Graph Processing With Wukong and Hadoop

As a last (for now) tutorial oriented post on Wukong, let's process a network graph.

Get Data

This airport data (airport edges) from Infochimps is one such network graph with over 35 million edges. It represents the number of flights and passengers transported between two domestic airports in a given month. Go ahead and download it.

Explore Data

We've got to actually look at the data before we can make any decisions about how to process it and what questions we'd like answered:

$: head data/flights_with_colnames.tsv | wu-lign 
origin_airport destin_airport passengers flights month 
MHK            AMW                    21       1 200810
EUG            RDM                    41      22 199011
EUG            RDM                    88      19 199012
EUG            RDM                    11       4 199010
MFR            RDM                     0       1 199002
MFR            RDM                    11       1 199003
MFR            RDM                     2       4 199001
MFR            RDM                     7       1 199009
MFR            RDM                     7       2 199011

So it's exactly what you'd expect; An adjacency list with (origin node,destination node,weight_1,weight_2,timestamp). There are thousands of data sets with similar characteristics...

Ask A Question

A simple question to ask (and probably the first question you should ask of a graph) is what the degree distribution is. Notice there are two flavors of degree in our graph:

1. Passenger Degree: For a given airport (node in the graph) the number of passengers in + the number of passengers out. Passengers in is called the 'in degree' and passengers out is (naturally) called the 'out degree'.

2. Flights Degree: For a given airport the number of flights in + the number of flights out.

Let's write the question wukong style:

#!/usr/bin/env ruby

require 'rubygems'
require 'wukong'

class EdgeMapper < Wukong::Streamer::RecordStreamer
  #
  # Yield both ways so we can sum (passengers in + passengers out) and (flights
  # in + flights out) individually in the reduce phase.
  #
  def process origin_code, destin_code, passengers, flights, month
    yield [origin_code, month, "OUT", passengers, flights]
    yield [destin_code, month, "IN", passengers, flights]
  end
end

class DegreeCalculator < Wukong::Streamer::AccumulatingReducer
  #
  # What are we going to use as a key internally?
  #
  def get_key airport, month, in_or_out, passengers, flights
    [airport, month]
  end

  def start! airport, month, in_or_out, passengers, flights
    @out_degree = {:passengers => 0, :flights => 0}
    @in_degree  = {:passengers => 0, :flights => 0}
  end

  def accumulate airport, month, in_or_out, passengers, flights
    case in_or_out
    when "IN" then
      @in_degree[:passengers] += passengers.to_i
      @in_degree[:flights]    += flights.to_i
    when "OUT" then
      @out_degree[:passengers] += passengers.to_i
      @out_degree[:flights]    += flights.to_i
    end
  end

  #
  # For every airport and month, calculate passenger and flight degrees
  #
  def finalize

    # Passenger degrees (out, in, and total)
    passengers_out   = @out_degree[:passengers]
    passengers_in    = @in_degree[:passengers]
    passengers_total = passengers_in + passengers_out

    # Flight degrees (out, in, and total)
    flights_out      = @out_degree[:flights]
    flights_in       = @in_degree[:flights]
    flights_total    = flights_in + flights_out

    yield [key, passengers_in, passengers_out, passengers_total, flights_in, flights_out, flights_total]
  end
end

#
# Need to use 2 fields for partition so every record with the same airport and
# month land on the same reducer
#
Wukong::Script.new(
  EdgeMapper,
  DegreeCalculator,
  :partition_fields  => 2 # use two fields to partition records
  ).run

Don't panic. There's a lot going on in this script so here's the breakdown (real gentle like):

Mapper

Here we're using wukong's RecordStreamer class which reads lines from $stdin and splits on tabs for us already. That's how we know exactly what arguments the process method gets.

Next, as is often the case with low level map-reduce, we've got to be a bit clever in the way we yield data in the map. Here we yield the edge both ways and attach an extra piece of information ("OUT" or "IN") depending on whether the passengers and flights were going into the airport in a month or out. This way we can distinguish between these two pieces of data in the reducer and process them independently.

Finally, we've carefully rearranged our records such that (airport,month) is always the first two fields. We'll partition on this as the key. (We have to say that explicitly at the bottom of the script)

Reducer

We've seen all these methods before except for one. The reducer needs to know what fields to use as the key (it defaults to the first field). Here we've explicitly told it to use the airport and month as the key with the 'get_key' method.

* start! - Here we initialize the internal state of the reducer with two ruby hashes. One, the @out_degree will count up all the passengers and flights out. The @in_degree will do the same but for passengers and flights in. (Let's all take a moment and think about how awful and unreadable that would be in java...)

* accumulate - Here we simply look at each record and decide which counters to increment depending on whether it's "OUT" or "IN".

* finalize - All we're doing here is taking our accumulated counts, creating the record we care about, and yielding it out. Remember, the 'key' is just (airport,month).

Get An Answer

We know how to put the data on the hdfs and run the script by now so we'll skip that part. Here's what the output looks like:

$: hdp-catd /data/domestic/flights/degree_distribution | head -n20 | wu-lign 
1B1 200906 1   1   2 1  1  2
ABE 200705 0  83  83 0  3  3
ABE 199206 0  31  31 0  1  1
ABE 200708 0 904 904 0 20 20
ABE 200307 0  91  91 0  2  2
ABE 200703 0  36  36 0  1  1
ABE 199902 0  84  84 0  1  1
ABE 200611 0 753 753 0 18 18
ABE 199209 0  99  99 0  1  1
ABE 200702 0  54  54 0  1  1
ABE 200407 0  98  98 0  1  1
ABE 200705 0 647 647 0 15 15
ABE 200306 0  27  27 0  1  1
ABE 200703 0 473 473 0 11 11
ABE 200309 0 150 150 0  1  1
ABE 200702 0 313 313 0  8  8
ABE 200103 0   0   0 0  1  1
ABE 199807 0 105 105 0  1  1
ABE 199907 0  91  91 0  1  1
ABE 199501 0  50  50 0  1  1 

At this point is where you might bring this back down to your local file system, crack open a program like R, make some plots, etc.

And we're done for now. Hurray.