Wednesday, January 26, 2011

OODB indexing inside memory (java and c++

We understand how indexing helps disk-based query, but large in-memory DB also need indexing. Gemfire is one example. A query without the aid of an index iterates through every object in the collection. If an index is available that matches the where-clause, the query iterates only over the indexed set, thus reducing the query processing time.

Best example is a point query "where trade.cusip=IBM". Suppose we have a gigantic hash map of 4GB with millions of {tradeId, trade} pairs. With index you iterate over a subset. Perhaps the index is a sorted map of {cusip, tradeId}. It's sorted to provide range queries, and index field must implement Comparable.

How large can a hash map grow? I once put 200MB into a hash map, easily.

In practice, to keep 100GB data in one machine is too expensive hardware-wise. When such a big server become obsolete, it's probably useless. Most people prefer the more economical server farm, where data is distributed.

The VMware book [[Virtualizing and Tuning Large Scale Java Platforms]] explains JVM memory size limits.

For a modest machine, the index itself could be too large to fit into one machine. Since in-memory index is usually a red-black tree (or skip list, but not a hash table), we need to split the index tree among physical machines. Beware b+tree is suitable for disk-based indexing only.

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