Partitioning is one of the most powerful tools at your disposal when managing large volumes of data and improving the performance of queries that would otherwise scan and filter lots and lots of data.
However, it can be tricky to determine why it isn’t helping when you thought it should.
Typically partitioning improves query performance by ensuring only the partition(s) needed to answer the business user’s query will be scanned rather than the entire table.
But how can you tell if you got partition pruning in an execution plan? Or worse yet, how do you determine why you didn’t get partitioning pruning when you were expecting it.
In the video below, I share the steps I use to determine if partition pruning has occurred and what to look at and correct if you don’t automatically get the partition pruning you were expecting.
This blog post is part of a series on SQL Tuning. I shared some simple steps to help you tune a SQL Statement using the wrong Join Type in part one. While part two deals with how to tackle a problem where the optimizer picks the wrong index, and part three shares tips on how to fix statements where the Optimizer chooses a nested loop join instead of a hash join.
At one point or another during your career, you will face a situation where the optimizer picks the wrong join method. As tempting as it is to brute-force the plan you want via a hint, it’s always better to understand why the Optimizer made a decision and provide the necessary information to allow the Optimizer to select the plan you want by default.
In this short video below, I explain how the Optimizer determines the join method based on the cardinality of the table on the left-hand side of the join. I also provide a simple set of steps to help you identify the common problems that can cause an incorrect join method to be used and guidance on how to supply the necessary information, so the Optimizer will automatically select the appropriate join method.
Adaptive joins, introduced in 12c, can help address some cases where the Optimizer selects the wrong join method but not in all cases. It’s always better to know how to save yourself rather than relying on the built-in safety net.
One of the most common SQL Tuning challenges you will encounter with enterprise applications is a SQL statement where the Optimizer picks the wrong index. As tempting as it is to brute-force the plan you want via an index hint, it’s always better to understand why the Optimizer made the decision and provide the necessary information and access structures to allow the Optimizer to select the plan you want by default.
In the short video below, I explain how the Optimizer costs each of the index accesses available to it and provide you with a simple set of steps to help you identify these types of problems and guidance on how to create indexes so the Optimizer will automatically select them.
This post is part two of a series of blog posts on SQL Tuning. In part one, I shared some simple steps to help you tune a SQL Statement using the wrong Join Type.
When it comes to SQL Tuning, I don’t typically recommend folks add one-off hints or look for magical underscore parameters to help improve their query performance.
Instead, I like to share some simple steps to
Accurately characterise the problem
Determine and apply a solution
Measure the effectiveness of that solution
But instead of talking about my approach, I thought it would be more beneficial to show you how I do it. So in the video below, you will see the exact steps I used to determine what was causing a Sales Report to run slowly and the two alternative approaches you can take to resolve similar problems.