Throwing Shapes
Sometimes data processing is better when separated into different processes that may run on the same machine or even on different ones. This is the well-known client-server technique. You can do it using a known protocol (such as http) or by developing your own, specific protocol. This approach needs implementation for constructor and parser procedures for each packet type (request and response). It’s possible for different packets to have the same structure so the constructor and parser will be always the same. Perhaps the simplest solution is to have key/value pairs packed with newline characters or with other separators inside a text block. Binary form with length encoding is another solution.
In an attempt to simplify this client-server interaction, the Remote Procedure Call (RPC) technique appeared. It tries to map functions inside the client code to their counterparts inside the server. RPC hides all the details between a client function call and the server function’s response. This includes argument serialization (to make data appropriate to transfer over the net, also known as marshaling), transport, the server function call, and returning response data back to the client (also serialized). In some implementations, RPC also tries to remove requirements for the client and the server to run on the same operating system or hardware, or to be written in the same programming language.
In the Perl world there are several modules that offer different kinds of RPC, including RPC::Simple, RPC::XML, DCE::RPC, and more.
In this article I’ll explain how to use Perl-specific features to develop a compact RPC implementation that I will name Perl-centric Remote Call (PerlRC). As the name suggests, it will run only with Perl clients and servers.
Shape
PerlRC needs to simulate a function call environment that seems familiar to the client. This requires handling the four key properties of a function call:
- Function name
- Function arguments
- Calling context
- Return data
The design of the Perl language allows generic argument handling, which means that it is possible to handle arguments without knowing them before the function call. There are also ways to discover the calling context. Finally, the caller can handle results in the same way as the called function’s arguments – generically, without knowing their details until the function call returns.
With this in mind, the PerlRC code must follow these steps:
Creating Transport Containers
Essentially these are the request and response packets. I’ll use hashes for both. Each one will be serialized to a scalar which the code will send to the other side with a trailing newline terminator.
A request container resembles:
# request hash $req1 = { 'ARGS' => [ # arguments list 2, 8 ], 'NAME' => 'power', # remote function name 'WANTARRAY' => 0 # calling context }; # result hash for scalar context $res1 = { 'RET_SCALAR' => 256 # result scalar }; # result hash for array context $res2 = { 'RET_ARRAY' => [ # result array 12, 13, 14, 15, 16, 17, 18, ] }; # result hash for error $res3 = { # error description 'ERROR' => 'No such function: test' };Arguments
To keep things simple, the first argument will represent the remote function name to call. This server must remove this argument from the list before passing on the rest to the remote function. The request container holds the name for the remote function and a separate reference to the argument list.
Calling Context Discovery
Find the calling context with the built-in
wantarrayfunction and put this value (0 for scalar and 1 for array context) in the request hash.Transfer Both to the Server
Serialize the request to scalar and escape newline chars with
\n. Append the newline terminator and send it to the server.Unpack Request Data
The server takes the request scalar, removes the trailing newline terminator, and unpacks the request data into a local hash that contains the function name, the calling context, and the argument list.
Server-side Function Call
Find and call the required function in appropriate context. Take the result data or the error. Create a result container with separate fields for scalar and array contexts and one field for any error.
Pack Result Data
Serialize the result hash, escape newlines, append a terminating newline, and send the result data to the client.
Client Unpack of the Result Data
When the client receives the result container, remove the trailing newline char. Unescape any newline chars and unpack the data into a local result hash. Depending on the calling context, return to the caller either the scalar or array field from the result hash or die with an error description if such exists.
The implementation uses two modules:
Storablehandles the serialization of arbitrary data. Serializing data converts it to a string of characters suitable for saving or sending across the network and unserializable later into the form of the original. The rest of the article will also refer to this process as packing and unpacking the data.- IO::Socket::INET handles the creation of Internet domain sockets.
Both modules are standard in the latest Perl distribution packages.
It is possible to use any serialization module including FreezeThaw, XML::Dumper, or even Data::Dumper + eval() instead of Storable.
Point of No Return
Enough background. Here’s the PerlRC implementation of the server:
use Storable qw( thaw nfreeze );
use IO::Socket::INET;
# function table, maps caller names to actual server subs
our %FUNC_MAP = (
power => \&power,
range => \&range,
tree => \&tree,
);
# create listen socket
my $sr = IO::Socket::INET->new( Listen => 5,
LocalAddr => 'localhost:9999',
ReuseAddr => 1 );
while(4)
{
# awaiting connection
my $cl = $sr->accept() or next; # accept new connection or loop on error
while( my $req = <$cl> ) # read request data, exit loop on empty request
{
chomp( $req );
my $thaw = thaw( r_unescape( $req ) ); # 'unpack' request data (\n unescape)
my %req = %{ $thaw || {} }; # copy to local hash
my %res; # result data
my $func = $FUNC_MAP{ $req{ 'NAME' } }; # find required function
if( ! $func ) # check if function exists
{
# function name is not found, return error
$res{ 'ERROR' } = "No such function: " . $req{ 'NAME' };
}
else
{
# function exists, proceed with execution
my @args = @{ $req{ 'ARGS' } }; # copy to local arguments hash
if( $req{ 'WANTARRAY' } ) # depending on the required context...
{
my @ret = &$func( @args ); # call function in array context
$res{ 'RET_ARRAY' } = \@ret; # return array
}
else
{
my $ret = &$func( @args ); # call function in scalar context
$res{ 'RET_SCALAR' } = $ret; # return scalar
}
}
my $res = r_escape( nfreeze( \%res ) ); # 'pack' result data (\n escape)
print $cl "$res\n"; # send result data to the client
}
}
The client side is also simple:
use Storable qw( thaw nfreeze );
use IO::Socket::INET;
# connect to the server
my $cl = IO::Socket::INET->new( PeerAddr => "localhost:9999" )
or die "connect error\n";
# this is interface sub to calling server
sub r_call
{
my %req; # request data
$req{ 'NAME' } = shift; # function name to call
$req{ 'WANTARRAY' } = wantarray ? 1 : 0; # context hint
$req{ 'ARGS' } = \@_; # arguments
my $req = r_escape( nfreeze( \%req ) ); # 'pack' request data (\n escape)
print $cl "$req\n"; # send to the server
my $res = <$cl>; # get result line
chomp( $res );
my $thaw = thaw( r_unescape( $res ) ); # 'unpack' result (\n unescape)
my %res = %{ $thaw || {} }; # copy result data to local hash
# server error -- break execution!
die "r_call: server error: $res{'ERROR'}\n" if $res{ 'ERROR' };
# finally return result in the required context
return wantarray ? @{ $res{ 'RET_ARRAY' } } : $res{ 'RET_SCALAR' };
}
On both sides there are two very simple functions that escape and unescape newline chars. This is necessary to prevent serialized data that contains newline chars from breaking the chosen packet terminator. (A newline works well there because it interacts well with the readline() operation on the socket.)
sub r_escape
{
my $s = shift;
# replace all newlines, CR and % with CGI-style encoded sequences
$s =~ s/([%\r\n])/sprintf("%%%02X", ord($1))/ge;
return $s;
}
sub r_unescape
{
my $s = shift;
# convert back escapes to the original chars
$s =~ s/%([0-9A-Fa-f]{2})/chr(hex($1))/ge;
return $s;
}
Waiting In The Wings
That’s the client and server. Now they need to do something useful. Here’s some code to run on the server from a client:
=head2 power()
arguments: a number (n) and power (p)
returns: the number powered (n**p)
=cut
sub power
{
my $n = shift;
my $p = shift;
return $n**$p;
}
=head2 range( f, t )
arguments: lower (f) and upper indexes (t)
returns: array with number elements between the lower and upper indexes
( f .. t )
=cut
sub range
{
my $f = shift;
my $t = shift;
return $f .. $t;
}
=head2 tree()
arguments: none
returns: in scalar context: hash reference to data tree
in array context: hash (array) of data tree
usage:
$data = tree(); $data->{ ... }
%data = tree(); $data{ ... }
=cut
sub tree
{
my $ret = {
this => 'is test',
nothing => [ qw( ever goes as planned ) ],
number_is => 42,
};
return wantarray ? %$ret : $ret;
}
To make these available to clients, the server must have a map of functions. It’s easy:
# function table, maps caller names to actual server subs
our %FUNC_MAP = (
power => \&power,
range => \&range,
tree => \&tree,
);
That’s all of the setup for the server. Now you can start it.
The client side calls functions in this way:
r_call( 'test', 1, 2, 3, 'opa' ); # this will receive 'not found' error
my $r = r_call( 'power', 2, 8 ); # $r = 256
my @a = r_call( 'range', 12, 18 ); # @a = ( 12, 13, 14, 15, 16, 17, 18 )
my %t = r_call( 'tree' ); # returns data as hash
my $t = r_call( 'tree' ); # returns data as reference
print( "Tree is:\n" . Dumper( \%t ) );
# this will print:
Tree is:
$VAR1 = {
'number_is' => 42,
'nothing' => [
'ever',
'goes',
'as',
'planned'
],
'this' => 'is test'
};
# and will be the same as
print( "Tree is:\n" . Dumper( $t ) );
One Wish
At this point everything works, but as usual, someone will want another feature. Suppose that the server and the client sides each had one wish.
The server side wish may be to have a built-in facility to find callable functions so as to build the function map can be built automatically.
Automatic map discovery has one major flaw which is that all functions in the current package are available to the client. This may not be always desirable. There are simple solutions to the problem. For example, all functions that need external visibility within a package could have a specific name prefix. A map discovery procedure can filter the list of all functions with this prefix and map those externally under the original names (without the prefix).
The following code finds all defined functions in the current namespace (the one that called r_map_discover()) and returns a hash with function-name keys and function-code-reference values:
sub r_map_discover
{
my ( $package ) = caller(); # get the package name of the caller
my $prefix = shift; # optional prefix
my %map;
# disable check for symbolic references
no strict 'refs';
# loop over all entries in the caller package's namespace
while( my ( $k, $v ) = each %{ $package . '::' } )
{
my $sym = $package . '::' . $k; # construct the full name of each symbol
next unless $k =~ s/^$prefix//; # allow only entries starting with prefix
my $r = *{ $sym }{ 'CODE' }; # take reference to the CODE in the glob
next unless $r; # reference is empty, no code under this name, skip
$map{ $k } = $r; # reference points to CODE, assign it to the map
}
return %map;
}
To make the use automatic discovery instead of a static function map, write:
# function table, maps caller names to actual server subs, initially empty
our %FUNC_MAP;
# run the automatic discovery function
%FUNC_MAP = r_map_discover();
Now %FUNC_MAP has all of the externally-visible functions in the current package (namespace). That means it’s time to modify the names in the module to work with automatic discovery. Suppose the prefix is x_:
sub x_power
{
...
}
sub x_range
{
...
}
The server will discover only those functions:
%FUNC_MAP = r_map_discover( 'x_' );
and the client will continue to call functions under their usual names:
my $r = r_call( 'power', 2, 8 ); # $r = 256
my @a = r_call( 'range', 12, 18 ); # @a = ( 12, 13, 14, 15, 16, 17, 18 )
That’s it for the server’s wish. Now it’s time to grant the client’s wish.
Call remote functions transparently might be most important client wish, avoiding the use of r_call().
Perl allows the creation of anonymous function references. It’s also possible to install that reference in a namespace under a real name. The result is a function created at run-time. If the function definition takes place in a specific lexical context, it will still have access to that context even when called later from outside that context. Those functions are closures and they are one way to avoid using r_call():
sub r_define_subs
{
my ( $package ) = caller(); # get the package name of the caller
for my $fn ( @_ ) # loop over the specified function names
{
my $sym = $package . '::' . $fn; # construct the full symbol name
no strict; # turn off symbolic refs check
*$sym = sub { r_call( $fn, @_ ); }; # construct and tie the closure
use strict; # turn the check back on
}
}
# define/import 'range' and 'tree' functions in the current package
r_define_subs( 'range', 'tree' );
# now call them as they are normal functions
my @a = range( 12, 18 ); # @a = ( 12 .. 18 )
my %t = tree(); # returns data as reference
This approach hides the use of r_call() to only one place which the client doesn’t see. Wish granted.
Limits
The biggest limitations of PerlRC relate to serialization.
First of all, both the client and server must have compatible serialization modules or versions. This is crucial! To avoid problems here, either you’ll have to write your own serialization code or perform some kind of version check. If you perform this check, be sure to do it before sending a request and response, in plain text, without using serialization at all.
Another problem is in what data you can serialize in the argument or result containers. Holding references there to something outside the same container may pull in more data than you want, if your serialization follows references, or it may not pull in enough data if your serialization process is very simple. Also there is no way to serialize file handles, compiled code, or objects (which are not in the same container really). In some cases, serializing code and objects may be possible if the serialization modules supports such features (as do Storable and FreezeThaw), if you have the required class modules on both sides, and if you trust code on either side.
The documentation of the serialization modules explain further limitations and workarounds for both approaches.
Conclusion
There is a bit more work to do on PerlRC before using it in production, but if you need simple RPC or you need to tweak the way RPC deals with data or communication, you may have good experiences writing your own implementation instead fitting your application around readymade modules. I hope this text is a good starting point.
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