CachesExplained

Caches

Example

LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
       .maximumSize(1000)
       .expireAfterWrite(10, TimeUnit.MINUTES)
       .removalListener(MY_LISTENER)
       .build(
           new CacheLoader<Key, Graph>() {
             public Graph load(Key key) throws AnyException {
               return createExpensiveGraph(key);
             }
           });

Applicability

Caches are tremendously useful in a wide variety of use cases. For example, you should consider using caches when a value is expensive to compute or retrieve, and you will need its value on a certain input more than once.

A Cache is similar to ConcurrentMap, but not quite the same. The most fundamental difference is that a ConcurrentMap persists all elements that are added to it until they are explicitly removed. A Cache on the other hand is generally configured to evict entries automatically, in order to constrain its memory footprint. In some cases a LoadingCache can be useful even if it doesn't evict entries, due to its automatic cache loading.

Generally, the Guava caching utilities are applicable whenever:

You are willing to spend some memory to improve speed.
You expect that keys will sometimes get queried more than once.
Your cache will not need to store more data than what would fit in RAM. (Guava caches are local to a single run of your application. They do not store data in files, or on outside servers. If this does not fit your needs, consider a tool like Memcached.)

If each of these apply to your use case, then the Guava caching utilities could be right for you!

Obtaining a Cache is done using the CacheBuilder builder pattern as demonstrated by the example code above, but customizing your cache is the interesting part.

Note: If you do not need the features of a Cache, ConcurrentHashMap is more memory-efficient -- but it is extremely difficult or impossible to duplicate most Cache features with any old ConcurrentMap.

Population

The first question to ask yourself about your cache is: is there some sensible default function to load or compute a value associated with a key? If so, you should use a CacheLoader. If not, or if you need to override the default, but you still want atomic "get-if-absent-compute" semantics, you should pass a Callable into a get call. Elements can be inserted directly, using Cache.put, but automatic cache loading is preferred as it makes it easier to reason about consistency across all cached content.

From a CacheLoader

A LoadingCache is a Cache built with an attached CacheLoader. Creating a CacheLoader is typically as easy as implementing the method V load(K key) throws Exception. So, for example, you could create a LoadingCache with the following code:

LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
       .maximumSize(1000)
       .build(
           new CacheLoader<Key, Graph>() {
             public Graph load(Key key) throws AnyException {
               return createExpensiveGraph(key);
             }
           });

...
try {
  return graphs.get(key);
} catch (ExecutionException e) {
  throw new OtherException(e.getCause());
}

The canonical way to query a LoadingCache is with the method get(K); however care must be taken not to callgetUncheckedon caches whoseCacheLoader`s declare checked exceptions.

LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
       .expireAfterAccess(10, TimeUnit.MINUTES)
       .build(
           new CacheLoader<Key, Graph>() {
             public Graph load(Key key) { // no checked exception
               return createExpensiveGraph(key);
             }
           });

...
return graphs.getUnchecked(key);

Bulk lookups can be performed with the method getAll(Iterable<? extends K>). By default, getAll will issue a a separate call to CacheLoader.load for each key which is absent from the cache. When bulk retrieval is more efficient than many individual lookups, you can override CacheLoader.loadAll to exploit this. The performance of getAll(Iterable) will improve accordingly.

Note that you can write a CacheLoader.loadAll implementation that loads values for keys that were not specifically requested. For example, if computing the value of any key from some group gives you the value for all keys in the group, loadAll might load the rest of the group at the same time.

From a Callable

All Guava caches, loading or not, support the method get(K, Callable<V>). This method returns the value associated with the key in the cache, or computes it from the specified Callable and adds it to the cache. No observable state associated with this cache is modified until loading completes. This method provides a simple substitute for the conventional "if cached, return; otherwise create, cache and return" pattern.

Cache<Key, Value> cache = CacheBuilder.newBuilder()
    .maximumSize(1000)
    .build(); // look Ma, no CacheLoader
...
try {
  // If the key wasn't in the "easy to compute" group, we need to
  // do things the hard way.
  cache.get(key, new Callable<Value>() {
    @Override
    public Value call() throws AnyException {
      return doThingsTheHardWay(key);
    }
  });
} catch (ExecutionException e) {
  throw new OtherException(e.getCause());
}

Inserted Directly

Values may be inserted into the cache directly with cache.put(key, value). This overwrites any previous entry in the cache for the specified key. Changes can also be made to a cache using any of the ConcurrentMap methods exposed by the Cache.asMap() view. Note that no method on the asMap view will ever cause entries to be automatically loaded into the cache. Further, the atomic operations on that view operate outside the scope of automatic cache loading, so Cache.get(K, Callable<V>) should always be preferred over Cache.asMap().putIfAbsent in caches which load values using either CacheLoader or Callable.

Eviction

The cold hard reality is that we almost certainly don't have enough memory to cache everything we could cache. You must decide: when is it not worth keeping a cache entry? Guava provides three basic types of eviction: size-based eviction, time-based eviction, and reference-based eviction.

Size-based Eviction

If your cache should not grow beyond a certain size, just use CacheBuilder.maximumSize(long). The cache will try to evict entries that haven't been used recently or very often. Warning: the cache may evict entries before this limit is exceeded -- typically when the cache size is approaching the limit.

Alternately, if different cache entries have different "weights" -- for example, if your cache values have radically different memory footprints -- you may specify a weight function with CacheBuilder.weigher(Weigher) and a maximum cache weight with CacheBuilder.maximumWeight(long). In addition to the same caveats as maximumSize requires, be aware that weights are computed at entry creation time, and are static thereafter.

LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
       .maximumWeight(100000)
       .weigher(new Weigher<Key, Graph>() {
          public int weigh(Key k, Graph g) {
            return g.vertices().size();
          }
        })
       .build(
           new CacheLoader<Key, Graph>() {
             public Graph load(Key key) { // no checked exception
               return createExpensiveGraph(key);
             }
           });

Timed Eviction

CacheBuilder provides two approaches to timed eviction:

expireAfterAccess(long, TimeUnit) Only expire entries after the specified duration has passed since the entry was last accessed by a read or a write. Note that the order in which entries are evicted will be similar to that of [size-based eviction].
[expireAfterWrite(long, TimeUnit)] Expire entries after the specified duration has passed since the entry was created, or the most recent replacement of the value. This could be desirable if cached data grows stale after a certain amount of time.

Timed expiration is performed with periodic maintenance during writes and occasionally during reads, as discussed below.

Testing Timed Eviction

Testing timed eviction doesn't have to be painful...and doesn't actually have to take you two seconds to test a two-second expiration. Use the [Ticker] interface and the [CacheBuilder.ticker(Ticker)] method to specify a time source in your cache builder, rather than having to wait for the system clock.

Reference-based Eviction

Guava allows you to set up your cache to allow the garbage collection of entries, by using [weak references] for keys or values, and by using [soft references] for values.

[CacheBuilder.weakKeys()] stores keys using weak references. This allows entries to be garbage-collected if there are no other (strong or soft) references to the keys. Since garbage collection depends only on identity equality, this causes the whole cache to use identity (==) equality to compare keys, instead of equals().
[CacheBuilder.weakValues()] stores values using weak references. This allows entries to be garbage-collected if there are no other (strong or soft) references to the values. Since garbage collection depends only on identity equality, this causes the whole cache to use identity (==) equality to compare values, instead of equals().
[CacheBuilder.softValues()] wraps values in soft references. Softly referenced objects are garbage-collected in a globally least-recently-used manner, in response to memory demand. Because of the performance implications of using soft references, we generally recommend using the more predictable [maximum cache size][size-based eviction] instead. Use of softValues() will cause values to be compared using identity (==) equality instead of equals().

Explicit Removals

At any time, you may explicitly invalidate cache entries rather than waiting for entries to be evicted. This can be done:

individually, using [Cache.invalidate(key)]
in bulk, using [Cache.invalidateAll(keys)]
to all entries, using [Cache.invalidateAll()]

Removal Listeners

You may specify a removal listener for your cache to perform some operation when an entry is removed, via [CacheBuilder.removalListener(RemovalListener)]. The [RemovalListener] gets passed a [RemovalNotification], which specifies the [RemovalCause], key, and value.

Note that any exceptions thrown by the RemovalListener are logged (using Logger) and swallowed.

CacheLoader<Key, DatabaseConnection> loader = new CacheLoader<Key, DatabaseConnection> () {
  public DatabaseConnection load(Key key) throws Exception {
    return openConnection(key);
  }
};
RemovalListener<Key, DatabaseConnection> removalListener = new RemovalListener<Key, DatabaseConnection>() {
  public void onRemoval(RemovalNotification<Key, DatabaseConnection> removal) {
    DatabaseConnection conn = removal.getValue();
    conn.close(); // tear down properly
  }
};

return CacheBuilder.newBuilder()
  .expireAfterWrite(2, TimeUnit.MINUTES)
  .removalListener(removalListener)
  .build(loader);

Warning: removal listener operations are executed synchronously by default, and since cache maintenance is normally performed during normal cache operations, expensive removal listeners can slow down normal cache function! If you have an expensive removal listener, use [RemovalListeners.asynchronous(RemovalListener, Executor)] to decorate a RemovalListener to operate asynchronously.

When Does Cleanup Happen?

Caches built with CacheBuilder do not perform cleanup and evict values "automatically," or instantly after a value expires, or anything of the sort. Instead, it performs small amounts of maintenance during write operations, or during occasional read operations if writes are rare.

The reason for this is as follows: if we wanted to perform Cache maintenance continuously, we would need to create a thread, and its operations would be competing with user operations for shared locks. Additionally, some environments restrict the creation of threads, which would make CacheBuilder unusable in that environment.

Instead, we put the choice in your hands. If your cache is high-throughput, then you don't have to worry about performing cache maintenance to clean up expired entries and the like. If your cache does writes only rarely and you don't want cleanup to block cache reads, you may wish to create your own maintenance thread that calls [Cache.cleanUp()] at regular intervals.

If you want to schedule regular cache maintenance for a cache which only rarely has writes, just schedule the maintenance using [ScheduledExecutorService].

Refresh

Refreshing is not quite the same as eviction. As specified in [LoadingCache.refresh(K)], refreshing a key loads a new value for the key, possibly asynchronously. The old value (if any) is still returned while the key is being refreshed, in contrast to eviction, which forces retrievals to wait until the value is loaded anew.

If an exception is thrown while refreshing, the old value is kept, and the exception is logged and swallowed.

A CacheLoader may specify smart behavior to use on a refresh by overriding [CacheLoader.reload(K, V)], which allows you to use the old value in computing the new value.

// Some keys don't need refreshing, and we want refreshes to be done asynchronously.
LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
       .maximumSize(1000)
       .refreshAfterWrite(1, TimeUnit.MINUTES)
       .build(
           new CacheLoader<Key, Graph>() {
             public Graph load(Key key) { // no checked exception
               return getGraphFromDatabase(key);
             }

             public ListenableFuture<Graph> reload(final Key key, Graph prevGraph) {
               if (neverNeedsRefresh(key)) {
                 return Futures.immediateFuture(prevGraph);
               } else {
                 // asynchronous!
                 ListenableFutureTask<Graph> task = ListenableFutureTask.create(new Callable<Graph>() {
                   public Graph call() {
                     return getGraphFromDatabase(key);
                   }
                 });
                 executor.execute(task);
                 return task;
               }
             }
           });

Automatically timed refreshing can be added to a cache using [CacheBuilder.refreshAfterWrite(long, TimeUnit)]. In contrast to expireAfterWrite, refreshAfterWrite will make a key eligible for refresh after the specified duration, but a refresh will only be actually initiated when the entry is queried. (If CacheLoader.reload is implemented to be asynchronous, then the query will not be slowed down by the refresh.) So, for example, you can specify both refreshAfterWrite and expireAfterWrite on the same cache, so that the expiration timer on an entry isn't blindly reset whenever an entry becomes eligible for a refresh, so if an entry isn't queried after it comes eligible for refreshing, it is allowed to expire.

Features

Statistics

By using [CacheBuilder.recordStats()], you can turn on statistics collection for Guava caches. The [Cache.stats()] method returns a [CacheStats] object, which provides statistics such as

[hitRate()], which returns the ratio of hits to requests
[averageLoadPenalty()], the average time spent loading new values, in nanoseconds
[evictionCount()], the number of cache evictions

and many more statistics besides. These statistics are critical in cache tuning, and we advise keeping an eye on these statistics in performance-critical applications.

`asMap`

You can view any Cache as a ConcurrentMap using its asMap view, but how the asMap view interacts with the Cache requires some explanation.

cache.asMap() contains all entries that are currently loaded in the cache. So, for example, cache.asMap().keySet() contains all the currently loaded keys.
asMap().get(key) is essentially equivalent to cache.getIfPresent(key), and never causes values to be loaded. This is consistent with the Map contract.
Access time is reset by all cache read and write operations (including Cache.asMap().get(Object) and Cache.asMap().put(K, V)), but not by containsKey(Object), nor by operations on the collection-views of Cache.asMap(). So, for example, iterating through cache.asMap().entrySet() does not reset access time for the entries you retrieve.

Interruption

Loading methods (like get) never throw InterruptedException. We could have designed these methods to support InterruptedException, but our support would have been incomplete, forcing its costs on all users but its benefits on only some. For details, read on.

get calls that request uncached values fall into two broad categories: those that load the value and those that await another thread's in-progress load. The two differ in our ability to support interruption. The easy case is waiting for another thread's in-progress load: Here we could enter an interruptible wait. The hard case is loading the value ourselves. Here we're at the mercy of the user-supplied CacheLoader. If it happens to support interruption, we can support interruption; if not, we can't.

So why not support interruption when the supplied CacheLoader does? In a sense, we do (but see below): If the CacheLoader throws InterruptedException, all get calls for the key will return promptly (just as with any other exception). Plus, get will restore the interrupt bit in the loading thread. The surprising part is that the InterruptedException is wrapped in an ExecutionException.

In principle, we could unwrap this exception for you. However, this forces all LoadingCache users to handle InterruptedException, even though the majority of CacheLoader implementations never throw it. Maybe that's still worthwhile when you consider that all non-loading threads' waits could still be interrupted. But many caches are used only in a single thread. Their users must still catch the impossible InterruptedException. And even those users who share their caches across threads will be able to interrupt their get calls only sometimes, based on which thread happens to make a request first.

Our guiding principle in this decision is for the cache to behave as though all values are loaded in the calling thread. This principle makes it easy to introduce caching into code that previously recomputed its values on each call. And if the old code wasn't interruptible, then it's probably OK for the new code not to be, either.

I said that we support interruption "in a sense." There's another sense in which we don't, making LoadingCache a leaky abstraction. If the loading thread is interrupted, we treat this much like any other exception. That's fine in many cases, but it's not the right thing when multiple get calls are waiting for the value. Although the operation that happened to be computing the value was interrupted, the other operations that need the value might not have been. Yet all of these callers receive the InterruptedException (wrapped in an ExecutionException), even though the load didn't so much "fail" as "abort." The right behavior would be for one of the remaining threads to retry the load. We have a bug filed for this. However, a fix could be risky. Instead of fixing the problem, we may put additional effort into a proposed AsyncLoadingCache, which would return Future objects with correct interruption behavior.

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