUCache<K, V> { private final int capacity; private final Map<K, V> cache; private final ReadWriteLock lock; public LRUCache(int capacity) { this.capacity = capacity; this.cache = new LinkedHashMap<K, V>(capacity, 0.75f, true) { @Override protected boolean removeEldestEntry(Map.Entry<K, V> eldest) { return size() > capacity; } }; this.lock = new ReentrantReadWriteLock(); } public V get(K key) { lock.readLock().lock(); try { return cache.get(key); } finally { lock.readLock().unlock(); } } public void put(K key, V value) { lock.writeLock().lock(); try { cache.put(key, value); } finally { lock.writeLock().unlock(); } } public void remove(K key) { lock.writeLock().lock(); try { cache.remove(key); } finally { lock.writeLock().unlock(); } } } In the above example, we added the Remove method to display the read -write conflict processing when the cache is deleted.By obtaining the writing lock, we can ensure the atomicity and consistency of the delete operation. Fifth, cache elimination treatment In a multi -threaded environment, when the cache space is insufficient, it is necessary to eliminate the most recently used data.To deal with this problem, we can use thread security data structures, such as ConcurrenThashMap, and combined with LinkedhashMap to implement the LRU mechanism. The following is the LRU cache implementation of the example, which demonstrates the processing of cache elimination: import java.util.LinkedHashMap; import java.util.Map; public class LRUCache<K, V> { private final int capacity; private final Map<K, V> cache; public LRUCache(int capacity) { this.capacity = capacity; this.cache = new LinkedHashMap<K, V>(capacity, 0.75f, true) { @Override protected boolean removeEldestEntry(Map.Entry<K, V> eldest) { return size() > capacity; } }; } public V get(K key) { return cache.get(key); } public void put(K key, V value) { cache.put(key, value); } } In the above example, we use LinkedhashMap to implement the LRU cache strategy.LinkedhashMap is an orderly hash table. By rewriting the RemoveEredestentry method, we can delete the longest unused data when the cache space is insufficient. 6. Summary In a multi -threaded environment, the LRU cache framework needs to be performed concurrently.This article introduces the problems of how to handle concurrent reading, concurrent writing, read and write conflict, and cache elimination, and provide the corresponding Java code example.When designing and implementing the LRU cache framework, we need to consider the safety and performance of concurrent treatment, and choose the appropriate concurrent treatment method to meet the needs.