hadoop 2.0 introduction – with hdp for windows...2015/05/14 · agenda • what is big data –...

Seele Lin

Hadoop 2.0 Introduction – with HDP for Windows

Who am I

•  Speaker: – 林彥⾠辰 –  A.K.A Seele Lin – Mail: [email protected]

•  Experience –  2010~Present

–  2013~2014 •  Trainer of Hortonworks Certificated Training lecture

– HCAHD（Hortonworks Certified Apache Hadoop Developer） – HCAHA （Hortonworks Certified Apache Hadoop Administrator）

Agenda

•  What is Big Data –  The Need for Hadoop

•  Hadoop Introduction –  What is Hadoop 2.0

•  Hadoop Architecture Fundamentals –  What is HDFS –  What is MapReduce –  What is YARN –  Hadoop eco-systems

•  HDP for Windows –  What is HDP –  How to install HDP on Windows –  The advantages of HDP –  What’s Next

•  Conclusion •  Q&A

What is Big Data

What is Big Data?

1.  In what timeframe do we now create the same amount of information that we created from the dawn of civilization until 2003?

2.  90% of the world’s data was created in the last (how many years)?

3.  This is data from 2010 report!

2 days

2 years

Sources: http://www.itbusinessedge.com/cm/blogs/lawson/just-the-stats-big-numbers-about-big-data/?cs=48051 http://techcrunch.com/2010/08/04/schmidt-data/

How large can it be?

•  1ZB = 1000 EB = 1,000,000 PB = 1,000,000,000 TB

Every minute…

•  http://whathappensontheinternetin60seconds.com/

The definition?

A set of files A database A single file

“Big Data is like teenage sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it too.”── Dan Ariely.

Big Data Includes All Types of Data S

truct

ured

•  Pre-defined schema

•  Relational database system

Sem

i-stru

ctur

ed

•  Inconsistent structure

•  Cannot be stored in rows in a single table

•  Logs, tweets •  Often has

nested structure

Uns

truct

ured

•  Irregular structure or..

•  Parts of it lack structure

•  Pictures •  Video

Time-sensitive Immutable

© Hortonworks Inc. 2013

6 Key Hadoop DATA TYPES

1.  Sentiment How your customers feel

2.  Clickstream Website visitors’ data

3.  Sensor/Machine Data from remote sensors and machines

4.  Geographic Location-based data

5.  Server Logs

6.  Text Millions of web pages, emails, and documents

Page

Value

4 V’s of Big Data

http://www.datasciencecentral.com/profiles/blogs/data-veracity

Next Product to Buy (NPTB)

Business Problem • Telecom product portfolios are complex • There are many cross-sell opportunities to installed base • Sales associates use in-person conversations to guess about NPTB

recommendations, with little supporting data

Solution • Hadoop gives telcos the ability to make confident NPTB

recommendations, based on data from all its customers • Confident NPTB recommendations empower sales associates and

improve their interactions with customers • Use the HDP data lake to reduce sales friction and create NPTB

advantage like Amazon’s advantage in eCommerce

Use case – Walmart prediction

Revenue ?

Friday

Beer Diapers

Localized, Personalized Promotions

Business Problem • Telcos can geo-locate their mobile subscribers • They could create localized and personalized promotions • This requires connections with both deep historical data and real-

time streaming data • Those connections have been expensive and complicated

Solution • Hadoop brings the data together to inexpensively localize and

personalize promotions delivered to mobile devices • Notify subscribers about local attractions, events and sales that

align with their preferences and location • Telcos can sell these promotional services to retailers

360° View of the Customer

Business Problem • Retailers interact with customers across multiple channels • Customer interaction and purchase data is often siloed • Few retailers can correlate customer purchases with marketing

campaigns and online browsing behavior • Merging data in relational databases is expensive

Solution • Hadoop gives retailers a 360° view of customer behavior • Store data longer & track phases of the customer lifecycle • Gain competitive advantage: increase sales, reduce supply chain

expenses and retain the best customers

Use case – Target case

•  Target mined their customer data and send coupons to shopper who have high “pregnancy prediction” score.

•  One angry father stormed into a Target to yell at them for sending his daughter coupons for baby clothes and cribs.

•  Guess what, she was pregnant, and hadn’t told her father yet.

Changes in Analyzing Data

Big data is fundamentally changing the way we analyze information.

–  Ability to analyze vast amounts of data rather than evaluating sample sets.

– Historically we have had to look at causes. Now we can look at patterns and correlations in data that give us much better perspective.

Recent day cases 1:

•  http://www.ibtimes.co.uk/global-smartphone-data-traffic-increase-eightfold-17-exabyte-2020-1475571

http://tech.naver.jp/blog/?p=2412

Recent day cases 1: Practice on LINE

Recent day cases 2: in Taiwan

–  The media analyze of 2014 Taipei City mayor election •  IMHO,⿊黑貘來說 http://gene.speaking.tw/2014/11/blog-post_28.html • 破解社群與APP⾏行銷

http://taiwansmm.wordpress.com/2014/11/26/⾏行銷絕不等於買廣告-2014年台北市⻑⾧長選舉柯⽂文哲與連/

Scale up or Scale out?

Guess what…

•  Traditionally, computation has been processor-bound •  For decades, the primary push was to increase the

computing power of a single machine –  Faster processor, more RAM

•  Distributed systems evolved to allow developers to use multiple machines for a single job –  At compute time, data is copied to the compute nodes

Scaling with a traditional database

•  scalling with a queue

•  sharding the database

•  fault-tolerane issue

•  corruption issue

•  problems

NOSQL

•  Not Only SQL •  provides a mechanism for storage and retrieval of data

that is modeled in means other than the tabular relations used in relational databases.

•  Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability.

•  Column: Accumulo, Cassandra, Druid, HBase, Vertica •  Document: Clusterpoint, Apache CouchDB, Couchbase,

MarkLogic, MongoDB

•  Key-value: Dynamo, FoundationDB, MemcacheDB, Redis, Riak, FairCom c-treeACE, Aerospike

•  Graph: Allegro, Neo4J, InfiniteGraph, OrientDB, Virtuoso, Stardog

First principles(1/2)

•  "At the most fundamental level, what does a data system do?"

•  a data system does: "A data system answers questions based on information that was acquired in the past".

•  "What is this person's name?”

•  "How many friends does this person have?”

•  A bank account web page answers questions like "What is my current balance?”

•  "What transactions have occurred on my account recently?"

First principles(2/2)

•  Data is often used interchangeably with the word "information".

•  You answer questions on your data by running functions that take data as input

•  The most general purpose data system can answer questions by running functions that take in the as input. In fact, any query can be answered entire dataset by running a function on the complete dataset

Desired Properties of a Big Data System

•  Robust and fault-tolerant •  Low latency reads and updates

•  Scalable

•  General

•  Extensible

•  Allow ad hoc queries

•  Minimal maintenance

The Lambda Architecture

•  There is no single tool that provides a complete solution.

•  You have to use The Lambda Architecture a variety of tools and techniques to build a complete Big Data system.

•  solves the problem of computing arbitrary functions on arbitrary data in realtime by decomposing the problem into three layers

•  batch layer, the serving layer, and the speed layer

The Lambda Architecture model

Batch Layer 1

•  The batch layer stores the master copy of the dataset and precomputes batch views on that master dataset.

•  The master dataset can be thought of us a very large list of records.

•  two things : –  store an immutable, constantly growing master dataset, –  and compute arbitrary functions on that dataset.

•  If you're going to precompute views on a dataset, you need to be able to do so for any view and any dataset.

•  There's a class of systems called "batch processing systems" that are built to do exactly what the batch layer requires

Batch Layer(2

What is Batch View

•  Everything starts from the "query = function(all data)" equation.

•  You could literally run your query functions on the fly on the complete dataset to get the results.

•  it would take a huge amount of resources to do and would be unreasonably expensive

•  Instead of computing the query on the fly, you read the results from the precomputed view.

How we get Batch View

Serving Layer 1

Serving Layer 2

•  The batch layer emits batch views as the result of its functions.

•  The next step is to load the views somewhere so that they can be queried.

•  The serving layer indexes the batch view and loads it up so it can be efficiently queried to get particular values out of the view.

Batch and serving layers satisfy almost all properties •  Robust and fault tolerant •  Scalable

•  General

•  Extensible

•  Allows ad hoc queries

•  Minimal maintenance

Speed Layer 1

Speed Layer 2

•  speed layer as similar to the batch layer in that it produces views based on data it receives.

•  One big difference is that in order to achieve the fastest latencies possible, the speed layer doesn't look at all the new data at once.

•  it updates the realtime view as it receives new data instead of recomputing them like the batch layer does.

•  "incremental updates” vs "recomputation updates".

•  Page view example

Speed Layer 3

•  complexity isolation：complexity is pushed into a layer whose results are only temporary

•  The last piece of the Lambda Architecture is merging the resutlts from the batch and realtime views

Summary of the Lambda Architecture

Summary of the Lambda Architecture •  All new data is sent to both the batch layer and the

speed layer

•  The master dataset is an immutable, append-only set of data

•  The batch layer pre-computes query functions from scratch

•  The serving layer indexes the batch views produced by the batch layer and makes it possible to get particular values out of a batch view very quickly

•  The speed layer compensates for the high latency of updates to the serving layer.

•  Queries are resolved by getting results from both the batch and realtime views and merging them together

The Need for Hadoop

•  Store and use all types of data •  Process all the data

•  Scalability

•  Commodity hardware

Traditional Database

SCALE (storage & processing)

Hadoop Platform

NoSQL MPP Analytics EDW

Hadoop as a Data Factory

•  A role Hadoop can play in an enterprise data platform is that of a data factory

Structured, semi-structured and raw data

Business value

Hadoop

Hadoop as a Data Lake

•  A larger more general role Hadoop can play in an enterprise data platform is that of a data lake.

Integrating Hadoop

WEB LOGS, CLICK STREAMS

MACHINE GENERATED

OLTP

EDW Data Marts

Hadoop

Big Data Data Analysis

Social Media

Messaging

Applications & Spreadsheets

Visualization & Intelligence

ODBC

ODBC Access for Popular BI Tools Tools

Staging Area

Connectors

Hadoop Introduction

– inspired by

•  Apache Hadoop project –  inspired by Google's MapReduce and Google File System

papers.

•  Open sourced, flexible and available architecture for large scale computation and data processing on a network of commodity hardware

•  Yahoo has been the largest contributor to the project and uses Hadoop extensively in its Web search and Ad business.

Hadoop Creator: Doug Cu:ng

Hadoop Concepts

•  Distribute the data as it is initially stored in the system •  Moving Computation is Cheaper than Moving Data

•  Individual nodes can work on data local to those nodes

•  Users can focus on developing applications.

Relational Databases vs. Hadoop

VS.

schema

speed

governance

best fit use

Relational Hadoop

processing

Required on write Required on read

Reads are fast Writes are fast

Standards and structured Loosely structured

Limited, no data processing Processing coupled with data

data types Structured Multi and unstructured

Interactive OLAP Analytics Complex ACID Transactions

Operational Data Store

Data Discovery Processing unstructured data Massive Storage/Processing

Different behaviors between RDBMS and Hadoop

– RDBMS

– Hadoop

Application Schema RDBMS SQL

Application Hadoop Schema MapReduce

Why we use Hadoop, not RDBMS?

•  Limitation of RDBMS – Capacity

•  100GB~100TB –  Speed – Cost

•  High-end devices’ price increases over than its linear proportion

•  Software cost on technical support or license fee

–  Too Complex

•  A Distributed File System is more likely fit our need – DFS usually provides backup and fault-

tolerance mechanism – More cheap than RDBMS when the data is

really huge enough

What is Hadoop 2.0?

•  The Apache Hadoop 2.0 project consists of the following modules: – Hadoop Common: the utilities that provide support for the other

Hadoop modules. – HDFS: the Hadoop Distributed File System –  YARN: a framework for job scheduling and cluster resource

management. – MapReduce: for processing large data sets in a scalable and

parallel fashion.

Difference between Hadoop 1.0 and 2.0

What is YARN

•  Yet Another Resource Negotiator •  Jira ticket (MAPREDUCE-279) raised in January 2008

by Hortonworks co-founder Arun Murthy.

•  YARN is the result of 5 years of subsequent development in the open community.

•  YARN has been tested by Yahoo! since September 2012 and has been in production across 30,000 nodes and 325PB of data since January 2013.

•  More recently, other enterprises such as Microsoft, eBay, Twitter, XING and Spotify have adopted a YARN-based architecture.

•  Apache Hadoop YARN wins Best Paper award at SoCC 2013! - Hortonworks

•  http://hortonworks.com/blog/apache-hadoop-yarn-wins-best-paper-award-at-socc-2013/

YARN: Taking Hadoop Beyond Batch

•  With YARN, applications run natively in Hadoop (instead of on Hadoop)

HDFS Federation

Hadoop Hadoop 2.0

http://hortonworks.com/blog/an-introduction-to-hdfs-federation/

/home/ /app/Hive /app/HBase

HDFS High Availability (HA)

•  Secondary Name Node is not Name Node •  http://www.youtube.com/watch?v=hEqQMLSXQlY

HDFS High Availability (HA)

https://issues.apache.org/jira/browse/HDFS-1623

Hadoop Architecture Fundamentals

What is HDFS

•  Shared multi-petabyte file system for an entire cluster

•  Managed by a single NameNode

•  Multiple DataNodes NameNode

DataNode DataNode DataNode

The Components of HDFS

•  NameNode –  The “master” node of HDFS – Determines and maintains how the chunks of data are

distributed across the DataNodes

•  DataNode –  Stores the chunks of data, and is responsible for replicating the

chunks across other DataNodes

Concept: What is NameNode

•  NameNode holds metadata for the files – One HDFS cluster only has one metadata

– NameNode is a single point of failure

•  Only one NameNode for One HDFS cluster – One HDFS cluster only has one namespace and one root

directory

•  Metadata saves in NameNode’s RAM in case to query it faster –  1G RAM can almost saves 1,000,000 blocks of the mapping

metadata information •  If the block size is 64MB, the metadata may mapping to 64TB actual

data

More on the Metadata

•  NameNode uses two important local files to save the metadata information︰

•  fsimage –  fsimage saves file directory tree information –  fsimage saves the mapping of the file and the blocks

•  edits –  edits saves the file system journal –  When the client tries to create / move a file, the operation will be

first recorded into edits. If the operation succeed, the data in RAM will later be changed.

–  fsimage WILL NOT instantly be changed.

The NameNode

fsimage edits

3. A client application creates a new file in HDFS.

4. The NameNode logs that transaction in the edits file.

1. When the NameNode starts, it reads the fsimage and edits files.

2. The transactions in edits are merged with fsimage, and edits is emptied.

NameNode

Data

File Name Replicas Block Sequence Others

/data/part-0 3 B1, B2, B3 user, group, ...

/data/part-1 3 B4, B5 user, group, ...

OP Code Operands

OP_SET_REPLICATION "/data/part-0", 2

OP_SET_OWNER "/data/part-1", "foo", "bar"

fsimage

edits

File Name Replicas Block Sequence Others

/data/part-0 2 B1, B2, B3 user, group, ...

/data/part-1 3 B4, B5 foo, bar, ...

Memory

Disk

Concept: What is DataNode

•  DataNode hold the actual blocks –  Each block will be 64MB or 128MB in size –  Each block is replicated three times on the cluster

•  DataNode communicates through Heartbeat with NameNode

Block backup and replication

•  Each block is replicated multiple times –  Default replica number = 3 –  Client can modify the configuration

•  Each block’s replica has the same ID –  System has no need to record which blocks are the same

•  Replicas can be set by rack awareness

–  First backup on a rack –  The other two backups are on another rack, but on the different

machines

The DataNodes

“I’m still alive! This is my latest

Blockreport.”

“I’m here! Here is my latest Blockreport.”

123

“Replicate block 123 to DataNode 1.”

NameNode

DataNode 1

DataNode 2

DataNode 3

DataNode 4

© Hortonworks Inc. 2013

Data

1. Client sends a request to the NameNode to add a file to HDFS

3. Client breaks the data into blocks and distributes the blocks to the DataNodes

4. The DataNodes replicate the blocks (as instructed by the NameNode)

2. NameNode tells client how and where to distribute the blocks

NameNode

DataNode 1

DataNode 2

DataNode 3

What is MapReduce

•  Two Functions – Mapper

•  Since we are processing huge amount of data, it’s nature to split the input data

•  The Mapper reads data in the form of key/value pairs •  M(K1, V1) à list(K2, V2)

– Reducer •  Since the input data are split, we would need another phase to

aggregate result in each split •  R(K2, list(V2)) à list(K3, V3)

Hadoop

MapReduce

Hadoop 1.0 Basic Core Architecture

Mapper Reducer

Hadoop Distributed File System (HDFS)

Shuffle/Sort Map Reduce

Words to Websites - Simplified •  From words provide locations •  Provides what to display for a search

–  Note: Page rank determines the order

•  For example – to find URLs with books on them

books www.eslite.com www.amazon.com email www.google.com www.yahoo.com www.facebook.com finance www.yahoo.com www.google.com groceries www.costco.com www.wellcome.com toolkits www.costco.com www.amazon.com

K, V

<url, keyword>

<keyword, url>

www.eslite.com books calendars www.yahoo.com sports finance email celebrity www.amazon.com shoes books toolkits www.google.com finance email search www.microso6.com operaHng-‐system producHvity system

Map

Reduce

Data Model

•  MapReduce works on <key, value> pairs (Key input, Value input)

(Key intermediate, Value intermediate)

(Key output, Value output)

Map

Reduce

(books, www.eslite.com www.amazon.com)

(books, www.eslite.com)

(www.eslite.com , books calendars) Other Compute result (other map result)

Job Tracker

Heartbeat, Task Report …

Worker Nodes

Task Tracker

M M M

R R

Task Tracker

M M M

R R

Task Tracker

M M M

R R

Task Tracker

M M M

R R

The M/R concept

Task Tracker D

Map -> Shuffle -> Reduce

Reducer 0

Task Tracker A

Mapper A A Sort

Task Tracker B

Mapper B B Sort

Task Tracker C

Mapper C C Sort

A

B

C

Merge Fetch

Map -> Shuffle -> Reduce

Reducer 0

Mapper A A0

A1

Mapper B B0

B1

Mapper C C0

C1

A0

B0

C0

A1

B1

C1

Reducer 1

Partition + Sort

Merge

Partition + Sort

Partition + Sort

Merge Fetch

Fetch

Word Count Example

Key: offset Value: line

Key: word Value: count

Key: word Value: sum of count

0:The cat sat on the mat 22:The aardvark sat on the sofa

What is YARN?

•  YARN is a re-architecture of Hadoop that allows multiple applications to run on the same platform

Why YARN •  support non-MapReduce workloads

–  reducing the need to move data between Hadoop HDFS and other storage systems

•  improve scalability –  2009 – 8 cores, 16GB of RAM, 4x1TB disk –  2012 – 16+ cores, 48-96GB of RAM, 12x2TB or 12x3TB of disk. –  scale to production deployments of ~5000 nodes of hardware of

2009 vintage

•  cluster utilization –  JobTracker views the cluster as composed of nodes (managed

by individual TaskTrackers) with distinct map slots and reduce slots

•  customer agility

How YARN Works

•  YARN’s original purpose was to split up the two major responsibilities of the JobTracker/TaskTracker into separate entities:

•  a global ResourceManager

•  a per-application ApplicationMaster

•  a per-node slave NodeManager

•  a per-application Container running on a NodeManager

MapReduce v1

The Hadoop 1.x and 2 Ecosystem

Hadoop

The Path to ROI

Hadoop Distributed File System

Structured Data

Raw Data

1.  Put the data into HDFS in its raw format

Answers to questions = $$

2. Use Pig to explore and transform

3. Data Analysts use Hive to query the data

4. Data Scientists use MapReduce, R and Mahout to mine the data

Hidden gems = $$

Flume & Sqoop

Flume / Sqoop – Data Integration Framework

What’s the problem for data collection •  Data collection is currently a priori and ad hoc

•  A priori – decide what you want to collect ahead of time

•  Ad hoc – each kind of data source goes through its own collection path

(and how can it help?)

•  A distributed data collection service

•  It efficiently collecting, aggregating, and moving large amounts of data

•  Fault tolerant, many failover and recovery mechanism

•  One-stop solution for data collection of all formats

Flume: High-Level Overview •  Logical Node •  Source

•  Sink

An example flow

Sqoop

•  Easy, parallel database import/export •  You want…

–  Insert data from RDBMS to HDFS –  Export data from HDFS back into RDBMS

Sqoop - import process

Sqoop - export process

•  Exports are performed in parallel using MapReduce

Why Sqoop

•  JDBC-based implementation – Works with many popular database vendors

•  Auto-generation of tedious user-side code – Write MapReduce applications to work with your data, faster

•  Integration with Hive –  Allows you to stay in a SQL-based environment

Pig & Hive

Why Hive and Pig?

•  Although MapReduce is very powerful, it can also be complex to master

•  Many organizations have business or data analysts who are skilled at writing SQL queries, but not at writing Java code

•  Many organizations have programmers who are skilled at writing code in scripting languages

•  Hive and Pig are two projects which evolved separately to help such people analyze huge amounts of data via MapReduce – Hive was initially developed at Facebook, Pig at Yahoo!

Pig

•  An engine for executing programs on top of Hadoop

•  A high-level scripting language (Pig Latin)

•  Process data one step at a time

•  Simple to write MapReduce program

•  Easy understand

•  Easy debug

A = load ‘a.txt’ as (id, name, age, ...) B = load ‘b.txt’ as (id, address, ...) C = JOIN A BY id, B BY id;STORE C into ‘c.txt’

– Initiated by

Hive – Developed by

•  What is Hive? –  An SQL-like interface to Hadoop

•  Treat your Big Data as tables

•  Data Warehouse infrastructure – Which provides

•  Data summarization – MapRuduce for execution

•  Ad hoc querying on top of Hadoop – Maintains metadata information about your Big Data stored on HDFS

•  Hive Query Language SELECT * FROM purchases WHERE price > 100 GROUP BY storeid

•  Input •  For the given sample input the map emits

•  the reduce just sums up the values

Hello World Bye World Hello Hadoop Goodbye Hadoop

< Hello, 1> < World, 1> < Bye, 1> < World, 1> < Hello, 1> < Hadoop, 1> < Goodbye, 1> < Hadoop, 1>

< Bye, 1> < Goodbye, 1> < Hadoop, 2> < Hello, 2> < World, 2>

WordCount Example

WordCount Example In MapReduce public class WordCount { public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "wordcount"); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } }

WordCount Example By Pig

A = LOAD 'wordcount/input' USING PigStorage as (token:chararray); B = GROUP A BY token; C = FOREACH B GENERATE group, COUNT(A) as count; DUMP C;

WordCount Example By Hive

CREATE TABLE wordcount (token STRING); LOAD DATA LOCAL INPATH ’wordcount/input' OVERWRITE INTO TABLE wordcount; SELECT count(*) FROM wordcount GROUP BY token;

Hive vs. Pig

Hive Pig Language HiveQL (SQL-like) Pig Latin, a scripting language Schema Table definitions

that are stored in a metastore

A schema is optionally defined at runtime

Programmait Access JDBC, ODBC PigServer

HCatalog in the Ecosystem

Java MapReduce

HCatalog

HDFS HBase ???

What is ?

•  A Java Web Application •  Oozie is a workflow scheduler for Hadoop

•  Crond for Hadoop

Job 1

Job 3

Job 2

Job 4 Job 5

How it triggered

•  Time –  Execute your workflow every 15 minutes

•  Event – Materialize your workflow every hour, but only run them when

the input data is ready.

00:15 00:30 00:45 01:00

01:00 02:00 03:00 04:00

Hadoop Input Data Exists?

Defining an Oozie Workflow

Start

End

Action

Control

Flow

Action

Action

Action

HDP on Windows

General Planning Considerations

•  Run on a single node? –  For test and perform simple operations – Not suitable for big data

•  Start from a small cluster – Maybe 4 or 6 nodes –  As the data grows, add more nodes.

•  Expand when necessary –  Storage is not enough –  Improve computing capability

Traditional Operating System Selection

•  RedHat Enterprise Linux •  CentOS

•  Ubuntu Server

•  SuSE Enterprise Linux

Topology

•  Master Node –  Active NameNode – ResourceManager –  Secondary NameNode ( or Standby NameNode)

•  Slave Node – DataNode – NodeManager

World

Network Topology

Switch Switch

Switch

Namenode

DN + NM DN + NM DN + NM DN + NM

Rack 1

Switch

RM

Rack 2

Switch

SNN

Rack 3

Switch

Rack n




Hadoop Distribution

•  Apache •  Cloudera

•  MapR

•  Hortonworks

•  Amazon EMR –  Apache Hadoop – MapR

•  Greenplum

•  IBM

Get all your Hadoop packages and …

•  Make sure the packages compatibility!

Who is Hortonworks? Upstream Community Projects Downstream Enterprise Product

Hortonworks Data Platform

Design & Develop

Distribute

Integrate & Test

Package & Certify

Apache Pig

Apache Hadoop

Test & Patch

Design & Develop

Release

No Lock-in: Integrated, tested & certified distribution lowers risk by ensuring close alignment with Apache projects

Virtuous cycle when development & fixed issues done upstream & stable project releases flow downstream

Apache Hive

Apache HBase

Other Apache Projects

Apache HCatalog

Apache Ambari

What is HDP

•  Enterprise Hadoop –  The Hortonworks Data

Platform (HDP)

–  The ONLY 100% open source and complete distribution

–  Enterprise grade, proven and tested at scale

–  Ecosystem endorsed to ensure interoperability

OS Cloud VM Appliance

HORTONWORKS DATA PLATFORM (HDP)

PLATFORM SERVICES

HADOOP CORE

Enterprise Readiness: HA, DR, Snapshots, Security, …

HDFS YARN

WEBHDFS MAP REDUCE

DATA SERVICES

HCATALOG

HIVE PIG HBASE

SQOOP

FLUME

OPERATIONAL SERVICES

Manage & Operate at

Scale OOZIE

AMBARI

The management for HDP

What is HDP for Windows

•  HDP for Windows significantly expands the ecosystem for the next generation big data platform. This means that the Microsoft partners and tools you already rely on can help you with your Big Data initiatives.

•  HDP for Windows is the Microsoft recommended way to deploy Hadoop on Windows Server environments.

•  Support – Windows server 2008 – Windows server 2012

Choose your HDP

HDP hardware recommendations

Machine Type Workload Pattern/ Cluster Type Storage Processor (# of

Cores) Memory (GB) Network

Slave Nodes

Balanced workload Twelve 2-3 TB disks 8 128-256 1 GB onboard, 2x10 GBE mezzanine/external

Compute-intensive workload Twelve 1-2 TB disks 10 128-256

1 GB onboard, 2x10 GBE mezzanine/external

Storage-heavy workload Twelve 4+ TB disks 8 128-256

1 GB onboard, 2x10 GBE mezzanine/external

NameNode Balanced workload Four or more 2-3 TB RAID 10 with spares

8 128-256 1 GB onboard, 2x10 GBE mezzanine/external

ResourceManager Balanced workload Four or more 2-3 TB RAID 10 with spares

8 128-256 1 GB onboard, 2x10 GBE mezzanine/external

The installation of HDP for Windows 1


•  #Log directory

•  HDP_LOG_DIR=d:\hadoop\logs

•  #Data directory •  HDP_DATA_DIR=d:\hdp\data

•  #Hosts

•  NAMENODE_HOST=NAMENODE_MASTER.acme.com •  SECONDARY_NAMENODE_HOST=SECONDARY_NAMENODE_MASTER.acme.com

•  RESOURCEMANAGER_HOST.acme.com •  HIVE_SERVER_HOST=HIVE_SERVER_MASTER.acme.com

•  OOZIE_SERVER_HOST=OOZIE_SERVER_MASTER.acme.com •  WEBHCAT_HOST=WEBHCAT_MASTER.acme.com

•  FLUME_HOSTS=FLUME_SERVICE1.acme.com,FLUME_SERVICE2.acme.com,FLUME_SERVICE3.acme.com

•  HBASE_MASTER=HBASE_MASTER.acme.com

•  HBASE_REGIONSERVERS=slave1.acme.com, slave2.acme.com, slave3.acme.com

•  ZOOKEEPER_HOSTS=slave1.acme.com, slave2.acme.com, slave3.acme.com •  SLAVE_HOSTS=slave1.acme.com, slave2.acme.com, slave3.acme.com


•  #Database host

•  DB_FLAVOR=derby •  DB_HOSTNAME=DB_myHostName

•  #Hive properties

•  HIVE_DB_NAME=hive •  HIVE_DB_USERNAME=hive

•  HIVE_DB_PASSWORD=hive

•  #Oozie properties •  OOZIE_DB_NAME=oozie

•  OOZIE_DB_USERNAME=oozie •  OOZIE_DB_PASSWORD=oozie

What is HDP for Windows(Con.)

The management of HDP for Windows

•  The Ambari SCOM integration is made possible by the pluggable nature of Ambari.

The management of HDP for Windows(Con.)

The Advantages of HDP for Windows

•  Hadoop on Windows Made Easy – With HDP for Windows, Hadoop is both simple to install and

manage. It demystifies the Hadoop distribution so you don’t need to choose and test the right combination of Hadoop projects to deploy.

•  Clean and Easy Management –  Apache Ambari, the open source choice for management of a

Hadoop cluster is integrated and extends Microsoft System Center so that IT Operators can manage their Hadoop clusters side-by-side with their databases, applications and other IT assets on a single screen.

•  Secure, Reliable, Enterprise-Ready Hadoop – Offering the most reliable, innovative and trusted distribution

available, Microsoft and Hortonworks together deliver tighter security through integration with Windows Server Active Directory, ease of management through System Center integration.

The Data Integration

•  The Hive ODBC Driver

Hive ODBC Driver

BI Tools Analytics Reporting

Using Hive with Excel

•  Using the Hive ODBC Driver, your Excel spreadsheets can query data stored in Hadoop

Querying Hive from Excel

Querying Hive from Excel (Con.)

Combine model using Power View in Excel

Then Next?

•  Spark

•  Ambari

•  Ranger

•  Falcon

Why

•  MapReduce is too slow •  Aims to make data analytics fast — both fast to run and

fast to write.

•  When you have the request: iterative algorithms

What is

•  In-memory distributed computing framework •  Create by UC Berkeley AMP Lab in 2010

•  Target Problem that Hadoop MR is bad at –  Iterative algorithm (Machine Learning ) –  Interactive data mining

•  More general purpose than Hadoop MR

•  Active contributions from ~15 companies

What Different between Hadoop and Spark

Data Source

Map()

Data Source 2

Join()

Cache() Transform

http://spark.incubator.apache.org

HDFS

Map

Reduce

Map

Reduce

What is

•  Provision a Hadoop Cluster –  Ambari provides a step-by-step wizard for installing Hadoop

services across any number of hosts. –  Ambari handles configuration of Hadoop services for the cluster.

•  Manage a Hadoop Cluster –  Ambari provides central management for starting, stopping, and

reconfiguring Hadoop services across the entire cluster.

•  Monitor a Hadoop Cluster –  Ambari provides a dashboard for monitoring health and status of

the Hadoop cluster.

Ambari installation Wizard

Ambari central dashboard

Conclusion

Container Container

Container

Application Master

Recap - Lifecycle of a YARN Application

Resource Manager

Node Manager

Client

Node Manager

Node Manager

Node Manager

Container •  Basic unit of allocation

Ex. Container A = 2GB, 1CPU

•  Fine-grained resource allocation

•  Replace the fixed map/reduce slots

Container

Container

Container Container

Hadoop 2.0 Eco-systems

•  a

Questions?

hadoop 2.0 introduction – with hdp for windows...2015/05/14 · agenda • what is big data –...

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