Unexplained poor table query performance

Hi All
I am really open to any advice as I have hit a kind of brick wall, a developer came to me asking about y a procedure was performing so slowly in beta as opposed to dev and after looking at exactly what it did I indentified the offending
select statement.
The query was basically passing some ids into a user defined table and using that thoses ids to filter.
Select gc.id
From temperatures as gcm left outer join
gauges gc ON ( gc.id = gcm.id Or gc.id IS NULL )
AND ( gc.countryid = gcm.countryid or gcm.countryid is null )
where souriceid = 3
So the gauges table has around 90K where as the temperatures has around 3 million .
K the test on the development server and the above returns in under 3 seconds where as the beta is just over 1 minute .
The beta in terms of processing power is much fast and both have the same version of SQL2012 sp1 ( 11.0.3128 ( x64))
having ran a quick query on index fragmentation i find there are a few indexes within the temperature table that are reasonable high. I then rebuild them and see that they are pretty much back to an acceptable level. Again I try the select
and a few times and get a range of times .
I then tried a restore from the weekend just to see if there was anything that may have changed and wondering if I was beginning to clutch at straws.
low and behold the restore was not only quick but from an index fragmentation point of view not in as great shape.
Ive compared the two tables which are identical with the only difference being in data to which I copied over to the restore and got the same 2 second result.
Any help on what to do next would be great , as I could replace the table with the restored one but I would like to know why this is happening .
Many Thanks
Robert

The query is a bit strange with the NULL checks on gc.id and gcm.countryid.
Since temperatures is the retained (outer) table, you can remove the part "or gcm.countryid is null".
Also, if table gauges does not allow NULLs (or does not have NULLs) in column id, you should remove the part "OR gc.id IS NULL".
If the query can be simplified as stated above, then all you need is a compound index on (id, countryid) or on (countryid, id) on both tables.
If the problem still persists, you can check the query plan to see what is different, and that should give you a clue about the issue.
Please note that for performance related queries, it is essential to show the exact query you are using. For example, if you are using a local variable or a parameter instead of "3" in your query, that makes a big difference.
If you need more help, then please post DDL for the tables and indexes that are involved.
Gert-Jan

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Speed up table query performance

Hi,
I have a table with 200mil rows (approx 30G size). What are the multiple ways to improve query performance on this table? We have tried indexing, but doesn't help much. Partitioning - I am not sure how to use it, since there is no clear criteria for creating partitions. What other options could I use?
If this is in the wrong forum, please suggest appropriate forum.
Thanks in advance.

Assuming you have purchased the partitioning option from Oracle and are not violating the terms of your license agreement then partitioning is the way to go.
For the concept docs go to http://tahiti.oracle.com
For demos of all of the various forms of partitioning go to Morgan's Library at www.psoug.org and look up partitioning.
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Why am I Observing Poor Geospatial Query Performance?

Our HANA Rev. 72 Amazon instance is performing very poorly when selecting geospatial polygons from tables. I'm hoping someone out there knows why.
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JeffKasper wrote:
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Poor report query performance

Hi Team, below is the report view which is causing slowness , let me know any suggestions if you have
CREATE VIEW [REPORT].[View_MachinePerformanceBySlot] AS
SELECT
      VA.SITE_NUM,
       VA.SLOT_NUMBER,
       VA.AREA_NAME,
       VA.MANUFACTURER_NAME,
       VA.ATYP_ID,
       VA.HOLD_PERCENTAGE,
       VA.SLOT_DENOM,
       VA.SERIAL_NUM,
       VA.AREA_ID,
       a.THEM_NAME,
    a.GAME_NAME,
       a.TCAT_LONG_NAME,
       a.TGRP_LONG_NAME,
       VA.MTYP_NAME,
       VA.OWNER_LABEL_KEY,
       vsmr.SDS_Bets AS HANDLE,
       vsmr.SDS_Plays AS HANDLE_PULL,
       vsmr.Days_On AS DAYS_ACTIVE,
       VA.GAME_TYPE,
       Mtr_NamedAsstID,
       Mtr_GameDay AS MVR_GDAY_DATE,
    vsmr.PTYP_ID AS MVR_PTYP_ID,
    Mtr_PeriodType AS PERIOD_TYPE,
    CAST(SDS_Bets AS FLOAT) AS MVR_BETS,
    CAST(SLIP_APJP_JACKPOT + SLIP_PROGRESSIVE_JAKPT
   + SLIP_MYST_JACKPOT + SLIP_CC_JACKPOT
   + SLIP_CELEBRATION_JACKPOT + vsmr.CASH_PROG+vsmr.NONCASH_PROG
    AS FLOAT)AS JACKPOTS,
    SDS_MachinePaidProgressiveWins,
    MVR_THEORETICAL_WIN,
    (CASE WHEN vsmr.SDS_Bets = 0 THEN 0
   ELSE (MVR_THEORETICAL_WIN * 100) / vsmr.SDS_Bets
    END) AS ACTUAL_PERCENTAGE,
    ((CASE WHEN vsmr.SDS_Bets = 0 THEN 0 ELSE (MVR_THEORETICAL_WIN * 100) / vsmr.SDS_Bets END)
- VA.HOLD_PERCENTAGE) AS VAR_HOLD_PERCENT,
    Days_On AS MVR_DAYS_ONLINE_VAL,
    (vsmr.SDS_1_bills + vsmr.SDS_5_bills + vsmr.SDS_10_bills + vsmr.SDS_20_bills
       + vsmr.SDS_50_bills + vsmr.SDS_100_bills + vsmr.SDS_CoinDrop) AS Bills_Coins,
       CAST(vsmr.SDS_1_bills + vsmr.SDS_5_bills + vsmr.SDS_10_bills + vsmr.SDS_20_bills
            + vsmr.SDS_50_bills + vsmr.SDS_100_bills + vsmr.SDS_CoinDrop +
            + vsmr.SDS_EFTInCashablePromo + vsmr.SDS_EFTInNonCashable
            + vsmr.SDS_EFTInCashable + vsmr.SDS_TicketInCashable + vsmr.SDS_TicketInNonCashable
            + vsmr.SDS_TicketInPromoCashable
            -( vsmr.SLIP_APJP_JACKPOT + vsmr.SLIP_PROG_JAKPT_ST + vsmr.SLIP_CC_JACKPOT +vsmr.SLIP_MYST_JACKPOT
    + vsmr.SLIP_DISPUTE + vsmr.SLIP_FILL+vsmr.SLIP_CELEBRATION_JACKPOT +vsmr.CASH_PROG
    +vsmr.NONCASH_PROG - vsmr.SLIP_BLEED
            -( vsmr.SDS_EFTOutCashablePromo+vsmr.SDS_EFTOutNonCashable+vsmr.SDS_EFTOutCashable+
     SDS_TicketOutNonCashable+SDS_TicketOutCashable
         AS FLOAT) AS SDS_WIN,
    (vsmr.SLIP_APJP_JACKPOT + vsmr.SLIP_PROG_JAKPT_ST + vsmr.SLIP_CC_JACKPOT+vsmr.SLIP_MYST_JACKPOT
   + vsmr.SLIP_DISPUTE + vsmr.SLIP_FILL+vsmr.SLIP_CELEBRATION_JACKPOT - vsmr.SLIP_BLEED
)AS SLIP_EXPENSES,
    -- WIN
    CAST((vsmr.SDS_1_bills + vsmr.SDS_5_bills + vsmr.SDS_10_bills + vsmr.SDS_20_bills
            + vsmr.SDS_50_bills + vsmr.SDS_100_bills + vsmr.SDS_CoinDrop +
            + vsmr.SDS_EFTInCashablePromo + vsmr.SDS_EFTInNonCashable
            + vsmr.SDS_EFTInCashable + vsmr.SDS_TicketInCashable + vsmr.SDS_TicketInNonCashable
            + vsmr.SDS_TicketInPromoCashable
        )AS FLOAT) AS WIN,
        -- SHORTS
        (vsmr.SLIP_APJP_JACKPOT +vsmr.SLIP_PROG_JAKPT_ST + vsmr.SLIP_CC_JACKPOT
   +vsmr.SLIP_DISPUTE +vsmr.SLIP_CELEBRATION_JACKPOT
   +(vsmr.SLIP_FILL - vsmr.SLIP_BLEED)
   +(vsmr.SDS_EFTOutCashablePromo+vsmr.SDS_EFTOutNonCashable+vsmr.SDS_EFTOutCashable
    +SDS_TicketOutNonCashable+SDS_TicketOutCashable)
)AS SHORTS,
-- MYSTERY_SHORT
        vsmr.SLIP_MYST_JACKPOT + vsmr.CASH_PROG + vsmr.NONCASH_PROG AS MYSTERY_SHORT,
-- SLIP_LINK_PROG_JAKPT
ISNULL(SLIP_LINK_PROG_JAKPT, 0) AS SLIP_LINK_PROG_JAKPT,
        -- ACTUAL_WIN
        (ISNULL(ActualMtr.ACTUAL_CASH_COUPON_VAL, 0)
        +ISNULL(ActualMtr.ACTUAL_NONCASH_COUPON_VAL, 0)
        +ISNULL((ActualMtr.ACTUAL_1_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_5_BILLS), 0)
        +ISNULL((ActualMtr.ACTUAL_10_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_20_BILLS), 0)
        +ISNULL((ActualMtr.ACTUAL_50_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_100_BILLS), 0)
        +ISNULL(vamcr.SCALE_AMT, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINCASH, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINNONCASH, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINPROMOCASH, 0)
        +(vsmr.SDS_EFTInCashablePromo + vsmr.SDS_EFTInNonCashable + vsmr.SDS_EFTInCashable )
        -(vsmr.SLIP_APJP_JACKPOT + vsmr.SLIP_PROG_JAKPT_ST + vsmr.SLIP_CC_JACKPOT+vsmr.SLIP_MYST_JACKPOT
            + vsmr.SLIP_DISPUTE + vsmr.SLIP_FILL +vsmr.SLIP_CELEBRATION_JACKPOT
            +vsmr.CASH_PROG+vsmr.NONCASH_PROG - vsmr.SLIP_BLEED)
        -(vsmr.SDS_EFTOutCashablePromo+vsmr.SDS_EFTOutNonCashable+vsmr.SDS_EFTOutCashable
    +SDS_TicketOutNonCashable+SDS_TicketOutCashable)
-ISNULL(SLIP_LINK_PROG_JAKPT, 0)
         ) AS ACTUAL_WIN
        ,(ISNULL(ActualMtr.ACTUAL_CASH_COUPON_VAL, 0)
        +ISNULL(ActualMtr.ACTUAL_NONCASH_COUPON_VAL, 0)
        +ISNULL((ActualMtr.ACTUAL_1_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_5_BILLS), 0)
        +ISNULL((ActualMtr.ACTUAL_10_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_20_BILLS), 0)
        +ISNULL((ActualMtr.ACTUAL_50_BILLS), 0) + ISNULL((ActualMtr.ACTUAL_100_BILLS), 0)
        +ISNULL(vamcr.SCALE_AMT, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINCASH, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINNONCASH, 0)
        +ISNULL(ActualMtr.ACTUAL_TKTINPROMOCASH, 0)
        +ISNULL(vsmr.SDS_EFTInCashablePromo,0 )
        +ISNULL(vsmr.SDS_EFTInNonCashable, 0)
        +ISNULL(vsmr.SDS_EFTInCashable, 0)) AS PHY_WIN
        ,(vsmr.CASH_PROG_SLOT_CONTRIBUTION + vsmr.CASH_PSR_ARV_AMT_VAL+vsmr.NON_CASH_PROG_SLOT_CONTRIBUTION
        +vsmr.NON_CASH_PSR_ARV_AMT_VAL ) AS PROVISION,
        ISNULL(a.PTBL_NO_OF_PAYLINES,0) AS PTBL_NO_OF_PAYLINES -- check with ARV
FROM ACCOUNTING.VIEW_SDS_METER_ROLLUP_WITH_PROG AS vsmr
JOIN REPORT.VIEW_ASSET VA
    ON VA. NAMED_ASSET_ID = Mtr_NamedAsstID
LEFT JOIN Accounting.VIEW_ACTUAL_METER_PERIODIC_ROLLUP AS ActualMtr
             ON (ActualMtr.NAMEDASSTID = vsmr.Mtr_NamedAsstID)
             AND (ActualMtr.GAMEDAY = vsmr.Mtr_GameDay)
             AND (ActualMtr.PTYP_ID = vsmr.PTYP_ID)
LEFT JOIN ACCOUNTING.VIEW_ACTUAL_METER_COIN_ROLLUP vamcr
             ON vamcr.CN_NAMEDASSTID = vsmr.Mtr_NamedAsstID
             AND vamcr.CN_GAMEDAY = vsmr.Mtr_GameDay
             AND vamcr.CN_PTYP_ID = vsmr.PTYP_ID
LEFT JOIN
(SELECT NAGI_NAST_ID,AT.THEM_NAME,TC.TCAT_LONG_NAME,Tg.TGRP_LONG_NAME ,PTBL.PTBL_NO_OF_PAYLINES,aco.USER_CUSTOM10 AS GAME_NAME FROM
ACCOUNTING.NAMED_ASSET_GAME_INFO
LEFT JOIN ACCOUNTING.GAME_INFO gf
            ON gf.GINFO_ID = NAGI_GINFO_ID
            AND NAGI_IS_LATEST=1
         JOIN ACCOUNTING.PAYTABLE PTBL
            ON PTBL.PTBL_ID = gf.GINFO_PTBL_ID
          JOIN ASSET.THEME AT
            ON AT.THEM_ID= gf.GINFO_ASST_THME_ID
         JOIN ASSET.THEME_CATEGORY tc
            ON TC.TCAT_ID = AT.THEME_PARENT_ID
         JOIN asset.THEME_GROUP tg
            on tc.TCAT_TGRP_ID=tg.TGRP_ID
         JOIN asset.THEME_TYPE TT
            on TT.TTYP_ID=AT.TTYP_ID
JOIN ACCOUNTING.NAMED_ASSET na
   on na.NAST_ID = NAGI_NAST_ID
JOIN ASSET.ASSET_CONFIGURATION ac
   on ac.ACNF_NUMBER = na.NAST_NAME AND ac.ACNF_DELETED_TS is null
JOIN ASSET.ASSET_CONFIGURATION_OPTION aco
   on aco.ACNF_ID = ac.ACNF_ID
            ) as a
            ON vsmr.Mtr_NamedAsstID =a.NAGI_NAST_ID

I would change the part below with a CTE
LEFT JOIN
(SELECT NAGI_NAST_ID,AT.THEM_NAME,TC.TCAT_LONG_NAME,Tg.TGRP_LONG_NAME ,PTBL.PTBL_NO_OF_PAYLINES,aco.USER_CUSTOM10 AS GAME_NAME FROM
ACCOUNTING.NAMED_ASSET_GAME_INFO
LEFT JOIN ACCOUNTING.GAME_INFO gf
            ON gf.GINFO_ID = NAGI_GINFO_ID
            AND NAGI_IS_LATEST=1
         JOIN ACCOUNTING.PAYTABLE PTBL
            ON PTBL.PTBL_ID = gf.GINFO_PTBL_ID
          JOIN ASSET.THEME AT
            ON AT.THEM_ID= gf.GINFO_ASST_THME_ID
         JOIN ASSET.THEME_CATEGORY tc
            ON TC.TCAT_ID = AT.THEME_PARENT_ID
         JOIN asset.THEME_GROUP tg
            on tc.TCAT_TGRP_ID=tg.TGRP_ID
         JOIN asset.THEME_TYPE TT
            on TT.TTYP_ID=AT.TTYP_ID
JOIN ACCOUNTING.NAMED_ASSET na
   on na.NAST_ID = NAGI_NAST_ID
JOIN ASSET.ASSET_CONFIGURATION ac
   on ac.ACNF_NUMBER = na.NAST_NAME AND ac.ACNF_DELETED_TS is null
JOIN ASSET.ASSET_CONFIGURATION_OPTION aco
   on aco.ACNF_ID = ac.ACNF_ID
            ) as a

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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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>

SELECT query performance : One big table Vs many small tables

Hello,
We are using BDB 11g with SQLITE support. I have a query about 'select' query performance when we have one huge table vs. multiple small tables.
Basically in our application, we need to run select query multiple times and today we have one huge table. Do you guys think breaking them into
multiple small tables will help ?
For test purposes we tried creating multiple tables but performance of 'select' query was more or less same. Would that be because all tables will map to only one database in backed with key/value pair and when we run lookup (select query) on small table or big table it wont make difference ?
Thanks.

Hello,
There is some information on this topic in the FAQ at:
http://www.oracle.com/technology/products/berkeley-db/faq/db_faq.html#9-63
If this does not address your question, please just let me know.
Thanks,
Sandra

Having more LTSs in logical dimension table hit the query performance?

Hi,
We have a logical table having around 19 LTSs. Having more LTSs in logical dimension table hit the query performance?
Thanks,
Anilesh

Hi Anilesh,
Its kind of both YES and NO. Here is why...
NO:
LTS are supposed to give BI Server an optimistic and logical way to retrieve the data. So, having more Optimistic LTS might help the BI Server with some good options tailored to a variety of analysis requests.
YES:
Many times, we have to bring in multiple physical tables as a part of single LTS (Mostly when the physical model is a snowflake) which might cause performance issues. Say there is a LTS with two tables "A" and "B", but for a ad-hoc analysis just on columns in "A", the query would still include the join with table "B" if this LTS is being used. We might want to avoid this kind of situations with multiple LTS each for a table and one for both of them.
Hope this helps.
Thank you,
Dhar

Poor query performance in Prod.

I am facing lots of issues in my queries.
The query is working fine in Dev. but after i transported it to Prod. the query is taking too much time to retreive the result.
Why i am facing this issue.
How can i do the performance tuning for the query.?
The query is built on multiprovider and it is also jumping to the ODS for ODS query.
But the query performance is really low and poor in Production.
And to surprise the query is wroking perfectly and faster in Dev.
What can be the suggestion.
Please send documents for performnace tuning, notes number... etc.

Are datavolumes huge in Prod Box...dat may be cause 4 d slow runtimes.
<b>Look at below performance improving techs</b>
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/cbd2d390-0201-0010-8eab-a8a9269a23c2
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/aec09790-0201-0010-8eb9-e82df5763455
Business Intelligence Performance Tuning [original link is broken]
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/cccad390-0201-0010-5093-fd9ec8157802
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/ce7fb368-0601-0010-64ba-fadc985a1f94
https://www.sdn.sap.com/irj/servlet/prt/portal/prtroot/docs/library/uuid/c8c4d794-0501-0010-a693-918a17e663cc
Note 565725 - Optimizing the performance of ODS objects

Poor query performance only with migrated 7.0 queries

Dear Team,
We are facing a serious query performance issue after migration of queries from 3.5 to 7.0.
I executed a query in 3.5 with some variable values which takes fraction of seconds to display the output. But the same migrated query with same variable entries is taking very long time and giving time out error.
We are not using any aggregates in the InfoProvider level.
Both the queries are based on same cube but 3.5 query is taking less time and 7.0 is taking very long time if more selection is done.
I checked for notes where I didn't find specific note for this particular scenario. I found notes only for general query performance improvement.
I want to know the reason why only in 7.0 the same 3.5 query is taking a long time and giving time out error. And please suggest some notes or suggestions related to this scenario.
Regards,
Chan

Hi,
Queries in BI 7.0 are almost the same as queries in 3.x format.
inorder to check if the problem is in the query runtime (database time) or JAVA runtime (probably rendering) you should try running it from RSRT once in JAVA web and once in ABAP web.
if the problem is only with JAVA web, than u should take the URL and add &profiling=X at the end.
after the query execution u can use statistics which will be shown at the top of the page.
With my experience, the problem is in the rendering phase of the query. Things that could be done is to limit the number of rows shown at each page, that could be done by changing the 0ANALYSIS web template - it's one of the web template parameters.
Tomer.

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
GURU

BW 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..

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 PM

I 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.

How to improve query performance using infoset

I create one infoset that including 4 char.and 3 DSO which all are time-dependent.When query run, system show very poor perfomance, sometimes no data show in BEX anayzer. In this case I have to close BEX analyzer at first and then open it again, after that it show real results. It seems very strange. Does anybody has experience on infoset performance improvement. pls info, thanks!

Hi
As info set itself doesn't have any data so it improves Performance
also go through the below tips.
Find the query Run-time
where to find the query Run-time ?
557870 'FAQ BW Query Performance'
130696 - Performance trace in BW
This info may be helpful.
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 particular day > check query execution time.
Statistical Records Part 4: How to read ST03N datasets from DB in NW2004
How to read ST03N datasets from DB
Try table rsddstats to get the statistics
Using cache memory 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. To check the performance of the aggregates,see the columns valuation and usage in aggregates.
Open the Aggregates...and observe VALUATION and USAGE columns.
"---" 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.
In valuation column,if there are more positive sign it means that the aggregate performance is good and it is useful to have this aggregate.But if it has more negative sign it means we need not better use that aggregate.
In usage column,we will come to know how far the aggregate has been used in query.
Thus we can check the performance of the aggregate.
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
performance ISSUE related to AGGREGATE
Note 356732 - Performance Tuning for Queries with Aggregates
Note 166433 - Options for finding aggregates (find optimal aggregates for an InfoCube)
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.
202469 - Using aggregate check tool
Note 646402 - Programs for checking aggregates (as of BW 3.0B SP15)
You can find out whether an aggregate is usefull or useless you can find out through a proccess of checking the tables RSDDSTATAGGRDEF*
Run the query in RSRT with statistics execute and come back you will get STATUID... copy this and check in the table...
This gives you exactly which infoobjects it's hitting, if any one of the object is missing it's useless aggregate.
6
Check SE11 > table RSDDAGGRDIR . You can find the last callup in the table.
Generate Report in RSRT
https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/cccad390-0201-0010-5093-fd9ec8157802
https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/4c0ab590-0201-0010-bd9a-8332d8b4f09c
Business Intelligence Journal Improving Query Performance in Data Warehouses
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=7891
Achieving BI Query Performance Building Business Intelligence
http://www.dmreview.com/issues/20051001/1038109-1.html
Assign points if useful
Cheers
SM

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:
Jitendra

Inaddition 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

Removing Stats improves query performance

Hi,
Today i have got a rare question regarding Stats collection,
In some cases removing existing stats will improve the query performance...
What is the reason behind this?
After a rigorous search i have landed here to here from you all.
Thanks in-advance.
Vara

Hi,
Before 11gR2 and adaptive cursor sharing, skewed data could result in wrong execution plan (histogramms and bind variable peaking could not fix it).
Sometimes statistics cannot be fresh enough, for example with temporary table, but also for regular tables which are massively loaded/modified, resulting in poor execution plan.
Also the optimizer might use guesses for complex predicates... which could have same consequences.
When no statistics are available, dynamic sampling may give a more accurate estimation of the cardinalities than statistics.
https://blogs.oracle.com/optimizer/entry/dynamic_sampling_and_its_impact_on_the_optimizer

Unexplained poor table query performance

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