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Hadoop Summit 2013- June 26th, 2013
Move to Hadoop, Go Faster and Save Millions - Mainframe Legacy Modernization
Sunilkumar Kakade – Director ITAashish Chandra – DVP, Legacy Modernization
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Legacy Rides The Elephant
Hadoop is disrupting the enterprise IT processing.
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Recognition - Contributors • Our Leaders
• Ted Rudman• Aashish Chandra
• Team • Simon Thomas• Sunil Kakade• Susan Hsu• Bob Pult• Kim Havens• Murali Nandula• Willa Tao• Arlene Pynadath• Nagamani Banda• Tushar Tanna• Kesavan Srinivasan
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The Enterprise Challenge
The Challenge
Growing Data
Volumes Shortened Processing Windows
Escalating Costs
Hitting Scalability Ceilings Demanding
Business Rqmts
ETL Complexity
Latency in Data
Tight IT Budgets
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Mainframe Migration - Overview• In spite of recent advances in computing, many core business
processes are batch-oriented running on mainframes.
• Annual Mainframe costs are counted in 6+ figure Dollars per year, potentially growing with capacity needs. In order to tackle the cost challenge, many organization have considered or attempted multi-year mainframe migration/re-hosting strategies.
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Batch Processing Characteristics
*Ref:. IBM Redbook
Characteristics*• Large amounts of input data are processed and stored (perhaps
terabytes or more).• Large numbers of records are accessed, and a large volume of
output is produced• Immediate response time is usually not a requirement,
however, must complete within a “batch window”• Batch jobs are often designed to run concurrently with online
transactions with minimal resource contention.
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Batch Processing Characteristics
Key infrastructure requirements:
• Sufficient data storage• Available processor capacity, or cycles• job scheduling• Programming utilities to process basic operations
(Sort/Filter/Split/Copy/Unload etc.)
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Why Hadoop and Why Now?THE ADVANTAGES:
• Cost reduction• Alleviate performance bottlenecks • ETL too expensive and complex• Mainframe and Data Warehouse processing Hadoop
THE CHALLENGE:• Traditional enterprises lack of awareness
THE SOLUTION:• Leverage the growing support system for Hadoop• Make Hadoop the data hub in the Enterprise• Use Hadoop for processing batch and analytic jobs
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The Architecture
• Enterprise solutions using Hadoop must be an eco-system
• Large companies have a complex environment:• Transactional system• Services• EDW and Data marts• Reporting tools and needs
• We needed to build an entire solution
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MetaScale’ s Hadoop Ecosystem
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Ha
doop based Ecosystem
for Legacy System
Modernization
Mysql
EnterpriseSystems
JQUERY/AJAXQuart
zJAXB
REST API
JDBC/IBATIS
JBOSSJ2EE/JBOSS/SPRING
Batch ProcessingHIVE
RUBY/MAPREDUCE
JBOSSHADOOP/PIG
DB2
EnterpriseSystems
JQUERY/AJAXQuart
zJAXB
REST APIJDBC/IBATIS
JBOSSJ2EE/WebSphere
Mainframe Batch Processing
VSAM
JBOSSCOBOL/JCL
MetaScale
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Mainframe Batch Processing Architecture
Mainframe Batch Processing Architecture
User Interface Data SourcesBatch
Processing
Datawarehouse
Input
Resultant Data
Resultant Data
Historical Data Sources
Input
Data Retention External Systems
Resultant Data
Input
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MetaScale Batch Processing Architecture With Hadoop
Hadoop EcoSystem
User Interface Data Sources
Hadoop EcoSystem Map Reduce basedBatch Processing
External Systems/
Datawarehouse
InputMove to Hadoop
Resultant Data
Move to Non-Hadoop
Resultant DataMove to Non-Hadoop platform
Datawarehouse
Resultant Data
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Typical Batch Processing Units (JCL) on Mainframe
Batch Processing - JOB FLOW
JCL1 - APPLICATION 1
Mainframe Batch Processing Flow
User Interface Data Sources Batch Processing
External Systems/
DatawarehouseInput
Resultant Data Resultant Data
SORT Input SPLITInput
SORT
Input COBOL
Input FILTER
Input FORMAT
JCL2 - APPLICATION 1
JCL3 - APPLICATION 2
LOAD TO DATABASE
COPY Input COBOL Input FORMAT
Input
Input
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Batch Processing Migration With HadoopSeamless migration of high MIPS processing jobs with no application alteration
Commodity Hardware Based Software Framework
Batch Processing - JOB FLOW
Batch Process - APPLICATION 1
Batch Processing - JOB FLOW - Legacy Platform
Invention - Migration methodology for Legacy Applications to Commodity Hardware
User Interface Data SourcesExternal Systems/
Datawarehouse
Batch ProcessingInput Resultant Data
PIG/MR Input PIG/MRInput
PIG/MR
Input PIG/MR
Input PIG/MR
Input PIG/MR
JCL2 - APPLICATION 1
JCL3 - APPLICATION 2
LOAD TO DATABASE
COPY Input COBOL Input FORMAT
Input
Input
Resultant Data
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Mainframe to Hadoop-PIG conversion example
Mainframe JCL//PZHDC110 EXEC PGM=SORT//SORTIN DD DSN=PZ.THDC100.PLMP.PRC,// DISP=(OLD,DELETE,KEEP)//SORTOUT DD DSN=PZ.THDC110.PLMP.PRC.SRT,LABEL=EXPDT=99000,// DISP=(,CATLG,DELETE),// UNIT=CART,// VOL=(,RETAIN),// RECFM=FB,LRECL=40//SYSIN DD DSN=KMC.PZ.PARMLIB(PZHDC11A),// DISP=SHR//SYSOUT DD SYSOUT=V//SYSUDUMP DD SYSOUT=D//*__________________________________________________
//* SORT FIELDS=(1,9,CH,A) - 500 Million Records sort took 45 minutes of clock time on A168 mainframe
PIG a = LOAD 'data' AS f1:char;b = ORDER a BY f1;
- 500 Million Records sort took less than 2 minutes
More benchmarking studies in progress
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Mainframe to Hadoop-PIG conversion example
Mainframe JCL//PZHDC110 EXEC PGM=SORT//SORTIN DD DSN=PZ.THDC100.PLMP.PRC,// DISP=(OLD,DELETE,KEEP)//SORTOUT DD DSN=PZ.THDC110.PLMP.PRC.SRT,LABEL=EXPDT=99000,// DISP=(,CATLG,DELETE),// UNIT=CART,// VOL=(,RETAIN),// RECFM=FB,LRECL=40//SYSIN DD DSN=KMC.PZ.PARMLIB(PZHDC11A),// DISP=SHR//SYSOUT DD SYSOUT=V//SYSUDUMP DD SYSOUT=D//*__________________________________________________
//* SORT FIELDS=(1,9,CH,A) - 500 Million Records sort took 45 minutes of clock time on A168 mainframe
PIG a = LOAD 'data' AS f1:char;b = ORDER a BY f1;
- 500 Million Records sort took less than 2 minutes
More benchmarking studies in progress
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Mainframe Migration – Value Proposition
MainframeMigration
Optimize
PiG / Hadoop Rewrites
Convert
High TCO
ResourceCrunch
Inert Business Practices
Mainframe ONLINE
-Tool based Conversion
-Convert COBOL & JCL to Java
Mainframe Optimization: -5% ~ 10% MIPS Reduction
-Quick Wins with Low hanging fruits
Mainframe BATCH
-ETL Modernization
-Move Batch Processing to Hadoop
Cost Savings Open Source Platform Simpler & Easier Code Business Agility Business & IT Transformation Modernized Systems IT Efficiencies
Companies can SAVE 60% ~ 80% of their Mainframe Costs with Modernization
Typically 60% ~ 65% of MIPS are used in Mainframes by BATCH processing
Estimated 45% of FUNCTIONALITY in mainframes is never used
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Mainframe Migration – Traditional Approach
• Traditional approaches to mainframe elimination call for large initial investments and carry significant risks – It is hard to match Mainframe performance and reliability.
• Many organizations still utilize mainframe for batch processing applications. Several solutions presented to move expensive mainframe computing to other distributed proprietary platform, most of them rely on end-to-end migration of applications.
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Mainframe Batch Processing MetaScale Architecture
• Using Hadoop, Sears/MetaScale developed an innovative alternative that enables batch processing migration to Hadoop Ecosystem, without the risks, time and costs of other methods.
• The solution has been adopted in multiple businesses with excellent results and associated cost savings, as Mainframes are physically eliminated or downsized: Millions of dollars in savings based on MIP reductions have been seen.
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MetaScale Mainframe Migration Methodology
Implement a Hadoop-
centric reference
architecture
Move enterprise
batch processing to Hadoop
Make Hadoop
the single point of
truth
Massively reduce ETL
by transforming
within Hadoop
Move results and
aggregates back to legacy
systems for consumption
Retain, within Hadoop,
source files at the finest
granularity for re-use
1 2 3 4 5 6
Key to our Approach:1) allowing users to continue to use familiar consumption interfaces2) providing inherent HA3) enabling businesses to unlock previously unusable data
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Mainframe Migration - Benefits
“MetaScale is the market
leader in moving mainframe batch
processing to Hadoop”
• Significant reduction in ISV costs & mainframe software licenses fees
• Open Source platform
• Saved ~ $2MM annually within 13 weeks by MIPS Optimization efforts
• Reduced 1000+ MIPS by moving batch processing to Hadoop
• Modernized COBOL, JCL, DB2, VSAM, IMS & so on
• Reduced batch processing in COBOL/JCL from over 6 hrs to less than 10 min in PiG Latin on Hadoop
• Simpler, and easily maintainable code
• Massively Parallel Processing
• Readily available resources & commodity skills
• Access to latest technologies
• IT Operational Efficiencies
• Moved 7000 lines of COBOL code to under 50 lines in PiG
• Ancient systems no longer bottleneck for business
• Faster time to Market
• Mission critical “Item Master” application in COBOL/JCL being converted by our tool in Java (JOBOL)
Cost Savings TransformI.T.
Skills & ResourcesBusiness Agility
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Summary• Hadoop can revolutionize Enterprise workload and make business
agile• Can reduce strain on legacy platforms• Can reduce cost• Can bring new business opportunities
• Must be an eco-system• Must be part of an data overall strategy• Not to be underestimated
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The LearningH
AD
OO
P We can dramatically reduce batch processing times for mainframe and EDW We can retain and analyze data at a much more granular level, with longer history Hadoop must be part of an overall solution and eco-system
IMP
LE
ME
NT
AT
ION We can reliably meet our production deliverable time-windows by using Hadoop
We can largely eliminate the use of traditional ETL tools New Tools allow improved user experience on very large data sets
UN
IQU
E
VA
LU
E
We developed tools and skills – The learning curve is not to be underestimated We developed experience in moving workload from expensive, proprietary mainframe and EDW
platforms to Hadoop with spectacular results
Over two years of Hadoop experience using Hadoop for Enterprise legacy workload.
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• Automation tools and techniques that ease the Enterprise integration of Hadoop
• Educate traditional Enterprise IT organizations about the possibilities and reasons to deploy Hadoop
• Continue development of a reusable framework for legacy workload migration
The Horizon – What do we need next?
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Legacy Modernization Service Offerings• Leveraging our patent pending and award-winning niche` products, we reduce
Mainframe MIPS, Modernize ETL processing and transform business and IT organizations to open source, cloud based, Big Data and agile platform
• MetaScale Legacy Modernization offers following services –
Legacy Modernization Assessment Services
Mainframe Migration Services• MIPS Reduction Services• Mainframe Application Migration
Legacy Distributed Modernization• ETL Modernization Services• Modernize Proprietary Systems and
Databases Managed Applications Support Support Transition Services
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For
mor
e in
form
atio
n,
visi
t:
www.metascale.com
Follow us on Twitter @LegacyModernizationMadeEasy
Join us on LinkedIn: www.linkedin.com/company/metascale-llc
Legacy Modernization Made Easy!
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