SlideShare ist ein Scribd-Unternehmen logo
1 von 9
Making Hadoop Real Time with
     Scala & GridGain

Nikita Ivanov, Founder & CEO                         GridGain Systems
September 2012                                        www.gridgain.com

                                      1065 East Hillsdale Blvd, Suite 230
                                                   Foster City, CA 94404




                               #gridgain
Table Of Contents:




         >        30%                      >       70%
              >    Why Real Time Hadoop?       >   Live Coding
              >    GridGain Overview           >   Real Time & Streaming Word Count
              >    In-Memory Computing
              >    Compute & Data Grids




www.gridgain.com                                                                Slide 2
Real Time Hadoop:




                     Why?




www.gridgain.com            Slide 3
GridGain Overview:
   >   Scalable In-Memory Data Platform                     >   Full ACID
       In-Memory Compute Grid + In-Memory Data Grid             Fully distributed ACID transactions
       Real Time & Streaming MapReduce, CEP

   >   Three Editions:
                                                            >   Simplicity and Productivity
                                                                Dramatically reduces cost of application development
        > “Compute Grid” Edition                                Demonstrably faster time-to-market
            Targeted at HPC market

        >   “Data Grid” Edition                                 Example:
            Targeted at Transactional Data Caching market
                                                                Full source code in Scala of world’s shortest real time MapReduce app
        >   “Big Data” Edition                                  built with GridGain. Works on one or thousands of computers with no
                                                                code changes required.
            Targeted at Real Time Big Data market

   >   Language support:
       Server: Java, Scala, Groovy,
       Clients: .NET, PHP, REST, C++

   >   Mobile platforms support:
       iOS/ObjectiveC, Android clients




www.gridgain.com                                                                                                         Slide 4
In-Memory Computing: Why Now?

                                 “In-memory will have an industry impact
                              comparable to web and cloud. RAM is a new disk,
                                          and disk is a new tape.”


Technology & Cost:                                                       Performance & Scalability Matters:
>   64-bit CPU can address 16 exabytes                                   >   Citi: 100ms == $1M loss
    Entire active data set on the planet is addressable by just 1 CPU.       Forex trading

>   Disk up to 105 times slower than DRAM                                >   Google: 500ms == 20% traffic drop
                                                                             Dropping 20% of revenue
    SSD drives are up to 103 times slower

>   Super effective in-memory parallelization
                                                                         >   SAP sees +206% in profit in Q112
                                                                             For in-memory SAP HANA products
    Enabled by modern multicore CPUs
                                                                         >   Software AG sees 3x revenue in 2012
>   DRAM prices drop 30% every 18 months                                     For in-memory Terracota products
    1TB RAM & 48 cores cluster ~ $40K (< $20K in 3 years)




www.gridgain.com                                                                                                Slide 5
GridGain: In-Memory Compute Grid


>   Direct API for split and aggregation
>   Pluggable failover, topology and collision resolution
>   Distributed task session
>   Distributed continuations & recursive split
>   Support for Streaming MapReduce
>   Support for Complex Event Processing (CEP)
>   Node-local cache
>   AOP-based, OOP/FP-based execution modes
>   Direct closure distribution in Java, Scala and Groovy
>   Cron-based scheduling
>   Direct redundant mapping support
>   Zero deployment with P2P class loading
>   Partial asynchronous reduction
>   Direct support for weighted and adaptive mapping
>   State checkpoints for long running tasks
>   Early and late load balancing
>   Affinity routing with data grid




    www.gridgain.com                                        Slide 6
GridGain: In-Memory Data Grid
>   Zero deployment for data
>   Local, full replicable and partitioned cache types
>   Pluggable expiration policies (LRU, LIRS, random, time-based)
>   Read-through and write-through logic with pluggable cache store
>   Synchronous and asynchronous cache operations
>   MVCC-based concurrency
>   Pluggable data overflow storage via new swap space SPI
>   PESSIMISTIC, OPTIMISTIC transactions
>   Standard isolation levels, JTA/JCA integration
>   Master/Master data replication/invalidation
>   Write-behind cache store support
>   Concurrent and transactional data preloading
>   Delayed preloading support
>   Affinity routing with compute grid
>   Partitioned cache with active replicas
>   Structured and unstructured data
>   Datacenter replication
>   JDBC driver for in-memory object data store
>   Off-heap memory support
>   Pluggable indexing via Indexing SPI
>   Tiered storage with on-heap, off-heap, swap space, SQL, and Hadoop
>   Distributed in-memory query capability
>   SQL, H2, Lucene, predicate-based affinity co-located queries



    www.gridgain.com                                                     Slide 7
Live Coding: GridGain + Scala




>       100% Live Coding:
    >       Nothing pre-built
    >       Every line & character
    >       Everything from the start




        www.gridgain.com                 Slide 8
Thank You!



  #gridgain

Weitere ähnliche Inhalte

Was ist angesagt?

Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
Yahoo Developer Network
 
Data infrastructure and Hadoop at LinkedIn
Data infrastructure and Hadoop at LinkedInData infrastructure and Hadoop at LinkedIn
Data infrastructure and Hadoop at LinkedIn
Hari Shankar Sreekumar
 
Girish Juneja - Intel Big Data & Cloud Summit 2013
Girish Juneja - Intel Big Data & Cloud Summit 2013Girish Juneja - Intel Big Data & Cloud Summit 2013
Girish Juneja - Intel Big Data & Cloud Summit 2013
IntelAPAC
 
Interactive Analytics in Human Time
Interactive Analytics in Human TimeInteractive Analytics in Human Time
Interactive Analytics in Human Time
DataWorks Summit
 

Was ist angesagt? (20)

Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
Apache Hadoop India Summit 2011 talk "Data Infrastructure on Hadoop" by Venka...
 
End to End Machine Learning Open Source Solution Presented in Cisco Developer...
End to End Machine Learning Open Source Solution Presented in Cisco Developer...End to End Machine Learning Open Source Solution Presented in Cisco Developer...
End to End Machine Learning Open Source Solution Presented in Cisco Developer...
 
MongoDB Sharding Webinar 2014
MongoDB Sharding Webinar 2014MongoDB Sharding Webinar 2014
MongoDB Sharding Webinar 2014
 
NoSQL on MySQL - MySQL Document Store by Vadim Tkachenko
NoSQL on MySQL - MySQL Document Store by Vadim TkachenkoNoSQL on MySQL - MySQL Document Store by Vadim Tkachenko
NoSQL on MySQL - MySQL Document Store by Vadim Tkachenko
 
In memory big data management and processing a survey
In memory big data management and processing a surveyIn memory big data management and processing a survey
In memory big data management and processing a survey
 
Benchmarking Apache Druid
Benchmarking Apache Druid Benchmarking Apache Druid
Benchmarking Apache Druid
 
Developing Software for Persistent Memory / Willhalm Thomas (Intel)
Developing Software for Persistent Memory / Willhalm Thomas (Intel)Developing Software for Persistent Memory / Willhalm Thomas (Intel)
Developing Software for Persistent Memory / Willhalm Thomas (Intel)
 
Dsdt meetup-january2018
Dsdt meetup-january2018Dsdt meetup-january2018
Dsdt meetup-january2018
 
Data infrastructure and Hadoop at LinkedIn
Data infrastructure and Hadoop at LinkedInData infrastructure and Hadoop at LinkedIn
Data infrastructure and Hadoop at LinkedIn
 
In-Memory Computing: Myths and Facts
In-Memory Computing: Myths and FactsIn-Memory Computing: Myths and Facts
In-Memory Computing: Myths and Facts
 
Dsdt meetup 2017 11-21
Dsdt meetup 2017 11-21Dsdt meetup 2017 11-21
Dsdt meetup 2017 11-21
 
Traveloka's journey to no ops streaming analytics
Traveloka's journey to no ops streaming analyticsTraveloka's journey to no ops streaming analytics
Traveloka's journey to no ops streaming analytics
 
Big Data/Hadoop Infrastructure Considerations
Big Data/Hadoop Infrastructure ConsiderationsBig Data/Hadoop Infrastructure Considerations
Big Data/Hadoop Infrastructure Considerations
 
Introduction to SQream and the IoT environment
Introduction to SQream and the IoT environmentIntroduction to SQream and the IoT environment
Introduction to SQream and the IoT environment
 
Webinar: Choosing the Right Shard Key for High Performance and Scale
Webinar: Choosing the Right Shard Key for High Performance and ScaleWebinar: Choosing the Right Shard Key for High Performance and Scale
Webinar: Choosing the Right Shard Key for High Performance and Scale
 
Maximize Greenplum For Any Use Cases Decoupling Compute and Storage - Greenpl...
Maximize Greenplum For Any Use Cases Decoupling Compute and Storage - Greenpl...Maximize Greenplum For Any Use Cases Decoupling Compute and Storage - Greenpl...
Maximize Greenplum For Any Use Cases Decoupling Compute and Storage - Greenpl...
 
Girish Juneja - Intel Big Data & Cloud Summit 2013
Girish Juneja - Intel Big Data & Cloud Summit 2013Girish Juneja - Intel Big Data & Cloud Summit 2013
Girish Juneja - Intel Big Data & Cloud Summit 2013
 
Interactive Analytics in Human Time
Interactive Analytics in Human TimeInteractive Analytics in Human Time
Interactive Analytics in Human Time
 
Brian Bulkowski. Aerospike
Brian Bulkowski. AerospikeBrian Bulkowski. Aerospike
Brian Bulkowski. Aerospike
 
How To Achieve Real-Time Analytics On A Data Lake Using GPUs
How To Achieve Real-Time Analytics On A Data Lake Using GPUsHow To Achieve Real-Time Analytics On A Data Lake Using GPUs
How To Achieve Real-Time Analytics On A Data Lake Using GPUs
 

Andere mochten auch (6)

CORPORATION
CORPORATIONCORPORATION
CORPORATION
 
Vicki Fullerton Presents to WCR 1960
Vicki Fullerton Presents to WCR 1960Vicki Fullerton Presents to WCR 1960
Vicki Fullerton Presents to WCR 1960
 
Buy Nevada Holiday Gift Guide 2014-15
Buy Nevada Holiday Gift Guide 2014-15Buy Nevada Holiday Gift Guide 2014-15
Buy Nevada Holiday Gift Guide 2014-15
 
BBB 2013 Annual Report
BBB 2013 Annual ReportBBB 2013 Annual Report
BBB 2013 Annual Report
 
Fredericksburg Reg. Bus. - Dec. 2014
Fredericksburg Reg. Bus. - Dec. 2014Fredericksburg Reg. Bus. - Dec. 2014
Fredericksburg Reg. Bus. - Dec. 2014
 
Successful Property Procurement: Another Amazing SJREI Event
Successful Property Procurement: Another Amazing SJREI EventSuccessful Property Procurement: Another Amazing SJREI Event
Successful Property Procurement: Another Amazing SJREI Event
 

Ähnlich wie Strangeloop2012

Ähnlich wie Strangeloop2012 (20)

Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)
 
Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
Advancing GPU Analytics with RAPIDS Accelerator for Spark and AlluxioAdvancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
 
NoSQL meetup July 2011
NoSQL meetup July 2011NoSQL meetup July 2011
NoSQL meetup July 2011
 
DDN: Massively-Scalable Platforms and Solutions Engineered for the Big Data a...
DDN: Massively-Scalable Platforms and Solutions Engineered for the Big Data a...DDN: Massively-Scalable Platforms and Solutions Engineered for the Big Data a...
DDN: Massively-Scalable Platforms and Solutions Engineered for the Big Data a...
 
GOAI: GPU-Accelerated Data Science DataSciCon 2017
GOAI: GPU-Accelerated Data Science DataSciCon 2017GOAI: GPU-Accelerated Data Science DataSciCon 2017
GOAI: GPU-Accelerated Data Science DataSciCon 2017
 
20201006_PGconf_Online_Large_Data_Processing
20201006_PGconf_Online_Large_Data_Processing20201006_PGconf_Online_Large_Data_Processing
20201006_PGconf_Online_Large_Data_Processing
 
ScyllaDB Virtual Workshop
ScyllaDB Virtual WorkshopScyllaDB Virtual Workshop
ScyllaDB Virtual Workshop
 
20190909_PGconf.ASIA_KaiGai
20190909_PGconf.ASIA_KaiGai20190909_PGconf.ASIA_KaiGai
20190909_PGconf.ASIA_KaiGai
 
PGConf.ASIA 2019 Bali - Full-throttle Running on Terabytes Log-data - Kohei K...
PGConf.ASIA 2019 Bali - Full-throttle Running on Terabytes Log-data - Kohei K...PGConf.ASIA 2019 Bali - Full-throttle Running on Terabytes Log-data - Kohei K...
PGConf.ASIA 2019 Bali - Full-throttle Running on Terabytes Log-data - Kohei K...
 
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
 
NVIDIA Rapids presentation
NVIDIA Rapids presentationNVIDIA Rapids presentation
NVIDIA Rapids presentation
 
Predictable Big Data Performance in Real-time
Predictable Big Data Performance in Real-timePredictable Big Data Performance in Real-time
Predictable Big Data Performance in Real-time
 
SFBigAnalytics_SparkRapid_20220622.pdf
SFBigAnalytics_SparkRapid_20220622.pdfSFBigAnalytics_SparkRapid_20220622.pdf
SFBigAnalytics_SparkRapid_20220622.pdf
 
Python and H2O with Cliff Click at PyData Dallas 2015
Python and H2O with Cliff Click at PyData Dallas 2015Python and H2O with Cliff Click at PyData Dallas 2015
Python and H2O with Cliff Click at PyData Dallas 2015
 
7 Reasons Not to Put an External Cache in Front of Your Database.pptx
7 Reasons Not to Put an External Cache in Front of Your Database.pptx7 Reasons Not to Put an External Cache in Front of Your Database.pptx
7 Reasons Not to Put an External Cache in Front of Your Database.pptx
 
GPU-Accelerating UDFs in PySpark with Numba and PyGDF
GPU-Accelerating UDFs in PySpark with Numba and PyGDFGPU-Accelerating UDFs in PySpark with Numba and PyGDF
GPU-Accelerating UDFs in PySpark with Numba and PyGDF
 
MySQL Transformation Case Study: 80% Cost Savings & Uninterrupted Availabilit...
MySQL Transformation Case Study: 80% Cost Savings & Uninterrupted Availabilit...MySQL Transformation Case Study: 80% Cost Savings & Uninterrupted Availabilit...
MySQL Transformation Case Study: 80% Cost Savings & Uninterrupted Availabilit...
 
BlazingSQL & Graphistry - Netflow Demo
BlazingSQL & Graphistry - Netflow DemoBlazingSQL & Graphistry - Netflow Demo
BlazingSQL & Graphistry - Netflow Demo
 
In memory computing principles by Mac Moore of GridGain
In memory computing principles by Mac Moore of GridGainIn memory computing principles by Mac Moore of GridGain
In memory computing principles by Mac Moore of GridGain
 
OSDC 2017 - Christos Erotocritou - Apache ignite in-memory data fabric
OSDC 2017 - Christos Erotocritou - Apache ignite in-memory data fabricOSDC 2017 - Christos Erotocritou - Apache ignite in-memory data fabric
OSDC 2017 - Christos Erotocritou - Apache ignite in-memory data fabric
 

Kürzlich hochgeladen

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

Kürzlich hochgeladen (20)

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 

Strangeloop2012

  • 1. Making Hadoop Real Time with Scala & GridGain Nikita Ivanov, Founder & CEO GridGain Systems September 2012 www.gridgain.com 1065 East Hillsdale Blvd, Suite 230 Foster City, CA 94404 #gridgain
  • 2. Table Of Contents: > 30% > 70% > Why Real Time Hadoop? > Live Coding > GridGain Overview > Real Time & Streaming Word Count > In-Memory Computing > Compute & Data Grids www.gridgain.com Slide 2
  • 3. Real Time Hadoop: Why? www.gridgain.com Slide 3
  • 4. GridGain Overview: > Scalable In-Memory Data Platform > Full ACID In-Memory Compute Grid + In-Memory Data Grid Fully distributed ACID transactions Real Time & Streaming MapReduce, CEP > Three Editions: > Simplicity and Productivity Dramatically reduces cost of application development > “Compute Grid” Edition Demonstrably faster time-to-market Targeted at HPC market > “Data Grid” Edition Example: Targeted at Transactional Data Caching market Full source code in Scala of world’s shortest real time MapReduce app > “Big Data” Edition built with GridGain. Works on one or thousands of computers with no code changes required. Targeted at Real Time Big Data market > Language support: Server: Java, Scala, Groovy, Clients: .NET, PHP, REST, C++ > Mobile platforms support: iOS/ObjectiveC, Android clients www.gridgain.com Slide 4
  • 5. In-Memory Computing: Why Now? “In-memory will have an industry impact comparable to web and cloud. RAM is a new disk, and disk is a new tape.” Technology & Cost: Performance & Scalability Matters: > 64-bit CPU can address 16 exabytes > Citi: 100ms == $1M loss Entire active data set on the planet is addressable by just 1 CPU. Forex trading > Disk up to 105 times slower than DRAM > Google: 500ms == 20% traffic drop Dropping 20% of revenue SSD drives are up to 103 times slower > Super effective in-memory parallelization > SAP sees +206% in profit in Q112 For in-memory SAP HANA products Enabled by modern multicore CPUs > Software AG sees 3x revenue in 2012 > DRAM prices drop 30% every 18 months For in-memory Terracota products 1TB RAM & 48 cores cluster ~ $40K (< $20K in 3 years) www.gridgain.com Slide 5
  • 6. GridGain: In-Memory Compute Grid > Direct API for split and aggregation > Pluggable failover, topology and collision resolution > Distributed task session > Distributed continuations & recursive split > Support for Streaming MapReduce > Support for Complex Event Processing (CEP) > Node-local cache > AOP-based, OOP/FP-based execution modes > Direct closure distribution in Java, Scala and Groovy > Cron-based scheduling > Direct redundant mapping support > Zero deployment with P2P class loading > Partial asynchronous reduction > Direct support for weighted and adaptive mapping > State checkpoints for long running tasks > Early and late load balancing > Affinity routing with data grid www.gridgain.com Slide 6
  • 7. GridGain: In-Memory Data Grid > Zero deployment for data > Local, full replicable and partitioned cache types > Pluggable expiration policies (LRU, LIRS, random, time-based) > Read-through and write-through logic with pluggable cache store > Synchronous and asynchronous cache operations > MVCC-based concurrency > Pluggable data overflow storage via new swap space SPI > PESSIMISTIC, OPTIMISTIC transactions > Standard isolation levels, JTA/JCA integration > Master/Master data replication/invalidation > Write-behind cache store support > Concurrent and transactional data preloading > Delayed preloading support > Affinity routing with compute grid > Partitioned cache with active replicas > Structured and unstructured data > Datacenter replication > JDBC driver for in-memory object data store > Off-heap memory support > Pluggable indexing via Indexing SPI > Tiered storage with on-heap, off-heap, swap space, SQL, and Hadoop > Distributed in-memory query capability > SQL, H2, Lucene, predicate-based affinity co-located queries www.gridgain.com Slide 7
  • 8. Live Coding: GridGain + Scala > 100% Live Coding: > Nothing pre-built > Every line & character > Everything from the start www.gridgain.com Slide 8
  • 9. Thank You! #gridgain

Hinweis der Redaktion

  1. \n
  2. \n
  3. \n
  4. Comments:\n---------------- \nExample:\nCounts number of non-space characters in given argument by spreading the processing on multiple remote nodes. \nThis is 100% full source code. Can be written in Java or Groovy just as easily.\nCompile and run it. Works transparently on one computer or on thousands.\nFully distributed and parallelized processing.\nNo deployment or provisioning required - 100% zero deployment.\nDevelop and start in the cloud in minutes.\nIt will automatically load balance and fail over, if necessary.\nIt will automatically scale up and down based on dynamically available resources.\n
  5. Comments:\n---------------\n- In-Memory Computing is high growth industry:\n- SAP has seen 206% increase in profit in Q112 in large due to SAP HANA, in-memory processing product\n- IBM grew in-memory computing revenue 80% in 2011\n- Software AG 3x revenue in 2012 for in-memory computing products\n- VMWare is looking at 30% increase in in-memory computing products\n\nGartner References:\n- Top 10 Strategic Technology Trends: In-Memory Computing Massimo Pezzini, Carl Claunch, Joseph Unsworth (G00230658)\n- Insight: Invest in In-Memory Computing for Breakthrough Competitive Advantage Massimo Pezzini (G00226070)\n- What CIOs Need to Know About In-Memory Database Management Systems Roxane Edjlali, Donald Feinberg (G00215735)\n- Need for Speed Powers In-Memory Business Intelligence James Richardson (G00212168)\n- Predicts 2012: Cloud and In-Memory Drive Innovation in Application Platforms Massimo Pezzini, Yefim Natis, Eric Knipp (G00226073)\n\nLinks:\n- http://slidesha.re/O0htOV\n- http://nyti.ms/Ngr4qF\n- http://bit.ly/Ngr8qh\n- http://bit.ly/NZu0Dw\n\n\n\n
  6. \n
  7. \n
  8. \n
  9. \n