The OTN database forum supplied a little puzzle a few days ago – starting with the old, old, question: “Why is the plan with the higher cost taking less time to run?”
The standard (usually correct) answer to this question is that the optimizer doesn’t know all it needs to know to predict what’s going to happen, and even if it had perfect information about your data the model used isn’t perfect anyway. This was the correct answer in this case, but with a little twist in the tail that made it a little more entertaining. Here’s the query, with the two execution plans and the execution statistics from autotrace:
From time to time we see a complaint on OTN about the stats history tables being the largest objects in the SYSAUX tablespace and growing very quickly, with requests about how to work around the (perceived) threat. The quick answer is – if you need to save space then stop holding on to the history for so long, and then clean up the mess left by the history that you have captured; on top of that you could stop gathering so many histograms because you probably don’t need them, they often introduce instability to your execution plans, and they are often the largest single component of the history (unless you are using incremental stats on partitioned objects***)
I gather that journalistic style dictates that if the headline is a question then the answer is no. So, following on from a discussion of possible side effects of partition exchange, let’s look at an example which doesn’t involve partitions. I’ve got a schema that holds nothing by two small, simple heap tables, parent and child, (with declared primary keys and the obvious referential integrity constraint) and I run a couple of very similar queries that produce remarkably different results:
A tweet from Connor McDonald earlier on today reminded me of a problem I managed to pre-empt a couple of years ago.
Partitioning is wonderful if done properly but it’s easy to get a little carried away and really foul things up. So company “X” decided they were going to use range/hash composite partitioning and, to minimise contention and (possibly) reduce the indexing overheads, they decided that they would create daily partitions with 1,024 subpartitions.
This, in testing, worked very well, and the idea of daily/1024 didn’t seem too extreme given the huge volume of data they were expecting to handle. There was, however, something they forgot to test; and I can demonstrate this on 12c with an interval/hash partitioned table:
I was setting up a few tests on a copy of 220.127.116.11 recently when I made a mistake creating the table – I forgot to put in a couple of CAST() calls in the select list, so I just patched things up with a couple of “modify column” commands. Since I was planning to smash the table in all sorts of ways and it had taken me several minutes to create the data set (10 million rows) I decided to create a clean copy of the data so that I could just drop the original table and copy back the clean version – and after I’d done this I noticed something a little odd.
Here’s the code (cut down to just 10,000 rows), with a little output:
If you don’t want to read the story, the summary for this article is:
If you create bitmap join indexes on a partitioned table and you use partition exchanges to load data into the table then make sure you create the bitmap join indexes on the loading tables in exactly the same order as you created them on the partitioned table or the exchange will fail with the (truthful not quite complete) error: ORA-14098: index mismatch for tables in ALTER TABLE EXCHANGE PARTITION.
An interesting observation came up on the Oracle-L list server a few days ago that demonstrated how clever the Oracle software is at minimising run-time work, and how easy it is to think you know what an execution plan means when you haven’t actually thought through the details – and the details might make a difference to performance.
The original question was about a very large table with several bitmap indexes, and an anomaly that appeared as a query changed its execution plan. Here are the critical sections from the plans (extracted from memory with rowsource execution statistics enabled):
I published a note on AllthingsOracle a few days ago discussing the options for dropping a column from an existing table. In a little teaser to a future article I pointed out that dropping columns DOESN’T reclaim space; or rather, probably doesn’t, and even if it did you probably won’t like the way it does it.
I will be writing about “massive deletes” for AllthingsOracle in the near future, but I thought I’d expand on the comment about not reclaiming space straight away. The key point is this – when you drop a column you are probably dropping a small fraction of each row. (Obviously there are some extreme variants on the idea – for example, you might have decided to move a large varchar2() to a separate table with shared primary key).
(To understand the title, see this Wikipedia entry)
The title could also be: “Do as I say, don’t do as I do”, because I want to remind you of an error that I regularly commit in my demonstrations. Here’s an example:
SQL> create table t (n number); Table created
Have you spotted the error yet ? Perhaps this will help:
I posted a note a few days ago about read consistency, the Cross Session PL/SQL Function Result Cache, deterministic functions, and scalar subqueries. The intent of the article was to make clear the point that while you might think that declaring a PL/SQL function to be deterministic or in the PL/SQL Result Cache might make a query that calls the function perform faster, if that function contained its own SQL statement then your code might not be producing self-consistent results and (even worse) if you had used the Result Cache option your code might actually cause other session to get wrong results if you tried to “set transaction read only” or “alter session set isolation_level = serializable”