Big Data WorkshopBy

Avinash RamineniShekhar Vemuri

Big Data• Big Data

– More than a single machine can process

– 100s of GB, TB, ?PB!!

• Why the Buzz now ?

– Low Storage Costs

– Increased Computing power

– Potential for Tremendous Insights

– Data for Competitive Advantage

– Velocity , Variety , Volume

• Structured

• Unstructured - Text, Images, Video, Voice, etc

Volume, Velocity & Variety

Big Data Use Cases

Traditional Large-Scale Computation

• Computation has been Processor and Memory bound– Relatively small amounts of data– Faster processor , more RAM

• Distributed Systems– Multiple machines for a single job– Data exchange requires synchronization– Finite bandwidth is available – Temporal dependencies are complicated– Difficult to deal with partial failures

• Data is stored on a SAN• Data is copied to compute nodes

Data Becomes the Bottleneck• Moore’s Law has held firm over 40 years

– Processing power doubles every two years– Processing speed is no longer the problem

• Getting the data to the processors becomes the bottleneck

• Quick Math– Typical disk data transfer rate : 75MB/Sec– Time taken to transfer 100GB of data to the processor ? 22

minutes !!• Assuming sustained reads• Actual time will be worse, since most servers have less than 100 GB of RAM

Need for New Approach• System must support partial Failure

– Failure of a component should result in a graceful degration of application performance

• If a component of the system fails, the workload needs to be picked up by other components.– Failure should not result in loss of any data

• Components should be able to join back upon recovery• Component failures during execution of a job impact the

outcome of the job• Scalability

– Increasing resources should support a proportional incease in load capacity.

Traditional vs Hadoop Approach• Traditional

– Application Server: Application resides– Database Server: Data resides– Data is retrieved from database and sent to application for

processing. – Can become network and I/O intensive for GBs of data.

• Hadoop– Distribute data on to multiple commodity hardware (data

nodes)– Send application to data nodes instead and process data in

parallel.

What is Hadoop

• Hadoop is a framework for distributed data processing with distributed

filesystem.

• Consists of two core components

– The Hadoop Distributed File System (HDFS)

– MapReduce

• Map and then Reduce

• A set of machines running HDFS and MapReduce is known as a Hadoop

Cluster.

• Individual machines are known as Nodes

Core Hadoop Concepts

• Distribute data as it is stored in to the system

• Nodes talk to each other as little as possible

–Shared Nothing Architecture

• Computation happens where the data is stored.

• Data is replicated multiple times on the system for

increased availability and reliability.

• Fault Tolerance

HDFS -1

• HDFS is a file system written in Java

– Sits on top of a native filesystem• Such as ext3, ext4 or xfs

• Designed for storing very large files with streaming data access patterns

• Write-once, read-many-times pattern

• No Random writes to files are allowed

– High Latency, High Throughput

– Provides redundant storage by replicating

– Not Recommended for lot of small files and low latency requirements

HDFS - 2

• Files are split into blocks of size 64MB or 128 MB

• Data is distributed across machines at load time

• Blocks are replicated across multiple machines, known as DataNodes

– Default replication is 3 times

• Two types of Nodes in HDFS Cluster – (Master – worker Pattern)

– NameNode (Master)• Keeps track of blocks that make up a file and where those blocks are located

– DataNodes • Hold the actual blocks

• Stored as standard files in a set of directories specified in the Configuration

HDFS

Block1

Block2

Block3

File150MB

DataNode 1Block1Block2Block3

DataNode 2Block1Block2

DataNode3Block1Block3

DataNode4Block2Block3

ReplicateSplit

Myfile.txt

Myfile.txt: block1,block2,block3

NameNode

Secondary NameNode

HDFS - 3

• HDFS Daemons

– NameNode

• Keeps track of which blocks make up a file and where those blocks are located

• Cluster is not accessible with out a NameNode

– Secondary NameNode

• Takes care of house keeping activities for NameNode (not a backup)

– DataNode

• Regularly report their status to namenode in a heartbeat

• Every hour or at startup sends a block report to NameNode

HDFS - 4

• Blocks are replicated across multiple machines, known as

DataNodes

• Read and Write HDFS files via the Java API

• Access to HDFS from the command line is achieved with the

hadoop fs command

– File system commands – ls, cat, put, get,rm

hadoop fs

"

hadoop fs –get

"

hadoop fs –put

"

Hands On: Using HDFS

MapReduce

• OK, You have data in HDFS, what next?

• Processing via Map Reduce

• Parallel processing for large amounts of data

•Map function, Reduce function

• Data Locality

• Code is shipped to the node closest to data

• Java, Python, Ruby, Scala…

MapReduce Features

• Automatic parallelization and distribution

• Fault-tolerance (Speculative Execution)

• Status and monitoring tools

• Clean abstraction for programmers

–MapReduce programs usually written in Java (Hadoop Streaming)

• Abstracts all the house keeping activities from developers

WordCount MapReduce

Hadoop Daemons• Hadoop is comprised of five separate daemons

• NameNode

• Secondary NameNode

• DataNode

• JobTracker

–Manages MapReduce jobs, distributes individual tasks to machines

• TaskTracker

– Instantiates and monitors individual Map and Reduce tasks

MapReduce

MapReduce Classes

Hadoop Cluster

Job Tracker

Name Node

MapReduce

HDFS

Task Tracker Task Tracker

Data Node Data Node

1,2,3 … n

Master Node Worker processes

MapReduce

•MR1

• only MapReduce

• aka - batch mode

•MR2

• YARN based

•MR is one implementation on top of YARN

• batch, interactive, online, streaming….

Hadoop 2.0

MapReduce

HDFS

NameNodeDataNode

MapReduce

JobTrackerTaskTracker

YARNContainer

NodeManagerAppMaster

ResourceManager

MapReduce

MapReduce

Hands On: Running a MR Job

How do you write MapReduce?

•Map Reduce is a basic building block,

programming approach to solve larger problems

• In most real world applications, multiple MR jobs

are chained together

• Processing pipeline - output of one - input to

another

How do you write MapReduce?

•Map Reduce is a basic building block,

programming approach to solve larger problems

• In most real world applications, multiple MR jobs

are chained together

• Processing pipeline - output of one - input to

another

Anatomy of Map Reduce Job (WordCount)

Hands On: Writing a MR Program

Hadoop Ecosystem

• Rich Ecosystem around Hadoop

•We have used many,

• Sqoop, Flume, Oozie, Mahout, Hive, Pig, Cascading

• Cascading, Lingual

•More out there!

• Impala, Tez, Drill, Stinger, Shark, Spark, Storm…….

Big Data RolesDate Engineer Vs Data Scientist Vs Data Analyst

Hive and Pig

• MapReduce code is typically written in Java• Requires:– A good java programmer– Who understands MapReduce– Who understands the problem– Who will be available to maintain and update the

code in the future as requirements change

Hive and Pig• Many organizations have only a few developers who can

write good MapReduce code• Many other people want to analyze data

– Business analysts– Data scientists– Statisticians– Data analysts

• Need a higher level abstraction on top of MapReduce– Ability to query the data without needing to know MapReduce

intimately– Hive and Pig address these needs

Hive

Hive

• Abstraction on top of MapReduce• Allows users to query data in the Hadoop cluster

without knowing Java or MapReduce• Uses HiveQL Language– Very Similar to SQL

• Hive Interpreter runs on a client machine– Turns HiveQL queries into MapReduce jobs– Submits those jobs to the cluster

Hive Data Model

• Hive ‘layers’ table definitions on top of data in HDFS

• Tables– Typed columns

• TINYINT , SMALLINT, INT, BIGINT• FLOAT,DOUBLE• STRING• BINARY• BOOLEAN, TIMESTAMP

• ARRAY , MAP, STRUCT

Hive Metastore

• Hive’s Metastore is a database containing table definitions and other metadata– By default, stored locally on the client machine in a

Derby database– Usually MySQL if the metastore needs to be shared

across multiple people– table definitions on top of data in HDFS

Hive Examples

Hive Examples

Hive Examples

Hive Limitations

• Not all ‘standard’ SQL is supported– Subqueries are only supported in the FROM clause

• No correlated subqueries– No support for UPDATE or DELETE– No support for INSERTing single rows

Hands-On: Hive

Apache Pig

Pig• Originally created at yahoo• High-level platform for data analysis and processing on Hadoop• Alternative to writing low-level MapReduce code• Relatively simple syntax• Features

– Joining datasets– Grouping data– Loading non-delimited data– Creation of user-defined functions written in java– Relational operations– Support for custom functions and data formats– Complex data structures

Pig

• No shared metadata like Hive• Installation requires no modification to the cluster• Components of Pig

– Data flow language – Pig Latin• Scripting Language for writing data flows

– Interactive Shell – Grunt Shell• Type and execute Pig Latin Statements• Use pig interactively

– Pig Interpreter • Interprets and executes the Pig Latin• Runs on the client machine

Grunt Shell

Pig Interpreter

1. Preprocess and parse Pig Latin2. Check data types3. Make optimizations4. Plan execution5. Generate MapReduce jobs6. Submit jobs to Hadoop7. Monitor progress

Pig Concepts

• A single element of data is an atom• A collection of atoms – such as a row or a

partial row – is a tuple• Tuples are collected together into bags• PigLatin script starts by loading one or more

datasets into bags, and then creates new bags by modifying those it already has

Sample Pig Script

Sample Pig Script

Hands-On: Pig Exercise

References

•Hadoop Definitive Guide - Tom White - must

read!

•http://highlyscalable.wordpress.com/

2012/02/01/mapreduce-patterns/

•http://hortonworks.com/hadoop/yarn/

Questions?

avinash@clairvoyantsoft.comshekhar@clairvoyantsoft.com