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CBO

CBO++

While browsing the web recently for articles on the HyperLogLog algorithm that Oracle uses for some of its approximate functions, I came upon a blog post written in Jan 2014 with the title Use Subqueries to Count Distinct 50X Faster. There are various ways that subqueries can be used to rewrite queries for improved performance, but when the title caught my eye I couldn’t think of a way in which they could improve “count distinct”.  It turned out that the word “subquery” was being used (quite correctly) in the sense of “inline view” while my mind had immediately turned to subqueries in the select list or where clause.

Virtual Partitions

Here’s a story of (my) failure prompted by a recent OTN posting.

The OP wants to use composite partitioning based on two different date columns – the table should be partitioned by range on the first date and subpartitioned by month on the second date. Here’s the (slightly modified) table creation script he supplied:

Debugging

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:

Set Operations

A recent post on the OTN database forum highlights a couple of important points ideas for optimising SQL. There are: (a) is there a logically equivalent way of stating the SQL and (b) is there a different “natural language” way of posing the problem.

The posting starts with a query, part of an execution plan, and a request to “get rid of the tablescan”. I guessed originally that the query came from an 11g instance, and the OP gave us some code to create the tables and indexes, so I’ve modelled the tables to get the indicated plan (then filled in the original numbers). This is the query, and my cosmetically adjusted version of the plan output that the OP probably got:

Semijoin_driver

Here’s one of those odd little tricks that (a) may help in a couple of very special cases and (b) may show up at some future date – or maybe it already does – in the optimizer if it is recognised as a solution to a more popular problem. It’s about an apparent restriction on how the optimizer uses the BITMAP MERGE operation, and to demonstrate a very simple case I’ll start with a data set with just one bitmap index:

Subquery Effects

Towards the end of last year I used a query with a couple of “constant” subqueries as a focal point for a blog note on reading parallel execution plans. One of the comments on that note raised a question about cardinality estimates and, coincidentally, I received an email about the cost calculations for a similar query a few days later.

Unfortunately there are all sorts of anomalies, special cases, and changes that show up across versions when subqueries come into play – it’s only in recent versions of 11.2, for example, that a very simple example I’ve got of three equivalent statements that produce the same execution plan report the same costs and cardinality. (The queries are:  table with IN subquery, table with EXISTS subquery, table joined to “manually unnested” subquery – the three plans take the unnested subquery shape.)

NLS Mess

The Oracle database has all sorts of little details built into it to help it deal with multi-national companies, but since they’re not commonly used you can find all sorts of odd “buggy” bits of behaviour when you start to look closely. I have to put “buggy” in quotes because some of the reported oddities are the inevitable consequences of (for example) how multi-byte character sets have to work; but some of the oddities look as if they simply wouldn’t be there if the programmer writing the relevant bit of code had remembered that they also had to cater for some NLS feature.

Column Groups

I think column groups can be amazingly useful in helping the optimizer to generate good execution plans because of the way they supply better details about cardinality; unfortunately we’ve already seen a few cases (don’t forget to check the updates and comments) where the feature is disabled, and another example of this appeared on OTN very recently.

Modifying the example from OTN to make a more convincing demonstration of the issue, here’s some SQL to prepare a demonstration:

Estimate_percent

Here’s a live one from OTN – here are a couple of extracts from the problem statement:

We’re experiencing an issue where it seems that the query plan changes from day to day for a particular procedure that runs once a night.
It’s resulting in a performance variance of 10 second completion time vs 20 minutes (nothing in between).
It started occurring about 2 months ago and now it’s becoming more prevalent where the bad query plan is coming up more often.
I noticed that the query plans vary for a simple query.
We do run gather statistics every night. (DBMS_STATS.GATHER_SCHEMA_STATS (ownname=>sys_context( ‘userenv’, ‘current_schema’ ), estimate_percent => 1);)

The query and two execution plans look like this:

Histogram Tip

I’ve just responded to the call for items for the “IOUG Quick Tips” booklet for 2015 – so it’s probably about time to post the quick tip that I put into the 2014 issue. It’s probably nothing new to most readers of the blog, but sometimes an old thing presented in a new way offers fresh insights or better comprehension.

Histogram Tips

A histogram, created in the right way, at the right time, and supported by the correct client-side code, can be a huge benefit to the optimizer; but if you don’t create and use them wisely they can easily become a source of inconsistent performance, and the automatic statistics gathering can introduce an undesirable overhead during the overnight batch. This note explains how you can create histograms very cheaply on the few columns where they are most likely to have a beneficial effect.