Java caching technology plays an important role in modern application development, which improves the access speed and responsiveness of applications. In actual application development scenarios, the size and depth of the cache are difficult to estimate, which involves the issue of automatic cache growth. This article will provide an in-depth introduction to cache auto-grow technology in Java cache.
Why do we need the cache to grow automatically?
First, let us understand why we need cache auto-grow. In some high-concurrency application scenarios, there is a large amount of data reading and writing. For these data read and write operations, if the database or other storage devices are accessed every time, it will have an impact on system performance.
In order to solve this problem, we can introduce caching technology to store data in memory, thereby improving the data reading and writing speed and responsiveness. However, the size of the cache is difficult to determine, especially in high-concurrency scenarios, it is easy to exceed the cache capacity, leading to cache overflow and data loss. Therefore, automatic cache growth becomes necessary.
How to implement cache automatic growth
There are two main methods to implement cache automatic growth in Java cache technology: LRU strategy and LFU strategy.
The full name of LRU is Least Recently Used, which means the least recently used. The LRU strategy means that when the cache is full, every time new data is added, the data with the earliest access time will be deleted from the cache, and then new data will be added.
The implementation of the LRU strategy can be achieved with the help of Java's LinkedHashMap class. The LinkedHashMap class implements the Map interface and uses a doubly linked list to maintain the order of elements.
In LinkedHashMap, you can automatically delete the oldest accessed data by overloading the removeEldestEntry method. The specific implementation method is as follows:
public class LRUCache<K, V> extends LinkedHashMap<K, V> {
private int maxCapacity;
public LRUCache(int maxCapacity){
super(16, 0.75f, true);
this.maxCapacity = maxCapacity;
}
@Override
protected boolean removeEldestEntry(Map.Entry eldest) {
return size() > maxCapacity;
}
}The full name of LFU is Least Frequently Used, which means the least frequently used recently. The problem that the LFU policy needs to solve is how to identify and delete infrequently used data when the cache capacity reaches the upper limit.
The implementation of the LFU strategy can be achieved with the help of Java's TreeMap class. The TreeMap class implements the Map interface and uses a red-black tree to maintain element order.
In TreeMap, you can automatically delete the least frequently used data by overloading the removeEldestEntry method. The specific implementation method is as follows:
public class LFUCache<K, V> extends TreeMap<LFUCache.Frequency, LinkedHashMap<K, V>> {
private int maxCapacity;
private int size = 0;
public LFUCache(int maxCapacity) {
super();
this.maxCapacity = maxCapacity;
}
public V get(Object key) {
LinkedHashMap<K, V> linkedHashMap = this.removeKey(key);
if (linkedHashMap != null) {
Frequency freq = linkedHashMap.entrySet().iterator().next().getValue().freq;
freq.increment();
this.put(freq, linkedHashMap);
return linkedHashMap.entrySet().iterator().next().getValue().value;
}
return null;
}
public V put(K key, V value) {
LinkedHashMap<K, V> linkedHashMap = this.removeKey(key);
if (linkedHashMap != null) {
size--;
}
if (maxCapacity == 0) {
return null;
}
if (size >= maxCapacity) {
removeEldestEntry();
}
Frequency freq = new Frequency();
LinkedHashMap<K, V> map = this.get(freq);
if (map == null) {
if (size < maxCapacity) {
map = new LinkedHashMap<K, V>();
this.put(freq, map);
size++;
} else {
removeEldestEntry();
map = new LinkedHashMap<K,V>();
this.put(freq, map);
size++;
}
}
map.put(key, new Node(value, freq));
return value;
}
private void removeEldestEntry() {
Entry<Frequency, LinkedHashMap<K, V>> first = this.firstEntry();
Entry<K, Node> eldest = first.getValue().entrySet().iterator().next();
first.getValue().remove(eldest.getKey());
if (first.getValue().isEmpty()) {
this.remove(first.getKey());
}
size--;
}
private LinkedHashMap<K, V> removeKey(Object key) {
for (Map.Entry<Frequency, LinkedHashMap<K, V>> entry : entrySet()) {
LinkedHashMap<K, V> value = entry.getValue();
if (value != null && value.containsKey(key)) {
value.remove(key);
if (value.isEmpty()) {
this.remove(entry.getKey());
}
return value;
}
}
return null;
}
private static class Frequency implements Comparable<Frequency> {
private int value;
public Frequency() {
this.value = 0;
}
public void increment() {
value++;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + value;
return result;
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Frequency other = (Frequency) obj;
if (value != other.value)
return false;
return true;
}
@Override
public int compareTo(Frequency o) {
return Integer.compare(this.value, o.value);
}
}
private static class Node<K, V> {
private V value;
private Frequency freq;
public Node(V value, Frequency freq) {
this.value = value;
this.freq = freq;
}
}
}Summary
This article mainly introduces the cache automatic growth technology in Java cache technology. Through the introduction and implementation of the LRU strategy and LFU strategy, I hope readers can understand the implementation of cache automatic growth and its corresponding application scenarios. In actual application development, the best caching strategy needs to be selected based on specific scenarios to improve application performance and reliability.
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