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Oracle database performance
Updated: 9 hours 53 min ago

Update hinted for wrong index

Fri, 2015-11-13 10:52

I worked with our support team to improve the performance of a PeopleSoft Financials update statement yesterday. The update statement had an index hint already in it but the index was not the best one of the available indexes.

Here is the original update statement:

 BANK_CD = :2,

I listed out the columns for the indexes on the table using the “querytuning” part of my standard sql tuning scripts.

Here are the columns for the hinted index:


The where clause includes only the first two columns.

But another similar index, PSEPYMNT_VCHR_XREF, exists with these columns:


The where clause has all three of these columns. So, why was the original query hinted this way? Does the E index not work better than the C index? I ran this query to see how selective the condition PYMNT_SELCT_STATUS = ‘N’ is.

>select PYMNT_SELCT_STATUS,count(*)
 4 AND B.REMIT_VENDOR = '12345678'

- ----------
C 5
N 979
P 177343
X 5485

I included the conditions on the first two columns that both indexes share, but removed the other conditions from the original update. A count on the number of rows that meet the conditions of only these two columns shows how many rows the original index will have to use to check the remaining where clause conditions.

I grouped by PYMNT_SELCT_STATUS to see how many rows met the condition PYMNT_SELCT_STATUS = ‘N’ and how many did not. Grouping on PYMNT_SELCT_STATUS shows how many rows the new index will use to check the remaining conditions in the where clause. I ran this query to see if the second index would use fewer rows than the first.

This query showed that only 979 of the over 180,000 rows met the condition. This made me think that the E index which includes PYMNT_SELCT_STATUS has a good chance of speeding up the original update. I ran a count with a hint forcing the C index and then again forcing the E index:

>set timing on
>select /*+ INDEX(B PSCPYMNT_VCHR_XREF) */ count(*)
 4 AND B.REMIT_VENDOR = '12345678'
 6 AND B.PYMNT_ID = ' '


Elapsed: 00:13:52.53
>select /*+ INDEX(B PSEPYMNT_VCHR_XREF) */ count(*)
 4 AND B.REMIT_VENDOR = '12345678'
 6 AND B.PYMNT_ID = ' '


Elapsed: 00:00:01.28

The original hint caused the select count(*) query to run in 13 minutes while the new hint caused it to run in 1 second. Clearly the new E index causes the query to run faster!

The developer that I was working with found the problem update statement in some PeopleCode and was able to edit the hint forcing it to use the better index. We migrated the modified code to production and the user was able to run the update statement without the web site timing out. Prior to the change the user was not able to complete the update because the SQL statement took so long it exceeded our application server timeout.



Categories: DBA Blogs

Tested 1000 Select Statements on New Exadata X5

Fri, 2015-11-06 17:37

I finished testing 1000 select statements on our new Exadata X5 to see if they would run faster or slower than on our older Exadata V2.  Our current production V2 has 12 nodes and the new X5 has only 2.  The memory and parallel server parameters on the X5 are 6 times are large as on the old one, since we have one sixth as many hosts and more than 6 times the memory and CPU per host. I think that memory parameters can sometimes change execution plans, and of course with the newer Exadata software who knows what other differences we might see.  I wanted to see if any plan changes or other issues caused some queries to run much slower on our newer Exadata system than the old one. I picked 1000 select statements at random from our current production and tested them comparing plans and execution time. In the end I did not find any bad plan changes and on average the tested select statements ran about 4 times faster on the X5 than on the older V2.

I used my testselect package that I have mentioned in several other posts. Here are some other examples of using this package for performance tuning:

In the other posts I was using the package to test the effect of some change on query plans and performance.  So, I was comparing two different situations on the same host. But, in this case I was comparing two different hosts with essentially the same data and settings. But they had different versions of Exadata hardware and larger parameters and fewer nodes on the newer host.  Here are the results of my first run with all 1000 statements.  I got the execution plan for all 1000 select statements but only executed the ones with different plans.  Here were the results:

>execute TEST_SELECT.display_results('X5','V2');
Select statements that ran 3 times faster with X5 than with V2.
        --------- -------------------- -------------------- --------------------- ---------------------
                3            287237826            287237826                     3                    34
                4           1245040971           1245040971                     1                    11
                9             36705296           2770058206                     4                    22

... edited out most of the lines for brevity ...

              997           2423577330           2423577330                     0                     9
              998           2217180459           3921538090                     1                    13
             1000           3842377551           1690646521                     2                    12
Number of selects=329
Select statements that ran 3 times faster with V2 than with X5.
        --------- -------------------- -------------------- --------------------- ---------------------
               95           3919277442           3919277442                     0                     2
              210           3508255766           3508255766                     0                     2
              282           3946849555           3085057493                     0                     6
              347           3278587008            789099618                    19                   170
              375            581067860            460184496                     0                     3
              429            534521834            534521834                     1                     6
              569           3953904703           3484839332                     0                     2
              681            946688683           3451337204                     1                     6
              697            908111030           2971368043                     0                     1
              699           3756954097           1915145267                     0                     1
              706           1121196591           1121196591                     0                     2
              708            581067860            460184496                     0                     4
              797            908111030           2841065272                     0                     5
              950            786005624           2571241212                    45                   460
              966           3151548044           3151548044                     1                     5
Number of selects=15
Summary of test results
        -------------------- ------------------------ ---------------- --------------------------
                          X5 5545.9999999999999999999              486                         11
                          V2                    21138              486                         43

Of the tested statements 329 ran 3 or more times faster on the X5.  But 15 selects ran 3 or more times faster on the old V2.  So, I needed to test the 15 selects again on both servers.

I’m not sure if it was smart or not, but I decided to run all the selects 5 times in a row to maximize caching.  The X5 is new and not in use so there wouldn’t be any activity to stimulate caching.  My test script for the X5 looked like this:

truncate table test_results;

execute TEST_SELECT.execute_all('X5');
execute TEST_SELECT.execute_all('X5');
execute TEST_SELECT.execute_all('X5');
execute TEST_SELECT.execute_all('X5');
execute TEST_SELECT.execute_all('X5');
execute TEST_SELECT.reexecute_errored('X5');
execute TEST_SELECT.reexecute_errored('X5');
execute TEST_SELECT.reexecute_errored('X5');
execute TEST_SELECT.reexecute_errored('X5');
execute TEST_SELECT.reexecute_errored('X5');

After we made sure that the system had cached everything, all 15 selects ran, on average, 4 times faster on the X5 than the V2:

-- ---------- ------------- ----------------- ----------------- ------------ ------------------ ------------------------ --------------- ------------- ------------------ -------------- ----------------------------------------------------------------
X5         95 54a8k0yhbgyfq                          3919277442            1                  0                       12            2583            14                  0              1
V2         95 54a8k0yhbgyfq                          3919277442            1                  1                       15            2583            14                  1              1
V2        210 b132ygmp743h4                          3508255766            0                  2                       19            1592            14                  0              1
X5        210 b132ygmp743h4                          3508255766            0                  2                        8            1430            14                  0              1
V2        282 aw5f12xsa8c2h                          3946849555            0                  0                       14            3468            14                  0              2
X5        282 aw5f12xsa8c2h                          3946849555            0                  0                        8            3322            14                  2              2
V2        347 8ncbyjttnq0sk                          3278587008            1                  3                      462         1203794            14                  0          61838
X5        347 8ncbyjttnq0sk                          3278587008            1                  2                      206         1126539            14                  4          51849
X5        375 4yq5jkmz2khv5                           581067860            0                  0                        9           14530            14                  0              2
V2        375 4yq5jkmz2khv5                           581067860            0                  0                       19           14686            14                  1              2
V2        429 49pyzgr4swm4p                           534521834            0                  2                       11            1814            14                  0              0
X5        429 49pyzgr4swm4p                           534521834            0                  0                        5            1638            14                  1              0
X5        569 3afmdkmzx6fw8                           630418386          694                  0                       74           70173            14                  3              0
V2        569 3afmdkmzx6fw8                          3527323087          694                  1                       73           68349            14                  0           3588
V2        681 dyufm9tukaqbz                           668513927            0                  0                       10            6298            14                  0              2
X5        681 dyufm9tukaqbz                          3317934314            0                  0                        8            6096            14                  0              2
V2        697 1fqc3xkzw8bhk                           908111030            0                  0                        3            1406            14                  0              1
X5        697 1fqc3xkzw8bhk                           908111030            0                  0                        2            1406            14                  0              1
V2        699 03qk2cjgr4q2k                          1915145267           31                  0                      476           95922            14                  1              0
X5        699 03qk2cjgr4q2k                          1915145267           31                  0                      272           96299            14                  0              0
V2        706 28fnjtdhjqwrg                          1121196591            0                  0                       21            1355            14                  0              4
X5        706 28fnjtdhjqwrg                          1121196591            0                  0                       13            1355            14                  0              4
V2        708 2yrkwqs46nju0                           581067860            0                  0                       14           14684            14                  0              0
X5        708 2yrkwqs46nju0                           581067860            0                  0                        9           14528            14                  0              0
V2        797 dc5481yn8pm85                           908111030            0                  0                        3            1407            14                  0              2
X5        797 dc5481yn8pm85                           908111030            0                  0                        2            1407            14                  0              2
V2        950 by6n1m74j82rt                           786005624            6                  7                     2087          249736            14                  1         245443
X5        950 by6n1m74j82rt                          2571241212            6                  0                      186           90897            14                  0              3
X5        966 5c2n74gfrxwxx                          3151548044           12                  0                       24          116360            14                  9          84949
V2        966 5c2n74gfrxwxx                          3151548044           12                  0                       52          119701            14                  1          88002

The summary of the results:

Select statements that ran 3 times faster with X5 than with V2.
	--------- -------------------- -------------------- --------------------- ---------------------
	       95           3919277442           3919277442                     0                     1
	      429            534521834            534521834                     0                     2
	      569            630418386           3527323087                     0                     1
	      950           2571241212            786005624                     0                     7
Number of selects=4
Select statements that ran 3 times faster with V2 than with X5.
	--------- -------------------- -------------------- --------------------- ---------------------
Number of selects=0
Summary of test results
	-------------------- ------------------------ ---------------- --------------------------
	                  X5                        4               15                          0
	                  V2                       16               15                          1

I guess it is no surprise that the X5 is faster than the five-year older V2.  But, I thought it was a good example of how to use my testselect package to do see how a set of queries will run in two different situations.


Categories: DBA Blogs

OakTable video of myself and others

Tue, 2015-11-03 10:53

You can find the full length video of my Delphix talk that I did at OakTable World on Tuesday here: url

Also, the OakTable folks have updated the OakTable World agenda page with video of all the talks. This has lots of good material and for free. Scroll down to the bottom of the page to find the links to the videos.


Categories: DBA Blogs

Final day – OpenWorld and Delphix Sync

Thu, 2015-10-29 20:54

This morning was my last day of Oracle OpenWorld sessions and this afternoon and evening finished off my day with Delphix Sync.

The first talk was my only NoSQL talk. It was interesting because the claim was that NoSQL was good for large numbers of simple transactions. This seems to be a theme across a couple of sessions. The funny thing is that the NoSQL code reminded me of my pre-SQL mainframe Datacom DB database programming days. You specified the table and the index and fetched rows etc. You are the optimizer! Of course, you can do the same with simple one table queries in SQL. But, Oracle’s NoSQL may have some concurrency modes that Oracle’s main RDBMS doesn’t have for what that’s worth. The fun thing was that they had examples using Python and I’ve taken Python on Edx so I could read the code. Also, they talked about the REST API and I had done a few REST commands with JSON working through a demo of the Oracle database cloud a few weeks back. So, there were synergies with things I already know.

Next I went to this packed session by someone from Tumbler describing their approach to sharding and scaling. The two packed sessions I went to this week were both MySQL sessions and both by internet companies – Tumbler and Ticketmaster. They were in kind of small rooms and it was a little warm and stuffy. But, I found both very interesting. Supporting large web apps is a pretty cool proposition. Something that in another life would be fun to work on.

Next I went to a PeopleSoft session. I’ve done PeopleSoft for 20 years and I’m bored with it but I figure I should keep up with the latest. It was actually more of a functional presentation on modules that I have never used so most of the information was of no use to me. But, the new Fluid User Interface that I had never seen before interested me so I stayed long enough to get a feel for it. It seems that Oracle built it for tablets and maybe smart phones.

Next it was off to the hip (or should I say hipster :)) Hotel Zetta for Delphix Sync. It was a very cool event with a fun venue and lots of good snacks. No dinner for me tonight. I got a chance to do a ten minutes lightning talk that I built from three slides from Tuesday’s presentation. I got positive feedback but I felt kind of intimidated by all the Delphix techies. There were a lot of Delphix leaders and developers present as well as a number of people from larger customers. I heard a great talk on Delphix performance and other customers and Delphix employees spoke as well. I learned a lot and it makes me think I need to delve back into our Delphix environment and give it a thorough check out.

So, my OpenWorld/Delphix Sync week is over and I am beat. Like always these conferences leave me with information overload. I’m back to the prioritization thing that always dogs my step. There is just too much to learn and do. Where do I put my time? We shall see.


Categories: DBA Blogs

Wednesday OpenWorld

Wed, 2015-10-28 21:27

Well, it was a long day but it ended in a fun way.

Today I was back to the normal OpenWorld sessions starting with the general session. It was eye-opening because the speakers described a new CPU chip that they were using in their latest servers.  It had some custom elements to support database processing.  It was strange because I have recently been studying the latest Intel x86 documentation and it was interesting to compare Intel’s chips with the latest Sun/Oracle chip.  I had read about the specialized SIMD instructions in the x86 family that Intel uses to speed up graphics. So, I was not surprised that Oracle is including more complex additions to their new chip with specialized instructions.  Still, I question whether people are really going to buy anything but Intel x86 at this point due to the price/performance.

Next I went to a session describing the way a company used a tool called Chef to manage their Weblogic deployments.  The session topic interested me because we also use Weblogic at US Foods.  But it was a little hard to follow.  Maybe it would have helped if I had been exposed to Chef before hearing the talk. Still, it was good to know that there are tools out there to help automate deployment of new systems.

Next I caught a PeopleSoft in the Cloud talk. It seems that you will install the latest version of PeopleTools in a very different way than you did in the past.  I got the feeling that this was just a part way step toward fully setting up PeopleSoft to run in the cloud.

Then I went to a really cool talk about how Ticketmaster sells 20,000 tickets in one minute. It was about their MySQL architecture.  They have a large farm of MySQL servers supporting the queries behind their ticketing web site.  But, they use Oracle for the updates related to ticket purchases.

Then I went to a talk on Oracle ZFS.  I get the feeling that I need to learn more about ZFS. It seems that ZFS is an ancestor of Delphix and I know that there is a free OpenZFS that I might play with. I think that Tim Gorman, who works for Delphix, mentioned something about OpenZFS  at his Ted talk at Oak Table World Tuesday so there may be some relationship.

Lastly I went to a talk about how you can use Oracle’s Enterprise Manager to support both on site and cloud databases.  It sounds good but I think it still need to mature over time to support the cloud systems more fully.

Then at 5:30 pm I went to a fun bloggers party sponsored by Pythian and the Oracle Technology Network (OTN).  I’m not a big party person but I had a good time.  It was easy to strike up a conversation with people since we had a lot in common.

Anyway, enough for today.  One more day Thursday and then my brain will overflow. :)


Categories: DBA Blogs

Tuesday OakTable World – brain fried!

Tue, 2015-10-27 19:08

Instead of going to the normal OpenWorld events today I went to OakTable World.  Now my brain is fried from information overload. :)

It started at 8 am with a nice talk about hash joins and Bloom filters.  Toon Koppelaars had some nice moving graphics showing how bloom filters work.  I’ve studied Bloom filters before but I’m not sure I understood it with the clarity that I had after this talk.

Then I did my talk at 9 am.  The best part for me was that we had a number of questions.  I ended up skipping several slides because of time but I felt like we helped people get what they wanted out of it by having the questions and discussion.  In retrospect my talk could have used more of an introduction to Delphix itself for this audience but I think we covered the essentials in the end.

Next Kellyn Pot’Vin-Gorman did more of a community service type of talk which was a change of pace.  She had a Raspberry Pi project which was a stuffed bear that would take your picture and post it on Twitter.  It was an example of the type of project that kids could do to get them interested in computer technology.

My brain began to turn to mush with Marco Gralike’s talk on XML and 12c In-Memory Column store.  I’m sure I’m absorbed something but I’m not that familiar with Oracle’s XML features.  Still, at least I know that there are in memory features for XML which I can file away for the future.

Several amusing 10 minute Ted talks followed.  Most notable to me was Jonathan Lewis’ talk about how virtual columns and constraints on virtual columns could improve cardinality estimates and thus query performance.

Cary Millsap talked about a variety of things including things like what he covered in his book.  He shared how he and Jeff Holt were hacking into what I assume is the C standard library to diagnose database performance issues, which was pretty techy.

Gwen Shapira’s talk on Kafka was a departure from the Oracle database topics but it was interesting to hear about this sort of queuing or logging service.  Reminds me in some ways of GGS and Tibco that we use at work but I’m sure it has different features.

Alex Gorbachev gave a high level overview of Internet of Things architectures.  This boiled down to how to connect many possibly low power devices to something that can gather the information and use it in many ways.

Lastly, we went back to the Oracle database and my brain slowly ground to a halt listening to Chris Antognini’s talk on Adaptive Dynamic Sampling.  I had studied this for my OCP but it has slowly leaked out of my brain and by 4 pm I wasn’t 100% efficient.  But, I got a few ideas about things that I can adjust when tuning this feature.

Anyway, brief overview.  I’m back to normal OpenWorld tomorrow but it was all OakTable today.  It was a good experience and I appreciated the chance to speak as well as to listen.


Categories: DBA Blogs

Monday OpenWorld

Mon, 2015-10-26 22:10

It was a good first full day at Oracle OpenWorld.  It started with the keynote led by Oracle’s CEO.  Of course he was very upbeat about Oracle’s products.  But, I found his comments about the economy and the way it affects IT spending more interesting than Oracle’s cloud products.  My translation is that companies have a lot of older systems that they can’t easily retire or upgrade but they want to quickly add all this new functionality.  I see that in my company so it rings true.  I don’t really believe that the cloud is the long-term answer but it makes me wonder what the real answer is.  I always come back to prioritization.  I think prioritizing spending is more important than moving things to the cloud.  You can’t afford to do everything so you have to make tough choices about what to spend your IT dollars on. That’s my opinion at least.

Next I went to a session on Coherence.  I kind of went out of a sense of obligation since our company owns the product.  But, it was a surprisingly good session.  It had a lot in it about Java 8 and the new features in it for parallel processing.  It made me want to dust off my Java skills and generally think about parallel processing in the Oracle database and how it relates to that in Hadoop, Scala, etc.

I went to two sessions on analytics, again out of a sense that I needed to learn about analytics and not due to any enthusiasm about it.  The first session was really interesting, but the 3:30 session almost literally put me to sleep.  The first session reminded me of some of the things in Craig Shallahamer’s forecasting book such as making a model of a system and doing validation of the model.  Analytics seems to follow a similar process. But, by the late afternoon a non-technical session on analytics in banking nearly knocked me out.

Wedged between my two analytics sessions I went to a very cool In-Memory Option boot camp.  I have not had the time or taken the time to look at the In-Memory Option and I got a nice fast hour-long exposure to it.  I don’t know if the other people in the class were lost because there were a lot of explain plans flying by but it is the type of stuff I’m interested in so it was nice that it was so technical.  The In-Memory Option reminded me a lot of Exadata smart scans and hybrid columnar compression.

Strangely enough multiple speakers pronounced columnar differently than I have done so I guess I will have to change.  They emphasize the second syllable but I usually emphasize the first.

I also snuck in to the OakTable World presentation by Tanel Poder.  It had to do with querying Hadoop clusters from Oracle databases using odbc/jdbc.  Pretty cool.  I also got to scope out the venue for my talk tomorrow in the process.

That’s enough for today.  I got a lot of good information.  I’ve slotted tomorrow for OakTable world so it will be interesting to see what all I can learn there.





Categories: DBA Blogs

Reviewing Delphix blog posts before OpenWorld

Thu, 2015-10-22 10:13

I just finished reviewing my Delphix blog posts in preparation for the talks that I will give during OpenWorld. I find myself referring back to my blog to help me remember what I have done in the past. I was thinking that I needed to jog my memory so that I could answer questions when I give my talks.  Some of the Delphix topics that I am speaking about occurred a year or two ago so my memories are fuzzy.  But, reading my own posts brought a lot of the details back.

I thought I would list the Delphix posts, even though people can find these just by searching my web site. If you are coming to my talk and want more details or if you just want all the Delphix related information on my site I have collected it here.

I have two talks scheduled during OpenWorld next week.

The first talk is at OakTable World at 9 am on Tuesday.

The second talk is at Delphix Sync between 3 and 4 pm Thursday.  The second talk is a ten minute “lightning talk” version of the first.

I hope to see you at one of the talks and if not I hope this post is valuable to you.


Categories: DBA Blogs

Toastmaster Talk – DBA Toolkit

Mon, 2015-10-19 16:55

Here is a YouTube video of a short Toastmasters talk of mine about a DBA topic.  It was probably too technical for the audience but may interest the readers of this blog.

Here was a post about a similar topic:

  • Bobby
Categories: DBA Blogs

My Delphix presentation at OakTable World

Wed, 2015-10-07 17:52

It is official.  I will be doing my Delphix presentation at OakTable World during the Oracle OpenWorld conference at the end of this month.  My talk is at 9 am on Tuesday, October 27.

I will describe our journey as a new Delphix customer with its ups and downs. I tried to have the spirit of a user group talk where you get a real person’s experience that you might not get from a more sales oriented vendor presentation.

Kyle Hailey, a OakTable member and Delphix employee, will host my talk.  I have been very impressed by Kyle’s technical knowledge and he will be with me to answer questions about Delphix that I could not answer.  I think it will be a good combination of my real world user experience and his depth of technical background in Delphix and Oracle performance tuning.

If you are going to OpenWorld and if you want to know more about Delphix come check it out.  Also, feel free to email me or post comments here if you have any questions about what the talk will cover.


Categories: DBA Blogs

AZORA – Arizona Oracle User Group new location

Mon, 2015-09-14 13:35

The Arizona Oracle User Group has moved tomorrow’s meeting to Oracle’s offices on Camelback road:

Meetup link with meeting details


Categories: DBA Blogs

September 15 Arizona Oracle User Group at US Foods in Tempe

Wed, 2015-09-09 11:12

Tuesday, September 15, we will be hosting the Arizona Oracle User Group on site at my employer US Foods at our office in Tempe.  The topic will be Oracle Enterprise Manager 12c.  Here is the meeting invite url: invite.

The invitation describes the content like this:

Join us for AZORA’s September meetup at US Foods. First, we’ll have Raj Chotalla from Intel sharing his team’s Oracle EM12c implementation strategy, architecture, and best-known methods. Next up will be Oracle’s Keith Anthony Marshall and Eric Dennis answering questions and providing live demos showing the power of Oracle Enterprise Manager for both Fusion Middleware and Oracle Database.

I hope to have some coworkers attend that can share US Foods experience with EM 12c as well maybe during the question and answer time.

I’m looking forward to it and I hope to see you there.


Categories: DBA Blogs

Plan change monitor prevents user impact from bad plan

Mon, 2015-08-31 13:07

This morning a colleague of mine noticed an alert from our plan change monitor and prevented any impact on the users by forcing a SQL’s plan to its normal fast plan before the users could be affected.  I want to share the monitor script and describe how we use its output.

I know this is long but I want to paste the SQL text of the monitor script here.  Review my comments on each step.  I can not remember the details of the script myself even though I wrote it but the comments hopefully will help:

set linesize 160
set pagesize 1000
set echo on
set termout on
set trimspool on
set define off

-- status active
-- this plan isn't the most commonly
-- run plan.
-- average this plan in v$sql > 10 x average of
-- most common plan

-- status active sessions
-- sql_id and plan_hash_value
-- elapsed and executions
-- max elapsed for the sql
-- eliminate pl/sql which has
-- plan hash value of 0

drop table active_sql;

create table active_sql as
max(sq.ELAPSED_TIME) elapsed,
max(sq.executions) executions
v$session vs,
v$sql sq
vs.sql_id=sq.sql_id and
vs.SQL_CHILD_NUMBER=sq.child_number and
vs.status='ACTIVE' and
sq.plan_hash_value <> 0
group by 

-- to get the most frequently
-- used plan first get the number
-- of exections by plan
-- for each of the active sqls

drop table plan_executions;

create table plan_executions as
sum(ss.executions_delta) total_executions
dba_hist_sqlstat ss,
active_sql a
group by 

-- use the previous table to get
-- the plans that are most frequently 
-- used. note that two plans could
-- have the same number of executions
-- but this is unlikely.

drop table most_frequent_executions;

create table most_frequent_executions as
from plan_executions pe1
pe1.total_executions =
(select max(pe2.total_executions)
from plan_executions pe2

-- handle special case of two plans with
-- same number of executions.
-- pick one with highest plan value
-- just to eliminate dups.

drop table most_frequent_nodups;

create table most_frequent_nodups as
from most_frequent_executions mfe1
mfe1.plan_hash_value =
(select max(mfe2.plan_hash_value)
from most_frequent_executions mfe2

-- get list of active sql that 
-- are not running the most
-- frequently executed plan

drop table not_most_freq;

create table not_most_freq as
select * from active_sql
(sql_id,plan_hash_value) not in
(select sql_id,plan_hash_value from most_frequent_nodups);

-- working on this part of the logic:
-- average this plan in v$sql > 10 x average of
-- most common plan

-- get average elapsed of most
-- frequently executed plans
-- add 1 to handle case of 0 executions

drop table avg_elapsed_most_f;

create table avg_elapsed_most_f as
most_frequent_nodups nd
ss.sql_id = nd.sql_id and
ss.plan_hash_value = nd.plan_hash_value
group by

-- get list of the sqls that are running
-- the plan that isn't most frequently 
-- executed and has an average elapsed 
-- more than 10 times the average of 
-- the most frequently executed plan
-- add 1 to executions to prevent 
-- divide by zero

drop table more_than_10x;

create table more_than_10x as
not_most_freq n,
avg_elapsed_most_f m
(n.elapsed/(n.executions+1)) > 10 * m.avg_elapsed and

spool planchangemonitor.log

select name db_name from v$database;

-- The listed sql_id and plan_hash_value items correspond to 
-- sql statements that have plans that may be
-- inefficient and need to be investigated.
-- The session id and username are included if a
-- session is currently running the sql with the plan.

'CHANGED '||'PLAN' flag,
more_than_10x m,
v$session s,
v$sql q
m.sql_id=s.sql_id(+) and
m.plan_hash_value=q.plan_hash_value(+) and
s.sql_id=q.sql_id and
order by

spool off

If I remember correctly I think the script looks for sessions running a plan whose current run time is 10 times that of the most frequently executed plan. This script is not perfect. The join to v$sql is not perfect and in some cases you can get duplicates.  People could quibble about the approach.  Why 10 times the previous run time?  I thought about more complex approaches but I just needed to get something in place.  But, on one database with a lot of small transactions we have made good use of this script, despite its flaws.

This morning my colleague noticed emails from the script that runs this SQL listing a query whose plan had changed.  The output looked like this:

------------ ------------- --------------- ---------- ---------
CHANGED PLAN 75ufmwrcmsuwz      2484041482         35 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        394 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        395 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        446 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        463 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        464 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        544 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        613 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        631 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        665 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        678 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        738 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        746 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        750 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482        752 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       1333 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       1416 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       1573 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       1943 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       1957 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       3038 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       3445 YOURUSER
CHANGED PLAN 75ufmwrcmsuwz      2484041482       3816 YOURUSER

I changed the real user to YOURUSER.  This output indicates that 23 sessions were all running the same SQL – sql_id=75ufmwrcmsuwz – and that this SQL was running on a new plan that was causing the SQL to run at least 10 times the normal run time.  In fact it was about 30 times as long.

To resolve the issue my colleague used our script to find the history of plans for 75ufmwrcmsuwz.

     select ss.sql_id,
  2  ss.plan_hash_value,
  4  ss.executions_delta,
  5  ELAPSED_TIME_DELTA/(executions_delta*1000) "Elapsed Average ms",
  6  CPU_TIME_DELTA/(executions_delta*1000) "CPU Average ms",
  7  IOWAIT_DELTA/(executions_delta*1000) "IO Average ms",
  8  CLWAIT_DELTA/(executions_delta*1000) "Cluster Average ms",
  9  APWAIT_DELTA/(executions_delta*1000) "Application Average ms",
 10  CCWAIT_DELTA/(executions_delta*1000) "Concurrency Average ms",
 11  BUFFER_GETS_DELTA/executions_delta "Average buffer gets",
 12  DISK_READS_DELTA/executions_delta "Average disk reads",
 13  ROWS_PROCESSED_DELTA/executions_delta "Average rows processed"
 15  where ss.sql_id = '75ufmwrcmsuwz'
 16  and ss.snap_id=sn.snap_id
 17  and executions_delta > 0
 19  order by ss.snap_id,ss.sql_id;

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
------------- --------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
75ufmwrcmsuwz      2297146707 30-AUG-15 AM              830         587.207673     49.9638554    541.617188                  0                      0                      0          5234.01928         125.083133              332.66747
75ufmwrcmsuwz      2297146707 30-AUG-15 AM             1178         578.205867     49.3972835    532.377174                  0                      0                      0          4870.22326         126.048387              324.50764
75ufmwrcmsuwz      2297146707 30-AUG-15 AM             1433         631.484713     49.1486392    585.826676                  0                      0                      0          4624.11305         125.446615              299.57083
75ufmwrcmsuwz      2297146707 30-AUG-15 PM             1620         592.593823     49.5987654     546.29731                  0                      0                      0          4744.17284         121.961728             312.735185
75ufmwrcmsuwz      2297146707 30-AUG-15 PM             1774         534.412339      51.059752     485.46836                  0                      0                      0          4983.44983         119.564825             326.067644
75ufmwrcmsuwz      2297146707 30-AUG-15 PM             1757         447.385105     44.9345475    404.415659                  0                      0                      0          4525.13147         107.277746             293.739328
75ufmwrcmsuwz      2297146707 30-AUG-15 PM             1626         431.718507      45.904059    388.200416                  0                      0                      0          4462.93296         118.027675             300.724477
75ufmwrcmsuwz      2297146707 30-AUG-15 PM             1080         375.905966      44.212963    334.434835                  0                      0                      0          4766.81574         109.157407             310.712037
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              707         368.289475     44.3140028    327.166223                  0                      0                      0          4894.20509         108.050919             315.565771
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              529         341.483588     39.6408318     305.47356                  0                      0                      0          4381.19849         96.2646503             288.030246
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              356         380.733635     41.5168539    342.034876                  0                      0                      0           4553.4691         105.272472             292.283708
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              313         435.449406     37.1565495    402.636489                  0                      0                      0          4144.30351         92.8690096             264.923323
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              214         516.455509     44.5794393    477.020692                  0                      0                      0          4567.67757         114.415888             289.607477
75ufmwrcmsuwz      2297146707 30-AUG-15 PM              182         720.749681     44.3956044    684.439467                  0                      0                      0          3811.83516         95.2362637             239.027473
75ufmwrcmsuwz      2297146707 30-AUG-15 PM               83          1043.1503     43.7349398    1008.41358                  0                      0                      0          3575.96386         114.289157             250.120482
75ufmwrcmsuwz      2484041482 30-AUG-15 PM                6         25314.6558     4311.66667    22971.4913                  0                      0                      0          78533.8333         69813.3333             157.833333
75ufmwrcmsuwz      2484041482 31-AUG-15 AM               96         25173.7346     5105.20833    21475.9516                  0                      0                      0          135242.802         62433.3125             118.395833
75ufmwrcmsuwz      2484041482 31-AUG-15 AM               39         26877.0626     5540.51282    22977.6229                  0                      0                      0          139959.308         68478.1795             93.7179487
75ufmwrcmsuwz      2484041482 31-AUG-15 AM               38          26993.419     5998.15789    22768.4285                  0                      0                      0          153843.342              74492             149.342105
75ufmwrcmsuwz      2484041482 31-AUG-15 AM               29         25432.5074     4952.06897    22288.7966                  0                      0                      0          112813.552         69803.0345             187.689655
75ufmwrcmsuwz      2484041482 31-AUG-15 AM               34         27281.7339     4541.47059    24543.1609                  0                      0                      0             95144.5         69187.3824             135.676471
75ufmwrcmsuwz      2484041482 31-AUG-15 AM              146         30512.9976     5421.43836    26984.2559                  0                      0                      0          115531.801         71886.6644             136.321918
75ufmwrcmsuwz      2484041482 31-AUG-15 AM              405         24339.6641     4853.40741    20794.0472                  0                      0                      0           115490.01         62004.4642             229.106173
75ufmwrcmsuwz      2484041482 31-AUG-15 AM              838         27552.3731     4903.06683    23661.2101                  0                      0                      0          111654.558         58324.9511             176.058473
75ufmwrcmsuwz      2484041482 31-AUG-15 AM             1653         30522.8358     4550.56261    26526.2183                  0                      0                      0           93818.418         49865.4701             137.212341

Based on this output my colleague chose 2297146707 as the good plan.  She ran coe_xfr_sql_profile.sql which is found in SQLT’s utl directory like this:

coe_xfr_sql_profile.sql 75ufmwrcmsuwz 2297146707

Then she ran the generated script:


This forced the plan back to its earlier efficient plan and my coworker did all of this early this morning before we reached our peak time of usage and before the bad plan could affect our users.


P.S. This example is from an database running on HP-UX Itanium.

Categories: DBA Blogs

Reminder: Great free computer science and Python programming class starts Wednesday

Mon, 2015-08-24 13:01

I mentioned this class earlier in a blog post but I wanted to remind people who read this blog that the class is starting again on Wednesday.  Here is the URL for the class: link

The class is completely free and taught at a very high level of quality.

It teaches computer science concepts that apply in any programming language but also teaches Python programming.

It is valuable information in the increasingly computer oriented world and economy and the class is free which is remarkable given its quality.

Here is the class name:

MITx: 6.00.1x Introduction to Computer Science and Programming Using Python


Categories: DBA Blogs

Memory added based on buffer pool advisory did not give desired result

Wed, 2015-08-19 14:37

Development asked me to look at ways to cut the run time of a series of PeopleSoft payroll batch jobs so I took an AWR report of the entire 4 hour period.  Based on the waits, the percentage of the elapsed time spent using I/O and the buffer pool advisory I chose to double the size of the buffer cache. But, this added memory did not improve the run time of the batch jobs. Maybe the affected blocks are only read into memory once so they would not get cached no matter how big the buffer pool was.

Here is the original run on June 22 with the original memory settings:

Cache Sizes

Begin End Buffer Cache: 3,328M 3,424M Std Block Size: 8K Shared Pool Size: 1,600M 1,520M Log Buffer: 7,208K

Top 5 Timed Foreground Events

Event Waits Time(s) Avg wait (ms) % DB time Wait Class db file sequential read 1,910,393 10,251 5 72.03 User I/O DB CPU 2,812 19.76 log file sync 35,308 398 11 2.80 Commit resmgr:cpu quantum 31,551 62 2 0.43 Scheduler db file scattered read 7,499 60 8 0.42 User I/O Buffer Pool Advisory
  • Only rows with estimated physical reads >0 are displayed
  • ordered by Block Size, Buffers For Estimate
P Size for Est (M) Size Factor Buffers (thousands) Est Phys Read Factor Estimated Phys Reads (thousands) Est Phys Read Time Est %DBtime for Rds D 336 0.10 41 2.71 6,513,502 1 9842530.00 D 672 0.20 83 2.42 5,831,130 1 8737799.00 D 1,008 0.29 124 2.18 5,241,763 1 7783636.00 D 1,344 0.39 166 1.96 4,720,053 1 6939010.00 D 1,680 0.49 207 1.77 4,250,981 1 6179603.00 D 2,016 0.59 248 1.59 3,825,904 1 5491420.00 D 2,352 0.69 290 1.43 3,438,372 1 4864023.00 D 2,688 0.79 331 1.28 3,083,734 1 4289879.00 D 3,024 0.88 373 1.15 2,758,459 1 3763273.00 D 3,360 0.98 414 1.02 2,459,644 1 3279504.00 D 3,424 1.00 422 1.00 2,405,118 1 3191229.00 D 3,696 1.08 455 0.91 2,184,668 1 2834329.00 D 4,032 1.18 497 0.80 1,931,082 1 2423784.00 D 4,368 1.28 538 0.71 1,696,756 1 2044421.00 D 4,704 1.37 579 0.62 1,479,805 1 1693185.00 D 5,040 1.47 621 0.53 1,278,370 1 1367070.00 D 5,376 1.57 662 0.45 1,090,505 1 1062925.00 D 5,712 1.67 704 0.38 914,112 1 777352.00 D 6,048 1.77 745 0.31 746,434 1 505888.00 D 6,384 1.86 786 0.24 580,310 1 236941.00 D 6,720 1.96 828 0.17 414,233 1 149325.00

In the SQL ordered by Elapsed Time report the top batch job SQL was 99.14% I/O

Based on this report it seems that the number of physical reads could be reduced to about 20% what they were on June 22 by doubling the size of the buffer cache. But, adding the memory did not cut the number of physical reads in any major way.

Here is yesterday’s run:

Cache Sizes

Begin End Buffer Cache: 6,848M 6,816M Std Block Size: 8K Shared Pool Size: 3,136M 3,136M Log Buffer: 16,572K

Top 5 Timed Foreground Events

Event Waits Time(s) Avg wait (ms) % DB time Wait Class db file sequential read 1,789,852 10,173 6 72.15 User I/O DB CPU 2,970 21.06 log file sync 37,562 200 5 1.42 Commit resmgr:cpu quantum 24,996 59 2 0.42 Scheduler db file scattered read 5,409 54 10 0.38 User I/O Buffer Pool Advisory
  • Only rows with estimated physical reads >0 are displayed
  • ordered by Block Size, Buffers For Estimate
P Size for Est (M) Size Factor Buffers (thousands) Est Phys Read Factor Estimated Phys Reads (thousands) Est Phys Read Time Est %DBtime for Rds D 672 0.10 83 11.25 516,440 1 1309098.00 D 1,344 0.20 166 5.98 274,660 1 683610.00 D 2,016 0.29 248 4.02 184,712 1 450915.00 D 2,688 0.39 331 2.90 133,104 1 317404.00 D 3,360 0.49 414 2.20 100,860 1 233990.00 D 4,032 0.59 497 1.80 82,768 1 187185.00 D 4,704 0.69 580 1.53 70,445 1 155305.00 D 5,376 0.79 663 1.31 60,345 1 129176.00 D 6,048 0.88 745 1.14 52,208 1 108127.00 D 6,720 0.98 828 1.01 46,477 1 93301.00 D 6,848 1.00 844 1.00 45,921 1 91862.00 D 7,392 1.08 911 0.95 43,572 1 85785.00 D 8,064 1.18 994 0.89 40,789 1 78585.00 D 8,736 1.28 1,077 0.85 38,889 1 73671.00 D 9,408 1.37 1,160 0.81 37,112 1 69073.00 D 10,080 1.47 1,242 0.77 35,490 1 64876.00 D 10,752 1.57 1,325 0.75 34,439 1 62158.00 D 11,424 1.67 1,408 0.73 33,353 1 59347.00 D 12,096 1.77 1,491 0.71 32,524 1 57204.00 D 12,768 1.86 1,574 0.69 31,909 1 55613.00 D 13,440 1.96 1,656 0.68 31,361 1 54194.00

After the memory add the same top batch job SQL was 98.80% I/O.  Some improvement but not nearly as much as I expected based on the buffer pool advisory.

I guess the moral of the story is that the buffer pool advisory does not apply to specific workloads and is only a general guideline.  Maybe this is the same kind of fallacy that you have with buffer cache hit ratios where certain workloads make the ratio irrelevant.  Here were the hit ratios:  Before 98.59% After 98.82%.  Basically these are the same.

I just thought I would share this to document a real case of using the buffer pool advisory and having it not produce the expected results.


Categories: DBA Blogs

Script to get previous month’s AWR report

Tue, 2015-08-18 11:58

We keep 6 weeks of history in the AWR on our databases, but I want to capture some information for long-term trending. What I really want to do is capture some metrics and put them in some database tables to use to generate reports, but I have not had time to build the scripts to do that.  So, instead I built a simple set of scripts to capture an AWR for the previous month. Since we have 6 weeks of history if I run my report in the first week of a month all the days of the previous month should still be in the AWR. I have just finished building this script so I can not promise that there is value in keeping monthly AWR reports but I thought it was worth sharing it.  Maybe something in the code will be useful to someone.  Here is the script:

-- Has to be run in the first week of the month so the entire
-- previous month is available. We keep 6 weeks of awr history.

-- setup columns for snapshots

column bsnap1 new_value bsnap1s noprint;
column esnap1 new_value esnap1s noprint;
column filenm new_value filenms noprint;

-- get snap id for first day of previous month

select min(snap_id) bsnap1
from dba_hist_snapshot
extract(month from END_INTERVAL_TIME)=
extract(month from (sysdate-to_number(to_char(sysdate,'DD'))))
(select max(STARTUP_TIME)
from dba_hist_snapshot
extract(month from END_INTERVAL_TIME)=
extract(month from (sysdate-to_number(to_char(sysdate,'DD')))));

-- get snap id for last day of previous month

select max(snap_id) esnap1
from dba_hist_snapshot
extract(month from END_INTERVAL_TIME)=
extract(month from (sysdate-to_number(to_char(sysdate,'DD'))));

-- get html file name

to_char(extract(month from 
to_char(extract(year from 
'.html' filenm
from v$database;

-- get awr report

define report_type='html';
define begin_snap = &bsnap1s;
define end_snap = &esnap1s;
define report_name = '&filenms';

define num_days = 0;


undefine report_type
undefine report_name
undefine begin_snap
undefine end_snap
undefine num_days

If the database bounced during the previous month we get the last set of snapshots after the last bounce.

I am not sure whether this approach will give us any benefits but I think it may help to show how to use queries to pick begin and end snapshots and then run an AWR report.

The tricky part of the code is this:

extract(month from (sysdate-to_number(to_char(sysdate,'DD'))))

It just returns the previous month as a number.  It is August now so here is what it returns today:

SQL> select
 2 extract(month from (sysdate-to_number(to_char(sysdate,'DD'))))
 3 prev_month
 4 from dual;


sysdate-to_number(to_char(sysdate,’DD’)) is the last day of the previous month:

SQL> select sysdate-to_number(to_char(sysdate,'DD')) last_day
 2 from dual;


– Bobby

Categories: DBA Blogs

Applied July Patch Sets To Test Databases

Tue, 2015-08-11 16:09

I applied the current July patch sets to a 11.2 and a 12.1 test database.  Now I have a and a test database.  It is helpful to have test databases that are on the most current patch sets and releases.  If I see unexpected behavior on some other database I can try the same thing on the patched test databases to see if some patch changed the behavior to what I expect.  Also, our production databases are all on or earlier releases so I can check whether the new fully patched 12.1 release has different behavior than our older systems.

Here are the patch numbers:

6880880 – current version of opatch

20760982 –

20831110 –

My test environments are on x86-64 Linux.

– Bobby

Categories: DBA Blogs

Registered for Oracle OpenWorld

Mon, 2015-08-10 13:41

I registered myself for Oracle OpenWorld and I have my hotel reserved and my flights ticketed.

I think it has been over 12 years – probably more like 15 years – since I went to OpenWorld. I went at least once between December 1994 and November 2003 when I still lived in Florida and was working on Oracle databases.  But since I moved from Florida I do not believe that I have been to the conference.  I have presented at Collaborate and ECOUG conferences since then.  I’m thinking that maybe next year I will try to present at the RMOUG conference.  I live in Arizona so RMOUG is close.  ECOUG was a nice distance when I still lived near the East Coast.  I like the smaller conferences and I have a better shot at getting a presentation accepted there.

But, this year it is OpenWorld and I am looking forward to it.  I may get a chance to interact with some Delphix employees and customers.  Also, I’m hoping to check out some technical presentations by the Oak Table members.  And it does not hurt to hear from Oracle itself on its technology.  No doubt there will be many of Oracle’s top technical leaders presenting.  And, any interaction I get with fellow DBA’s will be great.  It is always good to hear from people about their own experiences which may differ from mine.

Anyway, I’m all booked for OpenWorld.  Hope to see you there.

– Bobby



Categories: DBA Blogs

Check out 6.00.1x computer science class on edX!

Sat, 2015-07-25 11:15

I just finished the last program for a computer science class on edX and I urge you to try it.

I took this class:

MITx: 6.00.1x Introduction to Computer Science and Programming Using Python

I was more interested in how MIT taught the class than in the material itself because I already know the subjects covered.

The class taught the basics of programming – expressions, variables, loops, if statements, and functions.

It also had a large focus on bisection or binary search and the performance benefits of this type of search over sequentially reading through a list.

It also covered lists, hash tables, trees, stacks, and queues.

It discussed object-oriented programming.

The class concluded with the professor stating that the programming and computer science skills taught in this class are key to advancing your career, even if you do not work in a computer related job.

I interacted with a number of students in the class and found some that were in other fields and were having success taking the class.  Others were in business computing or IT and yet did not have a computer science background so they were good programmers but learning new concepts.  Many struggled with the class but, it is free, and is given often. The class starts up again August 26th.  Nothing stops you from taking it multiple times.

I tried to think about whether I should recommend this class to the people I work with as a method of helping develop my coworkers that do not have experience in these areas.  At first I thought that the subject is too academic and has no connection to their jobs. But, after thinking about it for a while, I now believe that just the opposite is true.

Searching for practical applications of the class, I first remembered the programs that we wrote that compared searching sequentially through a list to using binary search.  In one test case the sequential method took 15 seconds but the binary search took less than one second.  This reminded me so much of tuning Oracle SQL queries.  The sequential scan of the list was like a full table scan in Oracle.  The binary search was like looking up a single row using an index scan.  As I tune Oracle queries my computer science knowledge of binary search and binary trees makes it easy to understand index and full table scans.

In another example, we recently had slowness on a Weblogic portal server.  CPU was getting maxed out and the CPU spent most of its time in a Java ConcurrentHashMap object.  I don’t know the internals of Weblogic and I have never used a ConcurrentHashMap but I know how hashing works.  I know that hashing is very fast until your hash table fills up or if the hash function distributes the items in an unequal way. My knowledge of hashing helped me grasp why our portal server was using a lot of CPU despite my lack of access to its internals.

So, contrary to my original fear that the edX class was too academic and not practical I believe that the concepts covered are very practical.  If you do not know how binary search works or what a binary tree is you will benefit from 6.00.1x on edX.  If you can not explain how a hash table works and what causes hashing to slow down you can learn from 6.00.1x. And, if you have never written a computer program, although you may find the class difficult and have to take it more than once, you will benefit from 6.00.1x on edX.

– Bobby


Categories: DBA Blogs