Sometimes you just need a small simple cache for calculated values because the value generation is expensive in time or resources. In this post I show how such a cache can be implemented in a thread safe way with the Java8 version of the ConcurrentHashMap class with just 2 lines of relevant code.

For this example I use the calculation of the n-th fibonacci number and calculate the values for there input of 1 to 20.

## using no cache

The sample program and the first version of the fibonacci() method without caching are as follows:

package com.sothawo; import java.math.BigInteger; public class App { private BigInteger fibonacci(int n) { return (n <= 2) ? BigInteger.ONE : fibonacci(n - 1).add(fibonacci(n - 2)); } public static void main(String[] args) { new App().run(); } private void run() { for (int i = 1; i <= 20; i++) { System.out.println("fibonacci(" + i + ") is " + fibonacci(i)); } } }

This program produces the desired results, but due to the recursion there are 35.400 calls of the fibonacci() method, as already calculated values are recalculated again and again.

## pre-Java 8 cache

As a cache I use a HashMap and guard itâ€™s use with a lock object to ensure thread safety. The modified program looks like this:

package com.sothawo; import java.math.BigInteger; import java.util.HashMap; import java.util.Map; public class App { private Map<Integer, BigInteger> simpleCache = new HashMap<>(); private Object simpleCacheLock = new Object(); private BigInteger fibonacci(int n) { BigInteger fib; synchronized (simpleCacheLock) { fib = simpleCache.get(n); if (null == fib) { fib = (n <= 2) ? BigInteger.ONE : fibonacci(n - 1).add(fibonacci(n - 2)); simpleCache.put(n, fib); } } return fib; } public static void main(String[] args) { new App().run(); } private void run() { for (int i = 1; i <= 20; i++) { System.out.println("fibonacci(" + i + ") is " + fibonacci(i)); } } }

This works as expected and gets the number of calls to the fibonacci() method down to the minimum of 20, but is a lot of code.

## Java 8 version

The ConcurrentHashMap in Java 8 has got a nice addition, the `computeIfAbsent`

method. It takes two arguments: the key value and a function to calculate the value if not present in the cache. This leads to the following code:

package com.sothawo; import java.math.BigInteger; import java.util.concurrent.ConcurrentHashMap; public class App { private ConcurrentHashMap<Integer, BigInteger> betterCache = new ConcurrentHashMap<>(); private BigInteger fibonacci(int n) { return betterCache.computeIfAbsent(n, (i) -> (i <= 2) ? BigInteger.ONE : fibonacci(i - 1).add(fibonacci(i - 2))); } public static void main(String[] args) { new App().run(); } private void run() { for (int i = 1; i <= 20; i++) { System.out.println("fibonacci(" + i + ") is " + fibonacci(i)); } } }

The same result as before, but with just two lines of code.

How do we define a record eviction policy in such a local cache ?

I wouldn’t use this approach for more than the basic cache functionality, this post just showed how Java 8 added the possibility to do such things with lambdas and some functions like `computIfAbsent`.

For caches that are better configurable espeicially with regard to cache eviction I would recommend to look at the Google Guava Caches (https://github.com/google/guava/wiki/CachesExplained) before writing somethign yourself.

In no cache approach we could produce Fibonacci program result by bottom up approach(Dynamic programming). it takes less time than recursive and memoize approaches.

/*

* fib series by bottom up approach method

*/

public static int fib_by_bottom_up_approach(int number){

int[] bottom = new int[number];

bottom[0] = 1;

bottom[1] = 1;

for(int i=2;i<bottom.length;i++){

bottom[i] = bottom[i-1]+bottom[i-2];

}

return bottom[number-1];

}

Very nice post. Thank you.