One of the most critical issues that the PMF Development Team faces is insuring quick response times on reports, and the key factor in good response time is to extract data out of the core PMF "fact" table (MEASURES) as efficiently as possible Those of you who are familiar with the PMF database may already know that we have been using a WebFOCUS cluster join MEASURESDIM. MEASURESDIM incorporated the full set of joins you could possibly need to do any standard PMF report, so we could extract all required metrics values with a single TABLE FILE MEASURESDIM.

In the early releases of PMF, most of our customers had a relatively small volume of data (less than 1M rows), but in the last few years we have been seeing customers with 5M to 10M rows of data. When we did prototyping of larger databases, we observed that when doing a TABLE FILE MEASUREDIM, the database optimizers were making bad choices when selecting a single Scorecard; this was related to the fact the the database had to do extra joins. Since nearly every PMF report is based on a single Scorecard, we had to do something. The solution was to split out the fact query into two steps. The first step determines which Measures the report is using; the second step uses a new cluster join, PMF_CUBE_VW, which only contains joins from the MEASURES table to the immediately connected tables. With large databases, we observed order of magnitude improvements; and with smaller databases there was either no change or moderate improvements. These changes took effect starting with PMF release 5.2.1.

It almost goes without saying that database tuning is a highly complex and highly researched topic. If you do a web search on "database tuning" you will easily get 10s to 100s of thousands of hit depending on how you phrase your query. So this discussion is by nature rather superficial. If you would like do discuss this further, or if you have any questions, please feel free to respond to this posting.

Eric Heilner (AKA EricH)
Product Manageger

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