statistics

This is just a little shout-out about the Stats Advisor – if you decide to give it a go, what sort of things is it likely to tell you. The answer is in a dynamic performance view called v$stats_advisor_rules – which I’ve list below from an instance running 18.3.0.0.

Here’s an important thought if you’ve got any large tables which are purely hash partitioned. As a general guideline you should not need partition level stats on those tables. The principle of hash partitioned tables is that the rows are distributed uniformly and randomly based on the hash key so, with the assumption that the number of different hash keys is “large” compared to the number of partitions, any one partition should look the same as any other partition.

I’ve made use of the function a few times in the past, for example in this posting on the dangers of using reverse key indexes, but every time I’ve mentioned it I’ve only been interested in the “leaf blocks per key” option. There are actually four different variations of the function, relevant to different types of index and controlled by setting a flag parameter to one of 4 different values.

The call to sys_op_lbid() take 3 parameters: index (or index [sub]partition object id, a flag vlaue, and a table “rowid”, where the flag value can be one of L, R, O, or G. The variations of the call are as follows:

Way back in Oracle 8.0 we introduced the “Object-Relational” database, which was “the next big thing” in the database community back then. Every vendor was scrambling to show just how cool their database technology was with the object-oriented programming paradigm.

Don’t get me wrong – using the Oracle database object types and features associated with them has made my programming life a lot easier over the years. But for me, it’s always been pretty much limited to that, ie, programming, not actually using the object types in a database design as such. Nevertheless, using objects as columns, or even creating tables of objects is supported by the database. For example, I can create a object type of MY_OBJECT (which could itself be made up of objects) and then have a table, not with that object as a column, but actually a table of that object.

Here is a little note about the SYS.STATS_TARGET$ table used by the automatic statistics gathering job run at maintenance window, or when running it manually with:

exec dbms_auto_task_immediate.gather_optimizer_stats

This table is not documented and has no view on it, so those are only my guesses about what I observed, and comments are welcome. Basically, this table is used by the Auto Stats job to list the tables to process, from one execution to the other.

Note that in 12c the same information is updated into DBA_OPTSTAT_OPERATION_TASKS and visible through DBMS_STATS.REPORT_STATS_OPERATIONS. But I still use STATS_TARGET$ so see in real-time what is currently processed.

Columns description

STATUS

When the Auto Stats job lists the objects to process, they are in state PENDING (STATUS=0).

Just a short post today on something that came in as a question for the upcoming Office Hours session which I thought could be covered quickly in a blog post without needing a lot of additional discussion for which Office Hours is more suited to.

The question was:

“When I gather statistics using DBMS_STATS, can I just create a statistic table and pass that as a parameter to get the results of the gather”

And the answer simply is “No” but let me clear up the confusion.

This note is a variant of a note that I wrote a few months ago about the impact of nulls on column groups. The effect showed up recently on a client site with a little camouflage that confused the issue for a little while, so I thought it would be worth a repeat.  We’ll start with a script to generate some test data:

A recent thread on the ODC database forum highlighted a case where the optimizer was estimating 83,000 for a particular index full scan when the SQL Monitor output for the operation showed that it was returning 11,000,000 rows.

Apart from the minor detail that the OP didn’t specifically ask a question, the information supplied was pretty good. The OP had given us a list of bind variables, with values, and the SQL statement, followed by the text output of the Monitor’ed SQL and, to get the predicate section of the plan, the output from a call to dbms_xplan. This was followed by the DDL for the critical index and a list of the stats for all the columns in the index.

So far in this series I’ve written about the way that the optimizer estimates cardinality for an equijoin where one end of the join has a frequency histogram and the other end has a histogram of type:

I’ve got a data set which I’ve recreated in 11.2.0.4 and 12.2.0.1.

I’ve generated stats on the data set, and the stats are identical.

I don’t have any indexes or extended stats, or SQL Plan directives or SQL Plan Profiles, or SQL Plan Baselines, or SQL Patches to worry about.

I’m joining two tables, and the join column on one table has a frequency histogram while the join column on the other table has a height-balanced histogram.  The histograms were created with estimate_percent => 100%. (which explains why I’ve got a height-balanced histogram in 12c rather than a hybrid histogram.)

Here are the two execution plans, 11.2.0.4 first, pulled from memory by dbms_xplan.display_cursor():