Memoization is the name of a technique to speed up function calls by caching the return values. The idea is that if there is a function that we call with the same parameters several times during the life of our process, we can cache the result and eliminate the processing time.

More specifically, for every set of input, the first time the function is called, we let it compute the result, but then we store it in memory and the next, and subsequent times the function is called we just return the already computed data.

Using this technique assumes that for any given input the function is expected to return the same result. So no randomization involved. Time of execution is not involved. No other external information is taken in account. Not even the previous calls to this function have any impact on the result.

One of the most obvious, and not very useful, example is improving the speed of a recursive implementation of the Fibonacci series.

Another example would be computing the family tree of people based on hierarchical database, or building the pedigree of dogs based on raw data stored in an Relational Database such as MySQL.

## Fibonacci numbers

Let's see the case of the Fibonacci series.

examples/fibonacci.pl

```use 5.010;
use strict;
use warnings;

sub fibonacci {
my (\$n) = @_;
say "fibonacci(\$n)";
return 1 if \$n == 1 or \$n == 2;
return fibonacci(\$n-1) + fibonacci(\$n-2);
}

say fibonacci(6);
```

I've also include an extra say statement so we can see how many times was the function called.

The result of calling fibonacci(6) can be seen here:

```fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
fibonacci(2)
fibonacci(3)
fibonacci(2)
fibonacci(1)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
fibonacci(2)
8
```

As you can see fibonacci(3), which was called 3 times and fibonacci(2) was called 5 times. A total of 15 calls for fibonacci(6).

While in this case the computation is not heavy, but you can imagine that if calculating each function call took 1 second then we would be quite frustrated when we ran fibonacci(1000)

## Fibonacci numbers - cached

In this solution we manually added caching to the function computing the Fibonacci series. Not very nice, but it might help understanding the solution later on.

examples/fibonacci_with_cache.pl

```use 5.010;
use strict;
use warnings;

sub fibonacci {
my (\$n) = @_;

state %cache;
say "fibonacci(\$n)";
if (not exists \$cache{\$n}) {
if (\$n == 1 or \$n == 2) {
\$cache{\$n} = 1;
} else {
\$cache{\$n} = fibonacci(\$n-1) + fibonacci(\$n-2);
}
}
return \$cache{\$n};
}

say fibonacci(6);
```

We had to used a state variable introduced in perl 5.10.

The specialty of the state variables is that they are initialized only once and they retain their content between calls to the fibonacci function.

In the code we check if the %cache already has a value for the current \$n, if not we compute it using the recursive function calls and store it in the cache After that we can safely return \$cache{\$n} it will have the previously computed value.

If during the recursive calls we encounter a call that we have already computed we don't need to calculate it again. We just return the already calculated value. The output looks like this:

```fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
fibonacci(2)
fibonacci(3)
fibonacci(4)
8
```

This is already an improvement 9 calls instead of 15, but we still have a few duplicate calls. That's because in some cases the function is called again before the previous call with the same parameter had a chance to return and to update the cache.

There is another issue with this solution. We have to think about the caching during the implementation and the maintenance of the function. This made the function more complex. We could not use the return .. if .. statements any more. For more complex functions, where you have several points of return it will be even harder to add the caching.

The function also does now two things: computes Fibonacci numbers and caches results. Usually it is recommended that every function should do exactly one thing.

So let's separate the caching from the implementation of Fibonacci.

## Fibonacci numbers - external cache

examples/fibonacci_with_external_cache.pl

```use 5.010;
use strict;
use warnings;

sub fibonacci {
my (\$n) = @_;

state %cache;
if (not exists \$cache{\$n}) {
\$cache{\$n} = _fibonacci(\$n);
}
return \$cache{\$n};
}

sub _fibonacci {
my (\$n) = @_;
say "fibonacci(\$n)";
return 1 if \$n == 1 or \$n == 2;
return fibonacci(\$n-1) + fibonacci(\$n-2);
}

say fibonacci(6);
```

In this version we have two functions. The one called fibonacci is actually the one caching the results, the other one called _fibonacci is a "helper function" that users should not call directly and that's what is computing the fibonacci.

fibonacci has the %cache, it checks if it already has the results for \$n and calls _fibonacci(\$n) if it does not have the value yet.

_fibonacci implements the recursive computation of the Fibonacci numbers. It could be returned to the more simple syntax even using the return .. if .. constructs, but instead of calling itself, it calls fibonacci.

We have to do this because we wanted the recursive calls to be cached as well.

The result is what we expected, calling fibonacci() once for each number.

```fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
8
```

The separation of these two function is an improvement, but they are still interconnected. The developer and the maintainer of the _fibonacci function still needs to know about the caching. It also means that we cannot reuse our caching function.

Let's further separate the two.

## Memoize manually

In this version we have implemented a function called memoize that will accept the name of a function as a parameter and will change the behavior of that function adding caching to it. This technique requires some advanced knowledge, so if you are not interested in the the behind the scenes, then skip ahead to the next title.

examples/fibonacci_memoize_manually.pl

```use 5.010;
use strict;
use warnings;

sub memoize {
my (\$func) = @_;

my \$original = \&{\$func};
my %cache;
my \$sub = sub {
my (\$n) = @_;
if (not exists \$cache{\$n}) {
\$cache{\$n} = \$original->(\$n);
}
return \$cache{\$n};
};
no strict 'refs';
no warnings 'redefine';
*{\$func} = \$sub;
}

sub fibonacci {
my (\$n) = @_;
say "fibonacci(\$n)";
return 1 if \$n == 1 or \$n == 2;
return fibonacci(\$n-1) + fibonacci(\$n-2);
}

memoize('fibonacci');

say fibonacci(6);
```

The memoize function receives the name of a function as a parameter. Using the \&{\$func}; expression we copy the reference to the function to a temporary variable called \$original. We need it saved so later we can call the original function.

Then we create a new anonymous function and assign it to the variable \$sub. We are going to replace the original subroutine with this one. This new function accepts a single value \$n.

Inside the new subroutine we check the cache and if the value for \$n has not yet been computed we call the original function and pass the value of \$n to it. The returned value is saved in the cache.

Then we use the expression *{\$func} = \$sub; to replace the function name in \$func by the newly created function.

We had to add no strict 'refs' to avoid the Can't use string ... as a symbol ref while "strict refs" in use at ... error and we had to add no warnings 'redefine'; to avoid the Subroutine main::fibonacci redefined warning. After all we want to redefine the function.

You might have noticed that instead of having a state variable, this time %cache was defined with a simple my statement. This is because %cache, just as \$original are variables that are accessed from inside the anonymous function. As long as that function exists there variables will exist and they are going to be private for the anonymous function. They are part of the closure we have created here.

The result looks good:

```fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
8
```

While in this case the two functions don't access each other directly and in that way the memoize function is generic, there is still a slight problem with this solution. The internal function created by the memoize function assumes that there is going to be exactly one parameter. This is true for the fibonacci function, and this is true for many other functions, but this implementation of memoize is still limited that it can only handle functions with a single parameter.

## Memoize Fibonacci

In this version we use the generic memoize function of the Memoize module to add caching to the function generating the Fibonacci numbers:

examples/fibonacci_memoize.pl

```use 5.010;
use strict;
use warnings;

use Memoize qw(memoize);

sub fibonacci {
my (\$n) = @_;
say "fibonacci(\$n)";
return 1 if \$n == 1 or \$n == 2;
return fibonacci(\$n-1) + fibonacci(\$n-2);
}

memoize('fibonacci');

say fibonacci(6);
```

The result is the same as above.

```fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
8
```

There is not much to say here. It just works.

## Caveat

Memoization trades memory for speed. It uses more memory by caching results of previous computations in order to eliminate some code execution. You'll need to take in account the actual memory use if the memoized version if it is acceptable and if it is worth the effort.

Memoization has its overhead in CPU usage as well. If the original computation is very short then it might not worth the extra effort.

Memoization is an optimization technique. "Premature optimization is the root of all evil". Think hard before doing it. Better yet, measure processing time before and after to see if you have gained anything.

If the original function depends on external factors, or even on the previous calls of itself, then memoization will alter the results.