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Oracle database performance
Updated: 2 days 9 hours ago

AWR records top 30 SQLs by default

Fri, 2020-02-14 16:22

I forget that Oracle’s AWR only records the top 30 SQL statements in each snapshot by default. I am not sure how long this link will last but here is a 19c manual page describing the default: 19c manual – see the topnsql setting. A lot of my query tuning assumes that the problem query is in the AWR but for very efficient queries on active systems they may mysteriously disappear or be absent from the AWR. It sometimes takes me a while to remember that the snapshots only include a fixed number of SQLs.

I use my sqlstat3.sql query to look at a history of a particular sql_id’s executions. Often it shows the query running faster on one plan_hash_value than another. Then I look at why the sql_id changed plans. But what about when the good plan does not show up at all? Several times I have looked at sqlstat3.sql output and thought that a query had not run in the past with an efficient query even though it had. It had run so efficiently that it was not on the report, so it looked like the query was a new, slow, SQL statement.

Often I will fix a query’s plan with a SQL Profile and rerun sqlstat3.sql on a busy system after manually running dbms_workload_repository.create_snapshot to capture the most recent activity and the problem query with the new plan will not show up. Usually I remember that it is not in the top 30 queries and that is why it is missing but sometimes I forget. Here is a partial sqlstat3.sql output showing a long running SQL disappearing after I fixed its plan on Wednesday:

SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Ave ms
------------- --------------- --------------------- ---------------- ------------------
acn0557p77na2      3049654342 12-FEB-20 05.00.01 AM                1          16733.256
acn0557p77na2      3049654342 12-FEB-20 06.00.03 AM                2           49694.32
acn0557p77na2      3049654342 12-FEB-20 07.00.53 AM                6          47694.527
acn0557p77na2      3049654342 12-FEB-20 08.00.54 AM               11         50732.0651
acn0557p77na2      3049654342 12-FEB-20 09.00.33 AM               15         53416.5183
acn0557p77na2      3049654342 12-FEB-20 10.00.43 AM               21         86904.4385
acn0557p77na2      3049654342 12-FEB-20 11.00.02 AM               27          84249.859
acn0557p77na2      3049654342 12-FEB-20 12.00.20 PM               27         125287.757
acn0557p77na2      3049654342 12-FEB-20 01.00.36 PM               69         156138.176

Sometimes I query the V$ tables to verify it is currently running a good plan. Here is example output from vsqlarea.sql showing the good plan running today.

LAST_ACTIVE         SQL_ID        PLAN_HASH_VALUE Avg Elapsed ms
------------------- ------------- --------------- --------------
2020-02-14 16:11:40 acn0557p77na2       867392646             14

This is just a quick note to me as much as anyone else. A query that is missing from an AWR report or my sqlstat3.sql report may not have run at all, or it may have run so well that it is not a top 30 query.

Bobby

Categories: DBA Blogs

DBMS_UTILITY.FORMAT_CALL_STACK Change in 12.2 and later

Thu, 2020-02-13 15:47

Quick note. During my 11.2.0.4 to 19c upgrade that I have been writing about we found a difference in behavior of DBMS_UTILITY.FORMAT_CALL_STACK. I tested it on several versions, and it switched in 12.2. Now it puts the procedure name within the package in the stack.

Old output:

----- PL/SQL CALL STACK -----
  OBJECT      LINE  OBJECT
  HANDLE    NUMBER  NAME
0X15BFA6930         9  PACKAGE BODY MYUSER.MYPKG
0X10C988058         1  ANONYMOUS BLOCK

New output:

----- PL/SQL CALL STACK -----
  OBJECT      LINE  OBJECT
  HANDLE    NUMBER  NAME
0XA796DF28         9  PACKAGE BODY MYUSER.MYPKG.MYPROC
0X7ADFEEB8         1  ANONYMOUS BLOCK

Test code:

select * from v$version;

CREATE OR REPLACE PACKAGE MYPKG
AS

PROCEDURE MYPROC;

END MYPKG;
/
SHOW ERRORS

CREATE OR REPLACE PACKAGE BODY MYPKG
AS

PROCEDURE MYPROC
IS

BEGIN

DBMS_OUTPUT.PUT_LINE(UPPER(dbms_utility.format_call_stack));

END MYPROC;

END MYPKG;
/
SHOW ERRORS;

execute mypkg.myproc;
show errors;

Might be useful to someone else. We had some code that depended on the package name being the last thing on its line, but the new version includes the name of the procedure after the package name.

Bobby

Categories: DBA Blogs

Python 3.8.1 Linux Install Without Root

Tue, 2020-02-11 11:40

I wanted to install the latest Python on Linux in a low powered user’s home directory and not have the install interfere with the Python that comes with Linux. I wanted my own local copy of Python that I could install Python packages to without interfering with anything else. I was willing to use root to install the needed Linux packages, but everything else was done with my low powered user.

I did a yum update on my Oracle Enterprise Linux 7.7 install before I started to get everything on the latest version. As root I used yum to install the Linux packages I needed to install Python 3.8.1 from source:

[root@pythonvm ~]# yum install gcc openssl-devel bzip2-devel libffi-devel tcl* tk* -y
Loaded plugins: langpacks, ulninfo
mysql-connectors-community                                                   | 2.5 kB  00:00:00
mysql-tools-community                                                        | 2.5 kB  00:00:00
mysql57-community                                                            | 2.5 kB  00:00:00
ol7_UEKR4                                                                    | 2.5 kB  00:00:00
ol7_latest                                                                   | 2.7 kB  00:00:00
Package gcc-4.8.5-39.0.3.el7.x86_64 already installed and latest version
Package 1:openssl-devel-1.0.2k-19.0.1.el7.x86_64 already installed and latest version
Package bzip2-devel-1.0.6-13.el7.x86_64 already installed and latest version
Package libffi-devel-3.0.13-18.el7.x86_64 already installed and latest version
Package 1:tcl-devel-8.5.13-8.el7.x86_64 already installed and latest version
Package 1:tcl-8.5.13-8.el7.x86_64 already installed and latest version
Package tcl-pgtcl-2.0.0-5.el7.x86_64 already installed and latest version
Package 1:tk-devel-8.5.13-6.el7.x86_64 already installed and latest version
Package 1:tk-8.5.13-6.el7.x86_64 already installed and latest version
Nothing to do

I must have already installed these on this VM. I did the rest of these steps as my low powered user “bobby”. The next thing I did was download the latest Python source, the 3.8.1 version. I had to use the -k option to get around certificate issues:

[bobby@pythonvm ~]$ curl -k -O https://www.python.org/ftp/python/3.8.1/Python-3.8.1.tgz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 22.8M  100 22.8M    0     0  7830k      0  0:00:02  0:00:02 --:--:-- 7828k

For some reason I had to do this twice. The first download failed. Next I gunzipped and untarred this archive under my home directory:

tar zxfv Python-3.8.1.tgz

Python-3.8.1/Objects/sliceobject.c
Python-3.8.1/Objects/listobject.c
Python-3.8.1/Objects/typeslots.inc
Python-3.8.1/Objects/weakrefobject.c
Python-3.8.1/Objects/unicodeobject.c
Python-3.8.1/Objects/complexobject.c
Python-3.8.1/Objects/picklebufobject.c
Python-3.8.1/Objects/odictobject.c
Python-3.8.1/Objects/genobject.c
[bobby@pythonvm ~]$

I created a directory called “python” under my home directory to use as the top-level directory for my Python install. This directory tree will hold the binaries and any Python packages that I install.

[bobby@pythonvm ~]$ mkdir python

After changing directory to where I untarred the source files I configured the Python make and install to use the directory I just created as a “prefix”:


[bobby@pythonvm ~]$ cd Python-3.8.1

[bobby@pythonvm Python-3.8.1]$ pwd
/home/bobby/Python-3.8.1

./configure --prefix=/home/bobby/python

config.status: creating Misc/python-embed.pc
config.status: creating Misc/python-config.sh
config.status: creating Modules/ld_so_aix
config.status: creating pyconfig.h
creating Modules/Setup.local
creating Makefile


If you want a release build with all stable optimizations active (PGO, etc),
please run ./configure --enable-optimizations

Then I did the make and make altinstall from the same directory:

make
...
renaming build/scripts-3.8/idle3 to build/scripts-3.8/idle3.8
renaming build/scripts-3.8/2to3 to build/scripts-3.8/2to3-3.8
/usr/bin/install -c -m 644 ./Tools/gdb/libpython.py python-gdb.py
gcc -pthread -c -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall    -std=c99 -Wextra -Wno-unused-result -Wno-unused-parameter -Wno-missing-field-initializers -Werror=implicit-function-declaration  -I./Include/internal  -I. -I./Include    -DPy_BUILD_CORE -o Programs/_testembed.o ./Programs/_testembed.c
gcc -pthread     -Xlinker -export-dynamic -o Programs/_testembed Programs/_testembed.o libpython3.8.a -lcrypt -lpthread -ldl  -lutil -lm   -lm
sed -e "s,@EXENAME@,/home/bobby/python/bin/python3.8," < ./Misc/python-config.in >python-config.py
LC_ALL=C sed -e 's,\$(\([A-Za-z0-9_]*\)),\$\{\1\},g' < Misc/python-config.sh >python-config
[bobby@pythonvm Python-3.8.1]$

make altinstall
...
Looking in links: /tmp/tmpdrnzr3vb
Collecting setuptools
Collecting pip
Installing collected packages: setuptools, pip
Successfully installed pip-19.2.3 setuptools-41.2.0
[bobby@pythonvm Python-3.8.1]$

So, now Python 3.8.1 is installed in /home/bobby/python but I want to put the bin directory in the path so I can run python or pip and by default have them be this version. The install created python and pip as python3.8 and pip3.8 but I created links to them so that I could access them without 3.8 at the end of their names:

[bobby@pythonvm Python-3.8.1]$ cd /home/bobby/python/bin
[bobby@pythonvm bin]$ ls -al
total 16704
drwxr-xr-x. 2 bobby bobby     4096 Feb 10 15:36 .
drwxrwxr-x. 6 bobby bobby       52 Feb 10 15:36 ..
-rwxrwxr-x. 1 bobby bobby      109 Feb 10 15:36 2to3-3.8
-rwxrwxr-x. 1 bobby bobby      249 Feb 10 15:36 easy_install-3.8
-rwxrwxr-x. 1 bobby bobby      107 Feb 10 15:36 idle3.8
-rwxrwxr-x. 1 bobby bobby      231 Feb 10 15:36 pip3.8
-rwxrwxr-x. 1 bobby bobby       92 Feb 10 15:36 pydoc3.8
-rwxr-xr-x. 1 bobby bobby 17075312 Feb 10 15:35 python3.8
-rwxr-xr-x. 1 bobby bobby     3095 Feb 10 15:36 python3.8-config

[bobby@pythonvm bin]$ ln -s python3.8 python
[bobby@pythonvm bin]$ ln -s pip3.8 pip

Then I added/home/bobby/python/bin to the front of the path to keep these new python and pip links ahead of the python links or binaries in the rest of the path.

[bobby@pythonvm bin]$ cd
[bobby@pythonvm ~]$ vi .bashrc

export ORACLE_HOME=/home/oracle/app/oracle/product/12.1.0/client_1
export PATH=$PATH:$ORACLE_HOME/bin
export LD_LIBRARY_PATH=/home/oracle/app/oracle/product/12.1.0/client_1/lib
export PATH=/home/bobby/python/bin:$PATH

~
~
~
~
~
~
".bashrc" 17L, 451C written     

I logged out and in as bobby and tried python and pip to see that they were the correct version:

[bobby@pythonvm ~]$ python
Python 3.8.1 (default, Feb 10 2020, 15:33:01)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-39.0.3)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
[bobby@pythonvm ~]$ pip list
Package    Version
---------- -------
pip        19.2.3
setuptools 41.2.0
WARNING: You are using pip version 19.2.3, however version 20.0.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
[bobby@pythonvm ~]$   

Finally, I updated pip just to show that we can update something in this new install. Then I installed numpy to see if I could install a new package.

[bobby@pythonvm ~]$ pip install --upgrade pip
Collecting pip
  Downloading https://files.pythonhosted.org/packages/54/0c/d01aa759fdc501a58f431eb594a17495f15b88da142ce14b5845662c13f3/pip-20.0.2-py2.py3-none-any.whl (1.4MB)
     |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 1.4MB 863kB/s
Installing collected packages: pip
  Found existing installation: pip 19.2.3
    Uninstalling pip-19.2.3:
      Successfully uninstalled pip-19.2.3
Successfully installed pip-20.0.2

[bobby@pythonvm ~]$ pip list
Package    Version
---------- -------
pip        20.0.2
setuptools 41.2.0

[bobby@pythonvm ~]$ pip install numpy
Collecting numpy
  Downloading numpy-1.18.1-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
     |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 20.6 MB 62 kB/s
Installing collected packages: numpy
Successfully installed numpy-1.18.1
[bobby@pythonvm ~]$ pip list
Package    Version
---------- -------
numpy      1.18.1
pip        20.0.2
setuptools 41.2.0

This is a quick and dirty post, but I want to remember these steps for myself and it might be helpful for someone who uses Redhat/Centos/OEL.

Bobby

Categories: DBA Blogs

AZORA with Viscosity lunch and learn Thursday 2/13/20

Mon, 2020-02-03 17:51

Quick note. The next AZORA meetup will be next Thursday, 2/13/2020.

#meetup_oembed .mu_clearfix:after { visibility: hidden; display: block; font-size: 0; content: " "; clear: both; height: 0; }* html #meetup_oembed .mu_clearfix, *:first-child+html #meetup_oembed .mu_clearfix { zoom: 1; }#meetup_oembed { background:#eee;border:1px solid #ccc;padding:10px;-moz-border-radius:3px;-webkit-border-radius:3px;border-radius:3px;margin:0; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 12px; }#meetup_oembed h3 { font-weight:normal; margin:0 0 10px; padding:0; line-height:26px; font-family:Georgia,Palatino,serif; font-size:24px }#meetup_oembed p { margin: 0 0 10px; padding:0; line-height:16px; }#meetup_oembed img { border:none; margin:0; padding:0; }#meetup_oembed a, #meetup_oembed a:visited, #meetup_oembed a:link { color: #1B76B3; text-decoration: none; cursor: hand; cursor: pointer; }#meetup_oembed a:hover { color: #1B76B3; text-decoration: underline; }#meetup_oembed a.mu_button { font-size:14px; -moz-border-radius:3px;-webkit-border-radius:3px;border-radius:3px;border:2px solid #A7241D;color:white!important;text-decoration:none;background-color: #CA3E47; background-image: -moz-linear-gradient(top, #ca3e47, #a8252e); background-image: -webkit-gradient(linear, left bottom, left top, color-stop(0, #a8252e), color-stop(1, #ca3e47));disvplay:inline-block;padding:5px 10px; }#meetup_oembed a.mu_button:hover { color: #fff!important; text-decoration: none; }#meetup_oembed .photo { width:50px; height:50px; overflow:hidden;background:#ccc;float:left;margin:0 5px 0 0;text-align:center;padding:1px; }#meetup_oembed .photo img { height:50px }#meetup_oembed .number { font-size:18px; }#meetup_oembed .thing { text-transform: uppercase; color: #555; }
Kickstart to the Cloud Begin Your Journey and Optimize Along the Way

Thursday, Feb 13, 2020, 11:00 AM

The Henry
4455 E Camelback Rd Phoenix, AZ

2 AZORAS Attending

Register for one of two Lunch & Learns at The Henry in Phoenix, or stay for both presentations! Seating is limited and an RSVP is required. To RSVP for Session 1, Session 2 or the entire event go to https://tips.viscosityna.com/modernize2020-phoenix/ and complete the form and make your selection. Oracle continues to be the fastest, state-of-the-art…

Check out this Meetup →

Be sure to RSVP on Viscosity’s web site: https://tips.viscosityna.com/modernize2020-phoenix/

I don’t know about you but we are busy moving to or implementing on 19c so these sessions related to 19c and other topics should be valuable.

Bobby

Categories: DBA Blogs

Query Plan Change Diagnosis Example

Tue, 2020-01-28 15:02

This week I investigated an issue with a query that was suddenly a lot slower in one test environment than another. It runs about 2 seconds in the good case as well as in production. But it was now running more than 10 seconds which is painfully slow through the GUI and impacting the team’s ability to finish a software release. I was able to figure out that the query was running a worse plan and get it to run the good plan and helped the team finish their testing.

I thought I would write this blog post documenting the tools that I used and my reasoning while trying to resolve the issue. This post may be redundant with some of my other posts because they document similar tools and approaches, but I thought it could not hurt to put together a complete example. I considered just writing about one part of the process to focus in on that, but I thought I would write a longer post instead that shows the overall process with details along the way. This blog is my experience as an Oracle Database Administrator/Architect, and I work for an individual company so my experiences may not apply to everyone else’s situation. You may work somewhere that has licensed different versions or features of the Oracle database so your choices would be different. But I still can put out there what I am doing and let others apply it to their situation as they can. Also, there are lots of very sharp and hardworking Oracle people out there that write about these same topics and have contributed their own tools to the Oracle community so I wonder about the value of putting my tools on GitHub and writing about how I use them. Why not use their tools and their approaches? Maybe they will work better? Sure, but I know that my approach and tools work well for me. Also most of the ideas I use came from books, talks, and posts by the same sharp people that I have in mind so my approach really reflects my translation of ideas that are out there in the Oracle community with additions of my own so it is not a choice of me versus them. Anyway, I thought I would work through this example on this post so others can review it and find value where they can. Inevitably I will get a lot out of writing it down myself and it will help me remember what I was thinking at this time when I read this post in the future. Hopefully others will benefit as well.

Here is what I knew when I first looked at the problem. A coworker emailed me the SQL for the problem query and verified that the tables in both test environments were about the same size. Sometimes our test environments can be much smaller than production and different from each other in terms of data volume. Usually, when I get a performance question, I ask for two things: the name of the database and the date and time that the issue stopped and started, including time zone. In this case I knew the database name but not the time frame. But I figured that they had been working on it during the current day, so I just got an AWR report for that day from 8 am to present assuming they worked roughly in working hours.

Fortunately, the AWR report had the problem query as the top query so I did not have trouble finding it. It helped that I already knew the text of the SQL statement. The main thing I got from the AWR report at first is the SQL_ID value. I already had the SQL text, but it helps to get the SQL_ID to plug into other scripts I used.

Trimmed down version of AWR report showing problem SQL_ID 4n5ssud3fgsyq

My next step was to run my sqlstat.sql script to see if the problem query, 4n5ssud3fgsyq, has changed plans. Not sure why I did not use the updated version, sqlstat3.sql. I manually updated the query with the SQL_ID value:

where ss.sql_id = '4n5ssud3fgsyq'

and then I looked for plan changes in the output:


SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms
------------- --------------- ------------------------- ---------------- ------------------
4n5ssud3fgsyq        65249283 08-JAN-20 05.00.05.769 AM               12         47.4083333
4n5ssud3fgsyq        65249283 08-JAN-20 06.00.25.733 AM               21         44.0260952
4n5ssud3fgsyq        65249283 08-JAN-20 08.00.05.250 AM               16         54.5971875
4n5ssud3fgsyq        65249283 08-JAN-20 11.00.04.846 AM               22         165.703818
4n5ssud3fgsyq        65249283 08-JAN-20 12.00.24.960 PM               23          30.143913
4n5ssud3fgsyq        65249283 08-JAN-20 02.00.05.436 PM                1            298.585
4n5ssud3fgsyq        65249283 08-JAN-20 04.00.49.696 PM                3         323.666667
4n5ssud3fgsyq      2414495514 08-JAN-20 05.00.14.441 PM                4          14582.158
4n5ssud3fgsyq      2414495514 08-JAN-20 06.00.34.711 PM                6           9716.264
4n5ssud3fgsyq      2414495514 08-JAN-20 11.00.16.958 PM                8         10078.6125
4n5ssud3fgsyq      2414495514 09-JAN-20 12.00.36.802 AM               20         10876.6251
4n5ssud3fgsyq      2414495514 09-JAN-20 01.00.56.759 AM               10         10751.3919
4n5ssud3fgsyq      2414495514 09-JAN-20 02.00.16.085 AM               13          10297.929
4n5ssud3fgsyq      2414495514 09-JAN-20 03.00.36.804 AM                1          14824.517

I use the PLAN_HASH_VALUE as a name for the plan. I verified that the good plan, 65249283, was run on the other test server that was fine and that it was used in production. So, I had to figure out why the plan changed to 2414495514 on this one test server.

At some point in this process I paused and considered using a SQL Profile to just force the plan back to the good plan. I have written a number of posts about SQL Profiles but this did not seem like a situation where one made sense. Why not spend a couple hours trying to figure out why the plan changed? If I use a SQL Profile, then I will have to migrate the profile to production. And if the query changes the tiniest bit in a future release the SQL Profile will no longer be valid. I have one situation where we have a partitioned table with some almost empty partitions and queries that access the table with the partitioning column passed into the where clause as bind variables. I have been using SQL Profiles to handle that situation on this particular set of production and test databases, but it is a big pain. The last release I had to redo 7-9 SQL Profiles. I’m probably going to have to bite the bullet and find another way around it rather than updating profiles when a release modifies the affected queries. Probably will have to fake the stats on the empty partitions or see if they can be removed. I doubt I will be able to get development to take the partitioning columns out of bind variables. I guess some set of hints might work as well. But anyway, the point is that in this situation that this particular post is about I decided to dig into it and try to find the underlying problem instead of using a SQL Profile band aid.

Having decided to move forward with figuring out why the plan changed I next looked at the two plans. I used my getplans.sql script to extract the plans from the AWR based on SQL_ID and PLAN_HASH_VALUE. I just ran this when logged into the problem database using sqlplus:

@getplans 4n5ssud3fgsyq

I noticed that the slow plan did full table scans on two tables that the fast plan did index lookups on.

SQL_ID 4n5ssud3fgsyq
--------------------
...
Plan hash value: 65249283 - GOOD PLAN

----------------------------------------------------------
| Id  | Operation                             | Name     |
----------------------------------------------------------
...
|  16 |     TABLE ACCESS BY INDEX ROWID       | TABLE1   |
|  17 |      INDEX RANGE SCAN                 | TABLE1_2 |
...
|  47 |           TABLE ACCESS BY INDEX ROWID | TABLE2   |
|  48 |            INDEX UNIQUE SCAN          | TABLE2_0 |


Plan hash value: 2414495514 - BAD PLAN

----------------------------------------------------------
| Id  | Operation                             | Name     |
----------------------------------------------------------
...
|  16 |     TABLE ACCESS FULL                 | TABLE1   |
...						       
|  48 |           TABLE ACCESS FULL           | TABLE2 	 |
----------------------------------------------------------  
						       

I also looked at the Segments part of the AWR report and TABLE2 was at the top of the logical and physical reads reports.

Edited picture of AWR report. Top Table is TABLE2 in execution plans.

So, at this point in my attempt to understand why the plan changed I have the clue that the bad plan has full table scans, and that one of the two tables with the full scans has a substantial percentage of the overall I/O on the database. So, I had two ideas in mind. First, I wanted to check that the two tables had the same indexes as on the working system. Maybe the indexes got dropped during some development work. If the indexes were there, I could just look at the optimizer statistics for the two tables and compare them to what they have on the working test system and production.

I use my optimizer statistics scripts to look at indexes and information that the optimizer uses to choose the best plan. I know that there are other ways to get the same information, such as in a graphical program like Toad or OEM, but I like having scripts that give me exactly the information that I know is important. It is a little clunky to use but not difficult. The top level script is all.sql and I usually comment out the calls to scripts that produce an excessive amount of output. I probably should put all.sql out like this in GitHub:

@tablelist.sql
--@colpartstats.sql
@columnstats.sql
--@histograms.sql
@indexcolumns.sql
@indexstats.sql
--@indpartstats.sql
--@parthists.sql
@tablestats.sql
@tabpartstats.sql
@tabsubpartstats.sql

Seeing all the histogram information for every partition on a table with thousands of partitions is a lot of output and not really needed. If you have many subpartitions you might comment out tabsubpartstats.sql also. To use these scripts you just put your table owners and names in tablelist.sql and run all.sql. The script indexstats.sql listed out the indexes and statistics on them and indexcolumns.sql showed what columns the indexes were on. Unfortunately, in this case the indexes and their columns were the same for the two full scan tables on both test environments and on production. So the switch of the plan to full scans was not due to the lack of indexes. These tables were not partitioned so the tablestats.sql and columnstats.sql reports were the main ones I looked at to compare statistics. Maybe one of them had very out of date or even missing statistics. Unfortunately, the statistics on the two tables looked pretty similar on all three databases. So, my look at the indexes and statistics of the two tables with full scans on the bad plan did not explain the change in plan.

At some point in this process I decided to run the problem query to see if it gave me clues about the plan change. I am putting this post together remembering the steps I went through, but I may not have the exact chronological order. But I should be able to capture the main things I thought about in an order that makes sense. In order to test the query, I needed to come up with values for the bind variables. I used my bind2.sql script to extract some of the bind variable values on the problem test database. I edited bind2.sql to have the problem SQL_ID, 4n5ssud3fgsyq. I chose the most recent two sets of bind variable values to plug into the problem query. I extracted the query text using dumpsql.sql and used Toad to format it nicely. Then I plugged the formatted query into my test2.sql script. To use test2.sql you need to replace the select statement that is there on GitHub:

SELECT /*+gather_plan_statistics*/ * from dual where dummy <> ' ';

with your own query. But you have to make sure that the gather_plan_statistics hint stays in the select statement. There are different ways to handle bind variables and their values in a test script like this but in this case, I manually edited the test script replacing the bind variables with literals that I got from bind2.sql. I chose two sets because one set had nulls for a couple of values so I thought the nulls might cause the bad plan. There might be some odd choice of bind variable values on this test system that was causing the bad plan, or so I thought. But I ran the test2.sql script as described and it ran slow on the one test system for both sets of bind variable values and fast on the other.

My tests with the bind variable values showed that the problem was not related to those sets of bind variable values and that even with literals in place the problem query runs slow on the problem test database. The plan was not the same as the bad plan I had extracted for the original query, but it still had the two full scans. In the same way, my tests had index lookups on the two tables on the other test environment even with the literals replacing the bind variables. So, all this really proved so far was that the two sets of bind variable values that I chose did not affect the problem.

To go further I started looking at the output of test2.sql. The way I get the plan here shows estimated and actual rows. If I find discrepancies in estimated and actual row counts that could point me to where the problem lies. This query in test2.sql dumps out the plan in a format that shows estimated and actual rows:

select * from table(dbms_xplan.display_cursor(null,null,'ALLSTATS'));

Here is the edited output for the bad plan for the two full scan tables:

----------------------------------------------------------------------------------------
| Id  | Operation                    | Name    | Starts | E-Rows | A-Rows |   A-Time   |
----------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS FULL        | TABLE1  |      2 |   1593K|   3186K|00:00:01.36 |
...
|  48 |           TABLE ACCESS FULL  | TABLE2  |      2 |   1628K|   3344K|00:00:15.13 |
----------------------------------------------------------------------------------------

But I am not sure what this is telling me. For TABLE1 the optimizer expected 1593K rows and got 3186K. It expected 1.5 million rows but got 3 million. Similarly, about 1.6 million and 3.3. But how does this help me figure out what the problem is? I looked at the good plan in the same way but was equally puzzled:

-------------------------------------------------------------------------------------
| Id  | Operation                             | Name     | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS BY INDEX ROWID       | TABLE1   |      0 |      1 |      0 |
|* 17 |      INDEX RANGE SCAN                 | TABLE1_2 |      0 |      1 |      0 |
...
|  47 |           TABLE ACCESS BY INDEX ROWID | TABLE2   |      0 |      1 |      0 |
|* 48 |            INDEX UNIQUE SCAN          | TABLE2_0 |      0 |      1 |      0 |
-------------------------------------------------------------------------------------

Here the estimated rows is 1 but actual is 0. But how do I use that to figure out what changed the plan on the one test environment? Then I got the key insight to solve this whole thing. It was not new to me, but I don’t do this every day and it was a good reminder and I wanted to blog about it. I considered blogging about just this one point and maybe I should edit this post down to just this idea. But then I wouldn’t put out the overall steps and tools I used to get to this point. The key insight was to use a full set of outline hints to force the bad plan and good plan to run in both environments and to compare the estimated and actual rows with each plan. I am not sure how this post will end but that is the most important point. Presumably the optimizer considered both plans and gave each of them a cost and chose the lowest cost plan to run. On our problem system the much slower plan has a lower cost than the faster one. The opposite is true on the working systems. What we want is to force the query to run the bad plan both places and see where the estimated rows differ. Then do the same for the good plan. A key idea that I learned about query tuning from people like Tom Kyte, Jonathan Lewis, and others is that the optimizer chooses a bad plan because of bad row estimates or bad estimates of the cost per row of certain operations. Most of the time it comes down to bad row estimates. So, which table is getting the bad row estimates and why? My way of finding it out in this case was to use outline hints to force the same plan to run on both databases and then use the test2.sql output to look at estimated and actual rows.

I was not sure how to extract the plans from the AWR and get the outline hints. I’m sure there is a way but in my hurry I ended up just using my plan.sql script to use EXPLAIN PLAN to get the plan and pull the outline hint from there. I checked the plans to make sure they had the same index versus full scan issues as I have been seeing. At the end of the output for plan.sql is an outline hint section that starts like this:

Outline Data
-------------

  /*+
      BEGIN_OUTLINE_DATA

You just copy and paste everything from /*+ through */ as your hint. I put this in my test2.sql script that I had used before with one set of the bind variable values as constants and I merged the gather_plan_statistics hint with the outline hint so the query started like this:

 SELECT /*+gather_plan_statistics 
      BEGIN_OUTLINE_DATA

This way the gather_plan_statistics hint collects the actual and estimated rows and the outline hint forces the plan to be used.

At first, I looked at the estimated and actual rows on the good and bad plans on both systems for the two tables that switched to full scans on the problem system.

Good DB Bad Plan Full Scan Tables

------------------------------------------------------------------------------------
| Id  | Operation                             | Name    | Starts | E-Rows | A-Rows |
------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS FULL                 | TABLE1  |      1 |   1590K|   1593K|
...
|  48 |           TABLE ACCESS FULL           | TABLE2  |      1 |   1957K|   1957K|
------------------------------------------------------------------------------------

Bad DB Bad Plan Full Scan Tables

Plan hash value: 1397976319

------------------------------------------------------------------------------------
| Id  | Operation                             | Name    | Starts | E-Rows | A-Rows |
------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS FULL                 | TABLE1  |      1 |   1593K|   1593K|
...
|  48 |           TABLE ACCESS FULL           | TABLE2  |      1 |   1628K|   1672K|
------------------------------------------------------------------------------------

Good DB Good Plan Full Scan Tables

-------------------------------------------------------------------------------------
| Id  | Operation                             | Name     | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS BY INDEX ROWID       | TABLE1   |     16 |      1 |     18 |
|* 17 |      INDEX RANGE SCAN                 | TABLE1_2 |     16 |      1 |     18 |
...
|  47 |           TABLE ACCESS BY INDEX ROWID | TABLE2   |     12 |      1 |     12 |
|* 48 |            INDEX UNIQUE SCAN          | TABLE2_0 |     12 |      1 |     12 |
-------------------------------------------------------------------------------------

Bad DB Good Plan Full Scan Tables

-------------------------------------------------------------------------------------
| Id  | Operation                             | Name     | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------------------
...
|  16 |     TABLE ACCESS BY INDEX ROWID       | TABLE1   |     16 |      1 |     18 |
|* 17 |      INDEX RANGE SCAN                 | TABLE1_2 |     16 |      2 |     18 |
...
|  47 |           TABLE ACCESS BY INDEX ROWID | TABLE2   |     12 |      1 |     11 |
|* 48 |            INDEX UNIQUE SCAN          | TABLE2_0 |     12 |      1 |     11 |
-------------------------------------------------------------------------------------

No obvious pattern shows up on these to me. It seems like the optimizer has the same estimates for these two tables on both databases for both plans. But then I got the second key idea: look at the other tables. It is typical when a plan switches to a full scan for there to be something wrong with that table’s stats. But nothing pointed to that in this situation. So, I got the idea of stepping back and looking at the other tables. That led to me finding the actual issue. Two tables that were not the ones with the full scans had different degrees of discrepancies between actual and estimated rows on the bad and good databases.

Good DB Bad Plan Other Tables

-------------------------------------------------------------------------------------
| Id  | Operation                             | Name     | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------------------
...
|  12 |        TABLE ACCESS BY INDEX ROWID    | TABLE3   |      1 |      3 |      6 |
|* 13 |         INDEX RANGE SCAN              | TABLE3_1 |      1 |      3 |      6 |
|  14 |      TABLE ACCESS BY INDEX ROWID      | TABLE4   |      6 |     55 |     11 |
|* 15 |       INDEX RANGE SCAN                | TABLE4_1 |      6 |     55 |     11 |
...
-------------------------------------------------------------------------------------

Bad DB Bad Plan Other Tables

-------------------------------------------------------------------------------------
| Id  | Operation                             | Name     | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------------------
...
|  12 |        TABLE ACCESS BY INDEX ROWID    | TABLE3   |      1 |     22 |      6 |
|* 13 |         INDEX RANGE SCAN              | TABLE3_1 |      1 |     22 |      6 |
|  14 |      TABLE ACCESS BY INDEX ROWID      | TABLE4   |      6 |    153 |     11 |
|* 15 |       INDEX RANGE SCAN                | TABLE4_1 |      6 |    153 |     11 |
...
-------------------------------------------------------------------------------------

Good DB Good Plan Other Tables

----------------------------------------------------------------------------------
| Id  | Operation                          | Name     | Starts | E-Rows | A-Rows |
----------------------------------------------------------------------------------
...
|  12 |        TABLE ACCESS BY INDEX ROWID | TABLE3   |      1 |      3 |      6 |
|* 13 |         INDEX RANGE SCAN           | TABLE3_1 |      1 |      3 |      6 |
|  14 |      TABLE ACCESS BY INDEX ROWID   | TABLE4   |      6 |     55 |     11 |
|* 15 |       INDEX RANGE SCAN             | TABLE4_1 |      6 |     55 |     11 |

----------------------------------------------------------------------------------

Bad DB Good Plan Other Tables

----------------------------------------------------------------------------------
| Id  | Operation                          | Name     | Starts | E-Rows | A-Rows |
----------------------------------------------------------------------------------
...
|  12 |        TABLE ACCESS BY INDEX ROWID | TABLE3   |      1 |     22 |      6 |
|* 13 |         INDEX RANGE SCAN           | TABLE3_1 |      1 |     22 |      6 |
|  14 |      TABLE ACCESS BY INDEX ROWID   | TABLE4   |      6 |    153 |     11 |
|* 15 |       INDEX RANGE SCAN             | TABLE4_1 |      6 |    153 |     11 |
...
----------------------------------------------------------------------------------

Looking at these two tables the bad database had significantly higher row estimates than the good database and the actual rows was the same on both. Errors in estimated rows multiply through the plan as joins are considered. So, at this point I thought it could be that the estimates on these tables (TABLE3 and TABLE4) led to the switch to full scans on TABLE1 and TABLE2. So, I went back to my optimizer statistics scripts and took TABLE1 and 2 out of tablelist.sql and put TABLE3 and 4 in and looked for differences. I found the difference in the columnstats.sql output. The NUM_BUCKETS value for the first column of TABLE3 and TABLE4 was 1 on the bad database and 254 or 255 on the good. So, column statistics had been gathered differently on these two tables on the bad database at some point. Maybe in the process of testing or developing on this system statistics were gathered in a non-standard way. The database in question uses Oracle’s default statistics job. (This is 11.2.0.3, HP Unix Itanium by the way). So, I thought I would try gathering statistics on the two tables with dbms_stats.gather_table_stats with default options because that would be similar to what the default stats job would do. I did something like this:

execute dbms_stats.gather_table_stats('MYSCHEMA','TABLE3');

execute dbms_stats.gather_table_stats('MYSCHEMA','TABLE4');

After gathering stats on the two tables I verified that the first column of each had a 254/5 bucket histogram. Then I just tested the problem query on both, and they ran the same fast plan. Then the development team tested through the application and verified that it was back to normal. I flushed the shared pool before turning it over to them in case the bad plan was cached.

This problem was a little tricky because the stats were wrong on two tables that were not the ones that switched to full scans. A lot of times the situation is simpler such as a table is missing statistics altogether. But I thought it would be helpful to work through a description of the process that I took to get to that point and the tools that I used. The key point is the one I put in bold above which is to look at the estimated and actual rows for the same plan on both databases and look for differences. Once I did that it was just a matter of finding the tables with the different estimates on the two dbs. That led me to look at their statistics and to regather them in a better way. It took a little time to figure this out, but it was nice to find the problem and do the least amount of change necessary to resolve the issue instead of just randomly trying things. Sometimes people will just gather stats on all the tables in the query. That probably would have worked here if they gathered them in the right way. Or you could just use a SQL Profile and forget trying to figure out why the new plan popped up. But it was worthwhile to understand why the plan changed in this case. For one thing I was concerned whether the same plan change would happen in production, so I wanted to know why it changed on the test environment. Knowing that the two tables had statistics gathered on them in a way that we would not use in production I have more confidence that prod is fine. There really is value in digging into why a query’s plan has changed but it does take some work. Often the bad plan is a result of a bad row estimate in some part of the plan and it makes sense to track down which table has the bad row estimate and dig into why the estimate was off. In many cases bad row estimates relate to how and when statistics were gathered on the problem table as was the case with our two tables in this situation.

Categories: DBA Blogs

Datapump Import Partitioned Tables ORA-00600 qesmaGetPamR-NullCtx

Wed, 2019-12-18 10:28

I have not yet had time to build a test case and prove this out, but I wanted to document one last bug that we found so far in our 11.2.0.4 to 19c upgrade. We tried copying a bunch of partitioned tables on our source database to the new one using Datapump Import (impdp) over a database link. We got a boatload of errors like this:

ORA-00600: internal error code, arguments: [qesmaGetPamR-NullCtx], 

There are many Oracle bugs like this, but they seem to have been fixed in 11.2.0.4. For example:

Bug 12591399 – ORA-600[qesmagetpamr-nullctx] / ORA-14091 with distributed query with local partition table (Doc ID 12591399.8)

Puzzling. We ended up just exporting to disk and that has worked well so no big deal, but I wonder if this is some sort of recession of a fixed bug.

Anyway, I am off for the rest of the year. This should be my last post unless I mess with Nethack over vacation and post something about that. I hope everyone out there has a good new year.

Bobby

P.S. Created a simple partitioned table with 2 partitions and 100 rows in each one. I got the error importing over a link from 11.2.0.4 to 19c. It worked perfectly going from 11.2.0.4 to 11.2.0.4. Same source table. Parfile:

$ cat bobby_link_test.par
userid=MYUSER/MYPASSWORD
JOB_NAME=BOBBY_TEST
DIRECTORY=BOBBY_DIR
NETWORK_LINK=MYLINK
LOGFILE=bobby_link_test.log
tables=TEST

Table:

CREATE TABLE test
(
  PART_COL              NUMBER,
  data                  NUMBER
)
PARTITION BY RANGE (PART_COL)
(  
  PARTITION PART_COL_1 VALUES LESS THAN (100),  
  PARTITION PART_COL_2 VALUES LESS THAN (200)
)
;

PPS. Works fine going from 11.2.0.4 to 18c. Going to try a different 19c database just to be sure it isn’t the one that has the problem.

PPPS. Definitely a 19c bug. It fails on two different 19c databases but not on 18c. In every case source is same 11.2.0.4 database and same small partitioned table. Does anyone have time to file the bug report?

Categories: DBA Blogs

db_securefile PREFERRED results in ORA-60019 with small uniform extents

Tue, 2019-12-17 17:35

Last 19c upgrade issue. Working on our new 19c database, several things died off with errors like this:

SQL> execute DBMS_STATS.CREATE_STAT_TABLE ('MYSCHEMA','MYSTATTAB','MYTS');
BEGIN DBMS_STATS.CREATE_STAT_TABLE ('MYSCHEMA','MYSTATTAB','MYTS'); END;

*
ERROR at line 1:
ORA-60019: Creating initial extent of size 14 in tablespace of extent size 8
ORA-06512: at "SYS.DBMS_STATS", line 20827
ORA-06512: at "SYS.DBMS_STATS", line 20770
ORA-06512: at "SYS.DBMS_STATS", line 20765
ORA-06512: at line 1

Our tablespaces had small uniform extents and our 19c database had defaulted the parameter db_securefile to PREFERRED. We bumped our uniform extent sizes up to 1 megabyte and the problem went away. Setting db_securefile to PERMITTED also resolved the issue.

Oracle’s support site has a bunch of good information about this. This might be a relevant bug:

Bug 9477178 : ORA-60019: CREATING INITIAL EXTENT OF SIZE X IN TABLESPACE FOR SECUREFILES

Bobby

Categories: DBA Blogs

Datapump Import Fails on Tables With Extended Statistics

Tue, 2019-12-17 17:11

Quick post before I leave on vacation. We used Datapump to import a schema from an 11.2 HP-UX database to a 19c Linux database and got errors on a few tables like these:

ORA-39083: Object type TABLE:"MYSCHEMA"."TEST" failed to create with error:
ORA-00904: "SYS_STU0S46GP2UUQY#45F$7UBFFCM": invalid identifier

Failing sql is:
ALTER TABLE "MYSCHEMA"."TEST"  MODIFY ("SYS_STU0S46GP2UUQY#45F$7UBFFCM" NUMBER GENERATED
ALWAYS AS (SYS_OP_COMBINED_HASH("COL1","COL2","COL3")) VIRTUAL )

Workaround was to create the table first empty with no indexes, constraints, etc. and import. Today I was trying to figure out why this happened. Apparently, the table has extended statistics on the three primary key columns. I found a post by Jonathan Lewis that shows a virtual column like the one this table has with extended statistics. The error is on the datapump import, impdp, of the table that has extended statistics. This error is similar to some Oracle documented issues such as:

DataPump Import (IMPDP) Raises The Errors ORA-39083 ORA-904 Due To Virtual Columns Dependent On A Function (Doc ID 1271176.1)

But I could not immediately find something that says that extended statistics cause a table to not be importable using Datapump impdp.

If you want to recreate the problem, try added extended stats like this (which I derived from Jonathan Lewis’s post):

select dbms_stats.create_extended_stats(NULL,'TEST','(COL1, COL2, COL3)') name from dual;

select * from user_tab_cols where table_name='TEST';

Then export table from 11.2 and import to 19c database using datapump. Anyway, posting here for my own memory and in case others find it useful. Maybe this is a bug?

Bobby

Categories: DBA Blogs

Merge Always Updates Sequence Number

Tue, 2019-12-17 11:55

This is nothing new, but I wanted to throw out a quick post to document it. If you have a sequence.nextval in the insert part of a merge statement the merge calls nextval for all the updated rows as well.

Oracle has a bug report about this from a 9.2 issue, so this is nothing new:

Bug 6827003 : SEQUENCE # IN MERGE BEING UPDATED FOR BOTH INSERT AND UPDATE

I created a couple of testcases if you want to try them: sequencewithmerge.zip

Oracle’s bug report says you can work around the issue by encasing the sequence.nextval call in a function so I tried it and it works.

Anyway, you can’t count on the sequence only being advanced on inserted rows with merge statements if you include sequence.nextval in the insert part of the merge statement.

Bobby

Categories: DBA Blogs

Database Link to 9.2 Database from 19c

Fri, 2019-12-13 15:12

I have mentioned in previous posts that I am working on migrating a large 11.2 database on HP Unix to 19c on Linux. I ran across a database link to an older 9.2 database in the current 11.2 database. That link does not work in 19c so I thought I would blog about my attempts to get it to run in 19c. It may not be that useful to other people because it is a special case, but I want to remember it for myself if nothing else.

First, I’ll just create test table in my own schema on a 9.2 development database:

SQL> create table test as select * from v$version;

Table created.

SQL> 
SQL> select * from test;

BANNER
----------------------------------------------------------------
Oracle9i Enterprise Edition Release 9.2.0.5.0 - 64bit Production
PL/SQL Release 9.2.0.5.0 - Production
CORE	9.2.0.6.0	Production
TNS for HPUX: Version 9.2.0.5.0 - Production
NLSRTL Version 9.2.0.5.0 - Production

Next, I will create a link to this 9.2 database from a 19c database. I will hide the part of the link creation that has my password and the database details, but they are not needed.

SQL> create database link link_to_92
... removed for security reasons ...

Database link created.

SQL> 
SQL> select * from test@link_to_92;
select * from test@link_to_92
                   *
ERROR at line 1:
ORA-03134: Connections to this server version are no longer supported.

So I looked up ways to get around the ORA-03134 error. I can’t remember all the things I checked but I have a note that I looked at this one link: Resolving 3134 errors. The idea was to create a new database link from an 11.2 database to a 9.2 database. Then create a synonym on the 11.2 database for the table I want on the 9.2 system. Here is what that looks like on my test databases:

SQL> select * from v$version;

BANNER
--------------------------------------------------------------------------------
Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 - 64bit Production
... removed for brevity ...

SQL> create database link link_from_112
... removed for security ...

Database link created.

SQL> create synonym test for test@link_from_112;

Synonym created.

SQL> 
SQL> select * from test;

BANNER
----------------------------------------------------------------
Oracle9i Enterprise Edition Release 9.2.0.5.0 - 64bit Production

Now that I have the link and synonym on the 11.2 middleman database, I go back to the 19c database and create a link to the 11.2 database and query the synonym to see the original table:

SQL> select * from v$version;

BANNER                                                                           ...
-------------------------------------------------------------------------------- ...
Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 - Production           ...
...										    

SQL> create database link link_to_112
...

Database link created.
...
SQL> select * from v$version@link_to_112;

BANNER
--------------------------------------------------------------------------------
Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 - 64bit Production
...

SQL> select * from test@link_to_112;

BANNER
----------------------------------------------------------------
Oracle9i Enterprise Edition Release 9.2.0.5.0 - 64bit Production

So far so good. I am not sure how clear I have been, but the point is that I could not query the table test on the 9.2 database from a 19c database without getting an error. By jumping through an 11.2 database I can now query from it. But, alas, that is not all my problems with this remote 9.2 database table.

When I first started looking at these remote 9.2 tables in my real system, I wanted to get an execution plan of a query that used them. The link through an 11.2 database trick let me query the tables but not get a plan of the query.

SQL> truncate table plan_table;

Table truncated.

SQL> 
SQL> explain plan into plan_table for
  2  select * from test@link_to_112
  3  /

Explained.

SQL> 
SQL> set markup html preformat on
SQL> 
SQL> select * from table(dbms_xplan.display('PLAN_TABLE',NULL,'ADVANCED'));

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------
Error: cannot fetch last explain plan from PLAN_TABLE

SQL> 
SQL> select object_name from plan_table;

OBJECT_NAME
------------------------------------------------------------------------------

TEST

Kind of funky but not the end of the world. Only a small number of queries use these remote 9.2 tables so I should be able to live without explain plan. Next, I needed to use the remote table in a PL/SQL package. For simplicity I will show using it in a proc:

SQL> CREATE OR REPLACE PROCEDURE BOBBYTEST
  2  AS
  3  ver_count number;
  4  
  5  BEGIN
  6    SELECT
  7    count(*) into ver_count
  8    FROM test@link_to_112;
  9  
 10  END BOBBYTEST ;
 11  /

Warning: Procedure created with compilation errors.

SQL> SHOW ERRORS;
Errors for PROCEDURE BOBBYTEST:

LINE/COL ERROR
-------- -----------------------------------------------------------------
6/3      PL/SQL: SQL Statement ignored
6/3      PL/SQL: ORA-00980: synonym translation is no longer valid

I tried creating a synonym for the remote table but got the same error:

SQL> create synonym test92 for test@link_to_112;

...

SQL> CREATE OR REPLACE PROCEDURE BOBBYTEST
  2  AS
  3  ver_count number;
  4  
  5  BEGIN
  6    SELECT
  7    count(*) into ver_count
  8    FROM test92;
  9  
 10  END BOBBYTEST ;
 11  /

Warning: Procedure created with compilation errors.

SQL> SHOW ERRORS;
Errors for PROCEDURE BOBBYTEST:

LINE/COL ERROR
-------- -----------------------------------------------------------------
6/3      PL/SQL: SQL Statement ignored
6/3      PL/SQL: ORA-00980: synonym translation is no longer valid

Finally, by chance I found that I could use a view for the remote synonym and the proc would compile:

SQL> create view test92 as select * from test@link_to_112;

View created.

...

SQL> CREATE OR REPLACE PROCEDURE BOBBYTEST
  2  AS
  3  ver_count number;
  4  
  5  BEGIN
  6    SELECT
  7    count(*) into ver_count
  8    FROM test92;
  9  
 10  END BOBBYTEST ;
 11  /

Procedure created.

SQL> SHOW ERRORS;
No errors.
SQL> 
SQL> execute bobbytest;

PL/SQL procedure successfully completed.

SQL> show errors
No errors.

Now one last thing to check. Will the plan work with the view?

SQL> explain plan into plan_table for
  2  select * from test92
  3  /

Explained.

SQL> select * from table(dbms_xplan.display('PLAN_TABLE',NULL,'ADVANCED'));

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------
Error: cannot fetch last explain plan from PLAN_TABLE

Sadly, the view was not the cure all. So, here is a summary of what to do if you have a procedure on a 19c database that needs to access a table on a 9.2 database:

  • Create a link on a 11.2 database to the 9.2 database
  • Create a synonym on the 11.2 database pointing to the table on the 9.2 database
  • Create a link on the 19c database to the 11.2 database
  • Create a view on the 19c database that queries the synonym on the 11.2 database
  • Use the view in your procedure on your 19c database
  • Explain plans may not work with SQL that use the view

Bobby

Categories: DBA Blogs

So Far So Good with Force Logging

Mon, 2019-10-28 18:55

I mentioned in my previous two posts that I had tried to figure out if it would be safe to turn on force logging on a production database that does a bunch of batch processing on the weekend: post1, post2. We know that many of the tables are set to NOLOGGING and some of the inserts have the append hint. We put in force logging on Friday and the heavy weekend processing ran fine last weekend.

I used an AWR report to check the top INSERT statements from the weekend and I only found one that was significantly slower. But the table it inserts into is set for LOGGING, it does not have an append hint, and the parallel degree is set to 1. So, it is a normal insert that was slower last weekend for some other reason. Here is the output of my sqlstatsumday.sql script for the slower insert:

Day        SQL_ID        PLAN_HASH_VALUE Executions Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
---------- ------------- --------------- ---------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
2019-09-22 6mcqczrk3k5wm       472069319        129         36734.0024     20656.8462    462.098677                  0                      0             38.8160385          666208.285         1139.86923             486.323077
2019-09-29 6mcqczrk3k5wm       472069319        130         44951.6935     27021.6031    573.245664                  0                      0             21.8764885           879019.29         1273.52672             522.083969
2019-10-06 6mcqczrk3k5wm       472069319        130         9624.33742     7530.07634    264.929008                  0                      0             1.26370992          241467.023         678.458015             443.427481
2019-10-13 6mcqczrk3k5wm       472069319        130         55773.0864      41109.542    472.788031                  0                      0             17.5326031          1232828.64         932.083969             289.183206
2019-10-20 6mcqczrk3k5wm       472069319        130         89684.8089     59261.2977    621.276122                  0                      0             33.7963893          1803517.19         1242.61069             433.473282
2019-10-27 6mcqczrk3k5wm       472069319        130         197062.591     144222.595    561.707321                  0                      0             362.101267          10636602.9         1228.91603             629.839695

It averaged 197062 milliseconds last weekend but 89684 the previous one. The target table has always been set to LOGGING so FORCE LOGGING would not change anything with it.

One of the three INSERT statements that I expected to be slowed by FORCE LOGGING was faster this weekend than without FORCE LOGGING last weekend:

Day        SQL_ID        PLAN_HASH_VALUE Executions Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
---------- ------------- --------------- ---------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
2019-09-22 0u0drxbt5qtqk       382840242          1         2610257.66         391635    926539.984                  0                      0              13718.453             5483472           745816.5                3689449
2019-09-29 0u0drxbt5qtqk       382840242          1         17127212.3        1507065    12885171.7                  0                      0             14888.4595            18070434          6793555.5             15028884.5
2019-10-06 0u0drxbt5qtqk       382840242          1         3531931.07         420150    2355139.38                  0                      0             12045.0115             5004273            1692754                5101998
2019-10-13 0u0drxbt5qtqk       382840242          1         1693415.59         180730    1250325.41                  0                      0               819.7725           2242638.5           737704.5                2142812
2019-10-20 0u0drxbt5qtqk       382840242          1         5672230.17         536115    3759795.33                  0                      0             10072.9125             6149731            2332038              2806037.5
2019-10-27 0u0drxbt5qtqk       382840242          1         2421533.59         272585    1748338.89                  0                      0               9390.821           3311219.5           958592.5              2794748.5

It ran 2421533 milliseconds this weekend and 5672230 the prior one. So clearly FORCE LOGGING did not have much effect on its overall run time.

It went so well this weekend that we decided to leave FORCE LOGGING in for now to see if it slows down the mid-week jobs and the web-based front end. I was confident on Friday, but I am even more confident now that NOLOGGING writes have minimal performance benefits on this system. But we will let it bake in for a while. Really, we might as well leave it in for good if only for the recovery benefits. Then when we configure GGS for the zero downtime upgrade it will already have been there for some time.

The lesson for me from this experience and the message of my last three posts is that NOLOGGING writes may have less benefits than you think, or your system may be doing less NOLOGGING writes than you think. That was true for me for this one database. It may be true for other systems that I expect to have a lot of NOLOGGING writes. Maybe someone reading this will find that they can safely use FORCE LOGGING on a database that they think does a lot of NOLOGGING writes, but which really does not need NOLOGGING for good performance.

Bobby

Categories: DBA Blogs

Impact of Force Logging

Mon, 2019-09-23 17:29

I am working on upgrading an Oracle database from 11.2.0.4 to 19c and migrating it from HP Unix to Linux. This 15-terabyte database is too large to copy from the old to the new system during our normal weekend downtime window. It also has a ton of weekend batch updates that overlap the normal weekend change window so it would be best for our business processing if the cut over from the old to the new system was as fast as possible.

I want to use GoldenGate to minimize the downtime for the cutover using an approach similar to what is described in this Oracle document:

Zero Downtime Database Upgrade Using Oracle GoldenGate

You start GoldenGate collecting changes on the current production system and then take your time copying the 15 TB of data from the old to new system. Once you are done with the initial load you apply the changes that happened in the meanwhile. Finally, you cut over to the new system. You could even switch the direction of the replication to push changes on the new production system back to the old system to allow for a mid-week back out several days after your upgrade. Pretty cool. A teammate of mine successfully used this approach on an important database some years back.

But the database that I am working on now, unlike the one that my colleague worked on, has a lot of tables set to nologging. Under the right conditions inserts into tables set to nologging are not written to the redo logs and will be missed by GoldenGate. This Oracle article recommends setting your database to FORCE LOGGING so GoldenGate will not miss any updates:

In order to ensure that the required redo information is contained in the Oracle redo logs for segments being replicated, it is important to override any NOLOGGING operations which would prevent the required redo information from being generated. If you are replicating the entire database, enable database force logging mode.

Oracle GoldenGate Performance Best Practices

We could also switch all our application tables and partitions in the source system to logging but we have so many I think we would set the whole database to force logging.

But the big question which I touched on in my previous post is whether force logging will slow down our weekend batch processing so much that we miss our deadlines for weekend processing to complete and affect our business in a negative way. The more I investigate it the more convinced I am that force logging will have minimal impact on our weekend jobs. This is an unexpected and surprising result. I really thought that our batch processing relied heavily on nologging writes to get the performance they need. It makes me wonder why we are using nologging in the first place. It would be a lot better for backup and recovery to have all our inserts logged to the redo logs. Here is a nice Oracle Support document that lays out the pros and cons of using nologging:

The Gains and Pains of Nologging Operations (Doc ID 290161.1)

I have an entry in my notes for this upgrade project dated 8/26/19 in which I wrote “Surely force logging will bog the … DB down”. Now I think the opposite. So, what changed my mind? It started with the graph from the previous post:

Graph From Previous Post with Little Direct Writes I/O

I was really surprised that the purple line was so low compared to the other two. But I felt like I needed to dig deeper to make sure that I was not just misunderstanding these metrics. The last thing I want to do is make some production change that slows down our weekend processes that already struggle to meet their deadlines. I was not sure what other metrics to look at since I could not find something that directly measures non-logged writes. But then I got the idea of using ASH data.

In my “Fast way to copy data into a table” post I said that to copy data quickly between two Oracle tables “you want everything done nologging, in parallel, and using direct path”. I may have known then and forgotten but working on this now has me thinking about the relationship between these three ways of speeding up inserts into tables. I think there are the following two dependencies:

  • Nologging requires direct path
  • Parallel requires direct path

Oracle document “Check For Logging / Nologging On DB Object(s) (Doc ID 269274.1)” says the first one. In the second case if you have a target table set to parallel degree > 1 and you enable parallel DML you get direct path writes when you insert into the target table.

From all this I got the idea to look for direct path write waits in the ASH views. I could use ASH to identify insert statements that are using direct path. Then I could check that the target tables or partitions are set to nologging. Then I would know they are doing non-logged writes even if I did not have a metric that said so directly.

directwritesql.sql looked at all the SQL statements that had direct write waits over the entire 6 weeks of our AWR history. The output looks like this:

     select
  2  sql_id,count(*) active
  3  from DBA_HIST_ACTIVE_SESS_HISTORY a
  4  where
  5  event = 'direct path write'
  6  group by sql_id
  7  order by active desc;

SQL_ID            ACTIVE
------------- ----------
2pfzwmtj41guu         99
g11qm73a4w37k         88
6q4kuj30agxak         58
fjxzfp4yagm0w         53
bvtzn333rp97k         39
6as226jb93ggd         38
0nx4fsb5gcyzb         36
6gtnb9t0dfj4w         31
3gatgc878pqxh         31
cq433j04qgb18         25

These numbers startled me because they were so low. Each entry in DBA_HIST_ACTIVE_SESS_HISTORY represents 10 seconds of activity. So over 6 weeks our top direct path write waiter waited 990 seconds. Given that we have batch processes running full out for a couple of days every weekend 990 seconds over 6 weekends is nothing.

I took the top SQL ids and dumped out the SQL text to see what tables they were inserting into. Then I queried the LOGGING column of dba_tables and dba_tab_partitions to see which insert was going into a table or partition set to nologging.

select logging,table_name
from dba_tables
where owner='MYOWNER' and
table_name in
(
... tables inserted into ...
)
order by table_name;


select logging,table_name,count(*) cnt
from dba_tab_partitions
where table_owner='MYOWNER' and
table_name in
(
... tables inserted into ...
)
group by logging,table_name
order by table_name,cnt desc;

This simple check for LOGGING or NOLOGGING status eliminated several of the top direct path write waiters. This process reduced the list of SQL ids down to three top suspects:

SQL_ID            ACTIVE
------------- ----------
cq433j04qgb18         25
71sr61v1rmmqc         17
0u0drxbt5qtqk         11

These are all inserts that are not logged. Notice that the most active one has 250 seconds of direct path write waits over the past 6 weeks. Surely enabling force logging could not cause more than about that much additional run time over the same length of time.

I got the idea of seeing what percentage of the total ASH time was direct path write waits for each of these SQL statements. In every case it was small:

cq433j04qgb18

TOTAL_SAMPLE_COUNT DW_SAMPLE_COUNT DW_SAMPLE_PCT
------------------ --------------- -------------
              2508              25    .996810207
 
71sr61v1rmmqc

TOTAL_SAMPLE_COUNT DW_SAMPLE_COUNT DW_SAMPLE_PCT
------------------ --------------- -------------
              1817              17    .935608145

0u0drxbt5qtqk

TOTAL_SAMPLE_COUNT DW_SAMPLE_COUNT DW_SAMPLE_PCT
------------------ --------------- -------------
              8691              11    .126567714

TOTAL_SAMPLE_COUNT was all the samples for that SQL_ID value for the past 6 weeks. DW_SAMPLE_COUNT is the same count of samples that are direct write waits that we already talked about. DW_SAMPLE_PCT is the percentage of the total samples that were direct write wait events. They were all around 1% or lower which means that write I/O time was only about 1% of the entire run time of these inserts. The rest was query processing best I can tell.

Also I used my sqlstat3 script to look at the average run time for these inserts:

SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
------------- --------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
71sr61v1rmmqc      3333358322 01-SEP-19 12.00.46.381 PM                1         2768879.28         892080    207085.624                  0                      0                  3.817             9028323            1045428               19337954
71sr61v1rmmqc      3333358322 08-SEP-19 10.00.43.551 AM                0         264428.594          98840     28257.339                  0                      0                  3.657              177736             143345                      0
71sr61v1rmmqc      3333358322 08-SEP-19 11.00.49.648 AM                1          2352509.9         767440    160933.191                  0                      0                      0             8729437             791837               19110340
71sr61v1rmmqc      3333358322 15-SEP-19 11.00.03.027 AM                1         3090070.21         904310    190593.062                  0                      0                  2.192             9095421             949579               19470026

SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
------------- --------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
0u0drxbt5qtqk       382840242 01-SEP-19 02.00.23.436 AM                1         29281391.6        3211050    16624311.7                  0                      0              99532.905            37076159           14440303               24479240
0u0drxbt5qtqk       382840242 08-SEP-19 02.00.11.424 AM                1         3871668.37         424670    2563007.61                  0                      0               1236.003             4622248            2457057                2468983
0u0drxbt5qtqk       382840242 15-SEP-19 03.00.12.349 AM                0         5161808.16         615520    3358994.55                  0                      0              20656.365             6251060            2801828                      0
0u0drxbt5qtqk       382840242 15-SEP-19 04.00.33.661 AM                1         2412910.02         240650    1741053.89                  0                      0                699.717             3050529            1542895                4638794

SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
------------- --------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
cq433j04qgb18      1267318024 02-SEP-19 10.00.57.871 PM                1          77132.892          51180     10719.692                  0                      0                  2.003              460346              47055                 772468
cq433j04qgb18      1267318024 03-SEP-19 10.00.55.601 PM                1         116064.154          68350      9808.483                  0                      0              15746.609              911571              20422                1256808
cq433j04qgb18      1267318024 04-SEP-19 10.00.31.071 PM                1         106594.074          64030      6328.462                  0                      0              15603.102              777779              14945                1561172
cq433j04qgb18      1267318024 05-SEP-19 10.00.13.265 PM                0          44435.247          31810      2760.438                  0                      0                365.132              139637               5111                 257770
cq433j04qgb18      1267318024 09-SEP-19 10.00.18.346 PM                1         791385.928         324050    171504.931                  0                      0               7484.358             6430665             600703               14262960
cq433j04qgb18      1267318024 10-SEP-19 10.00.29.224 PM                0         1685763.14         676210    304045.354                  0                      0                283.296            11884045             838290               16268667
cq433j04qgb18      1267318024 11-SEP-19 10.01.00.800 PM                0         369644.825         172120     42679.357                  0                      0                  3.929             2443772             151369                3901044
cq433j04qgb18      1267318024 12-SEP-19 10.00.28.499 PM                0          30381.614          25630      1191.884                  0                      0                 422.55               98580               3389                 184812
cq433j04qgb18      1267318024 13-SEP-19 10.00.07.502 PM                0         173286.567         109990     11461.865                  0                      0                 359.37             1475324              63073                2360818
cq433j04qgb18      1267318024 16-SEP-19 10.00.52.175 PM                1         190203.822          93680     47585.666                  0                      0                122.658             1221886             348327                2955258

These queries run at most a couple of hours. If direct path writes are 1% of their total run time, I estimated that force logging would add about 1% to the elapsed time or about 2 minutes per execution.

The final step was to try to run one of these top nologging I/O inserts in a test environment with and without force logging to see if the test matches the expected performance slowdown. I was not able to run 0u0drxbt5qtqk without setting up a more elaborate test with the development team. My test of cq433j04qgb18 ran considerably faster with force logging than without it so I think other factors were hiding whatever effect force logging had. But 71sr61v1rmmqc had some nice results that matched my estimates well. This is on a Delphix clone of production so the data was up to date with prod but the underlying I/O was slower.

71sr61v1rmmqc results running 5 times normal 5 times force logging

The individual run times are in seconds and the averages are listed in seconds and in minutes. I ran the insert 5 times with no force logging and 5 times with it alternating. I dropped the primary key and unique index of the target table to keep from getting constraint errors. I rolled back the insert each time. It averaged about 1.2 minutes more out of 40 minutes of run time which is about a 3% increase. My estimate from ASH was about 1% so this test matches that well.

The final test remains. In some upcoming production weekend, I will put in a change to flip the database to force logging and see how it goes. My tests were run on a test system with a different storage system and with no other activity. We might see different results on a heavily loaded system with a queue for the CPU. But, after all this analysis and testing I feel confident that we won’t be able to tell that force logging is enabled. Unfortunately, we sometimes have performance issues anyway due to plan changes or data volume so the force logging switch might get blamed. But I feel confident enough to push for the final test and I think we ultimately will pass that test and be able to use force logging to enable GoldenGate to support a short cut over time for our migration and upgrade project.

Bobby

P.S. A good question came in as a comment about direct path write waits and asynchronous I/O. The system I am testing on does not support async I/O because it is HP Unix and a filesystem. This older blog post talks a bit about async and direct I/O on HP-UX:

https://www.bobbydurrettdba.com/2013/04/26/db_writer_processes-dbwr_io_slaves-with-no-asynch-io-on-hp-ux/

So, your mileage may vary (YMMV) if you do these same queries on a system with asynchronous writes. Linux filesystems support async writes and on HP-UX our RAC system on ASM supports it. It is one of the challenges of writing blog posts. Other people may be in different situations than I am.

Categories: DBA Blogs

Estimating how much write I/O is not logged

Thu, 2019-09-12 11:30

I am trying to figure out how much non-logged write I/O an Oracle database is doing. I want to run an ALTER DATABASE FORCE LOGGING command on the database so that I can use Oracle GoldenGate(GGS) which reads updates from Oracle’s logs. GGS will miss writes that are not logged. But if I turn on force logging it may slow down applications that depend on non-logged writes for good performance. So, I want to find some Oracle performance metrics that give me an idea about how much non-logged write I/O we have so I have an estimate of how much force logging will degrade performance.

I created SQL*Plus and PythonDBAGraphs reports based on DBA_HIST_IOSTAT_FUNCTION that gives some insight into the write I/O that is not logged. Here is the Python based graphical version of the report for one recent weekend:

Possible NOLOGGING Write I/O

The purple-blue line represents Direct Writes. These may or may not be logged. The red-orange line represents writes through the DBWR process. These are always logged. The light green line represents log I/O through the LGWR process. My theory is that if the purple line is above the green line the difference must be write I/O that is not logged. But if the green line is equal to or greater than the purple line you really do not know if there was any write I/O that was not logged. But if there is non-logged write I/O it cannot be more than the amount indicated by the purple line. So, this graph does not directly answer my question about how much write I/O was not logged but it does show some numbers that relate to the question.

I did some experiments with the V$IOSTAT_FUNCTION view that populates DBA_HIST_IOSTAT_FUNCTION to see what values it gives for Direct Writes, DBWR, and LGWR using different scenarios. Here is the zip of these scripts and their output: nologgingscriptsandlogs09122018.zip. I tested four scenarios:

  1. Insert append nologging
  2. Insert append logging
  3. Insert noappend logging
  4. Insert noappend nologging

1 and 2 did Direct Writes. 3 and 4 did DBWR writes. 2, 3, and 4 all did LGWR writes.

Here are the relevant sections of the output that correspond to these statements.

Insert append nologging:

FUNCTION_NAME      WRITE_DIFF_MEGABYTES
------------------ --------------------
Direct Writes                      4660
LGWR                                 46
DBWR                                 27

Insert append logging:

FUNCTION_NAME      WRITE_DIFF_MEGABYTES
------------------ --------------------
LGWR                               4789
Direct Writes                      4661
DBWR                                 37

Insert noappend logging:

FUNCTION_NAME      WRITE_DIFF_MEGABYTES
------------------ --------------------
DBWR                               6192
LGWR                               4528
Direct Writes                         2

Insert noappend nologging:

FUNCTION_NAME      WRITE_DIFF_MEGABYTES
------------------ --------------------
DBWR                               6213
LGWR                               4524
Direct Writes                         2

This pattern is similar to that in a Ask Tom post that I wrote about a while back. That post showed the different situations in which writes were logged or not. I also got some ideas about direct writes and logging from this Oracle support document:

Check For Logging / Nologging On DB Object(s) (Doc ID 269274.1)

It sounds like inserts into tables that go through the normal processing eventually get written to disk by DBWR but inserts with the append hint write directly to the datafiles and may or may not be logged and written out by LGWR.

These tests and documents gave me the idea of building a report and graph based on DBA_HIST_IOSTAT_FUNCTION showing the values for the Direct Writes, DBWR, and LGWR FUNCTION_NAME values. The graph above shows an example of a real system. I was surprised to see how high the DBWR and LGWR values were and how low the Direct Writes were. That made me think that it would be safe to try turning on FORCE LOGGING because it likely will have minimal impact on the overall weekend processing. It gave me enough evidence to push for approval to do a controlled test of enabling FORCE LOGGING in production over an upcoming weekend. I will update this post with the results if we move forward with the test.

Bobby

Categories: DBA Blogs

September 27 Arizona Oracle User Group Meeting

Wed, 2019-09-04 10:30

The Arizona Oracle User Group (AZORA) is cranking up its meeting schedule again now that the blazing hot summer is starting to come to an end. Our next meeting is Friday, September 27, 2019 from 12:00 PM to 4:00 PM MST.

Here is the Meetup link: Meetup

Thank you to Republic Services for allowing us to meet in their fantastic training rooms.

Thanks also to OneNeck IT Solutions for sponsoring our lunch.

OneNeck’s Biju Thomas will speak about three highly relevant topics:

  • Oracle’s Autonomous Database — “What’s the Admin Role?”
  • Oracle Open World #OOW 19 Recap
  • Let’s Talk AI, ML, and DL

I am looking forward to learning something new about these areas of technology. We work in a constantly evolving IT landscape so learning about the latest trends can only help us in our careers. Plus, it should be interesting and fun.

I hope to see you there.

Bobby

Categories: DBA Blogs

Analog Detour

Mon, 2019-08-26 23:04

Definition of analog: of, relating to, or being a mechanism or device in which information is represented by continuously variable physical quantities

Merriam Webster Dictionary Introduction My Analog Tools Before The Detour

I just finished going off on a major tangent away from my normal home computer pursuits such as studying algorithms, messing with Nethack source code, or practicing Python programming. Prior to this diversion I pursued these computer-related activities for learning and fun in my free time outside of work. But I spent the last three months pursuing activities related to pens, pencils, paper, and notebooks. Some people like to call using a pen and notebook an “analog” activity, so I used that word for the post title.

For several years my primary analog tools have been cheap wide ruled composition notebooks and Pilot G-2 gel pens. For example, I used a composition notebook to keep track of the homework and tests for my OpenCourseWare algorithms classes. I also used a composition notebook for non-technical purposes such as taking notes about things I read or heard, or just writing about things going on in my life. But otherwise most of what I wrote down was in computer form. I use Microsoft Outlook for my calendar at work and keep notes and tasks in Microsoft Word documents and text files, both in the office and on my home laptop. A lot of information was just kept in email. I have stuff on my iPhone.

But back in May I started looking at better ways to use pens, pencils and paper. I started looking on the internet for reviews and recommendations of what to buy and I ended up spending a lot of time and probably a few hundred dollars trying out different products and coming up with my own preferences. Finally, a couple or three weeks back I finally stepped back from going deeper and deeper into this exploration of pens, pencils, paper, and notebooks. I had spent enough money and time researching the best options. Now it was time for me to just use the tools I had and not buy any more and not read any more about them.

Now that I have stopped exploring these analog tools, I thought I should write a blog post about what I learned. I have a bunch of bookmarks of interesting web sites that I found helpful. I also have the results of my own use of the tools. Clearly, I am not a pen, pencil, paper, or notebook expert. This is an Oracle database blog and my strongest skills are in the database arena. Also, would people who read this blog for Oracle tuning scripts and information care about pens and paper? I am not sure. But the information that I learned and gathered has been helpful to me and it has been fun. Maybe others can benefit from my experience and if they want more expert advice, they can follow the links in this post to people who specialize in these areas.

I have decided to break this post, which is almost surely going to be my longest to date, into sections that alternate between things you write with and things you write on. Here is the outline:

  1. Introduction
  2. Pilot G-2 Gel Pens
  3. Graph Paper
  4. Pen Party
  5. Bullet Journal
  6. Fountain Pens
  7. Rhodia Dot Pads
  8. Pencils
  9. Conclusion

Lastly, I want to end the introduction with a caveat that is like those I find in a lot of the pen and paper blogs. I will have links to various businesses that sell products like pens or notebooks. I have not received any money to advertise these products, nor have I received any products for free. I bought all the products with my own money. As I mentioned in my privacy page, this blog is completely non-commercial and does not generate income in any way. I do not sell advertising or people’s emails to spammers or anything like that. My only income is from my job doing Oracle database work. I like having a blog and I cough up a couple hundred dollars a year for hosting, a domain name, and a certificate so I can put these posts out on the internet. So, don’t worry that I’m trying to sell you something because I am not.

Pilot G-2 Gel Pens Pilot G-2 Gel Pens of each size

I have been using Pilot G-2 gel pens for several years but did not realize that they came in different widths before I started researching all of these analog tools. I was using my gel pen with my composition notebook and kept accidentally smearing the ink. So, I finally Google searched for something like Pilot G-2 review and that really kicked off all of this research into types of pens and related tools. I found out that Pilot G-2 pens came in four width tips and that I had accidentally bought the widest tip pens which are the worst at smearing. They just put the most ink on the paper. I looked back at my Amazon orders and found that I bought a dozen “Fine Point” G-2 pens in 2015, but when I ran out and reordered 12 more in 2017, I got the “Bold Point” which has a thicker line. If you look at the pictures on Amazon, the boxes look very similar. You must know what the widths are named. So, as simple as this sounds, it was helpful to learn about the different tip sizes of my favorite gel pens and to try out each size to find which one I wanted. Here is a table of the four sizes:

Clip #MillimetersName# Colors0.38.38Ultra Fine405.5Extra Fine507.7Fine16101.0Bold8

The clips of the pens have numbers on them but only the .38 millimeter tip has a decimal point so that can be confusing. The .38 mm pen writes a very thin line. Evidently several manufacturers compete for the very thin line gel pen market. I have tried all four G-2 sizes and right now my favorite is the .5 mm extra fine.

I got the number of colors from Pilot’s site. It looks like the .7 mm fine has the most colors. I pretty much just use black, but I like red for editing.

A key thing that I learned about testing a new pen is that you must write a lot with it before you really know if you like it. Gel pens seem to start out a little uneven when you first write with them. Maybe they have been on a shelf somewhere for a long time. But after you write say 5 pages the ink starts to really flow well.

As I said in the introduction, I liked the Pilot G-2 gel pen before all this investigation began. But I know so much more about my favorite pen such as the different sizes. I will talk about this later, but one result of all this research is that I have started to like pens that write thinner lines. I accidentally bought a dozen of the thickest line G-2 pens in 2017 and it almost threw me off the pen. Now I have settled down with a G-2 pen with a tip half the size of the one I struggled with and it has really helped me out.

Graph Paper Graph paper with pseudo-code

I got the idea of switching to graph paper from the handwritten lecture notes from an algorithms class I was working through. Evidently the professor wrote out his notes using several color pens on graph paper. It made me wonder if I should use graph paper too.

I had already started using lined loose leaf filler paper in addition to composition notebooks. I think part of the problem is that it has been 30 years since I was a student in school, and I am out of practice using paper and 3 ring binders and all. My daughters use all these tools in school without thinking about it but for me the idea of using some loose filler paper is a revelation. Maybe some of this is not so much learning but relearning how to use these analog tools that I used regularly during my school days.

The lined filler paper was great as scratch paper when I was working on a problem for my online class but then I could clean up the answer and write it in my sturdy composition notebook to keep long term. But it made sense to get graph paper instead of lined paper because it would help to line things up along the vertical lines. If I need to write some code or pseudo-code and line up blocks within loops or if statements, I can use the vertical lines. The horizontal lines just separate the lines of code.

I ended up buying a nice pad of graph paper from Amazon but didn’t realize that some of the lines were darker than others. I prefer the lines to all be the same darkness. Then I ended up returning something that I bought from Office Depot with a gift card and they would not give me cash back. So, I used the gift card that was the refund to buy several pads of loose three-hole punched graph paper as well as two or three composition books with graph paper instead of lines. The composition books are kind of neat but they seem a little cheap and flimsy.

Of the three types of graph paper that I tried I like the Office Depot loose paper the best. All the lines are the same darkness and it is already three-hole punched so I can throw it in a binder if I want to keep it. I can use it for scratch paper, or I can save what I write down. So, at this point I like my loose paper to be graph paper but I still like the wide rule lined composition notebooks over the ones with graph paper.

Pen Party Pen Party Favorites

After reading the Pilot G-2 review I started reading reviews about other pens. Several web sites have best pen lists. The Gentleman Stationer has a nice best pens for 2019 page. The Pen Addict has lists of top 5 pens in different categories. Lastly, the online store JetPens, which specializes in Japanese pens, has their list of 33 best pens for 2019. One challenge that I had when I was looking at the different widths of G-2 pens is that I ended up buying packs of 4 or more pens on Amazon when I really just wanted one black pen to test. JetPens.com sells individual pens. If you spend $25 or more, you get free shipping. So, to test out some of the pens I had been reading about in the top pen lists I ordered several different kinds of pens, pencils, and an eraser, one of each kind.

Here was what I bought in my first order:

Uni-ball Signo UM-151 Gel Pen - 0.38 mm - Black         $2.85
Pentel EnerGel Euro Needle-PoinT - 0.35 mm - Black      $2.50
Platinum Preppy Fountain Pen - Black - 05 Medium Nib    $4.00
Tombow Mono 100 Pencil - HB                             $2.35
Zebra Sarasa Dry Gel Pen - 0.7 mm - Black               $2.95
Sakura Foam Eraser W 80                                 $1.50
Sakura Pigma Micron Pen - Size 02 - 0.3 mm - Black      $2.50
Tombow Mono Drawing Pen - 03 - Black                    $2.89
Tombow Fudenosuke Brush Pen - Hard - Black              $2.75
Uni Jetstream Sport Ballpoint Pen - 0.7 mm - Black Ink  $3.15

Some of these were suggested by Bullet Journal enthusiasts but I will talk about that in a later section. I will also talk about fountain pens and pencils later. The point here is that JetPens.com is cool. You must chunk up your purchase to be $25 or more but it is nice picking out individual pens to try them. One of the reasons I wanted to write this post is that I wanted to share about fun vendors like JetPens for people like me who had never heard of them before.

I took these things from my JetPens order and other pens and pencils that I already had and convinced my wife and three daughters to join me for a “pen party” to try them out. We sat around out kitchen table passing around groups of similar pens and pencils and tried each one out. After everyone finished trying them, we all picked our favorites.

My favorite was the .38 mm UM-151 or Signo DX gel pen. I have been using the Signo DX pen for tiny writing in a smaller notebook where it helps to have the smallest possible lines. I think it is slightly better than its G-2 .38 mm competitor. But I prefer the .5 mm G-2 gel pen for writing on full size 8 1/2 x 11 inch paper. So, my favorite gel pens are the .5 mm G-2 for normal writing and the .38 mm UM-151 for extra small writing.

My wife and two of my daughters preferred the Zebra Sarasa Dry .7 mm gel pen because it had the thick line of the .7 mm G-2 pen but with fast drying ink so that it did not smear. I’m not as big of a fan of the Sarasa Dry because the clip kind of sticks out and gets in my way. I may also just be a Pilot G-2 loyalist. Also, I have moved toward the thinner lines so the fast dry ink is not as important to me. We have a few of these Sarasa Dry pens in our pen cup in our kitchen.

My youngest daughter liked the Uni Jetstream Sport .7 mm ballpoint pen. This pen draws a finer line than you would think a .7 mm pen would because it is a ballpoint and not a gel pen. It also does not smear and is waterproof. We got her a bunch of these to take off to college as an incoming freshman this year.

We did not have this pen for our pen party but I wanted to mention the space pen that my family got me for Father’s Day. I carry it around in my pocket with my iPhone 8. I do not like the way the ink looks nearly as well as that of my gel pens, but the space pen is supposed to write at any angle and even on wet paper and it is mostly waterproof. Plus, it comes in a just under 4-inch-long bullet shape that fits comfortably in my pocket and cannot leak. The space pen gives me a way to write on just about anything when I am away from my home or office.

I am not a pen expert, but I thought I would pass along my own experiences as well as the links to the much more knowledgeable bloggers and vendors with their top pens lists. Someone out there might enjoy the top pens lists and trying out individual pens as much as we did. It was fun and even practical.

Bullet Journal Bullet Journal Supplies

At some point in this analog detour I started a Bullet Journal or BuJo for short. The main web site describes a BuJo as “The Analog Method for the Digital Age”. It sounds a little pretentious to call it “the” analog method instead of “an” analog method as if the Bullet Journal cures all ills. But I took the term analog from there to name this post. This post focuses on the analog tools and not the underlying philosophy or psychology behind the Bullet Journal. If you want to go deeper into that I recommend starting on the web site and then reading the book. I do not endorse everything in the book and web site or know if this method of journal writing really has all the claimed benefits. But I do endorse the tools that I have used such as the notebook which is very cool.

The Little Coffee Fox blog has a nice list of Bullet Journal supplies. I bought the black Leuchtturm1917 A5 Dotted Hardcover Notebook for my journal in May. A lot of the BuJo enthusiasts recommend this notebook. Even if you never get into bullet journals you might want to try one of these notebooks. The pages have dots instead of lines which is kind of like graph paper but less intrusive. The paper is nice quality and the notebook is hardbound and sturdy. The pages are numbered. I am writing this on August 25th, so I have been using my notebook for over 3 months. I like it a lot. Even if all the BuJo philosophy/psychology/method does not appeal to you the notebook itself is worth checking out.

I have tried several of the other BuJo supplies but I mainly use my Signo DX UM-151 .38 mm gel pen with my Leuchtturm A5 notebook along with a ruler. I got a foot long metal ruler with cork backing. I probably could have used any old straight edge just as well. I use it to draw lines and make boxes. I have not gotten into drawing and lettering as some BuJo enthusiasts do but I have purchased a couple of pens and a stencil to try. But I cannot really endorse something I do not use.

The Bullet Journal is all about using paper and pens instead of a computer which is really what this blog post is all about. What tools have I checked out to use offline? Can they work together with my computer tools to make me more productive and to help me have more fun?

Fountain Pens My First Two Fountain Pens – Preppies

I added a $4 fountain pen to my first JetPens order on a whim. I had to get the order up to $25 to get free shipping and $4 was reasonable for a fountain pen. If you look at the top pen lists above you will see that beginner fountain pens tend to run around $15 and yet my $4 Platinum Preppy was still considered a good pen. I got a second Preppy with my second JetPens order. The first was a medium nib and the second a fine nib. The medium has a .5 mm tip and the fine .3 mm. I had a lot of fun playing with these two pens. I ended up getting two matching converters so that I could use them with bottled ink and then I bought a nice bottle of black ink. Before I bought the full bottle of ink I got an ink sample and a pair of ink syringes so I could test out the ink.

While I experimented with my two Preppies I got a lot of helpful advice from the Reddit fountain pens group. Also vendors like JetPens and Goulet Pens have helpful videos and pages such as how to fill a fountain pen, how to clean a fountain pen, and how to use an ink sample. I think it makes good sense to start with a less expensive fountain pen and learn the ropes. The stereotypical experience of a new fountain pen user is that they do not learn how to take care of the pen, it stops working, and ends up in the back of a drawer unused. For example, I had trouble with the ink flow in my Preppies, so it helped to get advice on cleaning them and getting the ink flowing better.

After playing with my Preppies for a while I decided to get a nicer pen. If you read the top pen lists the break fountain pens into price ranges like “under $50”, “$50 to $100”, and “over $100”. I tried to be good and get a pen in the middle range, but I had my eye on several gold nib pens in the over $100 range. Japanese fountain pens mess up these neat price ranges because some pens that cost over $150 in the US can be purchased for less than $100 if you buy them directly from Japan. So, I told myself that I could get a $170 Platinum 3776 gold nib pen from Japan for under $100 and that is still in the middle range. This led to a lot of stress and frustration. I tried eBay first. A week after eBay charged over $80 to my credit card, I got an email from the seller saying that my pen was not in stock and did I want a blue one instead of the black one I ordered. I cancelled the order, but it took several days to get my money back. Then I ordered the same pen from a seller on Amazon and that was a total scam. Criminals broke into some poor unsuspecting inactive Amazon seller’s account and redirected the account to their bank account. Then they put out a bunch of bogus products at bargain prices including the fountain pen that I ordered. It took me a little over two weeks to get my money back.

After three or four weeks of frustration trying to buy an expensive fountain pen at a discount directly from Japan, I decided that it made more sense to buy from a reputable dealer in the US. I bought my 3776 at Pen Chalet and the buyer experience could not have been more different from my eBay and Amazon experiences. I got the black pen with gold trim and a fine nib. Lots of people on the fountain pens group on Reddit swear by buying fountain pens from Japan and they have more experience than I do. I suggest that wherever you buy your expensive fountain pen that you contact the seller first and ask them if they have the pen in stock. If they do not respond to your message, then run away very fast. Also, you probably should not try to get the best deal. Pay $20 more to buy the pen from a well-known dealer in Japan that sells a lot of fountain pens instead of going for the lowest price. Or just forget shopping for bargains from Japan and go with a well-regarded US vendor like Pen Chalet. I did contact Pen Chalet about something else before buying my pen and they responded quickly. A quick response to a question is a good sign for fountain pen sellers.

My Platinum 3776 pen writes like a dream. I have the matching gold colored converter and I use Aurora Black ink. It writes a nice thin black line, kind of like the ones my .5 mm G-2 and .38 mm Signo DX gel pens write. The big question is why spend $179 (including sales tax) for a fountain pen when a $3 gel pen makes pretty much the same line? I am not sure. Some people argue that fountain pens are better for the environment because you can fill them with bottled ink but with other pens you use them once and throw them away filling up landfills with plastic. Someone on the internet said, “gel pens are for work and school and fountain pens are spiritual”. I am not sure what they meant by spiritual but using a high-quality fountain pen is a nice experience. I keep mine at home, so I do not lose it somewhere like all my other pens. It is kind of nice to sit down at my kitchen table and write with my fountain pen. Maybe someone will read this post and find enjoyment in fountain pens themselves.

Rhodia Dot Pads Blog Post Outline on Rhodia Paper with Platinum 3776 Fountain Pen

Once I got into fountain pens, I needed some nice paper to write on. Many paper brands advertise as fountain pen friendly but I focused on Rhodia Dot Pads. These have dots like my Leuchtturm bullet journal notebook, but the pages are perforated so they can be removed. I started with the 6 x 8 1/4 inch pad because it was the best deal. I ended up writing on both sides of all 80 sheets and trying out different kinds of pens and pencils on it. We used these sheets in our family pen party. When I finished off this pad I bought the more expensive 8 1/4 by 11 3/4 inch pad and I really like it. I three-hole punch the pages after I rip them off and save them as fountain pen writing samples. I get a lot of enjoyment writing with my gold nib Platinum 3776 fountain pen on my full size Rhodia dot pad.

Before I started this analog detour, I wrote in a composition book with a gel pen. Today I write on my Rhodia pad with a fountain pen. One thing about the Rhodia paper is that it is smoother and less absorbent than cheaper paper. As a result, pens draw thinner lines on Rhodia paper. This probably would be more important with really wide fountain pen nibs, but it is nice that my fine nib pen leaves a nice sharp thin black line. The Rhodia paper is more expensive. At this instant you can get a full size Rhodia pad for $14.99. It has 80 sheets so that is about 19 cents per sheet. A 5 pack of Mead composition books will run you $16.75 for 500 sheets which is less than 4 cents per sheet. Is the Rhodia pad worth over 4 times as much? Why not stick with my Pilot G-2 .5 mm gel pen and write on Mead wide ruled composition books instead of using my Platinum 3776 fountain pen on a Rhodia Dot Pad? I think I could be happy with either. There is a small advantage to the more expensive pair. The fountain pen does not show through very much on the Rhodia paper. The gel pen shows through on the Mead composition book in my testing. At the end of the day, I just enjoy the Rhodia pad like I enjoy the nice fountain pen. It goes beyond simple practicality even though there are some practical benefits of the more expensive Rhodia pads.

Pencils My Favorite Pencil

The last type of analog tool that I checked out was pencils. I have not been using pencils at all in my work or in my computer science study at home. But I remember back in college writing my Pascal code in my first CS class all on paper. The TA that graded my programs said that I had the third least amount of CPU usage of anyone in the class and that the other two with less CPU usage than me dropped the class. I had been programming in BASIC and FORTRAN before coming to college so learning Pascal was not that hard. But I liked to write code out with pencil and edit using an eraser, so I did not spend a lot of time on the computer screen. These days I mainly do things on the screen. I think I need to get back to using pencils and erasers for writing and editing code and pseudo-code both in my online class and for work. I guess that goes along with the analog way of thinking like the bullet journal philosophy of using a notebook and pen instead of a program or app for your planner and journal.

My favorite pencil that I tried was the Tombow Mono 100 HB pencil that I bought in my first JetPens order. It is a pretty thing. It is basically a drawing pencil. It writes a nice dark line and is very smooth. When I was trying out various pencils, I found a fantastic pencil store called CW Pencil Enterprise. You can buy individual pencils of all kinds including types from countries all over the world. I only bought two or three pencils, but they were great to order from. They included a nice handwritten note written with a pencil. Vendors like CW Pencil motivated me to write this blog. JetPens, Goulet Pens, Pen Chalet, and CW Pencil were all very nice stores to buy from. I am sure that they are not perfect, but I had a great experience with them all and I wanted to share that with other people.

In addition to pencils I also looked at erasers. The Sakura Foam Eraser that I got from JetPens is a step up from the traditional pink hand held eraser. It erases all the pencil marks and leaves less stuff behind. A couple of my pencils like the Tombow Mono 100 did not have erasers on the end so I got a pack of Pentel Hi-Polymer Eraser Caps. These convert a drawing pencil into a more conventional writing pencil with eraser. When I use pencils for programming I alternate between the erasers on the end of the pencil and the stand-alone foam eraser.

As much fun as I had looking at pencils, I really did not find much difference between them when I used them for hand coding. The much less expensive Dixon Ticonderoga pencils that my children and wife swear by worked really well for me. I can barely tell the difference between them and the Tombow Mono 100. The Tombow is a little darker and smoother but it really does not matter much for what I need. So, I splurged on the expensive fountain pen but at this point I’m pretty much sold on the affordable and quite nice Dixon Ticonderoga standard in pencils.

Conclusion Rest of my analog stuff

I went back on forth on whether I should write this post and what to put in it. There is a lot more that I could talk about that goes beyond just the tools themselves and I thought about writing multiple posts. But, really, this is a DBA blog and not a pen, pencil, paper, and notebook blog so one post is probably enough. I thought about making this a lot shorter and just having links to the various web sites and products without explanation – bullet points with URLs behind them. I settled on a pretty long single post that weaved my personal experiences from the past 3 months or so in with links to the sites and products.

My exploration of these “analog” tools is like a breadth first search of a very large search tree of products and information. For example, there are many kinds of fountain pens and each pen has multiple nib sizes. How many pens would you have to buy to really try them all? If you look at Platinum’s 3776 site there are many different colors and designs of this one pen, plus multiple nib sizes for each. Then there are the other manufacturers each with multiple pens. It is a huge search tree. I got just a little way into this massive search and pulled the plug. This post documents the results of how far I got. I thought about waiting a year before writing a post about this to see if I am still using these tools and what benefits I got from them. But, by then I would have forgotten much of what I learned in my initial search. Maybe a year from now I can follow up with a post about whether this detour has had a lasting positive impact on my work and personal life.

Thanks to everyone who checks out this post. If you have questions or comments, it would be great if you left them below. I hope that something in here will be helpful to others. I had a lot of fun and learned a few useful things.

Bobby

Categories: DBA Blogs

Rolling out patched 19c home using gold image

Thu, 2019-08-08 18:49

For Oracle versions 11.2 through 18 I have been applying quarterly patches to a test database and then gathering the Oracle home into a tar file. When we want to roll out a new database server with the latest patches we untar the file and clone the Oracle home with a command like this:

$ORACLE_HOME/oui/bin/runInstaller -clone -silent ...

This command no longer works for 19c so now I am using a new command like this:

$ORACLE_HOME/runInstaller -silent -responseFile ...

Before running this command I had to unzip my gold image zip file into $ORACLE_HOME. I created the gold image zip file using a command like this:

./runInstaller -createGoldImage -destinationLocation ...

I ran this through MobaXterm to use their X server. I created the response file when I initially installed 19c on this test server. Then I patched the Oracle home with the July 2019 PSU and finally ran the above command to create the gold image.

Some useful links that I ran into:

Franck Pachot’s post about doing a silent 18c install using the new runInstaller

Oracle support document that says the old Oracle home cloning does not work in 19c:

19.x:Clone.pl script is deprecated and how to clone using gold-image (Doc ID 2565006.1)

Oracle documentation about installing silent with response file

DBA Stackexchange post about how you have to use a response file because the command line options don’t work

This is kind of terse but it has some of the key information. I may update it later if I can.

Bobby

Categories: DBA Blogs

Python Script To Backup Linux Directory To Windows

Thu, 2019-07-25 18:32

I found out that my blog backup script was failing so I had to rewrite it to handle dropped connections to my remote sftp server. In the process I broke out as much of the code as I could into a module that I could share. The module is backupremote.py in my miscpython repository. Might be helpful to someone else. It copies the directory tree on a remote Linux server down to a directory on a Windows machine (i.e. a laptop). Uses sftp.

The earlier version of this script was in this blog post: https://www.bobbydurrettdba.com/2018/05/30/python-script-to-backup-remote-directory-using-sftp/

Bobby

Categories: DBA Blogs

Batch Query Reduced from 12 hours to 45 Minutes

Thu, 2019-06-13 16:38

I was asked to look at a batch SQL query that ran for 12 hours on June 4th. I messed around with some other ideas and then went back to my best method for tuning long running batch queries. I ripped it apart. My code ran in 45 minutes.

This only works if you have the code and can change it. I work with a lot of PL/SQL code in Unix shell scripts running SQL*Plus. If someone comes to me with a SQL statement in this kind of code, I do not tear my hair out trying to tune it in some clever way. Also, this assumes that I cannot use a SQL Profile. SQL Profiles are my first choice for production performance problems. But for long running SQL that I have full control over and cannot use a SQL Profile I tear it apart.

Ripping or tearing a SQL statement apart means that I become the optimizer. In this case the problem query joined 5 tables. It summarized 3 months of data from a large fact table and the other 4 tables were joined together with the fact table. I replaced the one query with 5 queries each of which saved their results in a table. This first query summarized the fact table and the remaining four joined one more table to the current results. Something like this:

  • Summarize 3 months of fact table data – table 1
  • Join table 2 on surrogate key
  • Join table 3 on surrogate key
  • Join table 4 on natural keys
  • Join table 5 on natural keys

So, I created 5 tables each of which held the results of the previous joins. I dropped the tables as I was done with them to save space.

I have a feeling that I could use some clever hint to force the join order and access methods to match my 5 queries. But my time is short and this works, so I did not bother trying. When you have a query that runs for 12 hours it’s not very motivating to try various hints to get it to run faster. How long do you wait for each try before you give up? Working on one table at a time is nice. I have had this approach work for me time and time again. It is almost a mechanical process without a lot of painful thinking.

Anyway, I pass this on to the internet. People may think that breaking up a 5 table join into 5 queries is ugly, but it works.

Bobby

Categories: DBA Blogs

Another On Call Week, Another SQL Profile (or two)

Thu, 2019-06-13 16:14

I was on call again last week and Sunday night I used a SQL Profile to fix a performance problem. We rolled out some updates Saturday night and the modified query ran fine in some cases, but others ran for hours. When I got on there were 60 sessions running the bad plan and the load on the system was very high. It took me a while to identify the good plan, but then I found it, put in a SQL Profile, killed the 60 bad sessions, and the problem jobs ran in 2 minutes each. A similar situation came up Monday morning after I went off on call and a coworker took over and he ended up applying another SQL Profile on a similar query.

I spent the past couple of hours doing my typical SQL tuning exercise to see if I could figure out why Sunday’s query sometimes chose the bad plan.

The typical scenario includes these elements:

  1. Partitioned table with some near empty partitions and a lot of full partitions
  2. Bind variables used to determine partition choice

In our case we have certain tables that partition data by a code number. Certain numbers were used in the past or for other reasons are not active now. My guess is that the bad plans that we see come from bind variable values that point to the empty partitions. The bad plan works fine with empty partitions but then the optimizer uses it on full partitions and the query spins for hours.

I started to research this further to see if I could come up with a better fix than putting in SQL Profiles but did not get very far. I thought I would just pass this post along as is.

Bobby

P.S. I originally wrote this June 4th, but decided to just publish as is today.

Categories: DBA Blogs

Slides and My Impressions from May 17th AZORA Meetup

Tue, 2019-06-11 18:44

We have the slides from the two talks at our May 17th AZORA Meetup.

Here are Stephen Andert’s slides: Networking is NOT just cables and fiber!

Here are Doug Hood’s slides in three parts:

  1. Using SQL and PLSQL for Mid-Tier Database Caching
  2. Oracle TimesTen Scaleout – World’s Fastest OLTP DB
  3. Oracle In-Memory Data Processing

I wanted to share my impressions of the two talks and the meeting as well as share the slides. Of course, these are my opinions shaped by my own experiences and not universally true of everyone!

This meetup had two very different types of talks. I thought of Stephen’s talk as a career development or soft skills sort of talk. I have been to a number of talks like that at national Oracle user group conferences such as Collaborate. They help balance out the purely technical Oracle database content. Once Stephen got into his talk, I really started to appreciate the quality of the content. To me he was talking about keeping in touch with people in an intentional but sincere way. I like the idea of planning on contacting people a certain number of times per year for example.

Years ago, in my first job I worked for Campus Crusade for Christ (now Cru) and I raised money to support myself and my family. I networked with people that I met through churches and friends and family members. It was different than networking as part of a DBA career because I was directly asking for money instead of making career-oriented connections. But the core idea that I remember from Stephen’s talk applied then. Stephen’s idea was to genuinely seek to help the folks in your network without focusing on what they could do for you. In my CCC days the support raising training told us that we were not “fundraising” but instead “friend raising”. I had some great experiences meeting people and getting to know them and I think it was best when my focus was on how to inspire and encourage the people I met rather than to anxiously think about whether they could give money to support what I did.

The other less serious connection I saw between Stephen’s presentation and my Cru days is that Stephen has a hand-written database setup to keep track of his people. Back in the day I had a Paradox database from Borland running on MS-DOS to do a lot of the same things. So, hearing Stephen talk about his contact database was a blast from the past for me.

I am not really doing much in the way of networking myself these days. I write this blog. I speak at conferences every couple of years or so. I help with the local Oracle user group AZORA. But I am not intentionally making and maintaining relationships with other technical people in the way Stephen described so his talk gave me something to think about.

Doug Hood’s talk was at the other end of the spectrum with some cool technology. Doug spoke on several things and with a lot of detail so I cannot do a good job of summarizing what he said. Check the slides for more details. But I do want to write down my impressions. Listening to Doug’s talk reminded me of some of the computer science study that I have been doing on my own recently. I have sort of gone back to school as an independent learner. When Doug talked about the memory hierarchy and caching it tied right back to the assembly language and algorithms study I have been doing.

Doug presented some cool hardware that puts persistent memory close enough to the CPU that it changes the way we think about memory hierarchy. What if you replace your RAM with persistent RAM that did not get cleared when you power off your computer? Evidently in some architectures (maybe all the modern ones these days I don’t know) the RAM is closely connected to the CPU and does not have to be accessed over the bus in the way I/O is. So, persistent RAM would be much faster than some solid-state disk being read over the bus no matter how fast the SSD is. Anyway, see Doug’s slides. I am sure that I am butchering the details, but I am giving my impression and my memory so keep that in mind.

In general database work and database performance has a lot to do with caching. I have heard a little bit about how algorithms can be designed to work well with CPU caches. I recently read a chapter about the B-Tree data structure that is used in databases and it was a big contrast to the other data structures I had studied because it took disk reads and memory accesses into consideration. Anyway, at a high level I took away from Doug’s talk notions about memory and caching and different ways people can tweak the memory hierarchy to get better database performance.

I had to leave a little early to head for the mountains for the weekend but as always, I valued the time I spent at AZORA, my local Oracle user group. I appreciate Stephen and Doug stepping up and giving their presentations. I hope that my links and the way I characterized their talks is accurate enough. I am sure that I made mistakes, but I got something out of my time and appreciate their efforts.

AZORA is taking a break for the hot Arizona summer but coming back strong with our next meeting on September 27th. The details are being finalized so keep an eye on our Meetup page.

Bobby

Categories: DBA Blogs

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