268x Query Performance Bump for MongoDB

268x Query Performance Increase for MongoDB with Fractal Tree Indexes, SAY WHAT? by Tim Callaghan.

From the post:

Last week I wrote about our 10x insertion performance increase with MongoDB. We’ve continued our experimental integration of Fractal Tree® Indexes into MongoDB, adding support for clustered indexes. A clustered index stores all non-index fields as the “value” portion of the index, as opposed to a standard MongoDB index that stores a pointer to the document data. The benefit is that indexed lookups can immediately return any requested values instead of needing to do an additional lookup (and potential disk IOs) for the requested fields.

I’m trying to recover from learning about scalable subgraph matching, Efficient Subgraph Matching on Billion Node Graphs [Parallel Graph Processing], and now the nice folks at Tokutek post a 26,816% query performance increase for MongoDB.

They claim to not be MongoDB experts. I guess that’s right. The increase in performance would have been higher. 😉

Serious question: How long will it take this sort of performance increase to impact the modeling and design of information systems?

And in what way?

With high enough performance, can subject identity be modeled interactively?

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