Query performance concerning order by
Hi
The following query taks 0.2 seconds
select * from messagerecipients order by messageid
Also the following one takes same amount of time
select * from messagerecipients order by groupid
I want to order by both messageid and groupid so
I try both the following ways :
1)
select * from messagerecipients order by messageid, groupid
2)
select * from
( select * from messagerecipients order by messageid)
order by groupid
Both ways take around 2 seconds (10 times slower than
each query on each own).
Any ideas ?
Thanks !!
I just noticed the following:
messagerecipients has 39998 rows.
The following runs in 0.2 secs
select * from (
select * from messagerecipients
order by messageid, groupid
) where rownum < 38000
While the following in 2.5 secs
select * from (
select * from messagerecipients
order by messageid, groupid
) where rownum < 39000
Very strange !!!
Does that give you any ideas maybe ?
Thank you once again !!
Similar Messages
-
Hi All,
I have a two type of query below
Query 1:
Select A.name, C.description from A JOIN B ON A.id=B.id
LEFT JOIN C ON b.id=c.id
where B.status<>'PASS'
Query 2:
Select A.name, C.description from A JOIN B ON A.id=B.id AND B.status<>'PASS'
LEFT JOIN C ON b.id=c.id
which one will give the best performance in the above. Table A ,B And C have more than million records.
Regards,
Thiru
ThiruAs its an INNER JOIN in the above case the queries are equivalent and so is the plan created by the optimizer.
Please Mark This As Answer if it solved your issue
Please Vote This As Helpful if it helps to solve your issue
Visakh
My Wiki User Page
My MSDN Page
My Personal Blog
My Facebook Page -
Query performance concerning count
Hi
I join three tables based on indexed fields.
Two tables have 25000 records, the third has only
a couple of records. The result of the join is
25000 records.
The query runs in 0.2 seconds. Very fast.
I try to count the records replacing the select ID
with count(ID) or with count(*) and it takes 2 seconds
to count the rows.
Any ideas ?
Thank you !!COUNT(*) can most certainly make use of indexes, assuming you're using the cost-based optimizer and you have gathered statistics recently.
First, let's walk through what happens when there are no statistics on a table
scott@jcave > ANALYZE TABLE my_table DELETE STATISTICS;
scott@jcave > desc my_table;
Name Null? Type
MYKEY NOT NULL NUMBER
VALUE VARCHAR2(100)
scott@jcave > set autotrace on;
scott@jcave > select count(*) from my_table;
COUNT(*)
227610
Elapsed: 00:00:03.03
Execution Plan
0 SELECT STATEMENT Optimizer=CHOOSE
1 0 SORT (AGGREGATE)
2 1 TABLE ACCESS (FULL) OF 'MY_TABLE'
Statistics
36 recursive calls
0 db block gets
1145 consistent gets
1126 physical reads
480 redo size
381 bytes sent via SQL*Net to client
503 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processedSo, it took 3 seconds to full-scan the table when there were no statistics. Now, lets gather some statistics and try again. Note that I'll be using the ANALYZE command, but production databases should probably be gathering statistics with the dbms_stats package regularly.
scott@jcave > analyze table my_table compute statistics;
Table analyzed.
Elapsed: 00:00:09.07
scott@jcave > analyze table my_table compute statistics for all indexes;
Table analyzed.
Elapsed: 00:00:04.00
scott@jcave > select count(*) from my_table;
COUNT(*)
227610
Elapsed: 00:00:00.08
Execution Plan
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=43 Card=1)
1 0 SORT (AGGREGATE)
2 1 INDEX (FAST FULL SCAN) OF 'SYS_C003704' (UNIQUE) (Cost=4
3 Card=227610)
Statistics
55 recursive calls
2 db block gets
517 consistent gets
489 physical reads
120 redo size
381 bytes sent via SQL*Net to client
503 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processedWe've gone down to a fraction of a second, now that the CBO knows that the index will be useful.
The moral of the story is that if you haven't gathered statistics recently, you may want to try doing so.
Justin
Distributed Database Consulting, Inc.
www.ddbcinc.com/askDDBC -
I have a table with about 500 million records in it stored within both Oracle and MySQL (MyISAM). I have a column (say phone_number) with a standard b-tree index on it in both places. Without data or indexes cached (not enough memory to fully cache the index), I run about 10,000 queries using randomly generated phone numbers as criteria in 10 concurrent threads. It seems that the average time to retrieve a record in MySQL is about 200 milliseconds whereas in Oracle it is about 400 milliseconds. I'm just wondering if MyISAM/MySQL is inherently faster for a basic index search than Oracle is, or should I be able to tune Oracle to get comparable performance.
Of course the hardware configurations and storage configurations are the same. It's not the absolute time I'm concerned about here but the relative time. Twice as long to perform basically the same query seems concerning. I enabled tracing and it seems like some of the problem may be the recursive calls Oracle is making. Is there some way to optimize this a bit further?
Realize, I just want to look at query performance right now...ignoring all of the issues (locking, transactional integrity, etc.)
Thanks,
GregIn Oracle, a standard table is heap-organized. A b-tree index then contains index keys and ROWIDs, so if you need to read a particular row in the table, you first do a few I/O's on the index to get the ROWIDs and then look up the ROWIDs in the table. For any given key, ROWIDs are likely to be scattered throughout the table, so this latter step generally involves multiple scattered I/O's.
You can create an index-organized table or a hash cluster in Oracle in order to minimize the cost of this particular sort of lookup by clustering data with the same key physically near each other and, in the case of IOTs, potentially eliminating the need to store the index and table separately. Of course, there are costs to doing this in that inserts are now more expensive and secondary indexes are likely to be less useful. That probably gets you closer to what MySQL is doing if, as ajallen indicates, a MySQL table is generally stored more like an IOT than a heap-organized table.
If you get really creative, you could even partition this single table to potentially improve performance further.
Of course, if you're only storing one table, I'm not sure that you could really justify the cost of an Oracle license. This may well be the case where MySQL is more than sufficient for what this particular customer needs (knowing, nothing, of course, about the other requirements for the system).
Justin -
Hi
I have created a procedure that accepts two bind variables from a report. The user will select one or the other, both or neither of the variables. To return the appropriate results i have created a view with the entire result set and in the procedure are a number of if statements that determine what to place in the where clause selecting from the view, depending on what variables populated.
My concern is that the query that generates the view includes several joins and in total outputs around 150,000 records and seems to be rather slow to run.
Would you recommend another solution such as placing the query in the procedure itself repeated for every if statement?
Or should I work on the query performance?
What would be the most efficient solution for my problem?
Any advice would be greatly appreciated.
Thanks[url http://forums.oracle.com/forums/thread.jspa?threadID=501834&tstart=0]When your query takes too long
-
QUERY PERFORMANCE AND DATA LOADING PERFORMANCE ISSUES
WHAT ARE QUERY PERFORMANCE ISSUES WE NEED TO TAKE CARE PLEASE EXPLAIN AND LET ME KNOW T CODES...PLZ URGENT
WHAT ARE DATALOADING PERFORMANCE ISSUES WE NEED TO TAKE CARE PLEASE EXPLAIN AND LET ME KNOW T CODES PLZ URGENT
WILL REWARD FULL POINT S
REGARDS
GURUBW Back end
Some Tips -
1)Identify long-running extraction processes on the source system. Extraction processes are performed by several extraction jobs running on the source system. The run-time of these jobs affects the performance. Use transaction code SM37 Background Processing Job Management to analyze the run-times of these jobs. If the run-time of data collection jobs lasts for several hours, schedule these jobs to run more frequently. This way, less data is written into update tables for each run and extraction performance increases.
2)Identify high run-times for ABAP code, especially for user exits. The quality of any custom ABAP programs used in data extraction affects the extraction performance. Use transaction code SE30 ABAP/4 Run-time Analysis and then run the analysis for the transaction code RSA3 Extractor Checker. The system then records the activities of the extraction program so you can review them to identify time-consuming activities. Eliminate those long-running activities or substitute them with alternative program logic.
3)Identify expensive SQL statements. If database run-time is high for extraction jobs, use transaction code ST05 Performance Trace. On this screen, select ALEREMOTE user and then select SQL trace to record the SQL statements. Identify the time-consuming sections from the results. If the data-selection times are high on a particular SQL statement, index the DataSource tables to increase the performance of selection (see no. 6 below). While using ST05, make sure that no other extraction job is running with ALEREMOTE user.
4)Balance loads by distributing processes onto different servers if possible. If your site uses more than one BW application server, distribute the extraction processes to different servers using transaction code SM59 Maintain RFC Destination. Load balancing is possible only if the extraction program allows the option
5)Set optimum parameters for data-packet size. Packet size affects the number of data requests to the database. Set the data-packet size to optimum values for an efficient data-extraction mechanism. To find the optimum value, start with a packet size in the range of 50,000 to 100,000 and gradually increase it. At some point, you will reach the threshold at which increasing packet size further does not provide any performance increase. To set the packet size, use transaction code SBIW BW IMG Menu on the source system. To set the data load parameters for flat-file uploads, use transaction code RSCUSTV6 in BW.
6)Build indexes on DataSource tables based on selection criteria. Indexing DataSource tables improves the extraction performance, because it reduces the read times of those tables.
7)Execute collection jobs in parallel. Like the Business Content extractors, generic extractors have a number of collection jobs to retrieve relevant data from DataSource tables. Scheduling these collection jobs to run in parallel reduces the total extraction time, and they can be scheduled via transaction code SM37 in the source system.
8). Break up your data selections for InfoPackages and schedule the portions to run in parallel. This parallel upload mechanism sends different portions of the data to BW at the same time, and as a result the total upload time is reduced. You can schedule InfoPackages in the Administrator Workbench.
You can upload data from a data target (InfoCube and ODS) to another data target within the BW system. While uploading, you can schedule more than one InfoPackage with different selection options in each one. For example, fiscal year or fiscal year period can be used as selection options. Avoid using parallel uploads for high volumes of data if hardware resources are constrained. Each InfoPacket uses one background process (if scheduled to run in the background) or dialog process (if scheduled to run online) of the application server, and too many processes could overwhelm a slow server.
9). Building secondary indexes on the tables for the selection fields optimizes these tables for reading, reducing extraction time. If your selection fields are not key fields on the table, primary indexes are not much of a help when accessing data. In this case it is better to create secondary indexes with selection fields on the associated table using ABAP Dictionary to improve better selection performance.
10)Analyze upload times to the PSA and identify long-running uploads. When you extract the data using PSA method, data is written into PSA tables in the BW system. If your data is on the order of tens of millions, consider partitioning these PSA tables for better performance, but pay attention to the partition sizes. Partitioning PSA tables improves data-load performance because it's faster to insert data into smaller database tables. Partitioning also provides increased performance for maintenance of PSA tables for example, you can delete a portion of data faster. You can set the size of each partition in the PSA parameters screen, in transaction code SPRO or RSCUSTV6, so that BW creates a new partition automatically when a threshold value is reached.
11)Debug any routines in the transfer and update rules and eliminate single selects from the routines. Using single selects in custom ABAP routines for selecting data from database tables reduces performance considerably. It is better to use buffers and array operations. When you use buffers or array operations, the system reads data from the database tables and stores it in the memory for manipulation, improving performance. If you do not use buffers or array operations, the whole reading process is performed on the database with many table accesses, and performance deteriorates. Also, extensive use of library transformations in the ABAP code reduces performance; since these transformations are not compiled in advance, they are carried out during run-time.
12)Before uploading a high volume of transaction data into InfoCubes, activate the number-range buffer for dimension IDs. The number-range buffer is a parameter that identifies the number of sequential dimension IDs stored in the memory. If you increase the number range before high-volume data upload, you reduce the number of reads from the dimension tables and hence increase the upload performance. Do not forget to set the number-range values back to their original values after the upload. Use transaction code SNRO to maintain the number range buffer values for InfoCubes.
13)Drop the indexes before uploading high-volume data into InfoCubes. Regenerate them after the upload. Indexes on InfoCubes are optimized for reading data from the InfoCubes. If the indexes exist during the upload, BW reads the indexes and tries to insert the records according to the indexes, resulting in poor upload performance. You can automate the dropping and regeneration of the indexes through InfoPackage scheduling. You can drop indexes in the Manage InfoCube screen in the Administrator Workbench.
14)IDoc (intermediate document) archiving improves the extraction and loading performance and can be applied on both BW and R/3 systems. In addition to IDoc archiving, data archiving is available for InfoCubes and ODS objects.
Hope it Helps
Chetan
@CP.. -
How to improve query performance built on a ODS
Hi,
I've built a report on FI_GL ODS (BW3.5). The report execution time takes almost 1hr.
Is there any method to improve or optimize th query performance that build on ODS.
The ODS got huge volume of data ~ 300 Million records for 2 years.
Thanx in advance,
Guru.Hi Raj,
Here are some few tips which helps you in improving ur query performance
Checklist for Query Performance
1. If exclusions exist, make sure they exist in the global filter area. Try to remove exclusions by subtracting out inclusions.
2. Use Constant Selection to ignore filters in order to move more filters to the global filter area. (Use ABAPer to test and validate that this ensures better code)
3. Within structures, make sure the filter order exists with the highest level filter first.
4. Check code for all exit variables used in a report.
5. Move Time restrictions to a global filter whenever possible.
6. Within structures, use user exit variables to calculate things like QTD, YTD. This should generate better code than using overlapping restrictions to achieve the same thing. (Use ABAPer to test and validate that this ensures better code).
7. When queries are written on multiproviders, restrict to InfoProvider in global filter whenever possible. MultiProvider (MultiCube) queries require additional database table joins to read data compared to those queries against standard InfoCubes (InfoProviders), and you should therefore hardcode the infoprovider in the global filter whenever possible to eliminate this problem.
8. Move all global calculated and restricted key figures to local as to analyze any filters that can be removed and moved to the global definition in a query. Then you can change the calculated key figure and go back to utilizing the global calculated key figure if desired
9. If Alternative UOM solution is used, turn off query cache.
10. Set read mode of query based on static or dynamic. Reading data during navigation minimizes the impact on the R/3 database and application server resources because only data that the user requires will be retrieved. For queries involving large hierarchies with many nodes, it would be wise to select Read data during navigation and when expanding the hierarchy option to avoid reading data for the hierarchy nodes that are not expanded. Reserve the Read all data mode for special queriesu2014for instance, when a majority of the users need a given query to slice and dice against all dimensions, or when the data is needed for data mining. This mode places heavy demand on database and memory resources and might impact other SAP BW processes and tasks.
11. Turn off formatting and results rows to minimize Frontend time whenever possible.
12. Check for nested hierarchies. Always a bad idea.
13. If "Display as hierarchy" is being used, look for other options to remove it to increase performance.
14. Use Constant Selection instead of SUMCT and SUMGT within formulas.
15. Do review of order of restrictions in formulas. Do as many restrictions as you can before
calculations. Try to avoid calculations before restrictions.
17. Turn off warning messages on queries.
18. Check to see if performance improves by removing text display (Use ABAPer to test and validate that this ensures better code).
19. Check to see where currency conversions are happening if they are used.
20. Check aggregation and exception aggregation on calculated key figures. Before aggregation is generally slower and should not be used unless explicitly needed.
21. Avoid Cell Editor use if at all possible.
22. Make sure queries are regenerated in production using RSRT after changes to statistics, consistency changes, or aggregates.
23. Within the free characteristics, filter on the least granular objects first and make sure those come first in the order. -
Oracle Query Performance While calling a function in a View
Hi,
We have a performance issue in one of our Oracle queries.
Here is the scenario
We use a hard coded value (which is the maximum value from a table) in couple of DECODE statements in our query. We would like to remove this hard coded value from the query. So we wrote a function which will return a maximum value from the table. Now when we execute the query after replacing the hard coded value with the function, this function is called four times which hampers the query performance.
Pl find below the DECODE statements in the query. This query is part of a main VIEW.
Using Hardcoded values
=================
DECODE(pro_risk_weighted_ctrl_scr, 10, 9.9, pro_risk_weighted_ctrl_scr)
DECODE(pro_risk_score, 46619750, 46619749, pro_risk_score)
Using Functions
============
DECODE (pro_risk_weighted_ctrl_scr, rprowbproc.fn_max_rcsa_range_values ('CSR'), rprowbproc.fn_max_rcsa_range_values('CSR')- 0.1, pro_risk_weighted_ctrl_scr)
DECODE (pro_risk_score, rprowbproc.fn_max_rcsa_range_values ('RSR'), rprowbproc.fn_max_rcsa_range_values ('RSR') - 1, pro_risk_score)
Can any one suggest a way to improve the performance of the query.
Thanks & Regards,
Rajidrop table max_demo;
create table max_demo
(rcsa varchar2(10)
,value number);
insert into max_demo
select case when mod(rownum,2) = 0
then 'CSR'
else 'RSR'
end
, rownum
from dual
connect by rownum <= 10000;
create or replace function f_max (
i_rcsa in max_demo.rcsa%TYPE
return number
as
l_max number;
begin
select max(value)
into l_max
from max_demo
where rcsa = i_rcsa;
return l_max;
end;
-- slooooooooooooowwwwww
select m.*
, f_max(rcsa)
, decode(rcsa,'CSR',decode(value,f_max('CSR'),'Y - max is '||f_max('CSR'),'N - max is '||f_max('CSR'))) is_max_csr
, decode(rcsa,'RSR',decode(value,f_max('RSR'),'Y - max is '||f_max('RSR'),'N - max is '||f_max('RSR'))) is_max_rsr
from max_demo m
order by value desc;
-- ssllooooowwwww
with subq_max as
(select f_max('CSR') max_csr,
f_max('RSR') max_rsr
from dual)
select m.*
, decode(rcsa,'CSR',s.max_csr,'RSR',s.max_rsr) max
, decode(rcsa,'CSR',decode(value,s.max_csr,'Y - max is '||s.max_csr,'N - max is '||s.max_csr)) is_max_csr
, decode(rcsa,'RSR',decode(value,s.max_rsr,'Y - max is '||s.max_rsr,'N - max is '||s.max_rsr)) is_max_rsr
from max_demo m
, subq_max s
order by value desc;
-- faster
with subq_max as
(select /*+materialize */
f_max('CSR') max_csr,
f_max('RSR') max_rsr
from dual)
select m.*
, decode(rcsa,'CSR',s.max_csr,'RSR',s.max_rsr) max
, decode(rcsa,'CSR',decode(value,s.max_csr,'Y - max is '||s.max_csr,'N - max is '||s.max_csr)) is_max_csr
, decode(rcsa,'RSR',decode(value,s.max_rsr,'Y - max is '||s.max_rsr,'N - max is '||s.max_rsr)) is_max_rsr
from max_demo m
, subq_max s
order by value desc;
-- faster
with subq_max as
(select f_max('CSR') max_csr,
f_max('RSR') max_rsr,
rownum
from dual)
select m.*
, decode(rcsa,'CSR',s.max_csr,'RSR',s.max_rsr) max
, decode(rcsa,'CSR',decode(value,s.max_csr,'Y - max is '||s.max_csr,'N - max is '||s.max_csr)) is_max_csr
, decode(rcsa,'RSR',decode(value,s.max_rsr,'Y - max is '||s.max_rsr,'N - max is '||s.max_rsr)) is_max_rsr
from max_demo m
, subq_max s
order by value desc;
-- sloooooowwwwww
select m.*
, decode(rcsa,'CSR',s.max_csr,'RSR',s.max_rsr) max
, decode(rcsa,'CSR',decode(value,s.max_csr,'Y - max is '||s.max_csr,'N - max is '||s.max_csr)) is_max_csr
, decode(rcsa,'RSR',decode(value,s.max_rsr,'Y - max is '||s.max_rsr,'N - max is '||s.max_rsr)) is_max_rsr
from max_demo m
, (select /*+ materialize */
f_max('CSR') max_csr,
f_max('RSR') max_rsr
from dual) s
order by value desc;
-- faster
select m.*
, decode(rcsa,'CSR',s.max_csr,'RSR',s.max_rsr) max
, decode(rcsa,'CSR',decode(value,s.max_csr,'Y - max is '||s.max_csr,'N - max is '||s.max_csr)) is_max_csr
, decode(rcsa,'RSR',decode(value,s.max_rsr,'Y - max is '||s.max_rsr,'N - max is '||s.max_rsr)) is_max_rsr
from max_demo m
, (select f_max('CSR') max_csr,
f_max('RSR') max_rsr,
rownum
from dual) s
order by value desc; -
Poor query performance when joining CONTAINS to another table
We just recently began evaluating Oracle Text for a search solution. We need to be able to search a table that can have over 20+ million rows. Each user may only have visibility to a tiny fraction of those rows. The goal is to have a single Oracle Text index that represents all of the searchable columns in the table (multi column datastore) and provide a score for each search result so that we can sort the search results in descending order by score. What we're seeing is that query performance from TOAD is extremely fast when we write a simple CONTAINS query against the Oracle Text indexed table. However, when we attempt to first reduce the rows the CONTAINS query needs to search by using a WITH we find that the query performance degrades significantly.
For example, we can find all the records a user has access to from our base table by the following query:
SELECT d.duns_loc
FROM duns d
JOIN primary_contact pc
ON d.duns_loc = pc.duns_loc
AND pc.emp_id = :employeeID;
This query can execute in <100 ms. In the working example, this query returns around 1200 rows of the primary key duns_loc.
Our search query looks like this:
SELECT score(1), d.*
FROM duns d
WHERE CONTAINS(TEXT_KEY, :search,1) > 0
ORDER BY score(1) DESC;
The :search value in this example will be 'highway'. The query can return 246k rows in around 2 seconds.
2 seconds is good, but we should be able to have a much faster response if the search query did not have to search the entire table, right? Since each user can only "view" records they are assigned to we reckon that if the search operation only had to scan a tiny tiny percent of the TEXT index we should see faster (and more relevant) results. If we now write the following query:
WITH subset
AS
(SELECT d.duns_loc
FROM duns d
JOIN primary_contact pc
ON d.duns_loc = pc.duns_loc
AND pc.emp_id = :employeeID
SELECT score(1), d.*
FROM duns d
JOIN subset s
ON d.duns_loc = s.duns_loc
WHERE CONTAINS(TEXT_KEY, :search,1) > 0
ORDER BY score(1) DESC;
For reasons we have not been able to identify this query actually takes longer to execute than the sum of the durations of the contributing parts. This query takes over 6 seconds to run. We nor our DBA can seem to figure out why this query performs worse than a wide open search. The wide open search is not ideal as the query would end up returning records to the user they don't have access to view.
Has anyone ever ran into something like this? Any suggestions on what to look at or where to go? If anyone would like more information to help in diagnosis than let me know and i'll be happy to produce it here.
Thanks!!Sometimes it can be good to separate the tables into separate sub-query factoring (with) clauses or inline views in the from clause or an in clause as a where condition. Although there are some differences, using a sub-query factoring (with) clause is similar to using an inline view in the from clause. However, you should avoid duplication. You should not have the same table in two different places, as in your original query. You should have indexes on any columns that the tables are joined on, your statistics should be current, and your domain index should have regular synchronization, optimization, and periodically rebuild or drop and recreate to keep it performing with maximum efficiency. The following demonstration uses a composite domain index (cdi) with filter by, as suggested by Roger, then shows the explained plans for your original query, and various others. Your original query has nested loops. All of the others have the same plan without the nested loops. You could also add index hints.
SCOTT@orcl_11gR2> -- tables:
SCOTT@orcl_11gR2> CREATE TABLE duns
2 (duns_loc NUMBER,
3 text_key VARCHAR2 (30))
4 /
Table created.
SCOTT@orcl_11gR2> CREATE TABLE primary_contact
2 (duns_loc NUMBER,
3 emp_id NUMBER)
4 /
Table created.
SCOTT@orcl_11gR2> -- data:
SCOTT@orcl_11gR2> INSERT INTO duns VALUES (1, 'highway')
2 /
1 row created.
SCOTT@orcl_11gR2> INSERT INTO primary_contact VALUES (1, 1)
2 /
1 row created.
SCOTT@orcl_11gR2> INSERT INTO duns
2 SELECT object_id, object_name
3 FROM all_objects
4 WHERE object_id > 1
5 /
76027 rows created.
SCOTT@orcl_11gR2> INSERT INTO primary_contact
2 SELECT object_id, namespace
3 FROM all_objects
4 WHERE object_id > 1
5 /
76027 rows created.
SCOTT@orcl_11gR2> -- indexes:
SCOTT@orcl_11gR2> CREATE INDEX duns_duns_loc_idx
2 ON duns (duns_loc)
3 /
Index created.
SCOTT@orcl_11gR2> CREATE INDEX primary_contact_duns_loc_idx
2 ON primary_contact (duns_loc)
3 /
Index created.
SCOTT@orcl_11gR2> -- composite domain index (cdi) with filter by clause
SCOTT@orcl_11gR2> -- as suggested by Roger:
SCOTT@orcl_11gR2> CREATE INDEX duns_text_key_idx
2 ON duns (text_key)
3 INDEXTYPE IS CTXSYS.CONTEXT
4 FILTER BY duns_loc
5 /
Index created.
SCOTT@orcl_11gR2> -- gather statistics:
SCOTT@orcl_11gR2> EXEC DBMS_STATS.GATHER_TABLE_STATS (USER, 'DUNS')
PL/SQL procedure successfully completed.
SCOTT@orcl_11gR2> EXEC DBMS_STATS.GATHER_TABLE_STATS (USER, 'PRIMARY_CONTACT')
PL/SQL procedure successfully completed.
SCOTT@orcl_11gR2> -- variables:
SCOTT@orcl_11gR2> VARIABLE employeeid NUMBER
SCOTT@orcl_11gR2> EXEC :employeeid := 1
PL/SQL procedure successfully completed.
SCOTT@orcl_11gR2> VARIABLE search VARCHAR2(100)
SCOTT@orcl_11gR2> EXEC :search := 'highway'
PL/SQL procedure successfully completed.
SCOTT@orcl_11gR2> -- original query:
SCOTT@orcl_11gR2> SET AUTOTRACE ON EXPLAIN
SCOTT@orcl_11gR2> WITH
2 subset AS
3 (SELECT d.duns_loc
4 FROM duns d
5 JOIN primary_contact pc
6 ON d.duns_loc = pc.duns_loc
7 AND pc.emp_id = :employeeID)
8 SELECT score(1), d.*
9 FROM duns d
10 JOIN subset s
11 ON d.duns_loc = s.duns_loc
12 WHERE CONTAINS (TEXT_KEY, :search,1) > 0
13 ORDER BY score(1) DESC
14 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 4228563783
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 2 | 84 | 121 (4)| 00:00:02 |
| 1 | SORT ORDER BY | | 2 | 84 | 121 (4)| 00:00:02 |
|* 2 | HASH JOIN | | 2 | 84 | 120 (3)| 00:00:02 |
| 3 | NESTED LOOPS | | 38 | 1292 | 50 (2)| 00:00:01 |
| 4 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 5 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 6 | INDEX RANGE SCAN | DUNS_DUNS_LOC_IDX | 1 | 5 | 1 (0)| 00:00:01 |
|* 7 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("D"."DUNS_LOC"="PC"."DUNS_LOC")
5 - access("CTXSYS"."CONTAINS"("D"."TEXT_KEY",:SEARCH,1)>0)
6 - access("D"."DUNS_LOC"="D"."DUNS_LOC")
7 - filter("PC"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -- queries with better plans (no nested loops):
SCOTT@orcl_11gR2> -- subquery factoring (with) clauses:
SCOTT@orcl_11gR2> WITH
2 subset1 AS
3 (SELECT pc.duns_loc
4 FROM primary_contact pc
5 WHERE pc.emp_id = :employeeID),
6 subset2 AS
7 (SELECT score(1), d.*
8 FROM duns d
9 WHERE CONTAINS (TEXT_KEY, :search,1) > 0)
10 SELECT subset2.*
11 FROM subset1, subset2
12 WHERE subset1.duns_loc = subset2.duns_loc
13 ORDER BY score(1) DESC
14 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 153618227
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 38 | 1406 | 83 (5)| 00:00:01 |
| 1 | SORT ORDER BY | | 38 | 1406 | 83 (5)| 00:00:01 |
|* 2 | HASH JOIN | | 38 | 1406 | 82 (4)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 4 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("PC"."DUNS_LOC"="D"."DUNS_LOC")
4 - access("CTXSYS"."CONTAINS"("TEXT_KEY",:SEARCH,1)>0)
5 - filter("PC"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -- inline views (sub-queries in the from clause):
SCOTT@orcl_11gR2> SELECT subset2.*
2 FROM (SELECT pc.duns_loc
3 FROM primary_contact pc
4 WHERE pc.emp_id = :employeeID) subset1,
5 (SELECT score(1), d.*
6 FROM duns d
7 WHERE CONTAINS (TEXT_KEY, :search,1) > 0) subset2
8 WHERE subset1.duns_loc = subset2.duns_loc
9 ORDER BY score(1) DESC
10 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 153618227
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 38 | 1406 | 83 (5)| 00:00:01 |
| 1 | SORT ORDER BY | | 38 | 1406 | 83 (5)| 00:00:01 |
|* 2 | HASH JOIN | | 38 | 1406 | 82 (4)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 4 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("PC"."DUNS_LOC"="D"."DUNS_LOC")
4 - access("CTXSYS"."CONTAINS"("TEXT_KEY",:SEARCH,1)>0)
5 - filter("PC"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -- ansi join:
SCOTT@orcl_11gR2> SELECT SCORE(1), duns.*
2 FROM duns
3 JOIN primary_contact
4 ON duns.duns_loc = primary_contact.duns_loc
5 WHERE CONTAINS (duns.text_key, :search, 1) > 0
6 AND primary_contact.emp_id = :employeeid
7 ORDER BY SCORE(1) DESC
8 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 153618227
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 38 | 1406 | 83 (5)| 00:00:01 |
| 1 | SORT ORDER BY | | 38 | 1406 | 83 (5)| 00:00:01 |
|* 2 | HASH JOIN | | 38 | 1406 | 82 (4)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 4 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("DUNS"."DUNS_LOC"="PRIMARY_CONTACT"."DUNS_LOC")
4 - access("CTXSYS"."CONTAINS"("DUNS"."TEXT_KEY",:SEARCH,1)>0)
5 - filter("PRIMARY_CONTACT"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -- old join:
SCOTT@orcl_11gR2> SELECT SCORE(1), duns.*
2 FROM duns, primary_contact
3 WHERE CONTAINS (duns.text_key, :search, 1) > 0
4 AND duns.duns_loc = primary_contact.duns_loc
5 AND primary_contact.emp_id = :employeeid
6 ORDER BY SCORE(1) DESC
7 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 153618227
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 38 | 1406 | 83 (5)| 00:00:01 |
| 1 | SORT ORDER BY | | 38 | 1406 | 83 (5)| 00:00:01 |
|* 2 | HASH JOIN | | 38 | 1406 | 82 (4)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 4 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("DUNS"."DUNS_LOC"="PRIMARY_CONTACT"."DUNS_LOC")
4 - access("CTXSYS"."CONTAINS"("DUNS"."TEXT_KEY",:SEARCH,1)>0)
5 - filter("PRIMARY_CONTACT"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -- in clause:
SCOTT@orcl_11gR2> SELECT SCORE(1), duns.*
2 FROM duns
3 WHERE CONTAINS (duns.text_key, :search, 1) > 0
4 AND duns.duns_loc IN
5 (SELECT primary_contact.duns_loc
6 FROM primary_contact
7 WHERE primary_contact.emp_id = :employeeid)
8 ORDER BY SCORE(1) DESC
9 /
SCORE(1) DUNS_LOC TEXT_KEY
18 1 highway
1 row selected.
Execution Plan
Plan hash value: 3825821668
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 38 | 1406 | 83 (5)| 00:00:01 |
| 1 | SORT ORDER BY | | 38 | 1406 | 83 (5)| 00:00:01 |
|* 2 | HASH JOIN SEMI | | 38 | 1406 | 82 (4)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| DUNS | 38 | 1102 | 11 (0)| 00:00:01 |
|* 4 | DOMAIN INDEX | DUNS_TEXT_KEY_IDX | | | 4 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | PRIMARY_CONTACT | 4224 | 33792 | 70 (3)| 00:00:01 |
Predicate Information (identified by operation id):
2 - access("DUNS"."DUNS_LOC"="PRIMARY_CONTACT"."DUNS_LOC")
4 - access("CTXSYS"."CONTAINS"("DUNS"."TEXT_KEY",:SEARCH,1)>0)
5 - filter("PRIMARY_CONTACT"."EMP_ID"=TO_NUMBER(:EMPLOYEEID))
SCOTT@orcl_11gR2> -
Query Performance - Query very slow to run
I have built a query to show payroll costings per month per employee by cost centres for the current fiscal year. The cost centres are selected with a hierarchy variable - it's quite a latrge hierarchy. The problem is the query takes ages to run - nearly ten minutes. It's built on a DSO so I cant aggregate it. Is there anything I can do to improve performance.
Hi Joel,
Walkthrough Checklist for Query Performance:
1. If exclusions exist, make sure they exist in the global filter area. Try to remove exclusions by subtracting out inclusions.
2. Use Constant Selection to ignore filters in order to move more filters to the global filter area. (Use ABAPer to test and validate that this ensures better code)
3. Within structures, make sure the filter order exists with the highest level filter first.
4. Check code for all exit variables used in a report.
5. Move Time restrictions to a global filter whenever possible.
6. Within structures, use user exit variables to calculate things like QTD, YTD. This should generate better code than using overlapping restrictions to achieve the same thing. (Use ABAPer to test and validate that this ensures better code).
7. When queries are written on multiproviders, restrict to InfoProvider in global filter whenever possible. MultiProvider (MultiCube) queries require additional database table joins to read data compared to those queries against standard InfoCubes (InfoProviders), and you should therefore hardcode the infoprovider in the global filter whenever possible to eliminate this problem.
8. Move all global calculated and restricted key figures to local as to analyze any filters that can be removed and moved to the global definition in a query. Then you can change the calculated key figure and go back to utilizing the global calculated key figure if desired
9. If Alternative UOM solution is used, turn off query cache.
10. Set read mode of query based on static or dynamic. Reading data during navigation minimizes the impact on the R/3 database and application server resources because only data that the user requires will be retrieved. For queries involving large hierarchies with many nodes, it would be wise to select Read data during navigation and when expanding the hierarchy option to avoid reading data for the hierarchy nodes that are not expanded. Reserve the Read all data mode for special queriesu2014for instance, when a majority of the users need a given query to slice and dice against all dimensions, or when the data is needed for data mining. This mode places heavy demand on database and memory resources and might impact other SAP BW processes and tasks.
11. Turn off formatting and results rows to minimize Frontend time whenever possible.
12. Check for nested hierarchies. Always a bad idea.
13. If "Display as hierarchy" is being used, look for other options to remove it to increase performance.
14. Use Constant Selection instead of SUMCT and SUMGT within formulas.
15. Do review of order of restrictions in formulas. Do as many restrictions as you can before calculations. Try to avoid calculations before restrictions.
16. Check Sequential vs Parallel read on Multiproviders.
17. Turn off warning messages on queries.
18. Check to see if performance improves by removing text display (Use ABAPer to test and validate that this ensures better code).
19. Check to see where currency conversions are happening if they are used.
20. Check aggregation and exception aggregation on calculated key figures. Before aggregation is generally slower and should not be used unless explicitly needed.
21. Avoid Cell Editor use if at all possible.
22. Make sure queries are regenerated in production using RSRT after changes to statistics, consistency changes, or aggregates.
23. Within the free characteristics, filter on the least granular objects first and make sure those come first in the order.
24. Leverage characteristics or navigational attributes rather than hierarchies. Using a hierarchy requires reading temporary hierarchy tables and creates additional overhead compared to characteristics and navigational attributes. Therefore, characteristics or navigational attributes result in significantly better query performance than hierarchies, especially as the size of the hierarchy (e.g., the number of nodes and levels) and the complexity of the selection criteria increase.
25. If hierarchies are used, minimize the number of nodes to include in the query results. Including all nodes in the query results (even the ones that are not needed or blank) slows down the query processing. The u201Cnot assignedu201D nodes in the hierarchy should be filtered out, and you should use a variable to reduce the number of hierarchy nodes selected.
Regards
Vivek Tripathi -
Reg: Process Chain, query performance tuning steps
Hi All,
I come across a question like, There is a process chain of 20 processes.out of which 5 processes are completed at the 6th step error occured and it cannot be rectified. I should start the chain again from the 7th step.If i go to a prticular step i can do that particular step, How can i start the entair chain again from step 7.i know that i need to use a function module but i dont know the name of FM. Please somebody help me out.
Please let me know the steps involved in query performance tuning and aggregate tuning.
Thanks & Regards
Omkar.KHi,
Process Chain
Method 1 (when it fails in a step/request)
/people/siegfried.szameitat/blog/2006/02/26/restarting-processchains
How is it possible to restart a process chain at a failed step/request?
Sometimes, it doesn't help to just set a request to green status in order to run the process chain from that step on to the end.
You need to set the failed request/step to green in the database as well as you need to raise the event that will force the process chain to run to the end from the next request/step on.
Therefore you need to open the messages of a failed step by right clicking on it and selecting 'display messages'.
In the opened popup click on the tab 'Chain'.
In a parallel session goto transaction se16 for table rspcprocesslog and display the entries with the following selections:
1. copy the variant from the popup to the variante of table rspcprocesslog
2. copy the instance from the popup to the instance of table rspcprocesslog
3. copy the start date from the popup to the batchdate of table rspcprocesslog
Press F8 to display the entries of table rspcprocesslog.
Now open another session and goto transaction se37. Enter RSPC_PROCESS_FINISH as the name of the function module and run the fm in test mode.
Now copy the entries of table rspcprocesslog to the input parameters of the function module like described as follows:
1. rspcprocesslog-log_id -> i_logid
2. rspcprocesslog-type -> i_type
3. rspcprocesslog-variante -> i_variant
4. rspcprocesslog-instance -> i_instance
5. enter 'G' for parameter i_state (sets the status to green).
Now press F8 to run the fm.
Now the actual process will be set to green and the following process in the chain will be started and the chain can run to the end.
Of course you can also set the state of a specific step in the chain to any other possible value like 'R' = ended with errors, 'F' = finished, 'X' = cancelled ....
Check out the value help on field rspcprocesslog-state in transaction se16 for the possible values.
Query performance tuning
General tips
Using aggregates and compression.
Using less and complex cell definitions if possible.
1. Avoid using too many nav. attr
2. Avoid RKF and CKF
3. Many chars in row.
By using T-codes ST03 or ST03N
Go to transaction ST03 > switch to expert mode > from left side menu > and there in system load history and distribution for a particual day > check query execution time.
/people/andreas.vogel/blog/2007/04/08/statistical-records-part-4-how-to-read-st03n-datasets-from-db-in-nw2004
/people/andreas.vogel/blog/2007/03/16/how-to-read-st03n-datasets-from-db
Try table rsddstats to get the statistics
Using cache memoery will decrease the loading time of the report.
Run reporting agent at night and sending results to email.This will ensure use of OLAP cache. So later report execution will retrieve the result faster from the OLAP cache.
Also try
1. Use different parameters in ST03 to see the two important parameters aggregation ratio and records transferred to F/E to DB selected.
2. Use the program SAP_INFOCUBE_DESIGNS (Performance of BW infocubes) to see the aggregation ratio for the cube. If the cube does not appear in the list of this report, try to run RSRV checks on the cube and aggregates.
Go to SE38 > Run the program SAP_INFOCUBE_DESIGNS
It will shown dimension Vs Fact tables Size in percent.If you mean speed of queries on a cube as performance metric of cube,measure query runtime.
3. --- sign is the valuation of the aggregate. You can say -3 is the valuation of the aggregate design and usage. ++ means that its compression is good and access is also more (in effect, performance is good). If you check its compression ratio, it must be good. -- means the compression ratio is not so good and access is also not so good (performance is not so good).The more is the positives...more is useful the aggregate and more it satisfies the number of queries. The greater the number of minus signs, the worse the evaluation of the aggregate. The larger the number of plus signs, the better the evaluation of the aggregate.
if "-----" then it means it just an overhead. Aggregate can potentially be deleted and "+++++" means Aggregate is potentially very useful.
Refer.
http://help.sap.com/saphelp_nw70/helpdata/en/b8/23813b310c4a0ee10000000a114084/content.htm
http://help.sap.com/saphelp_nw70/helpdata/en/60/f0fb411e255f24e10000000a1550b0/frameset.htm
4. Run your query in RSRT and run the query in the debug mode. Select "Display Aggregates Found" and "Do not use cache" in the debug mode. This will tell you if it hit any aggregates while running. If it does not show any aggregates, you might want to redesign your aggregates for the query.
Also your query performance can depend upon criteria and since you have given selection only on one infoprovider...just check if you are selecting huge amount of data in the report
Check for the query read mode in RSRT.(whether its A,X or H)..advisable read mode is X.
5. In BI 7 statistics need to be activated for ST03 and BI admin cockpit to work.
By implementing BW Statistics Business Content - you need to install, feed data and through ready made reports which for analysis.
http://help.sap.com/saphelp_nw70/helpdata/en/26/4bc0417951d117e10000000a155106/frameset.htm
/people/vikash.agrawal/blog/2006/04/17/query-performance-150-is-aggregates-the-way-out-for-me
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/1955ba90-0201-0010-d3aa-8b2a4ef6bbb2
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/ce7fb368-0601-0010-64ba-fadc985a1f94
http://help.sap.com/saphelp_nw04/helpdata/en/c1/0dbf65e04311d286d6006008b32e84/frameset.htm
You can go to T-Code DB20 which gives you all the performance related information like
Partitions
Databases
Schemas
Buffer Pools
Tablespaces etc
use tool RSDDK_CHECK_AGGREGATE in se38 to check for the corrupt aggregates
If aggregates contain incorrect data, you must regenerate them.
Note 646402 - Programs for checking aggregates (as of BW 3.0B SP15)
Thanks,
JituK -
Hi All,
I worked on the query a month ago and the fix worked for me in test intance but failed in production. Following is the URL for the previous thread.
SQL query performance issues.
Following is the tkprof file.
CURSOR_ID:76 LENGTH:2383 ADDRESS:f6b40ab0 HASH_VALUE:2459471753 OPTIMIZER_GOAL:ALL_ROWS USER_ID:443 (APPS)
insert into cos_temp(
TRX_DATE, DEPT, PRODUCT_LINE, PART_NUMBER,
CUSTOMER_NUMBER, QUANTITY_SOLD, ORDER_NUMBER,
INVOICE_NUMBER, EXT_SALES, EXT_COS,
GROSS_PROFIT, ACCT_DATE,
SHIPMENT_TYPE,
FROM_ORGANIZATION_ID,
FROM_ORGANIZATION_CODE)
select a.trx_date,
g.segment5 dept,
g.segment4 prd,
m.segment1 part,
d.customer_number customer,
b.quantity_invoiced units,
-- substr(a.sales_order,1,6) order#,
substr(ltrim(b.interface_line_attribute1),1,10) order#,
a.trx_number invoice,
(b.quantity_invoiced * b.unit_selling_price) sales,
(b.quantity_invoiced * nvl(price.operand,0)) cos,
(b.quantity_invoiced * b.unit_selling_price) -
(b.quantity_invoiced * nvl(price.operand,0)) profit,
to_char(to_date('2010/02/28 00:00:00','yyyy/mm/dd HH24:MI:SS'),'DD-MON-RR') acct_date,
'DRP',
l.ship_from_org_id,
p.organization_code
from ra_customers d,
gl_code_combinations g,
mtl_system_items m,
ra_cust_trx_line_gl_dist c,
ra_customer_trx_lines b,
ra_customer_trx_all a,
apps.oe_order_lines l,
apps.HR_ORGANIZATION_INFORMATION i,
apps.MTL_INTERCOMPANY_PARAMETERS inter,
apps.HZ_CUST_SITE_USES_ALL site,
apps.qp_list_lines_v price,
apps.mtl_parameters p
where a.trx_date between to_date('2010/02/01 00:00:00','yyyy/mm/dd HH24:MI:SS')
and to_date('2010/02/28 00:00:00','yyyy/mm/dd HH24:MI:SS')+0.9999
and a.batch_source_id = 1001 -- Sales order shipped other OU
and a.complete_flag = 'Y'
and a.customer_trx_id = b.customer_trx_id
and b.customer_trx_line_id = c.customer_trx_line_id
and a.sold_to_customer_id = d.customer_id
and b.inventory_item_id = m.inventory_item_id
and m.organization_id
= decode(substr(g.segment4,1,2),'01',5004,'03',5004,
'02',5003,'00',5001,5002)
and nvl(m.item_type,'0') <> '111'
and c.code_combination_id = g.code_combination_id+0
and l.line_id = b.interface_line_attribute6
and i.organization_id = l.ship_from_org_id
and p.organization_id = l.ship_from_org_id
and i.org_information3 <> '5108'
and inter.ship_organization_id = i.org_information3
and inter.sell_organization_id = '5108'
and inter.customer_site_id = site.site_use_id
and site.price_list_id = price.list_header_id
and product_attr_value = to_char(m.inventory_item_id)
call count cpu elapsed disk query current rows misses
Parse 1 0.47 0.56 11 197 0 0 1
Execute 1 3733.40 3739.40 34893 519962154 11 188 0
total 2 3733.87 3739.97 34904 519962351 11 188 1
| Rows Row Source Operation
| ------------ ---------------------------------------------------
| 188 HASH JOIN (cr=519962149 pr=34889 pw=0 time=2607.35)
| 741 .TABLE ACCESS BY INDEX ROWID QP_PRICING_ATTRIBUTES (cr=519939426 pr=34889 pw=0 time=2457.32)
| 254644500 ..NESTED LOOPS (cr=519939265 pr=34777 pw=0 time=3819.67)
| 254643758 ...NESTED LOOPS (cr=8921833 pr=29939 pw=0 time=1274.41)
| 741 ....NESTED LOOPS (cr=50042 pr=7230 pw=0 time=11.37)
| 741 .....NESTED LOOPS (cr=48558 pr=7229 pw=0 time=11.35)
| 741 ......NESTED LOOPS (cr=47815 pr=7223 pw=0 time=11.32)
| 3237 .......NESTED LOOPS (cr=41339 pr=7223 pw=0 time=12.42)
| 3237 ........NESTED LOOPS (cr=38100 pr=7223 pw=0 time=12.39)
| 3237 .........NESTED LOOPS (cr=28296 pr=7139 pw=0 time=12.29)
| 1027 ..........NESTED LOOPS (cr=17656 pr=4471 pw=0 time=3.81)
| 1027 ...........NESTED LOOPS (cr=13537 pr=4404 pw=0 time=3.30)
| 486 ............NESTED LOOPS (cr=10873 pr=4240 pw=0 time=0.04)
| 486 .............NESTED LOOPS (cr=10385 pr=4240 pw=0 time=0.03)
| 486 ..............TABLE ACCESS BY INDEX ROWID RA_CUSTOMER_TRX_ALL (cr=9411 pr=4240 pw=0 time=0.02)
| 75253 ...............INDEX RANGE SCAN RA_CUSTOMER_TRX_N5 (cr=403 pr=285 pw=0 time=0.38)
| 486 ..............TABLE ACCESS BY INDEX ROWID HZ_CUST_ACCOUNTS (cr=974 pr=0 pw=0 time=0.01)
| 486 ...............INDEX UNIQUE SCAN HZ_CUST_ACCOUNTS_U1 (cr=488 pr=0 pw=0 time=0.01)
| 486 .............INDEX UNIQUE SCAN HZ_PARTIES_U1 (cr=488 pr=0 pw=0 time=0.01)
| 1027 ............TABLE ACCESS BY INDEX ROWID RA_CUSTOMER_TRX_LINES_ALL (cr=2664 pr=164 pw=0 time=1.95)
| 2063 .............INDEX RANGE SCAN RA_CUSTOMER_TRX_LINES_N2 (cr=1474 pr=28 pw=0 time=0.22)
| 1027 ...........TABLE ACCESS BY INDEX ROWID RA_CUST_TRX_LINE_GL_DIST_ALL (cr=4119 pr=67 pw=0 time=0.54)
| 1027 ............INDEX RANGE SCAN RA_CUST_TRX_LINE_GL_DIST_N1 (cr=3092 pr=31 pw=0 time=0.20)
| 3237 ..........TABLE ACCESS BY INDEX ROWID MTL_SYSTEM_ITEMS_B (cr=10640 pr=2668 pw=0 time=15.35)
| 3237 ...........INDEX RANGE SCAN MTL_SYSTEM_ITEMS_B_U1 (cr=2062 pr=40 pw=0 time=0.33)
| 3237 .........TABLE ACCESS BY INDEX ROWID OE_ORDER_LINES_ALL (cr=9804 pr=84 pw=0 time=0.77)
| 3237 ..........INDEX UNIQUE SCAN OE_ORDER_LINES_U1 (cr=6476 pr=47 pw=0 time=0.43)
| 3237 ........TABLE ACCESS BY INDEX ROWID MTL_PARAMETERS (cr=3239 pr=0 pw=0 time=0.04)
| 3237 .........INDEX UNIQUE SCAN MTL_PARAMETERS_U1 (cr=2 pr=0 pw=0 time=0.01)
| 741 .......TABLE ACCESS BY INDEX ROWID HR_ORGANIZATION_INFORMATION (cr=6476 pr=0 pw=0 time=0.10)
| 6474 ........INDEX RANGE SCAN HR_ORGANIZATION_INFORMATIO_FK2 (cr=3239 pr=0 pw=0 time=0.03)Please help.
Regards
Ashish| 254644500 ..NESTED LOOPS (cr=519939265 pr=34777 pw=0 time=3819.67)
| 254643758 ...NESTED LOOPS (cr=8921833 pr=29939 pw=0 time=1274.41)There is no way the optimizer should choose to process that many rows using nested loops.
Either the statistics are not up to date, the data values are skewed or you have some optimizer parameter set to none default to force index access.
Please post explain plan and optimizer* parameter settings. -
SQL query performance question
So I had this long query that looked like this:
SELECT a.BEGIN_DATE, a.END_DATE, a.DEAL_KEY, (select name from ideal dd where a.deal_key = dd.deal_key) DEALNAME, a.deal_term_key
FROM
ideal d, ideal_term a,( select deal_key, deal_term_key, max(createdOn) maxdate from Ideal_term B
where createdOn <= '03-OCT-12 10.03.00 AM' group by deal_key, deal_term_key ) B
WHERE a.begin_date <= '20-MAR-09 01.01.00 AM'
* and a.end_date >= '19-MAR-09 01.00.00 AM'*
* and A.deal_key = b.deal_key*
* and A.deal_term_key = b.deal_term_key*
* and a.createdOn = b.maxdate*
* and d.deal_key = a.deal_key*
* and d.name like 'MVPP1 B'*
order by
* a.begin_date, a.deal_key, a.deal_term_key;*
This performed very poorly for a record in one of the tables that has 43,000+ revisions. It took about 1 minute and 40 seconds. I asked the database guy at my company for help with it and he re-wrote it like so:
SELECT a.BEGIN_DATE, a.END_DATE, a.DEAL_KEY, (select name from ideal dd where a.deal_key = dd.deal_key) DEALNAME, a.deal_term_key
FROM ideal d
INNER JOIN (SELECT deal_key,
deal_term_key,
MAX(createdOn) maxdate
FROM Ideal_term B2
WHERE '03-OCT-12 10.03.00 AM' >= createdOn
GROUP BY deal_key, deal_term_key) B1
ON d.deal_key = B1.deal_key
INNER JOIN ideal_term a
ON B1.deal_key = A.deal_key
AND B1.deal_term_key = A.deal_term_key
AND B1.maxdate = a.createdOn
AND d.deal_key = a.deal_key + 0
WHERE a.begin_date <= '20-MAR-09 01.01.00 AM'
AND a.end_date >= '19-MAR-09 01.00.00 AM'
AND d.name LIKE 'MVPP1 B'
ORDER BY a.begin_date, a.deal_key, a.deal_term_key
this works much better, it only takes 0.13 seconds. I've bee trying to figure out why exaclty his version performs so much better. His only epxlanation was that the "+ 0" in the WHERE clause prevented Oracle from using an index for that column which created a bad plan initially.
I think there has to be more to it than that though. Can someone give me a detailed explanation of why the second version of the query performed so much faster.
Thanks.
Edited by: su**** on Oct 10, 2012 1:31 PMI used Autotrace in SQL developer. Is that sufficient? Here is the Autotrace and Explain for the slow query:
and for the fast query:
I said that I thought there was more to it because when my team members and I looked at the re-worked query the database guy sent us, our initial thoughts were that in the slow query some of the tables didn't have joins and because of that the query formed a Cartesian product and this resulted in a huge 43,000+ rows matrix.
In his version all tables had joins properly defined and in addition he had that +0 which told it to ignore the index for the attribute deal_key of table ideal_term. I spoke with the database guy today and he confirmed our theory. -
Hi,
I am working on a application Developed in Forms10g and Oralce 10g.
I have few very large transaction tables in db and most of the screens in my application based on these tables only.
When user performs a query (with out any filter conditions) the whole table(s) loaded into memory and takes very long time. Further queries on the same screen perform better.
How can I keep these tables in memory (buffer) always to reduce the initial query time?
or
Is there any way to share the session buffers with other sessions, sothat it does not take long time in each session?
or
Any query performance tuning suggestions will be appreciated.
Thanks in advanceThanks a lot for your posts, very large means around
12 million rows. Yep, that's a large table
I have set the query all records to "No". Which is good. It means only enough records are fetched to fill the initial block. That's probably about 10 records. All the other records are not fetched from the database, so they're also not kept in memory at the Forms server.
Even when I try the query in SQL PLUS it is taking
long time. Sounds like a query performance problem, not a Forms issue. You're probably better of asking in the database or SQL forum. You could at least include the SELECT statement here if you want any help with it. We can't guess why a query is slow if we have no idea what the query is.
My concern is, when I execute the same query again or
in another session (some other user or same user),
can I increase the performance because the tables are
already in memory. any possibility for this? Can I
set any database parameters to share the data between
sessions like that... The database already does this. If data is retrieved from disk for one user it is cached in the SGA (Shared Global Area). Mind the word Shared. This caching information is shared by all sessions, so other users should benefit from it.
Caching also has its limits. The most obvious one is the size of the SGA of the database server. If the table is 200 megabyte and the server only has 8 megabyte of cache available, than caching is of little use.
Am I thinking in the right way? or I lost some where?Don't know.
There's two approaches:
- try to tune the query or database to have better performance. For starters, open SQL*Plus, execute "set timing on", then execute "set autotrace traceonly explain statistics", then execute your query and look at the results. it should give you an idea on how the database is executing the query and what improvements could be made. You could come back here with the SELECT statement and timing and trace results, but the database or SQL forum is probably better
- MORE IMPORTANTLY: think if it is necessary for users to perform such time consuming (and perhaps complex) queries. Do users really need the ability to query all records. Are they ever going to browse through millions of records?
>
>
Thanks -
Inventory Ageing query performance
Hi All,
I have created inventory ageing query on our custom cube which is replica of 0IC_C03. We have data from 2003 onwards. the performance of the query is very poor the system almost hangs. I tried to create aggregates to improve performance but its failed. What i should do to improve the performance and why the aggregate filling is failed. Cube have compressed data. Pls guide.
Regards:
JitendraInaddition to the above posts
Check the below points ... and take action accordingly to increase the query performance.
mainly check --Is the Cube data Compressed. it will increase the performance of the query..
1)If exclusions exist, make sure they exist in the global filter area. Try to remove exclusions by subtracting out inclusions.
2)Check code for all exit variables used in a report.
3)Check the read mode for the query. recommended is H.
4)If Alternative UOM solution is used, turn off query cache.
5)Use Constant Selection instead of SUMCT and SUMGT within formulas.
6)Check aggregation and exception aggregation on calculated key figures. Before aggregation is generally slower and should not be used unless explicitly needed.
7)Check if large hierarchies are used and the entry hierarchy level is as deep as possible. This limits the levels of the hierarchy that must be processed.
Use SE16 on the inclusion tables and use the List of Value feature on the column successor and predecessor to see which entry level of the hierarchy is used.
8)Within the free characteristics, filter on the least granular objects first and make sure those come first in the order.
9)If hierarchies are used, minimize the number of nodes to include in the query results. Including all nodes in the query results (even the ones that are not needed or blank) slows down the query processing.
10)Check the user exits usage involved in OLAP run time?
11)Use Constant Selection instead of SUMCT and SUMGT within formulas.
12)
Turn on the BW Statistics: RSA1, choose Tools -> BW statistics for InfoCubes(Choose OLAP and WHM for your relevant Cubes)
To check the Query Performance problem
Use ST03N -> BW System load values to recognize the problem. Use the number given in table 'Reporting - InfoCubes:Share of total time (s)' to check if one of the columns %OLAP, %DB, %Frontend shows a high number in all InfoCubes.
You need to run ST03N in expert mode to get these values
based on the analysis and the values taken from the above - Check if an aggregate is suitable or setting OLAP etc.
Edited by: prashanthk on Nov 26, 2010 9:17 AM
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