FLASH MEMORY: THE BIG DATA from Structure:Data 2012


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Presentation from Scott Metzger, Violin Memory
More at http://event.gigaom.com/structuredata/

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FLASH MEMORY: THE BIG DATA from Structure:Data 2012

  1. 1. FLASH MEMORY: THE BIG DATA SPEAKER: Scott Metzger VP of Analytics Violin MemoryFriday, July 27, 2012
  2. 2. Flash Memory The Big Data Application Accelerant Q1 2011 Scott Metzger, VP AnalyticsFriday, July 27, 2012
  3. 3. Analytics with Big Data is “ Good tests kill flawed theories; we remain alive to guess ” again - Karl PopperFriday, July 27, 2012
  4. 4. Knowledge is Valuable How much is a 10% improvement worth to you?Friday, July 27, 2012
  5. 5. Big Data Analytics Straining Data  Volume More En##es  tracked Capacity Data  richness & Automated  data  collec#on Bandwid th Data  Velocity Real-­‐#me  ad-­‐hoc  queries More Con#nuous  updates IOPS Event-­‐based  triggers Data  Variety Lower More  complex  queries Latency More  fields  and  rela#onships More  indexesFriday, July 27, 2012
  6. 6. Memory Speed at Tier 1 1 PB VERY FAST No seek times 100 TB Non-volatile Green 10 TB 1 TB Flash Memory Arrays 8,000µs (2 orders of magnitude) 100 GB Emulating HDDs 15K Disk Array SATA Array SSDs 10 GB DRAM Multi-core 1 GB CPU Processor Cache ns 1µs 150µs 3ms 8ms 20ms TIME (Access Delay)Friday, July 27, 2012
  7. 7. Use   C ase:   F ortune   5 00   R etail   Background: Business transactions to be warehoused every day The longer the warehousing process takes the earlier the warehousing process needs to start Growing number of transactions and richness of data add to challenge of managing data Results: Flash memory array performance over spinning disk akin to 1G to 4G cellular data Solid state memory physical space 1/10th spinning disk Low latency -> more IO -> more work per unit of timeFriday, July 27, 2012
  8. 8. Use   C ase:   S ocial   N etworking   Background: User registration rates multiplied by quarter Exponential growth of concurrent user sessions Additional features require more complex real-time transactions Results: Memory arrays external to servers meet high availability requirements so when a component fails apps are still running Consistent user experience; page load time, custom content serving 80% reduction in power and cooling requirements Less compute requires less license costFriday, July 27, 2012
  9. 9. Use   C ase:   U .S.   G overnment   Background: Cost pressures for consolidation and desktop virtualization Virtual Desktop Infrastructure (VDI) is difficult to scale Training teams waiting too long for desktop and application boot times Results: 1/10th the wait time ‘Non-virtualized’ user experience for desktop apps Regained confidence in viability of VDI can be scaledFriday, July 27, 2012
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