SQL and PL/SQL Articles

virtual columns in 11g


virtual columns in 11g
Oracle has supported stored expressions for many years, in views and function-based indexes. Most commonly, views enable us to store and modularise computations and expressions based on their underlying tables' columns. In more recent versions (since around the 8i timeframe), we have been able to index expressions using function-based indexes. Now, with the release of 11g, Oracle enables us to store expressions directly in the base tables themselves as virtual columns.

Finding islands – 4 methods in Oracle


Finding islands are classic problems in PL/SQL. The basic concept is that you have some sort of numbers, like these: 1, 2, 3, 5, 6, 8, 9, 10, 15, 20, 21, 22, 23, 25, 26. The islands problem involves identifying ranges of existing values. For these numbers, the solution will be as follows:
first_island_element last_island_element
1 3
5 6
8 10
15 15
20 23
25 26

Finding gaps with analytic functions


Finding gaps is classic problem in PL/SQL. The basic concept is that you have some sort of numbers (like these: 1, 2, 3, 5, 6, 8, 9, 10, 15, 20, 21, 22, 23, 25, 26), where there’s supposed to be a fixed interval between the entries, but some entries could be missing. The gaps problem involves identifying the ranges of missing values in the sequence. For these numbers, the solution will be as follows:
4 4
7 7
11 14
16 19
24 24

First, run the following code, to create tab1 table:


The Bitmap Conspiracy


(with apologies to Robert Ludlum and Eric Van Lustbader)

The Bitmap Betrayal (Introduction)

Oracle performance tuning is an excellent source of myths. The very best ones have a group of adherents who continue to support the myth even when presented with counter-examples. Who’s heard of these?

  • Joins are faster than sub-queries
  • Sub-queries are faster than joins
  • Full Table Scans are bad

Those ones have been around as long as I can remember. Probably the single greatest concentration of Oracle performance tuning myths centres on Bitmap Indexes. Are these familiar?

  • Bitmap indexes are good for low-cardinality columns, whereas B-Tree indexes are good for high-cardinality columns.
  • Bitmap indexes are slow to update.
  • Bitmap indexes don't support concurrent updates.

Part 1 - The Bitmap Identity


What is a Bitmap Index?

This is first post of the four-part epic - The Bitmap Conspiracy - detailing the structure and behaviour of Bitmap Indexes. Later in the series we will cover the internal structure of Bitmap Indexes, how Oracle uses them, and finally we will expose some of the myths surrounding them. But before we get there let’s just get a clear understanding of what a Bitmap Index actually is.

Part 2 - The Bitmap Supremacy


The Structure of a Bitmap Index

I’ve been tuning Oracle database applications for a long time now. I started out recognising some simple patterns and applying template fixes (Got a full table scan? Use an index!) but such a collection of “Do this; don’t do that” anecdotes will only take you so far. If you are curious (I was), you can uncover the reasons why one method is faster than another; i.e. what is the computer doing to make slow code so slow. I found that a good understanding of the internals meant that you didn’t always need to know how to tune a specific example because you could work it out for yourself.

In a database application, these investigations frequently lead to data structures; how does the database store its information and how does it retrieve it? Good information on the internals of Bitmap Indexes is hard to piece together, so in Part 2 of this Bitmap Indexing epic we will look more closely at the internals of Bitmap indexes.

Part 3 - The Bitmap Dominion


Bitmap Execution Plans

This is Part 3 of The Bitmap Conspiracy, a four part epic on Bitmap Indexes.

In Part 1 we touched briefly on how Oracle can use Bitmap Indexes to resolve queries by translating equality and range predicates into bitmap retrievals. Now that we know more about how they are stored (see Part 2), let’s look closer at some of the operations that Oracle uses to access Bitmap Indexes and manipulate bitmaps.

Deterministic function vs scalar subquery caching. Part 1


I recently did a comparison caching mechanisms of scalar subquery caching(SSC) and deterministic functions in 11.2. Unfortunately, I do not have enough time to do a full analysis, so I will post it in parts.

Just another version of Tom Kyte’s runstats (runstats_pkg)


I want to share my modifications of Tom Kyte's runstats package, which include:

  • Any number of runs sets for analyzing

  • Standalone: No need to create other objects

  • Ability to specify session SID for statistics gathering

  • Ability to specify what to gather: latches, stats or both

  • Separate mask filters for output by statname and latchname

  • Ability to specify difference percentage for output separately for latches and statistics


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