Saving Cash Using Less Cache - 90% Savings in the Caching Tier

In a paper delivered at HotCloud '12 by a group from CMU and Intel Labs, Saving Cash by Using Less Cache (slides,  pdf), they show it may be possible to use less DRAM under low load conditions to save on operational costs. There are some issues with this idea, but in a give me more cache era, it could be an interesting source of cost savings for your product. 

Caching is used to:

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Paper: High Performance Scalable Data Stores

The world of scalable databases is not a simple one. They come in every race, creed, and color. Rick Cattell has brought some harmony to that world by publishing High Performance Scalable Data Stores, a nicely detailed one stop shop paper comparing scalable databases soley on the content of their character. Ironically, the first step in that evaluation is dividing the world into four groups:

• Key-value stores: Redis, Scalaris, Voldmort, and Riak.
• Document stores: Couch DB, MongoDB, and SimpleDB.
• Record stores: BigTable, HBase, HyperTable, and Cassandra.
• Scalable RDBMSs: MySQL Cluster, ScaleDB, Drizzle, and VoltDB.

The paper describes each system and then compares them on the dimensions of Concurrency Control, Data Storage Replication, Transaction Model, General Comments, Maturity, K-hits, License Language.

And the winner is: there are no winners. Yet. Rick concludes by pointing to a great convergence:

I believe that a few of these systems will gain critical mass and key players, and will pull away from the others by next year.  At that point, open source contributors will likely migrate to those players.

From the paper:

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A Yes for a NoSQL Taxonomy

NorthScale's Steven Yen in his highly entertaining NoSQL is a Horseless Carriage presentation has come up with a NoSQL taxonomy that thankfully focuses a little more on what NoSQL is, than what it isn't:

• key‐value‐cache
  • memcached, repcached, coherence, infinispan, eXtreme scale, jboss cache, velocity, terracoqa
•  key‐value‐store
  • keyspace, flare, schema‐free, RAMCloud
• eventually‐consistent key‐value‐store
  • dynamo, voldemort, Dynomite, SubRecord, Mo8onDb, Dovetaildb
• ordered‐key‐value‐store
  • tokyo tyrant, lightcloud, NMDB, luxio, memcachedb, actord
• data‐structures server
  •  redis
• tuple‐store
  • gigaspaces, coord, apache river
• object database
  • ZopeDB, db4o, Shoal
• document store
  •  CouchDB, Mongo, Jackrabbit, XML Databases, ThruDB, CloudKit, Perservere, Riak Basho, Scalaris
• wide columnar store
  • BigTable, Hbase, Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI

"Who will win?" Steven asks. He answers:  the most approachable API with enough power will win. Steven touts the contender with the most devastating knock out punch will be document stores because "everyone groks documents." Though the thought is there will be just a few winners and products will converge in functionality.

Steven is banking on the "worse is better" model of dominance, which is hard to argue with as it has been so successful an adoption pattern in our field. The convergence idea is something I also agree with. What we have now are a lot features masquerading as products. Over time they will merge together to become more full featured offerings.

The key question though is what is enough power to win? Just getting a value back for a key won't be enough. Who are you putting your money on?

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Papers: Readings in Distributed Systems

Marton Trencseni has collected a wonderful list of different papers on distributed systems. He's organized them into the following sections: The Google Papers, Distributed Filesystems, Non-relational Distributed Databases, The Lamport Papers, and Implementation Issues. Many old favorites on the list and some that are likely new to you. My new favorite is "Frangipani: A Scalable Distributed File System." How can you not love "Frangipani" as a word?

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