The approach this book has taken so far is to find optimum robust plans as if all driving-table filter conditions and all necessary join keys were already indexed. The implication is that, whenever you find that these optimum plans require an index you don't already have, you should create it and generate statistics for it so that the optimizer knows its selectivity. If you are tuning only the most important SQLSQL that contributes (or will contribute) significantly to the load and the perceived performance (from the perspective of the end user) of a real production systemany index you create by following this method will likely be well justified. Cary Millsap's book Optimizing Oracle Performance (O'Reilly), which I heartily recommend, provides a method for finding the most important SQL to tune.
Unfortunately, you must often tune SQL without much knowledge of its significance to overall production performance and load, especially early in the development process, when you have only tiny data volumes to experiment with and do not yet know the future end users' patterns of use.
To estimate how important a SQL statement will be to overall load and performance, ask the following questions:
Is it used online or only in batch? Waiting a few minutes for a batch job is usually no great hardship; end users can continue productive work while waiting for a printout or an electronic report. Online tasks should run in less than a second if they are at all frequent for a significant community of end users.
How many end users are affected by the application delay caused by the long-running SQL?
How often are the end users affected by the application delay per week?
Is there an alternate way the end user can accomplish the same task without a new index? For example, end users who are looking up employee data might have both Social Security numbers and names available to search on, and they need not have indexed paths to both if they have the freedom and information available to search on either. Some performance problems are best solved by training end users to follow the fast paths to the data that already exist.
Compare the runtimes of the best execution plan under the current indexes with the best plan under ideal indexes. How much slower is the best constrained plan that requires no (or fewer) new indexes? A nonideal index to the driving table, or even a full table scan, might be almost as fast as the ideal index, especially if the driving table is not the most expensive part of the query. A missing join-key index can force a plan that drives from a second-best-filtered or even worse driving node, where the alternate node has access to the whole join tree through current indexes. How much worse is that? The only way to know for sure is to try your query both ways. Alternatively, try hash joins when join-key indexes are missing, and see whether the improvement is enough to obviate the need for new indexes.
Estimate weekly lost productivity for online delays by using the length of each delay times the frequency of the task per end user times the number of end users. Summed delays of days per week add up to serious lost money. Summed delays of a couple of minutes per week amount to less than you might save by buying an extra coffee maker to save employees steps during coffee breaks; don't get carried away going after the little stuff!
Consider overriding external effects too. For example, online delays for a live customer-support application that cause customers to find a new vendor can be disproportionately expensive! Similarly, batch delays that trigger penalties for missed deadlines might carry huge costs. When SQL delays are costly and the improvement for adding a new index is significant, don't hesitate to add the index. Otherwise, consider the trade offs.