Execution plans

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Here’s a recent request from the OTN database forum – how do you make this query go faster (tkprof output supplied):

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Conditional SQL – 4

This is one of those posts where the investigation is left as an exercise – it’s not difficult, just something that will take a little time that I don’t have, and just might end up with me chasing half a dozen variations (so I’d rather not get sucked into looking too closely). It comes from an OTN question which ends up reporting this predicate:

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Unnest Oddity

Here’s a little oddity I came across in a few days ago – don’t worry too much about what the query is trying to do, or why it has been written the way I’ve done it, the only point I want to make is that I’ve got the same plan from two different strategies (according to the baseline/outline/hints), but the plans have a difference in cost.

Here’s the definition of my table and index, with query and execution plan:

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Parallel Execution – 3

It’s finally time to take a close look at the parallel versions of the execution plan I produced a little while ago for a four-table hash join. In this note I’ll examine the broadcast parallel distribution. First, here’s a list of the hints I’m going to use to get the effect I want:

		leading(t4 t1 t2 t3)
		full(t4) parallel(t4, 2)
		use_hash(t1) swap_join_inputs(t1) pq_distribute(t1 none broadcast)
		full(t1) parallel(t1, 2)
		use_hash(t2) swap_join_inputs(t2) pq_distribute(t2 none broadcast)
		full(t2) parallel(t2, 2)
		use_hash(t3) swap_join_inputs(t3) pq_distribute(t3 none broadcast)
		full(t3) parallel(t3, 2)

If you compare this set of hints to the hints I used for the serial plan you’ll see that the change involves two features:

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12c Subqueries

When you upgrade you often find that some little detail (of the optimizer) that didn’t receive a lot of attention in the “New Features” manuals introduces a few dramatic changes in execution plans. Here’s one example of a detail that is likely to catch a few unlucky people. We start with a very simple table which is just and id column with some padding, and then show the effect of a change in the handling of “constant subqueries”. Here’s my data set:

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Interesting Plan

A recent question on the OTN database forum included an execution plan that prompted one reader to ask: “but where has the existence subquery gone?” Here’s the original question showing the query, and here’s the later response showing the plan that prompted the question.

There were three possible reasons why that question may have been posed:

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Outline hassle

Here’s the output I got from a system after generating a stored outline on a query – then dropping the index that was referenced by the stored outline and creating an alternative index. Spot the problem:

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Not Pushing

Here’s an odd little optimizer glitch – probably irrelevant to most people, but an indication of the apparent randomness that appears as you combine features. I’ve created an example which is so tiny that the only explanation I can come up with the for optimizer not “behaving properly” is that I’ve found an undocumented restriction relating to a particular feature.

Here’s the basic schema structure with query and execution plan – there’s nothing particularly significant about the object definitions – they’re just a couple of (reduced) structures from a client site I visited a few years ago:

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Deferrable RI

Here’s a lovely little example that just came up on the OTN database forum of how things break when features collide. It’s a bug (I haven’t looked for the number) that seems to be fixed in All it takes is a deferrable foreign key and an outer join. I’ve changed the table and column names from the original, and limited the deferability to just the foreign key:

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Parallel Execution – 2

Since I’m going to write a couple of articles dissecting parallel execution plans, I thought I’d put up a reference post describing the set of tables I used to generate the plan, and the query (with serial execution plan) that I’ll be looking at. The setup is a simple star schema arrangement – which I’ve generated by created by creating three identical tables and then doing a Cartesian join across the three of them.

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