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！