Introduction to Big Data Technologies 2:HDFS, YARN, and MapReduce

In my first article in this series Introduction to Big Data Technologies 1: Hadoop Core Components, I explained what is meant by Big Data, the 5 Vs of Big Data, and brief definitions of all the major components of the Hadoop ecosystem. In this article, we will be diving into 3 backbones of Hadoop which are Hadoop File System(HDFS), Yet Another Resource Negotiator(YARN), and MapReduce. We will also do a hands-on example of MapReduce on the European Football Club Market Value dataset. We will use the concept of MapReduce to find the frequency of the age of players in European Football Clubs.

Hadoop File System(HDFS)

HDFS is the storage layer in Hadoop. It stores large files by splitting them into blocks. Each block is stored in a distributed manner, that is on different storage location across a cluster of machines. This helps in analyzing and processing the data quickly. The default block size in Hadoop 2.X is 128MB. Since the different blocks are store parallelly and distributedly, these blocks can also be processed parallelly and distributedly. Each block is replicated to make the system fault tolerance and highly available, the replica factor in HDFS is 3. Note it is not best for storing small data because it results in a lot of overhead. The major components of HDFS are NameNode and DataNodes. I will explain more about the components of HDFS later. The DataNodes are stored in racks and distributed systems contain several racks. HDFS has a Master/ Slave Architecture, where the NameNode is the master and the DataNodes are the slaves.

Terms in distributed systems

Fault tolerance: this is the ability for a system to continue functioning properly despite the failure of some of its components.

High Availability is the ability of a system to minimize downtime, that is, the system is always up. It is measure by the percentage of total uptime.

Scalability is the ability to increase and decrease resources based on demand.

Replication: this involves replicating the same block of data on difference DataNode.

Rack Awareness Algorithm: is used for managing the storage of blocks into different racks. This states that a replica of a block cannot be stored in the same rack.

Components of HDFS

NameNode stores the metadata of DataNodes and the blocks storage in them. For instance, the location of the blocks on the DataNodes, the replicas of the blocks, newly created blocks, modified blocks, deleted blocks, heartbeat and reports of all DataNode, etc. It maintains and manages DataNodes.

DataNode stores the actual block of data. DataNode communicates with each other to create and maintain replicas of blocks. A client read and write blocks to the DataNodes.

Reading a file in HDFS

  1. The Client sends a request to the NameNode to read a file.
  2. The NameNode returns the storage location (IP Addresses) of the blocks of that file on the DataNodes.
  3. The Client reads the blocks in the DataNodes based on the locations received from the NameNode.

Writing a file in HDFS

  1. The Client sends a request to write blocks of a file to the NameNode.
  2. The NameNode returns the IP addresses of storage locations where the blocks can be stored on DataNodes.
  3. The client stores the blocks of data in the DataNodes based on the IP addresses it received from the NameNode.
  4. DataNodes communicate with each other to replicate the blocks.
  5. Once the replicas are created successfully, the DataNodes send a success message to the client.
  6. Then the client sends the success message to the NameNode.
  7. The NameNode updates the metadata of the DataNodes and the blocks.

Blocks are stored in parallel while replicas are stored sequentially.

HDFS Command

HDFS commands are similar to normal Linux commands, the only addition is that you have to put “hadoop fs” before each command.

List file and directories in a directory

Make a new directory

Copy file from a local device to HDFS

YARN — Yet Another Resource Negotiator

It is the resource manager used in Hadoop 2.X. YARN was introduced to supports multiple and batch processing. It manages how processes are executed in Hadoop but can also run non-MapReduce applications. It contains a Resource Manager, Node Manager, Application Master, and Container.

Resource Manager

It manages resources and job scheduling. It contains the scheduler and the Application Manager. The scheduler is responsible for allocating resources to various running applications. Application Manager manages the list of submitted tasks. The application manager receives the request from the client, negotiate with the container on the required resources for executing the task.

Node Manager

Node Manager is on every DataNode that is where the data are stored. It is used for monitoring the health of Nodes and the usage of container resources.

Application Master

Application Master manages the application lifecycle, execution flow, and executes the task. Application Master request for resources from the resource manager.

Container

Container provides the execution environment.

MapReduce

MapReduce is a model used for processing large data distributedly and parallelly which is cheap, reliable, and fault-tolerant. In HDFS, the files are already stored in different storage locations. MapReduce process these data on those locations then returns an aggregated result. MapReduce functions can be writing in different programming languages like Python, Java, and Scala. The fastest of them is Scala. MapReduce processes data locally, that is data are processed where they are stored. This makes data processing faster. The results from each node are aggregated by the master node, then the final result is sent to the client. MapReduce view inputs and outputs as key, value pairs.

MapReduce Workflow

Using the European Football Market values Dataset on Kaggle, which contains web scrapped Market Value information and other related data on Players from the top 9 European leagues including Premier League, La Liga, Liga NOS, Ligue 1, Bundesliga, Seria A, Premier Liga, Eredivisie, and Jupiler Pro League. The dataset contains 35 columns and over 4200(rows) player details. We will focus on the 6th column which contains the age of the players. Let us use the instance of the 6 players and 4 columns.

Splitting: The data is divided into blocks.

Block 1

Block 2

Block 3

Mapper: takes a set of key-value pairs as input, transforms them, and returns another set of key-value pairs. Mappers focus on transforming its input. In this example, select the Age column and create another column that represents the count of a single player which is always 1.

Shuffling and Sorting: this is done automatically by MapReduce, it shuffles and sorts the output of the mapper based on keys.

Reducer: takes in a set of key-value pairs as input, performs some operation on them like sum, count, average, etc, then return reduced data. Reduce focus on aggregating input.

Final output

Code Example

  • Download and install Virtual Machine Oracle VirtualBox
  • Download Hortonworks HDP Sandbox
  • Import and start your Hortonworks on your Virtual Machine, it installs cent OS and Hadoop dependencies on the virtual machine.
  • On Windows, download Putty to create a terminal you can use in connecting to your virtual machine. You can connect directly from the terminal on Mac and Linux.

Host Name: maria_dev@127.0.0.1

Port: 2222

  • Login on the terminal using

Username : maria_dev

Password : maria_dev

  • Install dependencies
  • Install pip, mrjob, and an editor like VS Code, nano, vim, sublime, etc to write your program.
  • Download European Football Market Value from Kaggle. You can upload the data on Google Drive or on a server to be able to download it to your virtual machine with the “wget” command. You cannot automatically use wget to download data from Kaggle.
  • Download data to your virtual machine.
  • Create a python file with your editor
  • Run the python file using virtual machine resources
  • Run the Python file using Hadoop cluster

This shows information about the job ID, the status of the job, size of the input file, size of the output file, read and write operations.

This shows information about the job counter

This shows information about the MapReduce framework

The result shows that most players in the European football clubs are age 22, with a count of 390, next is age 24 with count 356, then 25 with count 338, and so on. From the data, it is advisable to start a football career very early so has to play in the senior team by age 20s.

I hope you have been able to grasp the concept of Hadoop File System(HDFS), Yet Another Resource Negotiator(YARN), and MapReduce. This article explained the components and structures of HDFS, YARN, and MapReduce, I also did a coding example using the European Football Market Value on Kaggle to drive in the concept of MapReduce.

Please, if you have any feedback or question, you can comment below. My next article in this series will be on Hadoop Pyspark.

Originally published at https://trojrobert.github.io on October 4, 2020.

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