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/**
* @param maxSize for caches that do not override {@link #sizeOf}, this is
* the maximum number of entries in the cache. For all other caches,
* this is the maximum sum of the sizes of the entries in this cache.
*/
public LruCache(int maxSize) {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
this.maxSize = maxSize;
this.map = new LinkedHashMap<K, V>(0, 0.75f, true);
}其中指定了最大缓存不能超过maxSize这个数值，其次，初始化了一个LinkedHashMap集合，我们知道，LinkedHashMap就是在HashMap中维护了一个链表记录插入的记录，如果我们把最后一个参数设置为true，那么我们取出的值就是我们按我们访问的顺序去取的。
另外，还有个方法是必须要实现的：
/**
* Returns the size of the entry for {@code key} and {@code value} in
* user-defined units. The default implementation returns 1 so that size
* is the number of entries and max size is the maximum number of entries.
*
* <p>An entry‘s size must not change while it is in the cache.
*/
protected int sizeOf(K key, V value) {
return 1;
}这个方法是测量我们我们实体的大小，不实现，它就会默认实现1了。所以，这个是硬要求。
放入缓存
public final V put(K key, V value) {
if (key == null || value == null) {
throw new NullPointerException("key == null || value == null");
}
V previous;
synchronized (this) {
putCount++;
size += safeSizeOf(key, value);
previous = map.put(key, value);
if (previous != null) {
size -= safeSizeOf(key, previous);
}
}
if (previous != null) {
entryRemoved(false, key, previous, value);
}
trimToSize(maxSize);
return previous;
}如果插入了空的key和value，它就会抛出异常。然后，放入的计数器加一，内存计数器加上新的Entry的大小。如果内存中已经存在这个值了，那么，我们的的Put操作算是覆盖操作，所以，我们得减去，刚才内存计数器加上的值。接下来，调用了
if (previous != null) {
entryRemoved(false, key, previous, value);
}有意思的是这个方法是一个空实现，是留给我们覆盖用的，相当于一个回调把。
protected void entryRemoved(boolean evicted, K key, V oldValue, V newValue) {}
大概意思就是说，当我们Put一个实体进去，如果是Map中是有相同key值的话，那么，我们相当于从内存中抹去了一个实体部分，什么key，oldValue，newValue都会给我们，evicted给了个false，先记着，继续看。接下来就调用了trimToSize()方法
public void trimToSize(int maxSize) {
while (true) {
K key;
V value;
synchronized (this) {
if (size < 0 || (map.isEmpty() && size != 0)) {
throw new IllegalStateException(getClass().getName()
+ ".sizeOf() is reporting inconsistent results!");
}
if (size <= maxSize || map.isEmpty()) {
break;
}
Map.Entry<K, V> toEvict = map.entrySet().iterator().next();
key = toEvict.getKey();
value = toEvict.getValue();
map.remove(key);
size -= safeSizeOf(key, value);
evictionCount++;
}
entryRemoved(true, key, value, null);
}
}这个方法就是一直循环，清理老的实体部分，直到满足，我们的LruCache所占内存小于我们初始化给定的maxSize或者是我们内内部的LinkedHashMap为空为止。有意思的是，每移除一个实体，又会调用entryRemoved()方法，只不过参数变成了 true,key,value,null。
获取缓存
public final V get(K key) {
if (key == null) {
throw new NullPointerException(“key == null”);
}
V mapValue;
synchronized (this) {
mapValue = map.get(key);
if (mapValue != null) {
hitCount++;
return mapValue;
}
missCount++;
}
/*
* Attempt to create a value. This may take a long time, and the map
* may be different when create() returns. If a conflicting value was
* added to the map while create() was working, we leave that value in
* the map and release the created value.
*/
V createdValue = create(key);
if (createdValue == null) {
return null;
}
synchronized (this) {
createCount++;
mapValue = map.put(key, createdValue);
if (mapValue != null) {
// There was a conflict so undo that last put
map.put(key, mapValue);
} else {
size += safeSizeOf(key, createdValue);
}
}
if (mapValue != null) {
entryRemoved(false, key, createdValue, mapValue);
return mapValue;
} else {
trimToSize(maxSize);
return createdValue;
}
}
第一步，如果传进来的参数是null的话，那么抛出个异常。第二步，如果缓存找到了，那么命中的计数器加一，返回我们从内存中找到的缓存。否则，没有找到的计数器加一，继续。第三步有点意思：
V createdValue = create(key);
if (createdValue == null) {
return null;
}如果LruCache中没有实体部分的话，就创建一个实体部分，好，我们点进create()这个方法：
protected V create(K key) {
return null;
}所以，第三步默认就是返回个null给我们了。这不是忽悠我们么，一起看看注释：
If a value for key exists in the cache when this method
returns, the created value will be released with entryRemoved and discarded.
原来，这个方法和是我们之前分析的entryRemoved和硬盘缓存结合起来用的，所以，知道之前，为什么要把抹去的实体部分和新增的实体部分回调给我们了吧？好，我们接着继续看，第四步，
synchronized (this) {
createCount++;
mapValue = map.put(key, createdValue);
if (mapValue != null) {
// There was a conflict so undo that last put
map.put(key, mapValue);
} else {
size += safeSizeOf(key, createdValue);
}
}我们把新创建的实体给放到我们的LinkedHashMap里面去，如果mapValue不为空的话，说明，之前LinkedHashMap已经有值了，我们也许是覆盖错了（可能key相同，但是value不相同），那么，我们再重新把覆盖的值给放进去，如果为空的话，那么，我们就要在所占内存的基础上加上这个值了。继续：
if (mapValue != null) {
entryRemoved(false, key, createdValue, mapValue);
return mapValue;
} else {
trimToSize(maxSize);
return createdValue;
}第六步就很简单了，就是根据我们的mapValue去选择是回调还是清理数据了。
清理缓存
清理一个实体就不用多说了，就是根据key来移除LinkedHashMap中的实体部分。
public final V remove(K key) {
if (key == null) {
throw new NullPointerException("key == null");
}
V previous;
synchronized (this) {
previous = map.remove(key);
if (previous != null) {
size -= safeSizeOf(key, previous);
}
}
if (previous != null) {
entryRemoved(false, key, previous, null);
}
return previous;
}我们来看看，清除所有缓存的方法：
public final void evictAll() {
trimToSize(-1); // -1 will evict 0-sized elements
}
给trimToSize方法传入了-1，点进去：
public void trimToSize(int maxSize) {
while (true) {
K key;
V value;
synchronized (this) {
if (size < 0 || (map.isEmpty() && size != 0)) {
throw new IllegalStateException(getClass().getName()
+ ".sizeOf() is reporting inconsistent results!");
}
if (size <= maxSize || map.isEmpty()) {
break;
}
Map.Entry<K, V> toEvict = map.entrySet().iterator().next();
key = toEvict.getKey();
value = toEvict.getValue();
map.remove(key);
size -= safeSizeOf(key, value);
evictionCount++;
}
entryRemoved(true, key, value, null);
}
}原来，我们的maxSize就变成了-1，这样就会一直循环删除缓存了，直到LinkedHashMap中的size为0为止。
private DiskLruCache(File directory, int appVersion, int valueCount, long maxSize) {
this.directory = directory;
this.appVersion = appVersion;
this.journalFile = new File(directory, JOURNAL_FILE);
this.journalFileTmp = new File(directory, JOURNAL_FILE_TMP);
this.valueCount = valueCount;
this.maxSize = maxSize;
}DiskLruCache的构造方法私有化，意味着，我们不能直接从外界new出这个对象，要借助open()来完成对它的初始化：
public static DiskLruCache open(File directory, int appVersion, int valueCount, long maxSize)
throws IOException {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
if (valueCount <= 0) {
throw new IllegalArgumentException("valueCount <= 0");
}
// prefer to pick up where we left off
DiskLruCache cache = new DiskLruCache(directory, appVersion, valueCount, maxSize);
if (cache.journalFile.exists()) {
try {
cache.readJournal();
cache.processJournal();
cache.journalWriter = new BufferedWriter(new FileWriter(cache.journalFile, true),
IO_BUFFER_SIZE);
return cache;
} catch (IOException journalIsCorrupt) {
// System.logW("DiskLruCache " + directory + " is corrupt: "
// + journalIsCorrupt.getMessage() + ", removing");
cache.delete();
}
}
// create a new empty cache
directory.mkdirs();
cache = new DiskLruCache(directory, appVersion, valueCount, maxSize);
cache.rebuildJournal();
return cache;
}一起看看它到底做了哪些工作，首先，构造出了DiskLruCache对象，其中传入的参数，文档上写的也很是详细：
* @param directory a writable directory * @param appVersion * @param valueCount the number of values per cache entry. Must be positive. * @param maxSize the maximum number of bytes this cache should use to store
继续往下看，如果DiskLruCache对象的日志文件存在的话，我们先读取日志文件，然后处理日志文件，然后生成一个BufferedWriter用于对日志文件的操作，最后返回DiskLruCache对象。如果，日志文件不存在，那么就默认初始化，也没什么难点。
日志文件
* This cache uses a journal file named "journal". A typical journal file * looks like this: * libcore.io.DiskLruCache * 1 * 100 * 2 * * CLEAN 3400330d1dfc7f3f7f4b8d4d803dfcf6 832 21054 * DIRTY 335c4c6028171cfddfbaae1a9c313c52 * CLEAN 335c4c6028171cfddfbaae1a9c313c52 3934 2342 * REMOVE 335c4c6028171cfddfbaae1a9c313c52 * DIRTY 1ab96a171faeeee38496d8b330771a7a * CLEAN 1ab96a171faeeee38496d8b330771a7a 1600 234 * READ 335c4c6028171cfddfbaae1a9c313c52 * READ 3400330d1dfc7f3f7f4b8d4d803dfcf6
我们参照这日志文件的格式，来分析对它的操作。
1.新建日志文件
private synchronized void rebuildJournal() throws IOException {
if (journalWriter != null) {
journalWriter.close();
}
Writer writer = new BufferedWriter(new FileWriter(journalFileTmp), IO_BUFFER_SIZE);
writer.write(MAGIC);
writer.write("\n");
writer.write(VERSION_1);
writer.write("\n");
writer.write(Integer.toString(appVersion));
writer.write("\n");
writer.write(Integer.toString(valueCount));
writer.write("\n");
writer.write("\n");
for (Entry entry : lruEntries.values()) {
if (entry.currentEditor != null) {
writer.write(DIRTY + ‘ ‘ + entry.key + ‘\n‘);
} else {
writer.write(CLEAN + ‘ ‘ + entry.key + entry.getLengths() + ‘\n‘);
}
}
writer.close();
journalFileTmp.renameTo(journalFile);
journalWriter = new BufferedWriter(new FileWriter(journalFile, true), IO_BUFFER_SIZE);
}首先把头信息写进入，然后内存中的LinkedHashMap的数据写到日志文件中，包括了“脏数据”和“干净的数据”。最后生成了一个日志文件的Writer。
2.读取日志文件
private void readJournal() throws IOException {
InputStream in = new BufferedInputStream(new FileInputStream(journalFile), IO_BUFFER_SIZE);
try {
String magic = readAsciiLine(in);
String version = readAsciiLine(in);
String appVersionString = readAsciiLine(in);
String valueCountString = readAsciiLine(in);
String blank = readAsciiLine(in);
if (!MAGIC.equals(magic)
|| !VERSION_1.equals(version)
|| !Integer.toString(appVersion).equals(appVersionString)
|| !Integer.toString(valueCount).equals(valueCountString)
|| !"".equals(blank)) {
throw new IOException("unexpected journal header: ["
+ magic + ", " + version + ", " + valueCountString + ", " + blank + "]");
}
while (true) {
try {
readJournalLine(readAsciiLine(in));
} catch (EOFException endOfJournal) {
break;
}
}
} finally {
closeQuietly(in);
}
}首先读取头部信息，如果不是我们的日志文件，那么就抛出个异常，如果是日志文件，就一行一行的读取内容了，
private void readJournalLine(String line) throws IOException {
String[] parts = line.split(" ");
if (parts.length < 2) {
throw new IOException("unexpected journal line: " + line);
}
String key = parts[1];
if (parts[0].equals(REMOVE) && parts.length == 2) {
lruEntries.remove(key);
return;
}
Entry entry = lruEntries.get(key);
if (entry == null) {
entry = new Entry(key);
lruEntries.put(key, entry);
}
if (parts[0].equals(CLEAN) && parts.length == 2 + valueCount) {
entry.readable = true;
entry.currentEditor = null;
entry.setLengths(copyOfRange(parts, 2, parts.length));
} else if (parts[0].equals(DIRTY) && parts.length == 2) {
entry.currentEditor = new Editor(entry);
} else if (parts[0].equals(READ) && parts.length == 2) {
// this work was already done by calling lruEntries.get()
} else {
throw new IOException("unexpected journal line: " + line);
}
}我们的内容都是通过空格键来区分的，parts数组的长度肯定大于2，这很好理解，如果小于2，说明这不是一个符合要求的行。如果读取的操作指令是REMOVE，则我们需要在LinkedHashMap删除掉这个记录。根据我们的日志文件的信息从内存中查找操作记录，接下来：
if (parts[0].equals(CLEAN) && parts.length == 2 + valueCount) {
entry.readable = true;
entry.currentEditor = null;
entry.setLengths(copyOfRange(parts, 2, parts.length));
} else if (parts[0].equals(DIRTY) && parts.length == 2) {
entry.currentEditor = new Editor(entry);
} else if (parts[0].equals(READ) && parts.length == 2) {
// this work was already done by calling lruEntries.get()
} else {
throw new IOException("unexpected journal line: " + line);
}如果是“干净的数据”，代码很好理解，至于“脏数据”和 READ标识的数据，我们就要从下面看了。
3.处理日志文件
private void processJournal() throws IOException {
deleteIfExists(journalFileTmp);
for (Iterator<Entry> i = lruEntries.values().iterator(); i.hasNext(); ) {
Entry entry = i.next();
if (entry.currentEditor == null) {
for (int t = 0; t < valueCount; t++) {
size += entry.lengths[t];
}
} else {
entry.currentEditor = null;
for (int t = 0; t < valueCount; t++) {
deleteIfExists(entry.getCleanFile(t));
deleteIfExists(entry.getDirtyFile(t));
}
i.remove();
}
}
}可以看到，通过我们读取日志文件，在内存中生成了对日志文件的映射，下面的代码，我们很清楚它到底要做什么了，根据currentEditor是否为空，来判断数据到底可用，如果currentEditor为空，那么我们记录它的大小，如果不为空，则删除文件。
写入缓存文件
public Editor edit(String key) throws IOException {
return edit(key, ANY_SEQUENCE_NUMBER);
}
private synchronized Editor edit(String key, long expectedSequenceNumber) throws IOException {
checkNotClosed();
validateKey(key);
Entry entry = lruEntries.get(key);
if (expectedSequenceNumber != ANY_SEQUENCE_NUMBER
&& (entry == null || entry.sequenceNumber != expectedSequenceNumber)) {
return null; // snapshot is stale
}
if (entry == null) {
entry = new Entry(key);
lruEntries.put(key, entry);
} else if (entry.currentEditor != null) {
return null; // another edit is in progress
}
Editor editor = new Editor(entry);
entry.currentEditor = editor;
// flush the journal before creating files to prevent file leaks
journalWriter.write(DIRTY + ‘ ‘ + key + ‘\n‘);
journalWriter.flush();
return editor;
}在正式读取文件之前，我们需要为它生成一个Editor对象，而这个Editor就是针对一个Entry来操作的，把通过和文件的key获取的Entry的currentEditor赋值成我们刚刚生成的editor，并且把在日志文件中记录这个key的文件是个“脏数据”。接下来，在Editor内部就要生成一个OutputStream用于写入文件了：
public OutputStream newOutputStream(int index) throws IOException {
synchronized (DiskLruCache.this) {
if (entry.currentEditor != this) {
throw new IllegalStateException();
}
return new FaultHidingOutputStream(new FileOutputStream(entry.getDirtyFile(index)));
}
}关于，index我们默认是传入0的，因为一个实体Entry内部可能会维护多个value，就是说，一个key，我们可以保存多个file，这个和初始化的valueCount属性有关系。
当写入操作结束后，或者写入异常，我们会调用这两个方法：
/**
* Commits this edit so it is visible to readers. This releases the
* edit lock so another edit may be started on the same key.
*/
public void commit() throws IOException {
if (hasErrors) {
completeEdit(this, false);
remove(entry.key); // the previous entry is stale
} else {
completeEdit(this, true);
}
}
/**
* Aborts this edit. This releases the edit lock so another edit may be
* started on the same key.
*/
public void abort() throws IOException {
completeEdit(this, false);
}至于hasErrors这个表示当我们写入的时候发生异常，我们就会把这个改为true。然而不管成功失败与否，都调用了completeEdit()这个方法：
private synchronized void completeEdit(Editor editor, boolean success) throws IOException {
Entry entry = editor.entry;
if (entry.currentEditor != editor) {
throw new IllegalStateException();
}
// if this edit is creating the entry for the first time, every index must have a value
if (success && !entry.readable) {
for (int i = 0; i < valueCount; i++) {
if (!entry.getDirtyFile(i).exists()) {
editor.abort();
throw new IllegalStateException("edit didn‘t create file " + i);
}
}
}
for (int i = 0; i < valueCount; i++) {
File dirty = entry.getDirtyFile(i);
if (success) {
if (dirty.exists()) {
File clean = entry.getCleanFile(i);
dirty.renameTo(clean);
long oldLength = entry.lengths[i];
long newLength = clean.length();
entry.lengths[i] = newLength;
size = size - oldLength + newLength;
}
} else {
deleteIfExists(dirty);
}
}
redundantOpCount++;
entry.currentEditor = null;
if (entry.readable | success) {
entry.readable = true;
journalWriter.write(CLEAN + ‘ ‘ + entry.key + entry.getLengths() + ‘\n‘);
if (success) {
entry.sequenceNumber = nextSequenceNumber++;
}
} else {
lruEntries.remove(entry.key);
journalWriter.write(REMOVE + ‘ ‘ + entry.key + ‘\n‘);
}
if (size > maxSize || journalRebuildRequired()) {
executorService.submit(cleanupCallable);
}
}第一步，editor肯定是当前操作的Entry的currentEditor，不然就不合法了；第二步，success && ! entry.readable 这个判断条件是什么意思呢？我们知道，当我们打开DiskLruCache的时候，会根据我们的日志文件来设置给他true的，就是说如果它的readable属性为true，肯定是有一次写入成功的操作的，如果不为true，则是第一次提交，那么，我们肯定有dirtyFile的，因为我们向外提供的输出流是针对dirtyFile操作的，
public OutputStream newOutputStream(int index) throws IOException {
synchronized (DiskLruCache.this) {
if (entry.currentEditor != this) {
throw new IllegalStateException();
}
return new FaultHidingOutputStream(new FileOutputStream(entry.getDirtyFile(index)));
}
}这样是不是一目了然了。第三步，如果成功了，我们就把dirtyFile改成cleanFile，提供外界读取，并累计缓存文件的大小。当然，如果读取失败，我们就删除掉dirtyFile。第四步，就是将readable设置为true，当然这是针对第一次写入操作的，然后就写入，CLEAD指令，如果写入不成功则写入REMOVE操作。第五步，就是针对缓存所做的处理：
private final Callable<Void> cleanupCallable = new Callable<Void>() {
@Override public Void call() throws Exception {
synchronized (DiskLruCache.this) {
if (journalWriter == null) {
return null; // closed
}
trimToSize();
if (journalRebuildRequired()) {
rebuildJournal();
redundantOpCount = 0;
}
}
return null;
}
};至于，什么时候rebuild日志文件，看看journalRebuildRequired()方法：
private boolean journalRebuildRequired() {
final int REDUNDANT_OP_COMPACT_THRESHOLD = 2000;
return redundantOpCount >= REDUNDANT_OP_COMPACT_THRESHOLD
&& redundantOpCount >= lruEntries.size();
}
这个就是防止，日志文件过大的一种处理策略，redundantOpCount是针对日志文件的操作次数。
读取缓存文件
public synchronized Snapshot get(String key) throws IOException {
checkNotClosed();
validateKey(key);
Entry entry = lruEntries.get(key);
if (entry == null) {
return null;
}
if (!entry.readable) {
return null;
}
/*
* Open all streams eagerly to guarantee that we see a single published
* snapshot. If we opened streams lazily then the streams could come
* from different edits.
*/
InputStream[] ins = new InputStream[valueCount];
try {
for (int i = 0; i < valueCount; i++) {
ins[i] = new FileInputStream(entry.getCleanFile(i));
}
} catch (FileNotFoundException e) {
// a file must have been deleted manually!
return null;
}
redundantOpCount++;
journalWriter.append(READ + ‘ ‘ + key + ‘\n‘);
if (journalRebuildRequired()) {
executorService.submit(cleanupCallable);
}
return new Snapshot(key, entry.sequenceNumber, ins);
}
相对于写入缓存文件来说，读取就比较简单了。前面都是一些是否可读的判断，到我们的Entry的去找我们的cleanFile，将他们的输入流封装到Snapshot对象供外界读取。
移除缓存文件
public synchronized boolean remove(String key) throws IOException {
checkNotClosed();
validateKey(key);
Entry entry = lruEntries.get(key);
if (entry == null || entry.currentEditor != null) {
return false;
}
for (int i = 0; i < valueCount; i++) {
File file = entry.getCleanFile(i);
if (!file.delete()) {
throw new IOException("failed to delete " + file);
}
size -= entry.lengths[i];
entry.lengths[i] = 0;
}
redundantOpCount++;
journalWriter.append(REMOVE + ‘ ‘ + key + ‘\n‘);
lruEntries.remove(key);
if (journalRebuildRequired()) {
executorService.submit(cleanupCallable);
}
return true;
}分析完写入的操作，这个理解起来真的太简单了，无非就是删除实体部分的cleanFile，写入REMOVE指令，并且移除了LinkedHashMap中的数据。
1、LruCache和DiskLruCache内部中都维护了一个LinkedHashMap，而LinkedHashMap中又维护了一条链表用于记录，插入或访问的顺序，我们根据这个特性，可以移除最不经常使用的实体部分。唯一不同的是，LruCache中value维护的就是真的实体部分，而DiskLruCache中value维护的是日志文件中的数据，我们根据DiskLruCache中维护的value去映射成一个个的实体部分，实体部分针对我们文件的操作，比如写入，读取。
2、需要注意的地方是，DiskLruCache是如何根据日志文件知道我们写入的状态呢，是根据日志文件中的指令，比如DIRTY,CLEAN,REMOVE等，如果初始化状态，我们对Entry进行了写入操作，那么写的文件是dirtyFile，只有成功之后，才将Entry的readable的表示改成true，把dirtyFile改成cleanFile，我们读取的时候就是根据readable是否为true，才去读取cleanFile的。
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原文地址：http://blog.csdn.net/yanghuinipurean/article/details/51733205
