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Workload Analysis of a Large-Scale Key-Value Store

Berk Atikoglu, Yuehai Xu , Eitan Fracthenberg , Song Yiang , Mike Paleczny. Workload Analysis of a Large-Scale Key-Value Store. Analyze Memcached at Facebook. +284,000,000,000 requests 5 different use cases Workload characteristics, locality, cache effectiveness.

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Workload Analysis of a Large-Scale Key-Value Store

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  1. Berk Atikoglu, YuehaiXu, EitanFracthenberg, Song Yiang, Mike Paleczny Workload Analysis of a Large-Scale Key-Value Store

  2. Analyze Memcached at Facebook • +284,000,000,000 requests • 5 different use cases • Workload characteristics, locality, cache effectiveness

  3. Why Is Caching Important? Database Cache Servers Web Servers

  4. Motivation • Understand workload characteristics • Identify factors affecting performance • Provide a benchmark for future studies

  5. Memcached • Distributed memory caching system • Key-value store for small objects Key Hash Function Memcached Servers

  6. Tracing Methodology • Capture traces through a Linux Kernel Module (LKM) • Process traces with Hive LKM

  7. Facebook Deployment Contains server related information Anything that doesn’t belong to a specific pool goes to ETC

  8. Analysis • Workload Characteristics • Locality, Cache Behavior

  9. Request Composition > 99.8% GET GET:UPDATE = 30:1

  10. Key Size Distribution 90% of VAR keys are 31B USR keys are 16B or 21B ETC is heterogeneous

  11. Value Size Distribution USR values are only 2B 90% of values are smaller than 500B

  12. Value Size Dist. By Overall Weight 90% of data is generated by values of 500B or smaller except ETC 90% is 10KB or smaller values for ETC

  13. Request Rate Over Time All pools show diurnal pattern except SYS

  14. Request Rate Over Time (ETC) North America starts its day Night time in Western Semiphere

  15. Analysis • Workload Characteristics • Locality, Cache Behavior

  16. Repeating Keys 0.0003% of keys in 10% of requests in ETC 1% of keys in 55% of requests in ETC Least frequent 50% of keys in 1% of requests in ETC

  17. Locality Over Time

  18. Reuse Period of Keys 99.9% of SYS keys are reused in 1hr 88.5% of ETC keys are reused in 1hr 96.4% of ETC keys are reused in 6hr

  19. Hit Rate Why? 98.2% 92.9% 81.4% 93.7% 98.7%

  20. Causes of ETC Cache Misses

  21. Conclusion • Analyzed 5 different memcached use cases • Different applications of memcached have extreme variations in access patterns • Answered pertinent questions to improve Facebook’s memcachedusage

  22. Thank You • Questions?

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