关于Android的 DiskLruCache磁盘缓存机制原理

目录

• 一、为什么用DiskLruCache
• 1、LruCache和DiskLruCache
• 2、为何使用DiskLruCache
• 二、DiskLruCache使用
• 1、添加依赖
• 2、创建DiskLruCache对象
• 3、添加 / 获取 缓存(一对一)
• 4、添加 / 获取 缓存(一对多)
• 三、源码分析
• 1、open()
• 2、rebuildJournal()
• 3、readJournal()
• 4、get()
• 5、validateKey
• 6、trimTOSize()
• 7、journalRebuildRequired()
• 8、save的过程

一、为什么用DiskLruCache

1、LruCache和DiskLruCache

LruCache和DiskLruCache两者都是利用到LRU算法，通过LRU算法对缓存进行管理，以最近最少使用作为管理的依据，删除最近最少使用的数据，保留最近最常用的数据;

LruCache运用于内存缓存，而DiskLruCache是存储设备缓存;

2、为何使用DiskLruCache

离线数据存在的意义，当无网络或者是网络状况不好时，APP依然具备部分功能是一种很好的用户体验;

假设网易新闻这类新闻客户端，数据完全存储在缓存中而不使用DiskLruCache技术存储，那么当客户端被销毁，缓存被释放，意味着再次打开APP将是一片空白;

另外DiskLruCache技术也可为app“离线阅读”这一功能做技术支持;

DiskLruCache的存储路径是可以自定义的，不过也可以是默认的存储路径，而默认的存储路径一般是这样的：/sdcard/Android/data/包名/cache，包名是指APP的包名。我们可以在手机上打开，浏览这一路径;

二、DiskLruCache使用

1、添加依赖

// add dependence
implementation 'com.jakewharton:disklrucache:2.0.2'

2、创建DiskLruCache对象

/*
 * directory – 缓存目录
 * appVersion - 缓存版本
 * valueCount – 每个key对应value的个数
 * maxSize – 缓存大小的上限
 */
DiskLruCache diskLruCache = DiskLruCache.open(directory, 1, 1, 1024 * 1024 * 10);

3、添加 / 获取 缓存(一对一)

/**
 * 添加一条缓存，一个key对应一个value
 */
public void addDiskCache(String key, String value) throws IOException {
    File cacheDir = context.getCacheDir();
    DiskLruCache diskLruCache = DiskLruCache.open(cacheDir, 1, 1, 1024 * 1024 * 10);
    DiskLruCache.Editor editor = diskLruCache.edit(key);
    // index与valueCount对应，分别为0,1,2...valueCount-1
    editor.newOutputStream(0).write(value.getBytes());
    editor.commit();
    diskLruCache.close();
}
/**
 * 获取一条缓存，一个key对应一个value
 */
public void getDiskCache(String key) throws IOException {
    File directory = context.getCacheDir();
    DiskLruCache diskLruCache = DiskLruCache.open(directory, 1, 1, 1024 * 1024 * 10);
    String value = diskLruCache.get(key).getString(0);
    diskLruCache.close();
}

4、添加 / 获取 缓存(一对多)

/**
 * 添加一条缓存，1个key对应2个value
 */
public void addDiskCache(String key, String value1, String value2) throws IOException {
    File directory = context.getCacheDir();
    DiskLruCache diskLruCache = DiskLruCache.open(directory, 1, 2, 1024 * 1024 * 10);
    DiskLruCache.Editor editor = diskLruCache.edit(key);
    editor.newOutputStream(0).write(value1.getBytes());
    editor.newOutputStream(1).write(value2.getBytes());
    editor.commit();
    diskLruCache.close();
}
/**
 * 添加一条缓存，1个key对应2个value
 */
public void getDiskCache(String key) throws IOException {
    File directory = context.getCacheDir();
    DiskLruCache diskLruCache = DiskLruCache.open(directory, 1, 2, 1024);
    DiskLruCache.Snapshot snapshot = diskLruCache.get(key);
    String value1 = snapshot.getString(0);
    String value2 = snapshot.getString(1);
    diskLruCache.close();
}

三、源码分析

1、open()

DiskLruCache的构造方法是private修饰，这也就是告诉我们，不能通过new DiskLruCache来获取实例，构造方法如下：

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_TEMP);
    this.journalFileBackup = new File(directory, JOURNAL_FILE_BACKUP);
    this.valueCount = valueCount;
    this.maxSize = maxSize;
}

但是提供了open()方法，供我们获取DiskLruCache的实例，open方法如下：

/**
   * Opens the cache in {@code directory}, creating a cache if none exists
   * there.
   *
   * @param directory a writable directory
   * @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
   * @throws IOException if reading or writing the cache directory fails
   */
  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");
    }
    // If a bkp file exists, use it instead.
    //看备份文件是否存在
    File backupFile = new File(directory, JOURNAL_FILE_BACKUP);
   //如果备份文件存在，并且日志文件也存在，就把备份文件删除
    //如果备份文件存在，日志文件不存在，就把备份文件重命名为日志文件
     if (backupFile.exists()) {
      File journalFile = new File(directory, JOURNAL_FILE);
      // If journal file also exists just delete backup file.
        //
      if (journalFile.exists()) {
        backupFile.delete();
      } else {
        renameTo(backupFile, journalFile, false);
      }
    }
    // Prefer to pick up where we left off.
    //初始化DiskLruCache,包括，大小，版本，路径，key对应多少value
    DiskLruCache cache = new DiskLruCache(directory, appVersion, valueCount, maxSize);
    //如果日志文件存在，就开始赌文件信息，并返回
    //主要就是构建entry列表
    if (cache.journalFile.exists()) {
      try {
        cache.readJournal();
        cache.processJournal();
        return cache;
      } catch (IOException journalIsCorrupt) {
        System.out
            .println("DiskLruCache "
                + directory
                + " is corrupt: "
                + journalIsCorrupt.getMessage()
                + ", removing");
        cache.delete();
      }
    }
    //不存在就新建一个
    // Create a new empty cache.
    directory.mkdirs();
    cache = new DiskLruCache(directory, appVersion, valueCount, maxSize);
    cache.rebuildJournal();
    return cache;
  }
open函数：如果日志文件存在，直接去构建entry列表;如果不存在，就构建日志文件;

2、rebuildJournal()

构建文件：
  //这个就是我们可以直接在disk里面看到的journal文件 主要就是对他的操作
 private final File journalFile;
 //journal文件的temp 缓存文件，一般都是先构建这个缓存文件，等待构建完成以后将这个缓存文件重新命名为journal
 private final File journalFileTmp;
/**
   * Creates a new journal that omits redundant information. This replaces the
   * current journal if it exists.
   */
  private synchronized void rebuildJournal() throws IOException {
    if (journalWriter != null) {
      journalWriter.close();
    }
    //指向journalFileTmp这个日志文件的缓存
    Writer writer = new BufferedWriter(
        new OutputStreamWriter(new FileOutputStream(journalFileTmp), Util.US_ASCII));
    try {
      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');
        }
      }
    } finally {
      writer.close();
    }
    if (journalFile.exists()) {
      renameTo(journalFile, journalFileBackup, true);
    }
     //所以这个地方 构建日志文件的流程主要就是先构建出日志文件的缓存文件，如果缓存构建成功 那就直接重命名这个缓存文件，这样做好处在哪里？
    renameTo(journalFileTmp, journalFile, false);
    journalFileBackup.delete();
    //这里也是把写入日志文件的writer初始化
    journalWriter = new BufferedWriter(
        new OutputStreamWriter(new FileOutputStream(journalFile, true), Util.US_ASCII));
  }

再来看当日志文件存在的时候，做了什么

3、readJournal()

private void readJournal() throws IOException {
StrictLineReader reader = new StrictLineReader(new FileInputStream(journalFile), Util.US_ASCII);
try {
//读日志文件的头信息
  String magic = reader.readLine();
  String version = reader.readLine();
  String appVersionString = reader.readLine();
  String valueCountString = reader.readLine();
  String blank = reader.readLine();
  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 + "]");
  }
//这里开始，就开始读取日志信息
  int lineCount = 0;
  while (true) {
    try {
    //构建LruEntries entry列表
      readJournalLine(reader.readLine());
      lineCount++;
    } catch (EOFException endOfJournal) {
      break;
    }
  }
  redundantOpCount = lineCount - lruEntries.size();
  // If we ended on a truncated line, rebuild the journal before appending to it.
  if (reader.hasUnterminatedLine()) {
    rebuildJournal();
  } else {
    //初始化写入文件的writer
    journalWriter = new BufferedWriter(new OutputStreamWriter(
        new FileOutputStream(journalFile, true), Util.US_ASCII));
  }
} finally {
  Util.closeQuietly(reader);
}
}

然后看下这个函数里面的几个主要变量：

//每个entry对应的缓存文件的格式 一般为1，也就是一个key,对应几个缓存，一般设为1，key-value一一对应的关系
private final int valueCount;
private long size = 0;
//这个是专门用于写入日志文件的writer
private Writer journalWriter;
//这个集合应该不陌生了，
private final LinkedHashMap<String, Entry> lruEntries =
        new LinkedHashMap<String, Entry>(0, 0.75f, true);
//这个值大于一定数目时 就会触发对journal文件的清理了
private int redundantOpCount;

下面就看下entry这个实体类的内部结构

private final class Entry {
        private final String key;
        /**
         * Lengths of this entry's files.
         * 这个entry中 每个文件的长度，这个数组的长度为valueCount 一般都是1
         */
        private final long[] lengths;
        /**
         * True if this entry has ever been published.
         * 曾经被发布过 那他的值就是true
         */
        private boolean readable;
        /**
         * The ongoing edit or null if this entry is not being edited.
         * 这个entry对应的editor
         */
        private Editor currentEditor;
        @Override
        public String toString() {
            return "Entry{" +
                    "key='" + key + '\'' +
                    ", lengths=" + Arrays.toString(lengths) +
                    ", readable=" + readable +
                    ", currentEditor=" + currentEditor +
                    ", sequenceNumber=" + sequenceNumber +
                    '}';
        }
        /**
         * The sequence number of the most recently committed edit to this entry.
         * 最近编辑他的序列号
         */
        private long sequenceNumber;
        private Entry(String key) {
            this.key = key;
            this.lengths = new long[valueCount];
        }
        public String getLengths() throws IOException {
            StringBuilder result = new StringBuilder();
            for (long size : lengths) {
                result.append(' ').append(size);
            }
            return result.toString();
        }
        /**
         * Set lengths using decimal numbers like "10123".
         */
        private void setLengths(String[] strings) throws IOException {
            if (strings.length != valueCount) {
                throw invalidLengths(strings);
            }
            try {
                for (int i = 0; i < strings.length; i++) {
                    lengths[i] = Long.parseLong(strings[i]);
                }
            } catch (NumberFormatException e) {
                throw invalidLengths(strings);
            }
        }
        private IOException invalidLengths(String[] strings) throws IOException {
            throw new IOException("unexpected journal line: " + java.util.Arrays.toString(strings));
        }
        //臨時文件創建成功了以後 就會重命名為正式文件了
        public File getCleanFile(int i) {
            Log.v("getCleanFile","getCleanFile path=="+new File(directory, key + "." + i).getAbsolutePath());
            return new File(directory, key + "." + i);
        }
        //tmp开头的都是临时文件
        public File getDirtyFile(int i) {
            Log.v("getDirtyFile","getDirtyFile path=="+new File(directory, key + "." + i + ".tmp").getAbsolutePath());
            return new File(directory, key + "." + i + ".tmp");
        }
}

DiskLruCache的open函数的主要流程就基本走完了;

4、get()

/**
   * Returns a snapshot of the entry named {@code key}, or null if it doesn't
   * exist is not currently readable. If a value is returned, it is moved to
   * the head of the LRU queue.
   * 通过key获取对应的snapshot
   */
  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!
      for (int i = 0; i < valueCount; i++) {
        if (ins[i] != null) {
          Util.closeQuietly(ins[i]);
        } else {
          break;
        }
      }
      return null;
    }
    redundantOpCount++;
    //在取得需要的文件以后 记得在日志文件里增加一条记录 并检查是否需要重新构建日志文件
    journalWriter.append(READ + ' ' + key + '\n');
    if (journalRebuildRequired()) {
      executorService.submit(cleanupCallable);
    }
    return new Snapshot(key, entry.sequenceNumber, ins, entry.lengths);
  }

5、validateKey

private void validateKey(String key) {
        Matcher matcher = LEGAL_KEY_PATTERN.matcher(key);
        if (!matcher.matches()) {
          throw new IllegalArgumentException("keys must match regex "
                  + STRING_KEY_PATTERN + ": \"" + key + "\"");
        }
  }

这里是对存储entry的map的key做了正则验证，所以key一定要用md5加密，因为有些特殊字符验证不能通过;

然后看这句代码对应的：

if (journalRebuildRequired()) {
      executorService.submit(cleanupCallable);
    }

对应的回调函数是：

/** This cache uses a single background thread to evict entries. */
  final ThreadPoolExecutor executorService =
      new ThreadPoolExecutor(0, 1, 60L, TimeUnit.SECONDS, new LinkedBlockingQueue<Runnable>());
  private final Callable<Void> cleanupCallable = new Callable<Void>() {
    public Void call() throws Exception {
      synchronized (DiskLruCache.this) {
        if (journalWriter == null) {
          return null; // Closed.
        }
        trimToSize();
        if (journalRebuildRequired()) {
          rebuildJournal();
          redundantOpCount = 0;
        }
      }
      return null;
    }
  };

其中再来看看trimTOSize()的状态

6、trimTOSize()

private void trimToSize() throws IOException {
    while (size > maxSize) {
      Map.Entry<String, Entry> toEvict = lruEntries.entrySet().iterator().next();
      remove(toEvict.getKey());
    }
  }

就是检测总缓存是否超过了限制数量,

再来看journalRebuildRequired函数

7、journalRebuildRequired()

/**
   * We only rebuild the journal when it will halve the size of the journal
   * and eliminate at least 2000 ops.
   */
  private boolean journalRebuildRequired() {
    final int redundantOpCompactThreshold = 2000;
    return redundantOpCount >= redundantOpCompactThreshold //
        && redundantOpCount >= lruEntries.size();
  }

就是校验redundantOpCount是否超出了范围，如果是，就重构日志文件;

最后看get函数的返回值 new Snapshot()

/** A snapshot of the values for an entry. */
//这个类持有该entry中每个文件的inputStream 通过这个inputStream 可以读取他的内容
  public final class Snapshot implements Closeable {
    private final String key;
    private final long sequenceNumber;
    private final InputStream[] ins;
    private final long[] lengths;
    private Snapshot(String key, long sequenceNumber, InputStream[] ins, long[] lengths) {
      this.key = key;
      this.sequenceNumber = sequenceNumber;
      this.ins = ins;
      this.lengths = lengths;
    }
    /**
     * Returns an editor for this snapshot's entry, or null if either the
     * entry has changed since this snapshot was created or if another edit
     * is in progress.
     */
    public Editor edit() throws IOException {
      return DiskLruCache.this.edit(key, sequenceNumber);
    }
    /** Returns the unbuffered stream with the value for {@code index}. */
    public InputStream getInputStream(int index) {
      return ins[index];
    }
    /** Returns the string value for {@code index}. */
    public String getString(int index) throws IOException {
      return inputStreamToString(getInputStream(index));
    }
    /** Returns the byte length of the value for {@code index}. */
    public long getLength(int index) {
      return lengths[index];
    }
    public void close() {
      for (InputStream in : ins) {
        Util.closeQuietly(in);
      }
    }
  }

到这里就明白了get最终返回的其实就是entry根据key 来取的snapshot对象，这个对象直接把inputStream暴露给外面;

8、save的过程

public Editor edit(String key) throws IOException {
    return edit(key, ANY_SEQUENCE_NUMBER);
}
//根据传进去的key 创建一个entry 并且将这个key加入到entry的那个map里 然后创建一个对应的editor
//同时在日志文件里加入一条对该key的dirty记录
private synchronized Editor edit(String key, long expectedSequenceNumber) throws IOException {
    //因为这里涉及到写文件 所以要先校验一下写日志文件的writer 是否被正确的初始化
    checkNotClosed();
    //这个地方是校验 我们的key的，通常来说 假设我们要用这个缓存来存一张图片的话，我们的key 通常是用这个图片的
    //网络地址 进行md5加密，而对这个key的格式在这里是有要求的 所以这一步就是验证key是否符合规范
    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;
}

然后取得输出流

public OutputStream newOutputStream(int index) throws IOException {
        if (index < 0 || index >= valueCount) {
            throw new IllegalArgumentException("Expected index " + index + " to "
                    + "be greater than 0 and less than the maximum value count "
                    + "of " + valueCount);
        }
        synchronized (DiskLruCache.this) {
            if (entry.currentEditor != this) {
                throw new IllegalStateException();
            }
            if (!entry.readable) {
                written[index] = true;
            }
            File dirtyFile = entry.getDirtyFile(index);
            FileOutputStream outputStream;
            try {
                outputStream = new FileOutputStream(dirtyFile);
            } catch (FileNotFoundException e) {
                // Attempt to recreate the cache directory.
                directory.mkdirs();
                try {
                    outputStream = new FileOutputStream(dirtyFile);
                } catch (FileNotFoundException e2) {
                    // We are unable to recover. Silently eat the writes.
                    return NULL_OUTPUT_STREAM;
                }
            }
            return new FaultHidingOutputStream(outputStream);
        }
    }

注意这个index 其实一般传0 就可以了，DiskLruCache 认为 一个key 下面可以对应多个文件，这些文件 用一个数组来存储，所以正常情况下，我们都是

一个key 对应一个缓存文件 所以传0

//tmp开头的都是临时文件
     public File getDirtyFile(int i) {
         return new File(directory, key + "." + i + ".tmp");
     }

然后你这边就能看到，这个输出流，实际上是tmp 也就是缓存文件的 .tmp 也就是缓存文件的 缓存文件 输出流;

这个流 我们写完毕以后 就要commit;

public void commit() throws IOException {
        if (hasErrors) {
            completeEdit(this, false);
            remove(entry.key); // The previous entry is stale.
        } else {
            completeEdit(this, true);
        }
        committed = true;
    }

这个就是根据缓存文件的大小 更新disklrucache的总大小 然后再日志文件里对该key加入clean的log

//最后判断是否超过最大的maxSize 以便对缓存进行清理
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 (!editor.written[i]) {
                editor.abort();
                throw new IllegalStateException("Newly created entry didn't create value for index " + i);
            }
            if (!entry.getDirtyFile(i).exists()) {
                editor.abort();
                return;
            }
        }
    }
    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');
    }
    journalWriter.flush();
    if (size > maxSize || journalRebuildRequired()) {
        executorService.submit(cleanupCallable);
    }
}

commit以后 就会把tmp文件转正 ，重命名为 真正的缓存文件了;

这个里面的流程和日志文件的rebuild 是差不多的，都是为了防止写文件的出问题。所以做了这样的冗余处理;

总结：

DiskLruCache，利用一个journal文件，保证了保证了cache实体的可用性(只有CLEAN的可用)，且获取文件的长度的时候可以通过在该文件的记录中读取。

利用FaultHidingOutputStream对FileOutPutStream很好的对写入文件过程中是否发生错误进行捕获，而不是让用户手动去调用出错后的处理方法;

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