tips to write effective queries and explain plan · queries and explain plan ... oracle first...
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Tips to Write Effective
Queries and EXPLAIN PLAN
Contents
SQL Statement Processing Phases
AutoTrace
EXPLAIN PLAN
Explain Plan Using SQL ID
Explain Plan from Active Session (Using TOP)
Explain Plan Operations Reference:
- Table Access Methods (Full Table Scans, Cluster, Hash, by Rowid, Index
Lookup)
- Index Access Methods (Unique scan, Range scan, Full scan, Fast full scan)
- Join Operation Techniques (Nested Loops, Merge Joins or Sort Joins, Hash
Joins)
- Operations (sort, filter, view)
Detect Driving Table
Several Tips to write better queries
SQL Statement Processing Phases The four statement processing phases in SQL are parsing binding, executing and
fetching.
PARSE: During the parse step, Oracle first verifies whether or not the SQL statement
is in the library cache. If it is, only little further processing is necessary, such as
verification of access rights. If not, the statement will need to be parsed, checked for
syntax errors, checked for correctness of table- and column-names, and optimized to
find the best access plan, etc. The former type of parse is called a soft parse and it is
considerably faster than the latter, a hard parse.
BIND: It scans the statement for bind variables and assigns a value to each variable.
EXECUTE: The Server applies the parse tree to the data buffers, performs necessary
I/O and sorts for DML statements.
FETCH: Retrieves rows for a SELECT statement during the fetch phase. Each fetch
retrieves multiple rows, using an array fetch.
A careful understanding of these steps will show that real user data are being
processed in the steps 2 through 4; and that the step 1 merely is present for the Oracle
engine to deal with the SQL statement.
This first step may take considerable time and resources, and as it is overhead seen
from the data processing point of view, applications should be written to minimize the
amount of time spent during this step. The most efficient way to do this is to avoid the
parse/optimization step as much as possible.
AUTOTRACE
The trace utility is very helpful to see the execution plan for a specific
query WITHOUT executing it. We can obtain the execution plan and some additional
statistics on running a SQL command automatically using AUTOTRACE.
SET AUTOTRACE <OPTIONS> <EXPLAIN or STATISTICS>
Setup:
- Create the PLAN_TABLE as SYS by executing:
@$ORACLE_HOME/rdbms/admin/utlxplan.sql create or replace public synonym PLAN_TABLE for
PLAN_TABLE;
grant all on PLAN_TABLE to PUBLIC;
- Setup the PLUSTRACE role (to be used with AUTOTRACE options) as SYS user: @$ORACLE_HOME/sqlplus/admin/plustrce.sql
grant plustrace to public;
If granting the 'plustrace' role to public doesn't work, you could also do the following: alter user &USER_NAME default role PLUSTRACE;
Note=
If you get problems with AUTOTRACE, then try the following as SYS: grant select on v_$session to plustrace;
Options to execute it Fist of all, we need to say that the format of it is hard to read. So I suggest to execute
the following at the top: set lines 100 wrap on trim on trimspool on
col plan_plus_exp format a100
OFF - Disables autotracing SQL statements
ON - Enables autotracing SQL Statements
TRACEONLY - Enables auto tracing SQL Statements, and Suppresses Statement
Output
EXPLAIN - Displays execution plans, but does not display statistics
STATISTICS Displays statistics, but does not display execution plans.
The best option is to use SET AUTOTRACE TRACE , this will not return the
selected data from the query, it will return the access path from plan table and its
statistics.
If you just want the execution plan, then you can use SET AUTOTRACE TRACE
EXP. These are the options:
set autotrace on explain; -> only the explain plan and the query result
set autotrace on statistics; -> only the result set and statistics. No
explain plan
set autotrace traceonly; -> only the explain plan and statistics . No query
result
set autotrace traceonly statistics; -> only the statistics. No query
result or explain plan
set autotrace traceonly explain; -> only the explain plan. No query
result or statistics
To disable use: SET AUTOTRACE OFF;
NOTE: The most important results are the db block gets, consistent gets, physical reads, redo size, sorts (memory) and sorts (disk).
Statistic Explanation • recursive calls: The number of internal calls Oracle has made to execute the
command. Those additional calls(sql) executed by Oracle implicitly to process your
(user) sql statement. Can be many things, hard parses, trigger executions , sort extent
allocations , data dictionary lookups/updates etc
• db block gets: The number of blocks retrieved to answer the query. A: A 'db
block get' is a current mode get. That is, it's the most up-to-date copy of the data in
that block, as it is right now, or currently. There can only be one current copy of a
block in the buffer cache at any time. Db block gets generally are used when DML
changes data in the database. In that case, row-level locks are implicitly taken on the
updated rows. There is also at least one well-known case where a select statement
does a db block get, and does not take a lock. That is, when it does a full table scan or
fast full index scan, Oracle will read the segment header in current mode
• consistent gets: The number of blocks retrieved that did not change the data and
therefore did not interfere with other users (i.e. by locking data). A 'consistent get' is
when Oracle gets the data in a block which is consistent with a given point in time, or
SCN. The consistent get is at the heart of Oracle's read consistency mechanism. When
blocks are fetched in order to satisfy a query result set, they are fetched in consistent
mode. If no block in the buffer cache is consistent to the correct point in time, Oracle
will (attempt to) reconstruct that block using the information in the rollback segments.
If it fails to do so, that's when a query errors out with the much dreaded, much feared,
and much misunderstood ORA-1555 "snapshot too old".
• physical reads: The number of blocks read from the disc. Basically those that
cannot be satisfied by the cache and those that are direct reads.
• redo size: The number of redo entries. The redo entries are written out to the
online redolog files from the log buffer cache by LGWR.
• bytes sent via SQL*Net to client: The number of bytes sent across the network
from the server to the client.
• bytes received via SQL*Net from client: The number of bytes sent across the
network from the client to the server.
• SQL*Net roundtrips to/from client: The number of exchanges between client
and server.
• sorts (memory): The number of data sorts performed in memory.
• sorts (disc): The number of data sorts performed on disc.
• rows processed: The number of rows processed by the query.
The db block gets, consistent gets and physical reads give the number of blocks that
were read to form the buffers or from the disc. For many queries, the number of
physical reads is low as the data is already in the database buffers. If the number of
physical reads is high then the query will be expected to be slow as there will be many
disc accesses.
The bytes received/sent via SQL*Net indicate how much data is being moved across
the network. This is important as moving a lot of data across the network may affect
the network's performance.
The sorts indicate the amount of work done in sorting data during the execution of the
query. Sorts are important as sorting data is a slow process.
EXPLAIN PLAN
The Explain Plan command uses a table to store information about the execution plan
chosen by the optimizer.
Oracle provides an autotrace facility to provide execution plan and some statistics.
There are two methods for looking at the execution plan
1. EXPLAIN PLAN command: Displays an execution plan for a SQL statement
without actually executing the statement
2. V$SQL_PLAN A dictionary view introduced in Oracle 9i that shows the execution
plan for a SQL statement that has been compiled into a cursor in the cursor cache
EXPLAIN PLAN COMMAND Perform the following to check it:
EXPLAIN PLAN FOR
your query.
Example
EXPLAIN PLAN FOR
SELECT * FROM emp e, dept d
WHERE e.deptno = d.deptno
AND e.ename = 'SMITH';
Finally use the DBMS_XPLAN.DISPLAY function to display the execution plan:
SET LINESIZE 130
SET PAGESIZE 0
SELECT *
FROM TABLE(DBMS_XPLAN.DISPLAY);
---------------------------------------------------------
----------
| Id | Operation | Name | Rows |
Bytes | Cost |
---------------------------------------------------------
----------
| 0 | SELECT STATEMENT | | 1 |
57 | 3 |
| 1 | NESTED LOOPS | | 1 |
57 | 3 |
|* 2 | TABLE ACCESS FULL | EMP | 1 |
37 | 2 |
| 3 | TABLE ACCESS BY INDEX ROWID| DEPT | 1 |
20 | 1 |
|* 4 | INDEX UNIQUE SCAN | PK_DEPT |
1 | | |
---------------------------------------------------------
----------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - filter("E"."ENAME"='SMITH')
4 - access("E"."DEPTNO"="D"."DEPTNO")
How To Read Query Plans?
The execution order in EXPLAIN PLAN output begins with the line that is the
furthest indented to the right.
The next step is the parent of that line.
If two lines are indented equally, then the top line is normally executed first.
---------------------------------------------------------
---------------------------------
| Id | Operation | Name |
Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------
---------------------------------
| 0 | SELECT STATEMENT | | 1
| 11 | 53 (2)| 00:00:01 |
| 1 | SORT AGGREGATE | | 1
| 11 | | |
| 2 | TABLE ACCESS BY INDEX ROWID| SKEW | 53
| 583 | 53 (2)| 00:00:01 |
|* 3 | INDEX RANGE SCAN | SKEW_COL1 | 54
| | 3 (0)| 00:00:01 |
---------------------------------------------------------
---------------------------------
The DBMS_XPLAN package supplies four table functions:
DISPLAY: to format and display the contents of a PLAN_TABLE.
Parameters: table_name, sql_id, format, filter_preds
DISPLAY_CURSOR: to format and display the contents of the execution plan
of any loaded cursor available in V$SQL. Parameters: sql_id, child_number,
format
DISPLAY_AWR: to format and display the contents of the execution plan of a
stored SQL statement in the AWR in DBA_HIST_SQLPLAN.
Parameters: sql_id, plan_hash_value, db_id, format
DISPLAY_SQLSET: to format and display the contents of the execution plan
of statements stored in a SQL tuning set, used in conjunction with the package
DBMS_SQLTUNE. Parameters: sqlset_name, sql_id, plan_hash_value,
format, sqlset_owner
The DBMS_XPLAN.DISPLAY function can accept 3 parameters:
1 table_name - Name of plan table, default value 'PLAN_TABLE'.
2 statement_id - Statement id of the plan to be displayed, default value NULL.
3 format - Controls the level of detail displayed, default value 'TYPICAL'. Other
values include 'BASIC', 'ALL', 'SERIAL'.
EXPLAIN PLAN SET STATEMENT_ID='TSH' FOR
SELECT *
FROM emp e, dept d
WHERE e.deptno = d.deptno
AND e.ename = 'SMITH';
SET LINESIZE 130
SET PAGESIZE 0
SELECT *
FROM TABLE(DBMS_XPLAN.DISPLAY('PLAN_TABLE','TSH','BASIC'));
---------------------------------------------
| Id | Operation | Name |
---------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | NESTED LOOPS | |
| 2 | TABLE ACCESS FULL | EMP |
| 3 | TABLE ACCESS BY INDEX ROWID| DEPT |
| 4 | INDEX UNIQUE SCAN | PK_DEPT |
---------------------------------------------
Explain Plan using the SQL ID
You can also grab the Explain Plan by using the SQL ID for an already executed
query. Example:
create table t ( x varchar2(30) primary key, y int );
exec dbms_stats.set_table_stats( user, 'T', numrows =>
1000000, numblks => 100000 );
declare
l_x_number number;
l_x_string varchar2(30);
begin
execute immediate 'alter session set
optimizer_mode=all_rows';
for x in (select * from t look_for_me where x =
l_x_number) loop null; end loop;
for x in (select * from t look_for_me where x =
l_x_string) loop null; end loop;
execute immediate 'alter session set
optimizer_mode=first_rows';
for x in (select * from t look_for_me where x =
l_x_number) loop null; end loop;
for x in (select * from t look_for_me where x =
l_x_string) loop null; end loop;
end;
/
Run this query to "catch" specific queries: select sql_id, child_number, sql_text
from v$sql
where upper(sql_text) like 'SELECT % FROM T%'
ORDER BY 2;
Then run the following to Show its plan select * from
table(DBMS_XPLAN.DISPLAY_CURSOR('&cursor_id', 0) ); -- The 0
is the child_number of the query
You can also view the plan in memory for a statement in the AWR Report select * from table(DBMS_XPLAN.DISPLAY_AWR('SQL_ID'));
Explain plan Hierarchy
Sample explain plan:
Query Plan
-----------------------------------------
SELECT STATEMENT [CHOOSE] Cost=1234
TABLE ACCESS FULL TPAIS [: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 TPAIS is the first operation.
This statement means we are doing a full table scan of table TPAIS 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 indicates 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.
Explain Plan from Active Session (Using TOP)
If you noticed that a session is using too much CPU, you can identify the actions
performed by that session using top and Explain Plan.
So first, use TOP to identify the session using high CPU and take a note of the PID.
set linesize 140
set pagesize 100
col username format a15
col machine format a20
ACCEPT Process_ID prompt 'Pid : '
select s.inst_id,p.spid,s.sid,s.serial#,s.username,s.machine
from gv$session s, gv$process p
where s.paddr=p.addr
and p.spid=&proceso;
Once you got the SID associated to that PID, then you can use it with explain plan: set lines 140
set pages 10000
set long 1000000
ACCEPT Process_SID prompt 'Sid : '
SELECT a.sql_id, a.sql_fulltext
FROM v$sqlarea a, v$session s
WHERE a.address = s.sql_address
AND s.sid = &proceso;
set lines 150
set pages 40000
col operation format a55
col object format a25
ACCEPT sentencia prompt 'Identificador de SQL ejecutado : '
select lpad(' ',2*depth)||operation||' '||options||decode(id, 0, substr(optimizer,1, 6)||'
Cost='||to_char(cost)) operation,
object_name object, cpu_cost, io_cost
from v$sql_plan where sql_id='&sentencia';
Explain Plan Operations Reference
Table Access Methods
1- FULL TABLE SCAN (FTS) - Read every row in the table, every block up to the
high water mark. 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 is the only way to reset the HWM back to the start of the table. 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 intends to run in parallel. Oracle uses multiblock
reads where it can.
2- CLUSTER - Access via an index cluster.
3- HASH - A hash key is issued to access one or more rows in a table with a matching
hash value.
4- BY ROWID - This is the quickest access method available. Oracle simply retrieves
the block specified and extracts the rows it is interested in. 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]
Another example where the 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
5- INDEX LOOKUP - The 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
Note 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
looking up values in the index first has produced the rowid. 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 next 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 unnecessary if the sort order required is the
same as the index. In the next example the index is sorted so the rows will be returned
in the order of the index hence a sort is unnecessary.
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 the next example we will forcing a full table scan. Because we have forced a FTS
the data is unsorted and we must sort the data after it has been retrieved.
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
Index Access Methods
There are 4 methods of index lookup:
b1.Index Unique Scan
b2.Index Range Scan
b3.Index Full Scan
b4.Index Fast Full Sscan
b5.Index Skip Scan
1. Index Unique Scan Only one row will be returned. Used when the statement contains a UNIQUE or a
PRIMARY KEY constraint that guarantees that only a single row is accessed
Example:
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
2. Index range scan This is a 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). e.g.
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]
3. 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). Oracle
chooses an index Full Scan when you have statistics that indicate that it is going to be
more efficient than a Full table scan and a sort. For example Oracle 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.
Processes all leaf blocks of an index, but only enough branch blocks to find 1st leaf
block. Used when all necessary columns are in index & order by clause matches index
struct or if sort merge join is done.
e.g. 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]
4. Index Fast Full Scan (not very used) Scans all the block in the index. Rows are not returned in sorted order. Note that
INDEX FAST FULL SCAN is the mechanism behind fast index create and recreate.
Scans all blocks in index used to replace a FTS when all necessary columns are in the
index. Using multi-block IO & can going parallel.
E.g.
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]
5. Index Skip Scan Skips the leading edge of the index & uses the rest Advantageous if there are few
distinct values in the leading column and many distinct values in the nonleading
column
Join Operations Techniques
There are three kinds of join conditions: nested loops, merge joins, and hash joins.
Each has specific performance implications, and each should be used in different
circumstances.
a. Nested loops work from one table (preferably the smaller of the two), looking up
the join criteria in the larger table. For every row in the outer table, Oracle accesses all
the rows in the
inner table Useful when joining small subsets of data and there is an efficient way to
access the second table (index look up). It’s helpful if the join column is indexed from
the larger table. Nested loops are useful when joining a smaller table to a larger table
and performs very well on smaller amounts of data. Nesting is when you perform the
same operation for every element in a data set: For each row in A do B
b. Hash joins read the smaller tables into a hash table in memory so the referenced
records can be quickly accessed by the hash key. Hash joins are great in data
warehouse scenarios where several smaller tables (with referential integrity defined)
are being referenced in the same SQL query as a single larger or very large table. The
hash join has ab initial overhead (of creating the hash tables) but performs rather well
no matter how many rows are involved.
c. Sort Merge or Merge joins work by selecting the result set from each table, and
then merging these two (or more) results together. Merge joins are useful when
joining two relatively large tables of about the same size together, the merge join
starts out with more overhead but remains rather consistent.
a. NESTED LOOPS JOIN - Nested Loops Joins are the most common and
straightforward type of nesting in Oracle. When joining two tables, for each row in
one table Oracle looks up the matching rows in the other table.Take the example of 2
tables joined as follows: Select *
From Table1 T1, Table2 T2
Where T1.Table1_Id = T2.Table1_id;
In the case of the Nested Loop Join, the rows will be accessed with an outer table
being chosen (say Table1 in this case) and for each row in the outer table, the inner
table (Table2) will be accessed with an index to retrieve the matching rows. Once all
matching rows from Table2 are found, then the next row on Table1 is retrieved and
the matching to Table2 is performed again
It's important that efficient index access is used on the inner table (Table2 in this
example) or that the inner table be a very small table. This is critical to prevent table
scans from being performed on the inner table for each row of the outer table that was
retrieved.
Optimizer uses nested loop when we are joining tables containing small number of
rows with an efficient driving condition. It is the most common join performed by
transactional (OLTP) systems
OUTER - A nested loops operation to perform an outer join statement.
Note: You will see more use of nested loop when using FIRST_ROWS optimizer mode
as it works on model of showing instantaneous results to user as they are fetched.
There is no need for selecting caching any data before it is returned to user. In case of
hash join it is needed and is explained below.
b. HASH JOIN - An operation that joins two sets of rows and returns the same result.
-ANTI - A hash anti-join.
-SEMI - A hash semi-join.
Hash joins are used when we are joining large tables. The optimizer uses the smaller
of the 2 tables to build a hash table in memory and the scans the large tables and
compares the hash value (of rows from large table) with this hash table to find the
joined rows.
The algorithm of hash join is divided in two parts
1. Build a in-memory hash table on smaller of the two tables.
2. Probe this hash table with hash value for each row second table\
Unlike nested loop, the output of hash join result is not instantaneous as hash joining
is blocked on building up hash table.
The Hash Join is is a very efficient join when used in the right situation. With the hash
join, one Table is chosen as the Outer table. This is the larger of the two tables in the
Join - and the other is chosen as the Inner Table. Both tables are broken into sections
and the inner Tables join columns are stored in memory (if hash_area_size is large
enough) and 'hashed'. This hashing provides an algorithmic pointer that makes data
access very efficient. Oracle attempts to keep the inner table in memory since it will
be 'scanned' many times. The Outer rows that match the query predicates are then
selected and for each Outer table row chosen, hashing is performed on the key and the
hash value is used to quickly find the matching row in the Inner Table. This join can
often outperform a Sort Merge join, particularly when 1 table is much larger than
another. No sorting is performed and index access can be avoided since the hash
algorithm is used to locate the block where the inner row is stored. Hash-joins are also
only used for equi-joins. Other important init.ora parms are: hash_join_enabled,
sort_area_size and hash_multiblock_io_count.
Note: You may see more hash joins used with ALL_ROWS optimizer mode, because it
works on model of showing results after all the rows of at least one of the tables are
hashed in hash table.
c. SORT MERGE JOIN or MERGE JOIN or Merge Scan - An operation that
accepts two sets of rows, each sorted by a specific value, combines each row from one
set with the matching rows from the other. Take an example of 2 tables being joined
and returning a large number of rows (say, thousands) as follows:
Select *
From Table1 T1, Table2 T2
Where T1.Table1_Id = T2.Table1_id;
The Merge Scan join will be chosen because the database has detected that a large
number of rows need to be processed and it may also notice that index access to the
rows are not efficient since the data is not clustered (ordered) efficiently for this join.
The steps followed to perform this type of join are as follows:
1) Pick an inner and outer table
2) Access the inner table, choose the rows that match the predicates in the Where
clause of the SQL statement.
3) Sort the rows retrieved from the inner table by the joining columns and store these
as a Temporary table. This step may not be performed if data is ordered by the keys
and efficient index access can be performed.
4) The outer table may also need to be sorted by the joining columns so that both
tables to be joined are sorted in the same manner. This step is also optional and
dependent on whether the outer table is already well ordered by the keys and whether
efficient index access can be used.
5) Read both outer and inner tables (these may be the sorted temporary tables created
in previous steps), choosing rows that match the join criteria. This operation is very
quick since both tables are sorted in the same manner and Database Prefetch can be
used.
6) Optionally sort the data one more time if a Sort was performed (e.g. an 'Order By'
clause) using columns that are not the same as were used to perform the join.
The Merge Join can be deceivingly fast due to database multi-block fetch (helped by
initialization parameter db_file_multiblock_read_count) capabilities and the fact that
each table is accessed only one time each. These are only used for equi-joins. The
other init.ora parm that can be tuned to help performance is sort_area_size.
OUTER - A merge join operation to perform an outer join statement.
-ANTI - A merge anti-join.
-SEMI - A merge semi-join.
Important point to understand is, unlike nested loop where driven (inner) table is read
as many number of times as the input from outer table, in sort merge join each of the
tables involved are accessed at most once. So they prove to be better than nested loop
when the data set is large.
When optimizer uses Sort merge join?
a) When the join condition is an inequality condition (like <, <=, >=). This is
because hash join cannot be used for inequality conditions and if the data set is
large, nested loop is definitely not an option.
b) If sorting is anyways required due to some other attribute (other than join) like
“order by”, optimizer prefers sort merge join over hash join as it is cheaper.
Note: Sort merge join can be seen with both ALL_ROWS and FIRST_ROWS optimizer
hint because it works on a model of first sorting both the data sources and then start
returning the results. So if the data set is large and you have FIRST_ROWS as
optimizer goal, optimizer may prefer sort merge join over nested loop because of
large data. And if you have ALL_ROWS as optimizer goal and if any inequality
condition is used the SQL, optimizer may use sort-merge join over hash join
Operations
The following operations show up in explain plans:
a. Sort
b. filter
c. view
a. Sorts There are a number of different operations that promote sorts
Order by clauses
Group by
Sort merge join
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.
b. 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 the next 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.
c. 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 that 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 that contains an aggregate function cannot be merged
into the main query. The explain plan shows this as a view step
Optimizer Method and how to know the Driving Table. A small "golden rule" is that your driving table should be the table that returns the
smallest number of rows (so you need to look at the where clause), and this is not
always the table with the smallest number of rows. But…. Where to specify the
driving Table?
Oracle processes result sets a table at a time. It starts by retrieving all the data for the
first (driving) table. Once this data is retrieved it is used to limit the number of rows
processed for subsequent (driven) tables. In the case of multiple table joins, the
driving table limits the rows processed for the first driven table. Once processed, this
combined set of data is the driving set for the second driven table etc. Roughly
translated into English, this means that it is best to process tables that will retrieve a small number of rows first. The optimizer will do this to the best of its
ability regardless of the structure of the DML, but some factors may help.
Both the Rule and Cost based optimizers select a driving table for each query.
In the RBO (Rule Based Optimizer) the driving table is the LAST TABLE in
the FROM CLAUSE (chooses the driving order by taking the tables in the FROM
clause RIGHT to LEFT).
In the CBO (Cost Based Optimizer) the driving table is is determinated from costs
derived from GATHERED STATISTICS. If there are no statistics or if the
optimizer_mode IS COST then CBO chooses the driving order of tables
from LEFT to RIGHT in the FROM clause, Place the most limiting tables first in the
FROM clause
If a decision cannot be made, the order of processing is FROM the END of the
FROM clause to the START. In RBO, we have a habit of ordering tables right-to-left in queries, right being the
driving table for the query.
In CBO, I had to adapt to ordering from left-to-right, left being the driving table. The
ORDERED hint used in CBO picks up tables left-to-right for processing. Take a pick.
Hence, it is important to have good statistics to pick up the correct driving table.
The WHERE clause is the main decision maker about which indexes to use. You
should always try to use your unique indexes first, and then if that is not possible then
use a non-unique index. For a query to use an index, one or more fields from that
index need to be mentioned in the WHERE clause. On concatenated indexes the index
will only be used if the first field in the index is mentioned.On 10g that in not needed
any more!!!
The more of its fields are mentioned in the where clause, the better an index is used.
So if you need to get statistics on your schema quickly, you can perform: BEGIN
dbms_stats.gather_schema_stats (ownname => 'SCOTT'
, estimate_percent => 10
, degree => 5
, cascade => true);
END;
/
OR
execute dbms_stats.gather_schema_stats(ownname =>
'SCOTT', estimate_percent => 10, degree => 5, cascade =>
true);
If you want to grab statistics for a Table and its indexes, then: EXEC DBMS_STATS.gather_table_stats('SCOTT', 'TEST',
cascade => TRUE);
More information HERE
TIPS to write better queries
Although two SQL statements may produce the same result, Oracle may process one
faster than the other. You can use the results of the EXPLAIN PLAN statement to
compare the execution plans and costs of the two statements and determine which is
more efficient. Following are some tips that help in writing efficient queries.
Before starting our discussion, once nice parameter to know:
Flushing the Buffer Cache Prior to Oracle Database 10g, the only way to flush the database buffer cache was to
shut down the database and restart it. Oracle Database 10g now allows you to flush
the database buffer cache with the alter system command using the flush buffer_cache
parameter. The FLUSH Buffer Cache clause is useful if you need to measure the
performance of rewritten queries or a suite of queries from identical starting points.
Use the following statement to flush the buffer cache. ALTER SYSTEM FLUSH BUFFER_CACHE; #This
command flushed the buffer cache in the SGA
ALTER SYSTEM FLUSH SHARED_POOL; #This command
flushed the shared pool
However, note that these clauses are intended for use only on a test database. It is not
advisable to use them on a production database, because subsequent queries will have
no hits, only misses.
Declare with Care!! The following table and then the sections after that offer some concrete advice on
potential issues you might encounter when declaring variables in PL/SQL
NUMBER If you don’t specify a precision, as in NUMBER(12,2), Oracle supports up
to 38 digits of precision. If you don’t need this precision, you’re wasting memory.
CHAR This is a fixed-length character string and is mostly available for compatibility
purposes with code written in earlier versions of Oracle. The values assigned to
CHAR variables are right-padded with spaces, which can result in unexpected
behavior. Avoid CHAR unless it’s specifically needed.
VARCHAR2 The greatest challenge you will run into with VARCHAR2 is to avoid
the tendency to hard-code a maximum length, as in VARCHAR2(30). Use %TYPE as
described later in this sectoin.
INTEGER If your integer values fall within the range of –231+1 .. 231–1 (a.k.a. –
2147483647 .. 2147483647), you should declare your variables as PLS_INTEGER.
This is the most efficient format for integer manipulation (until you get to Oracle
Database 10g Release 2, at which point BINARY_INTEGER, PLS_INTEGER and all
the other subtypes of BINARY_INTEGER offer the same performance).
Anchor variables to database datatypes using %TYPE and %ROWTYPE. When you declare a variable using %TYPE or %ROWTYPE, you “anchor” the type
of that data to another, previously defined element. If your program variable has the
same datatype as (and, as is usually the case, is acting as a container for) a column in a
table or view, use %TYPE to define it from that column. If your record has the same
structure as a row in a table or view, use %ROWTYPE to define it from that
table. Your code will automatically adapts to underlying changes in data structures.
1. Existence of a row Do not use ‘Select count(*)…’ to test the existence of a row. Instead, open an explicit
cursor, fetch once, and then check cursor%NOTFOUND :
If you are going to insert a row or update one if that exists, instead of: DECLARE
/* Declare variables which will be used in SQL statements */
v_LastName VARCHAR2(10) := 'Pafumi';
v_NewMajor VARCHAR2(10) := 'Computer';
v_exists number := 0;
BEGIN
Select count(1) into v_exists from students
WHERE last_name = v_LastName;
If v_exists = 1 then
/* Update the students table. */
UPDATE students
SET major = v_NewMajor
WHERE last_name = v_LastName;
else
INSERT INTO students (ID, last_name, major)
VALUES (10020, v_LastName, v_NewMajor);
END IF;
END;
/
Try to perform the following, is much faster !!!! DECLARE
/* Declare variables which will be used in SQL statements */
v_LastName VARCHAR2(10) := 'Pafumi';
v_NewMajor VARCHAR2(10) := 'Computer';
BEGIN
/* Update the students table. */
UPDATE students
SET major = v_NewMajor
WHERE last_name = v_LastName;
/* Check to see if the record was found. If not, then we need
to insert this record. */
IF SQL%NOTFOUND THEN
INSERT INTO students (ID, last_name, major)
VALUES (10020, v_LastName, v_NewMajor);
END IF;
END;
/
--Another Example if trying to insert values in a table with PK: INSERT INTO RecognitionLog(MachineName,StartDateTime)
values(p_MachineName,p_StartDateTime);
p_RowsAffected := SQL%ROWCOUNT;
COMMIT;
RETURN 0;
EXCEPTION
When Dup_val_on_index then
UPDATE RecognitionLog
SET EndDateTime = p_EndDateTime,
TotalRecognized = p_TotalRecognized,
TotalRecognitionFailed = p_TotalRecognitionFailed
WHERE MachineName = p_MachineName
AND StartDateTime = p_StartDateTime;
p_RowsAffected := SQL%ROWCOUNT;
commit;
RETURN 0;
when others then
rollback;
p_RowsAffected := 0;
return 1
If you just want to check the existance of a row, instead of the "classical": select count(*) from student where status = 10;
You can perform the following: SELECT COUNT(*) INTO v_count
FROM student where status = 10 AND ROWNUM = 1;
or
SELECT '1' INTO v_dummy
FROM student where status = 10 AND ROWNUM = 1;
In these examples only single a record is retrieved in the presence/absence check.
2. Avoid the use of NULL or IS NOT NULL.
Instead of:
Select * from clients where phone_number is null;
Use:
Select * from clients where phone_number = 0000000000000000;
3. Select the data that you need ONLY!!! When selecting from a table, be sure to only select the data that you need.
For example, if you only need 1 column from a 50 column table, be sure to do a
'select fld from table' and only retrieve what you need. If you do a
'select * from table' you will be fetching ALL columns of the table which increases
network traffic and causes the system to perform unnecessary work to retrieve data
that is not being used
4. Always use table alias and prefix
The parse phase for statements can be decreased by efficient use of aliasing. This
helps the speed of parsing the statements in two ways:
If an alias is not present, the engine must resolve which tables own the
specified columns.
A short alias is parsed more quickly than a long table name or alias. If possible,
reduce the alias to a single letter.
5. IN and EXISTS
Correlated Queries (Exists)
A subquery is said to be Correlated when it is joined to the outer query within the
Subquery. An example of this is: Select last_name, first_name
From Customer
Where customer.city = ‘Chicago’
and Exists
(Select customer_id
From Sales where
sales.total_sales_amt > 10000
and sales.customer_id =
customer.customer_id);
Notice that the last line in the above query is a join of the outer Customer table and
inner Sales tables. Given the query above, the outer query is read and for each
Customer in Chicago, the outer row is joined to the Subquery. Therefore, in the case
of a subquery, the inner query is executed once for every row read in the outer
query. EXISTS often result in a FULL TABLE SCAN
This is efficient where a relatively small number of rows are processed by the query,
but considerable overhead is incurred when a large number of rows are read.
Uncorrelated Queries (sub-query executes first) (IN)
A subquery is said to be uncorrelated (aka non-correlated) when the two tables are not
joined together in the inner query. In effect, the inner (sub) query is processed first
and then the result set is joined to the outer query. This is very efficient for queries
that return a large number of rows. An example of this is: Select last_name, first_name
From Customer
Where customer_id IN
(Select customer_id
From Sales where
sales.total_sales_amt > 10000);
The Sales table will be processed first and then all entries with a total_sales_amt >
10000 will be joined to the Customer table. This is efficient where a large number of
rows is being processed.
The optimizer is more likely to translate an IN into a join. It is important to
understand the number of rows to be returned by a query and then decide which
approach to use.
EXISTS vs. IN
use a join where possible
use IN over EXISTS (i.e. non-correlated subquery vs. correlated subquery).
The optimizer is more likely to translate IN into a Join than it is with EXISTS
IN executes a subquery once while Exists executes it once per outer row
In is similar to a merge-scan while Exists is similar to a nested-loop join.
There are some cases where EXISTS can outperform IN, but in more cases IN
will dramatically out-perform EXISTS. In general, IN is better than EXISTS.
EXISTS tries to satisfy the subquery as quickly as possible and returns ‘true’ if
the subquery returns 1 or more rows – it should be indexed. Optimize the
execution of the subquery.
Not In vs. Not Exists
Subqueries may be written using NOT IN and NOT EXISTS clauses. The NOT EXISTS clause
is sometimes more efficient since the database only needs to verify non-existence. With NOT IN
the entire result set must be materialized. Another consideration when using NOT IN, is if the
subquery returns NULLS, the results may not be returned (at all). With NOT EXISTS, a value in
the outer query that has a NULL value in the inner will be returned. Not In performs very well as
an anti-join using the cost-based optimizer and often performs Not Exists when this access path
is used. Outer joins can also be a very fast way to accomplish this.
Use IN instead of EXISTS. A simple trick to increase the speed of an EXISTS sub query is to replace it with IN. The IN
method is faster than EXISTS because it doesn’t check unnecessary rows in the comparison.
But this tip will be useful only if the inner query returns a small number of rows. If the inner
query retrieves a larger row set, then it is better to use EXISTS.
Example:
Before: select cgrfnbr from category where EXISTS (select cpcgnbr
from cgprrel where cpprnbr = 149 )
After: select cgrfnbr from category where cgrfnbr IN (select cpcgnbr from cgprrel where cpprnbr = 149 )
36% Time Reduction could be achieved.
6. Use Joins in place of EXISTS. SELECT *
FROM emp e
WHERE EXISTS (SELECT d.deptno
FROM dept d
WHERE e.deptno = d.deptno
AND d.dname = 'RESEARCH');
To improve performance use the following: SELECT *
FROM emp e, dept d
WHERE e.deptno = d.deptno
AND d.dname = ‘RESEARCH’;
7. Use EXISTS in place of DISTINCT. SELECT DISTINCT d.deptno, d.dname ,
FROM dept d, emp e
WHERE d.deptno = e.deptno;
The following SQL statement is a better alternative. SELECT d.deptno , d.dname
FROM dept d
WHERE EXISTS (SELECT 'X'
FROM emp e
WHERE d.deptno = e.deptno);
Another Example: SELECT DISTINCT hetitle, hename
FROM helpfiles h , merchant m
WHERE m.merfnbr = h.hemenbr;
Much Better: SELECT hetitle, hename
FROM helpfiles h WHERE EXISTS (SELECT m.merfnbr
FROM merchant m);
48% Time Reduction could be achieved.
8. Math Expressions. The optimizer fully evaluates expressions whenever possible and translates certain
syntactic constructs into equivalent constructs. This is done either because Oracle can
more quickly evaluate the resulting expression than the original expression or because
the original expression is merely a syntactic equivalent of the resulting expression.
Any computation of constants is performed only once when the statement is optimized
rather than each time the statement is executed. Consider these conditions that test for
monthly salaries greater than 2000:
sal > 24000/12
sal > 2000
sal*12 > 24000
If a SQL statement contains the first condition, the optimizer simplifies it into the
second condition.
Note that the optimizer does not simplify expressions across comparison operators.
The optimizer does not simplify the third expression into the second. For this reason,
application developers should write conditions that compare columns with constants
whenever possible, rather than conditions with expressions involving columns.
The Optimizer does not use index for the following statement. SELECT *
FROM emp
WHERE sal*12 > 24000;
Instead use the following statement. SELECT *
FROM emp
WHERE sal > 24000/12;
9. Never use NOT in an indexed column. Whenever Oracle encounters a NOT in an
index column, it will perform full-table scan.
SELECT *
FROM emp
WHERE NOT deptno = 0;
Instead use the following. SELECT *
FROM emp
WHERE deptno > 0;
10. Never use a function / calculation on an indexed column (unless you are SURE
that you are using an Index Function Based new in Oracle 8i). If there is any function
is used on an index column, optimizer will not use index. Use some other alternative.
If you don’t have another choice, keep functions on the right hand side of the equal
sign. The Concatenate || symbol will also disable indexes. Examples:
/** Do not use **/ SELECT * FROM emp WHERE SUBSTR (ENAME, 1,3) =
‘MIL’;
/** Suggested Alternative **/
Note: Optimizer uses the index only when optimizer_goal is set to FIRST_ROWS. SELECT * FROM emp WHERE ENAME LIKE 'MIL%’;
/** Do not use **/ SELECT * FROM emp WHERE sal! = 0;
Note: Index can tell you what is there in a table but not what is not in a table.
Note: Optimizer uses the index only when optimizer_goal = FIRST_ROWS.
/** Suggested Alternative **/ SELECT * FROM emp WHERE sal > 0;
/** Do not use **/ SELECT * FROM emp WHERE ename || job =
‘MILLERCLERK’;
Note: || is the concatenate function. Like other functions it disables index.
/** Suggested Alternative **/
Note: Optimizer uses the index only when optimizer_goal=FIRST_ROWS. SELECT *
FROM emp
WHERE ename = 'MILLER'
AND job = ‘CLERK’;
11. Whenever possible try to use bind variables
In Dynamic SQL, this is a MUST!!!
The next example would always require a hard parse when it is submitted: create or replace procedure dsal(p_empno in number) as
begin
execute immediate 'update emp set sal = sal*2
where empno = '||p_empno;
commit;
end;
/
Is more effective to use bind variables on the EXECUTE IMMEDIATE command as
follows: create or replace procedure dsal(p_empno in number) as
begin
execute immediate 'update emp set sal=sal*2 where empno
= :x' using p_empno;
commit;
end;
/
The Performance Killer Just to give you a tiny idea of how huge of a difference this can make performance
wise, you only need to run a very small test:
Here is the Performance Killer ....
SQL> alter system flush shared_pool;
SQL> set serveroutput on;
declare
type rc is ref cursor;
l_rc rc;
l_dummy all_objects.object_name%type;
l_start number default dbms_utility.get_time;
begin
for i in 1 .. 1000
loop
open l_rc for
'select object_name from all_objects
where object_id = ' || i;
fetch l_rc into l_dummy;
close l_rc;
-- dbms_output.put_line(l_dummy);
end loop;
dbms_output.put_line (round((dbms_utility.get_time-
l_start)/100, 2) || ' Seconds...' );
end;
/
101.71 Seconds...
... and here is the Performance Winner: declare
type rc is ref cursor;
l_rc rc;
l_dummy all_objects.object_name%type;
l_start number default dbms_utility.get_time;
begin
for i in 1 .. 1000
loop
open l_rc for
'select object_name from all_objects where object_id = :x' using i;
fetch l_rc into l_dummy;
close l_rc;
-- dbms_output.put_line(l_dummy);
end loop; dbms_output.put_line (round((dbms_utility.get_time-
l_start)/100, 2) || ' Seconds...' );
end;
/
1.9 Seconds...
That is pretty dramatic. The fact is that not only does this execute much faster (we
spent more time PARSING our queries then actually EXECUTING them!) it will let
more users use your system simultaneously.
12. Use the same convention for all your queries.
Oracle will put all your SQL or PL/SQL code in memory and will reuse statements
that are the same (saving parse time). So remember that:
Select * from emp where dept = :dept_no
Is different than
Select * from EMP where dept = :dept_no
Even differing spaces in the statement will cause this lookup to fail. Assuming the
statement does not have a cached execution plan it must be parsed before execution.
13. Tuning the WHERE Clause:
- When using AND Clauses in the WHERE Clause, put the most stringent
AND Clause furthest from the WHERE.
- When using OR Clauses in the WHERE Clause, put the most stringent OR
Clause closest to the WHERE.
14. Do not use the keyword HAVING use the keyword WHERE instead
The HAVING clause filters selected rows only after all rows have been
fetched. Using a WHERE clause helps reduce overheads in sorting, summing,
etc. HAVING clauses should only be used when columns with summary operations
applied to them are restricted by the clause.
Given Query Alternative
SELECT d.dname, AVG (e.sal)
FROM emp e, dept d
WHERE e.deptno = d.deptno
GROUP BY d.dname
HAVING dname != 'RESEAECH'
AND dname != 'SALES';
SELECT d.dname, AVG (e.sal)
FROM emp e, dept d
WHERE e.deptno = d.deptno
AND dname != 'RESEAECH'
AND dname != 'SALES'
GROUP BY d.dname;
26% Time Reduction could be achieved
15. Avoid Multiple Sub queries where possible Instead of this:
Update emp set emp_cat = (select max (category) from emp_categories),
sal_range = (select max(sal_range)
from emp_categories);
Use: Update emp set (emp_cat, sal_range) = (Select max
(category), max (sal_range) from emp_categories) ;
16. Use IN in place of OR Least Efficient:
Select ….
From location
Where loc_id = 10 or loc_id=20 or loc_id = 30
Most Efficient Select ….
From location
Where loc_id in (10,20,30)
17. Do not Commit inside a Loop Do not use a commit or DDL statements inside a loop or cursor, because that will
make the undo segments needed by the cursor unavailable.
Many applications commit more frequently than necessary, and their performance
suffers as a result. In isolation a commit is not a very expensive operation, but lots of
unnecessary commits can nevertheless cause severe performance problems. While a
few extra commits may not be noticed, the cumulative effect of thousands of extra
commits is very noticeable. Look at this test. Insert 1,000 rows into a test table -- first
as a single transaction, and then committing after every row. Your mileage may vary,
but these are my results, on an otherwise idle system show a performance blowout of
more than 100% when committing after every row.
create table t (n number);
--BAD METHOD
declare
start_time number;
begin
start_time := dbms_utility.get_time;
for i in 0..999 loop
insert into t values (i);
commit;
end loop;
dbms_output.put_line(dbms_utility.get_time -
start_time || ' centiseconds');
end;
/
102 centiseconds
truncate table t;
--GOOD METHOD
declare
start_time number;
begin
start_time := dbms_utility.get_time;
for i in 0..999 loop
insert into t values (i);
end loop;
commit;
dbms_output.put_line(dbms_utility.get_time -
start_time || ' centiseconds');
end;
/
44 centiseconds
18. Use UNION ALL instead of UNION
The problem is that in a UNION, Oracle finds all the qualifying rows and then "deduplicates" them. To see what I mean, you can simply compare the following queries:
select * from dual
union
select * from dual;
D
---
X
select * from dual
union ALL
select * from dual;
D
---
X X
Note how the first query returns only one record and the second returns two. A UNION forces a big sort and deduplication—a removal of duplicate values. Most of the time, this is wholly unnecessary. To see how this might affect you, I'll use the data dictionary tables to run a WHERE EXISTS query using UNION and UNION ALL and compare the results with TKPROF. The results are dramatic.
First, I'll do the UNION query:
SQL> select *
2 from dual
3 where exists
4 (select null from all_objects
5 union
6 select null from dba_objects
7 union
8 select null from all_users);
call cnt cpu ela query
---- --- ---- --- ------
Parse 1 0.01 0.00 0
Execute 1 2.78 2.75 192234
Fetch 2 0.00 0.00 3
----- ---- ---- ---- ------
total 4 2.79 2.76 192237
As you can see, that was a lot of work—more than 192,000 I/Os just to see if I should
fetch that row from DUAL. Now I add a UNION ALL to the query:
SQL> select *
2 from dual
3 where exists
4 (select null from all_objects
5 union all
6 select null from dba_objects
7 union all
8 select null from all_users);
call cnt cpu ela query
------ ---- ---- ---- -----
Parse 1 0.00 0.00 0
Execute 1 0.01 0.00 9
Fetch 2 0.00 0.00 3
------ ---- ---- ---- -----
total 4 0.01 0.00 12
Quite a change! What happened here was that the WHERE EXISTS stopped running the
subquery when it got the first row back, and because the database did not have to
bother with that deduplicate step, getting the first row back was very fast indeed.
The bottom line: If you can use UNION ALL, by all means use it over UNION to avoid a
costly deduplication step—a step that is probably not even necessary most of the time.
19. Check that your application is using the existing indexes
This is a CRITICAL point. So make use of Explain Plan!!!
20. Recommendation to work with dates.
If you need to get all the data for today's date, instead of: SELECT ImportedDate, State
FROM IssueData
WHERE TRUNC(ImportedDate ) = TRUNC(SYSDATE);
Use the following: SELECT ImportedDate, State
FROM IssueData
WHERE ImportedDate between trunc(SYSDATE) and
TRUNC(SYSDATE) + .99999;
21. Anti Joins
An anti-join is used to return rows from a table that that are present in another table. It
might be used for example between DEPT and EMP to return only those rows in
DEPT that didn't join to anything in EMP;
SELECT *
FROM dept
WHERE deptno NOT IN (SELECT deptno FROM EMP);
SELECT dept.*
FROM dept, emp
WHERE dept.deptno = emp.deptno (+)
AND emp.ROWID IS NULL;
SELECT *
FROM dept
WHERE NOT EXISTS (SELECT NULL FROM emp WHERE emp.deptno =
dept.deptno);
22. Full Outer Joins
Normally, an outer join of table A to table B would return every record in table A, and
if it had a mate in table B, that would be returned as well. Every row in table A would
be output, but some rows of table B might not appear in the result set. A full outer join
would return ebery row in table A, as well as every row in table B. The syntax for a
full outer join is new in Oracle 9i, but it is a syntactic convenience, it is possible to
produce full outer joins sets using conventional SQL.
update emp set deptno = 9 where deptno = 10;
commit;
Conventional SQL New Syntax
SELECT empno, ename, dept.deptno, dname
FROM emp, dept
WHERE emp.deptno(+) = dept.deptno
UNION ALL
SELECT empno, ename, emp.deptno, NULL
FROM emp, dept
WHERE emp.deptno = dept.deptno(+)
AND dept.deptno IS NULL
ORDER BY 1,2,3,4;
EMPNO ENAME DEPTNO DNAME
------ ------- ------- ----------
7369 SMITH 20 RESEARCH
7499 ALLEN 30 SALES
7521 WARD 30 SALES
7566 JONES 20 RESEARCH
7654 MARTIN 30 SALES
7698 BLAKE 30 SALES
7782 CLARK 9
7788 SCOTT 20 RESEARCH
7839 KING 9
7844 TURNER 30 SALES
7876 ADAMS 20 RESEARCH
7900 JAMES 30 SALES
7902 FORD 20 RESEARCH
7934 MILLER 9
10 ACCOUNTING
40 OPERATIONS
SELECT empno, ename,
NVL(dept.deptno,emp.deptno) deptno, dname
FROM emp FULL OUTER JOIN dept ON
(emp.deptno = dept.deptno)
ORDER BY 1,2,3,4;
EMPNO ENAME DEPTNO DNAME
------ ------- ------- ----------
7369 SMITH 20 RESEARCH
7499 ALLEN 30 SALES
7521 WARD 30 SALES
7566 JONES 20 RESEARCH
7654 MARTIN 30 SALES
7698 BLAKE 30 SALES
7782 CLARK 9
7788 SCOTT 20 RESEARCH
7839 KING 9
7844 TURNER 30 SALES
7876 ADAMS 20 RESEARCH
7900 JAMES 30 SALES
7902 FORD 20 RESEARCH
7934 MILLER 9
10 ACCOUNTING
40 OPERATIONS
23. Use BETWEEN instead of IN. The BETWEEN keyword is very useful for filtering out values in a specific range. It
is much faster than typing each value in the range into an IN.
Example:
Before: SELECT crpcgnbr FROM cgryrel WHERE crpcgnbr IN (508858, 508859, 508860, 508861,508862, 508863, 508864)
After: SELECT crpcgnbr FROM cgryrel WHERE crpcgnbr BETWEEN 508858 and 508864
59% Time Reduction could be achieved.