Apache Hadoop Overview

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Questions and Answers

What command is used to run a MapReduce job on a Hadoop cluster?

$ hadoop jar /$path$/hadoop-streaming-X.X.X.jar$

What is the purpose of the mapper in a MapReduce job?

The mapper processes input data and produces intermediate key-value pairs.

How do you specify the input file in a Hadoop streaming command?

-input input.txt

What is the output format of the results in the retail sales dataset example?

{barcode}\t{total_revenue} Signup and view all the answers

What library allows you to write MapReduce applications in Python?

MRJob Signup and view all the answers

What does HDFS stand for and what is its role in Hadoop?

Hadoop Distributed File System; it stores data across a distributed network of nodes. Signup and view all the answers

Can MRJob applications be run locally and on a cluster?

Yes, they can be run locally, on a Hadoop cluster, and on Amazon EMR. Signup and view all the answers

What does the reducer do in a MapReduce job?

The reducer aggregates and processes intermediate key-value pairs to produce final output. Signup and view all the answers

What is the primary function of Hive in the Hadoop ecosystem?

Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale using SQL-like queries. Signup and view all the answers

What is the main difference between Hadoop 1 (MRv1) and Hadoop 2 (MRv2) in terms of resource management?

Hadoop 1 uses Job Tracker for resource management, while Hadoop 2 introduces YARN, which separates resource management from data processing. Signup and view all the answers

In the context of Pig, what advantage does it provide when dealing with semi-structured or unstructured data?

Pig does not enforce a rigid schema, making it easier to process complex data pipelines. Signup and view all the answers

How does HBase accommodate very large tables in a distributed environment?

HBase supports scalable, distributed storage for large tables composed of billions of rows and millions of columns. Signup and view all the answers

What role does YARN play in the Hadoop ecosystem?

YARN (Yet Another Resource Negotiator) manages resources across the Hadoop cluster and schedules data processing tasks. Signup and view all the answers

What is a key advantage of using MapReduce in data processing?

MapReduce allows for parallel processing of large data sets across distributed computing environments. Signup and view all the answers

Describe the data model used by Pig.

Pig uses a data model based on nested 'bags' of items for processing data. Signup and view all the answers

What types of operations does Pig provide that resemble relational database functions?

Pig provides relational operators like JOIN and GROUP BY. Signup and view all the answers

What is the primary purpose of MapReduce in distributed computing?

To efficiently process large data sets across distributed clusters. Signup and view all the answers

Describe the logical flow of the MapReduce process.

The process consists of Input, Map, Shuffle & Sort, Reduce, and then Output. Signup and view all the answers

What type of applications is MapReduce particularly well-suited for?

MapReduce is well-suited for applications such as log processing and web index building. Signup and view all the answers

How does the streaming process in MapReduce enhance efficiency?

It reduces the number of seeks required to access data, allowing for faster data processing. Signup and view all the answers

What is the significance of the word count problem in the context of MapReduce?

It serves as a classic example to demonstrate the capabilities of the MapReduce model. Signup and view all the answers

What is the role of the 'Reduce' function in the MapReduce framework?

The 'Reduce' function aggregates the results from the 'Map' phase to produce final output. Signup and view all the answers

Explain the importance of 'Shuffle & Sort' in the MapReduce process.

'Shuffle & Sort' organizes the intermediate data before it is processed by the 'Reduce' phase. Signup and view all the answers

How does MapReduce resemble a Unix pipeline?

It follows a similar flow where data is passed through a sequence of processing stages. Signup and view all the answers

Study Notes

Apache Hadoop: HDFS, YARN, and MapReduce

Apache Hadoop is open-source software for reliable, scalable, and distributed computing
Hadoop processes large datasets across computer clusters
It scales from single servers to thousands of machines
It handles failures at the application layer, ensuring high availability
Hadoop is necessary because processing large datasets on large computer clusters requires a common infrastructure
Hadoop is efficient, reliable, and easy to use, and is open-source with an Apache License
Hadoop is used by Amazon, Facebook, Google, the New York Times, Veoh, and Yahoo! (and many more)

Hadoop Modules

Hadoop Common: Contains the Java libraries and tools needed to run Hadoop
Hadoop Distributed File System (HDFS): A distributed file system with high throughput access to application data.
Hadoop YARN: A framework for job scheduling and cluster resource management
Hadoop MapReduce: A YARN-based system for parallel processing of large datasets

Ambari: A web-based tool for provisioning, managing, and monitoring Hadoop clusters. It allows system administrators to provision, manage, and monitor Hadoop clusters.
ZooKeeper: A high-performance coordination service for distributed applications. It provides basic services including Naming, Configuration Management, Synchronization, group services
Flume: A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It's based on streaming data flows with tunable reliability

Oozie

Oozie is a workflow scheduler system to manage Hadoop jobs

Pig

Pig is a high-level scripting language built on top of Hadoop MapReduce for data analysis and transformation
It expresses sequences of MapReduce jobs, and offers Data models such as nested bags, and relational operators (e.g., JOIN, GROUP BY, etc.).
Pig provides an easy way to plug in Java functions for more complex pipelines

Hive

Hive is a distributed data warehouse system enabling analytics on a massive scale.
It's built on top of Apache Hadoop and supports storage such as S3 or HDFS.
It allows users to manage petabytes of data with SQL-like queries

HBase

Hbase is a scalable, distributed database supporting structured data storage for large tables.
It hosts billions of rows and millions of columns on commodity hardware

Hadoop 1 (MRv1) vs. Hadoop 2 (MRv2)

Hadoop 1: Hadoop MapReduce and HDFS. Job Tracker and Task Tracker coordinate jobs and tasks
Hadoop 2 (YARN): YARN replaces Job Tracker to split job execution into Resource Manager and Application Master to manage job execution.

YARN

YARN is the acronym for Yet Another Resource Negotiator and is an open-source framework for distributed processing.
YARN is the key feature of Hadoop version 2.0 of the Apache software foundation
YARN splits the responsibilities of JobTracker into a global Resource Manager and per-application Application Master

Hadoop Cluster Specification

Hadoop runs on commodity hardware, not expensive vendor-specific hardware
Clusters can start small and grow as needed. Storage and computation needs grow.
The number of nodes in a cluster is not constant, but the cluster's data capacity must grow consistently.

Hadoop Cluster Types

Master Nodes: Coordinate the cluster's work. Clients contact them for computations.
Worker/Slave Nodes: Where Hadoop data is stored and where data processing takes place

Network Topology

Typically, 30-40 servers per rack, connected by a 10Gb switch.
Top-of-rack (ToR) switches are needed for redundancy and performance with 10GbE switches recommended.

Cluster Size

Small: Single-rack deployment, self-contained but with few slave nodes
Medium: Multiple racks with distributed master nodes; resilience to failure is better
Large: Sophisticated networking architecture. Slave nodes on multiple racks can talk to any master node efficiently

Rack Awareness

Hadoop prefers within-rack transfers for better performance
The NameNode manages and optimizes data transfer location for faster processing

Hadoop Installation Modes

Standalone (Local): All services run in a single JVM
Pseudo-distributed: Services run on a single server, for simulation
Fully distributed: Services run on a cluster of machines

Hadoop Installation -- Your Task

Specific instructions for installing for a virtual machine (VM) running Ubuntu OS are provided; read the provided text file

Basic Commands for Managing HDFS

Listing files, creating directories, uploading, downloading, copying, moving, removing files and directories

Anatomy of a File Write/Read

Detailed explanation of writing and reading steps within the Hadoop Distributed File System

MapReduce

MapReduce is a distributed computing programming model that works like a Unix pipeline, optimizing data processing and data streaming

MapReduce Example (Word Count)

Demonstrates a word counting example using MapReduce to find the count of each unique word.

MapReduce Example 2 (Retail Sales)

Calculates the Total Sales Revenue for product codes, using MapReduce

Python MRJob

Python library allowing concise MapReduce programming in one class, eliminating separate programs.

Python MRJob - Example Word Count

Creates a Word Count program with Python

Assignment

Look for assignments and submission details in the related MS Teams channel

References

List of important references for the study on Hadoop

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Description

This quiz covers the fundamental concepts of Apache Hadoop, including its architecture and key modules like HDFS, YARN, and MapReduce. Participants will learn how Hadoop facilitates distributed computing and manages large datasets effectively. Test your knowledge on the tools and features that make Hadoop essential for modern data processing.