When the optimizer has to estimate the data volume (the BYTES column in the plan output), it usually bases this information on the column statistics, if applicable and available (think of complex expressions).However, whenever there is a VIEW operator in an execution plan, that represents an unmerged view, the optimizer obviously "loses" this information and starts applying defaults that are based on the column definition.Depending on the actual content of the columns this can lead to dramatic differences in data volume estimates.Both, under- and overestimates are possible, because for character based columns these defaults seem to be based on an assumed 50% fill grade, so a VARCHAR2(100 BYTE) column counts as 50 bytes data volume.For multi-byte character sets the same rule applies based on the maximum width of a column using the "char" semantics, so a VARCHAR2(1000 CHAR) column counts as 2000 byte
The TIMESTAMP WITH TIME ZONE data type that got introduced a long time ago is known for some oddities, for example Tony Hasler has a nice summary of some of them here.Here is another oddity that shows up when trying to aggregate on such a data type. Have a look at the following simple example:
create table t
rownum as id
, date '2000-01-01' + rownum - 1 as some_date
, cast(date '2000-01-01' + rownum - 1 as timestamp) as some_timestamp
, cast(date '2000-01-01' + rownum - 1 as timestamp with local time zone) as some_timestamp_with_local_tz
, cast(date '2000-01-01' + rownum - 1 as timestamp with time zone) as some_timestamp_with_timezone
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.
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
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 184.108.40.206 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:
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.
In a recent OTN thread I've been reminded of two facts about Dynamic Sampling that I already knew but had forgotten in the meantime:
1. The table level dynamic sampling hint uses a different number of blocks for sampling than the session / cursor level dynamic sampling. So even if for both for example level 5 gets used the number of sampled blocks will be different for most of the 10 levels available (obviously level 0 and 10 are exceptions)
2. The Dynamic Sampling code uses a different approach for partitioned objects if it is faced with the situation that there are more partitions than blocks to sample according to the level (and type table/cursor/session) of Dynamic Sampling
Note that all this here applies to the case where no statistics have been gathered for the table - I don't cover the case when Dynamic Sampling gets used on top of existing statistics.
In the initial part of this series I've explained some details regarding logical I/O using a Nested Loop Join as example.
To recap I've shown in particular:
- Oracle can re-visit pinned buffers without performing logical I/O
- There are different variants of consistent gets - a "normal" one involving buffer pin/unpin cycles requiring two latch acquisitions and a short-cut variant that visits the buffer while holding the corresponding "cache buffers chains" child latch ("examination") and therefore only requiring a single latch acquisition
- Although two statements use a similar execution plan and produce the same number of logical I/Os one is significantly faster and scales better than the other one