Dnsanon_rssac is an implementation of RSSAC002v4 processing for DNS statistics. It implements all of v5. In v4, it default to “lax” mode that provides a superset of v4. With –version v3 and v2 it also implements most of prior versions (all but zone size). Given the “RSSAC Advisory on Measurements of the Root Server System”, at https://www.icann.org/en/system/files/files/rssac-002-measurements-root-20nov14-en.pdf, it provides all values that can be computed from packet captures. Its processing can be parallelized and and done incrementally.

Design Goals

Explicit design goals:


Although not an explicit goal, this implementation is largely independent of the other implementations we know of. We depend on dnsanon, which includes some code from DSC (TCP reassembly).

The Basic Idea

The basic idea: nearly everything in RSSAC-002 is a specialized version of “word count”, if you write the words carefully. That lets one use Hadoop style-parallelism to process and combine data.

Get pcaps and extract the DNS queries to Fsdb format (Fsdb is tab-separated text with a header, see http://www.isi.edu/~johnh/FSDB.)

Convert each pcap’s queries to “rssacint” format, an internal format that supports easy aggregation. Each line of rssacint format is of the format (OPERATOR)(KEY) (COUNT). For example, for “+udp-ipv4-queries 10” the operator is “+”, the key is “udp-ipv4-queries” and we’ve seen 10 of them. The + means if we see two rows with the same key, we can add them together. (In practice we use terser keys because we move a lot of bytes around, so this key is actually “+3u04”.) Operators alllow one to compute sums, minimum and maximum, lists that check for completeness, and some others; see rssacint_reduce for details.

Rssacint files can be arbitrarily combined using the rssacint_reduce command. Just merge and sort two or more files then the reduce command will sum up counts (or more generally, apply the operator) without losing information.

As the last step, count the number of unique sources and convert to YAML. These steps loose information.

The Specific Workflow

A full pipeline is:

  1. collect pcaps of all traffic. We use LANDER. Alternates: dnscap.

    We assume pcaps show up as a series of files with dates and/or sequence numbers. For B, they look like 20151227-050349-00203216.lax.pcap, where the last set of numbers are a sequence number and “lax” is a site-name.

  2. extract the DNS queries to “message” format. We use dnsanon. Dnsanon is packaged separately at https://ant.isi.edu/software/dnsanon/index.html.

     < 20151227-050349-00203216.pcap  dnsanon -i - -o . -p mQ -f 20151227-050349-00203216

    will write the file 20151227-050349-00203216.message_question.xz

    (this code should actually be

     < 20151227-050349-00203216.pcap  dnsanon -i - -o - -p Q > 20151227-050349-00203216.message_question.xz

    but a bug in dnsanon-1.3 (to be fixed in dnsanon-1.4) causes this pipeline to not work.

  3. convert messages to rssacint format. Use ./message_to_rssacint.

     xzcat 20151227-050349-00203216.message_question.xz | \
     ./message_to_rssacint --file-seqno=203216 >20151227-050349-00203216.rssacint
  4. optionally (but recommended), process that rssacint format locally to reduce data size:

     < 20151227-050349-00203216.rssacint LC_COLLATE=C sort -k 1,1 | \
       ./rssacint_reduce > smaller.20151227-050349-00203216.rssacint
  5. merge all rssacint files into one big one and reduce it (can be done multiple times).

     cat smaller*.rssacint.fsdb | LC_COLLATE=C sort -k 1,1 | ./rssacint_reduce > complete.rssacint.fsdb
  6. reduce it again to count unique ips

     < complete.rssacint.fsdb ./rssacint_reduce --count-ips > complete.rssacfin.fsdb
  7. Convert rssacfin to yaml. We use ./rssacfin_to_rssacyaml

     < complete.rssacfin.fsdb ./rssacfin_to_rssacyaml

In Hadoop terms, steps 2 and 3 are the map phase, 4 is a combiner, step 5 is a reduce phase, and steps 6 and 7 are a second reduce phase. When we run with Hadoop we often do steps 6 and 7 as a single process.

(And there is nothing magical about Hadoop. The only requirement is that data be sorted before any rssacint_reduce step.)

Detailed Documentation and Sample Output

Each program has a manual page with examples and short sample input and output.

Extended sample output is included in the sample_data subdirectory. Run cd sample_data; make test to exercise this sample output as a test suite.

At B

For B-Root, we capture about 1 pcap file every minute or two (step 1), we process them incrementally over the day (steps 2 and 3 and 4). Every night we run steps 5 as a map-reduce job with Hadoop, and run the final reduce directly (without Hadoop).

On occasion we have re-run an entire day’s computation (steps 2 through 7). We can process that in a few hours on a moderate-size (about 120-core) Hadoop cluster.

Each pcap file is 2GB uncompressed.
Each message file is about 200MB compressed (xz). A merged rssacint file for a day of traffic is typically 10MB after xz compression. After counting unique IPs, this drops to about 2KB.


We have checked our computations for internal consistency and against the Hedgehog implementation of RSSAC-002. We believe our results are internally consistent. We see some differences with Hedgehog’s numbers, but they are close. We believe some differences are due to B-Root’s specific use of Hedgehog which triggers a limitation of Hedgehog that we have never worked-around.

The included program dsc_to_rssacint converts Hedgehog’s modified DSC output to rssacint. Although we do not recommend it for production use, it may be useful to compare implementations.


These program use the standard Perl build system. To install:

perl Makefile.PL
make test
make install

For customization options, see ExtUtils::MakeMaker::FAQ(3) or http://perldoc.perl.org/ExtUtils/MakeMaker/FAQ.html.

The current version of dnsanon_rssac is at https://ant.isi.edu/software/dnsanon_rssac/.

This program depends on dnsanon, available from https://ant.isi.edu/software/dnsanon/.



We are interested in feedback, particularly about correctness or other active users.

Please contact John Heidemann johnh@isi.edu with comments.