Oracle

tanelpoder's picture

Advanced Oracle Troubleshooting Guide – Part 12: control file reads causing enq: SQ – contention waits?

Vishal Desai systematically troubleshooted an interesting case where the initial symptoms of the problem showed a spike of enq: SQ – contention waits, but he dug deeper – and found the root cause to be quite different. He followed the blockers of waiting sessions manually to reach the root cause – and also used my @ash/ash_wait_chains.sql and @ash/event_hist.sql scripts to extract the same information more conveniently (note that he had modified the scripts to take AWR snap_ids as time range parameters instead of the usual date/timestamp):

marco's picture

Database landscape 2014 visualization

I saw this database landscape 2014 overview from “451 Research” with an very nice visualization…

Jonathan Lewis's picture

Golden Oldies

I’ve just been motivated to resurrect a couple of articles I wrote for DBAZine about 12 years ago on the topic of bitmap indexes. All three links point to Word 97 documents which I posted on my old website in September 2003. Despite their age they’re still surprisingly good.

Jonathan Lewis's picture

Manuals

From time to time I read a question (or, worse, an answer) on OTN and wonder how someone could have managed to misunderstand some fundamental feature of Oracle – and then, as I keep telling people everyone should do – I re-read the manuals and realise that that sometimes the manuals make it really easy to come to the wrong conclusion.

Having nothing exciting to do on the plane to Bucharest today, I decided it was time to read the Concepts manual again – 12c version – to remind myself of how much I’ve forgotten. Since I was reading the mobi version on an iPad mini I can’t quote page numbers, but at “location 9913 of 16157″ I found the following text in a sidebar:

“LGWR can write redo log entries to disk before a transaction commits. The redo entries become permanent only if the transaction later commits.”

oraclealchemist's picture

Real Push Button Refresh with Raspberry Pi

Push Button

In this post, we’re going to perform a push button refresh of an Oracle Database, Application Express (APEX) installation, and Tomcat webserver.

“But Oracle Alchemist,” you’re probably thinking, “we know about that. You’ve told us about how Delphix can provision and refresh data.” And yes, you’d be right. But I wasn’t done yet.

We’re going to perform a refresh of an Oracle Database, APEX installation, and Tomcat running in Amazon Web Services, replicated from a local Delphix Engine, by pressing a physical button wired to a Raspberry Pi running a python app that communicates with the Delphix REST API in the cloud over wifi.

Jonathan Lewis's picture

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.

Jonathan Lewis's picture

Not Exists

The following requirement appeared recently on OTN:

Jonathan Lewis's picture

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:

fritshoogland's picture

Hey dude, where’s my memory? part 2

In my previous article I started exploring the memory usage of a process on a recent linux kernel (2.6.39-400.243.1 (UEK2)), recent means “recent for the Enterprise Linux distributions” in this context, linux kernel developers would point out that the kernel itself is at version 3.19 (“stable version” at the time of writing of this blogpost).

Jonathan Lewis's picture

Not In CTAS

Everyone gets caught out some of the time with NOT IN.

NOT IN is not the opposite of IN.

This came up in a (fairly typical) question on OTN recently where someone had the task of “deleting 6M rows from a table of 18M”. A common, and perfectly reasonable, suggestion for dealing with a delete on this scale is to consider creating a replacement table holding the data you do want rather than deleting the data you don’t want.  In this case, however, the query for deleting the data looked like this:


DELETE FROM EI.CASESTATUS
     WHERE CASEID NOT IN (SELECT CASEID FROM DO.STG_CASEHEADER);

The suggested code for creating the kept data was this:

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