The inverted table format can deliver fast and flexible query capabilities, but is not widely used. ADABAS is probably the most successful implementation, but how often do you see that nowadays? Following is a description of how to implement inverted structures within a relational database. All code run on Oracle Database 12c, release 184.108.40.206.
There are three restrictions on indexing and partitioning: a unique index cannot be local non-prefixed; a global non-prefixed index is not possible; a bitmap index cannot be global. Why these limitations? I suspect that they are there to prevent us from doing something idiotic.
I see posts on Oracle related forums about various releases (anything that isn't 11.x or 12.x) being "unsupported". This is wrong. Of course you should upgrade any 9i or 10g databases, but you don't have to.
In this post, I will demonstrate a new feature introduced in 12c : In database archiving. It enables you to archive rows within a table by marking them as invisible. This is accomplshed by means of a hidden column ORA_ARCHIVE_STATE. These invisible rows are not visible to the queries but if needed, can be viewed , by setting a session parameter ROW ARCHIVAL VISIBILITY.
-- Create test user uilm, tablespace ilmtbs
-- Connect as user uilm
-- create and populate test table (5 rows) ilmtab with row archival clause
When you create a PDB, the database automatically creates and starts a service inside the CDB.The service has the same name as the PDB. It is possible that the name of the service will collide with an existing service name which is registered with the same listener. For example if two or more CDBs on the same computer system use the same listener, and the newly established PDB has the same service name as another PDB in these CDBs, then a collision occurs.
You must not attempt to operate a PDB that causes a collision with an existing service name.
Top 12 Features of Oracle 12C Summarized. This article is also available at www.ondemanddb.com
Summary: The Oracle 12C means different things to different people. It all depends on which areas you are looking at, as there are improvements in many areas. Summarized below is the list of Top 12 Features of Oracle 12C as I see it. I have summarized below, the top 12 which I found interesting.
Oracle is doing every thing to jump into the cloud bandwagon.
(with apologies to Robert Ludlum and Eric Van Lustbader)
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.
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.
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.
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.