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An extremely lite-weight cache framework, demonstrating how cache works.
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Cache-Lite - An extremely light-weight cache framework for Kotlin

Build Status License: GPL v3 Kotlin Version Kotlin Version Join the chat at

The Cache-Lite is an extremely light-weight cache framework implemented in Kotlin, it is for study case.

You can check out the source code to learn how caching works and learn how to implement a simple cache framework by yourself.


Caching is a critical technology in many high performance scalable applications. There are many choices in caching framework, including Ehcache, Memcache, cache2k etc. But today we are going to build one on our own, to learn what cache really does. Let's get started.

Naive Version

A cache is typically a key-value store, and in Kotlin/Java there is exactly a class representing this kind of data structure: Map. There are chances that we have already leveraged this class to do some caching tasks. So the very first thought to build a caching framework is simply using the class java.util.Map.

val cache = HashMap<Any, Any>()
cache["key"] = "Frequently used value taken from database"
val v = cache["key"]

Quite simple, isn't it? But according to the programming principle, "Program to Interface, not Implementation", we should not couple our caching framework to the specific implementation, which is java.util.Map here. You may argue that java.util.Map is an interface, not an implementation. True, but not applicable here. Here we are talking about a caching system, which means that, for end users, caching is an interface and java.util.Map is an implementation that they don't care. We have to define an interface that only describes what a cache does.

Defining Cache Interface

There are some basic operations on a cache, you may want to put a value into it, get a value by key from it, remove a value by key from it, clear it and know what's the size of it. After saying the sentence, our interface Cache is almost done.

interface Cache {
	val size: Int

	operator fun set(key: Any, value: Any)

	operator fun get(key: Any): Any?

	fun remove(key: Any): Any?

	fun clear()

Here is one thing different from what we just said, the method set. The reason why we name it set is that we want to use operator [] to put a value by key into the cache. By using operator function set, we are able to put a value into cache like this: cache["key"] = "value", instead of cache.put("key", "value").

Cache Forever

One of the challenges in designing a caching framework is to deal with the expiration of the cached items. But for simplicity, we are ignoring this now and build our first cache that caches items forever until we remove it manually.

It is true that we should program to interface not implementation, but when we are implementing an interface, we could leverage some other implementations, here we will use java.util.Map to implement our first cache, PerpetualCache:

class PerpetualCache : Cache {
	private val cache = HashMap<Any, Any>()

	override val size: Int
		get() = cache.size

	override fun set(key: Any, value: Any) {
		this.cache[key] = value

	override fun remove(key: Any) = this.cache.remove(key)

	override fun get(key: Any) = this.cache[key]

	override fun clear() = this.cache.clear()

All of the methods are delegated to cache, it is fine because cache is private so that we can change our implementation without effecting our end users. Now that we have our own cache interface Cache and one of implementations of it, PerpetualCache, we want to add more functionalities.

LRU Cache

By now our cache will keep all the entries until we remove them manually, it can be very memory-intensive. In many caching scenarios, we assume that the entries we recently used will be used again soon, if that is true (mostly it is), we can keep only a certain number of entries that are recently used and remove others, this kind of flush strategy is called Least Recently Used.

Since we already have PerpetualCache and we want to add responsibilitis to this class, the Decorator Pattern is the best choice here.

class LRUCache(private val delegate: Cache, private val minimalSize: Int = DEFAULT_SIZE) : Cache {
	private val keyMap = object : LinkedHashMap<Any, Any>(minimalSize, .75f, true) {
		override fun removeEldestEntry(eldest: MutableMap.MutableEntry<Any, Any>): Boolean {
			val tooManyCachedItems = size > minimalSize
			if (tooManyCachedItems) eldestKeyToRemove = eldest.key
			return tooManyCachedItems

	private var eldestKeyToRemove: Any? = null

	override val size: Int
		get() = delegate.size

	override fun set(key: Any, value: Any) {
		delegate[key] = value

	override fun remove(key: Any) = delegate.remove(key)

	override fun get(key: Any): Any? {
		return delegate[key]

	override fun clear() {

	private fun cycleKeyMap(key: Any) {
		keyMap[key] = PRESENT
		eldestKeyToRemove?.let { delegate.remove(it) }
		eldestKeyToRemove = null

	companion object {
		private const val DEFAULT_SIZE = 100
		private const val PRESENT = true

We only keep minimalSize entries at most. Here we leverage the class java.util.LinkedHashMap to trace the usage of entries, and eldestKeyToRemove is the one to be removed, which is the eldest entry ordered by used frequency. Method cycleKeyMap is responsible for removing entries that are too old and less used. Simple and straightforward.

Expirable Cache

As we said above, expiration is critical in caching framework because it prevent our cache from growing infinitely. With the experience of LRUCache implementation, we know how and when to remove entries, it's time to implement an expirable cache.

class ExpirableCache(private val delegate: Cache,
                     private val flushInterval: Long = TimeUnit.MINUTES.toMillis(1)) : Cache {
	private var lastFlushTime = System.nanoTime()

	override val size: Int
		get() = delegate.size

	override fun set(key: Any, value: Any) {
		delegate[key] = value

	override fun remove(key: Any): Any? {
		return delegate.remove(key)

	override fun get(key: Any): Any? {
		return delegate[key]

	override fun clear() = delegate.clear()

	private fun recycle() {
		val shouldRecycle = System.nanoTime() - lastFlushTime >= TimeUnit.MILLISECONDS.toNanos(flushInterval)
		if (!shouldRecycle) return

To make it simple, by saying expirable, we means that the cache is expirable, not a single entry in the cache is expirable. However, after knowing how the entire cache is expired, it's easy to implement a cache where entries are expired respectively.

We are given a flushInterval, and we will clear the cache every flushInterval milliseconds. It's typically a scheduled task, we can use a background thread to do the task, but again, to make it simple, we just recycle before every operation in our cache.

Other Implementations

Besides the three implementations we discussed above, here are several implementations such as FIFOCache, SoftCache and WeakCache, implemented with First-in-first-out algorithm, Soft Reference, and Weak Reference respectively.

You can check out the source code here in GitHub.

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