This will be the final post in my series on Result Caches. In my previous article, I had already got almost everything. Almost — four CPUs (cores) were still not enough to saturate the single latch. As you’ve probably already guessed, today we are going with an eight-way test.

Please note that today’s numbers are different since I’m using an entirely different hardware platform. While the four-way tests were done on a 2.4GHz Core 2 Quad box, today’s eight-way tests were done using four dual core Itanium 2 CPUs running at 1.1GHz.

Let’s take a look at the results:

I made a nice-looking graph from this:

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In my previous blog entry, I explained why I would expect Result Cache not to scale well. Unfortunately, at the time that blog entry was written, I had no access to hardware with more than two cores. That left me in an everything-but-the-proof state. “Theory without practice is sterile.” ©Albert Einstein.

Since then, I got a chance to re-run my test cases on a quad-core CPU, moving one step forward.

I re-executed my test cases with one to four processes against the Buffer Cache and the Result Cache in order to capture the number of lookups per second. I raised number of iterations to 1M to make the results more stable though.

Here is what I got: (more…)

In the previous article, I described my observations of RC Enqueue. Now it is time to take a look at the RC latches.

Latches, being serialization devices, are scalability inhibitors. Not that they inherently prevent you from scaling, quite the opposite is true. Serialization is a must if you expect your system to produce anything apart from GIGO (Garbage In Garbage Out). Concurrency is essentially made possible through serialization of shared resources. That being said, I would expect Result Cache to beat Oracle’s buffer cache on read-only workloads, since that is what RC was designed for. That is, Result Cache should perform faster and scale better.

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