Hadoop hbase mapreduce

What is Big Data ?

● How is big “Big Data” ?● Is 30 40 Terabyte big data ?● ….

● Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools

● Today Terabyte, Petabyte, Exabyte● Tomorrow ?

Enterprises & Big Data

● Most companies are currently using traditional tools to store data

● Big data: The next frontier for innovation, competition, and productivity

● The use of big data will become a key basis of competition

● Organisations across the globe need to take the rising importance of big data more seriously

Hadoop is an ecosystem, not a single product.

When you deal with BigData, the data center is your computer.

• A Brief History of Hadoop• Contributers and Development• What is Hadoop • Wyh Hadoop • Hadoop Ecosystem

• Hadoop has its origins in Apache Nutch

• Nutch was started in 2002

• Challenge : The billions of pages on the Web ?

• 2003 GFS (Google File System)

• 2004 NDFS (Nutch File System)

• 2004 Google published the paper of MapReduce

• 2005 Nutch Developers getting started with development of MapReduce

A Brief History of Hadoop


Contributers and Development

Lifetime patches contributed for all Hadoop-related projects: community members by current employer* source : JIRA tickets



* Resource: Kerberos Konference (Yahoo) – 2010

Development in ASF/Hadoop

● Resources● Mailing List● Wiki Pages , blogs● Issue Tracking – JIRA● Version Control SVN – Git


What is Hadoop

• Open-source project administered by the ASF

• Data Intensive Storage

• and Massivly Paralel Processing(MPP)

• Enables applications to work with thousands of nodes and petabytes of data

• Suitable for application with large data sets

What is Hadoop ?

• Scalable

• Fault Tolerance

• Reliable data storage using the Hadoop Distributed File System (HDFS)

• High-performance parallel data processing using a technique called MapReduce

What is Hadoop ?

• Hadoop Becoming defacto standard for large scale dataprocessing

• Becoming more than just MapReduce

• Ecosystem growing rapidly lot’s of great tools around it

What is Hadoop ?

Yahoo Hadoop Cluster

SGI Hadoop Cluster

38,000 machines distributed across 20 different clusters. Recource : Yahoo 2010

50,000 m : January 2012Resource http://www.computerworlduk.com/in-depth/applications/3329092/hadoop-could-save-you-money-over-a-traditional-rdbms/


Why Hadoop?

Why Hadoop?

Why Hadoop?

Why Hadoop? • Hadoop has its origins in Apache Nutch

• Can Process Big Data (Petabytes and more..)

• Unlimited Data Storage & Analyse

• No licence cost - Apache License 2.0

• Can be build out of the commodity hardware

• IT Cost Reduction

• Results

• Be One Step Ahead of Competition

• Stay there

Is hadoop alternative for RDBMs ?• At the moment Apache Hadoop is not a substitute for a database

• No Relation

• Key Value pairs

• Big Data

• unstructured (Text)

• semi structured (Seq / Binary Files)

• Structured (Hbase=Google BigTable)

• Works fine together with RDBMs


Hadoop Ecosystem

HDFS

(Hadoop Distributed File System)

HBase (Key-Value store)

MapReduce (Job Scheduling/Execution System)

Pig (Data Flow) Hive (SQL)

BI ReportingETL Tools

Sqoop

RDBMS

Hadoop Ecosystem

Important components of Hadoop

• HDFS: A distributed, fault tolerance file system

• MapReduce: A paralel data processing framework

• Hive : A query framework (like SQL)

• PIG : A query scripting tool

• HBase : realtime read/write access to your Big Data

Hadoop EcosystemHadoop is a Distributed Data Computing Platform

HDFS

HDFS

NameNode /DataNode interaction in HDFS. The NameNode keeps track of the file metadata—which files are in the system and how each file is broken down into blocks. The DataNodes provide backup store of the blocks and constantly report to the NameNode to keep the metadata current.»

Hadoop Cluster

Writing Files To HDFS

• Client consults NameNode

• Client writes block directly to

one DataNode

• DataNote replicates block

• Cycle repeats for next block

Reading Files From HDFS

• Client consults NameNode

• Client receives Data Node list for each block

• Client picks first Data Node for each block

• Client reads blocks sequentially

Rackawareness & Fault Tolerance

NameNode

Rack Aware Metadata

Rack 1:DN1DN2DN3DN5

Rack 5:DN5DN6DN7DN8

Rack N

File.txtBlk A:DN1,DN5,DN6

Blk B:DN1,DN2,DN9

BLKC:DN5,DN9,DN10

• Never loose all data if entire rack fails

• In Rack is higher bandwidth , lower latency

Cluster Healt

Hadoop Ecosystem






• HBase : A Column oriented Database for OLTP

MapReduce-Paradigm

• Simplified Data Processing on Large Clusters

• Splitting a Big Problem/Data into Little PiecesHive

• Key-Value

MapReduce-Batch Processing

• Phases

• Map

• Sort/Shuffle

• Reduce (Aggregation)

• Coordination

• Job Tracker

• Task Tracker

MapReduce-Map

MAP

MAP

MAP

K V1111

1111

1111

Datanode 1

Datanode 2

Datanode 3

MapReduce-Sort/Shuffle

1

1

1

1

1

1

1

1

1

1

1

1

SORT

SORT

SORT

Datanode 1

Datanode 2

Datanode 3

MapReduce-Reduce

1

1

1

1

1

1

1

1

1

1

1

1

4

3

3

2

REDUCE

REDUCE

REDUCE

SORT

SORT

SORT

K V

K V

K V

Datanode 1

Datanode 2

Datanode 3

MapReduce-All Phases

1

1

1

1

1

1

1

11

1

11

4

3

3

2

REDUCE

REDUCE

REDUCE

SORT

SORT

SORT

MAP

MAP

MAP

1111

1111

1111

MapReduce-Job & Task Tracker

JobTracker and TaskTracker interaction. After a client calls the JobTracker to begin a data processing job, the JobTracker partitions the work and assigns different map and reduce tasks to each TaskTracker in the cluster

Namenode

Datanodes

Summary of HDFS and MR

Hadoop Ecosystem







Hive

Hive

• Data warehousing package built on top of Hadoop

• It began its life at Facebook processing large amount of user

and log data

• Hadoop subproject with many contributors

• Ad hoc queries , summarization , and data analysis on Hadoop-

scale data

• Directly query data from different formats (text/binary) and file

formats (Flat/Sequence)

• HiveQL - like SQL

Hive ComponentsHDFS

Hive CLI

DDLQueriesBrowsing

Map Reduce

MetaStore

Thrift API

Execution

Hive QL

Parser

Planner

Mgm

t. W

eb

UI

*Thrift : Interface Definition Lang.

Hadoop Ecosystem







Pig

• The language used to express data flows, called Pig Latin

• Pig Latin can be extended using UDF (User Defined Functions)

• was originally developed at Yahoo Research

• PigPen is an Eclipse plug-in that provides an environment for

developing Pig programs

• Running Pig Programs

• Script ; script file that contains Pig commands

• Grunt ; interactive shell

• Embedded ; java

http://en.wikipedia.org/wiki/Yahoo

Piggrunt> records = LOAD 'input/ncdc/micro-tab/sample.txt' AS (year:chararray, temperature:int, quality:int);

grunt> DUMP records;(1950,0,1)(1950,22,1)(1950,-11,1)(1949,111,1)(1949,78,1)

grunt> DESCRIBE records;records: {year: chararray,temperature: int,quality: int}

grunt> filtered_records = FILTER records BY temperature != 22 );grunt> DUMP filtered_records;

grunt> grouped_records = GROUP records BY year;grunt> DUMP grouped_records;(1949,{(1949,111,1),(1949,78,1)})(1950,{(1950,0,1),(1950,22,1),(1950,-11,1)})

Hadoop Ecosystem







HBase

• Random, realtime read/write access to your Big Data

• Billions of rows X millions of columns

• Column-oriented store modeled after Google's BigTable

• provides Bigtable-like capabilities on top of Hadoop and HDFS

• HBase is not a column-oriented database in the typical RDBMS

sense, but utilizes an on-disk column storage format

HBase-Datamodel

• Think of tags. Values any length, no predefined names or widths

• Column names carry info (just like tags)

• (Table, RowKey, Family,Column, Timestamp) → Value

HBase-Datamodel


HBase-Datamodel


Create Sample Table hbase(main):003:0> create 'test', 'cf'

hbase(main):004:0> put 'test', 'row1', 'cf:a', 'value11'

hbase(main):004:0> put 'test', 'row1', 'cf:a', 'value12'

hbase(main):005:0> put 'test', 'row2', 'cf:b', 'value2'

hbase(main):006:0> put 'test', 'row3', 'cf:c', 'value3'

hbase(main):007:0> scan 'test'

ROW COLUMN+CELL

row1 column=cf:a, timestamp=1288380727188, value=value12

row2 column=cf:b, timestamp=1288380738440, value=value2

row3 column=cf:c, timestamp=1288380747365, value=value3

hbase(main):007:0> scan 'test', { VERSIONS => 3 }

ROW COLUMN+CELL



row2 column=cf:b, timestamp=1288380738440, value=value2

row3 column=cf:c, timestamp=1288380747365, value=value3

Hbase-Architecture

• Splits

• Auto-Sharding

• Master

• Region Servers

• HFile

Splits & RegionServers

• Rows grouped in regions and served by different servers• Table dynamically split into “regions” • Each region contains values [startKey, endKey) • Regions hosted on a regionserver

Hbase-Architecture

Other Components

• Flume

• Sqoop

Commertial Products

• Oracle Big Data Appliance

• Microsoft Azure + Excel + MapReduce

• Cloud Computing , Amazon elastic computing

• IBM Hadoop-based InfoSphere BigInsights

• VMWare Spring for Apache Hadoop

• Toad for Cloud Database

• Mapr , Cloudera , HortonWorks, Datameer

Thank You

Faruk Berksö[email protected]

Technology

Hadoop hbase mapreduce