Hadoop: An Industry Perspective

Hadoop: An Industry Perspective

Amr AwadallahFounder/CTO, Cloudera, Inc.Massive Data Analytics over the Cloud (MDAC’2010)Monday, April 26th, 2010

Amr Awadallah, Cloudera Inc 2

Outline

▪What is Hadoop?▪Overview of HDFS and MapReduce▪How Hadoop augments an RDBMS?▪Industry Business Needs:

▪Data Consolidation (Structured or Not)▪Data Schema Agility (Evolve Schema Fast)▪Query Language Flexibility (Data Engineering)

▪Data Economics (Store More for Longer)

▪Conclusion


What is Hadoop?

▪A scalable fault-tolerant distributed system for data storage and processing

▪Its scalability comes from the marriage of:▪ HDFS: Self-Healing High-Bandwidth Clustered Storage

▪ MapReduce: Fault-Tolerant Distributed Processing

▪Operates on structured and complex data▪A large and active ecosystem (many developers and additions like HBase, Hive, Pig, …)

▪Open source under the Apache License▪http://wiki.apache.org/hadoop/

http://wiki.apache.org/hadoop/


Hadoop History▪ 2002-2004: Doug Cutting and Mike Cafarella started working

on Nutch

▪ 2003-2004: Google publishes GFS and MapReduce papers

▪ 2004: Cutting adds DFS & MapReduce support to Nutch

▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch

▪ 2007: NY Times converts 4TB of archives over 100 EC2s

▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm

▪ April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910 nodes

▪ May 2009: ▪ Yahoo does fastest sort of a TB, 62secs over 1460 nodes▪ Yahoo sorts a PB in 16.25hours over 3658 nodes

▪ June 2009, Oct 2009: Hadoop Summit, Hadoop World

▪ September 2009: Doug Cutting joins Cloudera


Hadoop Design Axioms

1. System Shall Manage and Heal Itself

2. Performance Shall Scale Linearly 3. Compute Shall Move to Data4. Simple Core, Modular and

Extensible


Block Size = 64MBReplication Factor = 3

HDFS: Hadoop Distributed File System

Cost/GB is a few ¢/month vs $/month


MapReduce: Distributed Processing


Apache Hadoop Ecosystem

HDFS(Hadoop Distributed File System)

HBase (key-value store)

MapReduce (Job Scheduling/Execution System)

Pig (Data Flow) Hive (SQL)

BI ReportingETL Tools

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Sqoop

RDBMS

(Streaming/Pipes APIs)


Relational Databases: Hadoop:

Use The Right Tool For The Right Job

When to use?

• Affordable Storage/Compute

• Structured or Not (Agility)

• Resilient Auto Scalability

When to use?

• Interactive Reporting (<1sec)

• Multistep Transactions

• Lots of Inserts/Updates/Deletes


Typical Hadoop Architecture

Hadoop: Storage and Batch Processing

Data Collection

OLAP Data Mart

Business Intelligence

OLTP Data Store

Interactive Application

Business Users End Customers

Engineers


Complex Data is Growing Really FastGartner – 2009▪Enterprise Data will grow 650% in the next 5 years.

▪80% of this data will be unstructured (complex) data

IDC – 2008▪85% of all corporate information is in unstructured (complex) forms

▪Growth of unstructured data (61.7% CAGR) will far outpace that of transactional data

Data types

Complex


Data Consolidation: One Place For All

A single data system to enable processing across the universe of

data types.

Complex Data

DocumentsWeb feedsSystem logsOnline forums

Structured Data (“relational”)

CRMFinancialsLogisticsData Marts

SharePointSensor dataEMB archivesImages/Video

InventorySales recordsHR recordsWeb Profiles


Schema-on-Read:Schema-on-Write:

Data Agility: Schema on Read vs Write

• Schema must be created before data is loaded.

• An explicit load operation has to take place which transforms the data to the internal structure of the database.

• New columns must be added explicitly before data for such columns can be loaded into the database.

• Read is Fast.

• Standards/Governance.

• Data is simply copied to the file store, no special transformation is needed.

• A SerDe (Serializer/Deserlizer) is applied during read time to extract the required columns.

• New data can start flowing anytime and will appear retroactively once the SerDe is updated to parse them.

• Load is Fast

• Evolving Schemas/Agility


Query Language Flexibility

▪ Java MapReduce: Gives the most flexibility and performance, but potentially long development cycle (the “assembly language” of Hadoop).

▪ Streaming MapReduce: Allows you to develop in any programming language of your choice, but slightly lower performance and less flexibility.

▪Pig: A relatively new language out of Yahoo, suitable for batch data flow workloads

▪Hive: A SQL interpreter on top of MapReduce, also includes a meta-store mapping files to their schemas and associated SerDe’s. Hive also supports User-Defined-Functions and pluggable MapReduce streaming functions in any language.


Hive Extensible Data Types

▪STRUCTS:▪SELECT mytable.mycolumn.myfield FROM …

▪MAPS (Hashes):▪SELECT mytable.mycolumn[mykey] FROM …

▪ARRAYS:▪SELECT mytable.mycolumn[5] FROM …

• JSON:• SELECT get_json_object(mycolumn, objpath)


Data Economics (Return On Byte)

Low ROB

• Return on Byte = value to be extracted from that byte / cost of storing that byte.

• If ROB is < 1 then it will be buried into tape wasteland, thus we need cheaper active storage.

High ROB


Case Studies: Hadoop World ‘09▪ VISA: Large Scale Transaction Analysis

▪ JP Morgan Chase: Data Processing for Financial Services

▪ China Mobile: Data Mining Platform for Telecom Industry

▪ Rackspace: Cross Data Center Log Processing

▪ Booz Allen Hamilton: Protein Alignment using Hadoop

▪ eHarmony: Matchmaking in the Hadoop Cloud

▪ General Sentiment: Understanding Natural Language

▪ Yahoo!: Social Graph Analysis

▪ Visible Technologies: Real-Time Business Intelligence

▪ Facebook: Rethinking the Data Warehouse with Hadoop and Hive

Slides and Videos at http://www.cloudera.com/hadoop-world-nyc

http://www.cloudera.com/hadoop-world-nyc


Cloudera Desktop for Hadoop


Conclusion

Hadoop is a scalable distributed data processing system which enables:1. Consolidation (Structured or

Not)2. Data Agility (Evolving

Schemas)3. Query Flexibility (Any

Language)4. Economical Storage (ROB >

1)


Amr Awadallah

CTO, Cloudera Inc.

[email protected]

http://twitter.com/awadallah

Online Training Videos and Info:

http://cloudera.com/hadoop-training

http://cloudera.com/blog

http://twitter.com/cloudera

Contact Information

mailto:[email protected]

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MapReduce: The Programming Model

Split 1

Split i

Split N

Reduce 1

Reduce i

Reduce R

(sorted words, counts)

Shuffle(sorted words, counts)

Map 1(docid, text)

(docid, text) Map i

(docid, text)

Map M

(words, counts)

(words, counts)

“To Be Or Not

To Be?”

Be, 5

Be, 12

Be, 7

Be, 6

Output File 1

(sorted words, sum of counts)

Output File i


Output File R


Be, 30

SELECT word, COUNT(1) FROM docs GROUP BY word;cat *.txt | mapper.pl | sort | reducer.pl > out.txt


Hadoop High-Level Architecture

Name NodeMaintains mapping of file blocks

to data node slaves

Job TrackerSchedules jobs across

task tracker slaves

Data NodeStores and serves

blocks of data

Hadoop ClientContacts Name Node for data or Job Tracker to submit jobs

Task TrackerRuns tasks (work units)

within a job

Share Physical Node


Economics of Hadoop Storage▪ Typical Hardware:

▪ Two Quad Core Nehalems▪ 24GB RAM▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)▪ 1 Gigabit Ethernet card

▪ Cost/node: $5K/node

▪ Effective HDFS Space:▪ ¼ reserved for temp shuffle space, which leaves

9TB/node▪ 3 way replication leads to 3TB effective HDFS

space/node▪ But assuming 7x compression that becomes ~

20TB/node

Effective Cost per user TB: $250/TB

Other solutions cost in the range of $5K to $100K per user TB


Data Engineering vs Business Intelligence

▪Business Intelligence:▪The practice of extracting business numbers to monitor and evaluate the health of the business.

▪Humans make decisions based on these numbers to improve revenues or reduce costs.

▪Data Engineering:▪The science of writing algorithms that convert data into money Alternatively, how to automatically transform data into new features that increase revenues or reduce costs.

Education

Hadoop: An Industry Perspective