Category: Indexing

Brent Ozar asks (and answers) an interesting question:

Do Disabled Indexes Affect Missing Index Recommendations?

I’m so glad you asked! Let’s take a look.

Read on to learn if Betteridge’s law of headlines holds.

Eric Blinn explains how to use two particular DMVs to see which index changes you might want to make:

Missing Indexes

This group of DMVs records every scan and large key lookups.  When the optimizer declares that there isn’t an index to support a query request it generally performs a scan.  When this happens a row is created in the missing index DMV showing the table and columns that were scanned.  If that exact same index is requested a second time, by the same query or another similar query, then the counters are increased by 1.  That value will continue to grow if the workload continues to call for the index that doesn’t exist.  It also records the cost of the query with the table scan and a suspected percentage improvement if only that missing index had existed.  The below query calculated those values together to determine a value number.

Click through for sample scripts for this and the index usage stats DMV.  The tricky part is to synthesize the results of these DMVs into the minimum number of viable indexes.  Unlike the optimizer—which is only concerned with making the particular query that ran faster—you have knowledge of all of the queries in play and can find commonalities.

Jeff Schwartz continues his series on index tuning, including a section on what happens when you have too many indexes on a table:

Larger numbers of indices create exponentially more query plan possibilities. When too many choices exist, the Optimizer will give up partway through and just pick the best plan thus far. As more indices are added the problem worsens and compilation times, i.e., processor times, increase to a point. This can be illustrated best by reviewing an actual customer example. In this case, one table had 144 indices attached to it and several others had between 20 and 130 indices. The queries were quite complex with as many as fifteen joins, many of which were outer joins. Query and index tuning were impossible because query performance was often counterintuitive and sometimes nonsensical. Adding an index that addressed a specific query need often made the query run worse one time and better the next. Note: Cached query plan issues, e.g., parameter sniffing or plan reuse were not problems in this case. The only solution was to tear down ENTIRE indexing structure and rebuild it with SQL Server’s guidance and nine days’ worth of production queries. Table 5 summarizes the results of the index restructuring project. The performance of 98 percent of the queries was comparable to or better than it was when the large numbers of indices were present.

Don’t be that company.

Jeff Schwartz continues his series on index tuning:

Table 1 shows examples of queries that potentially need tuning based upon the number of executions, total reads, total duration, total CPU time, and average reads per execution. This kind of report immediately focuses attention on the queries that might benefit the most from either index or query tuning. The five queries highlighted in Table 1 underscore these criteria. The ones highlighted in yellow were the worst offenders because their executions collectively performed the most reads with the worst one totaling 3.5 BILLION reads. The ones highlighted in light green and orange accounted for the most CPU time as well as the longest total duration. The one highlighted in slate ran the most times, and the ones highlighted in gray performed the most reads per execution. This information is vital when determining where query and index tuning should be focused.

Jeff walks through some of his data collection and analysis process in this post, making it worth a read.

Jeff Schwartz gives us some guidance on finding good candidate indexes for tuning:

Index usage and tuning metrics became available on SQL Server 2005 with Dynamic Management Views and Functions, which will be discussed later. However, the meanings and significance of index DMV/DMF metrics are still not well understood by many despite only minor additions over the years. Specifically, the following list contains a synopsis of the topics that the author has observed to be the most salient index-related issues:

1. Identifying

   • Queries that need an index to function efficiently
   • Which indices, if any, are used by a query (and how they are used, e.g., randomly or sequentially)
   • Tables (and their indices) that merit evaluation and potential adjustment
   • Indices that duplicate functionality of others

2. Understanding when

   • A new index is truly needed and what improvement can be anticipated
   • An index can be deleted without harming performance
   • An index should be adjusted or added despite the query not producing any missing index warnings

3. Understanding why having too many indices results in

   • Inserts and updates taking too long and/or creating blocking
   • Suboptimal query plans being generated because there are too many index choices

4. Knowing Database Engine Tuning Advisor (DTA) pros & cons

Jeff starts with the basics of indexes, followed by some general strategy.  This promises to be the first of several posts on the topic.