10gR2

The basic formula for calculating the costs of a Nested Loop Join is pretty straightforward and has been described and published several times.

In principle it is the cost of acquiring the driving row source plus the cost of acquiring the inner row source of the Nested Loop as many times as the driving row source dictates via the cardinality of the driving row source.

Cost (outer rowsource) + Cost (inner rowsource) * Card (outer rowsource)

Obviously there are cases where Oracle has introduced refinements to the above formula where this is no longer true. Here is one of these cases that is probably not uncommon.

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.

Recently I used the COMMIT_WAIT and COMMIT_LOGGING parameters for solving (or, better, working around) a problem I faced while optimizing a specific task for one of my customers. Since it was the first time I used them in a production system, I thought to write this post not only to shortly explain the purpose of the these two parameters, but also to show a case where it is sensible to use them.

The purpose of the two parameters is the following:

COMMIT_WAIT

Recently I had to analyse a row lock contention problem that can be illustrated by the following test case:

Most execution plans can be interpreted by following few basic rules (in TOP, Chapter 6, I provide such a list of rules). Nevertheless, there are some special cases. One of them is when an index scan, in addition to the access predicate, has a filter predicate applying a subquery.

The following execution plan, taken from Enterprise Manager 11.2, is an example (click on the image to increase its size):

Notes:

Introduction

Another random note that I made during the sessions attended at OOW was about the SQL*Plus AUTOTRACE feature. As you're hopefully already aware of this feature has some significant shortcomings, the most obvious being that it doesn't pull the actual execution plan from the Shared Pool after executing the statement but simply runs an EXPLAIN PLAN on the SQL text which might produce an execution plan that is different from the actual one for various reasons.

Now the claim was made that in addition to these shortcomings the plan generated by the AUTOTRACE feature will stay in the Shared Pool and is eligible for sharing, which would mean that other statement executions could be affected by a potentially bad execution plan generated via AUTOTRACE rather then getting re-optimized on their own.

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.

Introduction

So this will be my Oracle related Christmas present for you: A prototype implementation that extends the DBMS_XPLAN.DISPLAY_CURSOR output making it hopefully more meaningful and easier to interpret. It is a simple standalone SQL*Plus script with the main functionality performed by a single SQL query. I've demoed this also during my recent "optimizer hacking sessions".

DBMS_XPLAN.DISPLAY_CURSOR together with the Rowsource Statistics feature (enabled via SQL_TRACE, GATHER_PLAN_STATISTICS hint, STATISTICS_LEVEL set to ALL or controlled via the corresponding hidden parameters "_rowsource_execution_statistics" and "_rowsource_statistics_sampfreq") allows since Oracle 10g a sophisticated analysis of the work performed by a single SQL statement.

Recently I was involved in a project where I had to trace the database calls of an application based on Oracle Portal 10.1.4. The basic requirements were the following:

• Tracing takes place in the production environment
• Tracing has to be enable for a single user only
• Instrumentation code cannot be added to the application

Given that Oracle Portal uses a pool of connections and that for each HTTP call it can use several database sessions, statically enable SQL trace for specific sessions was not an option.

At page 189 of TOP I wrote the following piece of text:

In summary, with the initialization parameter optimizer_secure_view_merging set to TRUE, the query optimizer checks whether view merging could lead to security issues. If this is the case, no view merging will be performed, and performance could be suboptimal as a result. For this reason, if you are not using views for security purposes, it is better to set this initialization parameter to FALSE.

What I didn’t consider when I wrote it, it is the implication of predicate move-around related to Virtual Private Database (VPD). In fact, as described in the documentation, that parameter controls view merging as well as predicate move-around.