Mark Champion

28 September 2017

• Redgate Monitor
• Database Performance Monitoring
• SQL Code Analysis

SQL Monitor Quick Tip: Using the SQL static code analysis performance rules

Static code analysis parses the source code, checking the syntax for compliance with a built-in set of rules. These rules are designed to encourage good coding practices and, applied during development and testing work, help you minimize the number of ‘code smells’ that creep into your application and database builds.

SQL Prompt provides a set of code analysis rules that will detect a range of problems, including: flawed table design, such as a missing clustered index; naming problems, such as use of reserved words; or problems with the syntax used in queries, or in routines such as functions and procedures, which could cause performance or security issues. SQL Monitor integrates the performance rules, which cover a range of general best practices for SQL performance, ranging from the need to qualify object names, to avoiding over-reliance of hints, to misuse of cursors:

Figure 1

If your SQL code flouts any of these rules, SQL Monitor will now highlight it automatically. If you haven’t noticed this until now, don’t worry, it’s quite subtle! Let’s look at a couple of simple examples. You’ll need to have SQL Monitor 7 or later installed, or alternatively you can visit the online demo.

Unqualified objects

Navigate to the Overview screen for one of your monitored SQL Server instances, and look at the query details for one of the Top 10 Queries. If you see any query text with a blue wiggly line underneath it, hover over that line, and a ToolTip will indicate the performance rule that has been contravened. In this case, it’s rule PE002, because the highlighted code fails to specify the schema name for a table.

Figure 2

Failing to qualify the owner of an object can cause performance problems for several reasons. For example, we force the engine to check for the object in two places, first in the authenticated user’s default schema and then in the dbo schema, instead of just one. Also, SQL Server can fail to reuse a perfectly valid execution plan, if for example a query is executed first with and again then without object qualification.

The required action is simply to rewrite such queries to schema-qualify all objects.

Over-reliance on hints

Figure 3 shows the Query Details screen, in SQL Monitor, for a Top 10 query that flouts another static code analysis performance rule, this time relating to the use of hints.

Figure 3

In this example, the developer has chosen to force the query optimizer to implement the INNER JOIN as a Merge join. Given a free hand, for example if we remove the join hint, the optimizer chooses a Nested Loops join.

Occasionally, during development, you will encounter cases where the optimizer appears to have erred in its decision making, and that better performance can be achieved by forcing it to make a different choice, via a query or table or join hint.

It’s rare that hints offer substantial performance benefits. Often their use results simply from the developer not performing enough iterations during testing to rule out statistical variation in performance.

Conversely, it’s common to find cases where hints cause performance problems, especially because over time they prevent the optimizer from making different choices, based on changes in the data, in the distribution of that data, or as a result of improvements in the optimizer with subsequent service packs or new releases.

What’s next?

These static analysis rules are not designed to offer a comprehensive query analysis tool, but to provide a useful first step in determining if there is something obviously wrong with your SQL, which needs further investigation. At the time of writing, SQL Monitor surfaces only SQL Code Guard’s static analysis performance rules, but there are many other rules that could be included. We want to get your feedback on how useful the feature would be in diagnosing poor performance. Are these rules useful? Is there anything you think we could add/remove? We’d welcome your feedback.

Further reading

• SQL Code Smells, by Phil Factor
• Database Code Analysis, by William Brewer
• SQL Prompt Code Analysis examples on Product Learning