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Voldemort on Solid
State Drives
Vinoth Chandar, Lei Gao, Cuong Tran
Linkedin Corp, Mountain View, CA
What is Voldemort?
• Distributed key-value storage system
  • Based on Amazon Dynamo
• Open source
  • https://github.com/voldemort/voldemort
• Widely used
  • Several TB of data @ Linkedin
  • Eharmony, Nokia, JIVE Software etc.
• We are on SSD!
SSD Migration
• Improve average latency
  • 20 ms to 2 ms
• Cluster expansion & data restoration
  • Improved 10x
• 95th & 99th latencies shot up
  • 30ms to 130ms and 240ms to 380ms
• GC Issues !!
          Client
          operations              BDB Cleaner        Retention Job


          BDB Cache
      App1 App2 .. AppN                         Server JVM Heap

                                  Linux
Benchmarking for SSD
• Java based systems
• Need for End-End Correlation
  • Linux page stealing
  • Use AlwaysPreTouch
• Run Workload causing Fragmentation
  • Promotion failure more likely
  • Defragmentation costly
• Run Workload with High memory churn
  • cost of CMS Initial Mark
  • High CMS initiating fraction
• Factor in higher IO Parallelism
  • Sequential IO benchmarks no longer use single thread
Takeaways
• GC impact significant
  • Big heaps = Big pauses
• Longer runs to account for SSD GC
  • SSD is log structured
  • Worst case 2 ms
• Special attention to 95th & 99th
  • Fragmentation eats up gains

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Voldemort on Solid State Drives

  • 1. Voldemort on Solid State Drives Vinoth Chandar, Lei Gao, Cuong Tran Linkedin Corp, Mountain View, CA
  • 2. What is Voldemort? • Distributed key-value storage system • Based on Amazon Dynamo • Open source • https://github.com/voldemort/voldemort • Widely used • Several TB of data @ Linkedin • Eharmony, Nokia, JIVE Software etc. • We are on SSD!
  • 3. SSD Migration • Improve average latency • 20 ms to 2 ms • Cluster expansion & data restoration • Improved 10x • 95th & 99th latencies shot up • 30ms to 130ms and 240ms to 380ms • GC Issues !! Client operations BDB Cleaner Retention Job BDB Cache App1 App2 .. AppN Server JVM Heap Linux
  • 4. Benchmarking for SSD • Java based systems • Need for End-End Correlation • Linux page stealing • Use AlwaysPreTouch • Run Workload causing Fragmentation • Promotion failure more likely • Defragmentation costly • Run Workload with High memory churn • cost of CMS Initial Mark • High CMS initiating fraction • Factor in higher IO Parallelism • Sequential IO benchmarks no longer use single thread
  • 5. Takeaways • GC impact significant • Big heaps = Big pauses • Longer runs to account for SSD GC • SSD is log structured • Worst case 2 ms • Special attention to 95th & 99th • Fragmentation eats up gains

Notes de l'éditeur

  1. Voldemort eng at Lnkd . 10 sec
  2. 20 sec. Set theme for talk. Not about distributed systems aspects. Experiences with Single node performance issues during SSD migration.
  3. 1 min. multi tenant. TODO : say explicitly “focus on diagram at the bottom”
  4. 2 min “Tackling these GC issues, gave us some intuitions for benchmarking java based applications on SSD”. In end-end correlation, “First of all, given Big data on SSD is relatively new, we need tools to correlate patterns across the stack and make sense out of it. For example – young gen pauses due to linux page stealing. Line up page scan data and system time components in young gen pause from gc log”
  5. 1 min