Oracle Database 11g Release 2: SQL Tuning Exam

* PX JOIN FILTER CREATE
The bloom filter is created in line 4.
* PX JOIN FILTER USE
The bloom filter is used in line 11.

Note:
* You can identify a bloom pruning in a plan when you see :BF0000 in the Pstart and Pstop columns of the execution plan and PART JOIN FILTER CREATE in the
operations column.
* A Bloom filter is a probabilistic algorithm for doing existence tests in less memory than a full list of keys would require. In other words, a Bloom filter is a method for representing a set of n elements (also called keys) to support membership queries.

* The Oracle database makes use of Bloom filters in the following 4 situations:

- To reduce data communication between slave processes in parallel joins: mostly in RAC
- To implement join-filter pruning: in partition pruning, the optimizer analyzes FROM and WHERE clauses in SQL statements to eliminate unneeded partitions when building the partition access list
- To support result caches: when you run a query, Oracle will first see if the results of that query have already been computed and cached by some session or user, and if so, it will retrieve the answer from the server result cache instead of gathering all of the database blocks
- To filter members in different cells in Exadata: Exadata performs joins between large tables and small lookup tables, a very common scenario for data warehouses with star schemas. This is implemented using Bloom filters as to determine whether a row is a member of the desired result set.

Automatic optimizer statistics collection calls the DBMS_STATS.GATHER_DATABASE_STATS_JOB_PROC procedure. This internal procedure operates similarly to the DBMS_STATS.GATHER_DATABASE_STATS procedure using the GATHER AUTO option. The main difference is that GATHER_DATABASE_STATS_JOB_PROCprioritizes database objects that require statistics, so that objects that most need updated statistics are processed first, before the maintenance window closes.

Note:
* The optimizer relies on object statistics to generate execution plans. If these statistics are stale or missing, then the optimizer does not have the necessary information it needs and can generate poor execution plans. The Automatic Tuning Optimizer checks each query object for missing or stale statistics, and produces two types of output:

/ Recommendations to gather relevant statistics for objects with stale or no statistics

Because optimizer statistics are automatically collected and refreshed, this problem occurs only when automatic optimizer statistics collection is disabled. See
"Managing Automatic Optimizer Statistics Collection".

/ Auxiliary statistics for objects with no statistics, and statistic adjustment factor for objects with stale statistics

The database stores this auxiliary information in an object called a SQL profile.

* Oracle recommends that you enable automatic optimizer statistics collection. In this case, the database automatically collects optimizer statistics for tables with absent or stale statistics. If fresh statistics are required for a table, then the database collects them both for the table and associated indexes.

Automatic collection eliminates many manual tasks associated with managing the optimizer. It also significantly reduces the risks of generating poor execution plans because of missing or stale statistics.

Automatic optimizer statistics collection calls the DBMS_STATS.GATHER_DATABASE_STATS_JOB_PROC procedure. This internal procedure operates similarly to the DBMS_STATS.GATHER_DATABASE_STATS procedure using the GATHER AUTO option. The main difference is that GATHER_DATABASE_STATS_JOB_PROC prioritizes database objects that require statistics, so that objects that most need updated statistics are processed first, before the maintenance window closes.
References:
Oracle Database Performance Tuning Guide, Managing Automatic Optimizer Statistics Collection

You can use an STS as input to SQL Tuning Advisor, which performs automatic tuning of the SQL statements based on other user-specified input parameters.

Note:
A SQL tuning set (STS) is a database object that includes one or more SQL statements along with their execution statistics and execution context, and could include a user priority ranking. You can load SQL statements into a SQL tuning set from different SQL sources, such as AWR, the shared SQL area, or customized SQL provided by the user. An STS includes:

A set of SQL statements

Associated execution context, such as user schema, application module name and action, list of bind values, and the cursor compilation environment

Associated basic execution statistics, such as elapsed time, CPU time, buffer gets, disk reads, rows processed, cursor fetches, the number of executions, the number of complete executions, optimizer cost, and the command type

Associated execution plans and row source statistics for each SQL statement (optional).

References:
Oracle Database Performance Tuning Guide, Managing SQL Tuning Sets

Note:
* Evolving a SQL plan baseline is the process by which the optimizer determines if non-accepted plans in the baseline should be accepted. As mentioned previously, manually loaded plans are automatically marked as accepted, so manual loading forces the evolving process. When plans are loaded automatically, the baselines are evolved using the EVOLVE_SQL_PLAN_BASELINE function, which returns a CLOB reporting its results.

SET LONG 10000 SELECT DBMS_SPM.evolve_sql_plan_baseline(sql_handle =>
'SYS_SQL_7b76323ad90440b9')
FROM dual;

* Manual plan loading can be used in conjunction with, or as an alternative to automatic plan capture. The load operations are performed using the DBMS_SPM package, which allows SQL plan baselines to be loaded from SQL tuning sets or from specific SQL statements in the cursor cache. Manually loaded statements are flagged as accepted by default. If a SQL plan baseline is present for a SQL statement, the plan is added to the baseline, otherwise a new baseline is created.

* fixed (YES/NO) : If YES, the SQL plan baseline will not evolve over time. Fixed plans are used in preference to non-fixed plans.

Whenever statistics in dictionary are modified, old versions of statistics are saved automatically for future restoration. Statistics can be restored using RESTORE procedures of DBMS_STATS package. These procedures use a time stamp as an argument and restore statistics as of that time stamp. This is useful in case newly collected statistics leads to some sub-optimal execution plans and the administrator wants to revert to the previous set of statistics.

References:
Oracle Database Performance Tuning Guide, Restoring Previous Versions of Statistics

A: OPTIMIZER_USE_SQL_PLAN_BASELINES enables or disables the use of SQL plan baselines stored in SQL Management Base. When enabled, the optimizer looks for a SQL plan baseline for the SQL statement being compiled. If one is found in SQL Management B ase, then the optimizer will cost each of the baseline plans and pick one with the lowest cost.

D: EVOLVE_SQL_PLAN_BASELINE Function

This function evolves SQL plan baselines associated with one or more SQL statements. A SQL plan baseline is evolved when one or more of its non-accepted plans is changed to an accepted plan or plans. If interrogated by the user (parameter verify = 'YES'), the execution performance of each non-accepted plan is compared against the performance of a plan chosen from the associated SQL plan baseline. If the non-accepted plan performance is found to be better than SQL plan baseline performance, the non-accepted plan is changed to an accepted plan provided such action is permitted by the user (parameter commit = 'YES').

Incorrect:
B: OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES enables or disables the automatic recognition of repeatable SQL statements, as well as the generation of SQL plan baselines for such statements.
C: ALTER_SQL_PLAN_BASELINE Function

This function changes an attribute of a single plan or all plans associated with a SQL statement using the attribute name/value format.

A: To speed table and index block access, Oracle uses the db_file_multiblock_read_count parameter (which defaults to 8) to aid in getting full-table scan and full-index scan data blocks into the data buffer cache as fast as possible.
B: The index nodes are not retrieved in index order, the rows will not be sequenced.

Note:

there are some requirements for Oracle to invoke the fast full-index scan.

All of the columns required must be specified in the index. That is, all columns in the select and where clauses must exist in the index.

The query returns more than 10 percent of the rows within the index. This 10 percent figure depends on the degree of multi-block reads and the degree of parallelism.

You are counting the number of rows in a table that meet a specific criterion. The fast full-index scan is almost always used for count(*) operations.

References:
index fast full scan tips

Example:

Additional set operations will be done for the customer dimension and the product dimension. At this point in the star query processing, there are three bitmaps.
Each bitmap corresponds to a separate dimension table, and each bitmap represents the set of rows of the fact table that satisfy that individual dimension's constraints.

These three bitmaps are combined into a single bitmap using the bitmap AND operation. This final bitmap represents the set of rows in the fact table that satisfy all of the constraints on the dimension table.

References:
Oracle Database Data Warehousing Guide, Star Transformation with a Bitmap Index

The defining query of a materialized view can select from tables, views, or A: materialized views owned by the user SYS, but you cannot enable QUERY REWRITE on such a materialized view.
B: You cannot specify the following CREATE MATERIALIZED VIEW clauses: CACHE or NOCACHE, CLUSTER, or ON PREBUILT TABLE.
Specify ON DEMAND to indicate that the materialized view will be refreshed on C:
demand by calling one of the three DBMS_MVIEW refresh procedures. If you omit both and ON DEMAND, ON DEMAND is the default.
ON COMMIT Incorrect:
E: Materialized views can only refresh ON COMMIT in certain situations. The materialized view cannot contain object types or Oracle-supplied types. The base tables will never have any distributed transactions applied to them.

Note:
Oracle uses materialized views (also known as snapshots in prior releases) to * replicate data to non-master sites in a replication environment and to cache expensive queries in a data warehouse environment.
* A materialized view is a replica of a target master from a single point in time. The master can be either a master table at a master site or a master materialized view at a materialized view site. Whereas in multimaster replication tables are continuously updated by other master sites, materialized views are updated from one or more masters through individual batch updates, known as a refreshes, from a single master site or master materialized view site.