OkHttp3源码详解(四)缓存策略

Android进阶开发 2019-11-18

编程语言 系统研发与运维 移动开发与客户端 网络与数据通信 android 日志 源码 Cache request editor

合理地利用本地缓存可以有效地减少网络开销,减少响应延迟。HTTP报头也定义了很多与缓存有关的域来控制缓存。今天就来讲讲OkHttp中关于缓存部分的实现细节。

  1. HTTP缓存策略
    首先来了解下HTTP协议中缓存部分的相关域。

1.1 Expires
超时时间,一般用在服务器的response报头中用于告知客户端对应资源的过期时间。当客户端需要再次请求相同资源时先比较其过期时间,如果尚未超过过期时间则直接返回缓存结果,如果已经超过则重新请求。

1.2 Cache-Control
相对值,单位时秒,表示当前资源的有效期。Cache-Control比Expires优先级更高:

Cache-Control:max-age=31536000,public
1.3 条件GET请求
1.3.1 Last-Modified-Date
客户端第一次请求时,服务器返回:

Last-Modified: Tue, 12 Jan 2016 09:31:27 GMT
当客户端二次请求时,可以头部加上如下header:

If-Modified-Since: Tue, 12 Jan 2016 09:31:27 GMT
如果当前资源没有被二次修改,服务器返回304告知客户端直接复用本地缓存。

1.3.2 ETag
ETag是对资源文件的一种摘要,可以通过ETag值来判断文件是否有修改。当客户端第一次请求某资源时,服务器返回:

ETag: "5694c7ef-24dc"
客户端再次请求时,可在头部加上如下域:

If-None-Match: "5694c7ef-24dc"
如果文件并未改变,则服务器返回304告知客户端可以复用本地缓存。

1.4 no-cache/no-store
不使用缓存

1.5 only-if-cached
只使用缓存

  1. Cache源码分析
    OkHttp的缓存工作都是在CacheInterceptor中完成的,Cache部分有如下几个关键类:

Cache:Cache管理器,其内部包含一个DiskLruCache将cache写入文件系统:

  • Cache Optimization


    *
  • To measure cache effectiveness, this class tracks three statistics:

  • {@linkplain #requestCount() Request Count:} the number of HTTP
  • requests issued since this cache was created.
  • {@linkplain #networkCount() Network Count:} the number of those
  • requests that required network use.
  • {@linkplain #hitCount() Hit Count:} the number of those requests
  • whose responses were served by the cache.

*Sometimes a request will result in a conditional cache hit. If the cache contains a stale copy ofthe response, the client will issue a conditional {@code GET}. The server will then send eitherthe updated response if it has changed, or a short 'not modified' response if the client's copyis still valid. Such responses increment both the network count and hit count.
*

The best way to improve the cache hit rate is by configuring the web server to return

cacheable responses. Although this client honors all href="https://yq.aliyun.com/go/articleRenderRedirect?url=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc7234">HTTP/1.1 (RFC 7234) cache headers, it doesn't cachepartial responses.
Cache内部通过requestCount,networkCount,hitCount三个统计指标来优化缓存效率

CacheStrategy:缓存策略。其内部维护一个request和response,通过指定request和response来描述是通过网络还是缓存获取response,抑或二者同时使用

[CacheStrategy.java]
/**

  • Given a request and cached response, this figures out whether to use the network, the cache, or
  • both.
    *
  • Selecting a cache strategy may add conditions to the request (like the "If-Modified-Since"

  • header for conditional GETs) or warnings to the cached response (if the cached data is
  • potentially stale).
    */

public final class CacheStrategy {
/* The request to send on the network, or null if this call doesn't use the network. /
public final Request networkRequest;

/* The cached response to return or validate; or null if this call doesn't use a cache. /
public final Response cacheResponse;
......
}
CacheStrategy$Factory:缓存策略工厂类根据实际请求返回对应的缓存策略

既然实际的缓存工作都是在CacheInterceptor中完成的,那么接下来看下CahceInterceptor的核心方法intercept方法源码:

[CacheInterceptor.java]
@Override public Response intercept(Chain chain) throws IOException {

//首先尝试获取缓存
Response cacheCandidate = cache != null
    ? cache.get(chain.request())
    : null;

long now = System.currentTimeMillis();

//获取缓存策略
CacheStrategy strategy = new CacheStrategy.Factory(now, chain.request(), cacheCandidate).get();
Request networkRequest = strategy.networkRequest;
Response cacheResponse = strategy.cacheResponse;

//如果有缓存,更新下相关统计指标:命中率
if (cache != null) {
  cache.trackResponse(strategy);
}

//如果当前缓存不符合要求,将其close
if (cacheCandidate != null && cacheResponse == null) {
  closeQuietly(cacheCandidate.body()); // The cache candidate wasn't applicable. Close it.
}

// 如果不能使用网络,同时又没有符合条件的缓存,直接抛504错误
if (networkRequest == null && cacheResponse == null) {
  return new Response.Builder()
      .request(chain.request())
      .protocol(Protocol.HTTP_1_1)
      .code(504)
      .message("Unsatisfiable Request (only-if-cached)")
      .body(Util.EMPTY_RESPONSE)
      .sentRequestAtMillis(-1L)
      .receivedResponseAtMillis(System.currentTimeMillis())
      .build();
}

// 如果有缓存同时又不使用网络,则直接返回缓存结果
if (networkRequest == null) {
  return cacheResponse.newBuilder()
      .cacheResponse(stripBody(cacheResponse))
      .build();
}

//尝试通过网络获取回复
Response networkResponse = null;
try {
  networkResponse = chain.proceed(networkRequest);
} finally {
  // If we're crashing on I/O or otherwise, don't leak the cache body.
  if (networkResponse == null && cacheCandidate != null) {
    closeQuietly(cacheCandidate.body());
  }
}

// 如果既有缓存,同时又发起了请求,说明此时是一个Conditional Get请求
if (cacheResponse != null) {
  // 如果服务端返回的是NOT_MODIFIED,缓存有效,将本地缓存和网络响应做合并
  if (networkResponse.code() == HTTP_NOT_MODIFIED) {
    Response response = cacheResponse.newBuilder()
        .headers(combine(cacheResponse.headers(), networkResponse.headers()))
        .sentRequestAtMillis(networkResponse.sentRequestAtMillis())
        .receivedResponseAtMillis(networkResponse.receivedResponseAtMillis())
        .cacheResponse(stripBody(cacheResponse))
        .networkResponse(stripBody(networkResponse))
        .build();
    networkResponse.body().close();

    // Update the cache after combining headers but before stripping the
    // Content-Encoding header (as performed by initContentStream()).
    cache.trackConditionalCacheHit();
    cache.update(cacheResponse, response);
    return response;
  } else {// 如果响应资源有更新,关掉原有缓存
    closeQuietly(cacheResponse.body());
  }
}

Response response = networkResponse.newBuilder()
    .cacheResponse(stripBody(cacheResponse))
    .networkResponse(stripBody(networkResponse))
    .build();

if (cache != null) {
  if (HttpHeaders.hasBody(response) && CacheStrategy.isCacheable(response, networkRequest)) {
    // 将网络响应写入cache中
    CacheRequest cacheRequest = cache.put(response);
    return cacheWritingResponse(cacheRequest, response);
  }

  if (HttpMethod.invalidatesCache(networkRequest.method())) {
    try {
      cache.remove(networkRequest);
    } catch (IOException ignored) {
      // The cache cannot be written.
    }
  }
}

return response;

}
核心逻辑都以中文注释的形式在代码中标注出来了,大家看代码即可。通过上面的代码可以看出,几乎所有的动作都是以CacheStrategy缓存策略为依据做出的,那么接下来看下缓存策略是如何生成的,相关代码实现在CacheStrategy$Factory.get()方法中:

[CacheStrategy$Factory]

/**
 * Returns a strategy to satisfy {@code request} using the a cached response {@code response}.
 */
public CacheStrategy get() {
  CacheStrategy candidate = getCandidate();

  if (candidate.networkRequest != null && request.cacheControl().onlyIfCached()) {
    // We're forbidden from using the network and the cache is insufficient.
    return new CacheStrategy(null, null);
  }

  return candidate;
}

/** Returns a strategy to use assuming the request can use the network. */
private CacheStrategy getCandidate() {
  // 若本地没有缓存,发起网络请求
  if (cacheResponse == null) {
    return new CacheStrategy(request, null);
  }

  // 如果当前请求是HTTPS,而缓存没有TLS握手,重新发起网络请求
  if (request.isHttps() && cacheResponse.handshake() == null) {
    return new CacheStrategy(request, null);
  }

  // If this response shouldn't have been stored, it should never be used
  // as a response source. This check should be redundant as long as the
  // persistence store is well-behaved and the rules are constant.
  if (!isCacheable(cacheResponse, request)) {
    return new CacheStrategy(request, null);
  }
    

  //如果当前的缓存策略是不缓存或者是conditional get,发起网络请求
  CacheControl requestCaching = request.cacheControl();
  if (requestCaching.noCache() || hasConditions(request)) {
    return new CacheStrategy(request, null);
  }

  //ageMillis:缓存age
  long ageMillis = cacheResponseAge();
  //freshMillis:缓存保鲜时间
  long freshMillis = computeFreshnessLifetime();

  if (requestCaching.maxAgeSeconds() != -1) {
    freshMillis = Math.min(freshMillis, SECONDS.toMillis(requestCaching.maxAgeSeconds()));
  }

  long minFreshMillis = 0;
  if (requestCaching.minFreshSeconds() != -1) {
    minFreshMillis = SECONDS.toMillis(requestCaching.minFreshSeconds());
  }

  long maxStaleMillis = 0;
  CacheControl responseCaching = cacheResponse.cacheControl();
  if (!responseCaching.mustRevalidate() && requestCaching.maxStaleSeconds() != -1) {
    maxStaleMillis = SECONDS.toMillis(requestCaching.maxStaleSeconds());
  }

  //如果 age + min-fresh >= max-age && age + min-fresh < max-age + max-stale,则虽然缓存过期了,     //但是缓存继续可以使用,只是在头部添加 110 警告码
  if (!responseCaching.noCache() && ageMillis + minFreshMillis < freshMillis + maxStaleMillis)      {
    Response.Builder builder = cacheResponse.newBuilder();
    if (ageMillis + minFreshMillis >= freshMillis) {
      builder.addHeader("Warning", "110 HttpURLConnection \"Response is stale\"");
    }
    long oneDayMillis = 24 * 60 * 60 * 1000L;
    if (ageMillis > oneDayMillis && isFreshnessLifetimeHeuristic()) {
      builder.addHeader("Warning", "113 HttpURLConnection \"Heuristic expiration\"");
    }
    return new CacheStrategy(null, builder.build());
  }

  // 发起conditional get请求
  String conditionName;
  String conditionValue;
  if (etag != null) {
    conditionName = "If-None-Match";
    conditionValue = etag;
  } else if (lastModified != null) {
    conditionName = "If-Modified-Since";
    conditionValue = lastModifiedString;
  } else if (servedDate != null) {
    conditionName = "If-Modified-Since";
    conditionValue = servedDateString;
  } else {
    return new CacheStrategy(request, null); // No condition! Make a regular request.
  }

  Headers.Builder conditionalRequestHeaders = request.headers().newBuilder();
  Internal.instance.addLenient(conditionalRequestHeaders, conditionName, conditionValue);

  Request conditionalRequest = request.newBuilder()
      .headers(conditionalRequestHeaders.build())
      .build();
  return new CacheStrategy(conditionalRequest, cacheResponse);
}

可以看到其核心逻辑在getCandidate函数中。基本就是HTTP缓存协议的实现,核心代码逻辑已通过中文注释说明,大家直接看代码就好。

  1. DiskLruCache
    Cache内部通过DiskLruCache管理cache在文件系统层面的创建,读取,清理等等工作,接下来看下DiskLruCache的主要逻辑:

public final class DiskLruCache implements Closeable, Flushable {

final FileSystem fileSystem;
final File directory;
private final File journalFile;
private final File journalFileTmp;
private final File journalFileBackup;
private final int appVersion;
private long maxSize;
final int valueCount;
private long size = 0;
BufferedSink journalWriter;
final LinkedHashMap lruEntries = new LinkedHashMap<>(0, 0.75f, true);

// Must be read and written when synchronized on 'this'.
boolean initialized;
boolean closed;
boolean mostRecentTrimFailed;
boolean mostRecentRebuildFailed;

/**

  • To differentiate between old and current snapshots, each entry is given a sequence number each
  • time an edit is committed. A snapshot is stale if its sequence number is not equal to its
  • entry's sequence number.
    */

private long nextSequenceNumber = 0;

/* Used to run 'cleanupRunnable' for journal rebuilds. /
private final Executor executor;
private final Runnable cleanupRunnable = new Runnable() {

public void run() {
    ......
}

};
...
}
3.1 journalFile
DiskLruCache内部日志文件,对cache的每一次读写都对应一条日志记录,DiskLruCache通过分析日志分析和创建cache。日志文件格式如下:

  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
 
 前5行固定不变,分别为:常量:libcore.io.DiskLruCache;diskCache版本;应用程序版本;valueCount(后文介绍),空行
 
 接下来每一行对应一个cache entry的一次状态记录,其格式为:[状态(DIRTY,CLEAN,READ,REMOVE),key,状态相关value(可选)]:
 - DIRTY:表明一个cache entry正在被创建或更新,每一个成功的DIRTY记录都应该对应一个CLEAN或REMOVE操作。如果一个DIRTY缺少预期匹配的CLEAN/REMOVE,则对应entry操作失败,需要将其从lruEntries中删除
 - CLEAN:说明cache已经被成功操作,当前可以被正常读取。每一个CLEAN行还需要记录其每一个value的长度
 - READ: 记录一次cache读取操作
 - REMOVE:记录一次cache清除
 

日志文件的应用场景主要有四个:

DiskCacheLru初始化时通过读取日志文件创建cache容器:lruEntries。同时通过日志过滤操作不成功的cache项。相关逻辑在DiskLruCache.readJournalLine,DiskLruCache.processJournal
初始化完成后,为避免日志文件不断膨胀,对日志进行重建精简,具体逻辑在DiskLruCache.rebuildJournal
每当有cache操作时将其记录入日志文件中以备下次初始化时使用
当冗余日志过多时,通过调用cleanUpRunnable线程重建日志
3.2 DiskLruCache.Entry
每一个DiskLruCache.Entry对应一个cache记录:

private final class Entry {

final String key;

/** Lengths of this entry's files. */
final long[] lengths;
final File[] cleanFiles;
final File[] dirtyFiles;

/** True if this entry has ever been published. */
boolean readable;

/** The ongoing edit or null if this entry is not being edited. */
Editor currentEditor;

/** The sequence number of the most recently committed edit to this entry. */
long sequenceNumber;

Entry(String key) {
  this.key = key;

  lengths = new long[valueCount];
  cleanFiles = new File[valueCount];
  dirtyFiles = new File[valueCount];

  // The names are repetitive so re-use the same builder to avoid allocations.
  StringBuilder fileBuilder = new StringBuilder(key).append('.');
  int truncateTo = fileBuilder.length();
  for (int i = 0; i < valueCount; i++) {
    fileBuilder.append(i);
    cleanFiles[i] = new File(directory, fileBuilder.toString());
    fileBuilder.append(".tmp");
    dirtyFiles[i] = new File(directory, fileBuilder.toString());
    fileBuilder.setLength(truncateTo);
  }
}
...
 
    /**
 * Returns a snapshot of this entry. This opens 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.
 */
Snapshot snapshot() {
  if (!Thread.holdsLock(DiskLruCache.this)) throw new AssertionError();

  Source[] sources = new Source[valueCount];
  long[] lengths = this.lengths.clone(); // Defensive copy since these can be zeroed out.
  try {
    for (int i = 0; i < valueCount; i++) {
      sources[i] = fileSystem.source(cleanFiles[i]);
    }
    return new Snapshot(key, sequenceNumber, sources, lengths);
  } catch (FileNotFoundException e) {
    // A file must have been deleted manually!
    for (int i = 0; i < valueCount; i++) {
      if (sources[i] != null) {
        Util.closeQuietly(sources[i]);
      } else {
        break;
      }
    }
    // Since the entry is no longer valid, remove it so the metadata is accurate (i.e. the cache
    // size.)
    try {
      removeEntry(this);
    } catch (IOException ignored) {
    }
    return null;
  }
}

}
一个Entry主要由以下几部分构成:

key:每个cache都有一个key作为其标识符。当前cache的key为其对应URL的MD5字符串
cleanFiles/dirtyFiles:每一个Entry对应多个文件,其对应的文件数由DiskLruCache.valueCount指定。当前在OkHttp中valueCount为2。即每个cache对应2个cleanFiles,2个dirtyFiles。其中第一个cleanFiles/dirtyFiles记录cache的meta数据(如URL,创建时间,SSL握手记录等等),第二个文件记录cache的真正内容。cleanFiles记录处于稳定状态的cache结果,dirtyFiles记录处于创建或更新状态的cache
currentEditor:entry编辑器,对entry的所有操作都是通过其编辑器完成。编辑器内部添加了同步锁
3.3 cleanupRunnable
清理线程,用于重建精简日志:

private final Runnable cleanupRunnable = new Runnable() {

public void run() {
  synchronized (DiskLruCache.this) {
    if (!initialized | closed) {
      return; // Nothing to do
    }

    try {
      trimToSize();
    } catch (IOException ignored) {
      mostRecentTrimFailed = true;
    }

    try {
      if (journalRebuildRequired()) {
        rebuildJournal();
        redundantOpCount = 0;
      }
    } catch (IOException e) {
      mostRecentRebuildFailed = true;
      journalWriter = Okio.buffer(Okio.blackhole());
    }
  }
}

};
其触发条件在journalRebuildRequired()方法中:

/**

  • We only rebuild the journal when it will halve the size of the journal and eliminate at least
  • 2000 ops.
    */

boolean journalRebuildRequired() {

final int redundantOpCompactThreshold = 2000;
return redundantOpCount >= redundantOpCompactThreshold
    && redundantOpCount >= lruEntries.size();

}
当冗余日志超过日志文件本身的一般且总条数超过2000时执行

3.4 SnapShot
cache快照,记录了特定cache在某一个特定时刻的内容。每次向DiskLruCache请求时返回的都是目标cache的一个快照,相关逻辑在DiskLruCache.get中:

[DiskLruCache.java]
/**

  • Returns a snapshot of the entry named {@code key}, or null if it doesn't exist is not currently
    1. If a value is returned, it is moved to the head of the LRU queue.
      */

public synchronized Snapshot get(String key) throws IOException {

initialize();

checkNotClosed();
validateKey(key);
Entry entry = lruEntries.get(key);
if (entry == null || !entry.readable) return null;

Snapshot snapshot = entry.snapshot();
if (snapshot == null) return null;

redundantOpCount++;
//日志记录
journalWriter.writeUtf8(READ).writeByte(' ').writeUtf8(key).writeByte('\n');
if (journalRebuildRequired()) {
  executor.execute(cleanupRunnable);
}

return snapshot;

}
3.5 lruEntries
管理cache entry的容器,其数据结构是LinkedHashMap。通过LinkedHashMap本身的实现逻辑达到cache的LRU替换

3.6 FileSystem
使用Okio对File的封装,简化了I/O操作。

3.7 DiskLruCache.edit
DiskLruCache可以看成是Cache在文件系统层的具体实现,所以其基本操作接口存在一一对应的关系:

Cache.get() —>DiskLruCache.get()
Cache.put()—>DiskLruCache.edit() //cache插入
Cache.remove()—>DiskLruCache.remove()
Cache.update()—>DiskLruCache.edit()//cache更新
其中get操作在3.4已经介绍了,remove操作较为简单,put和update大致逻辑相似,因为篇幅限制,这里仅介绍Cache.put操作的逻辑,其他的操作大家看代码就好:

[okhttp3.Cache.java]
CacheRequest put(Response response) {

String requestMethod = response.request().method();

if (HttpMethod.invalidatesCache(response.request().method())) {
  try {
    remove(response.request());
  } catch (IOException ignored) {
    // The cache cannot be written.
  }
  return null;
}
if (!requestMethod.equals("GET")) {
  // Don't cache non-GET responses. We're technically allowed to cache
  // HEAD requests and some POST requests, but the complexity of doing
  // so is high and the benefit is low.
  return null;
}

if (HttpHeaders.hasVaryAll(response)) {
  return null;
}

Entry entry = new Entry(response);
DiskLruCache.Editor editor = null;
try {
  editor = cache.edit(key(response.request().url()));
  if (editor == null) {
    return null;
  }
  entry.writeTo(editor);
  return new CacheRequestImpl(editor);
} catch (IOException e) {
  abortQuietly(editor);
  return null;
}

}
可以看到核心逻辑在editor = cache.edit(key(response.request().url()));,相关代码在DiskLruCache.edit:

[okhttp3.internal.cache.DiskLruCache.java]
synchronized Editor edit(String key, long expectedSequenceNumber) throws IOException {

initialize();

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.currentEditor != null) {
  return null; // 当前cache entry正在被其他对象操作
}
if (mostRecentTrimFailed || mostRecentRebuildFailed) {
  // The OS has become our enemy! If the trim job failed, it means we are storing more data than
  // requested by the user. Do not allow edits so we do not go over that limit any further. If
  // the journal rebuild failed, the journal writer will not be active, meaning we will not be
  // able to record the edit, causing file leaks. In both cases, we want to retry the clean up
  // so we can get out of this state!
  executor.execute(cleanupRunnable);
  return null;
}

// 日志接入DIRTY记录
journalWriter.writeUtf8(DIRTY).writeByte(' ').writeUtf8(key).writeByte('\n');
journalWriter.flush();

if (hasJournalErrors) {
  return null; // Don't edit; the journal can't be written.
}

if (entry == null) {
  entry = new Entry(key);
  lruEntries.put(key, entry);
}
Editor editor = new Editor(entry);
entry.currentEditor = editor;
return editor;

}
edit方法返回对应CacheEntry的editor编辑器。接下来再来看下Cache.put()方法的entry.writeTo(editor);,其相关逻辑:

[okhttp3.internal.cache.DiskLruCache.java]
public void writeTo(DiskLruCache.Editor editor) throws IOException {

  BufferedSink sink = Okio.buffer(editor.newSink(ENTRY_METADATA));

  sink.writeUtf8(url)
      .writeByte('\n');
  sink.writeUtf8(requestMethod)
      .writeByte('\n');
  sink.writeDecimalLong(varyHeaders.size())
      .writeByte('\n');
  for (int i = 0, size = varyHeaders.size(); i < size; i++) {
    sink.writeUtf8(varyHeaders.name(i))
        .writeUtf8(": ")
        .writeUtf8(varyHeaders.value(i))
        .writeByte('\n');
  }

  sink.writeUtf8(new StatusLine(protocol, code, message).toString())
      .writeByte('\n');
  sink.writeDecimalLong(responseHeaders.size() + 2)
      .writeByte('\n');
  for (int i = 0, size = responseHeaders.size(); i < size; i++) {
    sink.writeUtf8(responseHeaders.name(i))
        .writeUtf8(": ")
        .writeUtf8(responseHeaders.value(i))
        .writeByte('\n');
  }
  sink.writeUtf8(SENT_MILLIS)
      .writeUtf8(": ")
      .writeDecimalLong(sentRequestMillis)
      .writeByte('\n');
  sink.writeUtf8(RECEIVED_MILLIS)
      .writeUtf8(": ")
      .writeDecimalLong(receivedResponseMillis)
      .writeByte('\n');

  if (isHttps()) {
    sink.writeByte('\n');
    sink.writeUtf8(handshake.cipherSuite().javaName())
        .writeByte('\n');
    writeCertList(sink, handshake.peerCertificates());
    writeCertList(sink, handshake.localCertificates());
    // The handshake’s TLS version is null on HttpsURLConnection and on older cached responses.
    if (handshake.tlsVersion() != null) {
      sink.writeUtf8(handshake.tlsVersion().javaName())
          .writeByte('\n');
    }
  }
  sink.close();
}

其主要逻辑就是将对应请求的meta数据写入对应CacheEntry的索引为ENTRY_METADATA(0)的dirtyfile中。

最后再来看Cache.put()方法的return new CacheRequestImpl(editor);:

[okhttp3.Cache$CacheRequestImpl]
private final class CacheRequestImpl implements CacheRequest {

private final DiskLruCache.Editor editor;
private Sink cacheOut;
private Sink body;
boolean done;

public CacheRequestImpl(final DiskLruCache.Editor editor) {
  this.editor = editor;
  this.cacheOut = editor.newSink(ENTRY_BODY);
  this.body = new ForwardingSink(cacheOut) {
    @Override public void close() throws IOException {
      synchronized (Cache.this) {
        if (done) {
          return;
        }
        done = true;
        writeSuccessCount++;
      }
      super.close();
      editor.commit();
    }
  };
}

@Override public void abort() {
  synchronized (Cache.this) {
    if (done) {
      return;
    }
    done = true;
    writeAbortCount++;
  }
  Util.closeQuietly(cacheOut);
  try {
    editor.abort();
  } catch (IOException ignored) {
  }
}

@Override public Sink body() {
  return body;
}

}
其中close,abort方法会调用editor.abort和editor.commit来更新日志,editor.commit还会将dirtyFile重置为cleanFile作为稳定可用的缓存,相关逻辑在okhttp3.internal.cache.DiskLruCache$Editor.completeEdit中:

[okhttp3.internal.cache.DiskLruCache$Editor.completeEdit]
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 (!fileSystem.exists(entry.dirtyFiles[i])) {
      editor.abort();
      return;
    }
  }
}

for (int i = 0; i < valueCount; i++) {
  File dirty = entry.dirtyFiles[i];
  if (success) {
    if (fileSystem.exists(dirty)) {
      File clean = entry.cleanFiles[i];
      fileSystem.rename(dirty, clean);//将dirtyfile置为cleanfile
      long oldLength = entry.lengths[i];
      long newLength = fileSystem.size(clean);
      entry.lengths[i] = newLength;
      size = size - oldLength + newLength;
    }
  } else {
    fileSystem.delete(dirty);//若失败则删除dirtyfile
  }
}

redundantOpCount++;
entry.currentEditor = null;
//更新日志
if (entry.readable | success) {
  entry.readable = true;
  journalWriter.writeUtf8(CLEAN).writeByte(' ');
  journalWriter.writeUtf8(entry.key);
  entry.writeLengths(journalWriter);
  journalWriter.writeByte('\n');
  if (success) {
    entry.sequenceNumber = nextSequenceNumber++;
  }
} else {
  lruEntries.remove(entry.key);
  journalWriter.writeUtf8(REMOVE).writeByte(' ');
  journalWriter.writeUtf8(entry.key);
  journalWriter.writeByte('\n');
}
journalWriter.flush();

if (size > maxSize || journalRebuildRequired()) {
  executor.execute(cleanupRunnable);
}

}
CacheRequestImpl实现CacheRequest接口,向外部类(主要是CacheInterceptor)透出,外部对象通过CacheRequestImpl更新或写入缓存数据。

3.8总结
总结起来DiskLruCache主要有以下几个特点:

通过LinkedHashMap实现LRU替换
通过本地维护Cache操作日志保证Cache原子性与可用性,同时为防止日志过分膨胀定时执行日志精简
每一个Cache项对应两个状态副本:DIRTY,CLEAN。CLEAN表示当前可用状态Cache,外部访问到的cache快照均为CLEAN状态;DIRTY为更新态Cache。由于更新和创建都只操作DIRTY状态副本,实现了Cache的读写分离
每一个Cache项有四个文件,两个状态(DIRTY,CLEAN),每个状态对应两个文件:一个文件存储Cache meta数据,一个文件存储Cache内容数据

作者:李牙刷儿
原文链接:https://www.jianshu.com/p/87da91631a70

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