Query performance on materialized view vs master tables

Hi,
I am afraid of strange behavior in db, on my master tables UDBMOVEMENT_ORIG(26mil.rows) and UDBIDENTDATA_ORIG(18mil.rows) is created materialized view TMP_MS_UDB_MV (UDBMOVEMENT is synonym to this object) which meets some default conditions and join condition on these master tables. MV got about 12milions rows. I created MV to query not so huge objects, MV got 3GB, master tables toghether 12GB. But I don't understand that even physical reads and consistent gets are less on MV than on master tables, the final execution time is shorter on master tables. See my log below.
Why?
Thanks for answers.
SQL> set echo on
SQL> @flush
SQL> alter system flush buffer_cache;
System altered.
Elapsed: 00:00:00.20
SQL> alter system flush shared_pool;
System altered.
Elapsed: 00:00:00.65
SQL> SELECT
2 UDBMovement.zIdDevice, UDBMovement.sDevice, UDBMovement.zIdLocal, UDBMovement.sComputer, UDBMovement.tActionTime, UDBIdentData.sCardSubType, UDBIdentData.sCardType, UDBMovement.cEpan, UDBMovement.cText, UDBMovement.lArtRef, UDBMovement.sArtClassRef, UDBMovement.lSequenz, UDBMovement.sTransMark, UDBMovement.lBlock, UDBMovement.sTransType, UDBMovement.lGlobalID, UDBMovement.sFacility, UDBIdentData.sCardClass, UDBMovement.lSingleAmount, UDBMovement.sVAT, UDBMovement.lVATTot, UDBIdentData.tTarifTimeStart, UDBIdentData.tTarifTimeEnd, UDBIdentData.cLicensePlate, UDBIdentData.lMoneyValue, UDBIdentData.lPointValue, UDBIdentData.lTimeValue, UDBIdentData.tProdTime, UDBIdentData.tExpireDate
3 FROM UDBMOVEMENT_orig UDBMovement, Udbidentdata_orig UDBIdentData
4 WHERE
5 UDBMovement.lGlobalId = UDBIdentData.lGlobalRef(+) AND UDBMovement.sComputer = UDBIdentData.sComputer(+)
6 AND UDBMovement.sTransType > 0 AND UDBMovement.sDevice < 1000 AND UDBMovement.sDevice>= 0 AND UDBIdentData.sCardType IN (2) AND (bitand(UDBMovement.sSaleFlag,1) = 0 AND bitand(UDBMovement.sSaleFlag,4) = 0) AND UDBMovement.sArtClassRef < 100
7 AND UDBMovement.tActionTime >= TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.25 AND UDBMovement.tActionTime < TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.5
8 ORDER BY tActionTime, lBlock, lSequenz;
4947 rows selected.
Elapsed: 00:00:15.84
Execution Plan
Plan hash value: 1768406139
| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 7166 | 1238K| | 20670 (1)| 00:04:09 |
| 1 | SORT ORDER BY | | 7166 | 1238K| 1480K| 20670 (1)| 00:04:09 |
| 2 | NESTED LOOPS | | | | | | |
| 3 | NESTED LOOPS | | 7166 | 1238K| | 20388 (1)| 00:04:05 |
|* 4 | TABLE ACCESS BY INDEX ROWID| UDBMOVEMENT_ORIG | 7142 | 809K| | 7056 (1)| 00:01:25 |
|* 5 | INDEX RANGE SCAN | IDX_UDBMOVARTICLE | 10709 | | | 61 (0)| 00:00:01 |
|* 6 | INDEX UNIQUE SCAN | UDBIDENTDATA_PRIM | 1 | | | 1 (0)| 00:00:01 |
|* 7 | TABLE ACCESS BY INDEX ROWID | UDBIDENTDATA_ORIG | 1 | 61 | | 2 (0)| 00:00:01 |
Predicate Information (identified by operation id):
4 - filter("UDBMOVEMENT"."STRANSTYPE">0 AND "UDBMOVEMENT"."SDEVICE"<1000 AND
BITAND("SSALEFLAG",1)=0 AND "UDBMOVEMENT"."SDEVICE">=0 AND BITAND("UDBMOVEMENT"."SSALEFLAG",4)=0)
5 - access("UDBMOVEMENT"."TACTIONTIME">=TO_DATE(' 2011-05-05 06:00:00', 'syyyy-mm-dd
hh24:mi:ss') AND "UDBMOVEMENT"."TACTIONTIME"<TO_DATE(' 2011-05-05 12:00:00', 'syyyy-mm-dd
hh24:mi:ss') AND "UDBMOVEMENT"."SARTCLASSREF"<100)
filter("UDBMOVEMENT"."SARTCLASSREF"<100)
6 - access("UDBMOVEMENT"."LGLOBALID"="UDBIDENTDATA"."LGLOBALREF" AND
"UDBMOVEMENT"."SCOMPUTER"="UDBIDENTDATA"."SCOMPUTER")
7 - filter("UDBIDENTDATA"."SCARDTYPE"=2)
Statistics
543 recursive calls
0 db block gets
84383 consistent gets
4485 physical reads
0 redo size
533990 bytes sent via SQL*Net to client
3953 bytes received via SQL*Net from client
331 SQL*Net roundtrips to/from client
86 sorts (memory)
0 sorts (disk)
4947 rows processed
SQL> @flush
SQL> alter system flush buffer_cache;
System altered.
Elapsed: 00:00:00.12
SQL> alter system flush shared_pool;
System altered.
Elapsed: 00:00:00.74
SQL> SELECT UDBMovement.zIdDevice, UDBMovement.sDevice, UDBMovement.zIdLocal, UDBMovement.sComputer, UDBMovement.tActionTime, UDBMovement.sCardSubType, UDBMovement.sCardType, UDBMovement.cEpan, UDBMovement.cText, UDBMovement.lArtRef, UDBMovement.sArtClassRef, UDBMovement.lSequenz, UDBMovement.sTransMark, UDBMovement.lBlock, UDBMovement.sTransType, UDBMovement.lGlobalID, UDBMovement.sFacility, UDBMovement.sCardClass, UDBMovement.lSingleAmount, UDBMovement.sVAT, UDBMovement.lVATTot, UDBMovement.tTarifTimeStart, UDBMovement.tTarifTimeEnd, UDBMovement.cLicensePlate, UDBMovement.lMoneyValue, UDBMovement.lPointValue, UDBMovement.lTimeValue, UDBMovement.tProdTime
2 FROM UDBMOVEMENT WHERE
3 UDBMovement.sTransType > 0 AND UDBMovement.sDevice < 1000 AND UDBMovement.sDevice>= 0 AND UDBMovement.sCardType IN (2) AND (bitand(UDBMovement.sSaleFlag,1) = 0 AND bitand(UDBMovement.sSaleFlag,4) = 0) AND UDBMovement.sArtClassRef < 100
4 AND UDBMovement.tActionTime >= TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.25
5 AND UDBMovement.tActionTime < TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.5 ORDER BY tActionTime, lBlock, lSequenz;
4947 rows selected.
Elapsed: 00:00:26.46
Execution Plan
Plan hash value: 3648898312
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 2720 | 443K| 2812 (1)| 00:00:34 |
| 1 | SORT ORDER BY | | 2720 | 443K| 2812 (1)| 00:00:34 |
|* 2 | MAT_VIEW ACCESS BY INDEX ROWID| TMP_MS_UDB_MV | 2720 | 443K| 2811 (1)| 00:00:34 |
|* 3 | INDEX RANGE SCAN | EEETMP_MS_ACTTIMEDEVICE | 2732 | | 89 (0)| 00:00:02 |
Predicate Information (identified by operation id):
2 - filter("UDBMOVEMENT"."STRANSTYPE">0 AND BITAND("UDBMOVEMENT"."SSALEFLAG",4)=0 AND
BITAND("SSALEFLAG",1)=0 AND "UDBMOVEMENT"."SARTCLASSREF"<100)
3 - access("UDBMOVEMENT"."TACTIONTIME">=TO_DATE(' 2011-05-05 06:00:00', 'syyyy-mm-dd
hh24:mi:ss') AND "UDBMOVEMENT"."SDEVICE">=0 AND "UDBMOVEMENT"."SCARDTYPE"=2 AND
"UDBMOVEMENT"."TACTIONTIME"<TO_DATE(' 2011-05-05 12:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"UDBMOVEMENT"."SDEVICE"<1000)
filter("UDBMOVEMENT"."SCARDTYPE"=2 AND "UDBMOVEMENT"."SDEVICE"<1000 AND
"UDBMOVEMENT"."SDEVICE">=0)
Statistics
449 recursive calls
0 db block gets
6090 consistent gets
2837 physical reads
0 redo size
531987 bytes sent via SQL*Net to client
3953 bytes received via SQL*Net from client
331 SQL*Net roundtrips to/from client
168 sorts (memory)
0 sorts (disk)
4947 rows processed
SQL> spool off
Edited by: MattSk on Feb 4, 2013 2:20 PM

I have added some tkprof outputs on MV and master tables:
SELECT tmp_ms_udb_mv.zIdDevice, tmp_ms_udb_mv.sDevice, tmp_ms_udb_mv.zIdLocal, tmp_ms_udb_mv.sComputer, tmp_ms_udb_mv.tActionTime, tmp_ms_udb_mv.sCardSubType, tmp_ms_udb_mv.sCardType, tmp_ms_udb_mv.cEpan, tmp_ms_udb_mv.cText, tmp_ms_udb_mv.lArtRef, tmp_ms_udb_mv.sArtClassRef, tmp_ms_udb_mv.lSequenz, tmp_ms_udb_mv.sTransMark, tmp_ms_udb_mv.lBlock, tmp_ms_udb_mv.sTransType, tmp_ms_udb_mv.lGlobalID, tmp_ms_udb_mv.sFacility, tmp_ms_udb_mv.sCardClass, tmp_ms_udb_mv.lSingleAmount, tmp_ms_udb_mv.sVAT, tmp_ms_udb_mv.lVATTot, tmp_ms_udb_mv.tTarifTimeStart, tmp_ms_udb_mv.tTarifTimeEnd, tmp_ms_udb_mv.cLicensePlate, tmp_ms_udb_mv.lMoneyValue, tmp_ms_udb_mv.lPointValue, tmp_ms_udb_mv.lTimeValue, tmp_ms_udb_mv.tProdTime
FROM tmp_ms_udb_mv WHERE
tmp_ms_udb_mv.sTransType > 0 AND tmp_ms_udb_mv.sDevice < 1000 AND tmp_ms_udb_mv.sDevice>= 0 AND tmp_ms_udb_mv.sCardType IN (1) AND (bitand(tmp_ms_udb_mv.sSaleFlag,1) = 0 AND bitand(tmp_ms_udb_mv.sSaleFlag,4) = 0) AND tmp_ms_udb_mv.sArtClassRef < 100
AND tmp_ms_udb_mv.tActionTime >= TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.25
AND tmp_ms_udb_mv.tActionTime < TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.5
ORDER BY tActionTime, lBlock, lSequenz
call count cpu elapsed disk query current rows
Parse 1 0.04 0.10 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 596 0.17 27.07 2874 8894 0 8925
total 598 0.21 27.18 2874 8894 0 8925
Misses in library cache during parse: 1
Optimizer mode: ALL_ROWS
Parsing user id: 60
Rows Row Source Operation
8925 SORT ORDER BY (cr=8894 pr=2874 pw=0 time=27071773 us)
8925 MAT_VIEW ACCESS BY INDEX ROWID TMP_MS_UDB_MV (cr=8894 pr=2874 pw=0 time=31458291 us)
8925 INDEX RANGE SCAN EEETMP_MS_ACTTIMEDEVICE (cr=68 pr=68 pw=0 time=161347 us)(object id 149251)
SELECT
UDBMovement.zIdDevice, UDBMovement.sDevice, UDBMovement.zIdLocal, UDBMovement.sComputer, UDBMovement.tActionTime, UDBIdentData.sCardSubType, UDBIdentData.sCardType, UDBMovement.cEpan, UDBMovement.cText, UDBMovement.lArtRef, UDBMovement.sArtClassRef, UDBMovement.lSequenz, UDBMovement.sTransMark, UDBMovement.lBlock, UDBMovement.sTransType, UDBMovement.lGlobalID, UDBMovement.sFacility, UDBIdentData.sCardClass, UDBMovement.lSingleAmount, UDBMovement.sVAT, UDBMovement.lVATTot, UDBIdentData.tTarifTimeStart, UDBIdentData.tTarifTimeEnd, UDBIdentData.cLicensePlate, UDBIdentData.lMoneyValue, UDBIdentData.lPointValue, UDBIdentData.lTimeValue, UDBIdentData.tProdTime, UDBIdentData.tExpireDate
FROM UDBMOVEMENT_orig UDBMovement, Udbidentdata_orig UDBIdentData
WHERE
UDBMovement.lGlobalId = UDBIdentData.lGlobalRef(+) AND UDBMovement.sComputer = UDBIdentData.sComputer(+)
AND UDBMovement.sTransType > 0 AND UDBMovement.sDevice < 1000 AND UDBMovement.sDevice>= 0 AND UDBIdentData.sCardType IN (1) AND (bitand(UDBMovement.sSaleFlag,1) = 0 AND bitand(UDBMovement.sSaleFlag,4) = 0) AND UDBMovement.sArtClassRef < 100
AND UDBMovement.tActionTime >= TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.25
AND UDBMovement.tActionTime < TO_DATE('05/05/2011 00:00:00', 'dd/mm/yyyy hh24:mi:ss') + 0.5
ORDER BY tActionTime, lBlock, lSequenz
call count cpu elapsed disk query current rows
Parse 1 0.03 0.06 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 596 0.76 16.94 3278 85529 0 8925
total 598 0.79 17.01 3278 85529 0 8925
Misses in library cache during parse: 1
Optimizer mode: ALL_ROWS
Parsing user id: 60
Rows Row Source Operation
8925 SORT ORDER BY (cr=85529 pr=3278 pw=0 time=16942799 us)
8925 NESTED LOOPS (cr=85529 pr=3278 pw=0 time=15017857 us)
22567 TABLE ACCESS BY INDEX ROWID UDBMOVEMENT_ORIG (cr=17826 pr=1659 pw=0 time=7273473 us)
22570 INDEX RANGE SCAN IDX_UDBMOVARTICLE (cr=111 pr=111 pw=0 time=112351 us)(object id 143693)
8925 TABLE ACCESS BY INDEX ROWID UDBIDENTDATA_ORIG (cr=67703 pr=1619 pw=0 time=8154915 us)
22567 INDEX UNIQUE SCAN UDBIDENTDATA_PRIM (cr=45136 pr=841 pw=0 time=3731470 us)(object id 108324)

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SQL> select * from v$version ;
BANNER
Oracle Database 11g Enterprise Edition Release 11.2.0.1.0 - Production
PL/SQL Release 11.2.0.1.0 - Production
CORE     11.2.0.1.0     Production
TNS for Linux: Version 11.2.0.1.0 - Production
NLSRTL Version 11.2.0.1.0 - Production
SQL> desc employees
Name                                                                                            Null?    Type
EMPLOYEE_ID                                                                                     NOT NULL NUMBER(6)
FIRST_NAME                                                                                               VARCHAR2(20)
LAST_NAME                                                                                       NOT NULL VARCHAR2(25)
EMAIL                                                                                           NOT NULL VARCHAR2(25)
PHONE_NUMBER                                                                                             VARCHAR2(20)
HIRE_DATE                                                                                       NOT NULL DATE
JOB_ID                                                                                          NOT NULL VARCHAR2(10)
SALARY                                                                                                   NUMBER(8,2)
COMMISSION_PCT                                                                                           NUMBER(2,2)
MANAGER_ID                                                                                               NUMBER(6)
DEPARTMENT_ID                                                                                            NUMBER(4)
SQL> select count(*) from employees ;
COUNT(*)
       107
SQL> create table mv_table nologging as select department_id, sum(salary) as totalsal from employees group by department_id ;
Table created.
SQL> desc mv_table
Name                                                                                            Null?    Type
DEPARTMENT_ID                                                                                            NUMBER(4)
TOTALSAL                                                                                                 NUMBER
SQL> select count(*) from mv_table ;
COUNT(*)
        12
SQL> create materialized view mv_table on prebuilt table with reduced precision enable query rewrite as select department_id, sum(salary) as totalsal from employees group by department_id ;
Materialized view created.
SQL> select count(*) from mv_table ;
COUNT(*)
        12
SQL> select object_name, object_type from user_objects where object_name = 'MV_TABLE' ;
OBJECT_NAME                                                                                                                      OBJECT_TYPE
MV_TABLE                                                                                                                         TABLE
MV_TABLE                                                                                                                         MATERIALIZED VIEW
SQL> insert into mv_table values (999, 100) ;
insert into mv_table values (999, 100)
ERROR at line 1:
ORA-01732: data manipulation operation not legal on this view
SQL> update mv_table set totalsal = totalsal * 1.1 where department_id = 10 ;
update mv_table set totalsal = totalsal * 1.1 where department_id = 10
ERROR at line 1:
ORA-01732: data manipulation operation not legal on this view
SQL> delete from mv_table where totalsal <= 10000 ;
delete from mv_table where totalsal <= 10000
ERROR at line 1:
ORA-01732: data manipulation operation not legal on this view While investigating for this thread I actually made my own question redundant as the answer became gradually clear:
When using complex ETL's, I just need to make sure the complexity is located in the ETL loading the detailed table, not the aggregate
I'll try to clarify through an example:
- A detailed Table DET_SALES exists with Sales per Day, Store & Product
- An aggregated table AGG_SALES_MM exists with Sales, SalesStore per Month, Store & Product
- An ETL exists to load AGG_SALES_MM where Sales = SUM(Sales) & SalesStore = (SUM(Sales) Across Store)
--> i.e. the SalesStore measure will be derived out of a lookup
- A (Prebuilt) Materialized View will exist with the same column definitions as the ETL
--> to allow query-rewrite to know when to access the table
My concern was how to include the SalesStore in the materialized view definition (--> complex SQL!)
--> I should actually include SalesStore in the DET_SALES table, thus:
- including the 'Across Store' function in the detailed ETL
- rendering my Aggregation ETL into a simple GROUP BY
- rendering my materialized view definition into a simple GROUP BY as wellNot sure how close your example is to your actual problem. Also don't know if you are doing an incremental/complete data load and the data volume.
But the "SalesStore = (SUM(Sales) Across Store)" can be derived from the aggregated MV using analytical function. One can just create a normal view on top of MV for querying. It is hard to believe that aggregating in detail table during ETL load is the best approach but what do I know?

Materialized View with OLAP table function

Hi,
I am trying to materialize OLAP cubes into relational materialized views which works quite fine. After a few loadings in background in parallel with Database Jobs and DBMS_MVIEW package the peformance is getting poor. Steps I am performing:
1. Generate materialized view as DEFERRED and COMPLETE refresh
2. Generate database Jobs for refreshing views with DBMS_MVIEW.REFRESH function
3. Running Jobs in background
I have loading times the first time 10min after then over 4 hours. I also tried with ATOMIC_REFRESH=FALSE but the same result. Database is running in ARCHIVE LOGGING. Can this degrade the performance?
Any ideas?
Thanks,
Christian

Hi,
yes thats correct. I am creating MVs in 10.2.0.3
Here is an example:
CREATE MATERIALIZED VIEW "FCRSGX"."MV_F_ICCC_C11"
ORGANIZATION HEAP PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 COMPRESS NOLOGGING
STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645
PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT)
TABLESPACE "FCRSGX_CONSO_RELATIONAL"
BUILD DEFERRED
USING INDEX
REFRESH COMPLETE ON DEMAND
USING DEFAULT LOCAL ROLLBACK SEGMENT
DISABLE QUERY REWRITE
AS SELECT ENTITY, REVE_ICC, RP_ICC, MV_ICC, PERIOD, MEASURE, AMOUNT, R2C
FROM TABLE(OLAP_TABLE('FCRSGX.CONSODATA DURATION SESSION',
DIMENSION ENTITY as varchar2(8) FROM ENTITY
DIMENSION REVE_ICC as varchar2(8) FROM REVE_ICC
DIMENSION RP_ICC as varchar2(8) FROM RP_ICC
DIMENSION MV_ICC as varchar2(8) FROM MV_ICC
DIMENSION PERIOD as varchar2(8) FROM GMONTH
DIMENSION MEASURE as varchar2(30) FROM EXPR
MEASURE AMOUNT as number FROM ICCC.C11
LOOP CMPE.ICCC.C11
ROW2CELL R2C '))
WHERE OLAP_CONDITION(r2c, 'lmt entity to CMPE.ICCC.C11')=1
AND OLAP_CONDITION(r2c, 'lmt reve_icc to CMPE.ICCC.C11')=1
AND OLAP_CONDITION(r2c, 'lmt mmonth to sapload.per eq y')=1
AND OLAP_CONDITION(r2c, 'lmt gmonth to charl(mmonth) ')=1
AND OLAP_CONDITION(r2c, 'lmt rp_icc to CMP.ICCC.C11 ')=1
AND OLAP_CONDITION(r2c, 'lmt mv_icc to CMP.ICCC.C11 ')=1
AND OLAP_CONDITION(r2c, 'lmt expr to ''F.ICCC.C11'' ')=1
MODEL
DIMENSION BY(ENTITY,REVE_ICC,RP_ICC,MV_ICC,PERIOD,MEASURE)
MEASURES(AMOUNT,R2C)
RULES UPDATE SEQUENTIAL ORDER()
;

Do I need to tune the base query of my materialized view ?

Hi gurus,
We implement materialized view (FAST REFRESH ON COMMIT) on base table used in OLTP system. In fact the MV itself is also queried by the OLTP system itself, interactively.
Currently user complain that after they input transaction, they see that the MV is not refresh as soon as they commit the transaction.
(it takes 5 to 10 minutes before they see the update on the MV)
The query of the MV is attached below, it consists of union all of two queries but actually the real query has 13 queries on a UNION ALL.
The question is :
Do I need to tune the query that forms the MV ?
Does it impact the refresh time of the MV ?
Thank you for your help,
xtanto
SELECT GRNH.DOC_CODE ||' '|| GRNH.GRN_NO, GRNH.GRN_DATE, GRNH.REGS_CODE,
F_Convert_Qty_K(GRND.PRODUCT, GRND.QTY, GRND.UOM) QTY_IN, 0 QTY_OUT, 0 QTY_ADJ, 'N/A'.....
FROM GRN_LOCAL_D GRND
          JOIN GRN_LOCAL_H GRNH ON GRNH.GRNH_ID = GRND.GRNH_ID
          WHERE GRND.PRODUCT IS NOT NULL AND GRNH.WH_CODE IS NOT NULL
UNION ALL
SELECT GRNH.DOC_CODE ||' '|| GRNH.GRN_NO, GRNH.GRN_DATE, GRNH.REGS_CODE,
F_Convert_Qty_K(GRND.PRODUCT_B, GRND.QTY_B, GRND.UOM_B) QTY_IN, 0 QTY_OUT, 0 QTY_ADJ, 'N/A'...
FROM GRN_LOCAL_D GRND
JOIN GRN_LOCAL_H GRNH ON GRNH.GRNH_ID = GRND.GRNH_ID
WHERE GRND.PRODUCT_B IS NOT NULL AND GRNH.WH_CODE IS NOT NULL
UNION ALL
     ......

Hi Justin & APC,
Are they seeing a 5-10 minute delay in being able to commit their transaction?NO, the commit itself is completed normally, but after that when they go to the page (this is webbased app) that display data from the MV, the newly committed transactionis not there yet. Only after 5-10 minutes we can see the new data.
What does the F_Convert_Qty_K() function do?It is a function that convert the quantity from whatever Unit Of Measurement (UOM) into the smallest UOM. Below is the code.
So the question remains:
why intermittently theere is delay before the new data visible in the MV ?
Thanks you,
xtanto
CREATE OR REPLACE FUNCTION F_Convert_Qty (pProduct VARCHAR2, pQty NUMBER, pUOM VARCHAR2)
RETURN NUMBER
IS
vQtyBase NUMBER(13,5);
BEGIN
SELECT CASE WHEN pUOM = ( SELECT UOM_BASE FROM PRODUCTS WHERE PRODUCT = pProduct)
          THEN pQty
     ELSE
               SELECT ROUND(pQty * Coefficient / Divisor, 5)
               FROM (
                         SELECT Coefficient, Divisor FROM CONVERT_UOM
                         WHERE PRODUCT = pProduct AND UOM_CONVERT = pUOM
                         ) tmp
          END INTO vQtyBase FROM DUAL;
--RETURN (vQtyBase) ;
RETURN ( NVL(vQtyBase,0) ) ;
END F_Convert_Qty;
/

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>

Help with query rewrite and materialized views

Hello everybody,
I'm currently learning how to use Oracle (10G Enterprise) and in particular, Materialized Views.
I seem to have a problem making the optimizer use a materialized view. I have already set the OPTIMIZER_MODE, QUERY_REWRITE_ENABLED and QUERY_REWRITE_INTEGRITY as needed.
I need to create a materialized view for the following query:
Q1:
SELECT PS_SUPPKEY, PS_PARTKEY, PS_SUPPCOST
FROM PARTSUPPLIER E, PART WHERE PS_PARTKEY=P_PARTKEY and (lower(P_COMMENT) LIKE ''_o_a\%'' or lower(P_COMMENT) LIKE ''_o_u\%'')
and PS_SUPPCOST =
(SELECT min( PS_SUPPCOST)
FROM PARTSUPPLIER I
WHERE E.PS_PARTKEY=I.PS_PARTKEY)'
I created it using the following code:
CREATE MATERIALIZED VIEW mv_q1
ENABLE QUERY REWRITE
AS SELECT PS_SUPPKEY, PS_PARTKEY, PS_SUPPCOST
FROM PARTSUPPLIER E JOIN PART ON (PS_PARTKEY=P_PARTKEY)
WHERE lower(P_COMMENT) LIKE '_o_a%' or lower(P_COMMENT) LIKE '_o_u%'
and PS_SUPPCOST=
(SELECT min( PS_SUPPCOST)
FROM PARTSUPPLIER I
WHERE E.PS_PARTKEY=I.PS_PARTKEY);
I have created the statistics using:
execute dbms_stats.gather_table_stats('frandres',' mv_q1');
execute dbms_stats.gather_table_stats('frandres','PARTSUPPLIER');
execute dbms_stats.gather_table_stats('frandres','PART');
Both partsupplier and part are tables and not views.
When executing Q1, the plan does not use the materialized view. Furthermore, when using explain rewrite:
DECLARE
qrytxt VARCHAR2(3000) := 'SELECT PS_SUPPKEY, PS_PARTKEY, PS_SUPPCOST
FROM PARTSUPPLIER E, PART WHERE PS_PARTKEY=P_PARTKEY and (lower(P_COMMENT) LIKE ''_o_a\%'' or lower(P_COMMENT) LIKE ''_o_u\%'')
and PS_SUPPCOST =
(SELECT min( PS_SUPPCOST)
FROM PARTSUPPLIER I
WHERE E.PS_PARTKEY=I.PS_PARTKEY)';
BEGIN
dbms_mview.EXPLAIN_REWRITE
(qrytxt,'MV_Q1','MV_Q1');
END;
I get the following message:
MESSAGE
QSM-01150: query did not rewrite
QSM-01263: query rewrite not possible when query references a dictionary table o
r view
QSM-01219: no suitable materialized view found to rewrite this query
What I can't understand is why it says I am referencing the dictionary or a view?
If I remove the (lower(P_COMMENT) LIKE ''_o_a\%'' or lower(P_COMMENT) LIKE ''_o_u\%'') condition to the query (using the same materialized view), I get the following message from EXPLAIN_REWRITE:
MESSAGE
QSM-01150: query did not rewrite
QSM-01219: no suitable materialized view found to rewrite this query
Which is reasonable.
I don't know if the like condition is messing up my materialized view. Can anyone please help?
Thanks a lot in advance.
Edited by: user12072111 on Oct 29, 2009 9:43 PM

Bingo!
The 10.2.0.3 patch set is supposed to fix this ( [List of bugs fixed (MVs)|http://www.dbatools.net/doc/bug10203.html#MVIEW] )
In particular:
5052568 Query rewrite does not work for SQL with LIKE clause.
Thank you very much for your message!
The downside is that I'm only using Oracle for educational purposes and consequently have no Metalink id, so I can't install the patch. Thanks a lot though!

Materialized View UNION different tables 10g.

I am trying to create a materialized view from 2 different tables. According the documentation for 10G it should be available.
Here is my script.
DROP MATERIALIZED VIEW PERSON_MV_T16;
CREATE MATERIALIZED VIEW PERSON_MV_T16 refresh complete on demand
AS
SELECT
CAST(P.MARKER AS VARCHAR2(4)) AS MARKER,
P.ROWID P_ROW_ID,
CAST(P.ACTIVE_IND_DT AS DATE) AS ACTIVE_IND_DT
FROM PERSON_ORGS_APEX_MV P
UNION
SELECT
CAST(P.MARKER AS VARCHAR2(4)) AS MARKER,
P.ROWID P_ROW_ID,
CAST(P.ACTIVE_IND_DT AS DATE) AS ACTIVE_IND_DT
FROM PERSON_ORGS_APVX_MV P
delete from mv_capabilities_table;
begin
dbms_mview.explain_mview('PEOPLE.PERSON_MV_T16');
end;
select *
from mv_capabilities_table where capability_name not like '%PCT%' and capability_name = 'REFRESH_FAST_AFTER_INSERT';
I get the following error.
CAPABILITY_NAME = REFRESH_FAST_AFTER_INSERT
POSSIBLE = N
MSGTEXT = tables must be identical across the UNION operator
I wrapped them in CAST operations just to be sure they are the same type and size.

As far as I'm aware, you can create MV in standard and also there is no limitation that I'm aware off
Standard and Enterprise Edition
A. Basic replication (MV replication)
\- transaction based
- row-level
- asynchronous from master table to MV (Materialized View)
- DML replication only
- database 7 / 8.0 / 8i / 9i / 10g
Variants:
1. Read-only MV replication
2. Updateable MV replication:
2.1 asynchronous from MV to master
2.2 synchronous from MV to master
3. Writeable MV replication
Enterprise Edition only
B. Multimaster replication
\- transaction based
- row-level or procedural
- asynchronous or synchronous
- DML and DDL replication
- database 7 / 8.0 / 8i / 9i / 10g
- Enterprise Edition only
Variants:
1. row-level asynchronous replication
2. row-level synchronous replication
3. procedural asynchronous replication
4. procedural synchronous replication
C. Streams replication
(Standard Edition 10g can execute Apply process)
\- (redo) log based
- row-level
- asynchronous
- DML and DDL replication
- database 9i / 10g (10g has Down Streams Capture)

Usage of Query Rewrite in Materialized Views

Hi,
I have a star schema with fact table and and dimensions tables.
One of the dimension tables is time_dimension and I have created
a materialized view(time_sales_mv) on it and the fact table. I
have also created a dimension(time_dim) on the
table 'time_dimension' with hierarchies and attributes.
Following are the syntaxes -
--Dimension table
CREATE TABLE TIME_DIMENSION (
TIME_KEY NUMBER(9) NOT NULL,
DAY_OF_MONTH NUMBER(9),
WEEKDAY NUMBER(9),
WEEKEND NUMBER(9),
JULIAN_DAY NUMBER(9),
JULIAN_WEEK NUMBER(9),
JULIAN_YEAR NUMBER(9),
MONTH_NUMBER NUMBER(9),
MONTH_NAME VARCHAR2(15),
WEEK_OF_THE_YEAR NUMBER(9),
WEEKDAY_NAME VARCHAR2(10),
WEEK_DAY_NUMBER NUMBER(9),
THE_YEAR NUMBER(9),
DAY_OF_THE_YEAR NUMBER(9),
THE_DATE DATE,
THE_QUARTER NUMBER(9),
PRIMARY KEY ( TIME_KEY )) ;
--Fact table
CREATE TABLE SALES_FACT (
TIME_KEY NUMBER(9) NOT NULL,
PRODUCT_KEY NUMBER(9) NOT NULL,
PROMOTION_KEY NUMBER(9) NOT NULL,
CUSTOMER_KEY NUMBER(9) NOT NULL,
DOLLAR_SALES FLOAT,
UNIT_SALES NUMBER(9),
DOLLAR_COST FLOAT)
-- Dimension created
CREATE DIMENSION Time_dim
LEVEL THE_DATE IS TIME_DIMENSION.THE_DATE
LEVEL WEEK_OF_THE_YEAR IS time_dimension.WEEK_OF_THE_YEAR
LEVEL MONTH_NUMBER IS time_dimension.MONTH_NUMBER
LEVEL THE_QUARTER IS time_dimension.THE_QUARTER
LEVEL THE_YEAR IS time_dimension.THE_YEAR
HIERARCHY calendar_rollup (
     THE_DATE CHILD OF
     MONTH_NUMBER CHILD OF
     THE_QUARTER CHILD OF
     THE_YEAR )
HIERARCHY weekly_rollup (
     THE_DATE CHILD OF
WEEK_OF_THE_YEAR )
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.DAY_OF_MONTH
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.WEEKDAY
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.JULIAN_DAY
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.DAY_OF_THE_YEAR
ATTRIBUTE MONTH_NUMBER DETERMINES
time_dimension_sagar.month_name
ATTRIBUTE THE_YEAR DETERMINES
time_dimension_sagar.JULIAN_YEAR;
-- Materialized View
CREATE MATERIALIZED VIEW time_sales_mv
BUILD IMMEDIATE
REFRESH COMPLETE ON DEMAND
ENABLE QUERY REWRITE
AS
SELECT t.month_number, SUM
(dollar_sales) AS sum_dollar_sales
FROM sales_fact s,time_dimension t
WHERE t.time_key =
s.time_key GROUP BY
t.month_number
Now if I use the same query as in the MV and see the explain
plan it shows the MV is being used instead of the underlying
tables which is as expected. But if I change 'month_number'
to 'month_name' in the above query, the explain plan does not
use the MV which is not as expected. Since 'month_name' is an
attribute of 'month_number'(defined in the dimension
definition), we can use it and query rewrite feature will join
the MV to the time_dimension table. But in the actual plan, it
uses the fact table 'sales_fact' instead of the MV. Even when I
use the rewrite hint on the query it does not use the MV. I want
know why this is happening??
Query-
SELECT t.month_number, SUM(dollar_sales) AS
sum_dollar_sales FROM
sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_number
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=1 Card=82 Bytes=2132)
TABLE ACCESS (FULL) OF TIME_SALES_MV (Cost=1 Card=82
Bytes=2132)
Query(using month_name instead of month_number)-
SELECT t.month_name, SUM(dollar_sales)
FROM sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_name
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=151 Card=9053
Bytes=307802)
SORT (GROUP BY) (Cost=151 Card=9053 Bytes=307802)
HASH JOIN (Cost=16 Card=9053 Bytes=307802)
TABLE ACCESS (FULL) OF TIME_DIMENSION_SAGAR (Cost=1
Card=82 Bytes=1804)
TABLE ACCESS (FULL) OF SALES_FACT (Cost=10 Card=11040
Bytes=132480)
Query (using rewrite hint in the above query) -
SELECT /*+ rewrite(time_sales_mv)*/
t.month_name, SUM
(dollar_sales)
FROM sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_name
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=151 Card=9053
Bytes=307802)
SORT (GROUP BY) (Cost=151 Card=9053 Bytes=307802)
HASH JOIN (Cost=16 Card=9053 Bytes=307802)
TABLE ACCESS (FULL) OF TIME_DIMENSION_SAGAR (Cost=1
Card=82 Bytes=1804)
TABLE ACCESS (FULL) OF SALES_FACT (Cost=10 Card=11040
Bytes=132480)

Hi,
I have a star schema with fact table and and dimensions tables.
One of the dimension tables is time_dimension and I have created
a materialized view(time_sales_mv) on it and the fact table. I
have also created a dimension(time_dim) on the
table 'time_dimension' with hierarchies and attributes.
Following are the syntaxes -
--Dimension table
CREATE TABLE TIME_DIMENSION (
TIME_KEY NUMBER(9) NOT NULL,
DAY_OF_MONTH NUMBER(9),
WEEKDAY NUMBER(9),
WEEKEND NUMBER(9),
JULIAN_DAY NUMBER(9),
JULIAN_WEEK NUMBER(9),
JULIAN_YEAR NUMBER(9),
MONTH_NUMBER NUMBER(9),
MONTH_NAME VARCHAR2(15),
WEEK_OF_THE_YEAR NUMBER(9),
WEEKDAY_NAME VARCHAR2(10),
WEEK_DAY_NUMBER NUMBER(9),
THE_YEAR NUMBER(9),
DAY_OF_THE_YEAR NUMBER(9),
THE_DATE DATE,
THE_QUARTER NUMBER(9),
PRIMARY KEY ( TIME_KEY )) ;
--Fact table
CREATE TABLE SALES_FACT (
TIME_KEY NUMBER(9) NOT NULL,
PRODUCT_KEY NUMBER(9) NOT NULL,
PROMOTION_KEY NUMBER(9) NOT NULL,
CUSTOMER_KEY NUMBER(9) NOT NULL,
DOLLAR_SALES FLOAT,
UNIT_SALES NUMBER(9),
DOLLAR_COST FLOAT)
-- Dimension created
CREATE DIMENSION Time_dim
LEVEL THE_DATE IS TIME_DIMENSION.THE_DATE
LEVEL WEEK_OF_THE_YEAR IS time_dimension.WEEK_OF_THE_YEAR
LEVEL MONTH_NUMBER IS time_dimension.MONTH_NUMBER
LEVEL THE_QUARTER IS time_dimension.THE_QUARTER
LEVEL THE_YEAR IS time_dimension.THE_YEAR
HIERARCHY calendar_rollup (
     THE_DATE CHILD OF
     MONTH_NUMBER CHILD OF
     THE_QUARTER CHILD OF
     THE_YEAR )
HIERARCHY weekly_rollup (
     THE_DATE CHILD OF
WEEK_OF_THE_YEAR )
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.DAY_OF_MONTH
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.WEEKDAY
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.JULIAN_DAY
ATTRIBUTE THE_DATE DETERMINES
time_dimension_sagar.DAY_OF_THE_YEAR
ATTRIBUTE MONTH_NUMBER DETERMINES
time_dimension_sagar.month_name
ATTRIBUTE THE_YEAR DETERMINES
time_dimension_sagar.JULIAN_YEAR;
-- Materialized View
CREATE MATERIALIZED VIEW time_sales_mv
BUILD IMMEDIATE
REFRESH COMPLETE ON DEMAND
ENABLE QUERY REWRITE
AS
SELECT t.month_number, SUM
(dollar_sales) AS sum_dollar_sales
FROM sales_fact s,time_dimension t
WHERE t.time_key =
s.time_key GROUP BY
t.month_number
Now if I use the same query as in the MV and see the explain
plan it shows the MV is being used instead of the underlying
tables which is as expected. But if I change 'month_number'
to 'month_name' in the above query, the explain plan does not
use the MV which is not as expected. Since 'month_name' is an
attribute of 'month_number'(defined in the dimension
definition), we can use it and query rewrite feature will join
the MV to the time_dimension table. But in the actual plan, it
uses the fact table 'sales_fact' instead of the MV. Even when I
use the rewrite hint on the query it does not use the MV. I want
know why this is happening??
Query-
SELECT t.month_number, SUM(dollar_sales) AS
sum_dollar_sales FROM
sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_number
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=1 Card=82 Bytes=2132)
TABLE ACCESS (FULL) OF TIME_SALES_MV (Cost=1 Card=82
Bytes=2132)
Query(using month_name instead of month_number)-
SELECT t.month_name, SUM(dollar_sales)
FROM sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_name
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=151 Card=9053
Bytes=307802)
SORT (GROUP BY) (Cost=151 Card=9053 Bytes=307802)
HASH JOIN (Cost=16 Card=9053 Bytes=307802)
TABLE ACCESS (FULL) OF TIME_DIMENSION_SAGAR (Cost=1
Card=82 Bytes=1804)
TABLE ACCESS (FULL) OF SALES_FACT (Cost=10 Card=11040
Bytes=132480)
Query (using rewrite hint in the above query) -
SELECT /*+ rewrite(time_sales_mv)*/
t.month_name, SUM
(dollar_sales)
FROM sales_fact s, time_dimension t
WHERE t.time_key = s.time_key
GROUP BY t.month_name
Explain Plan -
SELECT STATEMENT Optimizer=CHOOSE (Cost=151 Card=9053
Bytes=307802)
SORT (GROUP BY) (Cost=151 Card=9053 Bytes=307802)
HASH JOIN (Cost=16 Card=9053 Bytes=307802)
TABLE ACCESS (FULL) OF TIME_DIMENSION_SAGAR (Cost=1
Card=82 Bytes=1804)
TABLE ACCESS (FULL) OF SALES_FACT (Cost=10 Card=11040
Bytes=132480)

Query performance on materialized view vs master tables

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