The Big Grid

Distributed Caching: Essential Lessons

Deadline was looming. My most productive (day)time in a week is often Wednesday and Thursday late afternoon. Fremont’s Peet’s Coffee was giving out free coffee to celebrate the one year anniversary of the store, and I was a bit over- caffeinated. :-P Under the temptation of getting more work done, I had almost forgone the ISTA meeting. It was a talk about distributed cache by Cameron Purdy from Tangosol.

Glad that I were there! Beside he scared me with a poor joke in the beginning, the talk was great. (I can no longer remember the joke)

I remembered his presentation as four parts.

  • 1/ Introduction of himself, the company, the problem space, and the product name (ie, what “coherence cache” means technically).

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  • 2/ the evolution (what, how, why) of the distribution cache. (from replication cache, partition, failover cache, local cache, standalone cache server, to write behind cache)

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  • 3/ highlights of technical challenge in the product implementation (finite state machine to model the communication; edge case in partition local cache; the gap in load the data and propagate it to cache; no absolute time in distributed system; network constraints: 12 ms latency, out of order delivery; cannot be proved correct, but can't find incorrect; leaving cluster etc.,)

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  • 4/ cluster system design guidelines (13 of them, [java] serialization/externalization, identity, define equals, idempotent, etc.)


    • With the great wealth of experience he had with real-life systems, full knowledge of the product since the beginning, the talk was vastly interesting. (and, no one fall from his/her chairs even the talk ran pretty long :-)

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