Optimizer performance slowdown from 10.1 to 10.2
------------------------------------------------
Key: DERBY-2130
URL: http://issues.apache.org/jira/browse/DERBY-2130
Project: Derby
Issue Type: Bug
Components: SQL, Performance
Affects Versions: 10.2.1.6, 10.3.0.0, 10.1.3.1
Reporter: Bryan Pendleton
Attachments: repro.sql
Attached is 'repro.sql', an IJ script which demonstrates what I
believe to be a serious performance issue in the Optimizer.
I have run this script in a number of configurations:
- 10.1.2.1: the script runs successfully. The 'prepare' statement
takes about 90 seconds, on a fairly powerful Windows machine
- 10.1.3.1: the script produces a NPE. I believe this is DERBY-1777
- 10.2.1.8/trunk: the script runs successfully. The 'prepare' statement
often takes about 220 seconds, on the same Windows machine
Intermittently, on 10.2 and on the trunk, the prepare statement takes
15+ minutes. I cannot reliably reproduce this; I run the same script
several times in a row and I cannot predict whether it will take 220
seconds or whether it will take 15+ minutes.
I am quite motivated to work on this problem, as this is blocking me from
using Derby for a project that I'm quite keen on, but I need some
suggestions and ideas about how to attack it. From my perspective
there are 3 primary topics:
1) Why did optimizer performance for this query degrade so significantly
from 10.1.2.1 to 10.2? The optimizer seems to be at least 2.5 times slower,
for this particular query at least, in 10.2. Sometimes it is 10x slower.
2) What is the source of the non-determinism? Why does the optimizer
often take 4 minutes to optimize this query on the trunk, but sometimes
take 15+ minutes? I don't believe that I'm changing anything from
run to run.
3) Can we improve the optimizer performance even beyond what it was
for 10.1.2? I realize that this is an ugly query, but I was hoping to
see an optimization time of 5-10 seconds, not 90 seconds (and certainly
not 220 seconds).
I have attempted to start answering some of these questions, with
limited success. Here is some of what I think I've discovered so far:
- the optimizer changes in 10.2 seem to have given the optimizer many
more choices of possible query plans to consider. I think this means
that, if the optimizer does not time out, it will spend substantially
more time optimizing because there are more choices to evaluate. Does
this by itself mean that the optimizer will take 2.5 times longer in
10.2 than it did in 10.1?
- something about this query seems to make the costing mechanism go
haywire, and produce extreme costs. While stepping through the
optimization of this query in the debugger I have seen it compute
costs like 1e63 and 1e200. This might be very closely related to
DERBY-1905, although I don't think I'm doing any subqueries here.
But maybe I'm misunderstanding the term "subquery" in DERBY-1905.
At any rate, due to the enormous estimated costs, timeout does not
occur.
- the WHERE clause in this query is converted during compilation to
an equivalent IN clause, I believe, which then causes me to run into
a number of the problems described in DERBY-47 and DERBY-713.
Specifically, rather than constructing a plan which involves 4
index probes for the 4 WHERE clause values, the optimizer decides
that an index scan must be performed and that it will have to process
the entire index (because the query uses parameter markers, not
literal values). So perhaps solving DERBY-47 would help me
- the optimizer in fact comes up with a "decent" query plan quite quickly.
I have experimented with placing a hard limit into the optimizer
timeout code, so that I can force optimization to stop after an
arbitrary fixed period of time. Then I have been able to set that
value to as low as 1 second, and the optimizer has produced plans
that then execute in a few milliseconds. Of course, I have only tried
this with a trivial amount of data in my database, so it's possible
that the plan produced by the optimizer after just a second of
optimizing is in fact poor, and I'm just not noticing it because my
data sizes are so small.
At this point, what would be really helpful to me would be some suggestions
about some general approaches or techniques to try to start breaking down
and analyzing this problem.
