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Analysis of domestic flights for Data Day Austin 2011
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plot
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README.textile
degree_distribution.pig
degree_distribution.rb
join_with_geo.pig

README.textile

Airports

WARNING: This is a technical and hands on workshop. Hoping for a lecture? Go to a different talk.

I will not be lecturing on Pig. By diving right in and actually using Apache Pig to solve an interesting problem we’ll see some of its peculiarities, how to work with it, and how to integrate it into a typical analytic workflow. The idea here is to use me, yourself, your neighbors, and possibly even google to work through the following outline:

Outline

The question we want to answer is a simple (but interesting) one:

“How have domestic airports changed since 1990?”

  • Get Data
    • We will gather all our raw data sources together
  • Explore Data
    • head, cut, etc just to see what it looks like
  • Load Data
    • We will upload our data to the HDFS (Hadoop Distributed File System)
  • Pig Setup
    • An aside moment to ensure everyone is on the same page with Apache Pig
  • Analyze Data (Pig!)
    • flights and passengers degree distribution
    • join with geo-location
  • Get answers
    • look at degree (passenger, seat, and flight) distributions as a function of time

Got it? Let’s take an hour and see how far we get. Go.


Get your tools

You will definitely need a laptop (or a desktop if you’re crazy enough to carry one in on your back like a sherpa), a unix terminal, and a text editor. You can follow along without these things but it’s going to be a lot less fun.

Get Data

Before we can even begin to answer our question we’ve got to have the data. Fortunately all of the data we need (and more) can be found at Infochimps. The first data set of interest contains over 9000 airports, their airport codes, and most importantly, their geo-locations. It can be downloaded here. Here’s a quick sample of that dataset:

airport_code latitude longitude airport_name city country country_abbv gmt_offset runway_length runway_elevation
MSW 15.67 39.3700 Massawa International Massawa Eritrea ER +3.0 11471 194
TES 15.1166 36.6833 Tessenei Tessenei Eritrea ER +3.0 6234 2018
ASM 15.2919 38.9105 Yohannes IV Asmara Eritrea ER +3.0 9843 7661
ASA 13.0716 42.6449 Assab International Assab Eritrea ER +3.0 7546 46
NLK -29.0416 167.9386 Norfolk Island Norfolk Island Norfolk Island NF +11.5 6400 371
URT 9.1 99.3333 Surat Thani Surat Thani Thailand TH -7.0 8202 19
PHZ 8.1666 98.2833 Phi Phi Island Phi Phi Island Thailand TH -7.0
|PHS
16.7833 100.2666 Phitsanulok Phitsanulok Thailand TH -7.0 9843 145
UTP 12.6666 100.9833 Utapao Utapao Thailand TH +7.0 11500 59
UTH 17.3863 102.7883 Udon Thani Udon Thani Thailand TH +7.0 10000 579

The last data set we need is really just a giant network graph detailing the number of flights and passengers between domestic airports for almost 20 years. It’s awesome. This can be downloaded for free here for data day only. And here’s what that data set looks like:

origin_airport destin_airport origin_city destin_city passengers seats flights distance month origin_pop destin_pop
MFR RDM Medford, OR Bend, OR 0 0 1 156 200810 200298 157730
AMA EKO Amarillo, TX Elko, NV 124 124 1 858 199308 202960 40259
TUS EKO Tucson, AZ Elko, NV 112 124 1 658 199308 711392 40259
AMA EKO Amarillo, TX Elko, NV 115 124 1 858 199406 206315 41668
ICT EKO Wichita, KS Elko, NV 100 124 1 1007 199607 552884 45034
SPS EKO Wichita Falls, TX Elko, NV 122 124 1 1059 199603 147683 45034

Note this data set normally lives on infochimps here

Explore Data

All the data here is small enough to explore on a single machine and with standard unix command-line tools. Take a moment to do so. Recommended: cat, head, cut, wc -l, uniq -c. This allows you to really understand what it is that you’re dealing with. Don’t fly blind.

Load Data

This step is (basically) optional. That is, if previously in the day you set up Hadoop in distributed (or pseudo-distributed) mode, this step is for you. Otherwise, put your data files in some suitable work directory (say /data) and keep exploring.

Assuming your data files are called flight_edges.tsv and airport_locations.tsv to upload simply do:


hadoop fs -mkdir /data/domestic/airports
hadoop fs -put flight_edges.tsv /data/domestic/airports/
hadoop fs -put airport_locations.tsv /data/domestic/airports/

will put your data files into a directory called /data/domestice/airports/ on the hdfs. Now pig can see them when ran in hadoop mode.

Now that we have the data on the hdfs (or locally) let’s open up a Pig interactive shell, load the data into pig, and dump out the first 10 lines or so. This will really quickly ensure we’ve got all our configuration in order:


$: pig
grunt> flight_edges = LOAD '/data/domestic/airports/flight_edges.tsv' AS (origin_code:chararray, destin_code:chararray, origin_city:chararray, destin_city:chararray, passengers:int, seats:int, flights:int, distance:float, month:int, origin_pop:int, destin_pop:int);
grunt> ten_flight_edges = LIMIT flight_edges 10;
grunt> DUMP ten_flight_edges;

You should see Pig start spamming your screen with all sorts of messages. Ultimately you’ll see 10 lines of data on your screen.

Do the same for the airport locations.

Notes: If pig doesn’t start up right away detecting your hadoop setup PIG_CLASSPATH is probably to blame. Aim it at the directory containing core-site.xml and mapred-site.xml. This is typically /etc/hadoop/conf.

Questions?

Analyze Data

The next step (finally, some pig!) is to actually analyze our data. Before we can do that we have to decide what question we want to answer:

How have domestic airports changed since 1990?

and break this into smaller, more specific, questions:

  • What is the yearly passenger degree distribution? That is, for every airport and year, what is the sum of inbound + outbound passengers? What about seats available? What about flights?
  • What airports or regions were most affected? the least?
  • Anything else interesting here?

And then decide how to answer these smaller questions with Pig syntax (some will require making plots for full effect).

Mapping Questions to “Pig Queries”

Degree Distribution

Before we can answer any of the above question we have to notice that our data is broken down by month and not year. Let’s fix that with a pig projection:


-- Cut off all monthly data portion, we'll sum everything for a whole year in the next step
year_data     = FOREACH flight_edges {
                  year = (int)month/(int)100;
                  GENERATE
                    origin_code AS origin_code,
                    destin_code AS destin_code,
                    passengers  AS passengers,
                    seats       AS seats,
                    flights     AS flights,
                    year        AS year
                  ;
                };

Notice all the nifty things we can do (ie. before the GENERATE part we could do a whole lot more, and we can rename and rearrange fields). Often you find yourself doing simple math during a projection as we’ll see next.

Next we are interested in the degree distributions for every year. Before we can do that we need to generate passengers, seats, and flights out for every airport as well as passengers, seats, and flights in for every airport. Pig allows us to say this very succinctly:


-- For every (airport,month) pair get passengers, seats, and flights out
edges_out     = FOREACH year_data GENERATE
                  origin_code AS airport,
                  year        AS year,
                  passengers  AS passengers_out,
                  seats       AS seats_out,
                  flights     AS flights_out
                ;

-- For every (airport,month) pair get passengers, seats, and flights in                
edges_in      = FOREACH year_data GENERATE
                  destin_code AS airport,
                  year        AS year,
                  passengers  AS passengers_in,
                  seats       AS seats_in,
                  flights     AS flights_in
                ;

This part wasn’t strictly necessary but it reads well. It doesn’t hurt us either because the pig optimizer rolls up all contiguous projections into one hadoop map task.

Finally, we’ll join the outbound data for a given year and airport togeher with the inbound data for the same year and aiport and sum the results:


-- group them together and sum
grouped_edges = COGROUP edges_in BY (airport,year), edges_out BY (airport,year);
degree_dist   = FOREACH grouped_edges {
                  passenger_degree = SUM(edges_in.passengers_in) + SUM(edges_out.passengers_out);
                  seats_degree     = SUM(edges_in.seats_in)      + SUM(edges_out.seats_out);
                  flights_degree   = SUM(edges_in.flights_in)    + SUM(edges_out.flights_out);
                  GENERATE
                    FLATTEN(group)   AS (airport, year),
                    passenger_degree AS passenger_degree,
                    seats_degree     AS seats_degree,
                    flights_degree   AS flights_degree
                  ;
                };

Here we’re using the COGROUP operator to join the two relations (edges out and edges in) by their common key (airport,year). This will result in two big bags that must be flattened in the projection. During the projection we SUM the appropriate meta data. One peculiarity of pig is that the join key after a GROUP or COGROUP operation is called group. We have to use the FLATTEN operator on it.

One final thing is to store the data out to disk:


STORE degree_dist INTO '/data/domestic/airports/degree_distribution';

and run it (save the code into a file called ‘degree_distribution.pig’ or just run the one in the git repo):


# local mode
pig -x local -p FLIGHT_EDGES=/data/domestic/airports/flight_edges.tsv -p DEG_DIST=/data/domestic/airports/degree_distribution degree_distribution.pig

# hadoop mode
pig -p FLIGHT_EDGES=/data/domestic/airports/flight_edges.tsv -p DEG_DIST=/data/domestic/airports/degree_distribution degree_distribution.pig

Geo-Location

Well, a simple degree distribution is nice and all, but what if we want to see what regions were more affected? We might want to plot our data on a map. To do that we’ll have to join our degree distribtion data with our geolocation data:

Here’s how to say that with Pig:


-- Load data (boring part)
deg_dist = LOAD '/data/domestic/airports/degree_distribution' AS (airport_code:chararray, year:int, passenger_degree:int, seats_degree:int, flights_degree:int);
airports = LOAD '/data/domestic/airports/airport_locations.tsv' AS (airport_code:chararray, latitude:float, longitude:float); -- other fields will be dropped
--

-- Join tables together with inner join on common field
with_geo      = JOIN airports BY airport_code, deg_dist BY airport_code;
with_geo_flat = FOREACH with_geo GENERATE
                  airports::airport_code     AS airport_code,
                  airports::latitude         AS latitude,
                  airports::longitude        AS longitude,
                  deg_dist::passenger_degree AS passenger_degree,
                  deg_dist::seats_degree     AS seats_degree,
                  deg_dist::flights_degree   AS flights_degree,
                  deg_dist::year             AS year
                ;

-- Store into a flat file
STORE with_geo_flat INTO '$DEG_DIST_GEO';

Notice that we use the projection after the JOIN to pick out the fields we want from the operation.

run it:


# local mode:
pig -x local -p DEG_DIST=/data/domestic/airports/degree_distribution -p AIRPORTS=/data/domestic/airports/airport_locations.tsv -p DEG_DIST_GEO=/data/domestic/airports/deg_dist_with_geo join_with_geo.pig

# hadoop mode:
pig -p DEG_DIST=/data/domestic/airports/degree_distribution -p AIRPORTS=/data/domestic/airports/airport_locations.tsv -p DEG_DIST_GEO=/data/domestic/airports/deg_dist_with_geo join_with_geo.pig

You can go a bit further if you choose to and think about some other things you might do with this data:


-- get the maximum flights in + out per year
by_year      = GROUP degree_dist BY year;
big_airports = FOREACH by_year {
                 max_flights = MAX(degree_dist.flights_degree);
                 GENERATE
                   group       AS year,
                   max_flights AS max_flights;
               };

and so on. The pig documentation here is a great overview. For more pig code examples see sounder.

Local Analysis

The last hadoop portion is to pull your result set down from the hdfs. Hadoop provides a nice convenience utility for doing this (only applies if you used hadoop):


# copy degree distribution
hadoop fs -copyToLocal /data/domestic/airports/degree_distribution data/deg_dist
cat data/deg_dist/part-* > data/yearly_degrees.tsv

# copy degree dist with geo
hadoop fs -copyToLocal /data/domestic/airports/deg_dist_with_geo data/deg_dist_with_geo
cat data/deg_dist_with_geo/part-* > data/yearly_degrees_with_geo.tsv

Now that your result data is local the next logical step is to import it into some analytics software and explore it with tools you already know. See the code in the plot directory for making simple plots in R and the www directory for a visualization of the degree distribution on a map using protovis

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