A Best Effort Cache Synchronization Library In Java

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a best effort cache synchronization library for distributed systems

GraceKelly is a best effort cache synchronization library designed to shield distributed systems and services from direct exposure to unpredictable request loads. It improves load and response SLA predictability in distributed environments. It also enables graceful degradation with stale data as fallback, in a degraded distributed ecosystem.

Why is it needed?

A chaotic place

Any big distributed environment is inherently complex and chaotic. This complexity arises due to the complex dependencies between different services. The variability of the requests and responses that this environment is exposed to makes it a chaotic place

This chaos means the predictability of load and latency is reduced. This makes the environment and it’s SLAs vulnerable to arbitrary request loads. It’s necessary to shield the environment from such externally induced unpredictability. Since service SLAs are affected by service load, such shielding also ensures their predictability. This means, one must systemically strive to hold on to as much predictability as possible when building a service/system.

Sheilds up

Caches act as sentinels in an distributed environment. Although their primary function is to reduce latency, when used appropriately they excel at bringing predictability to a system. That’s because a cache request is extremely predictable, with almost no variability, either in response times or the load per request. One could say that there is positive co-relation between the percentage of Cache hits and the predictability of a system/environment.

Cache expiry

Every time there is a cache miss the environment and SLAs become a little bit more vulnerable. In this context, the common cache usage pattern of expiry based on a ttl and subsequent re-population seems risky. Using cache expiry as a proxy/trigger for cache synchronization exposes the underlying system to potentially harmful request pattern load for the duration of synchronization.

The time between t1 and t2 is the duration of exposure. The predictability of the target service and all the services it depends on during this time is affected by the the per request load and the qps of all requests that result in a cache miss for c1.

What would be good to have is a cache library with regular caching semantics but one that accommodates refreshing a cache entry rather than expiring it based on ttl. This is exactly what GraceKelly is, it’s inspired by Gooogle Guava’s LoadingCache.

What does it do?

GraceKelly tries it’s best to refresh the cache entry that has expired. The refresh lifecycle is purely request triggered and doesn’t monitor/maintain the cache. For every request

Note that a cache entry is never removed(though it can be evicted by size constraints).

This does two things.

The Library

The library has a single Class Kelly that takes implementations of two different interfaces a CacheProvider and a CacheLoader. They pass around a CacheEntry


Kelly is the primary class for Gracekelly that reloads cacheEntries when they expire. It has a very simple interface for usage.

    * obtain an instance of a CacheProvider implementation
    * here the RemoteCache can be a wrapper to some kind of
    * memcached client.
    CacheProvider<CachedObject> cacheProvider = new RemoteCache();

    * obtain an instance of a CacheLoader implementation
    CacheLoader<CachedObject> cacheLoader = new MyCacheLoader();

    * Fix the threadpool size for the number of threads that will
    * be used to reload cache entries
    Integer threadPoolSize = 10;

    * Create a kelly reloading cache instance with the provided
    * cacheProvider, cacheLoader and threadPoolSize
    Kelly<CachedObject> cache = new Kelly(cacheProvider, cacheLoader, threadPoolSize);

    String key = "sample_key";
    CachedObject value = new CachedObject();
    long expiryTtl = 300;

    * Create a CacheEntry instance with the given
    * key, value and ttl for expiry
    CacheEntry cacheEntry = new CacheEntry(key, value, expiryTtl);

    //put a CacheEntry in Cache
    cache.put(key, cacheEntry);

    //get value from cache
    CachedObject cachedValue = cache.get(key);

    //expire a cache key
    cache.expire(key); //doesn't remove from cache

One has to note that expired entries are not replaced as soon as they have expired but an attempt is made to refresh them using the CacheLoader the first time an expired CacheEntry is encountered during a get request.


The CacheProvider interface is used to implement adapters to different cache implementations where the cached values are finally persisted and retrieved from. For eg: one would implement a CacheProvider for couchbase or memcached.

public interface CacheProvider <T>{
     * Returns a {@link CacheEntry}<T> if it is present in the underlying cache,
     * or it returns a null otherwise.
     * @param key
     * @return {@link CacheEntry}<T> for the given key or return null if not present
     * @throws CacheProviderException
    CacheEntry<T> get(String key) throws CacheProviderException;

     * Tries to update the cache with the given {@link CacheEntry}<T> for the given key
     * @param key
     * @param value
     * @return true or false based on the success of putting the {@link CacheEntry}<T>
     * into the cache.
     * @throws CacheProviderException
    Boolean put(String key, CacheEntry<T> value) throws CacheProviderException;

A trivial CacheProvider implementation for a local cache with a ConcurrentHashMap could look like the following.

    public class LocalCacheProvider implements CacheProvider<String>{

        private final Map<String,CacheEntry<String>> cache = new ConcurrentHashMap<String,CacheEntry<String>>();

        public CacheEntry<String> get(String key) throws CacheProviderException {
            return cache.get("key");

        public Boolean put(String key, CacheEntry<String> value) throws CacheProviderException {
            return true;


The CacheLoader provides a single method to reload cache, based on an existing entry in the cache. The implementation of CacheLoader should be able to reload the cache given the key of the and the previous value of the CacheEntry.

public interface CacheLoader<T> {

     * Takes a {@link String} key and a value/Object of type <T> and returns a
     * {@link CacheEntry}<T>. The implementation of this method is supposed to
     * return the CacheEntry with the latest Value for the given key.
     * @param key
     * @param prevValue
     * @return {@link CacheEntry} of the type parameter specified during
     * declaration of this instance of CacheLoader
     * @throws CacheLoaderException
    public CacheEntry<T> reload(String key, T prevValue) throws CacheLoaderException;


The CacheEntry class is a simple java object that holds data required to get, put and invalidate a cache entry. The generic parameter indicates the type of the object that will be stored against the given key. usage is as follows, where the ttl is in seconds

//cache entry valid for 5 minutes since time of creation
CacheEntry<CachedObject> cacheEntry = new CacheEntry<CachedObject>("key", someObject, 300);

String key = cacheEntry.getKey() //returns the key of the CacheEntry
CachedObject = cahceEntry.getValue() //returns value of the CacheEntry
long ttl = cacheEntry.getTtl() //returns the ttl in seconds

Maven Artifact

Add the following repository to your pom.xml

      <name>Clojars repository</name>

And add the following dependency to start using GraceKelly in your maven project.



The api docs can be found here

Contribution, Bugs and Feedback

For bugs, questions and discussions please use the Github Issues. Please follow the contribution guidelines when submitting pull requests.


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