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performance

SQL vs. PL/SQL

Which piece of code will be faster (clue – the table in question has no indexes):

Option 1 – pure SQL


update join1 set
        data = data||'#'
where   key_1=500
and     key_2=23851
and     key_3=57012
and     key_4=521
and     key_6=1
and     key_7=23352
;

Option 2 – a silly PL/SQL row by row approach:

Investigating the full table direct path / buffered decision.

A lot of blogposts and other internet publications have been written on the full segment scan behaviour of a serial process starting from Oracle version 11gR2. This behaviour is the Oracle engine making a decision between scanning the blocks of a segment into the Oracle buffercache or scanning these blocks into the process’ private process global area (PGA). This decision is even more important on the Exadata platform, because the Oracle engine must have made the decision to read the blocks into the process’ PGA in order to be able to do a smartscan. This means that if you are on Oracle 11gR2 already, and thinking about using the Exadata platform, the wait event ‘direct path read’ gives you an indication on how much potentially could be offloaded on Exadata, if you keep all the settings the same.

With Modern Storage the Oracle Buffer Cache is Not So Important.

With Oracle’s move towards engineered systems we all know that “more” is being done down at the storage layer and modern storage arrays have hundreds of spindles and massive caches. Does it really matter if data is kept in the Database Buffer Cache anymore?

Yes. Yes it does.

Time for a cool beer

Time for a cool beer

With much larger data sets and the still-real issue of less disk spindles per GB of data, the Oracle database buffer cache is not so important as it was. It is even more important.

If you use swingbench take note of sbutil

This is going to be a very short post for a change. I have used Swingbench extensively and really love the tool. Many thanks to Dominic Giles!

Recently he announced a new tool on his blog that you can use to inflate your data volume. So instead of using the “-scale” argument when executing oewizard you can just keep the defaults and later on create as much data as you like. Here is an example, the reason for this post.

Setting the Scene

Additional information on Oracle 12c big table caching

Teaching is on the things I like doing, and currently I am investigating the Oracle 12c features around caching data in the various memory areas. Since the In-Memory (cost) option has been discussed by other far more knowledgeable people I would like to share some findings about the big table caching here.

Some Background

In Oracle 12c you have two additional options to cache information: full database caching and big table caching. The first is great if you have a massively big machine with lots and lots of DRAM plus a clever OS that can deal with the inevitable ccNUMA setup you will have to tackle. And maybe don’t want to pay for the In-Memory option. This post is not about full database caching, but rather about the other possibility to cache blocks.

Efficiency

Here’s a question to which I don’t know the answer, and for which I don’t think anyone is likely to need the answer; but it might be an entertaining little puzzle for thr curious.

Assume you’re doing a full tablescan against a simple heap table of no more than 255 columns (and not using RAC, Exadata, In-memory, etc. etc. etc.), and the query is something like:


select  {columns 200 to 250}
from    t1
where   column_255 = {constant}
;

To test the predicate Oracle has to count its way along each row column by column to find column 255. Will it:

Exadata Fast Data File Creation

This post is the result of some testing I performed on Exadata data file creation. You may know that Exadata offloading incorporates SQL optimisations as well as some infrastructure work. For quite some time Exadata allowed the DBA to create data files a lot quicker than on traditional systems. This has been documented before, for example by @mpnsh here

Cartesian join

Some time ago I pulled off the apocryphal “from 2 hours to 10 seconds” trick for a client using a technique that is conceptually very simple but, like my example from last week, falls outside the pattern of generic SQL. The problem (with some camouflage) is as follows: we have a data set with 8 “type” attributes which are all mandatory columns. We have a “types” table with the same 8 columns together with two more columns that are used to translate a combination of attributes into a specific category and “level of relevance”. The “type” columns in the types table are, however, allowed to be null although each row must have at least one column that is not null – i.e. there is no row where every “type” column is null.

Not Exists

The following requirement appeared recently on OTN:

Counting

There’s a live example on OTN at the moment of an interesting class of problem that can require some imaginative thinking. It revolves around a design that uses a row in one table to hold the low and high values for a range of values in another table. The problem is then simply to count the number of rows in the second table that fall into the range given by the first table. There’s an obvious query you can write (a join with inequality) but if you have to join each row in the first table to several million rows in the second table, then aggregate to count them, that’s an expensive strategy.  Here’s the query (with numbers of rows involved) that showed up on OTN; it’s an insert statement, and the problem is that it takes 7 hours to insert 37,600 rows: