Common internet question: does the order of predicates in the where clause make a difference.
General answer: It shouldn’t, but sometimes it will thanks to defects in the optimizer.
There’s a nicely presented example on the OTN database forum where predicate order does matter (between 10.1.x.x and 18.104.22.168). Note particularly – there’s a script to recreate the issue; note, also, the significance of the predicate section of the execution plan.
It’s bug 6782665, fixed in 22.214.171.124
Recently appeared on Mos – “Bug 18219084 : DIFFERENT EXECUTION PLAN ACROSS RAC INSTANCES”
Now, I’m not going to claim that the following applies to this particular case – but it’s perfectly reasonable to expect to see different plans for the same query on RAC, and it’s perfectly possible for the two different plans to have amazingly different performance characteristics; and in this particular case I can see an obvious reason why the two nodes could have different plans.
Here’s the query reported in the bug:
Here’s a simple little demonstration of an enhancement to the optimizer in 12c that may result in some interesting changes in execution plans as cardinality estimates change from “guesses” to accurate estimates.
Here’s a little script to demonstrate an observation about a missed opportunity for avoiding work that appeared in my email this morning (that’s morning Denver time):
Here’s a recent request from the OTN database forum – how do you make this query go faster (tkprof output supplied):
from a, b
where A.MARK IS NULL
and a.cntry_code = b.cntry_code and b.dir_code='XX' and b.numb_type='XXX'
and upper(Trim(replace(replace(replace(replace(replace(replace(replace(a.co_name,'*'),'&'),'-'),'/'),')'),'('),' '))) like
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:
WHERE ( LENGTH ( :b7) IS NULL OR
UPPER (TRIM (CODFSC)) = UPPER (TRIM ( :b8)) or
UPPER (TRIM (CODUIC)) = UPPER (TRIM ( :b9)))
AND STATE = 0;
The three bind variables all hold the same value; there is a function-based index on upper(trim(codfsc)), and another on upper(trim(coduic)). The execution plan for this query is a full tablescan, but if you eliminate the first predicate Oracle can do a concatenation of two index range scans. This variation doesn’t surprise me, the optimizer’s ability to introduce concatenation is limited; however, I did wonder whether some small variation in the SQL would allow the optimizer to get just a little more clever.
Would you get concatenation if you changed the first predicate to (:b7 is null); if not, would a similar query that didn’t depend on function-based indexes do concatenation; if not is there any rewrite of this query that could do a tablescan ONLY for the case where :b7 was null ?
Demonstrations of any levels of success can be left in the comments if anyone’s interested. To get a fixed font that preserves space start the code with “sourcecode” and end with “/sourcecode” (removing the quotation marks and replacing them with square brackets).
Here’s a little oddity I came across in 126.96.36.199 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.
How not to write subqueries:
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)
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:
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:
Here’s the output I got from a 10.2.0.5 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:
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 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 188.8.131.52. 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:
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 from the previous post, and the query (with serial execution plan) that produced it. 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.