用guava快速打造两级缓存能力
首先，咱们都有一共识，即可以使用缓存来提升系统的访问速度！
现如今，分布式缓存这么强大，所以，大部分时候，我们可能都不会去关注本地缓存了！
而在一起高并发的场景，如果我们一味使用nosql式的缓存，如 redis, 那么也是好的吧！
但是有个问题我们得考虑下： redis 这样的缓存是快，但是它总有自己的瓶颈吧，如果什么东西我们都往里面存储，则在高并发场景下，应用瓶颈将受限于其缓存瓶颈吧！
所以，针对这种问题，在一些场景下，咱们可以使用本地缓存来存储一些数据，从而避免每次都将请求击穿到 redis 层面！
本文考虑的是使用 本地缓存 作为二级缓存存在，而非直接的充当缓存工具！
而使用本地缓存，则有几个讲究：
1. 缓存一致性问题;
2. 并发安全问题;
所谓缓存一致性问题，就是本地缓存，是否和redis等缓存中间件的数据保持一致，如果不一致的表现超过了可接受的程度，则要这访问速度也就没啥意义了！
所谓并发安全问题，即是，当使用本地缓存时，本地的缓存访问线程安全性问题，如果出现错乱情况，则严重了！
使用本地缓存，有什么好处？
1. 减少访问远程缓存的网络io,速度自然是要提升的;
2. 减少远程缓存的并发请求，从而表现出更大的并发处理能力;
本地缓存，都有什么应用场景？
1. 单机部署的应用咱们就不说了;
2. 读多写少的场景;(这也缓存的应用场景)
3. 可以容忍一定时间内的缓存不一致; (因涉及的本地缓存，分布式机器结果必可能不一致)
4. 应用对缓存的请求量非常大的场景; (如果直接打到redis缓存, 则redis压力巨大, 且应用响应速度将变慢)
所以，如果自己存在这样的使用场景，不防也考虑下，如何使用这本地缓存，来提升响应速度吧！
如果要求自己来实现这两级缓存功能，我想应该也是不能的！只要解决掉两个问题即可:
1. 缓存过期策略;
2. 缓存安全性;
其中一最简单直接的方式，就是使用一个定时刷新缓存的线程，在时间节点到达后，将缓存删除即可; 另一个安全问题，则可以使用 synchronized 或 并发包下的锁工具来实现即可。
但是真正做起来，可能不一定会简单，这也不是咱们想特别考虑的。
咱们主要看下 guava 是如何来解决这种问题的？以其思路来开拓自己的设想！
如何 guava 来作为我们的二级缓存？
1. 首先，咱们得引入 guava 的依赖
<dependency> <groupId>com.google.guava</groupId> <artifactId>guava</artifactId> <version>18.0</version> </dependency>
2. 创建guava缓存;
首先，创建的guava缓存实例，应是全局共用的，否则就失去了缓存的意义;
其次，一些过期参数，应支持配置化；
例子如下：
@Component @Slf4j public class LocalEnhancedCacheHolder { @Value("${guava.cache.max.size}") private Integer maxCacheSize; @Value("${guava.cache.timeout}") private Integer guavaCacheTimeout; /** * 字符串类型取值, k->v 只支持字符串存取，避免复杂化 */ private LoadingCache<String, String> stringDbCacheContainer; /** * hash 数据缓存, 展示使用多key 作为 guava 的缓存key */ private LoadingCache<HashDbItemEntry, byte[]> hashDbCacheContainer; @Resource private RedisTemplate redisTemplate; /** * 值为空 字符串标识 */ public static final String EMPTY_VALUE_STRING = ""; /** * 值为空 字节标识 */ public static final byte[] EMPTY_VALUE_BYTES = new byte[0]; @PostConstruct public void init() { Integer dbCount = 2; stringDbCacheContainer = CacheBuilder.newBuilder() .expireAfterWrite(guavaCacheTimeout, TimeUnit.SECONDS) .maximumSize(maxCacheSize) .build(new CacheLoader<String, String>() { @Override public String load(String key) throws Exception { log.info("【缓存】从redis获取配置:{}", key); String value = redisTemplate.get(key); return StringUtils.defaultIfBlank(value, EMPTY_VALUE_STRING); } }); hashDbCacheContainer = CacheBuilder.newBuilder() .expireAfterWrite(guavaCacheTimeout, TimeUnit.SECONDS) .maximumSize(maxCacheSize / dbCount) .build(new CacheLoader<HashDbItemEntry, byte[]>() { @Override public byte[] load(HashDbItemEntry keyHolder) throws Exception { log.info("【缓存】从redis获取配置:{}", keyHolder); byte[] valueBytes = redisTemplate.hgetValue( keyHolder.getBucketKey(), keyHolder.getSlotKey()); if(valueBytes == null) { valueBytes = EMPTY_VALUE_BYTES; } return valueBytes; } }); } /** * 获取k-v中的缓存值 * * @param key 键 * @return 缓存值，没有值时，返回 null */ public String getCache(String key) { try { return stringDbCacheContainer.get(key); } catch (ExecutionException e) { log.error("【缓存】获取缓存异常:{}, ex:{}", key, e); throw new RuntimeException(e); } } /** * 放入缓存，此处暂只实现为向redis写入值 * * @param key 缓存key * @param value 缓存value */ public void putCache(String key, String value) { redisTemplate.set(key, value, 0L); } /** * 放入缓存带超时时间设置，此处暂只实现为向redis写入值 * * @param key 缓存key * @param value 缓存value * @param timeout 超时时间，单位 s */ public void putCache(String key, String value, Long timeout) { redisTemplate.set(key, value, timeout); } /** * 删除单个kv缓存 * * @param key 缓存键 */ public void removeCache(String key) { redisTemplate.remove(key); } /** * 批量删除单个kv缓存 * * @param keyList 缓存键 列表，以 管道形式删除，性能更高 */ public void removeCache(Collection<String> keyList) { redisTemplate.remove(keyList); } /** * 从hash数据库中获取缓存值 * * @param bucketKey 桶key, 对应一系列值 k->v * @param slotKey 槽key, 对应具体的缓存值 * @return 缓存值 */ public byte[] getCacheFromHash(String bucketKey, String slotKey) { HashDbItemEntry entry = new HashDbItemEntry(bucketKey, slotKey); try { return hashDbCacheContainer.get(entry); } catch (ExecutionException e) { log.error("【缓存】获取缓存异常:{}, ex:{}", entry, e); throw new RuntimeException(e); } } /** * hash 数据结构存储 * * value 暂不存储相应值，只做查询使用 */ class HashDbItemEntry { private String bucketKey; private String slotKey; private Object value; public HashDbItemEntry(String bucketKey, String slotKey) { this.bucketKey = bucketKey; this.slotKey = slotKey; } public String getBucketKey() { return bucketKey; } public String getSlotKey() { return slotKey; } public Object getValue() { return value; } // 必重写 equals & hashCode, 否则缓存将无法复用 @Override public boolean equals(Object o) { if (this == o) return true; if (o == null || getClass() != o.getClass()) return false; HashDbItemEntry that = (HashDbItemEntry) o; return Objects.equals(bucketKey, that.bucketKey) && Objects.equals(slotKey, that.slotKey) && Objects.equals(value, that.value); } @Override public int hashCode() { return Objects.hash(bucketKey, slotKey, value); } @Override public String toString() { return "HashDbItemEntry{" + "bucketKey='" + bucketKey + '\'' + ", slotKey='" + slotKey + '\'' + ", value=" + value + '}'; } } }
如上例子，展示了两种缓存，一种是 简单的 string -> string 的缓存, 另一种是 (string, string) -> byte[] 的缓存; 不管怎么样，只是想说明，缓存的方式有多种！
我们就以简单的 string -> string 来说明吧！
stringDbCacheContainer = CacheBuilder.newBuilder() .expireAfterWrite(guavaCacheTimeout, TimeUnit.SECONDS) .maximumSize(maxCacheSize) .build(new CacheLoader<String, String>() { @Override public String load(String key) throws Exception { log.info("【缓存】从redis获取配置:{}", key); String value = redisTemplate.get(key); return StringUtils.defaultIfBlank(value, EMPTY_VALUE_STRING); } });
如上，咱们创建了一个缓存容器，它的最大容量是 maxCacheSize, 且每个key将在 guavaCacheTimeout 后过期, 过期后将从 redisTemplate 中获取数据！
如上，一个完整的 两级缓存组件就完成了，你大可以直接在项目进行相应的操作了！是不是很简单？
深入理解guava 二级缓存原理？
1. CacheBuilder 是如何创建的？
@GwtCompatible(emulated = true) public final class CacheBuilder<K, V> { private static final int DEFAULT_INITIAL_CAPACITY = 16; private static final int DEFAULT_CONCURRENCY_LEVEL = 4; private static final int DEFAULT_EXPIRATION_NANOS = 0; private static final int DEFAULT_REFRESH_NANOS = 0; static final Supplier<? extends StatsCounter> NULL_STATS_COUNTER = Suppliers.ofInstance( new StatsCounter() { @Override public void recordHits(int count) {} @Override public void recordMisses(int count) {} @Override public void recordLoadSuccess(long loadTime) {} @Override public void recordLoadException(long loadTime) {} @Override public void recordEviction() {} @Override public CacheStats snapshot() { return EMPTY_STATS; } }); static final CacheStats EMPTY_STATS = new CacheStats(0, 0, 0, 0, 0, 0); static final Supplier<StatsCounter> CACHE_STATS_COUNTER = new Supplier<StatsCounter>() { @Override public StatsCounter get() { return new SimpleStatsCounter(); } }; enum NullListener implements RemovalListener<Object, Object> { INSTANCE; @Override public void onRemoval(RemovalNotification<Object, Object> notification) {} } enum OneWeigher implements Weigher<Object, Object> { INSTANCE; @Override public int weigh(Object key, Object value) { return 1; } } static final Ticker NULL_TICKER = new Ticker() { @Override public long read() { return 0; } }; private static final Logger logger = Logger.getLogger(CacheBuilder.class.getName()); static final int UNSET_INT = -1; boolean strictParsing = true; int initialCapacity = UNSET_INT; int concurrencyLevel = UNSET_INT; long maximumSize = UNSET_INT; long maximumWeight = UNSET_INT; Weigher<? super K, ? super V> weigher; Strength keyStrength; Strength valueStrength; long expireAfterWriteNanos = UNSET_INT; long expireAfterAccessNanos = UNSET_INT; long refreshNanos = UNSET_INT; Equivalence<Object> keyEquivalence; Equivalence<Object> valueEquivalence; RemovalListener<? super K, ? super V> removalListener; Ticker ticker; Supplier<? extends StatsCounter> statsCounterSupplier = NULL_STATS_COUNTER; // TODO(fry): make constructor private and update tests to use newBuilder CacheBuilder() {} /** * Constructs a new {@code CacheBuilder} instance with default settings, including strong keys, * strong values, and no automatic eviction of any kind. */ public static CacheBuilder<Object, Object> newBuilder() { return new CacheBuilder<Object, Object>(); } /** * Sets the minimum total size for the internal hash tables. For example, if the initial capacity * is {@code 60}, and the concurrency level is {@code 8}, then eight segments are created, each * having a hash table of size eight. Providing a large enough estimate at construction time * avoids the need for expensive resizing operations later, but setting this value unnecessarily * high wastes memory. * * @throws IllegalArgumentException if {@code initialCapacity} is negative * @throws IllegalStateException if an initial capacity was already set */ public CacheBuilder<K, V> initialCapacity(int initialCapacity) { checkState(this.initialCapacity == UNSET_INT, "initial capacity was already set to %s", this.initialCapacity); checkArgument(initialCapacity >= 0); this.initialCapacity = initialCapacity; return this; } int getInitialCapacity() { return (initialCapacity == UNSET_INT) ? DEFAULT_INITIAL_CAPACITY : initialCapacity; } /** * Guides the allowed concurrency among update operations. Used as a hint for internal sizing. The * table is internally partitioned to try to permit the indicated number of concurrent updates * without contention. Because assignment of entries to these partitions is not necessarily * uniform, the actual concurrency observed may vary. Ideally, you should choose a value to * accommodate as many threads as will ever concurrently modify the table. Using a significantly * higher value than you need can waste space and time, and a significantly lower value can lead * to thread contention. But overestimates and underestimates within an order of magnitude do not * usually have much noticeable impact. A value of one permits only one thread to modify the cache * at a time, but since read operations and cache loading computations can proceed concurrently, * this still yields higher concurrency than full synchronization. * * <p> Defaults to 4. <b>Note:</b>The default may change in the future. If you care about this * value, you should always choose it explicitly. * * <p>The current implementation uses the concurrency level to create a fixed number of hashtable * segments, each governed by its own write lock. The segment lock is taken once for each explicit * write, and twice for each cache loading computation (once prior to loading the new value, * and once after loading completes). Much internal cache management is performed at the segment * granularity. For example, access queues and write queues are kept per segment when they are * required by the selected eviction algorithm. As such, when writing unit tests it is not * uncommon to specify {@code concurrencyLevel(1)} in order to achieve more deterministic eviction * behavior. * * <p>Note that future implementations may abandon segment locking in favor of more advanced * concurrency controls. * * @throws IllegalArgumentException if {@code concurrencyLevel} is nonpositive * @throws IllegalStateException if a concurrency level was already set */ public CacheBuilder<K, V> concurrencyLevel(int concurrencyLevel) { checkState(this.concurrencyLevel == UNSET_INT, "concurrency level was already set to %s", this.concurrencyLevel); checkArgument(concurrencyLevel > 0); this.concurrencyLevel = concurrencyLevel; return this; } int getConcurrencyLevel() { return (concurrencyLevel == UNSET_INT) ? DEFAULT_CONCURRENCY_LEVEL : concurrencyLevel; } /** * Specifies the maximum number of entries the cache may contain. Note that the cache <b>may evict * an entry before this limit is exceeded</b>. As the cache size grows close to the maximum, the * cache evicts entries that are less likely to be used again. For example, the cache may evict an * entry because it hasn't been used recently or very often. * * <p>When {@code size} is zero, elements will be evicted immediately after being loaded into the * cache. This can be useful in testing, or to disable caching temporarily without a code change. * * <p>This feature cannot be used in conjunction with {@link #maximumWeight}. * * @param size the maximum size of the cache * @throws IllegalArgumentException if {@code size} is negative * @throws IllegalStateException if a maximum size or weight was already set */ public CacheBuilder<K, V> maximumSize(long size) { checkState(this.maximumSize == UNSET_INT, "maximum size was already set to %s", this.maximumSize); checkState(this.maximumWeight == UNSET_INT, "maximum weight was already set to %s", this.maximumWeight); checkState(this.weigher == null, "maximum size can not be combined with weigher"); checkArgument(size >= 0, "maximum size must not be negative"); this.maximumSize = size; return this; } /** * Specifies the maximum weight of entries the cache may contain. Weight is determined using the * {@link Weigher} specified with {@link #weigher}, and use of this method requires a * corresponding call to {@link #weigher} prior to calling {@link #build}. * * <p>Note that the cache <b>may evict an entry before this limit is exceeded</b>. As the cache * size grows close to the maximum, the cache evicts entries that are less likely to be used * again. For example, the cache may evict an entry because it hasn't been used recently or very * often. * * <p>When {@code weight} is zero, elements will be evicted immediately after being loaded into * cache. This can be useful in testing, or to disable caching temporarily without a code * change. * * <p>Note that weight is only used to determine whether the cache is over capacity; it has no * effect on selecting which entry should be evicted next. * * <p>This feature cannot be used in conjunction with {@link #maximumSize}. * * @param weight the maximum total weight of entries the cache may contain * @throws IllegalArgumentException if {@code weight} is negative * @throws IllegalStateException if a maximum weight or size was already set * @since 11.0 */ @GwtIncompatible("To be supported") public CacheBuilder<K, V> maximumWeight(long weight) { checkState(this.maximumWeight == UNSET_INT, "maximum weight was already set to %s", this.maximumWeight); checkState(this.maximumSize == UNSET_INT, "maximum size was already set to %s", this.maximumSize); this.maximumWeight = weight; checkArgument(weight >= 0, "maximum weight must not be negative"); return this; } /** * Specifies the weigher to use in determining the weight of entries. Entry weight is taken * into consideration by {@link #maximumWeight(long)} when determining which entries to evict, and * use of this method requires a corresponding call to {@link #maximumWeight(long)} prior to * calling {@link #build}. Weights are measured and recorded when entries are inserted into the * cache, and are thus effectively static during the lifetime of a cache entry. * * <p>When the weight of an entry is zero it will not be considered for size-based eviction * (though it still may be evicted by other means). * * <p><b>Important note:</b> Instead of returning <em>this</em> as a {@code CacheBuilder} * instance, this method returns {@code CacheBuilder<K1, V1>}. From this point on, either the * original reference or the returned reference may be used to complete configuration and build * the cache, but only the "generic" one is type-safe. That is, it will properly prevent you from * building caches whose key or value types are incompatible with the types accepted by the * weigher already provided; the {@code CacheBuilder} type cannot do this. For best results, * simply use the standard method-chaining idiom, as illustrated in the documentation at top, * configuring a {@code CacheBuilder} and building your {@link Cache} all in a single statement. * * <p><b>Warning:</b> if you ignore the above advice, and use this {@code CacheBuilder} to build * a cache whose key or value type is incompatible with the weigher, you will likely experience * a {@link ClassCastException} at some <i>undefined</i> point in the future. * * @param weigher the weigher to use in calculating the weight of cache entries * @throws IllegalArgumentException if {@code size} is negative * @throws IllegalStateException if a maximum size was already set * @since 11.0 */ @GwtIncompatible("To be supported") public <K1 extends K, V1 extends V> CacheBuilder<K1, V1> weigher( Weigher<? super K1, ? super V1> weigher) { checkState(this.weigher == null); if (strictParsing) { checkState(this.maximumSize == UNSET_INT, "weigher can not be combined with maximum size", this.maximumSize); } // safely limiting the kinds of caches this can produce @SuppressWarnings("unchecked") CacheBuilder<K1, V1> me = (CacheBuilder<K1, V1>) this; me.weigher = checkNotNull(weigher); return me; } // Make a safe contravariant cast now so we don't have to do it over and over. @SuppressWarnings("unchecked") <K1 extends K, V1 extends V> Weigher<K1, V1> getWeigher() { return (Weigher<K1, V1>) MoreObjects.firstNonNull(weigher, OneWeigher.INSTANCE); } /** * Specifies that each entry should be automatically removed from the cache once a fixed duration * has elapsed after the entry's creation, or the most recent replacement of its value. * * <p>When {@code duration} is zero, this method hands off to * {@link #maximumSize(long) maximumSize}{@code (0)}, ignoring any otherwise-specificed maximum * size or weight. This can be useful in testing, or to disable caching temporarily without a code * change. * * <p>Expired entries may be counted in {@link Cache#size}, but will never be visible to read or * write operations. Expired entries are cleaned up as part of the routine maintenance described * in the class javadoc. * * @param duration the length of time after an entry is created that it should be automatically * removed * @param unit the unit that {@code duration} is expressed in * @throws IllegalArgumentException if {@code duration} is negative * @throws IllegalStateException if the time to live or time to idle was already set */ public CacheBuilder<K, V> expireAfterWrite(long duration, TimeUnit unit) { checkState(expireAfterWriteNanos == UNSET_INT, "expireAfterWrite was already set to %s ns", expireAfterWriteNanos); checkArgument(duration >= 0, "duration cannot be negative: %s %s", duration, unit); this.expireAfterWriteNanos = unit.toNanos(duration); return this; } long getExpireAfterWriteNanos() { return (expireAfterWriteNanos == UNSET_INT) ? DEFAULT_EXPIRATION_NANOS : expireAfterWriteNanos; } /** * Specifies that each entry should be automatically removed from the cache once a fixed duration * has elapsed after the entry's creation, the most recent replacement of its value, or its last * access. Access time is reset by all cache read and write operations (including * {@code Cache.asMap().get(Object)} and {@code Cache.asMap().put(K, V)}), but not by operations * on the collection-views of {@link Cache#asMap}. * * <p>When {@code duration} is zero, this method hands off to * {@link #maximumSize(long) maximumSize}{@code (0)}, ignoring any otherwise-specificed maximum * size or weight. This can be useful in testing, or to disable caching temporarily without a code * change. * * <p>Expired entries may be counted in {@link Cache#size}, but will never be visible to read or * write operations. Expired entries are cleaned up as part of the routine maintenance described * in the class javadoc. * * @param duration the length of time after an entry is last accessed that it should be * automatically removed * @param unit the unit that {@code duration} is expressed in * @throws IllegalArgumentException if {@code duration} is negative * @throws IllegalStateException if the time to idle or time to live was already set */ public CacheBuilder<K, V> expireAfterAccess(long duration, TimeUnit unit) { checkState(expireAfterAccessNanos == UNSET_INT, "expireAfterAccess was already set to %s ns", expireAfterAccessNanos); checkArgument(duration >= 0, "duration cannot be negative: %s %s", duration, unit); this.expireAfterAccessNanos = unit.toNanos(duration); return this; } long getExpireAfterAccessNanos() { return (expireAfterAccessNanos == UNSET_INT) ? DEFAULT_EXPIRATION_NANOS : expireAfterAccessNanos; } /** * Specifies that active entries are eligible for automatic refresh once a fixed duration has * elapsed after the entry's creation, or the most recent replacement of its value. The semantics * of refreshes are specified in {@link LoadingCache#refresh}, and are performed by calling * {@link CacheLoader#reload}. * * <p>As the default implementation of {@link CacheLoader#reload} is synchronous, it is * recommended that users of this method override {@link CacheLoader#reload} with an asynchronous * implementation; otherwise refreshes will be performed during unrelated cache read and write * operations. * * <p>Currently automatic refreshes are performed when the first stale request for an entry * occurs. The request triggering refresh will make a blocking call to {@link CacheLoader#reload} * and immediately return the new value if the returned future is complete, and the old value * otherwise. * * <p><b>Note:</b> <i>all exceptions thrown during refresh will be logged and then swallowed</i>. * * @param duration the length of time after an entry is created that it should be considered * stale, and thus eligible for refresh * @param unit the unit that {@code duration} is expressed in * @throws IllegalArgumentException if {@code duration} is negative * @throws IllegalStateException if the refresh interval was already set * @since 11.0 */ @Beta @GwtIncompatible("To be supported (synchronously).") public CacheBuilder<K, V> refreshAfterWrite(long duration, TimeUnit unit) { checkNotNull(unit); checkState(refreshNanos == UNSET_INT, "refresh was already set to %s ns", refreshNanos); checkArgument(duration > 0, "duration must be positive: %s %s", duration, unit); this.refreshNanos = unit.toNanos(duration); return this; } long getRefreshNanos() { return (refreshNanos == UNSET_INT) ? DEFAULT_REFRESH_NANOS : refreshNanos; } /** * Specifies a nanosecond-precision time source for use in determining when entries should be * expired. By default, {@link System#nanoTime} is used. * * <p>The primary intent of this method is to facilitate testing of caches which have been * configured with {@link #expireAfterWrite} or {@link #expireAfterAccess}. * * @throws IllegalStateException if a ticker was already set */ public CacheBuilder<K, V> ticker(Ticker ticker) { checkState(this.ticker == null); this.ticker = checkNotNull(ticker); return this; } Ticker getTicker(boolean recordsTime) { if (ticker != null) { return ticker; } return recordsTime ? Ticker.systemTicker() : NULL_TICKER; } /** * Specifies a listener instance that caches should notify each time an entry is removed for any * {@linkplain RemovalCause reason}. Each cache created by this builder will invoke this listener * as part of the routine maintenance described in the class documentation above. * * <p><b>Warning:</b> after invoking this method, do not continue to use <i>this</i> cache * builder reference; instead use the reference this method <i>returns</i>. At runtime, these * point to the same instance, but only the returned reference has the correct generic type * information so as to ensure type safety. For best results, use the standard method-chaining * idiom illustrated in the class documentation above, configuring a builder and building your * cache in a single statement. Failure to heed this advice can result in a {@link * ClassCastException} being thrown by a cache operation at some <i>undefined</i> point in the * future. * * <p><b>Warning:</b> any exception thrown by {@code listener} will <i>not</i> be propagated to * the {@code Cache} user, only logged via a {@link Logger}. * * @return the cache builder reference that should be used instead of {@code this} for any * remaining configuration and cache building * @throws IllegalStateException if a removal listener was already set */ @CheckReturnValue public <K1 extends K, V1 extends V> CacheBuilder<K1, V1> removalListener( RemovalListener<? super K1, ? super V1> listener) { checkState(this.removalListener == null); // safely limiting the kinds of caches this can produce @SuppressWarnings("unchecked") CacheBuilder<K1, V1> me = (CacheBuilder<K1, V1>) this; me.removalListener = checkNotNull(listener); return me; } // Make a safe contravariant cast now so we don't have to do it over and over. @SuppressWarnings("unchecked") <K1 extends K, V1 extends V> RemovalListener<K1, V1> getRemovalListener() { return (RemovalListener<K1, V1>) MoreObjects.firstNonNull(removalListener, NullListener.INSTANCE); } /** * Enable the accumulation of {@link CacheStats} during the operation of the cache. Without this * {@link Cache#stats} will return zero for all statistics. Note that recording stats requires * bookkeeping to be performed with each operation, and thus imposes a performance penalty on * cache operation. * * @since 12.0 (previously, stats collection was automatic) */ public CacheBuilder<K, V> recordStats() { statsCounterSupplier = CACHE_STATS_COUNTER; return this; } boolean isRecordingStats() { return statsCounterSupplier == CACHE_STATS_COUNTER; } Supplier<? extends StatsCounter> getStatsCounterSupplier() { return statsCounterSupplier; } /** * Builds a cache, which either returns an already-loaded value for a given key or atomically * computes or retrieves it using the supplied {@code CacheLoader}. If another thread is currently * loading the value for this key, simply waits for that thread to finish and returns its * loaded value. Note that multiple threads can concurrently load values for distinct keys. * * <p>This method does not alter the state of this {@code CacheBuilder} instance, so it can be * invoked again to create multiple independent caches. * * @param loader the cache loader used to obtain new values * @return a cache having the requested features */ public <K1 extends K, V1 extends V> LoadingCache<K1, V1> build( CacheLoader<? super K1, V1> loader) { checkWeightWithWeigher(); return new LocalCache.LocalLoadingCache<K1, V1>(this, loader); } /** * Builds a cache which does not automatically load values when keys are requested. * * <p>Consider {@link #build(CacheLoader)} instead, if it is feasible to implement a * {@code CacheLoader}. * * <p>This method does not alter the state of this {@code CacheBuilder} instance, so it can be * invoked again to create multiple independent caches. * * @return a cache having the requested features * @since 11.0 */ public <K1 extends K, V1 extends V> Cache<K1, V1> build() { checkWeightWithWeigher(); checkNonLoadingCache(); return new LocalCache.LocalManualCache<K1, V1>(this); } }
如上，使用建造者模式创建 LoadingCache<K, V> 缓存; 设置好 最大值，过期时间等参数;
2. 如何获取一个guava缓存？
其实就是一个get方法而已！ stringDbCacheContainer.get(key);
// com.google.common.cache.LocalCache // LoadingCache methods @Override public V get(K key) throws ExecutionException { // 两种数据来源，一是直接获取，二是调用 load() 方法加载数据 return localCache.getOrLoad(key); } // com.google.common.cache.LocalCache V getOrLoad(K key) throws ExecutionException { return get(key, defaultLoader); } V get(K key, CacheLoader<? super K, V> loader) throws ExecutionException { int hash = hash(checkNotNull(key)); // 还记得 ConcurrentHashMap 吗？ 先定位segment, 再定位 entry return segmentFor(hash).get(key, hash, loader); } Segment<K, V> segmentFor(int hash) { // TODO(fry): Lazily create segments? return segments[(hash >>> segmentShift) & segmentMask]; } // 核心取数逻辑在此get 中 // loading 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) { // 如果存在值，则依据 ticker 进行判断是否过期，从而直接返回值，具体过期逻辑稍后再说 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); } } } // 初次加载或过期之后，进入加载逻辑，重要 // 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(); } } // static class Segment<K, V> extends ReentrantLock // 整个 Segment 继承了 ReentrantLock, 所以 LocalCache 的锁是依赖于 ReentrantLock 实现的 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(); // 在更新值前，先把过期数据清除 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); // 处理 hash 碰撞时的链表查询 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, valueReference, 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, valueReference, 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; } } // 如果是第一次加载，则先创建 Entry, 进入 load() 逻辑 if (createNewEntry) { loadingValueReference = new LoadingValueReference<K, V>(); 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. // 同步加载数据源值, 从 loader 中处理 synchronized (e) { return loadSync(key, hash, loadingValueReference, loader); } } finally { // 记录未命中计数，默认为空 statsCounter.recordMisses(1); } } else { // The entry already exists. Wait for loading. // 如果有线程正在更新缓存，则等待结果即可，具体实现就是调用 Future.get() return waitForLoadingValue(e, key, valueReference); } } // 加载原始值 // at most one of loadSync.loadAsync may be called for any given LoadingValueReference V loadSync(K key, int hash, LoadingValueReference<K, V> loadingValueReference, CacheLoader<? super K, V> loader) throws ExecutionException { // loadingValueReference中保存了回调引用，加载原始值 ListenableFuture<V> loadingFuture = loadingValueReference.loadFuture(key, loader); // 存储数据入缓存，以便下次使用 return getAndRecordStats(key, hash, loadingValueReference, loadingFuture); } // 从 loader 中加载数据, public ListenableFuture<V> loadFuture(K key, CacheLoader<? super K, V> loader) { stopwatch.start(); V previousValue = oldValue.get(); try { // 如果原来没有值，则直接加载后返回 if (previousValue == null) { V newValue = loader.load(key); return set(newValue) ? futureValue : Futures.immediateFuture(newValue); } // 否则一般为无过期时间的数据进行 reload, 如果 reload() 的结果为空，则直接返回 // 须重写 reload() 实现 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 Futures.transform(newValue, new Function<V, V>() { @Override public V apply(V newValue) { LoadingValueReference.this.set(newValue); return newValue; } }); } catch (Throwable t) { if (t instanceof InterruptedException) { Thread.currentThread().interrupt(); } return setException(t) ? futureValue : fullyFailedFuture(t); } } // com.google.common.util.concurrent.Uninterruptibles /** * Waits uninterruptibly for {@code newValue} to be loaded, and then records loading stats. */ V getAndRecordStats(K key, int hash, LoadingValueReference<K, V> loadingValueReference, ListenableFuture<V> newValue) throws ExecutionException { V value = null; try { // 同步等待加载结果，注意，此处返回值不允许为null, 否则将报异常，这可能是为了规避缓存攻击漏洞吧 value = getUninterruptibly(newValue); if (value == null) { throw new InvalidCacheLoadException("CacheLoader returned null for key " + key + "."); } // 加载成功记录，此处扩展点，默认为空 statsCounter.recordLoadSuccess(loadingValueReference.elapsedNanos()); // 最后将值存入缓存容器中，返回（论hash的重要性） storeLoadedValue(key, hash, loadingValueReference, value); return value; } finally { if (value == null) { statsCounter.recordLoadException(loadingValueReference.elapsedNanos()); removeLoadingValue(key, hash, loadingValueReference); } } } /** * Invokes {@code future.}{@link Future#get() get()} uninterruptibly. * To get uninterruptibility and remove checked exceptions, see * {@link Futures#getUnchecked}. * * <p>If instead, you wish to treat {@link InterruptedException} uniformly * with other exceptions, see {@link Futures#get(Future, Class) Futures.get} * or {@link Futures#makeChecked}. * * @throws ExecutionException if the computation threw an exception * @throws CancellationException if the computation was cancelled */ public static <V> V getUninterruptibly(Future<V> future) throws ExecutionException { boolean interrupted = false; try { while (true) { try { return future.get(); } catch (InterruptedException e) { interrupted = true; } } } finally { if (interrupted) { Thread.currentThread().interrupt(); } } }
如上，就是获取一个缓存的过程。总结下来就是：
1. 先使用hash定位到 segment中，然后尝试直接到 map中获取结果；
2. 如果没有找到或者已过期，则调用客户端的load()方法加载原始数据；
3. 将结果存入 segment.map 中，本地缓存生效；
4. 记录命中情况，读取计数；
3. 如何处理过期？
其实刚刚我们在看get()方法时，就看到了一些端倪。
要确认两点: 1. 是否有创建异步清理线程进行过期数据清理？ 2. 清理过程中，原始数据如何自处？
其实guava的清理时机是在加载数据之前进行的！
// com.google.common.cache.LocalCache // static class Segment<K, V> extends ReentrantLock // 整个 Segment 继承了 ReentrantLock, 所以 LocalCache 的锁是依赖于 ReentrantLock 实现的 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(); // 在更新值前，先把过期数据清除 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); // 处理 hash 碰撞时的链表查询 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, valueReference, 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, valueReference, 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; } } // 如果是第一次加载，则先创建 Entry, 进入 load() 逻辑 if (createNewEntry) { loadingValueReference = new LoadingValueReference<K, V>(); 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. // 同步加载数据源值, 从 loader 中处理 synchronized (e) { return loadSync(key, hash, loadingValueReference, loader); } } finally { // 记录未命中计数，默认为空 statsCounter.recordMisses(1); } } else { // The entry already exists. Wait for loading. return waitForLoadingValue(e, key, valueReference); } } // 我们来细看下 preWriteCleanup(now); 是如何清理过期数据的 /** * Performs routine cleanup prior to executing a write. This should be called every time a * write thread acquires the segment lock, immediately after acquiring the lock. * * <p>Post-condition: expireEntries has been run. */ @GuardedBy("this") void preWriteCleanup(long now) { runLockedCleanup(now); } void runLockedCleanup(long now) { // 再次确保清理数据时，锁是存在的 if (tryLock()) { try { // 当存在特殊类型数据时，可以先进行清理 drainReferenceQueues(); // 清理过期数据，按时间清理 expireEntries(now); // calls drainRecencyQueue // 读计数清零 readCount.set(0); } finally { unlock(); } } } /** * Drain the key and value reference queues, cleaning up internal entries containing garbage * collected keys or values. */ @GuardedBy("this") void drainReferenceQueues() { if (map.usesKeyReferences()) { drainKeyReferenceQueue(); } if (map.usesValueReferences()) { drainValueReferenceQueue(); } } @GuardedBy("this") void expireEntries(long now) { // 更新最近的访问队列 drainRecencyQueue(); ReferenceEntry<K, V> e; // 从头部开始取元素，如果过期就进行清理 // 写队列超时: 清理, 访问队列超时: 清理 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(); } } } @Override public ReferenceEntry<K, V> peek() { ReferenceEntry<K, V> next = head.getNextInAccessQueue(); return (next == head) ? null : next; } // 清理指定类型的元素，如 过期元素 @GuardedBy("this") boolean removeEntry(ReferenceEntry<K, V> entry, int hash, RemovalCause cause) { 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 (ReferenceEntry<K, V> e = first; e != null; e = e.getNext()) { if (e == entry) { ++modCount; // 调用 removeValueFromChain, 清理具体元素 ReferenceEntry<K, V> newFirst = removeValueFromChain( first, e, e.getKey(), hash, e.getValueReference(), cause); newCount = this.count - 1; table.set(index, newFirst); this.count = newCount; // write-volatile return true; } } return false; } @GuardedBy("this") @Nullable ReferenceEntry<K, V> removeValueFromChain(ReferenceEntry<K, V> first, ReferenceEntry<K, V> entry, @Nullable K key, int hash, ValueReference<K, V> valueReference, RemovalCause cause) { enqueueNotification(key, hash, valueReference, cause); // 清理两队列 writeQueue.remove(entry); accessQueue.remove(entry); if (valueReference.isLoading()) { valueReference.notifyNewValue(null); return first; } else { return removeEntryFromChain(first, entry); } } @GuardedBy("this") @Nullable ReferenceEntry<K, V> removeEntryFromChain(ReferenceEntry<K, V> first, ReferenceEntry<K, V> entry) { int newCount = count; // 普通情况，则直接返回 next 元素链即可 // 针对有first != entry 的情况，则依次将 first 移动到队尾，然后跳到下一个元素返回 ReferenceEntry<K, V> newFirst = entry.getNext(); for (ReferenceEntry<K, V> e = first; e != entry; e = e.getNext()) { // 将first链表倒转到 newFirst 尾部 ReferenceEntry<K, V> next = copyEntry(e, newFirst); if (next != null) { newFirst = next; } else { removeCollectedEntry(e); newCount--; } } this.count = newCount; return newFirst; }
到此，我们就完整的看到了一个 key 的过期处理流程了。总结就是：
1. 在读取的时候，触发清理操作；
2. 使用 ReentrantLock 来进行线程安全的更新；
3. 读取计数器清零，元素数量减少；
3. 怎样主动放入一个缓存？
这个和普通的map的put方法一样，简单看下即可！
// com.google.common.cache.LocalCache$LocalManualCache @Override public void put(K key, V value) { localCache.put(key, value); } // com.google.common.cache.LocalCache @Override public V put(K key, V value) { checkNotNull(key); checkNotNull(value); int hash = hash(key); return segmentFor(hash).put(key, hash, value, false); } // com.google.common.cache.LocalCache$Segment @Nullable V put(K key, int hash, V value, boolean onlyIfAbsent) { lock(); try { long now = map.ticker.read(); preWriteCleanup(now); int newCount = this.count + 1; if (newCount > this.threshold) { // ensure capacity expand(); newCount = this.count + 1; } 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 (ReferenceEntry<K, V> 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(); V entryValue = valueReference.get(); if (entryValue == null) { ++modCount; if (valueReference.isActive()) { enqueueNotification(key, hash, valueReference, RemovalCause.COLLECTED); setValue(e, key, value, now); newCount = this.count; // count remains unchanged } else { setValue(e, key, value, now); newCount = this.count + 1; } this.count = newCount; // write-volatile evictEntries(); return null; } else if (onlyIfAbsent) { // Mimic // "if (!map.containsKey(key)) ... // else return map.get(key); recordLockedRead(e, now); return entryValue; } else { // clobber existing entry, count remains unchanged ++modCount; enqueueNotification(key, hash, valueReference, RemovalCause.REPLACED); setValue(e, key, value, now); evictEntries(); return entryValue; } } } // Create a new entry. ++modCount; ReferenceEntry<K, V> newEntry = newEntry(key, hash, first); setValue(newEntry, key, value, now); table.set(index, newEntry); newCount = this.count + 1; this.count = newCount; // write-volatile evictEntries(); return null; } finally { unlock(); postWriteCleanup(); } }
就这样，基于guava的二级缓存功能就搞定了。
老话：感谢你遇到的每一个bug！
不要害怕今日的苦，你要相信明天，更苦！
