Category: Top_Tip

SQL Tuning Workshop

Last week I had the pleasure of delivering a five-part SQL Tuning Workshop for my local Oracle User Group –  Northern California Oracle User Group. The workshop explains the fundamentals of the cost-based optimizer, the statistics that feed it, the hints that influence it and key tools you need to exam executions plans.

The workshop also provides a methodology for diagnosing and resolving the most common SQL execution performance problems. Given the volume of interest in this content, I want to share all of the material from the workshop here and give you links to additional material on each of the 5 topics.

Part 1 Understanding the Oracle Optimizer

The first part of the workshop covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it begins to optimize a query – query transformation.

Query transformations or the rewriting of the SQL statement into a semantically equivalent statement allows the Optimizer to consider alternative methods of processing or executing that query, which are often more efficient than the original SQL statement would allow. the majority of Oracle’s query transactions are now cost-based, which means the Optimizer will cost the plan with and with the query transformation and pick the plan with the lowest cost. With the help of the Optimizer development team, I’ve already blogged about a number of these transformations including:

You can also download the slides here.

Part 2 Best Practices for Managing Optimizer Statistics

Part 2 of the workshop focuses on Optimizer Statistics and the best practices for managing them, including when and how to gather statistics, including fixed object statistics.
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How do I scale an Autonomous Database?

Traditionally database deployments have been designed and provisioned for the peak possible workload. And in reality, a substantial margin of safety was also provisioned on top of that in order to make sure the system could cope with any unforeseen demands.

But peak workloads tend to occur infrequently, leaving most of this costly capacity idle the majority of the time.

In order to enable customers to pay for only the resources they need, Oracle Autonomous Database allow customers to elastically adjust their compute and storage resources when necessary.

An Autonomous Database can be scaled through the UI as shown in the video below or via our cloud APIs or CLI commands. In the video, you see how I can scale a 2 CPU  configuration to an 8 CPU configuration in under a minute to accommodate 48 concurrent users running a JSON workload.

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Getting started with Oracle Autonomous Transaction Processing

Getting started with Oracle Autonomous Transaction Processing is actually much easier than you might think. In fact, with Oracle’s $300 in free cloud credits you can probably get your first 30 days on the service for free. Please note, you will require an active email address and credit card in order to sign up for a trial account. Of course, if you have existing cloud credits you can skip this step.

Once you sign up for trail account you’ll get an email with your tenancy, username and password. Armed with this information, head on over to https://cloud.oracle.com to sign in. The video below explains in detailed the simple steps needed to provision a new Autonomous Transaction Processing database. I’ve also listed these steps below the video, for  easy reference.

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JEFF Talks From Kscope18

The first day of the ODTUG Kscope conference is always symposium Sunday. This year’s Database symposium, organized by @ThatJeffSmith, consisted of multiple, short, rapid  sessions, covering a wide variety of database and database tool topics, similar to Ted Talks but we called then JEFF Talks!

I was lucky enough to present 3 of this year’s JEFF Talks that I thought I would share on my blog since there wasn’t a way to uploaded to the conference site.

In the first session I covered  5 useful tips for getting the most out of your Indexes, including topics like reverse key indexes, partial indexes, and invisible indexes.

Next up was my session on JSON and the Oracle Database. In this session, I covered topics like what data type you should use to store JSON documents (varchar2, clob or blob) the pros and cons of using an IS JSON check constraint, and how to load, index, and query JSON documents.

In my finally JEFF talk I covered some of the useful PL/SQL packages that are automatically supplied with the Oracle Database. Since the talk was only 15 minutes I only touched on 4 of the 300 supplied packages you get with Oracle Database 18c but hopefully it will give you enough of a taste to get you interested in investigating some of the others!

 

 

Avoiding reparsing SQL queries due to partition level DDLs – Part 2

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.

Let’s begin by executing both queries and checking their execution plans.
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Avoiding reparsing SQL queries due to partition level DDLs – Part 1

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.

Continue reading “Avoiding reparsing SQL queries due to partition level DDLs – Part 1”

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