Distributed programming

A word on HDFS (Hadoop Distributed File System)

Last time we have made an introduction to Hadoop MapReduce. We have seen that it relied on its file system: Hadoop Distributed File System (HDFS). Today we are going to take a look at this.

What is HDFS?

The goal of HDFS is to stored large amount of data in a distributed and fault tolerant way. A large file loaded into HDFS will be divided into small chunks  and replicated several times into different nodes of the cluster. The block size and the replication factor is actually configurable per file. This allows to apply some computation on the data locally using MapReduce.  HDFS is said to be append only: once a file is created in HDFS it can only be appended.

Name nodes and Data nodes.

HDFS is based on a master/slave architecture. The NamedNode is the only node responsible for managing the access to the data by clients.  The DataNodes are the nodes responsible for storing the data. The NamedNode  actually stores the metadata of all the data stored in HDFS, and maintains a traditional hierarchical file organisation: the File System Namespace.

Some HDFS commands

These commands are rather simple. In order to do this, you should simply install Hadoop on a machine using a single node setup.

List contents: bin/hadoop fs -ls

Load a file: bin/hadoop fs -put sourcedir targetdir

Delete a file: bin/hadoop fs -rmr -r dir

Distributed programming

An introduction to Hadoop MapReduce

Today we are going to talk about a famous BigData framework called Hadoop MapReduce. In this article, after presenting the framework, we will make a small example using Java and MapReduce Hadoop on Linux Raspbian (yes I am testing Hadoop on a raspberry pi!).

What is MapReduce?

MapReduce is a framework to process very large amount of data easily and efficiently. Typically it gives a solution to this problem: we have terabytes of data, and we need to apply some computation on it to produce a result.

Ok, but how? In very short, MapReduce will use the memory, cpu and storage capacity of a cluster of nodes. The developer using this framework will just need to:

  • install and configure Hadoop on each node
  • make a program that implements two methods: map and reduce

And it is magic? It is near magic, yes! The key concept of understanding MapReduce is actually its file system: the Hadoop Distributed File System (HDSF). It splits huge data into small chunks (typically 68MB or 128MB) and stores them in different nodes of a cluster. During computation, nodes will execute in parallel on the data that is locally stored. Therefore, we are able to:

  • process extremely huge data by splitting them into smaller ones
  • optimize CPU/RAM by executing in different nodes concurrently
  • reduce network bandwidth because the data is located on the node where the computation happens

What does Map and Reduce Do?

Map Phase

Map takes key/value pairs and produce intermediary key/value pairs. The is known as the Map Phase and is executed in parallel by MapperTasks where each MapperTask process a different chunk of the input data.

(input) <k1, v1> -> Map -> <k2, v2> (intermediary ouput)

Reduce Phase

The framework then takes over in a process know as Shuffle and Sort. This happens before the ReducerTasks entering the Reduce phase.

Each intermediary key/value pairs is attributed to a specific ReducerTask (Shuffle): the only one responsible of a given key.  The pairs are then grouped by key (Sort) producing pairs of key/list of values. According to the documentation, the shuffle and sort phases occur simultaneously.

Finally,  ReducerTasks produces the final output for each key it is processing.

 (int. ouput) <k2, v2> -> Shuffle&Sort -> <k2, listOf v2> -> Reduce -> <k3,  v3> (ouput)

The complete process is:

<k1, v1> -> Map -> <k2, v2> -> Shuffle&Sort -> <k2, list of v2> -> Reduce -> <k3,  v3>

Example with WordSearch


We need to install Hadoop first. I have choosen to install it on Raspbian. For the example, we are going to set up a single node system for the purpose of simplicity.

Input data

The input consists of two files located on a folder in the Linux machine.

File01 contains: hello world bye hadoop

File02 contains: hello world hi harry home holiday

Nb: for simplicity, those files and folders are created manually without using HDFS commands.

Source File

Our Hadoop program is a Java program that looks like this. In the Map method we take only the word that starts with a specific string. In the Reduce method, we simply count the occurrences of each word filtered by the Map Phase.

Running the MapReduce program

First, we need to compile and package our program into a .jar file. Second we can run the program with Hadoop by choosing the following parameters:

  • the path of the java program
  • the name of the java class contaning Map/Reduce
  • the path of the input folder
  • the path of the output folder
  • the “startWith” string parameter

For the sake of simplicity let’s say that our jar, input and output are all located in Hadoop folder installation.

The command is to run MapReduce is:

bin/hadoop jar wordsearch.jar WordSearch input ouput ha


Looking at the file created in the output folder, we have a file named “part-r-00000” with all words starting with “ha” and the number of times they appear:

hadoop 1
harry 1

Which is correct!

Hadoop MapReduce is a cleanly thought distributed framework. In order to be use, one need a good comprehension of how things work.

Distributed programming

Introducing ZeroMQ with C# and Java native port

Zeromq is a messaging library written in C++ for building distributed applications. The library can be used when performance and stability both matters. In addition to the original library, there are bindings in language such as C# or Java that wraps the C++ dll. Moreover, 100% native ports exists in these same languages. In this post, I will use Jeromq (Java) and Netmq (.Net) to make a client and a server communicate in a very simple request-reply pattern.

The server in C#

We need to download the latest version of Netmq via Nuget. The server binds to an address so that client can connect to it, receives a message (the request) and sends a message (the reply).

The client in Java

We need to include the dependencies in the Maven file pom.xml.

Then we can create the client application. The client connects to the server, sends a message (the request), and receives a message (the reply).

Zeromq and their related ports can also be used in other patterns like publish-subscribe, push-pull, exclusive pair, etc… This might be a topic of an other post …