緩存按照進程可以分爲本地緩存和非本地緩存。本地緩存會把數據存儲在程序的進程內存中，比如這裏的guava。非本地緩存通常由另一個進程維護緩存，與應用程序是分割的，會涉及網絡io，比如redis。
redis這類緩存大家應該比較熟悉，這裏不做探討。
本地緩存使用起來更加輕量，且因爲沒有網絡io，效率更快。但是缺點也很明顯，緩存大小受限，否則會吃掉項目本身運行時的內存。如果我們的緩存大小不多，且沒有特殊的要求，本地緩存是不錯的選擇。
guava是google的一個工具包，是以jar包方式開放的，我們的項目如果要使用直接繼承jar包即可。guava中的一個重要功能就是localcache。使用方式暫且不說了，很多資料，這裏主要看下關鍵源碼。
guava cache的最重要的特徵就是 沒有使用多線程，所以真的方便，真的輕量。
guava緩存過期策略是一種延遲刪除策略，每次get的時候檢查時間戳，來決定這次get的值。而redis這種是採用多線程方式，定期檢查。
那麼我們就從get方法開始看。
V get(K key, int hash, CacheLoader<? super K, V> loader) throws ExecutionException {
checkNotNull(key);
checkNotNull(loader);
try {
if (count != 0) { // read-volatile
// don't call getLiveEntry, which would ignore loading values
ReferenceEntry<K, V> e = getEntry(key, hash);
if (e != null) {
long now = map.ticker.read();
V value = getLiveValue(e, now);
if (value != null) {
recordRead(e, now);
statsCounter.recordHits(1);
return scheduleRefresh(e, key, hash, value, now, loader);
}
ValueReference<K, V> valueReference = e.getValueReference();
if (valueReference.isLoading()) {
return waitForLoadingValue(e, key, valueReference);
}
}
}
// at this point e is either null or expired;
return lockedGetOrLoad(key, hash, loader);
} catch (ExecutionException ee) {
Throwable cause = ee.getCause();
if (cause instanceof Error) {
throw new ExecutionError((Error) cause);
} else if (cause instanceof RuntimeException) {
throw new UncheckedExecutionException(cause);
}
throw ee;
} finally {
postReadCleanup();
}
}guava緩存的get方法邏輯是，如果有，且不過期就返回，否則加載，存儲，返回。這是一種最常見的get方式，實現方式就在上面的代碼裏，這個get是在segment類裏，別忘了guava就是一個concurrentHashMap。
首先根據hash值取一個entry，再從entry裏取值，這裏最終通過getLiveValue函數實現，該函數會檢測value是否過期。如果過期了或者沒有值，就會走到函數的結束地方，調用lockedGetOrLoad方法load值。如果沒有過期，那麼接着會檢測該值是否需要刷新，由函數scheduleRefresh實現。
/**
* Gets the value from an entry. Returns null if the entry is invalid, partially-collected,
* loading, or expired.
*/
V getLiveValue(ReferenceEntry<K, V> entry, long now) {
if (entry.getKey() == null) {
tryDrainReferenceQueues();
return null;
}
V value = entry.getValueReference().get();
if (value == null) {
tryDrainReferenceQueues();
return null;
}
if (map.isExpired(entry, now)) {
tryExpireEntries(now);
return null;
}
return value;
}這裏通過調用isExpired函數檢測是否過期：
/** Returns true if the entry has expired. */
boolean isExpired(ReferenceEntry<K, V> entry, long now) {
checkNotNull(entry);
if (expiresAfterAccess() && (now - entry.getAccessTime() >= expireAfterAccessNanos)) {
return true;
}
if (expiresAfterWrite() && (now - entry.getWriteTime() >= expireAfterWriteNanos)) {
return true;
}
return false;
}isExpired會根據創建cache時的配置來判定過期。
一旦認定過期了，就會調用下面的函數來處理過期：
/** Cleanup expired entries when the lock is available. */
void tryExpireEntries(long now) {
if (tryLock()) {
try {
expireEntries(now);
} finally {
unlock();
// don't call postWriteCleanup as we're in a read
}
}
} @GuardedBy("this")
void expireEntries(long now) {
drainRecencyQueue();
ReferenceEntry<K, V> e;
while ((e = writeQueue.peek()) != null && map.isExpired(e, now)) {
if (!removeEntry(e, e.getHash(), RemovalCause.EXPIRED)) {
throw new AssertionError();
}
}
while ((e = accessQueue.peek()) != null && map.isExpired(e, now)) {
if (!removeEntry(e, e.getHash(), RemovalCause.EXPIRED)) {
throw new AssertionError();
}
}
}cache會維護兩個隊列來記錄操作cache的所有動作，access或者write。遍歷一遍隊列，淘汰掉過期的。如果過期了getLiveValue會返回null，否則返回相應的value。
所以get方法在getLiveValue調用完以後會檢測是否爲null。
如果不爲null，說明沒有過期，接下來就會判斷是否需要刷新。
V scheduleRefresh(
ReferenceEntry<K, V> entry,
K key,
int hash,
V oldValue,
long now,
CacheLoader<? super K, V> loader) {
if (map.refreshes()
&& (now - entry.getWriteTime() > map.refreshNanos)
&& !entry.getValueReference().isLoading()) {
V newValue = refresh(key, hash, loader, true);
if (newValue != null) {
return newValue;
}
}
return oldValue;
}如果不在更新，且達到了刷新閾值，就會refresh。
/**
* Refreshes the value associated with {@code key}, unless another thread is already doing so.
* Returns the newly refreshed value associated with {@code key} if it was refreshed inline, or
* {@code null} if another thread is performing the refresh or if an error occurs during
* refresh.
*/
@NullableDecl
V refresh(K key, int hash, CacheLoader<? super K, V> loader, boolean checkTime) {
final LoadingValueReference<K, V> loadingValueReference =
insertLoadingValueReference(key, hash, checkTime);
if (loadingValueReference == null) {
return null;
}
ListenableFuture<V> result = loadAsync(key, hash, loadingValueReference, loader);
if (result.isDone()) {
try {
return Uninterruptibles.getUninterruptibly(result);
} catch (Throwable t) {
// don't let refresh exceptions propagate; error was already logged
}
}
return null;
} ListenableFuture<V> loadAsync(
final K key,
final int hash,
final LoadingValueReference<K, V> loadingValueReference,
CacheLoader<? super K, V> loader) {
final ListenableFuture<V> loadingFuture = loadingValueReference.loadFuture(key, loader);
loadingFuture.addListener(
new Runnable() {
@Override
public void run() {
try {
getAndRecordStats(key, hash, loadingValueReference, loadingFuture);
} catch (Throwable t) {
logger.log(Level.WARNING, "Exception thrown during refresh", t);
loadingValueReference.setException(t);
}
}
},
directExecutor());
return loadingFuture;
}這裏很關鍵的是loadingValueReference的創建。guava由於需要的支持引用級別的過期，所以自己封了reference。loadingRef也是其中之一。該ref的特徵是isLoading方法返回true。
下面看該ref的創建：
/**
* Returns a newly inserted {@code LoadingValueReference}, or null if the live value reference
* is already loading.
*/
@NullableDecl
LoadingValueReference<K, V> insertLoadingValueReference(
final K key, final int hash, boolean checkTime) {
ReferenceEntry<K, V> e = null;
lock();
try {
long now = map.ticker.read();
preWriteCleanup(now);
AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
int index = hash & (table.length() - 1);
ReferenceEntry<K, V> first = table.get(index);
// Look for an existing entry.
for (e = first; e != null; e = e.getNext()) {
K entryKey = e.getKey();
if (e.getHash() == hash
&& entryKey != null
&& map.keyEquivalence.equivalent(key, entryKey)) {
// We found an existing entry.
ValueReference<K, V> valueReference = e.getValueReference();
if (valueReference.isLoading()
|| (checkTime && (now - e.getWriteTime() < map.refreshNanos))) {
// refresh is a no-op if loading is pending
// if checkTime, we want to check *after* acquiring the lock if refresh still needs
// to be scheduled
return null;
}
// continue returning old value while loading
++modCount;
LoadingValueReference<K, V> loadingValueReference =
new LoadingValueReference<>(valueReference);
e.setValueReference(loadingValueReference);
return loadingValueReference;
}
}
++modCount;
LoadingValueReference<K, V> loadingValueReference = new LoadingValueReference<>();
e = newEntry(key, hash, first);
e.setValueReference(loadingValueReference);
table.set(index, e);
return loadingValueReference;
} finally {
unlock();
postWriteCleanup();
}
}先上鎖，所以只有第一個refresh的線程才能進來，所做的就是查找到原來的entry，然後把它替換爲這裏的loadingEntry。結束以後，釋放鎖，第二個線程進來，檢測到loading尾true，就會返回null。所以只有第一個線程有機會去refresh值。
然後會到refresh函數當中，所有loadingValueReference爲null的都是沒有第一個獲取到鎖的，所以會返回原值（髒讀）。只有第一個獲取到鎖的會返回load的新值。
最後這個新值會被set到cache中，具體代碼在loadAsync中，這裏就不貼了。
所以，對於refresh，最重要的特徵是：併發的許多線程檢測到需要refresh，只有第一個會去load值，其餘的會返回原值。
這就是entry的value沒有過期的情況。之前說了如果過期了，會調用如下方法load值：
V lockedGetOrLoad(K key, int hash, CacheLoader<? super K, V> loader) throws ExecutionException {
ReferenceEntry<K, V> e;
ValueReference<K, V> valueReference = null;
LoadingValueReference<K, V> loadingValueReference = null;
boolean createNewEntry = true;
lock();
try {
// re-read ticker once inside the lock
long now = map.ticker.read();
preWriteCleanup(now);
int newCount = this.count - 1;
AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
int index = hash & (table.length() - 1);
ReferenceEntry<K, V> first = table.get(index);
for (e = first; e != null; e = e.getNext()) {
K entryKey = e.getKey();
if (e.getHash() == hash
&& entryKey != null
&& map.keyEquivalence.equivalent(key, entryKey)) {
valueReference = e.getValueReference();
if (valueReference.isLoading()) {
createNewEntry = false;
} else {
V value = valueReference.get();
if (value == null) {
enqueueNotification(
entryKey, hash, value, valueReference.getWeight(), RemovalCause.COLLECTED);
} else if (map.isExpired(e, now)) {
// This is a duplicate check, as preWriteCleanup already purged expired
// entries, but let's accomodate an incorrect expiration queue.
enqueueNotification(
entryKey, hash, value, valueReference.getWeight(), RemovalCause.EXPIRED);
} else {
recordLockedRead(e, now);
statsCounter.recordHits(1);
// we were concurrent with loading; don't consider refresh
return value;
}
// immediately reuse invalid entries
writeQueue.remove(e);
accessQueue.remove(e);
this.count = newCount; // write-volatile
}
break;
}
}
if (createNewEntry) {
loadingValueReference = new LoadingValueReference<>();
if (e == null) {
e = newEntry(key, hash, first);
e.setValueReference(loadingValueReference);
table.set(index, e);
} else {
e.setValueReference(loadingValueReference);
}
}
} finally {
unlock();
postWriteCleanup();
}
if (createNewEntry) {
try {
// Synchronizes on the entry to allow failing fast when a recursive load is
// detected. This may be circumvented when an entry is copied, but will fail fast most
// of the time.
synchronized (e) {
return loadSync(key, hash, loadingValueReference, loader);
}
} finally {
statsCounter.recordMisses(1);
}
} else {
// The entry already exists. Wait for loading.
return waitForLoadingValue(e, key, valueReference);
}
} V waitForLoadingValue(ReferenceEntry<K, V> e, K key, ValueReference<K, V> valueReference)
throws ExecutionException {
if (!valueReference.isLoading()) {
throw new AssertionError();
}
checkState(!Thread.holdsLock(e), "Recursive load of: %s", key);
// don't consider expiration as we're concurrent with loading
try {
V value = valueReference.waitForValue();
if (value == null) {
throw new InvalidCacheLoadException("CacheLoader returned null for key " + key + ".");
}
// re-read ticker now that loading has completed
long now = map.ticker.read();
recordRead(e, now);
return value;
} finally {
statsCounter.recordMisses(1);
}
}static class LoadingValueReference<K, V> implements ValueReference<K, V> {
volatile ValueReference<K, V> oldValue;
// TODO(fry): rename get, then extend AbstractFuture instead of containing SettableFuture
final SettableFuture<V> futureValue = SettableFuture.create();
final Stopwatch stopwatch = Stopwatch.createUnstarted();
public LoadingValueReference() {
this(null);
}
public LoadingValueReference(ValueReference<K, V> oldValue) {
this.oldValue = (oldValue == null) ? LocalCache.<K, V>unset() : oldValue;
}
@Override
public boolean isLoading() {
return true;
}
@Override
public boolean isActive() {
return oldValue.isActive();
}
@Override
public int getWeight() {
return oldValue.getWeight();
}
public boolean set(@NullableDecl V newValue) {
return futureValue.set(newValue);
}
public boolean setException(Throwable t) {
return futureValue.setException(t);
}
private ListenableFuture<V> fullyFailedFuture(Throwable t) {
return Futures.immediateFailedFuture(t);
}
@Override
public void notifyNewValue(@NullableDecl V newValue) {
if (newValue != null) {
// The pending load was clobbered by a manual write.
// Unblock all pending gets, and have them return the new value.
set(newValue);
} else {
// The pending load was removed. Delay notifications until loading completes.
oldValue = unset();
}
// TODO(fry): could also cancel loading if we had a handle on its future
}
public ListenableFuture<V> loadFuture(K key, CacheLoader<? super K, V> loader) {
try {
stopwatch.start();
V previousValue = oldValue.get();
if (previousValue == null) {
V newValue = loader.load(key);
return set(newValue) ? futureValue : Futures.immediateFuture(newValue);
}
ListenableFuture<V> newValue = loader.reload(key, previousValue);
if (newValue == null) {
return Futures.immediateFuture(null);
}
// To avoid a race, make sure the refreshed value is set into loadingValueReference
// *before* returning newValue from the cache query.
return transform(
newValue,
new com.google.common.base.Function<V, V>() {
@Override
public V apply(V newValue) {
LoadingValueReference.this.set(newValue);
return newValue;
}
},
directExecutor());
} catch (Throwable t) {
ListenableFuture<V> result = setException(t) ? futureValue : fullyFailedFuture(t);
if (t instanceof InterruptedException) {
Thread.currentThread().interrupt();
}
return result;
}
}
public V compute(K key, BiFunction<? super K, ? super V, ? extends V> function) {
stopwatch.start();
V previousValue;
try {
previousValue = oldValue.waitForValue();
} catch (ExecutionException e) {
previousValue = null;
}
V newValue = function.apply(key, previousValue);
this.set(newValue);
return newValue;
}
public long elapsedNanos() {
return stopwatch.elapsed(NANOSECONDS);
}
@Override
public V waitForValue() throws ExecutionException {
return getUninterruptibly(futureValue);
}
@Override
public V get() {
return oldValue.get();
}
public ValueReference<K, V> getOldValue() {
return oldValue;
}
@Override
public ReferenceEntry<K, V> getEntry() {
return null;
}
@Override
public ValueReference<K, V> copyFor(
ReferenceQueue<V> queue, @NullableDecl V value, ReferenceEntry<K, V> entry) {
return this;
}
}具體使用時，我們可以當refresh週期小於expire週期，這樣可以防止緩存雪崩。