Query performance data

Hi,
I have archived our dev database and reduced the table size from one million to 45000 records. The same table is having 1 million records in PROD. Before implementing the archival job in PROD I want to see the difference in query performance. When I ran
the same query in DEV and PROD both are taking 1 sec for execution. On a basic terms, there is not much performance improvement. But I ran io statistics and statistics time on for the queries in DEV and PROD.
DEV - Clustreredindex look up shows-88% and nonclustered index lookup is 2 %
PROD- Clustreredindex look up shows-18% and nonclustered index lookup is 67 %
I am not sure why this difference in query plan. But the logical read in DEV-1657 and CPU time = 12609 ms
PROD-logical read19434 and CPU time = 13625
Can any one help me in analysing the key differences to come to a conclusion that says there is some performance improvement?
Thanks

Hi
The statistics on DEV:
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 20, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'AirlineGroupMaster'. Scan count 0, logical reads 158, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#2F8E53E2'. Scan count 1, logical reads 79, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'TimefenceSubGroup'. Scan count 1, logical reads 9, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#3082781B'. Scan count 1, logical reads 1, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 16 ms, elapsed time = 10 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 15 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
(492 row(s) affected)
Table 'AirlineGroupMaster'. Scan count 0, logical reads 1024, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#2F8E53E2'. Scan count 1, logical reads 512, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'FLIGHTDETAILS'. Scan count 5, logical reads 1607, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#3082781B'. Scan count 1, logical reads 1, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 13000 ms, elapsed time = 13373 ms.
SQL Server Execution Times:
   CPU time = 13000 ms, elapsed time = 13373 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 13031 ms, elapsed time = 13392 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
IN PROD:
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 20, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'AirlineGroupMaster'. Scan count 0, logical reads 158, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#58010028'. Scan count 1, logical reads 79, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'TimefenceSubGroup'. Scan count 1, logical reads 9, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#58F52461'. Scan count 1, logical reads 1, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 96 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
Table 'Worktable'. Scan count 0, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
(518 row(s) affected)
Table 'AirlineGroupMaster'. Scan count 0, logical reads 2196, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'FLIGHTDETAILS'. Scan count 20, logical reads 19445, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#58010028'. Scan count 5, logical reads 5, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table '#58F52461'. Scan count 1, logical reads 1, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
(1 row(s) affected)
SQL Server Execution Times:
   CPU time = 13859 ms, elapsed time = 16827 ms.
SQL Server Execution Times:
   CPU time = 13859 ms, elapsed time = 16827 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 13859 ms, elapsed time = 16972 ms.
SQL Server parse and compile time:
   CPU time = 0 ms, elapsed time = 0 ms.
SQL Server Execution Times:
   CPU time = 0 ms, elapsed time = 0 ms.

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QUERY PERFORMANCE AND DATA LOADING PERFORMANCE ISSUES

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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.
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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.
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Hello.
I have created a dashboard view in SCOM 2012 R2 for power consumption of a number of servers. These servers are all part of a group. The power consists of a couple of different performance monitors (VMWare-sensor for VMs and Power Meter from the Power
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Hi,
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Hi All,
I am using oracle 9i DB and forms 6i.
In query form ,qry took long time to load the data into form.
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1) What is a crore? Wikipedia seems to indicate that it's either 10,000,000 or 500,000
http://en.wikipedia.org/wiki/Crore
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Hi Raj,
Here are some few tips which helps you in improving ur query performance
Checklist for Query Performance
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Performance data from system and DMS logs on PDW.

I want all of ther performance data as given below of the particular completed job with respective login ID from system or DMS logs on PDW
What is total Memory and CPU used for completing the job?
How many rows processed?
What is read and write data size?
What is disk I/O ?
How much time taken for completing the job?
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Hi Prasad, you may want to have a look at the following links:
http://download.microsoft.com/download/5/0/1/5015A62E-06BF-4DEC-B90A-37D52E279DE5/SQL_Server_2012_Parallel_Data_Warehouse_Breakthrough_Platform_White_Paper.pdf
http://saldeloera.wordpress.com/2012/08/27/pdw-tip-query-optimization-techniques-to-avoid-shufflemoves-and-partition-moves/
Regards, Leo

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Folks,
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Oracle 9.2.0.8 database, Oracle 9.2.0.1 driver, WL 8.1.6
We do not see the degradation in case of the following: -
Oracle 9.2.0.8 database, Oracle 9.2.0.1 driver, WL 8.1.5
Oracle 9.2.0.6 database, Oracle 9.2.0.1 driver, WL 8.1.5
This shows that the problem could be with the WL 8.1.6 version and I was wondering if any of you have faced this before? The query retrieves a set of data from Oracle none of which contain the AsciiStream data type, which is noted as a problem in WL 8.1.6, but that too, only for WL JDBC drivers.
Any ideas appreciated.

Folks,
We are upgrading WebLogic from 8.1.5 to 8.1.6 and Oracle from 9.2.0.6 to 9.2.0.8. We use the Oracle thin client driver for 9.2.0.8 to connect from the application to Oracle.
When we use the following combination of the stack we see SQL query performance degradation: -
Oracle 9.2.0.8 database, Oracle 9.2.0.8 driver, WL 8.1.6
Oracle 9.2.0.8 database, Oracle 9.2.0.1 driver, WL 8.1.6
We do not see the degradation in case of the following: -
Oracle 9.2.0.8 database, Oracle 9.2.0.1 driver, WL 8.1.5
Oracle 9.2.0.6 database, Oracle 9.2.0.1 driver, WL 8.1.5
This shows that the problem could be with the WL 8.1.6 version and I was wondering if any of you have faced this before? The query retrieves a set of data from Oracle none of which contain the AsciiStream data type, which is noted as a problem in WL 8.1.6, but that too, only for WL JDBC drivers.
Any ideas appreciated.

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

Query Performance Issues

Hi Gurus,
I m woking on performance tuning at the moment and wants some tips
regarding the Query performance tuning,if anyone can helpme in that
rfrence.
the thing is that i have got an idea about the system and now the
issues r with DB space, Abap Dumps, problem in free space in table
space, no number range buffering,cubes r using too many aggrigates,
large IC,Large ODS, and many others.
So my questionis that is anyone can tell me that how to resolve the
issues with the Large master data tables,and large ODS,and one more
importnat issue is KPI´s exceding there refrence valuses so any idea
how to deal with them.
waiting for the valuable responces.
thanks In advance
Redards
Amit

Hi Amit
For Query performance issue u can go for :-
Aggregates : They will help u a lot to make ur query faster becuase query doesnt hits ur cube it hits the aggregates which has very less number of records in comp to ur cube
secondly i wud suggest u is use CKF in place of formulaes if any in the query
other thing is avoid upto the extent possible the use fo nav attr . if u want to use them use it upto the minimal level reason i am saying so is during the query exec whn ever there is nav attr it provides unncessary join to ur MD and thus dec query perfo
be specifc to rows and columns if u r not sure of a kf or a char then better put it in a free char.
use filters if possible
if u follow these m sure ur query perfo will inc
Assign points if applicable
Thanks
puneet

Query Performance Issues on a cube sized 64GB.

Hi,
We have a non-time based cube whose size is 64 GB . Effectively, I can't use time dimension for partitioning. The transaction table has ~ 850 million records. We have 20+ dimensions among which 2 of the dimensions have 50 million records.
I have equally distributed the fact table records among 60 partitions. Each partition size is around 900 MB.
The processing of the cube is not an issue as it completes in 3.5 hours. The issue is with the query performance of the cube.
When an MDX query is submitted, unfortunately, in majority of the cases the storage engine has to scan all the partitions (as our cube is not time dependent and we can't find a suitable dimension that will fit the bill to partition measure group based
on it.)
I'm aware of the cache warming and usage based aggregation(UBO) techniques.
However, the cube is available for users to perform adhoc queries and hence the benefits of cache warming and UBO may cease to contribute to the performance gain as there is a high probability that each user may look at the data from different perspectives
(especially when we have 20 + dimensions) as day(s) progress.
Also, we have 15 + average calculations (calculated measures) in the cube. So, the storage engine sends all the granular data that the formula engine might have requested (could be millions of rows) and then perform the average calculation.
A look at the profiler suggested that considerable amount of time has been spent by storage engine to gather the records (from 60 partitions).
FYI - Our server has RAM 32 GB and 8 cores and it is exclusive to Analysis Services.
I would appreciate comments from anyone who has worked on a large cube that is not time dependent and the steps they took to improve the adhoc query performance for the users.
Thanks
CoolP

Hello CoolP,
Here is a good articles regarding how to tuning query performance in SSAS, please see:
Analysis Services Query Performance Top 10 Best Practices:
http://technet.microsoft.com/en-us/library/cc966527.aspx
Hope you can find some helpful clues to tuning your SSAS Server query performance. Moreover, there are two ways to improve the query response time for an increasing number of end-users:
Adding more power to the existing server (scale up)
Distributing the load among several small servers (scale out)
For detail information, please see:
http://technet.microsoft.com/en-us/library/cc966449.aspx
Regards,
Elvis Long
TechNet Community Support

How to improve Query performance on large table in MS SQL Server 2008 R2

I have a table with 20 million records. What is the best option to improve query performance on this table. Is partitioning the table into filegroups is a best option or splitting the table into multiple smaller tables?

Hi bala197164,
First, I want to inform that both to partition the table into filegroups and split the table into multiple smaller tables can improve the table query performance, and they are fit for different situation. For example, our table have one hundred columns and
some columns are not related to this table object directly (for example, there is a table named userinfo to store user information, it has columns address_street, address_zip,address_ province columns, at this time, we can create a new table named as Address,
and add a foreign key in userinfo table references Address table), under this situation, by splitting a large table into smaller, individual tables, queries that access only a fraction of the data can run faster because there is less data to scan. Another
situation is our table records can be grouped easily, for example, there is a column named year to store information about product release date, at this time, we can partition the table into filegroups to improve the query performance. Usually, we perform
both of methods together. Additionally, we can add index to table to improve the query performance. For more detail information, please refer to the following document:
Partitioning:
http://msdn.microsoft.com/en-us/library/ms178148.aspx
CREATE INDEX (Transact-SQL):
http://msdn.microsoft.com/en-us/library/ms188783.aspx
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Allen Li
TechNet Community Support

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.K

Hi,
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

Impact of real time cube on query performance and OLAP cache

Hi:
We have actual and plan cubes both setup as real time cubes (only plan cube is being planned against, not actual cube) and both cubes are compressed once a day.
We are planning on implementing BIA accelerator and have questions related to query performance optimization:
1/ Is there are any query performance benefits in changing the actual cube to be a basic cube (using program SAP_CONVERT_NORMAL_TRANS) if the F table is fully compressed
2/ Can OLAP cache be leveraged for the queries run against the real time cubes e.g. actual cubes
3/ What is the impact on BIA of having the actual cube as real time (whetehr or not there is data being loaded/planned during the day in that cube)
Thank you in advance,
Catherine

1) Is there are any query performance benefits in changing the actual cube to be a basic cube (using program SAP_CONVERT_NORMAL_TRANS) if the F table is fully compressed
From the performance point of view, tha actual cubes i.e std cubes are relatively better.
2) Yes OLAP cache can be leveraged for bringing up the plan query but all the calculations are done in the planning buffer.
3) Not sure.

Query performance and Transport

Guys, (Those of you that already work in the field maybe able to better answer this question.)
Let's say if an end-user comes and complaints about a certain query performance. The developer ends up creating aggregates on the cube. Would he be doing all this process on dev box then transport to QA and on to Production environment or directly on production environment and won't need to transport anything? Any kind of documentation will be helpful for this.
Thanks,
RG

Aggregates are created directly in production.
We create aggregate based on query statistics...which is based on data read by query and various times.
Since this is based of data in prod we develop directly in prod...i mean dev wont have any data so you dont have any basis to create aggregate. even if you use statistics data from prod to create aggregate in dev...you cannot again check performance improvement coz of aggregate in dev as it wont have data as prod has

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