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I have read on line about XS$NULL over the years and particularly the documentation that states that it has no privileges. The documentation states the following: An internal account that represents the absence of a user in a session. Because....[Read More]

Posted by Pete On 17/02/20 At 01:09 PM

A quirky little feature of interval partitioning showed up on Twitter today – a parallel insert that would only use a single PX slave to do the inserting. With 1.1 billion rows and the option for running parallel 32 this made the loading process rather slower than it ought to have been.

Fortunately it’s quite easy to model (and work around) the oddity. So here’s a small data set and an empty partitioned table to work with:

rem
rem     Script:         pt_int_load_anomaly.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2020
rem

create table t1 
nologging 
as
select 
        ao.* 
from 
        all_Objects ao, 
        (select rownum id from dual connect by level <= 20)
;

create table pt1
partition  by range (object_id) interval (1000000) (
        partition p1 values less than (1)
)
as
select  * 
from    all_Objects
where   rownum = 0
/

I’ve created a table by copying all_objects 20 times which, for my little sandbox, has given me a total of about 1.2M rows. Then I’ve created an empty interval-partitioned clone of all_objects, with the first partition defined to hold all rows where the object_id is less than 1 (and there’s no object in the database that could match that criterion). I’ve defined the interval to be 1,000,000 and since the highest object_id in my database is about 90,000 the first partition that gets added to this table will be able to hold all the data from t1.

So now we try to do a parallel insert from t1 into pt1, and check the execution plan and parallel execution statistics:

set serveroutput off

insert /*+ append enable_parallel_dml parallel(6) */ into pt1 select * from t1;

select * from table(dbms_xplan.display_cursor);

start pq_tqstat

Note how I’ve used the hint /*+ enable_parallel_dml */ (possible a 12c hint back-ported to 11.2.0.4) rather than using an “alter session”, it’s just a little convenience to be able to embed the directive in the SQL. The pq_tqstat script is one I published some time ago to report the contents of the session-specific dynamic performance view v$pq_tqstat immediately after running a parallel statement.

Here’s the plan:

SQL_ID  25hub68pf1z1s, child number 0
-------------------------------------
insert /*+ append enable_parallel_dml parallel(6) */ into pt1 select *
from t1

Plan hash value: 2888707464

-------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                   | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
-------------------------------------------------------------------------------------------------------------------------------------
|   0 | INSERT STATEMENT                            |          |       |       |   631 (100)|          |        |      |            |
|   1 |  PX COORDINATOR                             |          |       |       |            |          |        |      |            |
|   2 |   PX SEND QC (RANDOM)                       | :TQ10001 |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,01 | P->S | QC (RAND)  |
|   3 |    LOAD AS SELECT (HIGH WATER MARK BROKERED)| PT1      |       |       |            |          |  Q1,01 | PCWP |            |
|   4 |     OPTIMIZER STATISTICS GATHERING          |          |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,01 | PCWP |            |
|   5 |      PX RECEIVE                             |          |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,01 | PCWP |            |
|   6 |       PX SEND RANDOM LOCAL                  | :TQ10000 |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,00 | P->P | RANDOM LOCA|
|   7 |        PX BLOCK ITERATOR                    |          |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,00 | PCWC |            |
|*  8 |         TABLE ACCESS FULL                   | T1       |  1235K|   159M|   631  (10)| 00:00:01 |  Q1,00 | PCWP |            |
-------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   8 - access(:Z>=:Z AND :Z<=:Z)

Note
-----
   - Degree of Parallelism is 6 because of hint

The most important detail of this plan is that the PX slaves do the load as select (operation 3), then send a message to the query coordinator (PX send QC, operation 2) to tell it about the data load. They do not send their data to the QC for the QC to do the load.

So the plan says we will be doing parallel DM, but here’s what v$pq_tqstat tells us:

DFO_NUMBER      TQ_ID SERVER_TYPE     INSTANCE PROCESS           NUM_ROWS      BYTES ROW_SHARE DATA_SHARE      WAITS   TIMEOUTS AVG_LATENCY
---------- ---------- --------------- -------- --------------- ---------- ---------- --------- ---------- ---------- ---------- -----------
         1          0 Producer               1 P006                215880   34785363     17.47      16.86         16          0           0
                                             1 P007                202561   34436325     16.39      16.69         17          0           0
                                             1 P008                207519   34564496     16.79      16.75         17          0           0
                                             1 P009                208408   34594770     16.86      16.77         17          0           0
                                             1 P00A                198915   33529627     16.10      16.25         16          0           0
                                             1 P00B                202537   34430603     16.39      16.69         16          0           0
                      Consumer               1 P000                     0        144      0.00       0.00         51         47           0
                                             1 P001                     0        144      0.00       0.00         51         47           0
                                             1 P002               1235820  206340464    100.00     100.00         75         47           0
                                             1 P003                     0        144      0.00       0.00         51         47           0
                                             1 P004                     0        144      0.00       0.00       1138       1134           0
                                             1 P005                     0        144      0.00       0.00       1137       1133           0

                    1 Producer               1 P000                     0         24      0.00       5.91         51         42           0
                                             1 P001                     0         24      0.00       5.91         50         41           0
                                             1 P002                     2        286    100.00      70.44         58         14           0
                                             1 P003                     0         24      0.00       5.91         51         43           0
                                             1 P004                     0         24      0.00       5.91         51         42           0
                                             1 P005                     0         24      0.00       5.91         51         43           0
                      Consumer               1 QC                       2        406    100.00     100.00        311        179           0

19 rows selected.

The query did run parallel 6 as hinted – and 6 PX slaves scanned the t1 table; but they all sent all their data to one PX slave in the second slave set and that one PX slave did all the inserts. The plan was parallel, but the execution was effectively serial. (You’ll note there is something a little odd about the waits and timeout for p004 and p005 when they are acting as consumers. I may worry about that later, but it could be a host-based side effect of running parallel 6 on a VM with 4 CPUs).

The serialization leads to two questions

1. What went wrong?
2. How do we work around this and make the insert “truly” parallel

My answer to (1) is “I don’t know – but I’ll look at it if necessary” combined with the guess – it’s something to do with the table having only one partition at the outset and this has an unexpected side effect on the randomising function for the PX distribution.

My answer to (2) is “if I’m right about (1), why not try pre-defining two partitions, and I’ll even let both of them stay empty”.

So here’s my new definition for pt1:

create table pt1
partition  by range (object_id) interval (1000000) (
        partition p0 values less than (0),
        partition p1 values less than (1)
)
as
select  * 
from    all_Objects
where   rownum = 0
/

Re-running the test with the completely redundant, and permanently empty p0 partition the plan doesn’t change but the results from v$pq_tqstat change dramatically:

DFO_NUMBER      TQ_ID SERVER_TYPE     INSTANCE PROCESS           NUM_ROWS      BYTES ROW_SHARE DATA_SHARE      WAITS   TIMEOUTS AVG_LATENCY
---------- ---------- --------------- -------- --------------- ---------- ---------- --------- ---------- ---------- ---------- -----------
         1          0 Producer               1 P006                207897   34581153     16.82      16.76         23          4           0
                                             1 P007                215669   34786429     17.45      16.86         30          5           0
                                             1 P008                221474   36749626     17.92      17.81         28          5           0
                                             1 P009                204959   34497164     16.58      16.72         22          2           0
                                             1 P00A                177755   30141002     14.38      14.61         21          0           0
                                             1 P00B                208066   35585810     16.84      17.25         25          2           0
                      Consumer               1 P000                213129   35612973     17.25      17.26         82         57           0
                                             1 P001                200516   33570586     16.23      16.27         84         55           0
                                             1 P002                203395   33950449     16.46      16.45         83         56           0
                                             1 P003                205458   34235575     16.63      16.59         82         54           0
                                             1 P004                204111   33999932     16.52      16.48        581        555           0
                                             1 P005                209211   34971669     16.93      16.95        580        553           0

                    1 Producer               1 P000                     2        286     16.67      16.67        422        149           0
                                             1 P001                     2        286     16.67      16.67        398        130           0
                                             1 P002                     2        286     16.67      16.67        405        128           0
                                             1 P003                     2        286     16.67      16.67        437        161           0
                                             1 P004                     2        286     16.67      16.67        406        116           0
                                             1 P005                     2        286     16.67      16.67        440        148           0
                      Consumer               1 QC                      12       1716    100.00     100.00        242        111           0



19 rows selected.

Every consumer receives and inserts roughly 200,000 rows – it’s a totally fair parallel DML. Timings are pretty irrelevant for such a small data set but the excution time did drop from 7 seconds to 4 seconds when parallelism was working “properly”.

I’ve tested this script on 12.2.0.1 and 19.3.0.0 – the same anomaly appears in both versions though it might be worth noting that the strange skew in the waits and timeouts doesn’t appear in 19.3.0.0.

Team, Here is my testcase which got failed during Synchronous refresh in mview. <code>create table products as select rownum as prod_id, object_name as prod_name, object_type as prod_category, object_id as prod_category_id, data_object...

I have a package working fine in 11g version. But when I deploy the same package in 19c version, the behavior is different. PFB the description. Package specification has an cursor and created a table type with cursor%rowtype. Having a pipel...

Hi Tom, We are trying to do a sqoop import to hive from Oracle and struck with a weird error below: WARN[main] org.apache.hadoop.mapred.YarnChild: Exception running child : java.io.IOException: java.sql.SQLExcepion: ORA-00606:error occurred ...

Hello We have a Oracle 11g Release 2 database with five identical working Schemas being accessed by a VB Client Server business application The db server, with 32GB RAM just hosts this one database Copies of Client applications are installed a...

Hi Team, I have a DB, where a table is dropped from the schema accidentally. We are trying to find whether it got dropped due to manual execution of DROP query and by whom? Is there any way that we can find that the DROP query which is executed ...

HI, I am writing a query to find missing table partitions for next year using all_tab_partitions table, I am able to fetch the records with the help of column partition positions, but I have to extract the last partition date (YYYY-MM-DD) from HIG...

Late in 2019, Apple obsoleted my trusty iPad Mini II.  No more OS updates, may not run the latest apps, etc.  So I figured it was time for a new iPad... right up until the time I saw the prices. Yikes!!!

The expense of a new iPad, even a Mini, made me step back and reconsider my approach.  I started by considering my uses of the iPad.  I don't really create much with an iPad - it's just too limited for the type of work I do. And I don't communicate much with it, as I have an iPhone for calls, txt, and video chats. I use my iPad to consume:  books, movies, social media and news.

Once I understood my own use cases for the iPad, I realized I could fill my needs with a much less expensive device.  I settled on an Amazon Fire HD 8 (thanks to my kids for the nifty Christmas present).  And it works great for what I do, especially after setting it up to side load from Google Play.  My old Mini has been retired to a life as a digital photo frame and I'm really happy with the Fire.  Is the Fire as elegant of an experience as the Mini?  Not by a long shot.  But it gets the job done quite well.  And, at last check, the Fire is about 20% of the price of a new iPad Mini.  A great example of less being best.

I see this quite a bit in the way users work with enterprise applications - in my specific case, observations are from helping customers with Oracle HCM Cloud Enterprise Applications.  Users expend significant costs in terms of time and labor facilitating some very complicated use cases, many of which are either unnecessary or rarely encountered.  When we collaborate with those customers on those use cases, the end result is often that less is best.  Often they're focused on a specific use case because they have not considered another process for getting the job done. Other times it's a matter of the use case not being as critical to the business as originally thought.  And sometimes it's due to a legal or industry requirement that no longer exists.  And there are many more causes.  But it always makes my day when the lightbulb comes on and people realize they can make do quite well with much less than they originally thought.

I've seen this surface frequently in working with customers on the mobile-enabled Responsive UX.  Simplification was a driving design concept in building out Responsive UX, which means some less used and less important features were culled from the product.  Less is best, based on the feedback we've received from customers who have adopted the new UX.

So the upshot here?  Step back, take a breath, and think about how you use your enterprise apps.  Can you find instances where less would be best?  I'll bet you find more than one.

It is possible to take subquery factoring (common table expressions / CTEs) too far. The most important purpose of factoring is to make a complex query easier to understand – especially if you can identify a messy piece of text that is used in more than one part of the query – but I have seen a couple of patterns appearing that make the SQL harder to read.

• In one pattern each table is given its own factored subquery holding the non-join predicates (and, possibly, filter subqueries) and then the main query is just a simple join of the factored subqueries with nothing but join (ON) predicates.
• In another pattern each factored subquery consists of the previous subquery with one more table added to it, so every subquery is no more that a “two-table” query block and the final subquery is a simple “select from last_factored_subquery”.

Neither of these patterns is helpful – but today’s blog note is not going to be about going to extremes with subquery factoring; instead it’s an example of a fairly reasonable use of subquery factoring that ran into a “wrong results” bug.

Consider a system that collects data from some type of meters. Here’s a two-table definition for meters and meter readings:

create table meters (
        meter_id        number,
        meter_type      varchar2(10),  -- references meter_types
        date_installed  date,
        padding         varchar2(100),
        constraint met_pk primary key(meter_id)
)
;

create table meter_readings (
        meter_id        number,
        date_read       date,
        reading         number(10,3),
        padding         varchar2(100),
        constraint      mrd_pk primary key(meter_id, date_read),
        constraint      mrd_fk_met foreign key (meter_id) references meters
)
;

insert into meters
select
        1e6 + rownum,
        case mod(rownum,3)
                when 0 then 'A'
                when 1 then 'B'
                       else 'C'
        end,
        trunc(sysdate) - mod(rownum,5),
        rpad('x',100,'x')
from
        dual 
connect by 
        level <= 10
;

execute dbms_stats.gather_table_stats(null,'meters')

insert into meter_readings 
select
        met.meter_id,
        met.date_installed - v.id + 2,
        dbms_random.value,
        rpad('x',100,'x')
from
        meters met,
        (select rownum id from dual connect by level <= 4) v
;

commit;

execute dbms_stats.gather_table_stats(null,'meter_readings')

I’ve picked the obvious primary keys for the two tables and defined the appropriate referential integrity constraint – which means the optimzer should be able to choose the best possible strategies for any query that joins the two tables.

I’ve created a very small data set – a few meters installed in the last few days, and a few readings per meters over the last few days. So lets report the readings for the last 48 hours, and include in the output any meters that haven’t logged a reading in that interval.

Here’s the query I wrote, with its output, running on a 19.3 instance on 17th Feb 2020:

with mrd_cte as (
        select 
                meter_id, date_read, reading
        from 
                meter_readings
        where 
                date_read in (trunc(sysdate), trunc(sysdate)+1)
)
select
        met.meter_id, met.date_installed, mrd_cte.date_read, reading
from
        meters met
left join 
        mrd_cte
on      mrd_cte.meter_id = met.meter_id
;

  METER_ID DATE_INST DATE_READ    READING
---------- --------- --------- ----------
   1000001 16-FEB-20 17-FEB-20       .063
   1000002 15-FEB-20
   1000003 14-FEB-20
   1000004 13-FEB-20
   1000005 17-FEB-20 18-FEB-20        .37
   1000005 17-FEB-20 17-FEB-20       .824
   1000006 16-FEB-20 17-FEB-20       .069
   1000007 15-FEB-20
   1000008 14-FEB-20
   1000009 13-FEB-20
   1000010 17-FEB-20 17-FEB-20       .161
   1000010 17-FEB-20 18-FEB-20       .818

12 rows selected.

The query returns 12 rows – which SQL*Plus can report because it counts them as it fetches them so it can give you the total at the end of the query.

Of course, sometimes people write preliminary queries to find out how big the result set would be before they run the query to acquire the result set itself. In cases like that (where they’re just going to select a “count(*)” the optimizer may a choose different execution path from the base query – perhaps finding a way to do an index-only execution, and maybe eliminating a few table joins from the query. So let’s execute a count of the above query:

rem
rem     Script:         join_elimination_bug.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2020
rem

with mrd_cte as (
        select 
                meter_id, date_read, reading
        from 
                meter_readings
        where 
                date_read in (trunc(sysdate), trunc(sysdate)+1)
)
select count(*) from (
        select
                met.meter_id, met.date_installed, mrd_cte.date_read, mrd_cte.reading
        from
                meters met
        left join 
                mrd_cte
        on      mrd_cte.meter_id = met.meter_id
)
;

  COUNT(*)
----------
        10

1 row selected.

You’ll have to take my word for it, of course, but no-one else was using this database while I was running this test, and no-one else has access to the schema I was using anyway. Unfortunately when I count the 12 rows instead of reporting them Oracle thinks there are only 10 rows. Oops!

Step 1 in investigating the problem – check the execution plans to see if there are any differences in the structure of the plan, the use of predicates, or the outline information. I won’t bother with the plan for the base query because it was very obvious from the count query where the problem lay.

-------------------------------------------------------------------
| Id  | Operation        | Name   | Rows  | Cost (%CPU)| Time     |
-------------------------------------------------------------------
|   0 | SELECT STATEMENT |        |       |     1 (100)|          |
|   1 |  SORT AGGREGATE  |        |     1 |            |          |
|   2 |   INDEX FULL SCAN| MET_PK |    10 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('19.1.0')
      DB_VERSION('19.1.0')
      ALL_ROWS
      OUTLINE_LEAF(@"SEL$69B21C86")
      ELIMINATE_JOIN(@"SEL$00F67CF8" "METER_READINGS"@"SEL$1")
      OUTLINE(@"SEL$00F67CF8")
      MERGE(@"SEL$C43CA2CA" >"SEL$2")
      OUTLINE(@"SEL$2")
      OUTLINE(@"SEL$C43CA2CA")
      MERGE(@"SEL$D28F6BD4" >"SEL$E6E74641")
      OUTLINE(@"SEL$E6E74641")
      ANSI_REARCH(@"SEL$4")
      OUTLINE(@"SEL$D28F6BD4")
      MERGE(@"SEL$1" >"SEL$006708EA")
      OUTLINE(@"SEL$4")
      OUTLINE(@"SEL$006708EA")
      ANSI_REARCH(@"SEL$3")
      OUTLINE(@"SEL$1")
      OUTLINE(@"SEL$3")
      INDEX(@"SEL$69B21C86" "MET"@"SEL$3" ("METERS"."METER_ID"))
      END_OUTLINE_DATA
  */

This is the plan as pulled from memory by a call to dbms_xplan.display_cursor(). We note particularly the following: meter_readings doesn’t appear in the plan, there is no predicate section (and no asterisks against any of the operations that would tell us there ought to be some predicate information), and there’s a very revealing ELIMINATE_JOIN(@”SEL$00F67CF8″ “METER_READINGS”@”SEL$1”) in the outline information.

For some reason the optimizer has decided that it’s okay to remove meter_readings from the query (even though there may be many meter readings for each meter), so it was inevitable that it produced the wrong result.

Despite my opening note, this is not an issue with subquery factoring – it just looked that way when I started poking at the problem. In fact, if you rewrite the query using an inline view you get the same error, if you turn the inline view into a stored view you get the error, and if you turn the whole query into a simple (left) join with the date predicate as part of the ON clause you still get the error.

The problem lies somewhere in the join elimination transformation. If you go back to the outline information from the bad plan you’ll see the line: ELIMINATE_JOIN(@”SEL$00F67CF8″ “METER_READINGS”@”SEL$1”) – by changing this to NO_ELIMINATE_JOIN(…) and adding it to the main query block I got a suitable plan joining the two tables and producing the right result.

The problem appears in 12.2.0.1 and 19.5.0.0 (tested on livesql) – but does not appear in 12.1.0.2 or 11.2.0.4

There is a known bug associated with this problem:

Bug: 29182901
Abstract: WRONG COUNT WAS RETURNED WHEN _OPTIMIZER_ENHANCED_JOIN_ELIMINATION=TRUE
Query with Outer Join Returned a Wrong Result due to Join Elimination (Doc ID 29182901.8)

The bug is fixed in 20.1, with a fix that has been backported into the Jan 2020 patches for 19, 18, and 12.1

Seen the new look and feel for the spring.io? Worth a look

https://spring.io/

Unable to sudo su -:

[grid@ol7-fpp-fpps ~]$ sudo su -

We trust you have received the usual lecture from the local System
Administrator. It usually boils down to these three things:

    #1) Respect the privacy of others.
    #2) Think before you type.
    #3) With great power comes great responsibility.

[sudo] password for grid:
grid is not in the sudoers file.  This incident will be reported.

[grid@ol7-fpp-fpps ~]$ 

Group wheel has ALL privileges:

[root@ol7-fpp-fpps ~]# grep wheel /etc/sudoers
## Allows people in group wheel to run all commands
%wheel  ALL=(ALL)       ALL
# %wheel        ALL=(ALL)       NOPASSWD: ALL
[root@ol7-fpp-fpps ~]#

Modify user grid:

[root@ol7-fpp-fpps ~]# usermod -aG wheel grid

Or: usermod -a -G sudo user

Test sudo -:

[grid@ol7-fpp-fpps ~]$ sudo su -
[sudo] password for grid:
Last login: Sun Feb 16 08:35:52 -05 2020 on pts/0
[root@ol7-fpp-fpps ~]#

The Oracle E-Business Suite R12 Applications Database Administrator Certified Professional Certification is designed for individuals who possess a strong foundation and expertise in implementing Oracle E-Business Suite solutions. This exam is required as part of earning the new Oracle EBS R12 Applications Database Administrator Certified Professional certification. Check Out K21 Academy’s blog post at https://k21academy.com/appsdba65 […]

The post [1Z0-238] Oracle EBS R12 Certified Specialist Everything You Must Know About appeared first on Oracle Trainings for Apps & Fusion DBA.

Hi Tom, I am having a SQL output as follows. <code>A B C D E ---------------- ---------- ---------- ------------ ----------- 2020-02-12 221 68677 99.6...

Hello, Ask Tom team. I have the following query: <code>SELECT guid, sender_id, doc, status, arrived_date, register_date, last_updated_date FROM user1.table1 WHERE (sender_id=:SENDER OR :SENDER IS NULL ) AND (status=:STATUS OR :status IS NU...

Hi Chris, I want to restrict the length of the input string to 8 characters by adjusting all the digits (after 'MFT' in below example) from Input string. Means, want to accommodate all the digits in string. Ex.1. Input String is 'MFT123456' ...

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

The semantics of comparing character columns of different types can lead to some confusion, so before I get into the main body of this note here’s a little test based on a table with one row:

create table t1(c2 char(2), c3 char(3), vc2 varchar2(2), vc3 varchar2(3));

insert into t1 values ('XX','XX','XX','XX');
commit;

select count(*) c2_c3   from t1 where c2 = c3;
select count(*) c2_vc3  from t1 where c2 = vc3;
select count(*) c3_vc2  from t1 where c3 = vc2;
select count(*) c3_vc3  from t1 where c3 = vc3;

I’ve inserted one row, using the same value for every single column; then I’ve been counting the row(s) where various pairs of columns match. Which (if any) of the four queries return the value 1 and which (if any) return the value zero ?

To help you, here’s a quote from the relevant Oracle manual about character comparison semantics:

Blank-Padded and Nonpadded Comparison Semantics

With blank-padded semantics, if the two values have different lengths, then Oracle first adds blanks to the end of the shorter one so their lengths are equal. Oracle then compares the values character by character up to the first character that differs. The value with the greater character in the first differing position is considered greater. If two values have no differing characters, then they are considered equal. This rule means that two values are equal if they differ only in the number of trailing blanks. Oracle uses blank-padded comparison semantics only when both values in the comparison are either expressions of data type CHAR, NCHAR, text literals, or values returned by the USER function.

With nonpadded semantics, Oracle compares two values character by character up to the first character that differs. The value with the greater character in that position is considered greater. If two values of different length are identical up to the end of the shorter one, then the longer value is considered greater. If two values of equal length have no differing characters, then the values are considered equal. Oracle uses nonpadded comparison semantics whenever one or both values in the comparison have the data type VARCHAR2 or NVARCHAR2.

The first two queries return 1, the second two return zero.

1. Query 1: c2 is blank padded to match c3 in length before the comparison, so the values are ‘XX {space}’
2. Query 2: c2 is not padded, so the compared values are both ‘XX’
3. Query 3: c3 is three characters long, vc2 is only 2 characters long and does not get padded to match c3
4. Query 4: c3 is three characters long, vc3 is only 2 characters long and does not get padded to match c3

One interesting by-product of this example is this:

• c3 = c2 and c2 = vc3 but c3 != vc3     whatever happened to transitive closure!

So we come to the point of the article, which is this:

Be very careful about using char() (or nchar) types in your tables – especially if you’re thinking of using columns of type [n]char() in join predicates (or predicates that don’t start life as join predicates but become join predicates through transitive closure).

Here’s an interesting bug that has appeared (I think) as a side effect of the need for blank-padded semantics. We start with two tables that I’ll be joining with a hash join – one of them will be a small table that will be used as the “build” table, the other will be (faked to look like) a large table that will be used as the “probe” table.

rem
rem     Script:         bloom_prune_char_bug.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2020
rem
rem     Last tested 
rem             19.3.0.0
rem             12.2.0.1
rem

create table test_probe(status char(3)) partition by list(status) (partition st_1 values('00','BI'));

create table test_build(status char(2)); 

insert into test_build values('00');
insert into test_probe values('00');
insert into test_build values('BI');
insert into test_probe values('BI');

commit;
 
prompt  =====================================
prompt  Fake large table stats for test_probe
prompt  =====================================

exec dbms_stats.set_table_stats(null,'test_probe',numrows=>2000000);

spool bloom_prune_char_bug
set linesize 156
set pagesize 60

set serveroutput off

select  /*+ 
                gather_plan_statistics 
        */
        * 
from 
        test_build b,
        test_probe a 
where 
        a.status = b.status
;

select * from table(dbms_xplan.display_cursor(null,null,'projection partition allstats last'))
/

The two tables have a pair of matching rows – so the query should return two rows. But it doesn’t – it returns no rows, and the clue about why not is in the execution plan (which I’ve pulled from memory with lots of extra bits and pieces). Here’s the output from running this script (from the query onwards) on an instance of 12.2.0.1:

no rows selected


PLAN_TABLE_OUTPUT
-----------------------------------------------------------------
SQL_ID  2295z4p6m4557, child number 0
-------------------------------------
select /*+   gather_plan_statistics  */  * from  test_build b,
test_probe a where  a.status = b.status

Plan hash value: 177769189

--------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                | Name       | Starts | E-Rows | Pstart| Pstop | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT         |            |      1 |        |       |       |      0 |00:00:00.01 |       7 |       |       |          |
|*  1 |  HASH JOIN               |            |      1 |   2000K|       |       |      0 |00:00:00.01 |       7 |  2078K|  2078K|  766K (0)|
|   2 |   PART JOIN FILTER CREATE| :BF0000    |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   3 |    TABLE ACCESS FULL     | TEST_BUILD |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   4 |   PARTITION LIST SINGLE  |            |      1 |   2000K|KEY(AP)|KEY(AP)|      0 |00:00:00.01 |       0 |       |       |          |
|   5 |    TABLE ACCESS FULL     | TEST_PROBE |      0 |   2000K|     1 |     1 |      0 |00:00:00.01 |       0 |       |       |          |
--------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("A"."STATUS"="B"."STATUS")

Column Projection Information (identified by operation id):
-----------------------------------------------------------
   1 - (#keys=1) "B"."STATUS"[CHARACTER,2], "A"."STATUS"[CHARACTER,3]
   2 - INTERNAL_FUNCTION("B"."STATUS")[2], INTERNAL_FUNCTION("B"."STATUS")[2], "B"."STATUS"[CHARACTER,2]
   3 - "B"."STATUS"[CHARACTER,2]
   4 - (rowset=256) "A"."STATUS"[CHARACTER,3]
   5 - (rowset=256) "A"."STATUS"[CHARACTER,3]

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

The optimizer has used a Bloom filter to do partition pruning, and while we can see operation 4 reporting a “partition list single” operation using “and pruning” (AP), we can see that operation 5 reports zero starts. This is because the Bloom filter has been used to determine that there are no relevant partitions!

Looking down at the (rarely examined) projection information we can see why – operation 2 (the “part join filter create”) has a strange “Internal Function” in its projection, and references B.STATUS as character[2]. It looks as if the Bloom filter that identifies partitions has been built using a char(2) as the input to its hashing function – which is bad news when the resulting filter is used to check the hash values returned from the partition definition that is hash a char(3).

If my thoughts about the mismatch in how the Bloom filters for the build and probe tables are built then a test that would help to confirm the hypothesis would be disable Bloom filter pruning – which you can only do by setting a hidden parameter, possibly in a hint or SQL Patch):

select 
        /*+ 
                gather_plan_statistics 
                opt_param('_bloom_pruning_enabled','false') 
        */  
        * 
from 
        test_build b,
        test_probe a 
where
        a.status = b.status;

select * from table(dbms_xplan.display_cursor(null,null,'projection partition allstats last'))
/


ST STA
-- ---
00 00
BI BI

2 rows selected.


PLAN_TABLE_OUTPUT
------------------------------------------------------------------------
SQL_ID  9whuurpkm3wpw, child number 0
-------------------------------------
select  /*+   gather_plan_statistics
opt_param('_bloom_pruning_enabled','false')   subquery_pruning(a)  */
* from  test_build b,  test_probe a where  a.status = b.status

Plan hash value: 787868928

------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation              | Name       | Starts | E-Rows | Pstart| Pstop | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |            |      1 |        |       |       |      2 |00:00:00.01 |      45 |       |       |          |
|*  1 |  HASH JOIN             |            |      1 |   2000K|       |       |      2 |00:00:00.01 |      45 |  2078K|  2078K|  866K (0)|
|   2 |   TABLE ACCESS FULL    | TEST_BUILD |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   3 |   PARTITION LIST SINGLE|            |      1 |   2000K|     1 |     1 |      2 |00:00:00.01 |      38 |       |       |          |
|   4 |    TABLE ACCESS FULL   | TEST_PROBE |      1 |   2000K|     1 |     1 |      2 |00:00:00.01 |      38 |       |       |          |
------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("A"."STATUS"="B"."STATUS")

Column Projection Information (identified by operation id):
-----------------------------------------------------------
   1 - (#keys=1) "B"."STATUS"[CHARACTER,2], "A"."STATUS"[CHARACTER,3]
   2 - (rowset=256) "B"."STATUS"[CHARACTER,2]
   3 - (rowset=256) "A"."STATUS"[CHARACTER,3]
   4 - (rowset=256) "A"."STATUS"[CHARACTER,3]

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

Adding the hint opt_param(‘_bloom_pruning_enabled’,’false’) to the query we get the right results and, of course, we can see that there is no operation in the execution plan to generate and use the Bloom filter that is probably causing the problem.

Conclusion

If you are going to use char() types in your tables, and if you are going to compare columns of type char() make sure that the columns are defined to be exactly the same length – or that you include an explicit cast() to guarantee that the shorter column appears to be the same length as the longer column.

Footnote

This bug appeared in my MOS “hot topics”email a couple of days ago as

Bug 27661222: WRONG RESULTS WITH PARTITION PRUNING COMPARING CHAR COLUMNS OF DIFFERENT LENGTH

Reading the bug note the problem is described as a bug in “AND pruning” with a workaround of setting the hidden parameter “_and_pruning_enabled” to false (possibly through the opt_param() hint). I suspect that the underlying problem may be the Bloom filter itself and that disabling Bloom filter pruning for the query may be a slightly less aggressive workaround.

The bug is reported as fixed in 20.1 – but you don’t need to upgrade just yet because, apart from the workarounds, there are various patches available back to 19.x and 12.2.

The sample script above is basically the example in the bug note with a few minor changes.