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Technically this is Part 2 of Firewall-busting ASN-lookups. However, I said (in Part 1) that Part 2 would be about making a vectorized version and this is absolutely not about that. Rather than fib, I merely misdirect. Moving on…

As you can see in Part 1, we have to resort to a system() call to do the TXT record lookup with dig. Frankly, I really dislike that. It’s somewhat sloppy, wasteful of resources and we can do better. Much better (initially, just a little better, tho). R, like most modern interpreted languages, has a C interface. Hadley Wickham goes into far more detail in his epic online (and I’m assuming soon-to-be print) book Advanced R Programming than I will be doing in this post and Jonathan Callahan also has some great in-depth material you should review. You might want to take a peed at some of Dirk Eddelbuettel‘s work, too. This post will (should?) get you jumpstarted with the basics of integrating C & R and will dovetail nicely with Part 2 of the proper series, since we’ll not only be creating a vectorized version of the ip2asn() function but will also be putting it into a proper R package.

### Peeking Under The Covers

Even if you’ve only dabbled with R, you’ve already used traditional C-backed functions, and if you’ve done more extensive computing with R—say, worked with the RMySQL package to connect to a database—then you’ve absolutely used the more “modern”/prevalent Rcpp-backed functions. For example, the mysqlCloseConnection looks like this:

mysqlCloseConnection
function (con, ...)
{
if (!isIdCurrent(con))
return(TRUE)
rs <- dbListResults(con)
if (length(rs) > 0) {
if (dbHasCompleted(rs[[1]]))
dbClearResult(rs[[1]])
else stop("connection has pending rows (close open results set first)")
}
conId <- as(con, "integer")
.Call("RS_MySQL_closeConnection", conId, PACKAGE = .MySQLPkgName)
}
<environment: namespace:RMySQL>


PROTIP: you can see the source code for any R function by just typing the function name sans-parentheses & parameters at the R console prompt

Towards the bottom of the above code listing, you’ll see the .Call("RS_MySQL_closeConnection"...) line which is reaching out to the underlying C/C++ code (in RS-MySQL.c) that makes up the libary. Here’s the definition for that function:

/* open a connection with the same parameters used for in conHandle */
Con_Handle *
RS_MySQL_cloneConnection(Con_Handle *conHandle)
{
S_EVALUATOR

return RS_MySQL_createConnection(
RS_DBI_asMgrHandle(MGR_ID(conHandle)),
RS_MySQL_cloneConParams(RS_DBI_getConnection(conHandle)->conParams));
}


Now, we’re not working with MySQL in this post, but that’s a fairly familiar tool and provides the framework for us to discuss how we’ll be using C/C++, Rcpp & .Call() to make DNS calls from R in a much more efficient manner.

### Picking A DNS Library

For folks familiar with DNS, you may be thinking that we’re going to build an interface to the trusted old standard BIND libresolv library. While that was an option, we’re going to skip with tradition and use the ldns library from NLNet Labs, makers of the #spiffy Unbound validating recursive caching resolver (which uses ldns). Their ldns implementation has a simple but also robust API which supports IPv4, IPv6, TSIG & DNSEC plus is wicked fast, small and can make synchronous calls (which makes it easier to do a basic port). If you’re running Mac OS X, you’ll need to either use Homebrew or MacPorts or compile the library from source. I prefer Homebrew, and used:

brew install ldns


For Linux users, you’ll need both the ldns library and the bsd library (the latter primarily for strlcopy). I gravitate towards Ubuntu for Linux and used the following there:

sudo apt-get install libldns-dev libbsd-dev


You’ll note the lack of a Windows section. Consider this an open offer to anyone on Windows to augment our blog with a Windows version. The Rtools package can help you get started. Hit us up for details on how to join in the fun!

We are making the broad assumption that you have the necessary development environment setup on either Linux or Mac OS X. It’s unlikely you’d be this far along in the post if not 🙂

### Starting Small

Rather than build an entire R interace to the whole ldns library, we’re going to focus this post on:

• Getting a small Rcpp example built
• Interfacing with ldns to retreive a TXT record
• Building an ip2asn() R function that uses this new capability

NOTE: all of the code for this post is in this gist. You can download them all in one fell swoop with:

git clone https://gist.github.com/hrbrmstr/11286662,

and we’ll have a proper repository for the full package impementation in later posts.

We’ll begin with having you install the Rcpp package. Fire up an R console (or use the RStudio R console pane) and do:

> install.packages("Rcpp")


Next, create a directory (perhaps ip2asn for this limited example) and put the following code block into the file txt.cpp (or just use the one you cloned above):

// these three includes do a great deal of heavy lifting
// by making the necessary structures, functions and macros
// available to us for the rest of the code

#include <Rcpp.h>
#include <Rinternals.h>
#include <Rdefines.h>

#ifdef __linux__
#include <bsd/string.h>
#endif

// REF: http://www.nlnetlabs.nl/projects/ldns/ for API info
#include <ldns/ldns.h>

// need this for 'wrap()' which *greatly* simplifies dealing
// with return values
using namespace Rcpp;

// the sole function that does all the work. it accepts an
// R character vector as input (even though we're only expecting
// one string to lookuo) and returns a character vector (one row
// of the DNS TXT records)
RcppExport SEXP txt(SEXP ipPointer) {

ldns_resolver *res = NULL;
ldns_rdf *domain = NULL;
ldns_pkt *p = NULL;
ldns_rr_list *txt = NULL;
ldns_status s;

// SEXP passes in an R vector, we need this as a C++ StringVector
Rcpp::StringVector ip(ipPointer);

// we only passed in one IP address
domain = ldns_dname_new_frm_str(ip[0]);
if (!domain) { return(R_NilValue) ; }

s = ldns_resolver_new_frm_file(&res, NULL);
if (s != LDNS_STATUS_OK) { return(R_NilValue) ; }

p = ldns_resolver_query(res, domain, LDNS_RR_TYPE_TXT, LDNS_RR_CLASS_IN, LDNS_RD);

ldns_rdf_deep_free(domain); // no longer needed

if (!p) { return(R_NilValue) ; }

// get the TXT record(s)
if (!txt) {
ldns_pkt_free(p);
ldns_rr_list_deep_free(txt);
return(R_NilValue) ;
}

// get the TXT record (could be more than one, but not for our IP->ASN)

// get the TXT record (could be more than one, but not for our IP->ASN)

// get the character version via safe copy

// Max TXT record length is 255 chars, but for this example
// the Team CYMRU ASN resolver TXT records should never exceed
// 80 characters (from bulk analysis of large sets of IPs)

char ret[80] ;

Rcpp::StringVector result(1);
result[0] = ret ;

// clean up memory
ldns_rr_list_deep_free(txt);
ldns_pkt_free(p);
ldns_resolver_deep_free(res);

// return the TXT answer string which is ridiculously
// simple even for wonkier structures thanks to wrap()
return(wrap(result));

}


The code is commented pretty well and I won’t be covering all of the nuances of the individual ldns calls. Please note that the function has minimal error checking since it is serving first and foremost as a compact example. The full package version will have all i’s dotted and t’s crossed and I’ll make it a point to show the differences between a “toy” example and production-worthy code when we post the package follow-up.

The code flow pattern will be the same for most of these API library mappings:

• define data types that need to be passed in and returned
• convert them to structures C/C++ can handle
• perform your calculations/operations on that converted data
• clean up after yourself
• return a value R can handle

To compile that code into an object we can use in R, you need to do the following:

export PKG_LIBS=Rscript --vanilla -e 'Rcpp:::LdFlags()'
export PKG_CPPFLAGS=Rscript --vanilla -e 'Rcpp:::CxxFlags()'
R CMD SHLIB -lldns txt.cpp


The export lines setup environment variables that help R/Rcpp know where to look for libraries and define the proper compiler flags for your environment. The last line does the hard work of building the proper compilation and linking directives/commands. All three of them belong in a proper Makefile (or your build system of choice). Again, we’re taking a few shortcuts to make the overall concept a bit more digestible. Complexity coming soon!

If the build was successful, you’ll have txt.o and txt.so files in your directory. Now, on to the good bits!

### Interfacing With R

Having a compiled object is all well and good, but we need to be able to access the txt() function from R. It turns out that this part is pretty straightforward. Put the following into a file (perhaps ip2asn.R) or use the gist version:

# yes, this (dyn.load) is all it takes to expose the function we
# just created to R. and, yes, it's a bit more complicated than
# that, but for now bask in the glow of simplicity

# this function should look more than vaguely familiar
# http://dds.ec/blog/posts/2014/Apr/firewall-busting-asn-lookups/

ip2asn <- function(ip="216.90.108.31") {

orig <- ip

ip <- paste(paste(rev(unlist(strsplit(ip, "\\."))), sep="", collapse="."),
".origin.asn.cymru.com", sep="", collapse="")

# in essence, we replaced the system("dig ...") call with this

result <- .Call("txt", ip)

out <- unlist(strsplit(gsub("\"", "", result), "\ *\\|\ *"))

return(list(ip=orig, asn=out[1], cidr=out[2], cn=out[3], registry=out[4]))

}


To use this new function, make sure your R session is in the working directory of the library (via setwd()) and do:

source("ip2asn.R")
ip2asn()
## $ip ## [1] "216.90.108.31" ## ##$asn
## [1] "23028"
##
## $cidr ## [1] "216.90.108.0/24" ## ##$cn
## [1] "US"
##
\$registry
## [1] "arin"


That uses the function default IP address, but you can use any IP (and, it still only works with a single IP address). Kittens and polar bears will suffer greatly if you pass in anything but a single, 100% valid IP address (see, error checking saves wildlife and pets), but it gets the job done without a system() call and sets us up nicely for adding more capability.

### Wrapping Up

We gave you a whirlwind tour of interfacing with R and we’ll be re-visitng this topic in later posts. If any parts were a bit confusing or your setup has some errors, drop a note in the comments here or over at the gist and we’ll do our best to help you out.