Need for Speed: How In-Memory Changes Everything
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Transcript of Need for Speed: How In-Memory Changes Everything
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! Reveal the essential characteristics of enterprise software, good and bad
! Provide a forum for detailed analysis of today’s innovative technologies
! Give vendors a chance to explain their product to savvy analysts
! Allow audience members to pose serious questions... and get answers!
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! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
! December: Innovators
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! Disruptive Innovation produces an unexpected new market and value network, and is usually geared toward a new set of customers.
! Every once in a while, something comes along that blows everything else out of the water.
! The goal, of course, is to turn a disruptive technology into a sustaining technology, ie., one that overtakes or adequately competes with incumbent technologies.
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! Java based open source middleware for real time Big Data processing
! Based on a high performance in-memory processing platform that integrates compute and in-memory data grids
! As well as its Community Edition, GridGain offers three tailored editions: Compute Grid, Data Grid and Big Data
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Nikita Ivanov is founder and CEO of GridGain Systems, started in 2005 and funded by RTP Ventures. Nikita has led GridGain to develop advanced and distributed in-memory data and computational grid technologies – the top Java HPC (high performance computing) platform in the world today. He has over 20 years of experience in software application development, building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996. He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from Baltic State Technical University, Saint Petersburg, Russia.
GridGain: In-Memory Data Platform!
#gridgain!
!
!
Why Big Data?!
> Because:!> Cheap storage, exp. data growth!
> Three Vs: Volume, Velocity, Variety!> Fast Data: Adding “Speed” to Vs!
In-Memory Facts!
> 64-bit CPUs can address 16 exabytes!> RAM prices drop 30% every 18 months!
> 1GB costs < $1!
> 1TB RAM & 48 cores cluster ~ $40K!
> Multicore CPUs ideal for in-memory parallelization!
Why In-Memory?!
> Speed matters !> Citi: 100ms == $1M!
> Google: 500ms == 20% traffic drop!
> Disk up to 107 times slower than RAM!> In-memory computing tenet:!
> RAM is a new DISK!
> DISK is a new TAPE (overflow, recovery)!
Why In-Memory Big Data?!
Disk-Based:!> ETL data!
> Disk speed!
> Data warehouse"
In-Memory:"> Live data!
> RAM speed!
> Data processor"
Fast Data = Disk Warehouse + In-Memory Processing!
Why GridGain?!
> Scalable In-Memory Data Platform"> Compute Grid + In-Memory Data Grid
Real Time & Streaming MapReduce, CEP!> TBs of data and 1000s of nodes
Typical 10s of TBs and 100s of nodes!> In-Memory Speed, Database Reliability!
> Java-based, Scala and Groovy DSLs!
> C++, .NET, iOS, Android, PHP, REST!> IMDG vs. NoSQL vs. IMDB!
GridGain, At A Glance!
Compute Grid, MapReduce!
In-Memory Data Grid!
GridGain Use Cases!
> Trading Systems Handle large volumes of transactions!
> Real-time Risk Analysis Analysis of trading positions & risk!
> Online Gaming Online real-time backbone for gaming!
> Real Time Analytics Live real time insights and operational BI!
> Geo Mapping Real-time geographical route and traffic information!
> Bioinformatics Real-time DNA sequencing and matching !
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The Persistence Of Memory
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! Memory: ! Memory access is 100,000x faster than HDD! The disparity in speed is increasing! Memory cost declines @ 30% pa (roughly)! Memory cost 500x HDD cost
! SSD:! 3-5x faster than HDD! 15-20x in cost! Cost declines @ 50% pa (roughly)
! Local HDD:! 3x faster than SAN or NAS (usually)! Cost declines @ 35% pa (roughly) – so does SAN
and NAS
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When Memory Becomes The Prime Data Storage Medium
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! What is the biggest number of servers currently deployed by a GridGain customer? How much memory?
! My assumption is that GridGain is being used primarily for new applications. Is this the case?
! Surprisingly, Big Data applications appear possible. Is this a common request?
! Does GridGain support “in-memory fault tolerance?”
! The beguiling possibility of memory becoming the prime data store: Does application migration necessitate application rewrites?
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! How difficult is it to develop applications in the GridGain environment in practice. How much training do developers need? Do developers need to understand parallel programming (e.g. MapReduce)? If not, why not?
! Are any companies adopting this technology strategically?
! Are there any products that target the GridGain environment (In-memory databases, in memory ESBs, etc.)? Is there any associated cloud service specifically configured for GridGain deployment?
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! July: Disruption
! August: Analytics
! September: Integration
! October: Database
! November: Cloud
! December: Innovators
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