Memoize(3p) Perl Programmers Reference Guide Memoize(3p)

Memoize(3p) Perl Programmers Reference Guide Memoize(3p) #

Memoize(3p) Perl Programmers Reference Guide Memoize(3p)

NNAAMMEE #

 Memoize - Make functions faster by trading space for time

SSYYNNOOPPSSIISS #

         # This is the documentation for Memoize 1.03
         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’ #

DDEESSCCRRIIPPTTIIOONN #

 `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.

DDEETTAAIILLSS #

 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.

OOPPTTIIOONNSS #

 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.

IINNSSTTAALLLL #

 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".

NNOORRMMAALLIIZZEERR #

 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.

“"SSCCAALLAARR__CCAACCHHEE"”,, “"LLIISSTT__CCAACCHHEE"” #

 Normally, "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 _t_w_o 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 an array 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.

     Another reason to use "HASH" is to provide your own hash variable.
     You can then inspect or modify the contents of the hash to gain finer
     control over the cache management.

“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;
             { tie my %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 that the memoized function does not
     distinguish between list and sclar context, and that return values in
     both contexts should be stored together.  Both "LIST_CACHE => MERGE"
     and "SCALAR_CACHE => MERGE" mean the same thing.

     Consider this function:

             sub complicated {
               # ... time-consuming calculation of $result
               return $result;
             }

     The "complicated" function will return the same numeric $result
     regardless of whether it is called in list or in scalar context.

     Normally, the following code will result in two calls to
     "complicated", even if "complicated" is memoized:

         $x = complicated(142);
         ($y) = complicated(142);
         $z = complicated(142);

     The first call will cache the result, say 37, in the scalar cache;
     the second will cach the list "(37)" in the list cache.  The third
     call doesn't call the real "complicated" function; it gets the value
     37 from the scalar cache.

     Obviously, the second call to "complicated" 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.  "complicated" ends up being
     called only once, and both subsequent calls return 3 from the cache,
     regardless of the calling context.

 _L_i_s_t _v_a_l_u_e_s _i_n _s_c_a_l_a_r _c_o_n_t_e_x_t

 Consider this function:

     sub iota { return reverse (1..$_[0]) }

 This function normally returns a list.  Suppose you memoize it and merge
 the caches:

     memoize 'iota', SCALAR_CACHE => 'MERGE';

     @i7 = iota(7);
     $i7 = iota(7);

 Here the first call caches the list (1,2,3,4,5,6,7).  The second call
 does not really make sense. "Memoize" cannot guess what behavior "iota"
 should have in scalar context without actually calling it in scalar
 context.  Normally "Memoize" _w_o_u_l_d call "iota" in scalar context and
 cache the result, but the "SCALAR_CACHE => 'MERGE'" option says not to do
 that, but to use the cache list-context value instead. But it cannot
 return a list of seven elements in a scalar context. In this case $i7
 will receive the ffiirrsstt eelleemmeenntt of the cached list value, namely 7.

 _M_e_r_g_e_d _d_i_s_k _c_a_c_h_e_s

 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:".

OOTTHHEERR FFAACCIILLIITTIIEESS #

“"uunnmmeemmooiizzee"” There’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.

“"fflluusshh__ccaacchhee"” “flush_cache(function)” will flush out the caches, discarding _a_l_l 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 = ()".

CCAAVVEEAATTSS #

 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.

PPEERRSSIISSTTEENNTT CCAACCHHEE SSUUPPPPOORRTT #

 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 Storable for more details about this.)

 The "flush_cache()" function will raise a run-time error unless the tied
 package provides a "CLEAR" method.

EEXXPPIIRRAATTIIOONN SSUUPPPPOORRTT #

 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.

BBUUGGSS #

 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.

MMAAIILLIINNGG LLIISSTT #

 To join a very low-traffic mailing list for announcements about
 "Memoize", send an empty note to "mjd-perl-memoize-request@plover.com".

AAUUTTHHOORR #

 Mark-Jason Dominus ("mjd-perl-memoize+@plover.com"), Plover Systems co.

 See the "Memoize.pm" Page at http://perl.plover.com/Memoize/ for news and
 upgrades.  Near this page, at http://perl.plover.com/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'.)

 The author's book _H_i_g_h_e_r_-_O_r_d_e_r _P_e_r_l (2005, ISBN 1558607013, published by
 Morgan Kaufmann) discusses memoization (and many other topics) in
 tremendous detail. It is available on-line for free.  For more
 information, visit http://hop.perl.plover.com/ .

 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---fewer than two
 messages per year.

CCOOPPYYRRIIGGHHTT AANNDD LLIICCEENNSSEE #

 Copyright 1998, 1999, 2000, 2001, 2012  by Mark Jason Dominus

 This library is free software; you may redistribute it and/or modify it
 under the same terms as Perl itself.

TTHHAANNKK YYOOUU #

 Many thanks to Florian Ragwitz for administration and packaging
 assistance, to John Tromp for bug reports, 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.

perl v5.36.3 2016-07-25 Memoize(3p)