To mitigate SQL performance issues, I do make use of SQL Plan Baselines, SQL Profiles and SQL Patches, on a daily basis. Our environments are single-instance 126.96.36.199 CDBs, with over 2,000 PDBs. Our goal is Execution Plan Stability and consistent performance, over CBO plan flexibility. The CBO does a good job, considering the complexity imposed by current applications design. Nevertheless, some SQL require some help in order to enhance their plan stability.
I have written and shared a set of scripts that simply make the use of a bunch of APIs a lot easier, with better documented actions, and fully consistent within the organization. I have shared with the community these scripts in the past, and I keep them updated as per needs change. All these “CS” scripts are available under the download section on the right column.
Current version of the CS scripts is more like a toolset. You treat them as a whole. All of them call some other script that exists within the cs_internal subdirectory, then I usually navigate to the parent sql directory, and connect into SQL*Plus from there. All these scripts can be easily cloned and/or customized to your specific needs. They are available as “free to use” and “as is”. There is no requirement to keep their heading intact, so you can reverse-engineer them and make them your own if you want. Just keep in mind that I maintain, enhance, and extend this CS toolset every single day; so what you get today is a subset of what you will get tomorrow. If you think an enhancement you need (or a fix) is beneficial to the larger community (and to you), please let me know.
SQL Plan Baselines scripts
With the set of SQL Plan Baselines scripts, you can: 1) create a baseline based on a cursor or a plan stored into AWR; 2) enable and disable baselines; 3) drop baselines; 4) store them into a local staging table; 5) restore them from their local staging table; 6) promote as “fixed” or demote from “fixed”; 7) “zap” them if you have installed “El Zapper” (iod_spm).
Note: “El Zapper” is a PL/SQL package that extends the functionality of SQL Plan Management by automagically creating SQL Plan Baselines based on proven performance of a SQL statement over time, while considering a large number of executions, and a variety of historical plans. Please do not confuse “El Zapper” with auto-evolve of SPM. They are based on two very distinct premises. “El Zapper” also monitors the performance of active SQL Plan Baselines, and during an observation window it may disable a SQL Plan Baseline, if such plan no longer performs as “promised” (according to some thresholds). Most applications do not need “El Zapper”, since the use of SQL Plan Management should be more of an exception than a rule.
SQL Profiles scripts
With the set of SQL Profiles scripts, you can: 1) create a profile based on the outline of a cursor, or from a plan stored into AWR; 2) enable and disable profiles; 3) drop profiles; 4) store them into a local staging table; 5) restore them from their local staging table; 6) transfer them from one location to another (very similar to coe_xfr_sql_profile.sql, but on a more modular way).
Note: Regarding the transfer of a SQL Profile, the concept is simple: 1) on source location generate two plain text scripts, one that contains the SQL text, and a second that includes the Execution Plan (outline); 2) execute these two scripts on a target location, in order to create a SQL Profile there. The beauty of this method is not only that you can easily move Execution Plans between locations, but that you can actually create a SQL Profile getting the SQL Text from SQL_ID “A”, and the Execution Plan from SQL_ID “B”, allowing you to do things like: removing CBO Hints, or using a plan from a similar SQL but not quite the same (e.g. I can tweak a stand-alone cloned version of a SQL statement, and once I get the plan that I need, I associate the SQL Text from the original SQL, with the desired Execution Plan out of the stand-alone customized version of the SQL, after that I create a SQL Plan Baseline and drop the staging SQL Profile).
SQL Patches scripts
With the set of SQL Patches scripts, you can: 1) create a SQL patch based on one or more CBO Hints you provide (e.g.: GATHER_PLAN_STATISTICS MONITOR FIRST_ROWS(1) OPT_PARAM(‘_fix_control’ ‘5922070:OFF’) NO_BIND_AWARE); 2) enable and disable SQL patches; 3) drop SQL patches; 4) store them into a local staging table; 5) restore them from their local staging table.
Note: I use SQL Patches a lot, specially to embed CBO Hints that generate some desirable diagnostics details (and not so much to change plans), such as the ones provided by GATHER_PLAN_STATISTICS and MONITOR. In some cases, after I use the pathfinder tool written by Mauro Pagano, I have to disable a CBO patch (funny thing: I use a SQL Patch to disable a CBO Patch!). I also use a SQL Patch if I need to enable Adaptive Cursor Sharing (ACS) for one SQL (we disabled ACS for one major application). Bear in mind that SQL Plan Baselines, SQL Profiles and SQL Patches happily co-exist, so you can use them together, but I do prefer to use SQL Plan Baselines alone, whenever possible.
Today, we are very happy to release SQLdb360, a new tool that merges together eDB360 and SQLd360, under a single package.
Tools eDB360 and SQLd360 can still be used independently, but now there is only one package to download and keep updated. All the new features and updates to both tools are now in that one package.
The biggest change that comes with SQLdb360 is the kind invitation to everyone interested to contribute to its development. This is why the new blended name and its release format.
We do encourage your help and ideas to continue building a free, open-source, and hopefully a YMMV great tool!
Over the years, a few community members requested new features, but they were ultimately slowed down by our speed of reaction to their requests. Well, no more!
Few consumers of these tools implemented cool changes they needed, sometimes sending us the changes (or pull requests) until a later time. This means good ideas were available to others after some time. Not anymore!
If there is something you’d like to have as part of SQLdb360 (aka SQLd360 and eDB360), just write and test the additional code, then send us the pull request! Next, we will review, validate, and merge your code changes to the main tool.
There are several advantages to this new approach:
Carlos and Mauro won’t dictate the direction of the tool anymore: we will continue helping and contributing, but we won’t “own” it anymore (the community will!)
Carlos and Mauro won’t slow down the development anymore: nobody is the bottleneck now!
Carlos and Mauro wan’t run out of ideas anymore!!! The community has great ideas to share!!!
Due to the nature of this new collaborative effort, the way we now publish SQLdb360 is this:
Instead of linking to the current master repository, the tool now implements “releases”. This, in order to snapshot stable versions that bundle several changes together (better than creating separate versions per merge into master).
Links in our blogs are now getting updated, with references to the latest (and current) stable release of SQLdb360 (starting with v18.1).
Note: Version names sound awfully familiar to Oracle nomenclature, right? Well, we started using this numbering back in 2014!!!
This post is about: “Adapting and adopting SQL Plan Management (SPM) to achieve execution plan stability for sub-second queries on a high-rate OLTP mission-critical application”. In our case, such an application is implemented on top of several Oracle 12c multi tenant databases, where a consistent average execution time is more valuable than flexible execution plans. We successfully achieved plan stability implementing a simple algorithm using PL/SQL calling DBMS_SPM public APIs.
Chart below depicts a typical case where the average performance of a large set of business-critical SQL statements suddenly degraded from sub-millisecond to 15 or 20ms, then beccome more stable around 3ms. Wide spikes are a typical trademark of an Execution Plan for one or more SQL statements flipping for some time. In order to produce a more consistent latency we needed to improve plan stability, and of course the preferred tool to achieve that on an Oracle database is SQL Plan Management.
We tested and ruled out adaptive SQL Plan Management, which is an excellent 12c new feature. But, due to the dynamics of this application, where transactional data shifts so fast, allowing this “adaptive SPM” feature to evaluate auto-captured plans using bind variable values captured a few hours earlier, rendered unfortunately false positives. These false positives “evolved” as execution plans that were numerically optimal for values captured (at the time the candidate plan was captured), but performed poorly when executed on “current” values a few hours later. Nevertheless, this 12c “adaptive SPM” new feature is worth exploring for other applications.
We adapted SPM so it would only generate SQL Plan Baselines on SQL that executes often, and that is critical for the business. The algorithm has some complexity such as candidate evaluation and SQL categorization; and besides SPB creation it also includes plan demotion and plan promotion. We have successfully implemented it in some PDBs and we are currently doing a rollout to entire CDBs. The algorithm is depicted on diagram on the left, and more details are included in corresponding presentation slides listed on the right-hand bar. I plan to talk about this topic on an international Oracle Users Group in 2018.
This algorithm is scripted into a sample PL/SQL package, which you can find on a subdirectory on my shared scripts. If you consider using this sample script for an application of your own, be sure you make it yours before attempting to use it. In other words: fully understand it first, then proceed to customize it accordingly and test it thoroughly.
Chart below shows how average performance of business-critical SQL became more stable after implementing algorithm to adapt and adopt SPM on a pilot PDB. Not all went fine although: we had some outliers that required some tuning to the algorithm. Among challenges we faced: volatile data (creating a SPB when table was almost empty, then using it when table was larger); skewed values (create a SPB for non-popular value, then using it on a popular value); proper use of multiple optimal plans due to Adaptive Cursor Sharing (ACS); rejected candidates due to conservative initial restrictions on algorithm (performance per execution, number of executions, age of cursor, etc.)
If your OLTP application contains business critical SQL that executes at a high-rate, and where a spike on latency risks affecting SLAs, you may want to consider implementing SQL Plan Management. Consider then both: “adaptive SPM” if it satisfies your requirements, else build a PL/SQL library that can implement more complex logic for candidates evaluation and for SPBs maintenance. I do believe SPM works great, specially when you enhance its out-of-the-box functionality to satisfy your specific needs.