Histograms

A histogram is a special type of column statistic that provides more detailed information about the data distribution in a table column. A histogram sorts values into "buckets," as you might sort coins into buckets.

Based on the Number of Distinct Values and the distribution of the data, the database chooses the type of histogram to create. (In some cases, when creating a histogram, the database samples an internally predetermined number of rows.) The types of histograms are as follows:

· Frequency histograms and top frequency histograms

· Height-Balanced histograms (legacy)

· Hybrid histograms

· 

Purpose of Histograms

By default the optimizer assumes a uniform distribution of rows across the distinct values in a column.

When Oracle Database Creates Histograms

If DBMS_STATS gathers statistics for a table, and if queries have referenced the columns in this table, then Oracle Database creates histograms automatically as needed according to the previous query workload.

The basic process is as follows:

1. You run DBMS_STATS for a table with the METHOD_OPT parameter set to the default SIZE AUTO.

2. A user queries the table.

3. The database notes the predicates in the preceding query and updates the data dictionary table SYS.COL_USAGE$.

4. You run DBMS_STATS again, causing DBMS_STATS to query SYS.COL_USAGE$ to determine which columns require histograms based on the previous query workload.

Consequences of the AUTO feature include the following:

· As queries change over time, DBMS_STATS may change which statistics it gathers. For example, even if the data in a table does not change, queries and DBMS_STATSoperations can cause the plans for queries that reference these tables to change.

· If you gather statistics for a table and do not query the table, then the database does not create histograms for columns in this table. For the database to create the histograms automatically, you must run one or more queries to populate the column usage information in SYS.COL_USAGE$.

How Oracle Database Chooses the Histogram Type

Oracle Database uses several criteria to determine which histogram to create: frequency, top frequency, height-balanced, or hybrid.

The histogram formula uses the following variables:

· NDV (Number of Distinct Values)

This represents the number of distinct values in a column. For example, if a column only contains the values 100, 200, and 300, then the NDV for this column is 3.

· n

This variable represents the number of histogram buckets. The default is 254.

· p

This variable represents an internal percentage threshold that is equal to (1–(1/n)) * 100. For example, if n = 254, then p is 99.6.

An additional criterion is whether the estimate_percent parameter in the DBMS_STATS statistics gathering procedure is set to AUTO_SAMPLE_SIZE (default).

How do we gather stats for Histograms

If DBMS_STATS discovers an index whose columns are unevenly distributed, it will create histograms for that index to aid the cost-based SQL optimizer in making a decision about index versus full-table scan access.

Example

First we set up a table with some very skewed data - so skewed that when we query WHERE ID=1, Oracle Database will want to use an index on ID, and when we query WHERE ID=99, Oracle Database will not want to use an index.

sqlplus scott/tiger

CREATE TABLE skewed_data
AS
  SELECT DECODE(ROWNUM,1,1,99) id,
         all_objects.*
    FROM all_objects
/
Table created.

CREATE INDEX idx_skewed_data ON skewed_data (id);
Index created.

begin
    dbms_stats.gather_table_stats
    ( ownname     => USER,
      tabname     => 'SKEWED_DATA',
      method_opt  => 'for all indexed columns size 254',
      cascade     => TRUE
    );
end;
/
PL/SQL procedure successfully completed.

set autotrace traceonly explain

SELECT * FROM skewed_data WHERE id=1;

Execution Plan
----------------------------------------------------------
Plan hash value: 1799207757

-----------------------------------------------------------------------------------------------
| Id  | Operation                   | Name            | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                 |     1 |    96 |     2   (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID| SKEWED_DATA     |     1 |    96 |     2   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | IDX_SKEWED_DATA |     1 |       |     1   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------

SELECT * FROM skewed_data WHERE id=99;

Execution Plan
----------------------------------------------------------
Plan hash value: 746880940

---------------------------------------------------------------------------------
| Id  | Operation         | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |             | 51545 |  4832K|   190   (3)| 00:00:03 |
|*  1 |  TABLE ACCESS FULL| SKEWED_DATA | 51545 |  4832K|   190   (3)| 00:00:03 |
---------------------------------------------------------------------------------

The Table skewed_data contains a column ID, which is very much skewed most of the values are 99, with one record containing a value of 1. After we index and gather statistics on the table (generating histograms on that indexed column, so the optimizer knows that the data is skewed), we can see that the optimizer prefers an index range scan over a full scan when ID=1 is used and vice versa for ID=99.

Use Histograms!

The cost-based optimiser uses data value histograms to get accurate estimates of the distribution of column data. Histograms provide improved selectivity estimates in the presence of data skew, resulting in optimal execution plans with non-uniform data distributions.

Histograms can affect performance and should be used only when they substantially improve query plans. They are useful only when they reflect the current data distribution of a given column. If the data distribution of a column changes frequently, you must re-compute its histogram frequently.

Number of Histograms

The number of Histograms to used is specified with the SIZE parameter in method_opt:

method_opt  => 'for all indexed columns size 254',

How to read an Explain Plan

What's an explain plan?

An explain plan is a representation of the access path that is taken when a query is executed within Oracle.

Query processing can be divided into 7 phases:

[1] Syntactic	Checks the syntax of the query
[2] Semantic	Checks that all objects exist and are accessible
[3] View Merging	Rewrites query as join on base tables as opposed to using views
[4] Statement      Transformation	Rewrites query transforming some complex constructs into simpler ones where appropriate (e.g. subquery merging, in/or transformation)
[5] Optimization	Determines the optimal access path for the query to take. With the Rule Based Optimizer (RBO) it uses a set of heuristics to determine access path. With the Cost Based Optimizer (CBO) we use statistics to analyze the relative costs of accessing objects.
[6] QEP Generation	QEP = Query Evaluation Plan
[7] QEP Execution	QEP = Query Evaluation Plan

Steps [1]-[6] are handled by the parser. Step [7] is the execution of the statement.

The explain plan is produced by the parser. Once the access path has been decided upon it is stored in the library cache together with the statement itself. We store queries in the library cache based upon a hashed representation  of that query. When looking for a statement in the library cache, we first apply a hashing algorithm to the statement and then we look for this hash value in the library cache. This access path will be used until the query is reparsed.

Terminology

Row Source	A set of rows used in a query may be a select from a base object or the result set returned by joining 2 earlier row sources
Predicate	where clause of a query
Tuples	rows
Driving Table	This is the row source that we use to seed the query. If this returns a lot of rows then this can have a negative affect on all subsequent operations
Probed Table	This is the object we lookup data in after we have retrieved relevant key data from the driving table.

How does Oracle access data?

At the physical level Oracle reads blocks of data. The smallest amount of data read is a single Oracle block, the largest is constrained by operating system limits (and multiblock i/o). Logically Oracle finds the data to read by using the following methods:

o Full Table Scan (FTS)

o Index Lookup (unique & non-unique)

o Rowid

Explain plan Hierarchy

Simple explain plan:

Query Plan
-----------------------------------------
SELECT STATEMENT     [CHOOSE] Cost=1234
  TABLE ACCESS FULL LARGE [:Q65001] [ANALYZED]

The rightmost uppermost operation of an explain plan is the first thing that the explain plan will execute. In this case TABLE ACCESS FULL LARGE is the first operation. This statement means we are doing a full table scan of table LARGE. When this operation completes then the resultant row source is passed up to the
next level of the query for processing. In this case it is the SELECT STATEMENT which is the top of the query.

[CHOOSE] is an indication of the optimizer_goal for the query. This DOES NOT necessarily indicate that plan has actually used this goal. The only way to confirm this is to check the
cost= part of the explain plan as well. For example the following query indicates that the CBO has been used because there is a cost in the cost field:

SELECT STATEMENT     [CHOOSE] Cost=1234

However the explain plan below indicates the use of the RBO because the cost field is blank:

SELECT STATEMENT     [CHOOSE] Cost=

The cost field is a comparative cost that is used internally to determine the best cost for particular plans. The costs of different statements are not really directly comparable.

[:Q65001] indicates that this particular part of the query is being executed in parallel. This number indicates that the operation will be processed by a parallel query slave as opposed to being executed serially.

[ANALYZED] indicates that the object in question has been analyzed and there are currently statistics available for the CBO to use. There is no indication of the 'level' of analysis done.

Access Methods in detail

Full Table Scan (FTS)

In a FTS operation, the whole table is read up to the high water mark (HWM). The HWM marks the last block in the table that has ever had data written to it. If you have deleted all the rows then you will still read up to the HWM. Truncate resets the HWM back to the start of the table. FTS uses multiblock i/o to read the blocks from disk. Multiblock i/o is controlled by the parameter <PARAM:db_block_multi_block_read_count>.

This defaults to:

db_block_buffers / ( (PROCESSES+3) / 4 )

Maximum values are OS dependant

Buffers from FTS operations are placed on the Least Recently Used (LRU) end of the buffer cache so will be quickly aged out. FTS is not recommended for large tables unless you are reading >5-10% of it (or so) or you intend to run in parallel.

Example FTS explain plan:

SQL> explain plan for select * from dual;

Query Plan
-----------------------------------------
SELECT STATEMENT     [CHOOSE] Cost=
  TABLE ACCESS FULL DUAL

Index lookup

Data is accessed by looking up key values in an index and returning rowids. A rowid uniquely identifies an individual row in a particular data block. This block is read via single block i/o.

In this example an index is used to find the relevant row(s) and then the table is accessed to lookup the ename column (which is not included in the index):

SQL> explain plan for
select empno,ename from emp where empno=10;
Query Plan

------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
TABLE ACCESS BY ROWID EMP [ANALYZED]
    INDEX UNIQUE SCAN EMP_I1

Notice the 'TABLE ACCESS BY ROWID' section. This indicates that the table data is not being accessed via a FTS operation but rather by a rowid lookup. In this case the rowid has been produced by looking up values in the index first. The index is being accessed by an 'INDEX UNIQUE SCAN' operation. This is explained below. The index name in this case is EMP_I1. If all the required data resides in the index then a table lookup may be unnecessary and all you will see is an index access with no table access.

In the following example all the columns (empno) are in the index. Notice that no table access takes place:

SQL> explain plan for
select empno from emp where empno=10;

Query Plan
------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
  INDEX UNIQUE SCAN EMP_I1

Indexes are presorted so sorting may be unecessary if the sort order required is the same as the index.

SQL> explain plan for select empno,ename from emp
where empno > 7876 order by empno;

Query Plan
-------------------------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=1
TABLE ACCESS BY ROWID EMP [ANALYZED]
  INDEX RANGE SCAN EMP_I1 [ANALYZED]

In this case the index is sorted so ther rows will be returned in the order of the index hence a sort is unecessary.

SQL> explain plan for
select /*+ Full(emp) */ empno,ename from emp
where empno> 7876 order by empno;

Query Plan
-------------------------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=9
  SORT ORDER BY
    TABLE ACCESS FULL EMP [ANALYZED]  Cost=1 Card=2 Bytes=66

Because we have forced a FTS the data is unsorted and so we must sort the data
after it has been retrieved.

There are 4 methods of index lookup:

o index unique scan

o index range scan

o index full scan

o index fast full scan

Index unique scan

Method for looking up a single key value via a unique index. Always returns a single value You must supply AT LEAST the leading column of the index to access data via the index, However this may return > 1 row as the uniqueness will not be guaranteed.

SQL> explain plan for
select empno,ename from emp where empno=10;

Query Plan
------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
TABLE ACCESS BY ROWID EMP [ANALYZED]
    INDEX UNIQUE SCAN EMP_I1

Index range scan

Method for accessing multiple column values You must supply AT LEAST the leading column of the index to access data via the index Can be used for range operations (e.g. > < <> >= <= between)

SQL> explain plan for select empno,ename from emp
where empno > 7876 order by empno;

Query Plan
-------------------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=1
TABLE ACCESS BY ROWID EMP [ANALYZED]
  INDEX RANGE SCAN EMP_I1 [ANALYZED]

A non-unique index may return multiple values for the predicate col1 = 5 and will use an index range scan

SQL> explain plan for select mgr from emp where mgr = 5

Query plan
--------------------
SELECT STATEMENT [CHOOSE] Cost=1
  INDEX RANGE SCAN EMP_I2 [ANALYZED]

Index Full Scan

In certain circumstances it is possible for the whole index to be scanned as opposed to a range scan (i.e. where no constraining predicates are provided for a table). Full index scans are  only available in the CBO as otherwise we are unable to determine whether a full scan would be a good idea or not. We choose an index Full Scan when we have statistics that indicate that it is going to be more efficient than a Full table scan and a sort.

For example we may do a Full index scan when we do an unbounded scan of an index and want the data to be ordered in the index order. The optimizer may decide that selecting all the information from the index and not sorting is more efficient than doing a FTS or a Fast Full Index Scan and then sorting.

An Index full scan will perform single block i/o's and so it may prove to be inefficient. Index BE_IX is a concatenated index on big_emp (empno,ename)

SQL> explain plan for select empno,ename
     from big_emp order by empno,ename;

Query Plan
------------------------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=26
  INDEX FULL SCAN BE_IX [ANALYZED]

Index Fast Full Scan

Scans all the block in the index Rows are not returned in sorted order Introduced in 7.3 and requires V733_PLANS_ENABLED=TRUE and CBO may be hinted using INDEX_FFS hint uses multiblock i/o can be executed in parallel can be used to access second column of concatenated indexes. This is because we are selecting all of the index.

Note that INDEX FAST FULL SCAN is the mechinism behind fast index create and recreate. Index BE_IX is a concatenated index on big_emp (empno,ename)

SQL> explain plan for select empno,ename from big_emp;

Query Plan
------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=1
  INDEX FAST FULL SCAN BE_IX [ANALYZED]

Selecting the 2nd column of concatenated index:

SQL> explain plan for select ename from big_emp;

Query Plan
------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=1
  INDEX FAST FULL SCAN BE_IX [ANALYZED]

Rowid

This is the quickest access method available Oracle simply retrieves the block specified and extracts the rows it is interested in. Most frequently seen in explain plans as Table access by Rowid

SQL> explain plan for select * from dept where rowid = ':x';

Query Plan
------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
TABLE ACCESS BY ROWID DEPT [ANALYZED]

Table is accessed by rowid following index lookup:

SQL> explain plan for
select empno,ename from emp where empno=10;

Query Plan
------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
TABLE ACCESS BY ROWID EMP [ANALYZED]
    INDEX UNIQUE SCAN EMP_I1

Joins

A Join is a predicate that attempts to combine 2 row sources We only ever join 2 row sources together Join steps are always performed serially even though underlying row sources may have been accessed in parallel. Join order - order in which joins are performed

The join order makes a significant difference to the way in which the query is executed. By accessing particular row sources first, certain predicates may be satisfied that are not satisfied by with other join orders. This may prevent certain access paths from being taken.

Suppose there is a concatenated index on A(a.col1,a.col2). Note that a.col1 is the leading column. Consider the following query:

select A.col4
from   A,B,C
where  B.col3 = 10
and    A.col1 = B.col1
and    A.col2 = C.col2
and    C.col3 = 5

We could represent the joins present in the query using the following schematic:

  B     <---> A <--->    C
col3=10                col3=5

There are really only 2 ways we can drive the query: via B.col3 or C.col3. We would have to do a Full scan of A to be able to drive off it. This is unlikely to be efficient with large tables;

If we drive off table B, using predicate B.col3=10 (as a filter or lookup key) then we will retrieve the value for B.col1 and join to A.col1. Because we have now filled the leading column of the concatenated index on table A we can use this index to give us values for A.col2 and join to A.

However if we drive of table c, then we only get a value for a.col2 and since this is a trailing column of a concatenated index and the leading column has not been supplied at this point, we cannot use the index on a to lookup the data.

So it is likely that the best join order will be B A C. The CBO will obviously use costs to establish whether the individual access paths are a good idea or not.

If the CBO does not choose this join order then we can hint it by changing the from
clause to read:

from B,A,C

and using the /*+ ordered */ hint. The resultant query would be:

select /*+ ordered */ A.col4
from   B,A,C
where  B.col3 = 10
and    A.col1 = B.col1
and    A.col2 = C.col2
and    C.col3 = 5

Join Types

o Sort Merge Join (SMJ)

o Nested Loops (NL)

o Hash Join

Sort Merge Join

Rows are produced by Row Source 1 and are then sorted Rows from Row Source 2 are then produced and sorted by the same sort key as Row Source 1. Row Source 1 and 2 are NOT accessed concurrently Sorted rows from both sides are then merged together (joined)

                   MERGE
                 /      \
            SORT        SORT
             |             |
        Row Source 1  Row Source 2

If the row sources are already (known to be) sorted then the sort operation is unecessary as long as both 'sides' are sorted using the same key. Presorted row sources include indexed columns and row sources that have already been sorted in earlier steps. Although the merge of the 2 row sources is handled serially, the row sources could be accessed in parallel.

SQL> explain plan for
select /*+ ordered */ e.deptno,d.deptno
from emp e,dept d
where e.deptno = d.deptno
order by e.deptno,d.deptno;

Query Plan
-------------------------------------
SELECT STATEMENT [CHOOSE] Cost=17
  MERGE JOIN
    SORT JOIN
      TABLE ACCESS FULL EMP [ANALYZED]
    SORT JOIN
      TABLE ACCESS FULL DEPT [ANALYZED]

Sorting is an expensive operation, especially with large tables. Because of this, SMJ is often not a particularly efficient join method.

Nested Loops

First we return all the rows from row source 1 Then we probe row source 2 once for each row returned from row source 1

Row source 1
~~~~~~~~~~~~
Row 1 --------------       -- Probe ->       Row source 2
Row 2 --------------       -- Probe ->       Row source 2
Row 3 --------------       -- Probe ->       Row source 2

Row source 1 is known as the outer table
Row source 2 is known as the inner table

Accessing row source 2 is known a probing the inner table For nested loops to be efficient it is important that the first row source returns as few rows as possible as this directly controls the number of probes of the second row source. Also it helps if the access method for row source 2 is efficient as this operation is being repeated once for every row returned by row source 1.

SQL> explain plan for
select a.dname,b.sql
from dept a,emp b
where a.deptno = b.deptno;

Query Plan
-------------------------
SELECT STATEMENT [CHOOSE] Cost=5
  NESTED LOOPS
    TABLE ACCESS FULL DEPT [ANALYZED]
    TABLE ACCESS FULL EMP [ANALYZED]

Hash Join

New join type introduced in 7.3 More efficient in theory than NL & SMJ Only accessible via the CBO Smallest row source is chosen and used to build a hash table and a bitmap The second row source is hashed and checked against the hash table looking for joins. The bitmap is used as a quick lookup to check if rows are in the hash table and are especially useful when the hash table is too large to fit in memory.

SQL> explain plan for
select /*+ use_hash(emp) */ empno
from emp,dept
where emp.deptno = dept.deptno;

Query Plan
----------------------------
SELECT STATEMENT  [CHOOSE] Cost=3
  HASH JOIN
    TABLE ACCESS FULL DEPT
    TABLE ACCESS FULL EMP

Hash joins are enabled by the parameter HASH_JOIN_ENABLED=TRUE in the init.ora or session. TRUE is the default in 7.3

Cartesian Product

A Cartesian Product is done where they are no join conditions between 2 row sources and there is no alternative method of accessing the data Not really a join as such as there is no join! Typically this is caused by a coding mistake where a join has been left out. It can be useful in some circumstances - Star joins uses cartesian products.

Notice that there is no join between the 2 tables:

SQL> explain plan for
select emp.deptno,dept,deptno
from emp,dept

Query Plan
------------------------------
SLECT STATEMENT [CHOOSE] Cost=5
  MERGE JOIN CARTESIAN
    TABLE ACCESS FULL DEPT
    SORT JOIN
      TABLE ACCESS FULL EMP

The CARTESIAN keyword indicate that we are doing a cartesian product.

Operations

Operations that show up in explain plans

o sort

o filter

o view

Sorts

There are a number of different operations that promote sorts

o order by clauses

o group by

o sort merge join

Note that if the row source is already appropriately sorted then no sorting is required. This is now indicated in 7.3:

SORT GROUP BY NOSORT
     INDEX FULL SCAN .....

In this case the group by operation simply groups the rows it does not do the sort operation as this has already been completed.

Sorts are expensive operations especially on large tables where the rows do not fit in memory and spill to disk. By default sort blocks are placed into the buffer cache. This may result in aging out of other blocks that may be reread by other processes. To avoid this you can use the parameter <Parameter:SORT_DIRECT_WRITES> which does not place sort blocks into the buffer cache.

Filter

Has a number of different meanings used to indicate partition elimination may also indicate an actual filter step where one row source is filtering another functions such as min may introduce filter steps into query plans

In this example there are 2 filter steps. The first is effectively like a NL except that it stops when it gets something that it doesn't like (i.e. a bounded NL). This is there because of the not in. The second is filtering out the min value:

SQL> explain plan for select * from emp
     where empno not in (select min(empno)
     from big_emp group by empno);

Query Plan
------------------
SELECT STATEMENT [CHOOSE]  Cost=1
  FILTER     **** This is like a bounded nested loops
    TABLE ACCESS FULL EMP [ANALYZED]
     FILTER   **** This filter is introduced by the min
        SORT GROUP BY NOSORT
          INDEX FULL SCAN BE_IX

This example is also interesting in that it has a NOSORT function. The group by does not need to sort because the index row source is already pre sorted.

Views

When a view cannot be merged into the main query you will often see a projection view operation. This indicates that the 'view' will be selected from directly as opposed to being broken down into joins on the base tables. A number of constructs make a view non mergeable. Inline views are also non mergeable.

In the following example the select contains an inline view which cannot be merged:

SQL> explain plan for
select ename,tot
from emp,
    (select empno,sum(empno) tot from big_emp group by empno) tmp
where emp.empno = tmp.empno;

Query Plan
------------------------
SELECT STATEMENT [CHOOSE]
  HASH JOIN
    TABLE ACCESS FULL EMP [ANALYZED]
    VIEW
      SORT GROUP BY
        INDEX FULL SCAN BE_IX

In this case the inline view tmp which contains an aggregate function cannot be merged into the main query. The explain plan shows this as a view step.

Partition Views

Allows a large table to be broken up into a number of smaller partitions which can be queried much more quickly than the table as a whole a union all view is built over the top to provide the original functionality Check constraints or where clauses provide partition elimination capabilities

SQL> explain plan for
select /*+ use_nl(p1,kbwyv1) ordered */  sum(prc_pd)
from parent1 p1,  kbwyv1
where p1.class = 22
and   kbwyv1.bitm_numb = p1.bitm_numb
and   kbwyv1.year = 1997
and   kbwyv1.week between 32 and 33 ;

Query Plan
-----------------------------------------
SELECT STATEMENT   [FIRST_ROWS] Cost=1780
  SORT AGGREGATE
    NESTED LOOPS   [:Q65001] Ct=1780 Cd=40 Bt=3120
      TABLE ACCESS FULL PARENT1 [:Q65000] [AN] Ct=20 Cd=40 Bt=1040
      VIEW  KBWYV1 [:Q65001]
        UNION-ALL PARTITION  [:Q65001]
          FILTER   [:Q64000]
            TABLE ACCESS FULL KBWYT1 [AN] Ct=11 Cd=2000 Bt=104000
          TABLE ACCESS FULL KBWYT2 [AN] Ct=11 Cd=2000 Bt=104000
          TABLE ACCESS FULL KBWYT3 [AN] Ct=11 Cd=2000 Bt=104000
          FILTER   [:Q61000]
            TABLE ACCESS FULL KBWYT4 [AN] Ct=11 Cd=2000 Bt=104000

KBWYV1 is a view on 4 tables KBWYT1-4. KBWYT1-4 contain rows for week 31-34 respectively and are maintained by check constraints. This query should only return rows from partions 2 & 3. The filter operation indicates this. Partitions 1 & 4 are eliminated at execution time. The view line indicates that the view is not merged. The union-all partion information indicates that we have recognised this as a partition view. Note that the tables can be accessed in parallel.

Remote Queries

Only shows remote in the OPERATION column OTHER column shows query executed on remote node OTHER_NODE shows where it is executed Different operational characteristics for RBO & CBO

RBO - Drags everything across the link and joins locally
CBO - Uses cost estimates to determine whether to execute remotely or locally

SQL>  explain plan for
select *
from dept@loop_link;

Query Plan
-------------------------------------------------------
SELECT STATEMENT REMOTE  [CHOOSE] Cost=1
  TABLE ACCESS FULL DEPT [SJD.WORLD] [ANALYZED]

In this case the whole query has been sent to the remote site. The other column shows nothing.

SQL> explain plan for
select a.dname,avg(b.sal),max(b.sal)
from dept@loop_link a, emp b
where a.deptno=b.deptno
group by a.dname
order by max(b.sal),avg(b.sal) desc;

Query Plan
-----------------------------------------------------
SELECT STATEMENT   [CHOOSE] Cost=20
  SORT ORDER BY  [:Q137003] [PARALLEL_TO_SERIAL]
    SORT GROUP BY  [:Q137002] [PARALLEL_TO_PARALLEL]
      NESTED LOOPS   [:Q137001] [PARALLEL_TO_PARALLEL]
        REMOTE   [:Q137000] [PARALLEL_FROM_SERIAL]
        TABLE ACCESS FULL EMP [:Q137001] [ANALYZED]
        [PARALLEL_COMBINED_WITH_PARENT]

Bind Variables

Bind variables are recommended in most cases because they promote sharing of sql code
At parse time the parser has NO IDEA what the bind variable contains. With RBO this makes no difference but with CBO, which relies on accurate statistics to produce plans, this can be a problem.

Defining bind variables in sqlplus:

variable x varchar2(18);
assigning values:
begin
:x := 'hello';
end;
/

SQL> explain plan for
select *
from dept
where rowid = ':x';

Query Plan
------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1
  TABLE ACCESS BY ROWID DEPT [ANALYZED]

Parallel Query

Main indicators that a query is using PQO:

o [:Q1000004] entries in the explain plan

o Checkout the other column for details of what the slaves are executing

o v$pq_slave will show any parallel activity

Columns to look in for information

o other - contains the query passed to the slaves

o other_tag - describes the contents of other

o object_node - indicates order of pqo slaves

Parallel Query operates on a producer/consumer basis. When you specify parallel degree 4 oracle tries to allocate 4 producer slaves and 4 consumer slaves. The producers can feed any of the consumers. If there are only 2 slaves available then we use these. If there is only 1 slave available then we go serial If there are none available then we use serial. If parallel_min_percent is set then we error ora 12827 instead of using a lower number of slaves or going serial

Consumer processes typically perform a sorting function. If there is no requirement for the data to be sorted then the consumer slaves are not produced and we end up with the number of slaves used matching the degree of parallelism as opposed to being 2x the degree.

Parallel Terms

PARALLEL_FROM_SERIAL	This means that source of the data is serial but it is passed to a parallel consumer
PARALLEL_TO_PARALLEL	Both the consumer and the producer are  parallel
PARALLEL_COMBINED_WITH_PARENT	This operation has been combined with the parent operator. For example in a sort merge join the sort operations would be shown as PARALLEL_COMBINED_WITH_PARENT because the sort and the merge are handled as 1 operation.
PARALELL_TO_SERIAL	The source of the data is parallel but it is passed to a serial consumer. This typically will happen at the top of the explain plan but could occur anywhere

Examples of parallel queries

Assumptions

OPTIMIZER_MODE = CHOOSE
DEPT is small compared to EMP
DEPT has an index (DEPT_INDX) on deptno column

Three examples are presented

Query #1:  Serial
Query #2:  Parallel
Query #3:  Parallel, with forced optimization to RULE and forced usage of DEPT_INDX

Sample Query #1 (Serial)

select A.dname, avg(B.sal), max(B.sal)
from  dept A, emp B
where A.deptno = B.deptno
group by A.dname
order by max(B.sal), avg(B.sal) desc;

Execution Plan #1 (Serial)

OBJECT_NAME                      OBJECT_NODE OTHER
-------------------------------  ----------- -------
SELECT STATEMENT
 SORT ORDER BY
   SORT GROUP BY
     MERGE JOIN
       SORT JOIN
         TABLE ACCESS FULL emp
       SORT JOIN
         TABLE ACCESS FULL dept

Notice that the object_node and other columns are empty

Sample Query #2 (Query #1 with parallel hints)

select /*+ parallel(B,4) parallel(A,4) */
A.dname, avg(B.sal), max(B.sal)
from  dept A, emp B
where A.deptno = B.deptno
group by A.dname
order by max(B.sal), avg(B.sal) desc;

Execution Plan #2  (Parallel)

OBJECT_NAME                      OBJECT_NODE OTHER
-------------------------------  ----------- -------
SELECT STATEMENT      Cost = ??
 SORT ORDER BY                   :Q55004     **[7]**
   SORT GROUP BY                 :Q55003     **[6]**
     MERGE JOIN                  :Q55002     **[5]**
       SORT JOIN                 :Q55002     **[4]**
         TABLE ACCESS FULL emp   :Q55001     **[2]**
       SORT JOIN                 :Q55002     **[3]**
         TABLE ACCESS FULL dept  :Q55000     **[1]**

Execution Plan #2  -- OTHER column

**[1]**  (:Q55000) "PARALLEL_FROM_SERIAL"

Serial execution of SELECT DEPTNO, DNAME FROM DEPT

**[2]**  (:Q55001) "PARALLEL_TO_PARALLEL"

        SELECT /*+ ROWID(A1)*/
        A1."DEPTNO" C0, A1."SAL" C1
        FROM "EMP" A1
        WHERE ROWID BETWEEN :1 AND :2

**[3]**  (:Q55002) "PARALLEL_COMBINED_WITH_PARENT"
**[4]**  (:Q55002) "PARALLEL_COMBINED_WITH_PARENT"
**[5]**  (:Q55002) "PARALLEL_TO_PARALLEL"

        SELECT /*+ ORDERED USE_MERGE(A2)*/
        A2.C1 C0, A1.C1 C1
        FROM :Q55001 A1,:Q55000 A2
        WHERE A1.C0=A2.C0

**[6]**  (:Q55003) "PARALLEL_TO_PARALLEL"

        SELECT MAX(A1.C1) C0, AVG(A1.C1) C1, A1.C0 C2
        FROM :Q55002 A1
        GROUP BY A1.C0

**[7]**  (:Q55004) "PARALLEL_FROM_SERIAL"

        SELECT A1.C0 C0, A1.C1 C1, A1.C2 C2
        FROM :Q55003 A1
        ORDER BY A1.CO, A1.C1 DESC

Sample Query #3 (Query #2 with fudged hints)

select /*+ index(A dept_indx) parallel(B,4) parallel(A,4) */
      A.dname, avg(B.sal), max(B.sal)
from  dept A, emp B
where A.deptno = B.deptno
group by A.dname
order by max(B.sal), avg(B.sal) desc;

Execution Plan #3  (Parallel)

OBJECT_NAME                         OBJECT_NODE OTHER
----------------------------------- ----------- -------
SELECT STATEMENT          Cost = ??
 SORT ORDER BY                      :Q58002     **[6]**
   SORT GROUP BY                    :Q58001     **[5]**
     NESTED LOOPS JOIN              :Q58000     **[4]**
       TABLE ACCESS FULL emp        :Q58000     **[3]**
       TABLE ACCESS BY ROWID dept   :Q58000     **[2]**
         INDEX RANGE SCAN dept_indx :Q58000     **[1]**

Execution Plan #3  -- OTHER column

**[1]**  (:Q58000) "PARALLEL_COMBINED_WITH_PARENT"
**[2]**  (:Q58000) "PARALLEL_COMBINED_WITH_PARENT"
**[3]**  (:Q58000) "PARALLEL_COMBINED_WITH_PARENT"
**[4]**  (:Q58000) "PARALLEL_TO_PARALLEL"

        SELECT /*+ ORDERED USE_NL(A2) INDEX(A2) */
        A2."DNAME" C0, A1.C0 C1
        FROM
          (SELECT /*+ ROWID(A3) */
           A3."SAL" CO, A3."DEPTNO" C1
           FROM "EMP" A3
           WHERE ROWID BETWEEN :1 AND :2) A1,
          "DEPT" A2
        WHERE A2."DEPTNO" = A1.C1

**[5]**  (:Q58001) "PARALLEL_TO_PARALLEL"

        SELECT MAX(A1.C1) C0, AVG(A1.C1) C1, A1.C0 C2
        FROM :Q58000 A1
        GROUP BY A1.C0

**[6]**  (:Q58002) "PARALLEL_TO_SERIAL"

        SELECT A1.C0 C0, A1.C1 C1, A1.C2 C2
        FROM :Q58001 A1
        ORDER BY A1.C0, A1.C1 DESC

How to obtain explain plans

Explain plan for

Main advantage is that it does not actually run the query - just parses the sql. This means that it executes quickly. In the early stages of tuning explain plan gives you an idea of the potential performance of your query without actually running it. You can then make a judgement as to any modifications you may choose to make.

Autotrace

Autotrace can be configured to run the sql & gives a plan  and statistics afterwards or just give you an explain plan without executing the query.

Tkprof

Analyzes trace file