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In either case, it is important to understand that all optimizations come at a cost. By implementing indexing on your objects, you are making add, remove, and update operations slower on your objects and, in exchange, gaining tremendous benefits for optimizing searches. When you use indexing properly on large collections of business objects in memory on fields where comparison is a costly operation (such as strings), you can gain performance improvements that are several orders of magnitude faster. However, using indexing on fields that are not searched frequently and/or are updated frequently can actually cause performance to degrade. It is critical to have a sense for how you re going to use your child class when you start to use the Indexable attribute on properties within it, just like you would be careful about how you apply an index to a database table.

Both queries fetched exactly the same number of rows, but the HEAP table performed considerably more logical I/O. As the degree of concurrency on the system goes up, we would likewise expect the CPU used by the HEAP table to go up more rapidly as well, while the query possibly waits for latches into the buffer cache. Using runstats (a utility of my own design; refer to the introductory section Setting Up Your Environment for This Book for details), we can measure the difference in latching. On my system, I observed the following STAT...consistent gets STAT...consistent gets from ca STAT...no work - consistent re STAT...session logical reads STAT...consistent gets from ca STAT...table fetch by rowid STAT...buffer is not pinned co LATCH.cache buffers chains STAT...physical read total byt STAT...physical read bytes STAT...cell physical IO interc 723,073 723,073 362,683 723,131 359,225 360,385 648,695 1,090,022 30,703,616 30,703,616 30,703,616 438,048 438,048 77,651 438,086 72,982 72,085 288,324 523,161 40,747,008 40,747,008 40,747,008 -285,025 -285,025 -285,032 -285,045 -286,243 -288,300 -360,371 -566,861 10,043,392 10,043,392 10,043,392

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Run1 latches total versus runs -- difference and pct Run1 Run2 Diff Pct 1,204,593 624,834 -579,759 192.79% where Run1 was the HEAP_ADDRESSES table and Run2 was the IOT_ADDRESSES table. As you can see, there was a dramatic and repeatable decrease in the latching taking place, mostly due to the cache buffers chains latch (the one that protects the buffer cache). The IOT in this case would provide the following benefits: Increased buffer cache efficiency, as any given query needs to have fewer blocks in the cache. Decreased buffer cache access, which increases scalability. Less overall work to retrieve our data, as it is faster. Less physical I/O per query possibly, as fewer distinct blocks are needed for any given query and a single physical I/O of the addresses most likely retrieves all of them (not just one of them, as the heap table implementation does).

When a CSLA .NET collection is serialized, the indices within the collection do not pass the serialization boundary. This would be possible in theory, but the performance implications of having not only the objects themselves but all the indices on those objects get passed over the wire are not insignificant. In addition, the indexing mechanism depends on hash-code generation of objects that is only consistent at the scope of a physical machine that is, hash codes are not guaranteed to be equivalent on, say, a 64-bit operating system and a 32-bit operating system so it would be impractical to translate the index values during serialization in the absence of a generic hash code generator that could guarantee durability across machine boundaries. Although the index itself is not passed over the serialization boundary, the index is re-created according to the options specified on the child class. This typically happens upon the first instance of a query that utilizes the index on the other side of the serialization boundary.

The same would be true if you frequently use BETWEEN queries on a primary or unique key. Having the data stored physically sorted will increase the performance of those queries as well. For example, I maintain a table of stock quotes in my database. Every day, for hundreds of stocks, I gather together the stock ticker, date, closing price, days high, days low, volume, and other related information. The table looks like this: ops$tkyte@ORA11GR2> create table stocks 2 ( ticker varchar2(10), 3 day date,

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