Enhancements in SQL Plan Management, SecureFiles LOB Write Performance, and Column Width

Join Lois Houston and Nikita Abraham, along with Senior Principal Database & Security Instructor Ron Soltani, as they discuss how the new Automatic SQL Plan Management feature in Oracle Database 23ai improves performance consistency and simplifies management. Then, Senior Principal Database & MySQL Instructor Bill Millar shares insights into two new features: one that enhances SecureFiles LOB Write Performance, improving read and write speeds, and another that increases the column limit in a table to 4,096, making it easier to handle complex data.   Oracle MyLearn: https://mylearn.oracle.com/ou/course/oracle-database-23ai-new-features-for-administrators/137192/207062   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and joining me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we looked at the Oracle Database 23ai enhancements that have been made to Hybrid Columnar Compression and Fast Ingest. In today’s episode, we’ll talk about the 23ai new feature for Automatic SQL Plan Management with Ron Soltani, a Senior Principal Database & Security Instructor with Oracle University.  01:01 Nikita: And later on, we’ll be joined by Bill Millar, another Senior Principal Database & MySQL Instructor, who will tell us about the 23ai automatic feature that enhances SecureFiles LOB Write Performance. We’ll also get him to talk about the Wide Columns update. So, let’s get started. Hi Ron! What have been the common challenges with SQL plans and database performance? Ron: One of the problems that we have always had, if you remember, was when data changes, database setting configuration, parameter changes, SQL that were operating very well could now behave badly using the SQL plan that were associated to them. And remember, the same SQL plan generally Oracle likes to continuously reuse.  So the SQL plans were put in the baseline in the past, and we could have those SQL plan baseline, which are a set of approved plans to be used for a SQL from the SQL history stored in AWR, then could be used for the optimizer to choose from. However, which plan to choose and which one would be the best one to use, this is what the problem has been in managing the SQL plan baselines, and a lot of the operation would have been done manually.  02:22 Lois: And what have we done to overcome this?  Ron: So now this new system will going to perform all of those operations automatically for us. Now it can search the Automatic Workload Repository. It can find SQL plans for a particular SQL statement, then look for any alternative plans that may available in alternate sources like SQL tuning sets. And then validate those plans and see if those plans are going to be good and to be used as SQL plan baseline for executing SQL statement by the optimizer. 03:00 Nikita: So we now have the Automatic SQL Plan Management Evolve Advisor to help manage operations automatically, right? Can you tell us a little more about it? How does it ensure optimal performance? Ron: This is an automatic advisor that is created that can go look for different plans and validate the plans by examining them, making sure that they are not causing any regression compared to the previous operation, and then evolve that plan into a good baseline.  This simplifies management of the baseline repository for a SQL statement. So as data changes, as parameters changes, optimizer could come up with different type of plans that are set within this baseline that has been validated to be good baseline for each situational operation. So this way you reduce a lot of hard parsing operations.  04:00 Lois: And how does the SQL Evolve Advisor work, Ron? Ron: First, it will check the AWR to find what are the top SQLs that has been found. Then it will look to see if these top SQLs who did not perform well with the plan that they have, that's why they're top SQL, have other alternative plans that are stored in the SQL plan history, in AWR, or available in any other sources.  Then if it finds any additional plans, it will go ahead and add all of those plans into the plan history. So in the plan history, now you have accumulation of all the plans available in AWR and anything that has been brought from other sources. Then it will test every one of those plans and validate that by use of the plan, the SQL statement will not deprivate and get slower. The performance is either similar or actually better. So normally, there is a percentage that the SQL should improve. So we will then validate these baselines.  And finally, once the baselines or those plans have been validated, they will be accepted, and then they will be added as SQL plan baselines. They will remain in the statement history, in the AWR, and will be available for optimizer for the future use.  05:28 Nikita: What are the benefits of this? Ron: Number one is Autonomous Database. As you know, they want to automate all management, including management of the SQL execution due to changes that are happening for the application, for the data, or the database and its environment.  It totally eliminates any manual intervention for management of the statement, and it can transparently repair any statement that had been affected by a major change.  06:00 Lois: What sort of problems does this feature solve for us? Ron: Of course, this is a performance consistency. We want to make sure that every statement performed to its best performance and any specific changes that may impact those SQL statements would be taken into an account, and a better plan, if available, would then be available for use.  It also improves the application performance level, therefore database service level will get much improvement. And the SQL execution plans will be automatically managed behind the scene by expanding these baselines, by managing all of these baseline history and all of that that is managed by this automatic SQL plan management environment automatically.  06:50 Nikita: And when do we use this?  Ron: If there is a change in a database environment, like you add SGA, the change into the shared pool, change in the size of the buffer cache or any type of storage effects. So all of those can actually affect the SQL execution.  Now all of those changes, including data changes, can cause a SQL plan to not behave very well or behave as well as it was doing before. Therefore, if particular plans do not perform as well as they did before, that affects the performance of the application. This also affects the performance of the database and the instance.  07:35 Lois: So, how do we use this environment?  Ron: Well, best news that I have for you in that is that there is nothing manual needs to be done. All we need to do is, number one, make sure that we enable foreground automatic SQL plan management that we done through the package for the DBMS SPM for SQL plan management.  You will use the package with the configure option, and you enable the auto SPM evolve task, and you set it to auto. Once this is done, now the SQL evolve plan management and advisor are enabled, and they will then monitor your statements, review all of the top SQLs as they are found with all of the ADDM operation, and then do their work in looking for better plans and being able to maintain the SQL plan baselines we talked about.  Now for you to be able to view, monitor, and see how these operations are going, if it is enabled, you can take a look at the DBA SQL plan baseline's view. There are many, many columns in that particular baseline, and there are also columns that has been added that tell you where is the plan generated from, if a plan is approved, and any other user interaction with the plan or settings can then be verified using that DBA SQL plan baseline view.  09:13 Are you looking for practical use cases to help you plan and apply configurations that solve real-world challenges? With the new Applied Learning courses for Cloud Applications, you'll be able to practically apply the concepts learned in our implementation courses and work through case studies featuring key decisions and configurations encountered during a typical Oracle Cloud Applications implementation. Applied learning scenarios are currently available for General Ledger, Payables, Receivables, Accounting Hub, Global Human Resources, Talent Management, Inventory, and Procurement, with many more to come! Visit mylearn.oracle.com to get started. 09:54 Nikita: Welcome back! Let’s bring Bill into the conversation. Hi  Bill! Can you tell us about the 23ai automatic feature that enhances SecureFiles LOB Write Performance?  Bill: The key here is that it is automatic and transparent. There's no parameters set. Nothing to configure in table, no hints, and nothing that you have to do with these improvements. It is tightly integrated with SecureFiles LOB infrastructure.  So now, multiple LOBs can be handled in a single transaction and can be buffered simultaneously. This will help with mixed workloads, switching between the LOBs that are writing in a single transaction. The PGA will adaptively resize based off the size for these large writes for the LOBs if you're using the No Cache option. Remember, no cache is going to bypass the buffer cache and does direct reads and writes from the PGA.  JSON type will be transformed into the OSON Oracle data type. It is an optimized native binary storage format for JSON data.  11:15 Lois: Ok. So, going forward, there will be better read and write performance for LOBs. Bill: Multiple LOBs in a single transaction can be buffered simultaneously, improving mixed workloads. We just talked about the PGA. Automatically, the buffer is automatically resized.  And the improved JSON support. The reason it will recognize, hey, this is a JSON data type. But traditionally, JSON data types were small.  So they were small to medium size. So the range from 32k to 32 meg was considered small to medium whereas LOBs were designed for data types larger than 100 meg. So by recognizing this a JSON data type, it can take advantage of the LOB architecture.  Other enhancements will also include the acceleration of compressed LOBs, the pen and compression caching, and improves the poor performance of your reads and writes to compressed LOBs. It's faster than previously.  12:24 Nikita: Bill, what do you think about the recent increase in the column limit? Previously, the limit was 1000 columns per table, which sometimes posed issues when migrating from other systems that allowed more than 1,000 columns, right?  Bill: Maybe because of workload requirements, the whole machine learning, the internet of things workloads, IOTs can have hundreds of thousands of attributes, dimensional attribute columns for that. And even our very own blockchain tables reserves up to 40 hidden virtual columns, so that takes away from the total amount.  Virtual columns count towards the column limits and some applications as they drop columns, what it does, it just converts them to unused, and it still applies towards the limit the number of columns that you can have to that limit. There were workarounds. However, they were most likely not the best way to do it, like column switching, table splitting for that. But big data really use cases, really saw where files have or required more than 1,000 columns.  13:42 Lois: So, now that we can have 4,096 columns in a table, I’m sure it’s made handling complex data a lot easier.  Bill: So by increasing this, since other systems do support higher column limits, it can-- the increase can make migration from other systems easier and possibly even a little bit more attractive while it can make applications a little bit simpler because the 1,000 column limit was not always optimal for analytics. Where 1,000 might have been plenty for OLTP type environments, but not for the analytics, especially when it comes to machine learning and those internet of things that we talked about, where the previous workarounds, like splitting the tables, really caused more performance issue than anything else.  So we want to avoid those suboptimal workarounds. And the nice thing is there's no change to the SQL. So once you have that-- well, if we were doing SQL, if we had tables that were split and we're trying to do things that is actually going to help improve that SQL, now, we don't have multiple objects that we're dealing with.  14:57 Nikita: How do we actually go about increasing the column limit to 4,096? Bill: You do have to have the compatibility set to 23c. Why? Because it's a new feature. There is a new initialization parameter called Max columns, and you do set that. There's two different ways, two different values. We can set it to standard or we can set it to extended.  It is dynamic. When it's set to standard, it's only 1,000. When we set it to extended, it's going to allow the 4,096. It is modifiable at the PDB level. However, it will inherit what's at the root level, if it's not explicitly set at a PDB. It can't alter it in a session for that. And multiple instances of the RAC environment must use the same value.  Now one thing, notice that it cannot be set to standard if I created a table that had more than 1,000 columns. One thing that might get you, when you drop a table that has more 1,000 columns and you try to set it back to standard, it might tell you, hey, you have tables that have more than 1,000 columns. Don't forget your recycle bin unless you did a drop table purge.  16:09 Lois: Are there any performance considerations to keep in mind, Bill? Bill: There's really no DML or query performance degradation for the tables. However, it might require, as you would expect, the increase in memory when we have the new column limits. It might require additional shared pool, additional SGA with the additional columns, more buffer cache as we're bringing blocks in.  So that's shared pool along with the PGA. And also we can add in buffer cache in there, because that increased column count is going to be increase in the total PGA memory usage. And those are kind of expected for that. But the big advantage is it gives us the ability to eliminate some of these suboptimal workarounds that we had in the past.  17:02 Nikita: Ok! We covered a lot today so thank you Bill and Ron.  Lois: To learn more about what we discussed today, visit mylearn.oracle.com and search for the Oracle Database 23ai New Features for Administrators course. Join us next week for a discussion on some more Oracle Database 23ai new features. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 17:27 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.