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I wrote a short note a little while ago demonstrating how flexible Oracle 12.2 can be about physically rebuilding a table online to introduce or change the partitioning while discarding data, and so on.  But what do you do (as a recent question on ODC asked) if you want to upgrade a customer’s database to meet the requirements of a new release of your application by changing a partitioned table into a composite partitioned table and don’t have enough room to do an online rebuild. Which could require two copies of the data to exist at the same time.)

If you’ve got the down time (and not necessarily a lot is needed) you can fall back on “traditional methods” with some 12c enhancements. Let’s start with a range partitioned table:

Danger – Hints

It shouldn’t be possible to get the wrong results by using a hint – but hints are dangerous and the threat may be there if you don’t know exactly what a hint is supposed to do (and don’t check very carefully what has happened when you’ve used one that you’re not familiar with).

This post was inspired by a blog note from Connor McDonald titled “Being Generous to the Optimizer”. In his note Connor gives an example where the use of “flexible” SQL results in an execution plan that is always expensive to run when a more complex version of the query could produce a “conditional” plan which could be efficient some of the time and would be expensive only when there was no alternative. In his example he rewrote the first query below to produce the second query:

Stats advisor

This is just a little shout-out about the Stats Advisor – if you decide to give it a go, what sort of things is it likely to tell you. The answer is in a dynamic performance view called v$stats_advisor_rules – which I’ve list below from an instance running

12c Snapshots

I published a note a few years ago about using the 12c “with function” mechanism for writing simple SQL statements to takes deltas of dynamic performance views. The example I supplied was for v$event_histogram but I’ve just been prompted by a question on ODC to supply a couple more – v$session_event and v$sesstat (joined to v$statname) so that you can use one session to get an idea of the work done and time spent by another session – the first script reports wait time:


The title is the name of an Oracle hint that came into existence in Oracle and made an appearance recently in a question on the rarely used “My Oracle Support” Community forum (you’ll need a MOS account to be able to read the original). I wouldn’t have found it but the author also emailed me the link asking if I could take a look at it.  (If you want to ask me for help – without paying me, that is – then posting a public question in the Oracle (ODC) General Database or SQL forums and emailing me a private link is the strategy most likely to get an answer, by the way.)

The question was about a very simple query using a straightforward index – with a quirky change of plan after upgrading from to Setting the optimizer_features_enable to ‘’ in the system re-introduced the 10g execution plan. Here’s the query:

Upgrades – again

I’ve got a data set which I’ve recreated in and

I’ve generated stats on the data set, and the stats are identical.

I don’t have any indexes or extended stats, or SQL Plan directives or SQL Plan Profiles, or SQL Plan Baselines, or SQL Patches to worry about.

I’m joining two tables, and the join column on one table has a frequency histogram while the join column on the other table has a height-balanced histogram.  The histograms were created with estimate_percent => 100%. (which explains why I’ve got a height-balanced histogram in 12c rather than a hybrid histogram.)

Here are the two execution plans, first, pulled from memory by dbms_xplan.display_cursor():

Upgrade threat

Here’s one I’ve just discovered while trying to build a reproducible test case – that didn’t reproduce because an internal algorithm has changed.

If you upgrade from 12c to 18c and have a number of hybrid histograms in place you may find that some execution plans change because of a change in the algorithm for producing hybrid histograms (and that’s not just if you happen to get the patch that fixes the top-frequency/hybrid bug relating to high values).

Here’s a little test to demonstrate how I wasted a couple of hours trying to solve the wrong problem – first a simple data set:

Hybrid Fake

Oracle 12c introduced the “Hybrid” histogram – a nice addition to the available options and one that (ignoring the bug for which a patch has been created) supplies the optimizer with better information about the data than the equivalent height-balanced histogram. There is still a problem, though, in the trade-off between accuracy and speed: just as it does with height-balanced histograms when using auto_sample_size Oracle samples (typically) about 5,500 rows to create a hybrid histogram, and the SQL it uses to generate the necessary summary is essentially an aggregation of the sample, so either you have a small sample with the risk of lower accuracy or a large sample with an increase in workload.

Case Study

A question about reading execution plans and optimising queries arrived on the ODC database forum a little while ago; the owner says the following statement is taking 14 minutes to return 30,000 rows and wants some help understanding why.

If you look at the original posting you’ll see that we’ve been given the text of the query and the execution plan including rowsource execution stats. There’s an inconsistency between the supplied information and the question asked, and I’ll get back to that shortly, but to keep this note fairly short I’ve excluded the 2nd half of the query (which is a UNION ALL) because the plan says the first part of the query took 13 minutes and 20 second and the user is worried about a total of 14 minutes.

Hacking for Skew

In my presentation to the UKOUG SIG yesterday “Struggling with Statistics – part 2” I described a problem that I wrote about a few months ago: when you join a fact table with a massively skewed distribution on one of the surrogate key columns to a dimension holding the unique list of keys and descriptions a query against a description “loses” the skew. Here’s an demo of the problem that’s a little simpler than the one in the previous article.