## Hot slice

### Easy-as-Pie local caching

*This post was originally published over
at jooq.org as part of a special series
focusing on all things Java 8, including how take advantage of
lambda expressions, extension methods, and other great
stuff. You’ll find the
source code on GitHub.*

### Java 8 Goodie: Easy-as-Pie Local Caching

Now get ready for one of the most awesome revelations in this
series so far. We’ll show you an easy-as-pie way to implement a
local cache using the good old `ConcurrentHashMap`

and
lambda expressions. Because, `Map`

now has a new way
of
atomically calculating a new value in case a key is absent.
Perfect for caches. Let’s delve into code:

public static void main(String[] args) { for (int i = 0; i < 10; i++) System.out.println( "f(" + i + ") = " + fibonacci(i)); } static int fibonacci(int i) { if (i == 0) return i; if (i == 1) return 1; System.out.println("Calculating f(" + i + ")"); return fibonacci(i - 2) + fibonacci(i - 1); }

Yes. That’s the naive way of doing things. Even for small
numbers like`fibonacci(5)`

, the above algorithm will
print out a huge amount of lines, as we’re repeating the same
calculations exponentially:

Calculating f(6) Calculating f(4) Calculating f(2) Calculating f(3) Calculating f(2) Calculating f(5) Calculating f(3) Calculating f(2) Calculating f(4) Calculating f(2) Calculating f(3) Calculating f(2) f(6) = 8

What we want to do is build a cache of previously calculated fibonacci numbers. The most straightforward technique is to memoize all values in a cache. Here’s how we build a cache:

```
static Map<Integer, Integer> cache
= new ConcurrentHashMap<>();
```

Done! As mentioned before, we’re using the newly
added `Map.computeIfAbsent()`

method
to calculate a new value from a `source`

function
only if we don’t already have a value for a given key. Caching! And
since this method is guaranteed to execute atomically, and since
we’re using a `ConcurrentHashMap`

,
this cache is even thread-safe without resorting to manually
applying `synchronized`

anywhere. And it can
be reused for stuff other than calculating fibonacci numbers. But
let’s first apply this cache to
our `fibonacci()`

function.

```
static int fibonacci(int i) {
if (i == 0)
return i;
if (i == 1)
return 1;
return cache.computeIfAbsent(i, (key) ->
fibonacci(i - 2)
+ fibonacci(i - 1));
}
```

That’s it. It can’t get any simpler than this! Want proof? We’ll log a message on the console every time we actually evaluate a new value:

static int fibonacci(int i) { if (i == 0) return i; if (i == 1) return 1; return cache.computeIfAbsent(i, (key) -> { System.out.println( "Slow calculation of " + key); return fibonacci(i - 2) + fibonacci(i - 1); }); }

The above program will print

f(0) = 0 f(1) = 1 Slow calculation of 2 f(2) = 1 Slow calculation of 3 f(3) = 2 Slow calculation of 4 f(4) = 3 Slow calculation of 5 f(5) = 5 Slow calculation of 6 f(6) = 8 Slow calculation of 7 f(7) = 13 Slow calculation of 8 f(8) = 21 Slow calculation of 9 f(9) = 34

### How would we have done it in Java 7?

Good question. With lots of code. We’d probably write something like this using double-checked locking:

static int fibonacciJava7(int i) { if (i == 0) return i; if (i == 1) return 1; Integer result = cache.get(i); if (result == null) { synchronized (cache) { result = cache.get(i); if (result == null) { System.out.println( "Slow calculation of " + i); result = fibonacci(i - 2) + fibonacci(i - 1); cache.put(i, result); } } } return result; }

Convinced?

Note, that your *actual* solution would
probably make use of Guava
Caches.

### Conclusion

Lambdas are only one part of Java 8. An important part, but let’s not forget all the new features that were added to the libraries and that can be leveraged with lambdas now!

This is really exciting and…

We can greatly improve our code bases without resorting to new libraries. All of the above can be run with the JDK’s libraries only.

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