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Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov
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Improving MapReduce: Scala and Gridgain to the rescue! Nikita Ivanov

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The topic of this presentation is about demonstrating how Scala and GridGain software allows to build a real time, MapReduce application in a record time and how GridGain’s approach is different to …

The topic of this presentation is about demonstrating how Scala and GridGain software allows to build a real time, MapReduce application in a record time and how GridGain’s approach is different to the one employed by Hadoop MapReduce framework that is based on offline batch-oriented processing. Most of the presentation will be concentrate on the live “from scratch” coding of the application that takes an ubiquitious Hadoop example of calculating word frequencies in the large body of text and implementing it using GridGain to work in 100% real time context instead. The live coding part of the presentation will be done using Scala programming language that is natively supported by GridGain. The initial 10 minutes of the presentation will be devoted to explaining the MapReduce concept. High level explanation about GridGain and Hadoop approaches to solving it, and its respective tradeoffs. Overall - the presentation is extremely hands-on and requires a significant level of understanding of Java or Scala, and basic level of understanding for distributed programming concepts such as In-Memory Data Grid and Compute Grids.

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  • 1. Improving MapReduce:GridGain and Scala To The Rescue! Nikita Ivanov, Founder & CEO GridGain Systems October 2012 www.gridgain.com #gridgain
  • 2. Table Of Contents: > 30% > 70% > Why Real Time Hadoop/MR? > Live Coding > GridGain Overview > Real Time & Streaming Word Count > In-Memory Computing > Compute & Data Gridswww.gridgain.com Slide 2
  • 3. Real Time MapReduce/Hadoop: Why?www.gridgain.com Slide 3
  • 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 4
  • 5. GridGain: In-Memory Data Platform > In-Memory Compute Grid + > Full ACID Transactions In-Memory Data Grid Fully distributed ACID transactions > Real Time & Streaming MapReduce, CEP > Simplicity and Productivity Dramatically reduces cost of application development > Three Editions: Demonstrably faster time-to-market > In-Memory HPC HPC market Example: > In-Memory Data Grid Full source code in Scala of world’s shortest real time MapReduce app Transactional Data Caching market built with GridGain. Works on one or thousands of computers with no code changes required. > In-Memory Hadoop Real Time Big Data market > Language support: Server: Java, Scala, Groovy, Clients: .NET, PHP, REST, C++ > Mobile platforms support: iOS/ObjectiveC, Android clientswww.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

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