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CBO

FIRST_ROWS_10 CBO Is Hopeless, It’s Using The Wrong Index !! (Weeping Wall)

There’s an organisation I had been dealing with on and off over the years who were having all sorts of issues with their Siebel System and who were totally convinced their performance issues were due directly to being forced to use the FIRST_ROWS_10 optimizer. I’ve attempted on a number of occasions to explain that their […]

Join Cardinality – 5

So far in this series I’ve written about the way that the optimizer estimates cardinality for an equijoin where one end of the join has a frequency histogram and the other end has a histogram of type:

Join Cardinality – 4

In previous installments of this series I’ve been describing how Oracle estimates the join cardinality for single column joins with equality where the columns have histograms defined. So far I’ve  covered two options for the types of histogram involved: frequency to frequency, and frequency to top-frequency. Today it’s time to examine frequency to hybrid.

My first thought about this combination was that it was likely to be very similar to frequency to top-frequency because a hybrid histogram has a list of values with “repeat counts” (which is rather like a simple frequency histogram), and a set of buckets with variable sizes that could allow us to work out an “average selectivity” of the rest of the data.

Column Groups

Sometimes a good thing becomes at bad thing when you hit some sort of special case – today’s post is an example of this that came up on the Oracle-L listserver a couple of years ago with a question about what the optimizer was doing. I’ll set the scene by creating some data to reproduce the problem:

Join Cardinality – 2

In the previous note I posted about Join Cardinality I described a method for calculating the figure that the optimizer would give for the special case where you had a query that:

Join Cardinality

Following up my “Hacking for Skew” article from a couple of days ago, Chinar Aliyev has written an article about a method for persuading the optimizer to calculate the correct cardinality estimate without using any undocumented, or otherwise dubious, mechanisms.

Column Group Catalog

I seem to have written a number of aricles about column groups – the rather special, and most useful, variant on extended stats. To make it as easy as possible to find the right article I’ve decided to produce a little catalogue (catalog) of all the relevant articles, with a little note about the topic each article covers. Some of the articles will link to others in the list, and there are a few items in the list from other blogs. There are also a few items which are the titles of drafts which have been hanging around for the last few years.

MERGE JOIN CARTESIAN: a join method or a join type?

By Franck Pachot

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I’ll present about join methods at POUG and DOAG. I’ll show how the different join methods work in order to better understand them. The idea is to show Nested Loops, Hash Join, Sort Merge Join, Merge Join Cartesian on the same query. I’ll run a simple join between DEPT and EMP with the USE_NL, USE_HASH, USE_MERGE and USE_MERGE_CARTESIAN hints. I’ll show the execution plan, with SQL Monitoring in text mode. And I’ll put some gdb breakpoints on the ‘qer’ (query execution rowsource) functions to run the plan operations step by step. Then I’ll do the same on a different query in order to show in detail the 12c adaptive plans.

Extended Histograms

Today’s little puzzle comes courtesy of the Oracle-L mailing list. A table has two columns (c2 and c3), which contain only the values ‘Y’ and ‘N’, with the following distribution:


select   c2, c3, count(*)
from     t1
group by c2, c3
;

C C   COUNT(*)
- - ----------
N Y       1994
Y N      71482

2 rows selected.

The puzzle is this – how do you get the optimizer to predict a cardinality of zero (or, using its best approximation, 1) if you execute a query where the predicate is:

where   c2 = 'N' and c3 = 'N'

Here are 4 tests you might try:

Rebuilding Indexes: Danger With Clustering Factor Calculation (Chilly Down)

Let me start by saying if you don’t already following Jonathan Lewis’s excellent Oracle blog, do yourself a favour. In a recent article, Jonathan highlighted a danger with rebuilding indexes (or indeed creating an index) when used in relation to collecting index statistics with the TABLE_CACHED_BLOCKS preference. I’ve discussed the importance of the TABLE_CACHED_BLOCKS statistics […]