OkHttp3源码详解(四)缓存策略

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