Another year has passed. I take the opportunity to thank you for visiting and to wish you a Happy New Year 2016!
In case you didn’t recognize: That is supposed to look like fireworks, The Oracle Instructor style ;-)
2015 was a great year for uhesse.com with 345,000+ views and the crossing of the one million hits threshold. Top countries with more than 4,000 views in 2015 were
While creating the lightweight monitoring package, I could see that I could use the same technique for another little project I wanted to create. A fast instance wide countdown, that was not dependent on a table or a sequence. Since I already had the basic framework ready, it was pretty fast to implement.
As mentioned in the first and second part of this instalment the different available distribution methods of the new parallel FILTER are selected automatically by the optimizer - in this last post of this series I want to focus on that optimizer behaviour.It looks like there are two new optimizer related parameters that control the behaviour of the new feature: "_px_filter_parallelized" is the overall switch to enable/disable the new parallel filter capability - and defaults to "true" in 12c, and "_px_filter_skew_handling" influences how the optimizer determines the distribution methods - the parameter naming suggests that it somehow has to do with some kind of "skew" - note that the internal parameter that handles the new
#222222;">A recent post on LinkedIn had this image:
The problem is as old as development. The tester finds a bug but the developer can’t reproduce the bug so closes it as unreproducible.
… but how else can testing show development the bug? Should the developer come over to the testers desk and work on the testers systems while the tester waits?
A variation on Jonathan Lewis's SNAP_MY_STATS package to report the resource consumption of a unit of work between two snapshots. Designed to work under constrained developer environments, this version has enhancements such as time model statistics and the option to report on specific statistics. ***Update*** Now available in two formats: 1) as a PL/SQL package and 2) as a free-standing SQL*Plus script (i.e. no installation/database objects needed). June 2007 (updated November 2015)
I just got back from watching Star Wars: The Force Awakens.
I won’t give any spoilers, so don’t worry if you’ve not seen it yet!
Overall I thought it was a really good film. I went to see it with some friends and their kids, so ages in our group ranged from 6 to 60+. Everyone came out saying it was good, and the kids wanted all the toys and were arguing over which one of the characters they would be… So they pretty much nailed it as far as setting up this trilogy!
This is how an upgrade with pluggable databases looks conceptually:
You have two multitenant databases from different versions in place. Preferably they share the same storage, which allows to do the upgrade without having to move any datafiles
You unplug the pluggable database from the first multitenant database, then you drop it. That is a fast logical operation that does not delete any files
Next step is to plug in the pluggable database into the multitenant database from the higher version
I think the “column group” variant of extended stats is a wonderful addition to the Oracle code base, but there’s a very important detail about using the feature that I hadn’t really noticed until a question came up on the OTN database forum recently about a very bad join cardinality estimate.
The point is this: if you have a multi-column equality join and the optimizer needs some help to get a better estimate of join cardinality then column group statistics may help if you create matching stats at both ends of the join. There is a variation on this directive that helps to explain why I hadn’t noticed it before – multi-column indexes (with exactly the correct columns) have the same effect and, most significantly, the combination of one column group and a matching multi-column index will do the trick.
Previous installment: Day Three: Just a Mess Without a Clue
I know a funny little man,
As quiet as a mouse,
Who does the mischief that is done
In everybody’s house!
There’s no one ever sees his face,
And yet we all agree
That every plate we break was cracked
By Mr. Nobody.