Bobby Durrett's DBA Blog

On December 2nd we ran into a problem with one of my scripts that uses the DBA_HIST_SQLSTAT view. It did not show the full run time of a SQL statement that ran for several hours. So, it hid the fact that the plan that the SQL statement was using was much less efficient than another better plan. This post will document the two DBA_HIST_SQLSTAT scripts that I have been using so far and will introduce a new script that should make it clearer that a SQL statement is running for several hours on a plan.

There are two scripts that are based on DBA_HIST_SQLSTAT that I use most often for tuning: sqlstat.sql, sqlstat2.sql. I use these scripts all the time to look at the performance of queries and the different execution plans that they use. But in this case the output of sqlstat.sql was misleading. Here is part of its output for the problem SQL statement am47wcwn336yj:

PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms
--------------- ------------------------- ---------------- ------------------
     3771021445 04-NOV-18 05.00.15.814 AM                1            2250696
     2117190322 11-NOV-18 03.01.00.265 AM                6         223502.352
     1484468961 18-NOV-18 06.00.22.364 AM                1         2695964.96
     2117190322 25-NOV-18 02.00.34.302 AM                3         198192.968
     1139203276 02-DEC-18 07.00.21.784 AM                1          322110.53

We got a performance ticket at about 9 am on December 2nd about a session with SQL ID am47wcwn336yj running for longer than normal. Based on the sqlstat.sql output above you would think that the current plan is 1139203276 and that it is a little slower than the other plans but not terrible. The problem is that sqlstat.sql only shows you SQL executions that have completed during the hour prior to the END_INTERVAL_TIME. So, one execution of am47wcwn336yj completed between 6 and 7 am on December 2nd, but the output makes it look like it only ran for 322110.53 milliseconds which is about 5 minutes. Really it probably ran for several hours and completed around 6:05 am. So, we thought that 1139203276 was an ok plan when really 2117190322 is the much more efficient plan. But, the sqlstat.sql output confused us because it did not show the hours where am47wcwn336yj was running with plan 1139203276 but had not completed. It is easy to see why sqlstat.sql leaves out these hours if you look at this condition in its where clause:

and executions_delta > 0

This part of the where clause conditions prevents you from seeing where the SQL statement is still running. But the select clause contains a division by executions_delta so you cannot have divide by zero so that is why I excluded it.

When I am working on a long running SQL statement that spans two or more hours, I run sqlstat2.sql after I run sqlstat.sql. Sqlstat2.sql shows all the time spent per hour regardless of whether a SQL statement’s execution completed. It does not have the condition requiring executions_delta to be > 0 because it does not divide by that column. Here is the output for am47wcwn336yj:

PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA ELAPSED_TIME_DELTA
--------------- ------------------------- ---------------- ------------------
     3771021445 04-NOV-18 03.00.43.465 AM                0         3584030311
     3771021445 04-NOV-18 04.00.27.516 AM                0         3588264762
     3771021445 04-NOV-18 05.00.15.814 AM                1         2250695998
     2117190322 11-NOV-18 03.01.00.265 AM                6         1341014109
     1484468961 18-NOV-18 02.00.07.772 AM                0         3171408998
     1484468961 18-NOV-18 03.00.58.545 AM                0         3648125526
     1484468961 18-NOV-18 04.00.44.680 AM                0         3586687594
     1484468961 18-NOV-18 05.00.32.832 AM                0         3592026797
     1484468961 18-NOV-18 06.00.22.364 AM                1         2695964960
     2117190322 25-NOV-18 02.00.34.302 AM                3          594578904
     1139203276 02-DEC-18 02.00.14.082 AM                0          480039949
     1139203276 02-DEC-18 03.00.05.255 AM                0         3591464884
     1139203276 02-DEC-18 04.00.50.936 AM                0         3645014119
     1139203276 02-DEC-18 05.00.40.304 AM                0         3591817904
     1139203276 02-DEC-18 06.00.31.820 AM                0         3587861998
     1139203276 02-DEC-18 07.00.21.784 AM                1          322110530
     1139203276 02-DEC-18 09.00.58.127 AM                0         2028205794
     1139203276 02-DEC-18 10.00.37.793 AM                0         3582326580

Here you can see that plan 1139203276 ran between 1 and 7 am on 02-DEC-18 and completed and then ran between 8 and 10 am and never completed. We ended up killing the long running session and we reran the SQL another way so there is no more history of it. The problem with sqlstat2.sql is that it does not show average elapsed time per execution like sqlstat.sql does. It just shows the total elapsed time for all executions, if any. This is because we don’t divide by executions_delta. Also, the units are microseconds instead of milliseconds. I find milliseconds to be easier to use.

To me looking at the output from sqlstat.sql and the output from sqlstat2.sql 2117190322 is the best plan. It is clear to me because I wrote these two scripts and am used to the way they look in this situation. If you go back to the first output, you can see that plan 2117190322 averages less than 224 seconds per execution (223502.352 ms and 198192.968 ms). Plans 3771021445, 1484468961, and 1139203276 ran for multiple hours. You can tell this because there are multiple consecutive hours with those plans and 0 executions.

I have used sqlstat.sql and sqlstat2.sql a lot as they are but today, I got the idea of combining them into a script that shows average elapsed time per execution for a given hour and includes hours where an execution did not complete. I have not used it yet in a real performance tuning situation but here it is, cleverly named sqlstat3.sql. Here is its output for this situation:

PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms
--------------- ------------------------- ---------------- ------------------
     3771021445 04-NOV-18 03.00.43.465 AM                0         3584030.31
     3771021445 04-NOV-18 04.00.27.516 AM                0         3588264.76
     3771021445 04-NOV-18 05.00.15.814 AM                1            2250696
     2117190322 11-NOV-18 03.01.00.265 AM                6         223502.352
     1484468961 18-NOV-18 02.00.07.772 AM                0            3171409
     1484468961 18-NOV-18 03.00.58.545 AM                0         3648125.53
     1484468961 18-NOV-18 04.00.44.680 AM                0         3586687.59
     1484468961 18-NOV-18 05.00.32.832 AM                0          3592026.8
     1484468961 18-NOV-18 06.00.22.364 AM                1         2695964.96
     2117190322 25-NOV-18 02.00.34.302 AM                3         198192.968
     1139203276 02-DEC-18 02.00.14.082 AM                0         480039.949
     1139203276 02-DEC-18 03.00.05.255 AM                0         3591464.88
     1139203276 02-DEC-18 04.00.50.936 AM                0         3645014.12
     1139203276 02-DEC-18 05.00.40.304 AM                0          3591817.9
     1139203276 02-DEC-18 06.00.31.820 AM                0            3587862
     1139203276 02-DEC-18 07.00.21.784 AM                1          322110.53
     1139203276 02-DEC-18 09.00.58.127 AM                0         2028205.79
     1139203276 02-DEC-18 10.00.37.793 AM                0         3582326.58

The nice thing is that you can see that 2117190322 ran 6 times averaging 223502.352 milliseconds on November 11 and 3 times averaging 198192.968 milliseconds on November 25th. Plus, you can see that the other plans span multiple hours. Note that the plans that span a full hour run for about 3600000 milliseconds which is an hour so that supports the idea that the SQL statement is active throughout the entire hour. I have only shown some of the total output to fit on the screen, but the full output includes more information such as a CPU and IO breakdown of the run time.

Anyway, the point is that the main DBA_HIST_SQLSTAT query that I use, sqlstat.sql does not show SQL statements that run for several hours very clearly. You can figure it out using sqlstat2.sql in addition to sqlstat.sql. The new sqlstat3.sql script may make it easier to diagnose multi-hour SQL statement executions in the future without having to use two scripts.

Bobby

P.S. See this post about how I consolidated sqlstat.sql, sqlstat2.sql and sqlstat3.sql into one script, sqlstat.sql:

https://www.bobbydurrettdba.com/2020/04/14/fixed-issue-with-dba_hist_sqlstat-script/

As 2018 is ending I am thinking about what I spend my time on. Do I have my priorities correct? I know that I should spend some part of my time interacting with people over the Internet. I have gotten so much benefit from talking with other people about Oracle database issues through web-based forums. I have also had some in person interactions. I’m excited about the sort of rebirth of AZORA, my local Arizona Oracle user group. I talk to human beings in person about Oracle at user group meetings and that has great value. But I noticed that it had been a while since my last blog post of any substance. So, I asked myself if I am losing interest in blogging. I have not lost interest in writing blog posts, but I have had many other things tugging for my time, including other ways to interact with people over the Internet. So, I need to think about how much time to allocate to this blog next year.

I blame part of my lack of blog verbosity on Python. I have written a number of Python related posts, but Python has also drawn me into interacting with Rosetta Code and Stack Overflow. I’m trying to keep up my Python skills so that I have a general-purpose programming language available in my tool kit. But any time I might have devoted to my blog may have gotten used up by these Python related sites. Even GitHub is a form of distraction because maintaining Python repositories there takes time. But I don’t regret any time spent on Python because it is such a popular language now and it has really helped me in my work.

I guess the second time sink has been my PeopleSoft work. I don’t really talk much about my PeopleSoft work on this blog, but I have done a lot more of this sort of work in 2018 than I ever would have expected. With the push to move to the cloud and move to non-Oracle databases I have nevertheless been doing a bunch of old fashioned on premises ERP support, PeopleSoft applications on Oracle databases. I’ve been doing PeopleSoft on Oracle database for at least 24 years now so if my employer needs this sort of work, I’m capable of doing it. But PeopleSoft doesn’t excite me as much as database internals and performance tuning so that’s why I don’t blog about it much.

Speaking of the cloud and non-Oracle database, I have done some work in these areas in 2018 but not as much as I would have liked. I probably wouldn’t blog about the basics of using AWS or MySQL RDS, but if I can dig into some MySQL internals and use or build some MySQL performance tools, I can see blogging about that. My experience is mostly with Oracle, but I think open source is neat. I like having the MySQL and PostgreSQL source code even if I am unfamiliar with it. So, I guess I haven’t blogged about the cloud and non-Oracle databases because I just haven’t gotten very far. Maybe next year.

But, why have I not blogged about Oracle performance tuning? That really is the question. There have been several performance issues that I have worked on over the past few months, but in many cases, they were not anything new to me. Still, I think I may have missed out by not documenting the steps I went through in some of these real issues. I think it is easy to feel like what I am writing is not anything new and that there are better Oracle performance tuning bloggers out there. Also, I worry that I will make a mistake and confuse people or mislead them with wrong information. I would not intentionally mislead anyone, but I can certainly be wrong! I think going forward in 2019 I want to make the effort to write blog posts about Oracle performance issues that I have resolved even if they are not especially new territory. I probably can find some interesting angle from most issues. I think it might help people to see how I am using my SQL*Plus and Python scripts in different scenarios. Anyway, I hope to get back to blogging about Oracle performance.

Topics of my 15 blog posts in 2018 to this point:

My Shakespeare inspiration (To be, or not to be):

To be, or not to be, that is the question:
Whether ’tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take Arms against a Sea of troubles…

Hamlet. Act III, Scene I

Recently during testing a merge statement was running forever in our qat environment after a simple change. We had simply added a few columns to an existing table. After looking around we found that our qat environment had the parameter parallel_degree_policy set to AUTO, but our other environments had it set to MANUAL. I thought I would show how we figured this out.

I had the SQL_ID for the problem merge statement and used my sqlstat.sql script to see if the same merge statement ran in production. I did not find any production executions. I was told that this merge statement was already in our production code and ran all the time without an issue. So, then I used my findsql.sql script to find the SQL_ID of the production version of the merge statement. 

To use findsql.sql you have to edit it to replace MYUSER with a real user on your database. I use my own personal user. Then you have to update the LIKE statement in the where clause to identify the query that you want. I replaced this line in the findsql.sql text on GitHub:

SQL_TEXT like '%a.FISC_WK_OF_Yr < to_number(to_char(sysdate+1, ''iW''))%' and

with this line which is part of the text of the problem merge statement:

upper(SQL_TEXT) like upper('%MERGE INTO DDMADM.FWB_MDL MDL%') and

I easily found the equivalent merge statement in production. The QAT merge statement had SQL_ID 00cmatqz5wymd and the prod one had 7pgqd82bgdah2. My coworker, who had brought me in on this issue, said that on QAT the query was not running in parallel. So, I used my getplans.sql script to look at the two plans. On prod the plan used parallelism:

-----------------------------------------
| Id  | Operation                        
-----------------------------------------
|   0 | MERGE STATEMENT                  
|   1 |  MERGE                           
|   2 |   TEMP TABLE TRANSFORMATION      
|   3 |    PX COORDINATOR                
|   4 |     PX SEND QC (RANDOM)          
|   5 |      LOAD AS SELECT              
|   6 |       UNION-ALL                  
|   7 |        WINDOW SORT               
|   8 |         PX RECEIVE               
|   9 |          PX SEND HASH            
|  10 |           VIEW                   
|  11 |            UNPIVOT               
|  12 |             PX BLOCK ITERATOR    
|  13 |              TABLE ACCESS FULL   

On QAT it did not:

-----------------------------------------------------
| Id  | Operation                                    
-----------------------------------------------------
|   0 | MERGE STATEMENT                              
|   1 |  MERGE                                       
|   2 |   VIEW                                       
|   3 |    NESTED LOOPS OUTER                        
|   4 |     VIEW                                     
|   5 |      TEMP TABLE TRANSFORMATION               
|   6 |       LOAD AS SELECT                         
|   7 |        UNION-ALL                             
|   8 |         WINDOW SORT                          
|   9 |          VIEW                                
|  10 |           UNPIVOT                            
|  11 |            PARTITION RANGE SINGLE            
|  12 |             TABLE ACCESS BY LOCAL INDEX ROWID
|  13 |              INDEX RANGE SCAN                

I found this extra line at the end of the QAT plan:

- automatic DOP: Computed Degree of Parallelism is 1 because of parallel threshold

This triggered a memory of a coworker of mine who was trying out some degree of parallelism parameters a couple of years ago. After searching the internet, I quickly found that the automatic DOP message was related to the parameter parallel_degree_policy. We found that it was set to manual on prod, so we set it to manual on QAT and the merge statement went back to the expected performance.

I guess the point of this post is to keep a close watch on the messages that show up at the end of your execution plan. They could lead you to a parameter setting that is the source of your problem.

Lastly, this is my first post using the new WordPress Gutenberg plugin. What do you think? It was pretty easy to put in the output from my scripts in little unformatted boxes by themselves. Much easier than with the previous WordPress editor.

Bobby

P.S. I have found one negative to Gutenberg. I can’t find a way to use the Jetpack Proofreading function to check my grammar. So, if you see that I have used passive voice in a bunch of my sentences you will know why. Hopefully they will add this into a future update. Of course, I could just use my brain to check the grammar but where is the fun in that?

P.P.S. I started working on another blog post and found a simple solution to my grammar checking needs using Gutenberg. I just copy and paste the blog post text into Microsoft Word and use its grammar checker. Pretty neat.

I am going to giving a talk about why Python is a good programming language for Oracle DBAs in Scottsdale on November 16th for the Arizona Oracle User Group, AZORA. We may get some other speakers together for the meeting but I will be there for sure. I did this talk for Collaborate and for the RMOUG Training Days earlier this year. I may try to make it a little more interactive if I can get it together in time. I’m thinking of encouraging people to bring their laptops and install Python 3.7.1 so they can play around with a couple of features during the talk. Here is the Meetup information:

https://www.meetup.com/Arizona-Oracle-User-Group/events/255752135/

Let me know if you have any questions about this meeting. It should be fun.

Our meeting sponsor Clarivoyant graciously agreed to provide us with pizza and drinks so don’t worry about lunch!

Search my blog for Python posts if you want to see examples of how I have been using Python for my Oracle DBA work:

https://www.bobbydurrettdba.com/?s=Python

Bobby

I have a couple of Oracle performance reports that I upload from my work Windows 7 laptop to our corporate SharePoint site on Office 365 on a regular basis. I thought that it would save me time to automate the uploading of these reports using Python. This post describes the continuing saga of my attempts to build a working Python script just to upload a file to SharePoint. I have a script that works for me today but there are no guarantees it will work in the future. Still, it might be helpful to other Oracle DBAs and others so I thought it was worth documenting.

The first Python script that I wrote that really helped me in my job was the ashcpu.py script within PythonDBAGraphs. I run this report just about every day at work to monitor the CPU usage on an important database. After I review the report which is an image of a graph I manually copy the report to SharePoint in a certain folder. It doesn’t take long to find the bookmark for the SharePoint folder and to copy the files over. But, what if I could just click on an icon and the files would be copied to SharePoint automatically?

Back in 2015 I wanted to start saving some historical Oracle performance data that went back further than the 6 weeks of AWR history that we keep on our Oracle databases. Ideally we would setup some sort of AWR data warehouse that we could mine for useful information based on data from the past. But setting up an AWR data warehouse would require money and approvals and time. Instead I built a simple script to get monthly AWR reports for all of our production databases. If the database had been up for the entire month it would get a report for that month, otherwise it would report on a subset of the month. I forget how I chose the subset. I think I picked the longest number of days. Anyway, the script tars and gzips the html AWR files for all of the databases into a single .tar.gz file. So, each month I use ftp to copy the .tar.gz file down to my laptop. Then I extract all of the html files and zip them up. Then I copy the zip to Sharepoint. So, in this case I wanted a Python script that would do the ftp, ungzip and untar, zip, and copy the zip to Sharepoint. It is only once a month but it would be nice to just click the button and have it done.

The script to upload the images is pretty simple. All it does is look for .png image files in the right directory and with the right pattern and upload them and then delete them from the source directory.

The script that uploads the zip files with AWR reports is a little more complicated because it does the ftp, tar, gunzip, zip, and copies to SharePoint.

But things like untaring and zipping the files are easy in Python:

The hard part was getting the file to SharePoint.

I had a fully working script that I was using to upload both of these types of reports, images and awr zips, to SharePoint. It probably worked for a week at most. Here is the code on GitHub that no longer works: url

I tried to find out why this script stopped working by asking on Stack Overflow and opening an issue on the GitHub site for the package that my script depended on. But I didn’t get an immediate answer so I moved on to create the copyfiletosharepoint script. It wasn’t easy getting copyfiletosharepoint to work. It is based on Microsoft Graph which seems to be Microsoft’s preferred way to interact with SharePoint on Office 365 through Python. Anyway, as of today 7/27/2018 it works for the two reports that I want to upload. Hopefully it will keep working for longer than my first script!

It has been quite the saga and has taken much longer than I expected. But hopefully the scripts that I have built will make me more productive by automating some of the mindless tasks that I do on a regular basis.

Bobby

A coworker of mine asked me to look at a MySQL query that was running longer than we want it too. If they added a LIMIT 1000 clause at the end of the query it popped up the results in a couple of minutes but without the LIMIT clause it apparently hung forever without returning a row of the result set. I figured out that the query was returning 4 million rows with a bunch of columns in each row which is why it was slow. But the question remained why did it pop up the first row faster with LIMIT clause? I checked the plan with and without the LIMIT clause and didn’t see a difference. Then I did more testing and realized that even a simple query that returns a large result set took a long time to display the first row. This is on MySQL 5.7 running in AWS RDS.

I asked a question about this on two forums that answer MySQL questions:

https://dba.stackexchange.com/questions/208161/mysql-select-from-table-without-conditions-does-not-return-rows-right-away

https://forums.mysql.com/read.php?24,666643

I appreciate people responding to my questions but I didn’t really get the answer I was looking for. I really wanted to know why large result sets made the mysql client take a long time to display the first row. But, I guess people thought that you should just add a LIMIT clause and not worry about it. Why would you want to return the first rows of a large result set quickly? I just want to understand it and I still don’t.

I built a test case and ran it against an Oracle database and a MySQL database and the Oracle database returned the first row immediately and the MySQL version hung for a couple of minutes. I used the Oracle sqlplus utility and MySQL mysql command line client.

Here is the test case:

I ran this from my laptop at home over a VPN so they both had to send results over the internet. But the MySQL database was in AWS so it had to go over the internet twice to get to me. The Oracle database was in our data center. The select * from test statement pops up the first rows instantly on Oracle but took at least a minute to show up on MySQL. A simple Python script against the same MySQL table pops up rows instantly.

So, this leaves me wondering if the slowness in displaying the first row is due to some processing that occurs in the mysql client. I assume, but don’t know, that running the query through Python is no different from through the mysql client from the server’s perspective.

I guess the point is that there is a lot that I don’t know about MySQL’s architecture because most of my experience is with Oracle. But it was helpful to me to know that the mysql client is slow to produce the first row of a large result set and to know that a Python script could be used as a workaround to this slowness.

Bobby

This is the sftp version of the ftp script I published in an earlier post. It logs into a remote host using sftp and backs up a remote directory recursively backing up all subdirectories.

This one uses Paramiko instead of ftplib so you can do encrypted file copies.

Bobby

A few days or weeks ago I saw a Twitter post about database links failing on Oracle databases next year. So, I have researched this issue. My company has a bunch of Oracle databases with a web of database links connecting them. After researching this for a while I have convinced myself that our company will not experience an issue. I want to share some of what I have found in this post.

First, I have read some helpful documents on Oracle’s support site. I recommend that you review these if you want to understand the issue:

Mandatory Patching Requirement for Database Versions 11.2.0.3 or Earlier, Using DB Links (Doc ID 2335265.1)

Recommended patches and actions for Oracle databases versions 12.1.0.1, 11.2.0.3 and earlier – before June 2019 (Doc ID 2361478.1)

Master Note: Overview for SCN issues (Doc ID 1503937.1)

System Change Number (SCN), Headroom, Security and Patch Information (Doc ID 1376995.1)

ORA-19706 and Related Alert Log Messages (Doc ID 1393360.1)

Bug 23130347 : HIGH SCN GROWTH RATE

Besides reading Oracle support documents I wanted to recreate a database link issue on a couple of test databases. My idea was to build a database that was not patched enough based on Oracle’s recommendations and one that was. Then connect them and set the date forward to after June 23, 2019 and see if I could create the issues that could happen at that time. I was not successful in recreating the issue. But I did prove out one interesting fact about database links and SCN numbers. When you use a database link the Oracle RDBMS synchronizes the SCN numbers of the two databases. Here is an example. I have 11.2.0.3.0 and 12.1.0.2 databases running on small Linux VMs on top of VirtualBox on my laptop.

Here are their SCN numbers after I create a database link between the two and before I use the database link:

NLSRTL Version 11.2.0.3.0 - Production

ORCL:SYSTEM>ORCL:SYSTEM>
     CURRENT_SCN
----------------
         1003971
         
NLSRTL Version 12.1.0.2.0 - Production

ORCL:SYSTEM>ORCL:SYSTEM>
     CURRENT_SCN
----------------
         1858602

Here are their SCN numbers after an update over a link:

NLSRTL Version 11.2.0.3.0

ORCL:SYSTEM>ORCL:SYSTEM>
     CURRENT_SCN
----------------
         1859090
 
NLSRTL Version 12.1.0.2.0

ORCL:SYSTEM>ORCL:SYSTEM>
     CURRENT_SCN
----------------
         1859091

I did the same thing with a select statement and got the same sort of synchronization of SCN numbers.

Knowing that database links cause SCNs to sync I started looking at our SCNs on all of our databases and found that there were two groups of databases with very similar SCNs. One group’s SCN was around 475,000,000,000 and the other group’s SCN was around 970,000,000,000.

But, based on the documents that I have read the max SCN for May 16th, 2018, today, is almost 16 trillion. We are under 1 trillion on our busiest system so we have a lot of headroom. The Oracle support documents say that there is some internal limit to how much your SCN can grow and Oracle bases the limit on the number of seconds since midnight January 1, 1988. You multiply the seconds by 16K SCN numbers per second to get the max SCN for today’s date. I found a lot of this math in scnhealthcheck.sql which Oracle’s Doc ID 1503937.1 includes.  I hacked it into different pieces that I found helpful but I didn’t think I should post them because it is a copyrighted script. Just download it from Oracle support and see if what I’m saying makes sense.

Anyway, after reading up on all this stuff I don’t think that the SCN issues with database links apply to us. We have far too much headroom. Maybe a company with higher transaction rate databases would be closer to the limit. We will find out on June 23, 2019 if I am right.

Bobby

So, I was at the Collaborate 2018 Oracle user group conference last week and I am on call this week. I have not posted anything about the conference so I thought I would try to get something out there.

I did my Python and Toastmasters talks. I’m not sure how people received these. Both of these talks sort of loaded extra burden on the Oracle DBAs who attended. One talk said they should learn Python and the other said they should join Toastmasters. I think both had good information and valuable advice. But I got the feeling looking into people’s eyes that I was just adding a weight to their already heavy load. Maybe next time I do some talks I should focus on teaching something that can help lighten people’s load. They should take something away from the talk that they could use right away and not have to go do a bunch of extra work. I’m not sure.

The Toastmasters talk was poorly attended just as it was at RMOUG Training days. I was able to convince the people who run the conferences to let me do the talk but it didn’t attract attendees. Maybe the next time that I do a talk on soft skills I should give specific helpful advice instead of just talking about a program that people could join. A couple of IOUG leaders at my talk made similar suggestions. I guess with the two conferences behind me I’m reviewing what I did and how I can improve. Of course people were appreciative of my efforts since I’m just a volunteer speaker. But, I would like to get better.

I think next time I talk about Python I want to do a demo. They say a picture is worth a thousand words. I think the same is true about demos. I talked about why Python is a good language for Oracle DBAs to use with just a bunch of slides. Maybe I could have made similar points in the context of a live demo. I have some Toastmasters talks coming up. I may work in a demo or two to get some practice doing that sort of talk.

I enjoyed attending the sessions. When I wasn’t presenting I attended a variety of sessions. In the past I would focus on Oracle performance tuning talks but this year I looked for other topics that are current.

I did go to two Oracle new features talks. One funny thing happened. I think both speakers talked about Oracle’s new autonomous data warehouse and how it would get rid of a lot of normal DBA work. Then both speakers went on to detail all the complex new features that are in Oracle 12.2 and 18c. It made me laugh because in the same hour talk they contradicted themselves. First they said all this DBA work is going away and then they listed a pile of new features that DBAs need to learn. Funny. I don’t buy all the autonomous stuff for a second. It is all hype like a lot of things. Think about SQL optimization. People have tried for decades to write a SQL optimizer and they still can’t make one that works. I think there are some things that we just can’t do and SQL optimization is one of them. I have no reason to think that the machine learning and AI that will supposedly drive the autonomous data warehouse will work any better than the optimizer. Time will tell.

I went to three talks on Kafka which was a little redundant. I also went to a talk on MySQL, Mongo DB, Devops, and a couple of talks related to SQL on Hadoop. I went to a couple of talks by Amazon Web Services employees including one on Oracle on RDS. I went to a couple of talks on block chain. Lastly I went to a little different talk about graph theory or network science. All of this is to say I was looking at a lot of non-Oracle stuff instead of just going to Oracle database performance sessions.

The funny thing is that I’m on a project at work that is talking about putting in a SQL on Hadoop system. I was able to apply the Hadoop stuff that I learned at Collaborate in my first day back in the office. I’m not a Hadoop expert but the exposure was valuable.

The graph theory stuff intrigues me. I learned some graph theory in the edX and OCW classes that I have worked through. I can’t help wondering if there are business or IT problems that we could be modelling with graphs in my work. I’m working on this issue of database links between older and newer versions of Oracle. I guess you could look at Oracle databases as nodes and database links as edges. But, I’m not sure beyond that.

Anyway, going to a conference always gives me a lot to think about.  It was good to do my two talks and to hear what others had to say.

Bobby

I’m leaving for Las Vegas a week from this Sunday for the Collaborate 2018 conference #C18LV.

I’m doing the same two talks that I did in Denver at RMOUG Training Days.

Here are the times and locations for the talks if you will be there at C0llaborate:

Session: 1415: Python for the Oracle DBA
Date/time: Tue, Apr 24, 2018 (09:45 AM – 10:45 AM)
Location: Jasmine H

Session: 1416: Toastmasters for the Oracle DBA
Date/time: Wed, Apr 25, 2018 (08:30 AM – 09:30 AM)
Location: South Pacific I, North Convention Center

I haven’t looked at these talks since the conference in February and I have focused my attention on other things so now I am starting to get my mind back on these talks. I am passionate about both topics so hopefully that will come across. I got good feedback at the conference in Denver so I think that those who hear my talks in Vegas will benefit from them. If you can make it to Collaborate I would love to have you attend.

Las Vegas will be just a bit warmer than the 6 degree Fahrenheit mornings that we had in Denver, so that will be fun. 🙂

Bobby