缓存淘汰算法--LRU算法
参考：
https://www.cnblogs.com/dailidong/p/7571178.html
https://blog.csdn.net/wangxilong1991/article/details/70172302
https://www.cnblogs.com/lzrabbit/p/3734850.html
java实现极简的LRU算法
import java.util.LinkedHashMap;
import java.util.Map;
/**
* LRU (Least Recently Used)
*/
public class LRUCache<K, V> extends LinkedHashMap<K, V> {
/**
*
*/
private static final long serialVersionUID = 1L;
//缓存大小
private int cacheSize;
public LRUCache(int cacheSize) {
//第三个参数true是关键
super(10, 0.75f, true);
this.cacheSize = cacheSize;
}
/**
* 缓存是否已满
*/
@Override
protected boolean removeEldestEntry(Map.Entry<K, V> eldest) {
boolean r = size() > cacheSize;
if(r){
System.out.println("清除缓存key："+eldest.getKey());
}
return r;
}
LRUCache<String, String> cache = new LRUCache<String, String>(5);
cache.put("1", "1");
cache.put("2", "2");
cache.put("3", "3");
cache.put("4", "4");
cache.put("5", "5");
System.out.println("初始：");
System.out.println(cache.keySet());
System.out.println("访问3：");
cache.get("3");
System.out.println(cache.keySet());
System.out.println("访问2：");
cache.get("2");
System.out.println(cache.keySet());
System.out.println("增加数据6,7：");
cache.put("6", "6");
cache.put("7", "7");
System.out.println(cache.keySet());
}
import java.util.Map;
/**
* LRU (Least Recently Used)
*/
public class LRUCache<K, V> extends LinkedHashMap<K, V> {
/**
*
*/
private static final long serialVersionUID = 1L;
//缓存大小
private int cacheSize;
public LRUCache(int cacheSize) {
//第三个参数true是关键
super(10, 0.75f, true);
this.cacheSize = cacheSize;
}
/**
* 缓存是否已满
*/
@Override
protected boolean removeEldestEntry(Map.Entry<K, V> eldest) {
boolean r = size() > cacheSize;
if(r){
System.out.println("清除缓存key："+eldest.getKey());
}
return r;
}
//测试
public static void main(String[] args) {LRUCache<String, String> cache = new LRUCache<String, String>(5);
cache.put("1", "1");
cache.put("2", "2");
cache.put("3", "3");
cache.put("4", "4");
cache.put("5", "5");
System.out.println("初始：");
System.out.println(cache.keySet());
System.out.println("访问3：");
cache.get("3");
System.out.println(cache.keySet());
System.out.println("访问2：");
cache.get("2");
System.out.println(cache.keySet());
System.out.println("增加数据6,7：");
cache.put("6", "6");
cache.put("7", "7");
System.out.println(cache.keySet());
}
}
运行结果如下：
初始化：
[1, 2, 3, 4, 5]
访问3：
[1, 2, 4, 5, 3]
访问2：
[1, 4, 5, 3, 2]
增加数据6,7：
清除缓存key：1
清除缓存key：4
[5, 3, 2, 6, 7]
缓存淘汰算法--LRU算法(java代码实现)
LRU
原理
LRU（Least recently used，最近最少使用）算法根据数据的历史访问记录来进行淘汰数据，其核心思想是“如果数据最近被访问过，那么将来被访问的几率也更高”。
实现1
最常见的实现是使用一个链表保存缓存数据，详细算法实现如下： 
1. 新数据插入到链表头部；
2. 每当缓存命中（即缓存数据被访问），则将数据移到链表头部；
3. 当链表满的时候，将链表尾部的数据丢弃。
分析
【命中率】
当存在热点数据时，LRU的效率很好，但偶发性的、周期性的批量操作会导致LRU命中率急剧下降，缓存污染情况比较严重。
【复杂度】
实现简单。
【代价】
命中时需要遍历链表，找到命中的数据块索引，然后需要将数据移到头部。
import java.util.ArrayList;
import java.util.Collection;
import java.util.LinkedHashMap;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;
import java.util.Map;
/**
* 类说明：利用LinkedHashMap实现简单的缓存， 必须实现removeEldestEntry方法，具体参见JDK文档
*
* @author dennis
*
* @param <K>
* @param <V>
*/
public class LRULinkedHashMap<K, V> extends LinkedHashMap<K, V> {
private final int maxCapacity;
private static final float DEFAULT_LOAD_FACTOR = 0.75f;
private final Lock lock = new ReentrantLock();
public LRULinkedHashMap(int maxCapacity) {
super(maxCapacity, DEFAULT_LOAD_FACTOR, true);
this.maxCapacity = maxCapacity;
}
@Override
protected boolean removeEldestEntry(java.util.Map.Entry<K, V> eldest) {
return size() > maxCapacity;
}
@Override
public boolean containsKey(Object key) {
try {
lock.lock();
return super.containsKey(key);
} finally {
lock.unlock();
}
}
@Override
public V get(Object key) {
try {
lock.lock();
return super.get(key);
} finally {
lock.unlock();
}
}
@Override
public V put(K key, V value) {
try {
lock.lock();
return super.put(key, value);
} finally {
lock.unlock();
}
}
public int size() {
try {
lock.lock();
return super.size();
} finally {
lock.unlock();
}
}
public void clear() {
try {
lock.lock();
super.clear();
} finally {
lock.unlock();
}
}
public Collection<Map.Entry<K, V>> getAll() {
try {
lock.lock();
return new ArrayList<Map.Entry<K, V>>(super.entrySet());
} finally {
lock.unlock();
}
}
} 实现2
LRUCache的链表+HashMap实现
传统意义的LRU算法是为每一个Cache对象设置一个计数器，每次Cache命中则给计数器+1，而Cache用完，需要淘汰旧内容，放置新内容时，就查看所有的计数器，并将最少使用的内容替换掉。
它的弊端很明显，如果Cache的数量少，问题不会很大， 但是如果Cache的空间过大，达到10W或者100W以上，一旦需要淘汰，则需要遍历所有计算器，其性能与资源消耗是巨大的。效率也就非常的慢了。
它的原理： 将Cache的所有位置都用双连表连接起来，当一个位置被命中之后，就将通过调整链表的指向，将该位置调整到链表头的位置，新加入的Cache直接加到链表头中。
这样，在多次进行Cache操作后，最近被命中的，就会被向链表头方向移动，而没有命中的，而想链表后面移动，链表尾则表示最近最少使用的Cache。
当需要替换内容时候，链表的最后位置就是最少被命中的位置，我们只需要淘汰链表最后的部分即可。
上面说了这么多的理论， 下面用代码来实现一个LRU策略的缓存。
非线程安全，若实现安全，则在响应的方法加锁。
import java.util.HashMap;
import java.util.Map.Entry;
import java.util.Set;
public class LRUCache<K, V> {
private int currentCacheSize;
private int CacheCapcity;
private HashMap<K,CacheNode> caches;
private CacheNode first;
private CacheNode last;
public LRUCache(int size){
currentCacheSize = 0;
this.CacheCapcity = size;
caches = new HashMap<K,CacheNode>(size);
}
public void put(K k,V v){
CacheNode node = caches.get(k);
if(node == null){
if(caches.size() >= CacheCapcity){
caches.remove(last.key);
removeLast();
}
node = new CacheNode();
node.key = k;
}
node.value = v;
moveToFirst(node);
caches.put(k, node);
}
public Object get(K k){
CacheNode node = caches.get(k);
if(node == null){
return null;
}
moveToFirst(node);
return node.value;
}
public Object remove(K k){
CacheNode node = caches.get(k);
if(node != null){
if(node.pre != null){
node.pre.next=node.next;
}
if(node.next != null){
node.next.pre=node.pre;
}
if(node == first){
first = node.next;
}
if(node == last){
last = node.pre;
}
}
return caches.remove(k);
}
public void clear(){
first = null;
last = null;
caches.clear();
}
private void moveToFirst(CacheNode node){
if(first == node){
return;
}
if(node.next != null){
node.next.pre = node.pre;
}
if(node.pre != null){
node.pre.next = node.next;
}
if(node == last){
last= last.pre;
}
if(first == null || last == null){
first = last = node;
return;
}
node.next=first;
first.pre = node;
first = node;
first.pre=null;
}
private void removeLast(){
if(last != null){
last = last.pre;
if(last == null){
first = null;
}else{
last.next = null;
}
}
}
@Override
public String toString(){
StringBuilder sb = new StringBuilder();
CacheNode node = first;
while(node != null){
sb.append(String.format("%s:%s ", node.key,node.value));
node = node.next;
}
return sb.toString();
}
class CacheNode{
CacheNode pre;
CacheNode next;
Object key;
Object value;
public CacheNode(){
}
}
public static void main(String[] args) {
LRUCache<Integer,String> lru = new LRUCache<Integer,String>(3);
lru.put(1, "a"); // 1:a
System.out.println(lru.toString());
lru.put(2, "b"); // 2:b 1:a
System.out.println(lru.toString());
lru.put(3, "c"); // 3:c 2:b 1:a
System.out.println(lru.toString());
lru.put(4, "d"); // 4:d 3:c 2:b
System.out.println(lru.toString());
lru.put(1, "aa"); // 1:aa 4:d 3:c
System.out.println(lru.toString());
lru.put(2, "bb"); // 2:bb 1:aa 4:d
System.out.println(lru.toString());
lru.put(5, "e"); // 5:e 2:bb 1:aa
System.out.println(lru.toString());
lru.get(1); // 1:aa 5:e 2:bb
System.out.println(lru.toString());
lru.remove(11); // 1:aa 5:e 2:bb
System.out.println(lru.toString());
lru.remove(1); //5:e 2:bb
System.out.println(lru.toString());
lru.put(1, "aaa"); //1:aaa 5:e 2:bb
System.out.println(lru.toString());
}
}
LRU缓存实现(Java)
LRU是Least Recently Used 的缩写，翻译过来就是“最近最少使用”，LRU缓存就是使用这种原理实现，简单的说就是缓存一定量的数据，当超过设定的阈值时就把一些过期的数据删除掉，比如我们缓存10000条数据，当数据小于10000时可以随意添加，当超过10000时就需要把新的数据添加进来，同时要把过期数据删除，以确保我们最大缓存10000条，那怎么确定删除哪条过期数据呢，采用LRU算法实现的话就是将最老的数据删掉，废话不多说，下面来说下Java版的LRU缓存实现
Java里面实现LRU缓存通常有两种选择，一种是使用LinkedHashMap，一种是自己设计数据结构，使用链表+HashMap
LRU Cache的LinkedHashMap实现
LinkedHashMap自身已经实现了顺序存储，默认情况下是按照元素的添加顺序存储，也可以启用按照访问顺序存储，即最近读取的数据放在最前面，最早读取的数据放在最后面，然后它还有一个判断是否删除最老数据的方法，默认是返回false，即不删除数据，我们使用LinkedHashMap实现LRU缓存的方法就是对LinkedHashMap实现简单的扩展，扩展方式有两种，一种是inheritance，一种是delegation，具体使用什么方式看个人喜好
//LinkedHashMap的一个构造函数，当参数accessOrder为true时，即会按照访问顺序排序，最近访问的放在最前，最早访问的放在后面
public LinkedHashMap(int initialCapacity, float loadFactor, boolean accessOrder) {
super(initialCapacity, loadFactor);
this.accessOrder = accessOrder;
}
//LinkedHashMap自带的判断是否删除最老的元素方法，默认返回false，即不删除老数据
//我们要做的就是重写这个方法，当满足一定条件时删除老数据
protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
return false;
}
LRU缓存LinkedHashMap(inheritance)实现
采用inheritance方式实现比较简单，而且实现了Map接口，在多线程环境使用时可以使用 Collections.synchronizedMap()方法实现线程安全操作
package cn.lzrabbit.structure.lru;
import java.util.LinkedHashMap;
import java.util.Map;
/**
* Created by liuzhao on 14-5-15.
*/
public class LRUCache2<K, V> extends LinkedHashMap<K, V> {
private final int MAX_CACHE_SIZE;
public LRUCache2(int cacheSize) {
super((int) Math.ceil(cacheSize / 0.75) + 1, 0.75f, true);
MAX_CACHE_SIZE = cacheSize;
}
@Override
protected boolean removeEldestEntry(Map.Entry eldest) {
return size() > MAX_CACHE_SIZE;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
for (Map.Entry<K, V> entry : entrySet()) {
sb.append(String.format("%s:%s ", entry.getKey(), entry.getValue()));
}
return sb.toString();
}
}
这样算是比较标准的实现吧，实际使用中这样写还是有些繁琐，更实用的方法时像下面这样写，省去了单独见一个类的麻烦
final int cacheSize = 100;
Map<String, String> map = new LinkedHashMap<String, String>((int) Math.ceil(cacheSize / 0.75f) + 1, 0.75f, true) {
@Override
protected boolean removeEldestEntry(Map.Entry<String, String> eldest) {
return size() > cacheSize;
}
};
LRU缓存LinkedHashMap(delegation)实现
delegation方式实现更加优雅一些，但是由于没有实现Map接口，所以线程同步就需要自己搞定了
package cn.lzrabbit.structure.lru;
import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Set;
/**
* Created by liuzhao on 14-5-13.
*/
public class LRUCache3<K, V> {
private final int MAX_CACHE_SIZE;
private final float DEFAULT_LOAD_FACTOR = 0.75f;
LinkedHashMap<K, V> map;
public LRUCache3(int cacheSize) {
MAX_CACHE_SIZE = cacheSize;
//根据cacheSize和加载因子计算hashmap的capactiy，+1确保当达到cacheSize上限时不会触发hashmap的扩容，
int capacity = (int) Math.ceil(MAX_CACHE_SIZE / DEFAULT_LOAD_FACTOR) + 1;
map = new LinkedHashMap(capacity, DEFAULT_LOAD_FACTOR, true) {
@Override
protected boolean removeEldestEntry(Map.Entry eldest) {
return size() > MAX_CACHE_SIZE;
}
};
}
public synchronized void put(K key, V value) {
map.put(key, value);
}
public synchronized V get(K key) {
return map.get(key);
}
public synchronized void remove(K key) {
map.remove(key);
}
public synchronized Set<Map.Entry<K, V>> getAll() {
return map.entrySet();
}
public synchronized int size() {
return map.size();
}
public synchronized void clear() {
map.clear();
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
for (Map.Entry entry : map.entrySet()) {
sb.append(String.format("%s:%s ", entry.getKey(), entry.getValue()));
}
return sb.toString();
}
}
LRU Cache的链表+HashMap实现
注：此实现为非线程安全，若在多线程环境下使用需要在相关方法上添加synchronized以实现线程安全操作
package cn.lzrabbit.structure.lru;
import java.util.HashMap;
/**
* Created by liuzhao on 14-5-12.
*/
public class LRUCache1<K, V> {
private final int MAX_CACHE_SIZE;
private Entry first;
private Entry last;
private HashMap<K, Entry<K, V>> hashMap;
public LRUCache1(int cacheSize) {
MAX_CACHE_SIZE = cacheSize;
hashMap = new HashMap<K, Entry<K, V>>();
}
public void put(K key, V value) {
Entry entry = getEntry(key);
if (entry == null) {
if (hashMap.size() >= MAX_CACHE_SIZE) {
hashMap.remove(last.key);
removeLast();
}
entry = new Entry();
entry.key = key;
}
entry.value = value;
moveToFirst(entry);
hashMap.put(key, entry);
}
public V get(K key) {
Entry<K, V> entry = getEntry(key);
if (entry == null) return null;
moveToFirst(entry);
return entry.value;
}
public void remove(K key) {
Entry entry = getEntry(key);
if (entry != null) {
if (entry.pre != null) entry.pre.next = entry.next;
if (entry.next != null) entry.next.pre = entry.pre;
if (entry == first) first = entry.next;
if (entry == last) last = entry.pre;
}
hashMap.remove(key);
}
private void moveToFirst(Entry entry) {
if (entry == first) return;
if (entry.pre != null) entry.pre.next = entry.next;
if (entry.next != null) entry.next.pre = entry.pre;
if (entry == last) last = last.pre;
if (first == null || last == null) {
first = last = entry;
return;
}
entry.next = first;
first.pre = entry;
first = entry;
entry.pre = null;
}
private void removeLast() {
if (last != null) {
last = last.pre;
if (last == null) first = null;
else last.next = null;
}
}
private Entry<K, V> getEntry(K key) {
return hashMap.get(key);
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
Entry entry = first;
while (entry != null) {
sb.append(String.format("%s:%s ", entry.key, entry.value));
entry = entry.next;
}
return sb.toString();
}
class Entry<K, V> {
public Entry pre;
public Entry next;
public K key;
public V value;
}
}
LinkedHashMap的FIFO实现
FIFO是First Input First Output的缩写，也就是常说的先入先出，默认情况下LinkedHashMap就是按照添加顺序保存，我们只需重写下removeEldestEntry方法即可轻松实现一个FIFO缓存，简化版的实现代码如下
final int cacheSize = 5;
LinkedHashMap<Integer, String> lru = new LinkedHashMap<Integer, String>() {
@Override
protected boolean removeEldestEntry(Map.Entry<Integer, String> eldest) {
return size() > cacheSize;
}
};
调用示例
测试代码
package cn.lzrabbit.structure.lru;
import cn.lzrabbit.ITest;
import java.util.LinkedHashMap;
import java.util.Map;
/**
* Created by liuzhao on 14-5-15.
*/
public class LRUCacheTest {
public static void main(String[] args) throws Exception {
System.out.println("start...");
lruCache1();
lruCache2();
lruCache3();
lruCache4();
System.out.println("over...");
}
static void lruCache1() {
System.out.println();
System.out.println("===========================LRU 链表实现===========================");
LRUCache1<Integer, String> lru = new LRUCache1(5);
lru.put(1, "11");
lru.put(2, "11");
lru.put(3, "11");
lru.put(4, "11");
lru.put(5, "11");
System.out.println(lru.toString());
lru.put(6, "66");
lru.get(2);
lru.put(7, "77");
lru.get(4);
System.out.println(lru.toString());
System.out.println();
}
static <T> void lruCache2() {
System.out.println();
System.out.println("===========================LRU LinkedHashMap(inheritance)实现===========================");
LRUCache2<Integer, String> lru = new LRUCache2(5);
lru.put(1, "11");
lru.put(2, "11");
lru.put(3, "11");
lru.put(4, "11");
lru.put(5, "11");
System.out.println(lru.toString());
lru.put(6, "66");
lru.get(2);
lru.put(7, "77");
lru.get(4);
System.out.println(lru.toString());
System.out.println();
}
static void lruCache3() {
System.out.println();
System.out.println("===========================LRU LinkedHashMap(delegation)实现===========================");
LRUCache3<Integer, String> lru = new LRUCache3(5);
lru.put(1, "11");
lru.put(2, "11");
lru.put(3, "11");
lru.put(4, "11");
lru.put(5, "11");
System.out.println(lru.toString());
lru.put(6, "66");
lru.get(2);
lru.put(7, "77");
lru.get(4);
System.out.println(lru.toString());
System.out.println();
}
static void lruCache4() {
System.out.println();
System.out.println("===========================FIFO LinkedHashMap默认实现===========================");
final int cacheSize = 5;
LinkedHashMap<Integer, String> lru = new LinkedHashMap<Integer, String>() {
@Override
protected boolean removeEldestEntry(Map.Entry<Integer, String> eldest) {
return size() > cacheSize;
}
};
lru.put(1, "11");
lru.put(2, "11");
lru.put(3, "11");
lru.put(4, "11");
lru.put(5, "11");
System.out.println(lru.toString());
lru.put(6, "66");
lru.get(2);
lru.put(7, "77");
lru.get(4);
System.out.println(lru.toString());
System.out.println();
}
}
运行结果
"C:Program Files (x86)Javajdk1.6.0_10injava" -Didea.launcher.port=7535 "-Didea.launcher.bin.path=C:Program Files (x86)JetBrainsIntelliJ IDEA 13.0.2in" -Dfile.encoding=UTF-8 -classpath "C:Program Files (x86)Javajdk1.6.0_10jrelibcharsets.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibdeploy.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjavaws.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjce.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjsse.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibmanagement-agent.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibplugin.jar;C:Program Files (x86)Javajdk1.6.0_10jrelib
esources.jar;C:Program Files (x86)Javajdk1.6.0_10jrelib
t.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextdnsns.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextlocaledata.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunjce_provider.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunmscapi.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunpkcs11.jar;D:SVNprojectsJavaJava.Algorithm arget est-classes;D:SVNprojectsJavaJava.Algorithm argetclasses;C:Program Files (x86)JetBrainsIntelliJ IDEA 13.0.2libidea_rt.jar" com.intellij.rt.execution.application.AppMain Main
start...
===========================LRU 链表实现===========================
5:11 4:11 3:11 2:11 1:11
4:11 7:77 2:11 6:66 5:11
===========================LRU LinkedHashMap(inheritance)实现===========================
1:11 2:11 3:11 4:11 5:11
5:11 6:66 2:11 7:77 4:11
===========================LRU LinkedHashMap(delegation)实现===========================
1:11 2:11 3:11 4:11 5:11
5:11 6:66 2:11 7:77 4:11
===========================FIFO LinkedHashMap默认实现===========================
{1=11, 2=11, 3=11, 4=11, 5=11}
{3=11, 4=11, 5=11, 6=66, 7=77}
over...
Process finished with exit code 0
"C:Program Files (x86)Javajdk1.6.0_10injava" -Didea.launcher.port=7535 "-Didea.launcher.bin.path=C:Program Files (x86)JetBrainsIntelliJ IDEA 13.0.2in" -Dfile.encoding=UTF-8 -classpath "C:Program Files (x86)Javajdk1.6.0_10jrelibcharsets.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibdeploy.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjavaws.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjce.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibjsse.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibmanagement-agent.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibplugin.jar;C:Program Files (x86)Javajdk1.6.0_10jrelib
esources.jar;C:Program Files (x86)Javajdk1.6.0_10jrelib
t.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextdnsns.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextlocaledata.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunjce_provider.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunmscapi.jar;C:Program Files (x86)Javajdk1.6.0_10jrelibextsunpkcs11.jar;D:SVNprojectsJavaJava.Algorithm arget est-classes;D:SVNprojectsJavaJava.Algorithm argetclasses;C:Program Files (x86)JetBrainsIntelliJ IDEA 13.0.2libidea_rt.jar" com.intellij.rt.execution.application.AppMain Main
start...
===========================LRU 链表实现===========================
5:11 4:11 3:11 2:11 1:11
4:11 7:77 2:11 6:66 5:11
===========================LRU LinkedHashMap(inheritance)实现===========================
1:11 2:11 3:11 4:11 5:11
5:11 6:66 2:11 7:77 4:11
===========================LRU LinkedHashMap(delegation)实现===========================
1:11 2:11 3:11 4:11 5:11
5:11 6:66 2:11 7:77 4:11
===========================FIFO LinkedHashMap默认实现===========================
{1=11, 2=11, 3=11, 4=11, 5=11}
{3=11, 4=11, 5=11, 6=66, 7=77}
over...
Process finished with exit code 0