Storm TimeCacheMap RotatingMap源码分析

TimeCacheMap是Twitter Storm里面一个类， Storm使用它来保存那些最近活跃的对象，并且可以自动删除那些已经过期的对象。

不过在storm0.8之后TimeCacheMap被弃用了，取而代之的是RotatingMap。

RotatingMap与TimeCacheMap的区别如下：

• 1.前者去掉了自动清理的线程，让用户自己去控制清理过期的数据，控制清理数据用rotate()方法，就是去尾加新头。
• 2.前者get,put等方法都不加锁了，需要用户自己控制锁

总之就是提供了更大的自由度，让开发者去控制这个数据结构！下面先具体分析TimeCacheMap，而后RotatingMap就一目了然了

我直接在源码中，加上中文的注释分析源码TimeCacheMap

package backtype.storm.utils;

import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.Map;
import java.util.Map.Entry;
import backtype.storm.utils.Time;

/**
 * Expires keys that have not been updated in the configured number of seconds.
 * The algorithm used will take between expirationSecs and
 * expirationSecs * (1 + 1 / (numBuckets-1)) to actually expire the message.
 *
 * get, put, remove, containsKey, and size take O(numBuckets) time to run.
 *
 * The advantage of this design is that the expiration thread only locks the object
 * for O(1) time, meaning the object is essentially always available for gets/puts.
 */
/**
 *如果在配置的时间内没有更新数据，这个数据就会被删
 *expirationSecs * (1 + 1 / (numBuckets-1))解释：
 *
 *假设_cleaner线程刚刚清理数据，put函数调用发生将key放入桶中，那么一条数据的超时时间为：
 *expirationSecs / (numBuckets-1) * numBuckets = expirationSecs * (1 + 1 / (numBuckets-1))
 *然而，假设put函数调用刚刚执行结束，_cleaner线程就开始清理数据，那么一条数据的超时时间为：
 *expirationSecs / (numBuckets-1) * numBuckets - expirationSecs / (numBuckets-1) = expirationSecs
 *
 *这个数据结构最大的好处是：数据分成多个桶，锁的粒度小，只要O（1）的复杂度就可以删掉过期数据。因此，大部分时间都可以进行get和put操作
 */
//deprecated in favor of non-threaded RotatingMap
//虽然在storm0.8之后TimeCacheMap被弃用了，不过其设计还是很独到的，值得一探究竟
@Deprecated
public class TimeCacheMap<K, V> {
    //this default ensures things expire at most 50% past the expiration time
    private static final int DEFAULT_NUM_BUCKETS = 3;

    //回调函数实现这个接口就可以，至少可以把删掉的元素传回去
    public static interface ExpiredCallback<K, V> {
        public void expire(K key, V val);
    }

    //把数据分成多个桶，用链表是因为在头尾的增减操作时O（1）
    private LinkedList<HashMap<K, V>> _buckets;

    private final Object _lock = new Object();
    private Thread _cleaner;
    private ExpiredCallback _callback;

    public TimeCacheMap(int expirationSecs, int numBuckets, ExpiredCallback<K, V> callback) {
        if(numBuckets<2) {
            throw new IllegalArgumentException("numBuckets must be >= 2");
        }
        //构造函数中，按照桶的数量，初始桶
        _buckets = new LinkedList<HashMap<K, V>>();
        for(int i=0; i<numBuckets; i++) {
            _buckets.add(new HashMap<K, V>());
        }

        _callback = callback;
        final long expirationMillis = expirationSecs * 1000L;
        final long sleepTime = expirationMillis / (numBuckets-1);
        _cleaner = new Thread(new Runnable() {
            public void run() {
                try {
                    while(true) {
                        Map<K, V> dead = null;
                        Time.sleep(sleepTime);
                        synchronized(_lock) {
                            //删掉最后一个桶，在头补充一个新的桶，最后一个桶的数据是最旧的
                            dead = _buckets.removeLast();
                            _buckets.addFirst(new HashMap<K, V>());
                        }
                        if(_callback!=null) {
                            for(Entry<K, V> entry: dead.entrySet()) {
                                _callback.expire(entry.getKey(), entry.getValue());
                            }
                        }
                    }
                } catch (InterruptedException ex) {

                }
            }
        });
        //作为守护线程运行，一旦主线程不在，这个线程自动结束
        _cleaner.setDaemon(true);
        _cleaner.start();
    }

    public TimeCacheMap(int expirationSecs, ExpiredCallback<K, V> callback) {
        this(expirationSecs, DEFAULT_NUM_BUCKETS, callback);
    }

    public TimeCacheMap(int expirationSecs) {
        this(expirationSecs, DEFAULT_NUM_BUCKETS);
    }

    public TimeCacheMap(int expirationSecs, int numBuckets) {
        this(expirationSecs, numBuckets, null);
    }

    public boolean containsKey(K key) {
        synchronized(_lock) {
            for(HashMap<K, V> bucket: _buckets) {
                if(bucket.containsKey(key)) {
                    return true;
                }
            }
            return false;
        }
    }

    public V get(K key) {
        synchronized(_lock) {
            for(HashMap<K, V> bucket: _buckets) {
                if(bucket.containsKey(key)) {
                    return bucket.get(key);
                }
            }
            return null;
        }
    }

    public void put(K key, V value) {
        synchronized(_lock) {
            Iterator<HashMap<K, V>> it = _buckets.iterator();
            HashMap<K, V> bucket = it.next();
            //在第一个桶上更新数据
            bucket.put(key, value);
            //去掉后面桶的数据
            while(it.hasNext()) {
                bucket = it.next();
                bucket.remove(key);
            }
        }
    }

    public Object remove(K key) {
        synchronized(_lock) {
            for(HashMap<K, V> bucket: _buckets) {
                if(bucket.containsKey(key)) {
                    return bucket.remove(key);
                }
            }
            return null;
        }
    }

    public int size() {
        synchronized(_lock) {
            int size = 0;
            for(HashMap<K, V> bucket: _buckets) {
                size+=bucket.size();
            }
            return size;
        }
    }

    //这个方法也太迷惑人了，作用就是把清理线程杀掉，这样数据就不会过期了，应该改名叫neverCleanup
    public void cleanup() {
        //中断清理线程中的sleep，_cleaner线程会抛出异常，然后_cleaner线程就死了，不再清理过期数据了
        _cleaner.interrupt();  //调用了interrupt后，再跑sleep就会抛InterruptedException异常
    }
}

RotatingMap源码几乎和TimeCacheMap一样，就是去掉清理线程去掉锁，加了一个rotate()方法开发者自己清理过期数据

package backtype.storm.utils;

import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.Map;
import java.util.Map.Entry;

/**
 * Expires keys that have not been updated in the configured number of seconds.
 * The algorithm used will take between expirationSecs and
 * expirationSecs * (1 + 1 / (numBuckets-1)) to actually expire the message.
 *
 * get, put, remove, containsKey, and size take O(numBuckets) time to run.
 *
 * The advantage of this design is that the expiration thread only locks the object
 * for O(1) time, meaning the object is essentially always available for gets/puts.
 */
public class RotatingMap<K, V> {
    //this default ensures things expire at most 50% past the expiration time
    private static final int DEFAULT_NUM_BUCKETS = 3;

    public static interface ExpiredCallback<K, V> {
        public void expire(K key, V val);
    }

    private LinkedList<HashMap<K, V>> _buckets;

    private ExpiredCallback _callback;

    public RotatingMap(int numBuckets, ExpiredCallback<K, V> callback) {
        if(numBuckets<2) {
            throw new IllegalArgumentException("numBuckets must be >= 2");
        }
        _buckets = new LinkedList<HashMap<K, V>>();
        for(int i=0; i<numBuckets; i++) {
            _buckets.add(new HashMap<K, V>());
        }

        _callback = callback;
    }

    public RotatingMap(ExpiredCallback<K, V> callback) {
        this(DEFAULT_NUM_BUCKETS, callback);
    }

    public RotatingMap(int numBuckets) {
        this(numBuckets, null);
    }   

    public Map<K, V> rotate() {
        Map<K, V> dead = _buckets.removeLast();
        _buckets.addFirst(new HashMap<K, V>());
        if(_callback!=null) {
            for(Entry<K, V> entry: dead.entrySet()) {
                _callback.expire(entry.getKey(), entry.getValue());
            }
        }
        return dead;
    }

    public boolean containsKey(K key) {
        for(HashMap<K, V> bucket: _buckets) {
            if(bucket.containsKey(key)) {
                return true;
            }
        }
        return false;
    }

    public V get(K key) {
        for(HashMap<K, V> bucket: _buckets) {
            if(bucket.containsKey(key)) {
                return bucket.get(key);
            }
        }
        return null;
    }

    public void put(K key, V value) {
        Iterator<HashMap<K, V>> it = _buckets.iterator();
        HashMap<K, V> bucket = it.next();
        bucket.put(key, value);
        while(it.hasNext()) {
            bucket = it.next();
            bucket.remove(key);
        }
    }

    public Object remove(K key) {
        for(HashMap<K, V> bucket: _buckets) {
            if(bucket.containsKey(key)) {
                return bucket.remove(key);
            }
        }
        return null;
    }

    public int size() {
        int size = 0;
        for(HashMap<K, V> bucket: _buckets) {
            size+=bucket.size();
        }
        return size;
    }
}