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Memoize - Make functions faster by trading space for time
# This is the documentation for Memoize 1.01
use Memoize;
memoize('slow_function');
slow_function(arguments); # Is faster than it was before
This is normally all you need to know. However, many options are available:
memoize(function, options...);
Options include:
NORMALIZER => function
INSTALL => new_name
SCALAR_CACHE => 'MEMORY'
SCALAR_CACHE => ['HASH', \%cache_hash ]
SCALAR_CACHE => 'FAULT'
SCALAR_CACHE => 'MERGE'
LIST_CACHE => 'MEMORY'
LIST_CACHE => ['HASH', \%cache_hash ]
LIST_CACHE => 'FAULT'
LIST_CACHE => 'MERGE'
`Memoizing' a function makes it faster by trading space for time. It
does this by caching the return values of the function in a table.
If you call the function again with the same arguments, memoize
jumps in and gives you the value out of the table, instead of letting
the function compute the value all over again.
Here is an extreme example. Consider the Fibonacci sequence, defined by the following function:
# Compute Fibonacci numbers
sub fib {
my $n = shift;
return $n if $n < 2;
fib($n-1) + fib($n-2);
}
This function is very slow. Why? To compute fib(14), it first wants
to compute fib(13) and fib(12), and add the results. But to compute
fib(13), it first has to compute fib(12) and fib(11), and then it
comes back and computes fib(12) all over again even though the answer
is the same. And both of the times that it wants to compute fib(12),
it has to compute fib(11) from scratch, and then it has to do it
again each time it wants to compute fib(13). This function does so
much recomputing of old results that it takes a really long time to
run---fib(14) makes 1,200 extra recursive calls to itself, to compute
and recompute things that it already computed.
This function is a good candidate for memoization. If you memoize the
`fib' function above, it will compute fib(14) exactly once, the first
time it needs to, and then save the result in a table. Then if you
ask for fib(14) again, it gives you the result out of the table.
While computing fib(14), instead of computing fib(12) twice, it does
it once; the second time it needs the value it gets it from the table.
It doesn't compute fib(11) four times; it computes it once, getting it
from the table the next three times. Instead of making 1,200
recursive calls to `fib', it makes 15. This makes the function about
150 times faster.
You could do the memoization yourself, by rewriting the function, like this:
# Compute Fibonacci numbers, memoized version
{ my @fib;
sub fib {
my $n = shift;
return $fib[$n] if defined $fib[$n];
return $fib[$n] = $n if $n < 2;
$fib[$n] = fib($n-1) + fib($n-2);
}
}
Or you could use this module, like this:
use Memoize;
memoize('fib');
# Rest of the fib function just like the original version.
This makes it easy to turn memoizing on and off.
Here's an even simpler example: I wrote a simple ray tracer; the program would look in a certain direction, figure out what it was looking at, and then convert the `color' value (typically a string like `red') of that object to a red, green, and blue pixel value, like this:
for ($direction = 0; $direction < 300; $direction++) {
# Figure out which object is in direction $direction
$color = $object->{color};
($r, $g, $b) = @{&ColorToRGB($color)};
...
}
Since there are relatively few objects in a picture, there are only a
few colors, which get looked up over and over again. Memoizing
ColorToRGB sped up the program by several percent.
This module exports exactly one function, memoize. The rest of the
functions in this package are None of Your Business.
You should say
memoize(function)
where function is the name of the function you want to memoize, or
a reference to it. memoize returns a reference to the new,
memoized version of the function, or undef on a non-fatal error.
At present, there are no non-fatal errors, but there might be some in
the future.
If function was the name of a function, then memoize hides the
old version and installs the new memoized version under the old name,
so that &function(...) actually invokes the memoized version.
There are some optional options you can pass to memoize to change
the way it behaves a little. To supply options, invoke memoize
like this:
memoize(function, NORMALIZER => function,
INSTALL => newname,
SCALAR_CACHE => option,
LIST_CACHE => option
);
Each of these options is optional; you can include some, all, or none of them.
If you supply a function name with INSTALL, memoize will install
the new, memoized version of the function under the name you give.
For example,
memoize('fib', INSTALL => 'fastfib')
installs the memoized version of fib as fastfib; without the
INSTALL option it would have replaced the old fib with the
memoized version.
To prevent memoize from installing the memoized version anywhere, use
INSTALL => undef.
Suppose your function looks like this:
# Typical call: f('aha!', A => 11, B => 12);
sub f {
my $a = shift;
my %hash = @_;
$hash{B} ||= 2; # B defaults to 2
$hash{C} ||= 7; # C defaults to 7
# Do something with $a, %hash
}
Now, the following calls to your function are all completely equivalent:
f(OUCH);
f(OUCH, B => 2);
f(OUCH, C => 7);
f(OUCH, B => 2, C => 7);
f(OUCH, C => 7, B => 2);
(etc.)
However, unless you tell Memoize that these calls are equivalent,
it will not know that, and it will compute the values for these
invocations of your function separately, and store them separately.
To prevent this, supply a NORMALIZER function that turns the
program arguments into a string in a way that equivalent arguments
turn into the same string. A NORMALIZER function for f above
might look like this:
sub normalize_f {
my $a = shift;
my %hash = @_;
$hash{B} ||= 2;
$hash{C} ||= 7;
join(',', $a, map ($_ => $hash{$_}) sort keys %hash);
}
Each of the argument lists above comes out of the normalize_f
function looking exactly the same, like this:
OUCH,B,2,C,7
You would tell Memoize to use this normalizer this way:
memoize('f', NORMALIZER => 'normalize_f');
memoize knows that if the normalized version of the arguments is
the same for two argument lists, then it can safely look up the value
that it computed for one argument list and return it as the result of
calling the function with the other argument list, even if the
argument lists look different.
The default normalizer just concatenates the arguments with character 28 in between. (In ASCII, this is called FS or control-\.) This always works correctly for functions with only one string argument, and also when the arguments never contain character 28. However, it can confuse certain argument lists:
normalizer("a\034", "b")
normalizer("a", "\034b")
normalizer("a\034\034b")
for example.
Since hash keys are strings, the default normalizer will not
distinguish between undef and the empty string. It also won't work
when the function's arguments are references. For example, consider a
function g which gets two arguments: A number, and a reference to
an array of numbers:
g(13, [1,2,3,4,5,6,7]);
The default normalizer will turn this into something like
"13\034ARRAY(0x436c1f)". That would be all right, except that a
subsequent array of numbers might be stored at a different location
even though it contains the same data. If this happens, Memoize
will think that the arguments are different, even though they are
equivalent. In this case, a normalizer like this is appropriate:
sub normalize { join ' ', $_[0], @{$_[1]} }
For the example above, this produces the key ``13 1 2 3 4 5 6 7''.
Another use for normalizers is when the function depends on data other than those in its arguments. Suppose you have a function which returns a value which depends on the current hour of the day:
sub on_duty {
my ($problem_type) = @_;
my $hour = (localtime)[2];
open my $fh, "$DIR/$problem_type" or die...;
my $line;
while ($hour-- > 0)
$line = <$fh>;
}
return $line;
}
At 10:23, this function generates the 10th line of a data file; at
3:45 PM it generates the 15th line instead. By default, Memoize
will only see the $problem_type argument. To fix this, include the
current hour in the normalizer:
sub normalize { join ' ', (localtime)[2], @_ }
The calling context of the function (scalar or list context) is
propagated to the normalizer. This means that if the memoized
function will treat its arguments differently in list context than it
would in scalar context, you can have the normalizer function select
its behavior based on the results of wantarray. Even if called in
a list context, a normalizer should still return a single string.
SCALAR_CACHE, LIST_CACHENormally, Memoize caches your function's return values into an
ordinary Perl hash variable. However, you might like to have the
values cached on the disk, so that they persist from one run of your
program to the next, or you might like to associate some other
interesting semantics with the cached values.
There's a slight complication under the hood of Memoize: There are
actually two caches, one for scalar values and one for list values.
When your function is called in scalar context, its return value is
cached in one hash, and when your function is called in list context,
its value is cached in the other hash. You can control the caching
behavior of both contexts independently with these options.
The argument to LIST_CACHE or SCALAR_CACHE must either be one of
the following four strings:
MEMORY
FAULT
MERGE
HASH
or else it must be a reference to a list whose first element is one of
these four strings, such as [HASH, arguments...].
MEMORY
MEMORY means that return values from the function will be cached in
an ordinary Perl hash variable. The hash variable will not persist
after the program exits. This is the default.
HASH
HASH allows you to specify that a particular hash that you supply
will be used as the cache. You can tie this hash beforehand to give
it any behavior you want.
A tied hash can have any semantics at all. It is typically tied to an
on-disk database, so that cached values are stored in the database and
retrieved from it again when needed, and the disk file typically
persists after your program has exited. See perltie for more
complete details about tie.
A typical example is:
use DB_File;
tie my %cache => 'DB_File', $filename, O_RDWR|O_CREAT, 0666;
memoize 'function', SCALAR_CACHE => [HASH => \%cache];
This has the effect of storing the cache in a DB_File database
whose name is in $filename. The cache will persist after the
program has exited. Next time the program runs, it will find the
cache already populated from the previous run of the program. Or you
can forcibly populate the cache by constructing a batch program that
runs in the background and populates the cache file. Then when you
come to run your real program the memoized function will be fast
because all its results have been precomputed.
TIE
This option is no longer supported. It is still documented only to
aid in the debugging of old programs that use it. Old programs should
be converted to use the HASH option instead.
memoize ... [TIE, PACKAGE, ARGS...]
is merely a shortcut for
require PACKAGE;
{ my %cache;
tie %cache, PACKAGE, ARGS...;
}
memoize ... [HASH => \%cache];
FAULT
FAULT means that you never expect to call the function in scalar
(or list) context, and that if Memoize detects such a call, it
should abort the program. The error message is one of
`foo' function called in forbidden list context at line ...
`foo' function called in forbidden scalar context at line ...
MERGE
MERGE normally means the function does not distinguish between list
and sclar context, and that return values in both contexts should be
stored together. LIST_CACHE => MERGE means that list context
return values should be stored in the same hash that is used for
scalar context returns, and SCALAR_CACHE => MERGE means the
same, mutatis mutandis. It is an error to specify MERGE for both,
but it probably does something useful.
Consider this function:
sub pi { 3; }
Normally, the following code will result in two calls to pi:
$x = pi();
($y) = pi();
$z = pi();
The first call caches the value 3 in the scalar cache; the second
caches the list (3) in the list cache. The third call doesn't call
the real pi function; it gets the value from the scalar cache.
Obviously, the second call to pi is a waste of time, and storing
its return value is a waste of space. Specifying LIST_CACHE =>
MERGE will make memoize use the same cache for scalar and list
context return values, so that the second call uses the scalar cache
that was populated by the first call. pi ends up being called only
once, and both subsequent calls return 3 from the cache, regardless
of the calling context.
Another use for MERGE is when you want both kinds of return values
stored in the same disk file; this saves you from having to deal with
two disk files instead of one. You can use a normalizer function to
keep the two sets of return values separate. For example:
tie my %cache => 'MLDBM', 'DB_File', $filename, ...;
memoize 'myfunc',
NORMALIZER => 'n',
SCALAR_CACHE => [HASH => \%cache],
LIST_CACHE => MERGE,
;
sub n {
my $context = wantarray() ? 'L' : 'S';
# ... now compute the hash key from the arguments ...
$hashkey = "$context:$hashkey";
}
This normalizer function will store scalar context return values in
the disk file under keys that begin with S:, and list context
return values under keys that begin with L:.
unmemoizeThere's an unmemoize function that you can import if you want to.
Why would you want to? Here's an example: Suppose you have your cache
tied to a DBM file, and you want to make sure that the cache is
written out to disk if someone interrupts the program. If the program
exits normally, this will happen anyway, but if someone types
control-C or something then the program will terminate immediately
without synchronizing the database. So what you can do instead is
$SIG{INT} = sub { unmemoize 'function' };
unmemoize accepts a reference to, or the name of a previously
memoized function, and undoes whatever it did to provide the memoized
version in the first place, including making the name refer to the
unmemoized version if appropriate. It returns a reference to the
unmemoized version of the function.
If you ask it to unmemoize a function that was never memoized, it croaks.
flush_cacheflush_cache(function) will flush out the caches, discarding all
the cached data. The argument may be a function name or a reference
to a function. For finer control over when data is discarded or
expired, see the documentation for Memoize::Expire, included in
this package.
Note that if the cache is a tied hash, flush_cache will attempt to
invoke the CLEAR method on the hash. If there is no CLEAR
method, this will cause a run-time error.
An alternative approach to cache flushing is to use the HASH option
(see above) to request that Memoize use a particular hash variable
as its cache. Then you can examine or modify the hash at any time in
any way you desire. You may flush the cache by using %hash = ().
Memoization is not a cure-all:
Do not memoize a function whose behavior depends on program state other than its own arguments, such as global variables, the time of day, or file input. These functions will not produce correct results when memoized. For a particularly easy example:
sub f {
time;
}
This function takes no arguments, and as far as Memoize is
concerned, it always returns the same result. Memoize is wrong, of
course, and the memoized version of this function will call time once
to get the current time, and it will return that same time
every time you call it after that.
Do not memoize a function with side effects.
sub f {
my ($a, $b) = @_;
my $s = $a + $b;
print "$a + $b = $s.\n";
}
This function accepts two arguments, adds them, and prints their sum.
Its return value is the numuber of characters it printed, but you
probably didn't care about that. But Memoize doesn't understand
that. If you memoize this function, you will get the result you
expect the first time you ask it to print the sum of 2 and 3, but
subsequent calls will return 1 (the return value of
print) without actually printing anything.
Do not memoize a function that returns a data structure that is modified by its caller.
Consider these functions: getusers returns a list of users somehow,
and then main throws away the first user on the list and prints the
rest:
sub main {
my $userlist = getusers();
shift @$userlist;
foreach $u (@$userlist) {
print "User $u\n";
}
}
sub getusers {
my @users;
# Do something to get a list of users;
\@users; # Return reference to list.
}
If you memoize getusers here, it will work right exactly once. The
reference to the users list will be stored in the memo table. main
will discard the first element from the referenced list. The next
time you invoke main, Memoize will not call getusers; it will
just return the same reference to the same list it got last time. But
this time the list has already had its head removed; main will
erroneously remove another element from it. The list will get shorter
and shorter every time you call main.
Similarly, this:
$u1 = getusers();
$u2 = getusers();
pop @$u1;
will modify $u2 as well as $u1, because both variables are references
to the same array. Had getusers not been memoized, $u1 and $u2
would have referred to different arrays.
Do not memoize a very simple function.
Recently someone mentioned to me that the Memoize module made his program run slower instead of faster. It turned out that he was memoizing the following function:
sub square {
$_[0] * $_[0];
}
I pointed out that Memoize uses a hash, and that looking up a
number in the hash is necessarily going to take a lot longer than a
single multiplication. There really is no way to speed up the
square function.
Memoization is not magical.
You can tie the cache tables to any sort of tied hash that you want
to, as long as it supports TIEHASH, FETCH, STORE, and
EXISTS. For example,
tie my %cache => 'GDBM_File', $filename, O_RDWR|O_CREAT, 0666;
memoize 'function', SCALAR_CACHE => [HASH => \%cache];
works just fine. For some storage methods, you need a little glue.
SDBM_File doesn't supply an EXISTS method, so included in this
package is a glue module called Memoize::SDBM_File which does
provide one. Use this instead of plain SDBM_File to store your
cache table on disk in an SDBM_File database:
tie my %cache => 'Memoize::SDBM_File', $filename, O_RDWR|O_CREAT, 0666;
memoize 'function', SCALAR_CACHE => [HASH => \%cache];
NDBM_File has the same problem and the same solution. (Use
Memoize::NDBM_File instead of plain NDBM_File.)
Storable isn't a tied hash class at all. You can use it to store a
hash to disk and retrieve it again, but you can't modify the hash while
it's on the disk. So if you want to store your cache table in a
Storable database, use Memoize::Storable, which puts a hashlike
front-end onto Storable. The hash table is actually kept in
memory, and is loaded from your Storable file at the time you
memoize the function, and stored back at the time you unmemoize the
function (or when your program exits):
tie my %cache => 'Memoize::Storable', $filename;
memoize 'function', SCALAR_CACHE => [HASH => \%cache];
tie my %cache => 'Memoize::Storable', $filename, 'nstore';
memoize 'function', SCALAR_CACHE => [HASH => \%cache];
Include the `nstore' option to have the Storable database written
in `network order'. (See the Storable manpage for more details about this.)
The flush_cache() function will raise a run-time error unless the
tied package provides a CLEAR method.
See Memoize::Expire, which is a plug-in module that adds expiration functionality to Memoize. If you don't like the kinds of policies that Memoize::Expire implements, it is easy to write your own plug-in module to implement whatever policy you desire. Memoize comes with several examples. An expiration manager that implements a LRU policy is available on CPAN as Memoize::ExpireLRU.
The test suite is much better, but always needs improvement.
There is some problem with the way goto &f works under threaded
Perl, perhaps because of the lexical scoping of @_. This is a bug
in Perl, and until it is resolved, memoized functions will see a
slightly different caller() and will perform a little more slowly
on threaded perls than unthreaded perls.
Some versions of DB_File won't let you store data under a key of
length 0. That means that if you have a function f which you
memoized and the cache is in a DB_File database, then the value of
f() (f called with no arguments) will not be memoized. If this
is a big problem, you can supply a normalizer function that prepends
"x" to every key.
To join a very low-traffic mailing list for announcements about
Memoize, send an empty note to mjd-perl-memoize-request@plover.com.
Mark-Jason Dominus (mjd-perl-memoize+@plover.com), Plover Systems co.
See the Memoize.pm Page at http://www.plover.com/~mjd/perl/Memoize/
for news and upgrades. Near this page, at
http://www.plover.com/~mjd/perl/MiniMemoize/ there is an article about
memoization and about the internals of Memoize that appeared in The
Perl Journal, issue #13. (This article is also included in the
Memoize distribution as `article.html'.)
My upcoming book will discuss memoization (and many other fascinating topics) in tremendous detail. It will be published by Morgan Kaufmann in 2002, possibly under the title Perl Advanced Techniques Handbook. It will also be available on-line for free. For more information, visit http://perl.plover.com/book/ .
To join a mailing list for announcements about Memoize, send an
empty message to mjd-perl-memoize-request@plover.com. This mailing
list is for announcements only and has extremely low traffic---about
two messages per year.
Copyright 1998, 1999, 2000, 2001 by Mark Jason Dominus
This library is free software; you may redistribute it and/or modify it under the same terms as Perl itself.
Many thanks to Jonathan Roy for bug reports and suggestions, to
Michael Schwern for other bug reports and patches, to Mike Cariaso for
helping me to figure out the Right Thing to Do About Expiration, to
Joshua Gerth, Joshua Chamas, Jonathan Roy (again), Mark D. Anderson,
and Andrew Johnson for more suggestions about expiration, to Brent
Powers for the Memoize::ExpireLRU module, to Ariel Scolnicov for
delightful messages about the Fibonacci function, to Dion Almaer for
thought-provoking suggestions about the default normalizer, to Walt
Mankowski and Kurt Starsinic for much help investigating problems
under threaded Perl, to Alex Dudkevich for reporting the bug in
prototyped functions and for checking my patch, to Tony Bass for many
helpful suggestions, to Jonathan Roy (again) for finding a use for
unmemoize(), to Philippe Verdret for enlightening discussion of
Hook::PrePostCall, to Nat Torkington for advice I ignored, to Chris
Nandor for portability advice, to Randal Schwartz for suggesting the
'flush_cache function, and to Jenda Krynicky for being a light in
the world.
Special thanks to Jarkko Hietaniemi, the 5.8.0 pumpking, for including this module in the core and for his patient and helpful guidance during the integration process.