1. Tampa Bay Relational Users Group
Query diagnosis
IBM Silicon Valley Lab, U.S.A.
Ó IBM Corporation 2003

2. Query analysis and tuning
Format the SQL statement
Prepare the statement for human tuning
Separate sections for:
SELECT list
FROM clause
WHERE clause …
Tools support
Data Studio fixpack includes SQL formatting
Show transformed SQL text

3. Sample unformatted query
EXPLAIN PLAN SET QUERYNO = 1 FOR
SELECT DISTINCT ITEM.ITEM_NBR AS ITEM_NBR, ITEM.PRDT_ID, STOREITEM.WK_STRT_DT AS WK_STRT_DT ,STOREITEM.DC_ID AS DC_ID FROM PROD.TIPA004_STITM_PROJ AS STOREITEM , PROD.TITM001_ITEM AS ITEM WHERE ITEM.BUS_UNIT_ID = ‘GS‘ AND ITEM.BUS_UNIT_ID = STOREITEM.BUS_UNIT_ID AND ITEM.MJR_CATG_ID = '00754‘ AND ITEM.INTMD_CATG_ID = '00043‘ AND ITEM.ITEM_NBR = STOREITEM.ITEM_NBR AND ITEM.MJR_CATG_ID = STOREITEM.MJR_CATG_ID AND ITEM.INTMD_CATG_ID = STOREITEM.INTMD_CATG_ID AND STOREITEM.RTL_DEPT_NBR = 1 AND AD_ITEM_FLG = 'Y‘ AND WK_STRT_DT = ' ';
Unformatted SQL,
where to start?

4. Formatted EXPLAIN PLAN SET QUERYNO = 1 FOR
SELECT DISTINCT ITEM.ITEM_NBR AS ITEM_NBR, ITEM.PRDT_ID, STOREITEM.WK_STRT_DT AS WK_STRT_DT ,STOREITEM.DC_ID AS DC_ID
FROM PROD.TIPA004_STITM_PROJ AS STOREITEM
,PROD.TITM001_ITEM AS ITEM
WHERE ITEM.BUS_UNIT_ID = STOREITEM.BUS_UNIT_ID
AND ITEM.MJR_CATG_ID = STOREITEM.MJR_CATG_ID
AND ITEM.INTMD_CATG_ID = STOREITEM.INTMD_CATG_ID
AND ITEM.ITEM_NBR = STOREITEM.ITEM_NBR
AND ITEM.BUS_UNIT_ID = ‘GS‘
AND ITEM.MJR_CATG_ID = '00754‘
AND ITEM.INTMD_CATG_ID = '00043‘
AND STOREITEM.AD_ITEM_FLG = 'Y‘
AND STOREITEM.RTL_DEPT_NBR = 1
AND STOREITEM.WK_STRT_DT = ' ';

5. Analyzing query Observe “interesting predicates”
Optimizer may produce inaccurate filter factor estimate
Range predicates with parameter markers
Predicates using interesting literals
Probable defaults
Complex predicates
Complex OR expressions
Negation predicates
Column expressions
Non-column expressions

6. Sample query
Pat’s diagnosis

7. Query breakdown SELECT … FROM SETL_TRANS S ,BRANCH CUST ,BRANCH_ADDR A
WHERE S.ADV_ABA_R = ?
AND S.PROCESS_DT < ' ‘
AND S.TYPE_CD IN ('A', ‘C’, ‘X’)
AND S.CLR_CYCLE_CD IN ('EOD', 'IMD‘, ‘OPN’)
AND S.STLMT_DT = ?
AND S.ACCT_NUM = CUST.ACCT_NUM
AND CUST.CUST_EFCT_DT <= ?
AND CUST.CUST_INACTV_DT > ?
AND A.ACCT_NUM = CUST.ACCT_NUM
AND A.CUST_EFCT_DT <= ?
AND A.CUST_INACTV_DT > ?
AND A.ADDR_TYP_CD = ' '

8. Identify peculiar predicates
SELECT …
FROM SETL_TRANS S
,BRANCH CUST
,BRANCH_ADDR A
WHERE S.ADV_ABA_R = ?
AND S.PROCESS_DT < ‘ ’  MAX DATE
AND S.TYPE_CD IN ('A', 'C', ‘X‘, ‘Z’)
AND S.CLR_CYCLE_CD IN ('EOD', 'IMD‘, ‘OPN’)
AND S.STLMT_DT = ?
AND S.ACCT_NUM = CUST.ACCT_NUM
AND CUST.CUST_EFCT_DT <= ?  Range with marker
AND CUST.CUST_INACTV_DT > ?  Range with marker
AND A.ACCT_NUM = CUST.ACCT_NUM
AND A.CUST_EFCT_DT <= ?  Range with marker
AND A.CUST_INACTV_DT > ?  Range with marker
AND A.ADDR_TYP_CD = ' ‘  COL = blank

9. Why are they peculiar? Predicates with typical default often skewed.
AND S.PROCESS_DT < ‘ ’  MAX DATE
AND A.ADDR_TYP_CD = ' ‘  COL = blank
Range predicates with parameter markers
- Impossible to estimate without literal
AND CUST.CUST_EFCT_DT <= ?  Range with marker
AND CUST.CUST_INACTV_DT > ?  Range with marker
AND A.CUST_EFCT_DT <= ?  Range with marker
AND A.CUST_INACTV_DT > ?  Range with marker

10. Range predicate interpolation
Table 104. Default filter factors for interpolation
Note: Op is one of these operators: <, <=, >, >=.
COMMENT: This is DB2’s documented guess for an impossible to estimate
Filter factor.
COLCARDF
Filter factor for OP
Filter Factor for
LIKE / BETWEEN
>= 100,000,000
1 / 10,000
3 / 100,000
>= 10,000,000
1 / 3,000
>= 1,000,000
1 / 1,000
3 / 10,000
>= 100,000
1 / 300
>= 10,000
1 / 100
3 / 1,000
>= 1,000
1 / 30
>= 100
1 / 10
3 / 100
>= 2
1 / 3
= 1 1
>= 0

11. Analyzing query Embed information within statement
Table information
CARDF
NPAGES
Column information for predicates
Local predicates
Join predicates
Observe where the filtering is
Selectivity of a predicate is relative to table cardinality
Investigate “suspicious” predicates
Determine actual versus estimated filtering
If there is a problem, identify options

12. Embed statistics SELECT …
FROM SETL_TRANS S CARDF 1,600,254 NPAGES 21,627
,BRANCH CUST CARDF 31,696 NPAGES 1132
,BRANCH_ADDR A CARDF 58,627 NPAGES 2791
WHERE S.ADV_ABA_R = ? COLCARDF 19,712
AND S.PROCESS_DT < ‘ ’ COLCARDF 11
LOW2KEY HIGH2KEY
AND S.TYPE_CD IN ('A', 'C', ‘X‘, ‘Z’) COLCARDF 4
AND S.CLR_CYCLE_CD IN ('EOD', 'IMD', ‘OPN') COLCARDF 3
AND S.STLMT_DT = ? COLCARDF 13
AND S.ACCT_NUM = CUST.ACCT_NUM COLCARDF / 26,527
AND CUST.CUST_EFCT_DT <= ? COLCARDF 2,496
LOW2KEY HIGH2KEY
AND CUST.CUST_INACTV_DT > ? COLCARDF 279
LOW2KEY HIGH2KEY
AND A.ACCT_NUM = CUST.ACCT_NUM COLCARDF 26,527 / 26,527
AND A.CUST_EFCT_DT <= ? COLCARDF 2,496
AND A.CUST_INACTV_DT > ? COLCARDF 274
LOW2KEY ‘ ’ HIGH2KEY ‘ ’
AND A.ADDR_TYP_CD = ‘ ‘ COLCARDF 5

13. Suspicious predicate analysis
1) The first range predicate, we’re looking for all values less than ‘
So the predicate searches for all values less than a number significantly greater
Than the HIGH2KEY – so basically, all of the rows qualify here.
(since the optimizer has the literal value, it KNOWS that all rows qualify).
2) For the column = blank predicate, I don’t believe a skew search was ever done.
You could look to see how many values are blank. Is it > 20%? 1/5 = 20%.
1) AND S.PROCESS_DT < ' ‘ COLCARDF 11
LOW2KEY HIGH2KEY
2) AND A.ADDR_TYP_CD = ' ‘ COLCARDF 5
Conclusion: First predicate is should not be causing this SQL statement any
Problems.

14. Suspicious predicate analysis
The literal value used for each of the parameter markers in this case happened
To be the same, and the value was
Comparing the literal value to the HIGH2KEY and what range would qualify
Is how I determined the ESTIMATED FF WITH LITERAL.
The ESTIMATED FF WITH MARKER is from the chart in the Admin guide.
The “error” is how different the optimizers DEFAULT estimate is from ACTUAL filtering.
3) AND CUST.CUST_EFCT_DT <= ? COLCARDF 2,496
LOW2KEY HIGH2KEY
ESTIMATED FF WITH LITERAL: = 100%
ESTIMATE WITH MARKER: 1/30 = 3% ( 97% error )
4) AND CUST.CUST_INACTV_DT > ? COLCARDF 279
LOW2KEY HIGH2KEY
ESTIMATED FF WITH LITERAL: = 99%
ESTIMATE WITH MARKER: 1/10 = 10% ( 89% error )
5) AND A.CUST_EFCT_DT <= ? COLCARDF 2,496
6) AND A.CUST_INACTV_DT > ? COLCARDF 274
LOW2KEY ‘ ’ HIGH2KEY ‘ ’

15. Suspicious predicate analysis
Conclusion
The range predicates with parameter markers introduce significant filter factor error. So we should recognize that this filter factor error can cause significant cost estimation problems for the optimizer – possibly resulting in poor access path choice.

16. Where’s the filtering? WHERE S.ADV_ABA_R = ? COLCARDF 19,712
(Very selective predicate)
AND S.PROCESS_DT < ‘ ’ COLCARDF 11
(This predicate doesn’t filter anything, known from suspicious predicate analysis)
AND S.TYPE_CD IN ('A', 'C', ‘X', ‘Z') COLCARDF 4
(In-list looking for 4 values, COLCARDF 4 – not filtering)
AND S.CLR_CYCLE_CD IN ('EOD', 'IMD', ‘OPN') COLCARDF 3
(In-list looking for 3 values, COLCARDF 3 – not filtering)
AND S.STLMT_DT = ? COLCARDF 13
(COL = LIT, COLCARDF 13 – somewhat filtering, but not great selectivity)
AND S.ACCT_NUM = CUST.ACCT_NUM COLCARDF / 26,527
(For the range predicates, we know that optimizer PERCIEVES them to be selective but
In reality, they are not. This was determined during suspicious predicate analysis)
AND CUST.CUST_EFCT_DT <= ? COLCARDF 2,496
AND CUST.CUST_INACTV_DT > ? COLCARDF 279
AND A.ACCT_NUM = CUST.ACCT_NUM COLCARDF 26,527 / 26,527
AND A.CUST_EFCT_DT <= ? COLCARDF 2,496
AND A.CUST_INACTV_DT > ? COLCARDF 274
AND A.ADDR_TYP_CD = ‘ ‘ COLCARDF 5
(COL = blank. Probably this column is skewed on blank. COLCARDF 5, not typically
Very filtering)

17. Where’s the filtering? Most selective by far SELECT …
FROM SETL_TRANS S CARDF 1,600,254 NPAGES 21,627
,BRANCH CUST CARDF 31,696 NPAGES 1132
,BRANCH_ADDR A CARDF 58,627 NPAGES 2791
WHERE S.ADV_ABA_R = ? COLCARDF 19,712
AND S.PROCESS_DT < ‘ ’ COLCARDF 11
LOW2KEY HIGH2KEY
AND S.TYPE_CD IN ('A', 'C', ‘X', ‘Z') COLCARDF 4
AND S.CLR_CYCLE_CD IN ('EOD', 'IMD', ‘OPN') COLCARDF 3
AND S.STLMT_DT = ? COLCARDF 13
AND S.ACCT_NUM = CUST.ACCT_NUM COLCARDF / 26,527
AND CUST.CUST_EFCT_DT <= ? COLCARDF 2,496
LOW2KEY HIGH2KEY
AND CUST.CUST_INACTV_DT > ? COLCARDF 279
LOW2KEY HIGH2KEY
AND A.ACCT_NUM = CUST.ACCT_NUM COLCARDF 26,527 / 26,527
AND A.CUST_EFCT_DT <= ? COLCARDF 2,496
AND A.CUST_INACTV_DT > ? COLCARDF 274
LOW2KEY ‘ ’ HIGH2KEY ‘ ’
AND A.ADDR_TYP_CD = ‘ ‘ COLCARDF 5
Most selective by far

18. Index analysis One significant input to the optimizer is…
Available indexes
What join sequence they encourage
Some index performance considerations
Provide efficient access for local predicates
Encourages table to be outer table
Provide efficient access for join predicates
Encourage access to table as INNER table of join
Provide ordering to avoid sort
Analysis:
Are there appropriate indexes to support this query?

19. Identify indexes Table: SETL_TRANS INDEX IXSTRN01
(PROCESS_DT, CLR_CYCLE_CD, ADV_ABA_R, TYPE_CD, ACCT_NUM, STLMT_DT)
TABLE: BRANCH
INDEX: IXBRNC01
(CUST_INACTV_DT, CUST_EFCT_DT)
INDEX: IXBRNC02
(ACCT_NUM, CUST_EFCT_DT)
TABLE: BRANCH_ADDR
INDEX: IXBRAD01
INDEX: IXBRAD02
(ACCT_NUM, ADDR_TYP_CD, CUST_EFCT_DT)

20. Index candidate usage Key: RED = Range predicate, stops matching
Table: AJT_SETL_TRANS
INDEX IXSTRN01
(PROCESS_DT, CLR_CYCLE_CD, ADV_ABA_R, TYPE_CD, ACCT_NUM, STLMT_DT)
TABLE: BRANCH
INDEX: IXBRNC01
(CUST_INACTV_DT, CUST_EFCT_DT)
INDEX IXBRNC02
(ACCT_NUM, CUST_EFCT_DT)
TABLE: BRANCH_ADDR
INDEX: IXBRAD01
INDEX: IXBRAD02
(ACCT_NUM, ADDR_TYP_CD, CUST_EFCT_DT)
Key:
RED = Range predicate, stops matching
BLUE: Join predicate
GREEN: Local equals predicate / in-list

21. Index design analysis (by table)
BRANCH table (Index design OK!)
Index IXBRNC02 supports local access
CONCERN: Predicate on this column has filter factor grossly overestimated, so optimizer will perceive the access to be more efficient to this table than what really occurs!
Index IXBRNC01 supports join access
BRANCH_ADDR table (Index design OK!)
Index IXBRAD01 leading column on local filtering
Predicate on this column has filter factor grossly over estimated
Allows table to be considered as inner table efficiently
Index IXBRAD02 leading column supports join
Allows table to be an efficient inner table

22. Index design analysis (by table)
SETL_TRANS table (Not OK!)
INDEX IXSTRN01 has one index.
No efficient for join
join predicate needs to be leading col)
No efficient index for outer access
Leading column of index qualifies ALL rows

23. Biggest table, worst index
Overlay table size
Table: SETL_TRANS CARDF 1,600,254 NPAGES 21,627
INDEX IXSTRN01
(PROCESS_DT, CLR_CYCLE_CD, ADV_ABA_R, TYPE_CD, ACCT_NUM, STLMT_DT)
TABLE: BRANCH CARDF 31,696 NPAGES 1132
INDEX: IXBRNC02
(CUST_INACTV_DT, CUST_EFCT_DT)
INDEX: IXBRNC01
(ACCT_NUM, CUST_EFCT_DT)
TABLE: BRANCH_ADDR CARDF 58,627 NPAGES 2791
INDEX: IXBRAD01
INDEX: IXBRAD02
(ACCT_NUM, ADDR_TYP_CD, CUST_EFCT_DT)
Key:
RED = Range predicate, stops matching
BLUE: Join predicate
GREEN: Local equals predicate / in-list
Biggest table, worst index
Options. Must scan
1.6 million rows!

24. Possible new indexes Existing index IXSTRN01
(PROCESS_DT, CLR_CYCLE_CD, ADV_ABA_R, TYPE_CD, ACCT_NUM, STLMT_DT)
Efficient outer table access
INDEX opt_1
(ADV_ABA_R, STLMT_DT, ACCT_NUM)
Efficient inner table access:
INDEX opt_2
(ACCT_NUM)

25. Summary of this SQL
Indexes on BRANCH, BRANCH_ADDR look better than they are
Range predicate with parameter marker estimates 3% of rows qualify
In reality, 99% qualify
Inefficient index available on SETL_TRANS table
No efficient outer table index available
No efficient inner table index available
This is the biggest table, with the best filter!!!
Optimizer bad join method due to combination of above factors
Performed full scan of transaction index 26,000 times
Resolution:
Providing new index on SETL_TRANS should provide more stable, faster access than ever before
REOPT, or providing literal values avoids the disaster without new index

26. SQL 2
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8

27. Local predicate analysis
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005  ???
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2  ???
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958  Skewed, not selective
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126  skewed, selective
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998  skewed, not selective
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018  skewed, selective
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
Both ‘A’ and ‘B’ tables have selective predicates.
COUNTRY_CD and PART_CD predicates – there is skew, optimizer assumes uniform distribution
B.PART_NUM – Slightly skewed. 3% one value. Uniform estimate is 0.4%.
PREFERRED – skewed, query searches for an infrequently occurring value.
Without looking at indexes, seems ‘A’ and ‘B’ will compete to be outer table
Qualified rows of 67.1 and 77.8 pretty close

28. Local index analysis – ‘A’
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005  ???
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2  ???
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958  Skewed, not selective
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPRT Y U PART_CD
COUNTRY_CD
FILE DR
SECTOR
PDV
IXPRT N D PART_CD
PART_TYPE
PDV
FILE
IXPRT N D PART_TYPE
PART_CD
COUNTRY_CD
IXPRT N D PART_TYPE
PART_NUM

29. Local index analysis – ‘A’
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005  ???
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2  ???
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958  Skewed, not selective
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPRT Y U PART_CD
COUNTRY_CD
FILE DR
SECTOR
PDV
IXPRT N D PART_CD
PART_TYPE
PDV
FILE
IXPRT N D PART_TYPE
PART_CD
COUNTRY_CD
IXPRT N D PART_TYPE
PART_NUM

30. Local index analysis B
SELECT COLS
FROM CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
WHERE B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126  skewed, selective
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998  skewed, not selective
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCTR Y P PART_NUM
CONTRACTOR_ID
CONT_TYPE
IXCTR N D PART_NUM
IXCTR N D BEGIN_DT
CONTRACTOR_ID
IXCTR N D CONTRACTOR_ID
PART_NUM
BEGIN_DT
END_DT
IXCTR N D CONTRACTOR_ID
BEGIN_DT

31. Local index analysis B
SELECT COLS
FROM CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
WHERE B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126  skewed, selective
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998  skewed, not selective
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCTR Y P PART_NUM
CONTRACTOR_ID
CONT_TYPE
IXCTR N D PART_NUM
IXCTR N D CONTRACTOR_ID
PART_NUM
BEGIN_DT
END_DT
IXCTR N D CONTRACTOR_ID
BEGIN_DT
Note: SUB_CONTRACTOR is selective due to search for least frequent value. Is not in any candidate index.
Otherwise, local index support looks good.
May be able to drop IXCTR02 with reverse index scan support.

32. Local index analysis C Table C
SELECT COLS
FROM CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
WHERE C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018  skewed, selective
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCPR N D PREFERRED
CONTRACTOR_ID
PRODUCT_ID
Table C
There is index support for local filtering.
Trailing join column (good)

33. Indexes for local summary
Each table with local filtering had efficient indexes to support local filtering
Positives:
Efficient access paths exist.
Negatives:
Each table will compete for the outer
More “apparently efficient” choices, more stress on optimizer, opportunity for incorrect choice

34. Join graph B C E D A
Two most selective tables ‘A’ and ‘B’ not joined directly
C – D – E each join on same column (PRODUCT_ID)
Shaping up like ‘A’ with 67 outer rows as outer vs ‘B’ with 77 rows as outer

35. Join considerations Index support for certain join sequences
Indexes available to support matching index access for different desirable join sequences?
Join reduction / fan-out considerations
Consider expansion / contraction of result size through different join sequences

36. Join indexes A INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPRT N P PART_NUM
IXPRT N D PART_NUM
PART_TYPE
PART_CD
IXPRT N D PART_TYPE
PART_NUM
IXPRTxx N D PART_NUM
PART_CD
COUNTRY_CD

37. Join indexes A Join access available through join the ‘D’ table only
Via PART_NUM if ‘D’ is the outer
There are multiple indexes to support ‘A’ as inner
IXPRT02 and IXPRTxx appear redundant
IXPRTxx is superset of IXPRT02, same column sequence
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPRT N P PART_NUM
IXPRT N D PART_NUM
PART_TYPE
PART_CD
IXPRT N D PART_TYPE
PART_NUM
IXPRTxx N D PART_NUM
PART_CD
COUNTRY_CD

38. Join indexes B INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCTR Y P PART_NUM
CONTRACTOR_ID
CONT_TYPE
IXCTR N D CONTRACTOR_ID
PART_NUM
BEGIN_DT
END_DT
IXCTR N D CONTRACTOR_ID
BEGIN_DT

39. Join indexes B INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
Join access available through join the ‘C’ table only
Via CONTRACTOR_ID if ‘C’ is the outer
There are multiple indexes to support ‘B’ as inner
IXCTR01 has PART_NUM as leading local
Join from outer will hit far fewer leaf pages due to leading local predicate
Smaller “swath” of leaf pages: NLEAF * 1/PART_NUM COLCARDF
210 * (1/260) ~= 1 leaf page
Makes this index “outstanding” from inner index access perspective
Also an effective “outer” index since it provides good local filtering and join order for a join to ‘C’ table as inner
IXCTR04, IXCTR05 lead with join predicate
Support the join effectively
Join scattered over all leaf pages
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCTR Y P PART_NUM
CONTRACTOR_ID
CE_TYPE
IXCTR N D CONTRACTOR_ID
PART_NUM
CE_DTDIFFREEL
CE_DTLANCREEL
IXCTR N D CONTRACTOR_ID
CE_DTDIFFREEL

40. Join indexes C INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCPR Y U PRODUCT_ID
CONTRACTOR_ID
CO_DTHRCONTACT
IXCPR N D CONTRACTOR_ID
PRODUCT_ID
IXCPR N D CONTRACTOR_ID
CO_PHASECONTACT
PRODUCT_ID
IXCPR N D PREFERRED
CONTRACTOR_ID
PRODUCT_ID

41. Join indexes C
Join access available through join the ‘B’, ‘D’, and ‘E’ tables
Via CONTRACTOR_ID if ‘B’ is the outer composite
Via PRODUCT_ID if ‘D’ or ‘E’ are in the outer composite
There is support for either join sequence.
CPNQCC02 has PRODUCT_ID as leading column to support ‘D’ or ‘E’ in outer composite
CPNQXC02 and IXCPR03 have CONTRACTOR_ID as leading join column if ‘B’ is the outer composite
IXCPR03 would also be a candidate if B were cartesianed with D or E. Not that I think that’s likely.
CPMQXCOH would likely be preferred index if ‘B’ were in outer composite
Selective leading local on PREFERRED bounds the leaf pages that would be hit to < 2% of all leaf pages
Makes ‘C’ a possible efficient outer – good local filtering, provides join ordering for join to ‘B’ table
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXCPR Y U PRODUCT_ID
CONTRACTOR_ID
CO_DTHRCONTACT
IXCPR N D CONTRACTOR_ID
PRODUCT_ID
IXCPR N D CONTRACTOR_ID
CO_PHASECONTACT
PRODUCT_ID
IXCPR N D PREFERRED
CONTRACTOR_ID
PRODUCT_ID

42. Join indexes D INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPDA Y P PRODUCT_ID
IXPDA N D PART_NUM
PRODUCT_ID
IXPDA N D PRODUCT_ID
PART_NUM

43. Join indexes D ‘D’ is accessed in multiple directions
Via PART_NUM if ‘A’ is the outer
Via PRODUCT_ID if accessed through ‘C’ or ‘E’
Both join direction supported by matching index access.
RT_ENTID leading column of IXPDA02
PRODUCT_ID leading column of IXPDA05, IXPDA06
The non-primary key indexes are defined as allowing duplicates – but they cannot.
PRODUCT_ID is the primary key and is included in a unique index.
Any index which contains PRODUCT_ID therefore is unique. Defining as unique would save some space in the index. Duplicate indexes have slightly larger control structures to allow for duplicate RIDS.
DB2 must allow for duplicates if the index is not explicitly defined as unique since you could drop the unique index.

44. Join indexes E Join access available through C and E tables
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
INDEX CLU UR NLEAF NLEVEL CR KEYCOLNAME COLCARDF MCARDF
IXPPA N U PRODUCT_ID
Join access available through C and E tables
Both tables join on PRODUCT_ID column
Join is supported via IXPDA01 index
PRODUCT_ID only column
Unique index (no fan-out when joining to this table)

45. Join fan-out Look at join fan-out issues
SELECT COLS
FROM PART A CARDF=17,598 QUALIFIED_ROWS=67.1 NPAGESF=1,467
, CONTRACTOR B CARDF=34,728 QUALIFIED_ROWS=77.8 NPAGESF=724
, CONT_PARTS C CARDF=2,093,750 QUALIFIED_ROWS=38,382 NPAGESF=52,189
, PARTS_PROD_ASMBLY D CARDF=7,058,356 QUALIFIED_ROWS=7,058,356 NPAGESF=68,644
, PARTS_PROD_ASM_DTL E CARDF=21,366,326 QUALIFIED_ROWS=21,366,320 NPAGESF=1,236,490
WHERE A.COUNTRY_CD = ? COLCARDF=208 MAX_FREQ=36.408% FF=0.005
‘FR’ = 36.4% ‘GB’ = 17% ‘DE’=10%
AND A.PART_CD = ? COLCARDF=5 MAX_FREQ=47.199% FF=0.2
4 = 47%, 2 = 27%, 6 = 17%, 1 = 8%, blank = < 1%
AND A.PART_TYPE IN ( 'F', 'I', 'P' ) COLCARDF=8 MAX_FREQ=79.867% FF=0.958
AND B.PART_NUM = ? COLCARDF=260 MAX_FREQ=3.032% FF=0.004
AND B.SUB_CONTRACTOR = 'Y' COLCARDF=2 MAX_FREQ=87.402% FF=0.126
LOW2KEY=N HIGH2KEY=Y
AND B.SUSPENDED = 'N' COLCARDF=2 MAX_FREQ=99.833% FF=0.998
AND C.PREFERRED = 'Y' COLCARDF=3 MAX_FREQ=76.832% FF=0.018
AND B.CONTRACTOR_ID = C.CONTRACTOR_ID COLCARDF=1,047/316 FF=9.551E-4
AND D.PART_NUM = A.PART_NUM COLCARDF=6,132/17,598 FF=5.682E-5
AND C.PRODUCT_ID = D.PRODUCT_ID COLCARDF=1,391,650/7,058,356 FF=1.417E-7
AND C.PRODUCT_ID = E.PRODUCT_ID COLCARDF=1,391,650/21,366,326 FF=4.68E-8
AND E.PRODUCT_ID = D.PRODUCT_ID COLCARDF=21,366,326/7,058,356 FF=4.68E-8
Look at join fan-out issues
Qualified outer rows * (CARDF of inner / MAX(join colcardf)
A  D
67.1 rows * (7,058,356 / 17598) ~= 27,000 rows
B  C or C  B
77.8 rows * (2,093,750 / 1047) ~= 155,500 rows (after local filtering on C, down to 38K)
So B  C expected to fan-out far more.

46. Explain
PLANNO
METHOD
MERGE_COLS
TB_NAME
MATCH
COLS
ACCESS_TYPE
ACCESS
NAME
IX_ONLY
SORTN_JOIN
SORTC_JOIN 1
PART 2 I
IXPRT01 N
PARTS_PROD_ASSEMBLY
IXPPA02 Y 3
CONT_PARTS
IXPRD04 4
CONTRACTOR
IXCTR01 5
PARTS_PROD_ASM_DTL
IXPDA01
Join sequence
Access ‘A’ via index IXPRT01 (PART_CD, COUNTRY_CD, …) ~67 rows
Nested loop join to ‘D’ using index IXPDA02 (RV_ENT_ID, PRODUCT_ID) ~27,000 rows
Sort merge join to C
Sorting composite into PRODUCT_ID sequence
Access ‘C’ via IXCPR04 (PREFERRED, CONTRACTOR_ID)
Sorting new into PRODUCT_ID sequence ~7,900 rows
Nested loop join to B via index IXCTR ~7,900 rows
(PART_NUM, CONTRACTOR_ID, CE_TYPE)
Nested loop join to E via index IXPDA ~7,900 rows
(PRODUCT_ID)
Blue = local predicate
Green = join predicate

47. Issues – A as outer? Is local filtering to ‘A’ table accurate?
There is skew, but use of markers precludes recognition of skew
Qualified rows and fan-out could be much worse than estimated
‘A’ as outer could be underestimated, depends on what values being used
Sort merge join to ‘C’ to avoid 27K probes
Does not want to probe 27k times matching + fan-out on PRODUCT_ID
Uses efficient local index instead
1 probe to scan of 38k rows via PREFERRED
27K probes * 2 rows per inner via index on PRODUCT_ID
Index on PREFERRED, PRODUCT_ID likely would might avert SMJ in this context
Hesitant to recommend index – since A  D  C could be an inefficient sequence.

48. Issues – B / C as outer? B as outer
Less skew on B.PART_NUM = ? – less uncertainty in cost estimate
Fan-out to 38K rows is discouraging
B  C supported by efficient local + equals index
(PREFERRED, CONTRACTOR_ID, PRODUCT_ID)
C also a desirable outer
Index on (PREFERRED,CONTRACTOR_ID,PRODUCT_ID) provides good local filter
Could access B via local filtering on B.PART_NUM = ?, materialize 77 rows into workfile for sort merge join

49. Summary Query 2 Bottom line: Multiple choices
Uniform distribution estimate on ‘A’ table allows it to compete very favorably.
If ‘FR’, ‘GB’, ‘DE’ values used for COUNTRY_CD – ‘A’ as outer no longer desirable.
Are ‘FR’, ‘GB’, ‘DE’ values frequently used for this query?
If PART_CD = ‘4’ value is used frequently – ‘A’ as outer no longer desirable.
Is ‘4’ used frequently?
Split query, REOPT, OPTHINTS…
Multiple choices
Local filtering spread across several tables
Estimated filtering looks good
Efficient access paths (index to support local, join predicates) exist
More difficult for optimizer to identify the cheapest path
Scenario more regression prone
Optimizer may need more statistics, ability to use more statistics (REOPT) for optimizer identify the cheapest path

50. Commentary How to perform SQL analysis Format query so it’s readable
Annotate with important statistics
Tables:
Table cardinality, NPAGES, qualified number of rows
Predicates
COLCARDF, LOW2KEY, HIGH2KEY, filter factor estimate
Are table level estimates reasonable based on your knowledge?
If you don’t know – perform counts to find out if estimates are accurate
If you don’t know how selective things are, how will you know what the best path should be?
Are predicate level filtering estimates reasonable?
Reference table, index, indexed columns report
Is the best local filtering supported through matching index access?
Any mis-estimated local filtering that’s also matching indexable (may cause one path to look far more efficient than reality)
With trailing join predicates to provide order to next desired table (bonus)
Is there adequate (matching) index support for desired join sequences?
Develop understanding of “plausible” and “desirable” access paths
Examine EXPLAIN output
Does optimizer choose the path you expect?
If not, you should have better understanding of what makes other access paths competitive, tuning can be more targeted
Eg. Certain predicate appears filtering, but is not.
Can use REOPT, or trick – targeted to solve a specific problem.
Skilled targeted tuning is less susceptible to re-regress than blind tuning (where problem is not understood)