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Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
Caching technology comparison
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Caching technology comparison

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Dynacache and Data Replication Service vs Memcached

Dynacache and Data Replication Service vs Memcached

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    • 1.
        Caching Technologies for Java Applications
        Martin Presler-Marshall ICS Performance
      09/30/2011
    • 2.
        Technologies
      • General object cache attributes
      • 3. Memcached
      • 4. DistributedMap (part of Dynacache)
    • 5.
        General Object Cache Characteristics
      • An object cache is a Key-Value lookup table
        • Similar to java.util.Hashtable
      • Have configurable maximum sizes
        • May be configured number of cache entries or memory size
        • 6. Objects typically discarded on least-recently-used policy when cache is full
          • Some caches may offer other algorithms, such as priority-based schemes
      • Have configurable lifetimes per cache entry
        • Items are automatically discarded once lifetime is exceeded
    • 7. Overview: Cache Instances
      • A cache instance is a logical grouping of cache keys & values
      • 8. Identical keys in two separate cache instances do not collide
      • 9. Cache statistics can typically be monitored on an instance level
      • 10. Support for creating multiple instances varies by cache implementation
    • 11.
        Overview: Memcached
      • “ Free & open source, high-performance, distributed memory object caching system” (from memcached.org)
      • 12. Main components:
        • memcached server – standalone server storing cached items
          • Each cluster of servers is a single cache instance
          • 13. To run multiple instances, must run multiple servers on each node
        • memcached client software
          • Available for multiple programming languages
          • 14. Keys & values must be serialized, then transmitted over TCP/IP
      • Cluster design:
        • Client uses hashing algorithm to assign keys to servers
        • 15. Each key is cached on at most one server
        • 16. Explicit invalidations go only to the server which should store that key
    • 17.
        Overview: DistributedMap
      • Built-in component in WAS Network Deployment
        • Only available to Java clients
      • Content is cached in JVM memory
        • Cache operations are POJO calls
      • Multiple cache instances can be created simply
        • Each is treated separately for monitoring, synchronization, LRU and invalidation
      • Cluster design:
        • Cache keys may be cached in multiple nodes
        • 18. Explicit invalidation notifies peers to discard the cache key
        • 19. Option available to push entries to peers on cache insert
    • 20. Advantages: Dynacache
      • Fast cache operations - the key raison d'etre for a cache
        • Lookups through POJO calls - no network delays
        • 21. No serialization/ deserialization of keys and values – lower CPU consumption
      • Successful experience with many IBM products & customers
        • We know how to use it well
      • Mature, supported part of WAS
      • 22. Integration points: Extreme Scale, OpenJPA
      • Flexibility
        • Easy support for multiple cache instances
        • 23. Priority-based LRU algorithm
        • 24. Disk offload
      • Monitoring/management tools: (PMI, Mbean, Cache Monitor)
    • 25. Advantages: Memcached
      • Does not consume memory in Java heap
        • This is less important on a 64-bit JVM
        • 26. We've successfully run WAS apps with 6+GB heaps
      • Active community support
        • ...though available documentation is not great
      • Usable in Tomcat, WAS CE, php applications
      • 27. Used successfully by multiple public sites
      • Single instance of each cache key
        • No peer-to-peer traffic on invalidations
        • 28. A key inserted by one node is immediately available to all peers
          • Typically will increase hit rates
      • Cache size increases as nodes are added
      • 29. Free tools exist for monitoring
        • ...but somewhat hard to use
    • 30. Open Questions
      • Need some direct performance comparisons
        • Have done some limited benchmarking of memcached using various clients
        • 31. Small-scale (single server, small cachesize)
      • Monitor memcached servers in LotusLive performance environment
        • May provide useful data on memcached performance in a nearly-real-world use case

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