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.
In my pervious post, I promised to provide an alternative solution to avoiding repasing SQL queries due to partition level DDLs.
So, what is it?
In Oracle Database 12 Release 2 we implementing a fine-grained cursor invalidation mechanism, so that cursors can remain valid if they access partitions that are not the target of an EXCHANGE PARTITION, TRUNCATE PARTITION or MOVE PARTITION command.
As I said in my previous post, this enhancements can’t help in the case of a DROP PARTITION command due to the partition number changing but hopefully you can change the DROP to either an EXCHANGE PARTITION or a TRUNCATE PARTITION command to avoid the hard parse, as I have done in the example below.
If you recall, we have a METER_READINGS table that is partitioned by time, with each hour being stored in a separate partition. Once an hour we will now TRUNCATE the oldest partition in the table as a new partition is added. We also had two versions of the same SQL statement, one that explicitly specifies the partition name in the FROM clause and one that uses a selective WHERE clause predicate to prune the data set down to just 1 partition.
A couple of weeks ago, I published a blog post that said specifying a partition name in the FROM clause of a query would prevent an existing cursor from being hard parsed when a partition is dropped from the same table. This was not correct.
It’s actually impossible for us not to re-parse the existing queries executing against the partitioned table when you drop a partition, because all of the partition numbers change during a drop operation. Since we display the partition numbers in the execution plan, we need the re-parse each statement to generate a new version of the plan with the right partition information.
What actually happened in my example was the SQL statement with the partition name specified in the FROM clause reused child cursor 0 when it was hard parsed after the partition drop, while the SQL statement that just specified the table name in theFROM clause got a new child cursor 0.
But it’s not all bad news. I do have a solution that will reduce hard parses when executing DDL operations on partitioned tables that you can check out in part 2 of this blog post. But before you click over to read the alternative solution, let me explain in detail what was really happening in the original example I posted.
If you recall, we have a METER_READINGS table that is partitioned by time, with each hour being stored in a separate partition. Once an hour we drop the oldest partition in the table as a new partition is added. We also had two versions of the same SQL statement, one that explicitly specifies the partition name in the FROM clause and one that uses a selective WHERE clause predicate to prune the data set down to just 1 partition.
Back in January, I promised I would write a series of blogs on some of my favorite new 12c enhancements, designed to make your life easier. I’m finally getting around to keeping that promise with this weeks blog post on my favorite partitioning enhancements.
Imagine you have a large SALES table that contains information on all of the sales we have had in our chain of department stores.