Generating an AWR report for Autonomous Databases

Oracle Autonomous Database automates the lifecycle management of a database, everything from provisioning, scaling, backups and patching, but what it doesn’t do yet is fully tune your application.

You are still on the hook to make sure your app doesn’t have any concurrency bottlenecks or poorly written SQL that Auto Indexing can’t address.

So, what can you do to monitor your app while it’s running on an Autonomous Database?

The first place you can start is the Performance Hub tab on the cloud console. Here you’ll find both real-time and historical performance data in the form of Active Session History (ASH) information for the last seven days, and SQL Monitor reports for the high-load SQL. You can aggregate the ASH data several different ways including by wait class, database service, resource group or SQL ID. The same information is also available in Oracle Management Cloud (OMC), and SQL Developer Web.

But if you are trying to get a holistic view of how your app is behaving on a database, nothing beats an Automatic Workload Repository report or AWR report.

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How do I scale an Autonomous Database?

Traditionally database deployments have been designed and provision 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 an 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 the most out of Oracle SQL Monitor

I’m often asked what is the best tool for viewing execution plans and for me the answer is always SQL Monitor (included in the Oracle Tuning Pack). It really is the most invaluable tool if you need to determine what is happening during the execution of any long running SQL statements.

In order to help you get the very most out of using SQL Monitor, I wanted to share with you some of the tips and tricks I‘ve learnt over the years from the original Database Manageability team, especially Cecilia Grant!

So, why do I love it so much?

There are so many reasons to love SQL Monitor; it’s hard for me to know where to begin. So, instead of giving you an exhaustive list I’ve put together a short video to demonstrate how I use SQL Monitor (be sure to set your resolution to 720p).

How do I control what statements are monitored?

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Oracle Database 12c and DevOps

Over the last month or so I’ve been on the road presenting at different user group conferences and meeting with customers. I’ve gotten a number of requests via blog comments to share the slides for the sessions I presented.

One of the most requested sessions is actually one I delivered, as a joint session with @GeraldVenzl, called Oracle Database 12c and DevOps.

In this session Gerald takes on the role of the lead developer on a project to deploy a RESTful web-based application, while I play the role of the DBA. Through the course of the session, we learn to work together to find a solution that will allow our (fictitious) company  to embrace a more agile development approach, as well as the latest technology trends without exposing the business to painful availability or security vulnerabilities.

Although the slides don’t do our witty repartee justice , hopefully they will inspire you to try out some of the technologies we describe in the session!

Below is the other session that I got a lot of requests for was the keynote session I gave at this year’s Georgia Oracle Users Group Tech Days 2017 , “What to expect from Oracle Database 12c”.

Best Practices For Large Volume or IoT Workloads

Over the last few years there has been a rapid surge in the adoption of smart devices. Everything from phones and tablets, to smart meters and fitness devices, can connect to the Internet and share data. You only have to follow @MarkRittman and his experiences with getting his kettle to boil remotely to see just how many devices within your own home can connect to the internet.

With all of these smart devices, comes a huge increase in the frequency and volume of data being ingested into and processed by databases. This scenario is commonly referred to as the Internet of Things or IoT.

Some people assume that a NoSQL database is required for an IoT workload because the ingest rate required exceeds the capabilities of a traditional relational database. This is simply not true.

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Oracle Storage Index

If you are on Exadata or taking advantage of Database In-Memory it’s possible your queries will benefit for the automatically created and maintained Storage Indexes.

But what exactly are Storage Indexes and why don’t I always see a benefit from them?

Let me start by describing what Storage Indexes are in relation to Database In-Memory but remember they behavior in exactly the same way on the Exadata storage cell.

A Storage Index keeps track of minimum and maximum values for each column in an In-Memory Compression Unit (IMCU) or 1MB chunk on the Exadata storage cells. When a query specifies a WHERE clause predicate, the In-Memory Storage Index on the referenced column(s) is examined to determine if any entries with the specified value exist.

If you are on Exadata or taking advantage of Database In-Memory it’s possible your queries will benefit for the automatically created and maintained Storage Indexes.

But what exactly are Storage Indexes and why don’t I always see a benefit from them?

Let me start by describing what Storage Indexes are in relation to Database In-Memory but remember they behavior in exactly the same way on the Exadata storage cell.

A Storage Index keeps track of minimum and maximum values for each column in an In-Memory Compression Unit (IMCU) or 1MB chunk on the Exadata storage cells. When a query specifies a WHERE clause predicate, the In-Memory Storage Index on the referenced column(s) is examined to determine if any entries with the specified value exist.

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