Hadoop, a distributed

framework for Big Data

 Introduction

1. Introduction: Hadoop’s history and

advantages

2. Architecture in detail

3. Hadoop in industry
 What is Hadoop?

• Apache top level project, open-source

implementation of frameworks for reliable,
scalable, distributed computing and data
storage.
• It is a flexible and highly-available
architecture for large scale computation
and data processing on a network of
commodity hardware.
Brief History of Hadoop

• Designed to answer the question:

“How to process big data with
reasonable cost and time?”
 Search engines in 1990s

1996

1996

1996

1997
Google search engines

1998

2013
 Hadoop’s Developers

2005: Doug Cutting and Michael J. Cafarella developed

Hadoop to support distribution for the Nutch search
engine project.
Doug Cutting
The project was funded by Yahoo.

2006: Yahoo gave the project to Apache

Software Foundation.
 Google Origins

2003

2004

2006
 Some Hadoop Milestones

• 2008 - Hadoop Wins Terabyte Sort Benchmark (sorted 1 terabyte

of data in 209 seconds, compared to previous record of 297 seconds)

• 2009 - Avro and Chukwa became new members of Hadoop

Framework family

• 2010 - Hadoop's Hbase, Hive and Pig subprojects completed, adding

more computational power to Hadoop framework

• 2011 - ZooKeeper Completed

• 2013 - Hadoop 1.1.2 and Hadoop 2.0.3 alpha.

- Ambari, Cassandra, Mahout have been added
 What is Hadoop?

• Hadoop:

• an open-source software framework that supports data-

intensive distributed applications, licensed under the
Apache v2 license.

• Goals / Requirements:

• Abstract and facilitate the storage and processing of

large and/or rapidly growing data sets
• Structured and non-structured data
• Simple programming models

• High scalability and availability

• Use commodity (cheap!) hardware with little redundancy

• Fault-tolerance

• Move computation rather than data

Hadoop Framework Tools
 Hadoop’s Architecture

• Distributed, with some centralization

• Main nodes of cluster are where most of the computational power

and storage of the system lies

• Main nodes run TaskTracker to accept and reply to MapReduce

tasks, and also DataNode to store needed blocks closely as
possible

• Central control node runs NameNode to keep track of HDFS

directories & files, and JobTracker to dispatch compute tasks to
TaskTracker

• Written in Java, also supports Python and Ruby

Hadoop’s Architecture
 Hadoop’s Architecture

• Hadoop Distributed Filesystem

• Tailored to needs of MapReduce

• Targeted towards many reads of filestreams

• Writes are more costly

• High degree of data replication (3x by default)

• No need for RAID on normal nodes

• Large blocksize (64MB)

• Location awareness of DataNodes in network

 Hadoop’s Architecture

NameNode:

• Stores metadata for the files, like the directory structure of a

typical FS.

• The server holding the NameNode instance is quite crucial,

as there is only one.

• Transaction log for file deletes/adds, etc. Does not use

transactions for whole blocks or file-streams, only metadata.

• Handles creation of more replica blocks when necessary

after a DataNode failure
 Hadoop’s Architecture

DataNode:

• Stores the actual data in HDFS

• Can run on any underlying filesystem (ext3/4, NTFS, etc)

• Notifies NameNode of what blocks it has

• NameNode replicates blocks 2x in local rack, 1x elsewhere

Hadoop’s Architecture: MapReduce Engine
 Hadoop’s Architecture

MapReduce Engine:

• JobTracker & TaskTracker

• JobTracker splits up data into smaller tasks(“Map”) and

sends it to the TaskTracker process in each node

• TaskTracker reports back to the JobTracker node and

reports on job progress, sends data (“Reduce”) or requests
new jobs
 Hadoop’s Architecture

• None of these components are necessarily limited to using

HDFS

• Many other distributed file-systems with quite different

architectures work

• Many other software packages besides Hadoop's

MapReduce platform make use of HDFS
 Hadoop in the Wild

• Hadoop is in use at most organizations that handle big data:

o Yahoo!
o Facebook
o Amazon
o Netflix
o Etc…

• Some examples of scale:

o Yahoo!’s Search Webmap runs on 10,000 core Linux
cluster and powers Yahoo! Web search

o FB’s Hadoop cluster hosts 100+ PB of data (July, 2012)

& growing at ½ PB/day (Nov, 2012)
 Hadoop in the Wild

Three main applications of Hadoop:

• Advertisement (Mining user behavior to generate

recommendations)

• Searches (group related documents)

• Security (search for uncommon patterns)

 Hadoop in the Wild

• Non-realtime large dataset computing:

o NY Times was dynamically generating PDFs of articles

from 1851-1922

o Wanted to pre-generate & statically serve articles to

improve performance

o Using Hadoop + MapReduce running on EC2 / S3,

converted 4TB of TIFFs into 11 million PDF articles in
24 hrs
 Hadoop in the Wild: Facebook Messages

• Design requirements:

o Integrate display of email, SMS and

chat messages between pairs and
groups of users

o Strong control over who users

receive messages from

o Suited for production use between

500 million people immediately after
launch

o Stringent latency & uptime

requirements
Hadoop in the Wild

• System requirements

o High write throughput

o Cheap, elastic storage

o Low latency

o High consistency (within a

single data center good
enough)

o Disk-efficient sequential
and random read
performance
 Hadoop in the Wild

• Classic alternatives

o These requirements typically met using large MySQL cluster &

caching tiers using Memcached

o Content on HDFS could be loaded into MySQL or Memcached

if needed by web tier

• Problems with previous solutions

o MySQL has low random write throughput… BIG problem for

messaging!

o Difficult to scale MySQL clusters rapidly while maintaining

performance

o MySQL clusters have high management overhead, require

more expensive hardware
 Hadoop Highlights

• Distributed File System

• Fault Tolerance
• Open Data Format
• Flexible Schema
• Queryable Database
 Why use Hadoop?
• Need to process Multi Petabyte Datasets
• Data may not have strict schema
• Expensive to build reliability in each application
• Nodes fails everyday
• Need common infrastructure
• Very Large Distributed File System
• Assumes Commodity Hardware
• Optimized for Batch Processing
• Runs on heterogeneous OS
 DataNode

• A Block Sever
– Stores data in local file system
– Stores meta-data of a block - checksum
– Serves data and meta-data to clients
• Block Report
– Periodically sends a report of all existing blocks to
NameNode
• Facilitate Pipelining of Data
– Forwards data to other specified DataNodes
 Block Placement

• Replication Strategy
– One replica on local node
– Second replica on a remote rack
– Third replica on same remote rack
– Additional replicas are randomly placed
• Clients read from nearest replica
 Data Correctness

• Use Checksums to validate data – CRC32

• File Creation
– Client computes checksum per 512 byte
– DataNode stores the checksum
• File Access
– Client retrieves the data and checksum from
DataNode
– If validation fails, client tries other replicas
 Data Pipelining

• Client retrieves a list of DataNodes on

which to place replicas of a block
• Client writes block to the first DataNode
• The first DataNode forwards the data to
the next DataNode in the Pipeline
• When all replicas are written, the client
moves on to write the next block in file
 Hadoop MapReduce

• MapReduce programming model

– Framework for distributed processing of large
data sets
– Pluggable user code runs in generic
framework
• Common design pattern in data
processing
– cat * | grep | sort | uniq -c | cat > file
– input | map | shuffle | reduce | output
 MapReduce Usage

• Log processing
• Web search indexing
• Ad-hoc queries
 Closer Look

• MapReduce Component
– JobClient
– JobTracker
– TaskTracker
– Child
• Job Creation/Execution Process
 MapReduce Process
(org.apache.hadoop.mapred)

• JobClient
– Submit job
• JobTracker
– Manage and schedule job, split job into tasks
• TaskTracker
– Start and monitor the task execution
• Child
– The process that really execute the task
 Inter Process Communication
IPC/RPC (org.apache.hadoop.ipc)

• Protocol
JobSubmissionProtocol
– JobClient <-------------> JobTracker

InterTrackerProtocol
– TaskTracker <------------> JobTracker

TaskUmbilicalProtocol
– TaskTracker <-------------> Child
• JobTracker impliments both protocol and works as server in
both IPC
• TaskTracker implements the TaskUmbilicalProtocol; Child
gets task information and reports task status through it.