Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages (part 2: distribute classification with hadoop)

elephant-drinking

In this post, we are going to categorize the tweets by distributing the classification on the hadoop cluster. It can make the classification faster if there is a huge number of tweets to classify.

To go through this tutorial you would need to have run the commands in the post Using the Mahout Naive Bayes Classifier to automatically classify Twitter messages.

To distribute the classification on the hadoop nodes, we are going to define a mapreduce job:

  • the csv containing the tweets to classify is split into several chunks
  • each chunk is sent to the hadoop node that will process it by running the map class
  • the map class loads the naive bayes model and some document/word frequency into memory
  • for each tweet of the chunk, it computes the best matching category. The result is written in the output file. We are not using a reducer class as we don’t need to do aggregations.

To download the code used in this post, you can fetch it from github:

$ git clone https://github.com/fredang/mahout-naive-bayes-example2.git

To compile the project:

$ mvn clean package assembly:single

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Installing and comparing MySQL/MariaDB, MongoDB, Vertica, Hive and Impala (Part 1)

impalaA common thing a data analyst does in his day to day job is to run aggregations of data by generally summing and averaging columns using different filters. When tables start to grow to hundreds of millions or billions of rows, these operations become extremely expensive and the choice of a database engine is crucial. Indeed, the more queries an analyst can run during the day, the better he can be at understanding the data.

In this post, we’re going to install 5 popular databases on Linux Ubuntu (12.04):

  • MySQL / MariaDB 10.0: Row based database
  • MongoDB 2.4: NoSQL database
  • Vertica Community Edition 6: Columnar database (similar to Infobright, InfiniDB, …)
  • Hive 0.10: Datawarehouse built on top of HDFS using Map/Reduce
  • Impala 1.0:  Database implemented on top of HDFS (compatible with Hive) based on Dremel that can use different data formats (raw CSV format, Parquet columnar format, …)

Then we’ll provide some scripts to populate them with some test data, run some simple aggregation queries and measure the response time. The tests will be run on only one box without any tuning using a relatively small dataset (160 million rows) but we’re planning on running more thorough tests in the cloud later with much bigger datasets (billions of rows). This is just to give a general idea on the performance of each of the database.
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Playing with Apache Hive and SOLR

As described in a previous post, Apache SOLR can perform very well to provide low latency analytics. Data logs can be pre-aggregated using Hive and then synced to SOLR. To this end, we developed a simple Storage Handler for SOLR so that data can be read and written to SOLR transparently using an external table.

We will show in this post how to install our SOLR storage handler and then run a simple example where we sync some data from Hive to SOLR.
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Playing with Apache Hive, MongoDB and the MTA

Apache Hive is a popular datawarehouse system for Hadoop that allows to run SQL queries on top of Hadoop by translating queries into Map/Reduce jobs. Due to the high latency incurred by Hadoop to execute Map/Reduce jobs, Hive cannot be used in applications that require fast access to data. One common technique is to use Hive to pre-aggregate data logs stored in HDFS and then sync the data to a Datawarehouse.

In this post we’re going to describe how to install Hive and then, as New York City straphangers, we’re going to load subway train movement data from the MTA in HDFS, execute Hive queries to aggregate the number of daily average train movements per line and store the result in MongoDB.
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Playing with the Mahout recommendation engine on a Hadoop cluster

Elephant and riderApache Mahout is an open source library which implements several scalable machine learning algorithms. They can be used among other things to categorize data, group items by cluster, and to implement a recommendation engine.

In this tutorial we will run the Mahout recommendation engine on a data set of movie ratings and show the movie recommendations for each user.

For more details on the recommendation algorithm, you can look at the tutorial from Jee Vang.

Requirement

  • Java (to run hadoop)
  • Hadoop (used by Mahout)
  • Mahout
  • Python (use to show the result)

Running Hadoop

In this section, we are going to describe how to quickly install and configure hadoop on a single machine.
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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., GeoLiteCity_20130101.zip ) from: http://geolite.maxmind.com/download/geoip/database/GeoLiteCity_CSV/.

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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Deploying Hadoop on EC2 with Whirr

Apache Whirr is a set of tools to deploy cloud services. It can be used on Amazon Elastic Cloud(EC2), Rackspace Cloud and many other cloud providers.

Requirement

You need to have an account on Amazon EC2. If you don’t have an account yet, that’s a good news because you are eligible for the AWS Free Tier (750 hours of cloud computing per month for free for 12 month). In the example below, we are using micro instances so you are not going to pay anything (up to 750 hours) with the free tier plan.

Make sure that you have Java JDK 6 or 7 installed on your machine.

Installation

You can download whirr at http://www.apache.org/dyn/closer.cgi/whirr/

Uncompress the archive:

tar xvfz whirr-0.8.1.tar.gz

Now we are going to write a config file to tell whirr how to deploy hadoop on amazon ec2. Create the file ~/hadoop-ec2.properties with the following content:
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