In-Memory Computing: Myths and Facts

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In-Memory Computing is here and it is going to profoundly change the way that applications are built. Jon Webster, GridGain's Vice President of Product Management, will discuss common misconceptions about In-Memory Computing along with the most important potential benefits to your company. He will also present use cases of actual GridGain customers that illustrate the power of In-Memory Computing. If processing large volumes of data quickly is important to you, don't miss this webinar!

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In-Memory Computing: Myths and Facts

  1. 1. In-Memory Computing: Facts & Myths Jon Webster VP of Product Management www.gridgain.com #gridgain
  2. 2. What is In-Memory Computing? In-Memory Computing uses high-performance, integrated, distributed memory systems to compute and transact on large-scale data sets in realtime - orders of magnitude faster than legacy disk-based systems.
  3. 3. Why Now?
  4. 4. Paradigm Shift à la 1970s 1970s:! >  >  IBM released “Winchester” IBM 340 disk - Era of HDD
 Tapes start to decline" SQL
 Era of Structured Data! 2010s: ! >  >  64-bit CPUs + DRAM prices drop 30% YoY - Era of Memory
 HDDs start to decline" NoSQL + SQL
 Era of Unstructured Data! ! RAM is a new disk, disk is a new tape.
  5. 5. Memory First vs. Disk First >  Memory First Architecture:
 Memory is primary storage, disk for backups
 Reading Record: API call <-> pointer arithmetic
 Latency: nanoseconds" >  Disk First Architecture:
 Disk as primary storage, memory for caching
 Reading Record: API call <-> OS I/O <-> I/O controller <-> disk 
 Latency: milliseconds
  6. 6. Bring Computations To Data Client-Server, J2EE, SMP 1990s-2005s" >  >  Data is moved to application layer for processing:
 Data not-partitioned and stored in central RDBMS
 Data sizes are relatively small
 Technically impossible to distributed computations to central RDBMS" In-Memory Computing, Hadoop, MPP 2005s+" >  >  Computations are moved to data:
 Data is partitioned and stored in distributed systems
 Data overall sizes are massive
 Technically possible to distribute computations for distributed data
  7. 7. Myth #1: Too Expensive Facts:! >  >  >  >  2013: 1TB DRAM cluster ~$25K" 2015: 1TB DRAM cluster ~$10K
 30% reduction YoY" Memory Channel Storage (MCS) 
 NAND in DRAM form factor, 2x speed of flash, same price as flash" Storage Class Memory (SCM)
 ~10x slower than DRAM, Flash price, non-volatile
  8. 8. Myth #2: Not Durable Facts:! >  >  >  IMC have durable backups and disk storage
 Active or passive replicas, transactional read-through and write-through! Mature IMC provide tiered storage
 DRAM - Local Swap - RDBMS/HDFS" Operational vs. Historical datasets
 99% of operational datasets < 10TB
  9. 9. Myth #3: Flash Is Fast Enough Facts:! >  Flash on PCI-E is still... a block device, i.e. disk. A faster one.
 Still going through OS I/O, I/O controller, marshaling, buffering.!
  10. 10. Myth #4: Only In-Memory Databases Facts:! >  >  >  >  IMC is not a product - it is a technology
 It is applied to different products and payloads! In-Memory Database is important use case for today
 Easiest adoption and a “low-hanging fruit”! Streaming is an ideal use case for IMC going forward
 Streaming CAN ONLY be supported on IMC" Vertical and PnP products are the future
 Minimal integration, maximum benefit

  11. 11. Four Use Cases >  >  >  >  Real-Time Risk Analytics
 Able to grow book of business while reducing latencies" Railroad Logistics
 Able to ingest and process sensor data for instant logistics" Energy Generation
 Able to decide on trade vs. generate in real-time as demand spikes" Oil & Gas Drilling
 Able to provide real-time safety monitoring during fracking
  12. 12. Thank you! www.gridgain.com #gridgain

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