Using Hadoop Pig with MongoDB

In this post, we’ll see how to install MongoDB support for Pig and we’ll illustrate it with an example where we join 2 MongoDB collections with Pig and store the result in a new collection.


Building Mongo Hadoop

We’re going to use the GIT project  developed by 10gen but with a slightly modification that we made. Because the Pig language doesn’t support variable that starts with underscore (e.g., _id) which is used in MongoDB, we added the ability to use it by replacing the _ prefix with u__ so _id becomes u__id.

First get the source:

$ git clone

Compile the Hadoop pig part of it:

$ ./sbt package
$ ./sbt mongo-hadoop-core/package
$ ./sbt mongo-hadoop-pig/package
$ mkdir ~/pig_libraries
$ cp ./pig/target/mongo-hadoop-pig-1.1.0-SNAPSHOT.jar \
./target/mongo-hadoop-core-1.1.0-SNAPSHOT.jar ~/pig_libraries

Running a join query with Pig on MongoDB collections

One of the thing you can’t do in MongoDB is to do a join between 2 collections. So let’s see how we can do it simply with a pig script.
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Playing with Hadoop Pig

Hadoop Pig is a tool to manipulate data from various sources (CSV file, MySQL, MongoDB, …) using a procedural language (Pig Latin). It can run standalone or distributed with Hadoop. Unlike Hive, it can manipulate non-relational data and do things like aggregations, joins (including left joins), regular expression, sorting, …

In our post, in order to simplify we will consider only standalone executions of Pig.

To install Pig in Ubuntu:

$ sudo apt-get install hadoop-pig

Let’s take a simple example by considering the geo location database from Maxmind. Download the latest GeoLite City file in CSV format (e.g., ) from:

The file in the zip we’re interested in is GeoLiteCity-Location.csv.

Let’s remove the header from the csv file:

$ tail -n +3  GeoLiteCity-Location.csv > location.csv

Let’s first start by loading the CSV file and display it:

data = LOAD 'location.csv' USING PigStorage(',')
       AS (locId, country, region, city, postalCode,
           latitude, longitude, metroCode, areaCode);
dump data;

To run it:

$ pig -x local script.pig

The first line maps the different columns in the CSV to fields which are by default chararray (e.g., city:chararray).
Let’s get rid of information we don’t need and only keep city, region and country:

cities = FOREACH data GENERATE city, region, country;

Now let’s only keep US cities where the city name is set:

usCities = FILTER cities BY country == '"US"' AND city != '""';

Now let’s try to see what city names are popular, i.e., see how many states use the city names.
In SQL, the query would be something like:
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