A new major release (version 3.0) of my XPLAN_ASH tool is available for download.
In addition to many changes to the way the information is presented and many other smaller changes to functionality there is one major new feature: XPLAN_ASH now also supports S-ASH, the free ASH implementation.
If you run XPLAN_ASH in a S-ASH repository owner schema, it will automatically detect that and adjust accordingly.
XPLAN_ASH was tested against the latest stable version of S-ASH (2.3). There are some minor changes required to that S-ASH release in order to function properly with XPLAN_ASH. Most of them will be included in the next S-ASH release as they really are only minor and don't influence the general S-ASH functionality at all.
In the previous post I've demonstrated an unexpected Nested Loop Join caused by an extreme data distribution. Although unexpected at first sight, the performance of the execution plan selected by the optimizer is decent - provided the estimates are in the right ballpark.Here is another case of an unexpected execution plan, this time about Merge Joins.
In order to appreciate why the execution plan encountered is unexpected, first a quick summary about how Merge Joins work:A Merge Join is essentially a Nested Loop operation from one sorted row source into another sorted row source.
Recently I came across some interesting edge cases regarding the costing of joins. They all have in common that they result in (at first sight) unexpected execution plans, but only some of them are actual threats to performance.
The first one is about outer joins with an extreme data distribution. Consider the following data setup:
create table t1
rownum as id
, rpad('x', 100) as filler
, case when rownum > 1e6 then rownum end as null_fk
level <= 1e6
exec dbms_stats.gather_table_stats(null, 't1')
create table t2
rownum as id
, rpad('x', 100) as filler
level <= 1e6
When performing aggregate GROUP BY operations an additional filter on the aggregates can be applied using the HAVING clause.Usually aggregates are one of the last steps executed before the final result set is returned to the client.However there are various reasons, why a GROUP BY operation might be somewhere in the middle of the execution plan operation, for example it might be part of a view that cannot be merged (or was hinted not to be merged using the NO_MERGE hint), or in the more recent releases (11g+) the optimizer decided to use the GROUP BY PLACEMENT transformation that deliberately can move the GROUP BY operation to a different execution step of the plan.In such cases, when the GROUP BY operation will be input to some other operation, it becomes essential for the overall efficiency of the execution plan preferred by the optimizer that the cardinality estimates are in the right ballpark, as it will influe
A new version 2.0 of the XPLAN_ASH utility introduced here is available for download.You can download the latest version here.The change log tracks the following changes:- Access check- Conditional compilation for different database versions- Additional activity summary- Concurrent activity information (what is/was going on at the same time)- Experimental stuff: Additional I/O summary- More pretty printing- Experimental stuff: I/O added to Average Active Session Graph (renamed to Activity Timeline)- Top Execution Plan Lines and Top Activities added to Activity Timeline- Activity Timeline is now also shown for serial execution when TIMELINE option is specified- From 126.96.36.199 on: We get the ACTUAL DOP from the undocumented PX_FLAGS colu
Here is an odd bug that can lead to some nasty side effects when using the EXCHANGE PARTITION technique. It is probably there for a very long time, simply because it depends on the usage of virtual columns, and the basic technique of virtual columns was introduced way back in the Oracle 8i times with the introduction of Function Based Indexes.
The problem isn't the exchange partition operation itself, but the accompanying swap of object statistics information, in particular the column statistics.
Look the following sequence of DDL and DML commands and pay then special attention to the output for the column statistics before and after the EXCHANGE PARTITION operation:
Note: This blog post actually serves three purposes:
Usually the Cost-Based Optimizer arrives at a reasonable execution plan if it gets the estimates regarding cardinality and data scattering / clustering right (if you want to learn more about that why not watch my Webinar available at "AllThingsOracle.com"?).
Here is an example I've recently come across where this wasn't case - the optimizer obviously preferred plans with a significantly higher cost.
The setup to reproduce the issue is simple:
Oracle 11g added Extended Statistics support for column groups in order to detect correlated columns for filter predicates using an equal comparison.
Note that Oracle 11g also added the ability to use the number of distinct keys of a composite index as an upper limit for the cardinality estimates for matching column predicates, which means that the optimizer is now capable of detecting correlated columns without the explicit addition of Extended Statistics / Column Groups.
Oracle 11.2 introduced a set of new Query Transformations, among others the ability to coalesce subqueries which means that multiple correlated subqueries can be merged into a number of less subqueries.
Timur Akhmadeev already demonstrated the basic principles in a blog entry, but when I was recently involved into supporting a TPC-H benchmark for a particular storage vendor I saw a quite impressive application of this optimization that I would like to share here.