This article shows you ways to improve the performance of SQL statements by analyzing the access plans that are generated by the DB2 SQL Optimizer (referred to as optimizer hereafter). It includes scenarios used by DB2 support and development that show you how problems are diagnosed and resolved, including how you can use the RUNSTATS tool, the REOPT bind option, and the EXPLAIN options in DB2 Version 9.7 to help you increase performance by improving optimizer cardinality estimates.
Scenario 1: Using RUNSTATS to update out-of-date statistics
In this scenario, a query performs poorly on the production system as compared to the development system. To improve the query performance, you analyze the access plan and run RUNSTATS to update any out-of-date statistics.
Listing 1. Original statement
-- Note: VW is a view select * from db2inst1.vw v, db2inst1.t1 t1 where v.x = t1.x
Listing 2. Optimized statement
SELECT ... FROM DB2INST1.T4 AS Q1, DB2INST1.T3 AS Q2, DB2INST1.T2 AS Q3, DB2INST1.T1 AS Q4 WHERE (Q1.T2ID = Q2.T2ID) AND (Q3.T4ID = Q1.T4ID) AND (Q1.Y = 'Y') AND (Q1.Z = 'N') AND (Q3.X = Q4.X) AND (Q3.Y IS NULL )
Listing 3. Query access plan
9.906 NLJOIN ( 2) 11476.2 2892 /--------------+--------------\ 247.66 0.04 NLJOIN FETCH ( 3) ( 8) 11333.4 100.125 2888 4.00412 /---------+--------\ /---+--\ 0.0033 74416 1 712084 NLJOIN TBSCAN IXSCAN TABLE: DB2INST1 ( 4) ( 7) ( 9) T2 0.0199984 11333.4 75.0188 52 2888 3 /-------+------\ | | 0.0208 0.16 74416 712084 IXSCAN IXSCAN TABLE: DB2INST1 INDEX: DB2INST1 ( 5) ( 6) T1 IDX_1 0.0104877 0.00951078 0 1 | | 13 100 INDEX: DB2INST1 INDEX: DB2INST1 IDX_T4 IDX_T3
Your first step in analyzing this access plan is to look at the estimated
number of rows by the optimizer. If the estimate error is large, then
cardinality estimation is likely a major factor in choosing a non-optimal
access plan. Estimates less than one row are likely suspects, unless the
operator is the inner (right) input of a nested loop join (referred to as
NLJOIN hereafter). The inner input cardinality
is a per-outer estimate, and an estimate less than one row is common.
Furthermore, when you analyze access plans, you should start from the
bottom and work your way up the graph.
The access plan in Listing 3 shows a cardinality
estimate of less than one row,
IXSCAN (5). This operator is not on
the inner input of a
NLJOIN, so it is a good
candidate for further investigation.
Listing 4. Predicates applied at IXSCAN(5)
Predicates: ---------- 4) Start Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 0.04 Predicate Text: -------------- (Q1.Y = 'Y') 4) Stop Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 0.04 Predicate Text: -------------- (Q1.Y = 'Y') 5) Start Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 0.04 Predicate Text: -------------- (Q1.Z = 'N') 5) Stop Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 0.04 Predicate Text: -------------- (Q1.Z = 'N')
When you further examine the details of the predicates that are applied at
IXSCAN (5) in Listing
4, you notice that the two predicates are shown with
P2, and the
filter factor with
P1: Q1.Y = 'Y' FF=0.04
P2: Q1.Z = 'N' FF=0.04
a filter factor of
0.04, so assuming
independence, then the cardinality estimate at
IXSCAN(5) is calculated as follows.
CARD_AT_IXSCAN(5) = FF(P1) * FF(P2) * INPUT_CARD
13, and represents the number of rows flowing
IXSCAN(5). As a result, the cardinality is
estimated as the following:
CARD_AT_IXSCAN(5) = 0.04 * 0.04 * 13 = 0.0208
When computing the filter factor of these predicates, the optimizer can
make use of available column distribution statistics to account for
non-uniformly distributed data. Or, if the data is uniformly distributed,
the optimizer can use the column cardinality (referred to as
colcard hereafter). In the worst case, if
statistics are not available, the optimizer fabricates statistics based on
the size of the table. It then gives a warning in the Extended
Diagnostic Information section of the
db2exfmt output for each table that the
optimizer fabricated statistics.
Listing 5. Extended diagnostic information
Diagnostic Identifier: 1 Diagnostic Details: EXP0045W. The table named "DB2INST1.T4" has fabricated statistics. This can lead to poor cardinality and predicate filtering estimates. The size of the table changed significantly since the last time the RUNSTATS command was run.
The information in Listing 5 shows that since the statistics are outdated,
the optimizer fabricates the statistics. If the statistics show a smaller
number of records in the table compared to the
Insert (UDI) counters, then the fabricated
statistics are saved in the internal packed descriptor. If the newly
fabricated statistics show a larger number of records compared to the
UDI counters, then the same fabricated
statistics may still remain until it is reset by
RUNSTATS. The change in the fabricated
statistics may lead to changes in the plan chosen for the query
Listing 6 shows that two
count(*) queries are submitted to further
confirm the real cardinality estimates before and after the predicates are
Listing 6. COUNT(*) queries to confirm the actual counts
SELECT COUNT(*) FROM "DB2INST1 "."T4"; RESULT: 13 ROWS SELECT COUNT(*) FROM "DB2INST1 "."T4" AS Q1 WHERE (Q1.Y = 'Y') AND (Q1.Z = 'N'); RESULT: 13 ROWS
count(*) queries show that the actual
13 remains the same before and
after the predicates are applied. Compared to the estimated cardinality of
0.0208, the error in the estimate is a factor
13/0.0208 = 625. This is significant since
the optimizer estimates less than 1 row at
NLJOIN (4), and thus expects to probe the inner
NLJOIN only once. However, with the
counts collected above, that is not true and you can expect the join to
result in at least
625 * CARD_EST_NLJOIN(4) =~ 2 rows. As a
result, you can expect that the inner is probed twice at
(3) as shown in Listing 3. The inner of
NLJOIN (3) is a full table scan at
TBSCAN (7), although the access may not perform
optimally if you have to probe it more than once.
Solution: You should execute
RUNSTATS on the table
DB2INST1.T4 to collect up-to-date statistics.
Listing 7 shows the access plan chosen as a
result of updating the statistics, and the query performed to your
Listing 7. Access plan after RUNSTATS is executed
6191.41 HSJOIN ( 2) 11948.8 2892 /--------------------+-------------------\ 74416 2.08 NLJOIN HSJOIN ( 3) ( 7) 11945.4 0.0277668 2892 0 /------+------\ /-------+------\ 74416 1 13 4 TBSCAN FETCH IXSCAN IXSCAN ( 4) ( 5) ( 8) ( 9) 11333.4 100.125 0.0156447 0.0112472 2888 4.00412 0 0 | /---+---\ | | 74416 1 712084 13 4 TABLE: DB2INST1 IXSCAN TABLE: DB2INST1 INDEX: DB2INST1 INDEX: DB2INST1 T1 ( 6) T2 IDX_T4 IDX_T3 75.0188 3 | 712084 INDEX: DB2INST1 IDX_1
The filter factors of the two predicates are estimated as
1 with the updated statistics.
Listing 8. The predicate filter factors for the two predicates at IXSCAN (8) after RUNSTATS executed
Predicates: ---------- 3) Start Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 1 Predicate Text: -------------- (Q1.Z = 'N') 3) Stop Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 1 Predicate Text: -------------- (Q1.Z = 'N') 4) Start Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 1 Predicate Text: -------------- (Q1.Y = 'Y') 4) Stop Key Predicate Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 1 Predicate Text: -------------- (Q1.Y = 'Y')
Using the updated filter factors, the optimizer estimates the cardinality at IXSCAN(8) as
FF(P1) * FF(P2) * INPUT_CARD = 1 * 1 * 13 = 13
Scenario 2: Collecting distribution statistics to improve query execution performance
In this scenario, the optimizer computes a query access plan with a non-optimal index access.
Listing 9. Original statement
select ... from db2inst1.name n1 where ... n1.lastname like ‘BAKER%' and n1.firstname like ‘LENNY%'
Listing 10. Optimized statement
Optimized Statement: ------------------- SELECT ... FROM DB2INST1.NAME AS Q1 WHERE ... AND (Q1.LASTNAME LIKE ‘LENNY%') AND (Q1.FIRSTNAME LIKE ‘BAKER%')
Listing 11. A snippet of the query access plan
0.40474 FETCH ( 3) 37.86 126.234 /---+---\ 139.289 9.3475e+08 RIDSCN TABLE: DB2INST1 ( 4) NAME 22.7192 Q1 4 | 139.289 SORT ( 5) 22.7187 4 | 139.289 IXSCAN ( 6) 22.6784 4 | 9.3475e+08 INDEX: DB2INST1 INDX3 Q1
FIRSTNAME "LENNY" appears 1.14 million times
DB2INST1.NAME. But, for the query you looked
at in Listing 9, the optimizer chooses an index
scan access of
DB2INST1.INDX3 with the key
containing only the
FIRSTNAME field. Listing 11 shows how the optimizer computes a
very low cost estimate for this index access,
IXSCAN(6). A second index called
DB2INST1.INDX1 with a key that includes both
FIRSTNAME is also defined on the table.
Listing 12. Statistics details
NUM_FREQVALUES 100 NUM_QUANTILES 50 RUNSTATS ON TABLE DB2INST1.NAME WITH DISTRIBUTION AND DETAILED INDEXES ALL Table Cardinality: UPDATE SYSSTAT.TABLES SET CARD=934750120, NPAGES=17905266, FPAGES=17905266, OVERFLOW=0, ACTIVE_BLOCKS=0 WHERE TABNAME = 'NAME' AND TABSCHEMA = 'DB2INST1 '; Column Cardinalities: COLNAME COLCARD ---------- ------- LASTNAME 6856495 FIRSTNAME 3829720 Indexes and their associated statistics: INDEX KEY NLEAF NLEVELS SEQ PAGES CLUSTER FACTOR ------ ----------- ------- ------- --------- -------------- INDX1 LASTNAME 3829525 5 3828184 0.084789 FIRSTNAME NAME_MIDDLE NUMKEY INDX2 LASTNAME 4790072 5 4788313 0.089595 SIGNIN INDX3 FIRSTNAME 2596262 5 2595551 0.075572 BIRTH
The index statistics in Listing 12 shows how the clustering of the table is very poor on all the indexes in column CLUSTER FACTOR. If the data stream is not reduced significantly at the index scan level, the random I/O's may cause the large amount of data pages that are fetched to perform poorly.
The first step is to determine the accuracy of this estimate because the
output cardinality of the
IXSCAN (6) operator
in Listing 11 is very small compared to the 934
million rows of the input cardinality.
Listing 13. Details of the predicates applied at IXSCAN (6)
Predicates: ---------- 6) Stop Key Predicate Comparison Operator: Less Than or Equal (<=) Subquery Input Required: No Filter Factor: 0.59615 Predicate Text: -------------- (Q1.FIRSTNAME <= 'LENNY........................') 7) Start Key Predicate Comparison Operator: Less Than or Equal (<=) Subquery Input Required: No Filter Factor: 0.40385 Predicate Text: -------------- ('LENNY........................' <= Q1.FIRSTNAME) Input Streams: ------------- 1) From Object DB2INST1.INDX3 Estimated number of rows: 9.3475e+08 Number of columns: 2 Subquery predicate ID: Not Applicable Column Names: ------------ +Q1.FIRSTNAME(A)+Q1.$RID$ Output Streams: -------------- 2) To Operator #5 Estimated number of rows: 139.289 Number of columns: 1 Subquery predicate ID: Not Applicable Column Names: ------------ +Q1.FIRSTNAME(A)
The rewrite phase of query optimization generates equivalent range
predicates for each of the
LIKE predicates in
the statement, which improves query performance by applying the generated
range predicates as
start/stop keys on the
index scan. The range predicate is constructed to search for the range of
values between the lowest string with
the prefix, and the highest string with
as the prefix. The filter factors for the
predicates are shown as follows.
P4: n1.lastname like 'LENNY%' FF=1.49012e-07
P5: n1.firstname like 'BAKER%' FF=0.00290581
WITH DISTRIBUTION clause is used in the
RUNSTATS command, the quantile statistics
collected on the column provide the optimizer with the required
information to accurately estimate the filter factor of range predicates.
For this scenario, Table 1 shows the quantiles of
Table 1. Quantiles for predicate columns
The calculation of the filter factor estimate for the range predicate is best shown by using the diagram in Listing 14.
Listing 14. Calculating FF of range predicates
| | 'LENNY.....' <= Q1.FIRSTNAME = FF2 | | |------------------------------------------------->| | | FF(RANGE) | | | | = FF1 + FF2 – 1 | | |<-------------------------------------------------| | | Q1.FIRSTNAME <= 'LENNY.....' = FF1 | | |<------------------------------------------------------------------------------->| | FULL RANGE OF VALUES IN FIRSTNAME |
When quantile statistics are available, the filter factor of the
LIKE predicate is computed by estimating the
individual filter factor of each range (FF1,FF2) individually, and
determining the overlap between the two predicates as:
FF(RANGE) = (FF1 + FF2) - 1.
As a result, the filter factor for the predicate
FIRSTNAME LIKE 'LENNY%' is computed as:
FF1 = FF('LENNY......' <= Q1.FIRSTNAME) = 0.40385 FF2 = FF(Q1.FIRSTNMAE <= 'LENNY......') = 0.59615 FF(RANGE) = FF1 + FF2 – 1 = 0.59615 + 0.40385 – 1 = 0
This shows the estimated filter factor is
using linear interpolation of the quantile statistics, but the optimizer
further bounds the filter factor using the frequent value statistics as a
Following the same procedure as described in Scenario
1, the cardinality estimate can be verified using
Listing 15. COUNT(*) queries to confirm the actual counts
select count(*) from db2inst1.name n8 where n1.lastname like 'BAKER%' RESULT: 2739268 select count(*) from db2inst1.name n8 where n1.firstname like 'LENNY%' RESULT: 1161991 select count(*) from db2inst1.name n1 where n1.firstname like 'LENNY%' AND n1.lastname like 'BAKER%' RESULT: 3853
The counts show that the filter factor estimate for
LASTNAME is fine, but
FIRSTNAME is under-estimated. From Listing 13 you can see that:
FF(n1.lastname like 'BAKER%') = 0.00290581
Therefore, as seen below, the estimated cardinality is very accurate after applying this predicate.
INPUT_CARD * FF(n1.lastname like 'BAKER%') = 934750120 * 0.00290581 = 2,716,206.25
FIRSTNAME, the optimizer estimates only
139.289 rows, but the actual count is 1,161,991. The error in the estimate
Why is the optimizer underestimating the cardinality? For
range predicates, the optimizer considers using the quantile statistics to
estimate the filter factor. From the statistics listed in Table 1, there are
557,252,038 – 538,016,212 =
rows between positions 29 and 30 that contain values between
frequent values shown below that one of the values within this range
exists in 2.29 million rows.
UPDATE SYSSTAT.COLDIST SET COLVALUE='LESTER', VALCOUNT=2295040 WHERE COLNAME = 'FIRSTNAME' AND TABNAME = 'NAME' AND TABSCHEMA = 'DB2INST1 ' AND TYPE = 'F' AND SEQNO = 58;
That value only covers a small fraction of the range, and there are 17 million other rows unaccounted for. Listing 16 includes a diagram that shows the unknown values.
Listing 16. Quantile bucket for FIRSTNAME
LARRY LESTER LIN |<-------UNKNOWN------->|<----2.29M---->|<------UNKNOWN------>| ... +--------------+--------+---------------+---------------------+ ... | | | | 538016212 LENNY? 557252038 (SEQNO 29) (SEQNO 30)
Solution: Since the number of distinct possible values
LIN can be very large, if you can reduce the
ranges in the quantiles, then the error in the filter factor estimate can
be reduced. Initially, you were collecting 50 quantiles, so if you collect
more quantiles, then the error in the optimizer's estimates should be
reduced, and should lead to an optimal access plan.
Listing 17. Snippet of access plan after increasing quantiles
511.05 IXSCAN ( 3) 11111.8 11242.9 | 9.3475e+08 INDEX: DB2INST1 INDX1 Q1
Increasing the quantiles collected on
to 200 greatly reduced the error in the cardinality estimate, resulting in
the optimizer choosing an index access that led to optimal query execution
Scenario 3: Using REOPT to improve query execution performance
In this scenario, you run a static query from an application that completes in 11 minutes. But if the query is submitted from the command line (CLP), it completes in 10 seconds. Why is there a difference in performance between the two methods of executing the same query?
Listing 18. Query from static package using host variables
SELECT ... FROM DB2INST1.T1 A, DB2INST1.T2 B, DB2INST1.T3 C, WHERE A.T1ID = B.T2ID and B.T2ID = C.T2ID and B.X = :HV00001 :HI00001 and C.Y <> ‘10’ and A.X in ('00') and A.Y in ('000','001','005','006') and A.Z between :HV00002 :HI00002 and :HV00003 :HI00003
Listing 19. Query from CLP using literal values
SELECT ... FROM DB2INST1.T1 A, DB2INST1.T2 B, DB2INST1.T3 C, WHERE A.T1ID = B.T1ID and B.T2ID = C.T2ID and B.X = 'CAD' and C.Y <> ‘10’ and A.X in ('00') and A.Y in ('000','001','005','006') and A.Z between ‘2007-10-01' and '2007-10-30'
When the optimizer compiles a query with predicates in the
WHERE clause that contains parameter markers,
special registers, or host variables, it does not know the actual value
when computing the filter factor. For equality predicates, the
COLCARD statistic is used, and for range
LOW2KEY statistics are used. In this scenario,
you collected distribution statistics, so the optimizer can estimate a
more accurate cardinality when there is significant skew in the column
data, and the actual literal values are used in the query when submitted
from the CLP.
Solution: Bind the package using the
REOPT ALWAYS option as follows:
db2 BIND <filename> ... REOPT ALWAYS ...
REOPT ONCE option can also be considered,
but if there is significant skew in the data, then multiple variations of
the plan may be required to achieve optimal performance. With
REOPT ONCE, if you do not seed it with a
representative set of values, then it may not be beneficial. The following
guideline shows when to use
- Non-uniform data distribution
- Distribution statistics are collected
- REOPT ALWAYS: compilation time isn’t a concern
- REOPT ONCE: if compilation time is a concern, REOPT-imizing on the first set of values might produce a plan that is good enough for all ranges of values
You can use the
db2pd tool to confirm that
REOPT is being used, as shown in Listing 20.
Listing 20. Using db2pd to confirm REOPT is being used
db2pd –db <dbname> -REOPT Database Partition 0 -- Database PCTMS00D -- Active -- Up 15 days 00:28:27 -- Date ... Dynamic Cache Reoptimization: Dynamic SQL Statements: Address AnchID StmtUID NumEnv NumVar NumRef NumExe Text 0x2E787090 41 94 1 4 5 5 SELECT ... FROM DB2INST1.T1 A, DB2INST1.T1 B, DB2INST1.T1 C, WHERE A.T1ID = B.T1ID and B.T2ID = C.T2ID and B.X = :HV00001 :HI00001 and C.Y <> ‘10’ and A.X in ('00') and A.Y in ('000','001','005','006') and A.Z between :HV00002 :HI00002 and :HV00003 :HI00003
NumVar value represents the number of
variations of the query, and the
is used to map to the variations, as shown in Listing
Listing 21. Dynamic SQL variations
Address AnchID StmtUID EnvID VarID NumRef Typ ... Time ... 0x2F1175E0 41 94 2 4 1 6 2008-11-06-16.34.25.040149 0x2F0E7C50 41 94 2 3 1 6 2008-11-06-220.127.116.114376 0x2F2E6A60 41 94 2 2 1 6 2008-11-06-18.104.22.1681584 0x2F2D61C0 41 94 2 1 1 6 2008-11-06-22.214.171.1246402
Listing 21 shows the different variations for the
same query, executed at different times. Due to performance reasons, when
REOPT ALWAYS, the values illustrated by
the empty output in Listing 22 are not tracked
Listing 22. REOPT values not available under REOPT ALWAYS
Address AnchID StmtUID EnvID VarID NumRef Typ ... Time ...
Scenario 4: Collecting column group statistics to improve query execution performance
In this scenario, the optimizer underestimates the cardinality when computing a query access plan, causing the query to run for 2 hours.
Listing 23. Original statement
SELECT ... FROM T1, T2, T3, T4 WHERE ... AND T1.ACCT_NUM = T2.ACCT_NUM AND T2.ACCT_NUM = T3.ACCT_NUM AND T2.ID_NUM = T3.ID_NUM AND T1.ACCT_NUM = T4.ACCT_NUM AND T1.REP_NUM = T4.REP_NUM AND T4.ACCT_NUM = T3.ACCT_NUM AND T4.ID_NUM = T3.ID_NUM AND T4.ACCT_IND = T3.ACCT_IND AND ('1800-01-01-00.00.00.000000' < T1.DATE_MOD) AND T1.COMM IN ('A', 'D') ...
Listing 24. Query access plan
55.8297 NLJOIN ( 2) 226198 66138.3 /------------+-----------\ 55.8297 1 NLJOIN FETCH ( 3) ( 10) 221047 92.2672 65932.4 3.68909 /----+---\ /---+---\ 57.0648 0.978356 3.05367 3.81189e+06 TBSCAN IXSCAN IXSCAN TABLE: DB2INST1 ( 4) ( 9) ( 11) T3 218193 50.0315 50.0303 65818.2 2 2 | | | 57.0648 1.06432e+06 3.81189e+06 SORT INDEX: DB2INST1 INDEX: DB2INST1 ( 5) T2_IDX T3_IDX 218193 65818.2 | 57.0648 HSJOIN ( 6) 218193 65818.2 /------+-----\ 6.69807e+06 1.15013e+06 TBSCAN TBSCAN ( 7) ( 8) 60191.6 33264.5 33540 17057 | | 6.69807e+06 3.3602e+06 TABLE: DB2INST1 TABLE: DB2INST1 T4 T1
The query in Listing 23 contains multiple equality join predicates between each of the tables in the join.
Group 1: P2: T2.ACCT_NUM = T3.ACCT_NUM P3: T2.ID_NUM = T3.ID_NUM Group 2: P4: T1.ACCT_NUM = T4.ACCT_NUM P5: T1.REP_NUM = T4.REP_NUM Group 3: P6: T4.ACCT_NUM = T3.ACCT_NUM P7: T4.ID_NUM = T3.ID_NUM P8: T4.ACCT_IND = T3.ACCT_IND
The access plan in Listing 24 shows a significant
reduction in cardinality at
HSJOIN (6) when
compared to the two inputs into the join. The detailed predicates section
in Listing 25 are from the
Group 2 set, shown as
Q4 identify tables
Listing 25. The predicates applied at HSJOIN(6)
2) Predicate used in Join Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 2.7307e-07 Predicate Text: -------------- (Q4.ACCT_NUM = Q1.ACCT_NUM) <--- P4 6) Predicate used in Join Relational Operator: Equal (=) Subquery Input Required: No Filter Factor: 2.71267e-05 Predicate Text: -------------- (Q1.REP_NUM = Q4.REP_NUM) <--- P5
The estimated cardinality of the hash join is computed as the product of the input cardinalities, and the filter factor of each join predicate, as shown below.
HSJOIN_CARD = 2.7307e-07 * 2.71267e-05 * 6.69807e+06 * 1.15013e+06 =~ 57.0648
count(*) query may be used to
calculate the error in the estimate of this join cardinality, as shown
SELECT COUNT(*) FROM T4 AS Q1, T1 AS Q4 WHERE (Q1.REP_NUM = Q4.REP_NUM) AND (Q4.ACCT_NUM = Q1.ACCT_NUM) AND ('1800-01-01-00.00.00.000000' < Q4.DATE_MOD) AND Q4.COMM IN ('A', 'D'); RESULT: 1,155,273 rows
The individual filter factors for the two local predicates on
T1 were verified and confirmed as not being
very selective. Column group statistics may be a solution because there
are two or more equality join predicates between the pair of tables, and
the error in the cardinality estimate of the join is large, so the two
join predicates might be statistically correlated. The simple approach is
to collect column group statistics on both tables, but only a column group
on the parent is required, so the steps necessary to determine the parent
side in this join are shown.
Table 2. Column statistics
From the column statistics in Table 2,
T4 is the parent in the join because the
REP_NUM is greater than T1's respective
COLCARD statistics, the
HIGH2KEY for each column is greater, and the
LOW2KEY are the same (requirement is
less than or equal to). As a result, a column
group statistic on (
T4.ACCT_NUM, T4.REP_NUM) is
considered by the optimizer to account for statistical correlation between
the two join predicates.
Solution: Collect the column group statistic on
T4. If an index is already defined on
ACCT_NUM, REP_NUM) as the leading columns in
the key (the order of the two columns is not important), then the
FIRST2KEYCARD statistic of the index would
already be available for use by the optimizer to account for the
statistical correlation. In this scenario, such an index does not exist,
RUNSTATS command is executed with the
column group included, as shown below.
RUNSTATS ON TABLE DB2INST1.T4 ON ALL COLUMNS AND COLUMNS ((ACCT_NUM, REP_NUM)) WITH DISTRIBUTION AND SAMPLED DETAILED INDEXES ALL
Listing 26 shows how collecting the column group statistics improves the cardinality estimate computed by the optimizer, which produces a better performing access plan.
Listing 26. Access plan after collecting column group statistics
1.6838e+06 HSJOIN ( 2) 420201 170275 /--------------+--------------\ 1.72106e+06 1.06671e+06 HSJOIN HSJOIN ( 3) ( 6) 248948 170618 69854 100421 /------+-----\ /------+-----\ 6.70386e+06 1.20558e+06 3.81669e+06 1.06671e+06 TBSCAN TBSCAN TBSCAN IXSCAN ( 4) ( 5) ( 7) ( 8) 60245.9 33859.8 154124 15675.8 33569 17359 91530 8891 | | | | 6.70386e+06 3.42018e+06 3.81669e+06 1.06671e+06 TABLE: DB2INST1 TABLE: DB2INST1 TABLE: DB2INST1 INDEX:DB2INST T4 T1 T3 T2_IDX
Scenario 5: Using Section Actuals when analyzing access plans to improve query performance
Determining the appropriate set of
queries can be difficult and time consuming, especially for large queries.
You can remedy this by using the new feature called
Section Actuals, from DB2 Version 9.7 Fix Pack
In this scenario, you repeat Scenario 1 using
Section Actuals to illustrate its usefulness.
Do the following steps to capture the
- Enable Section Actuals
- Create the workload manager and event monitor
- Collect Section Actuals for the statement of interest
- Locate the Application, UOW and Activity ID for the data
- Populate the data into EXPLAIN tables
- Run db2exfmt to generate the access plan
- Examine the output
Step 1: Enable Section Actuals
You must explicitly enable the
feature by setting the
configuration parameter to
BASE as follows.
db2 update db cfg for <dbname> using section_actuals base
Once enabled, the information is captured using the
Step 2: Create the workload manager and event monitor
You must create a workload manager and event monitor to use
Actuals. You can use
the default workload manager instead of creating one as described
create workload MYWORKLOAD current client_acctng('MYWORKLOAD') service class sysdefaultuserclass collect activity data on all database partitions with details, section; grant usage on workload MYWORKLOAD to public; create event monitor MYMON for activities write to table;
Step 3: Collect Section Actuals for the statement of interest
delete from ACTIVITYSTMT_MYMON; call wlm_set_client_info(null, null, null, 'MYWORKLOAD', null); set event monitor MYMON state 1; -- a subset of the statement from scenario 1 SELECT * FROM DB2INST1.T4 AS Q1 WHERE (Q1.Y = 'Y') AND (Q1.Z = 'N') set event monitor MYMON state 0; call wlm_set_client_info(null, null, null, null, null);
Step 4: Locate the application, UOW and activity ID for the data
select appl_id, uow_id, activity_id, substr(stmt_text,1,80) as stmt from ACTIVITYSTMT_MYMON ; APPL_ID UOW_ID ACTIVITY_ID STMT -------------------------- ------- ---------------- ----------------------------- *LOCAL.DB2.100530150552 20 1 SELECT * FROM DB2INST1.T4 AS Q1 WHERE (Q1.Y = 'Y') AND (Q1.Z = 'N')
Step 5: Populate the data into the EXPLAIN tables
explain_from_activity procedure is called
to populate the explain tables for the statement of interest. The first
APPL_ID, UOW_ID, ACTIVITY_ID, are
determined for the statement of interest from the output in step 4. The fourth input is the event monitor
identifier set in step 3, and the fifth input is the
schema name of the
EXPLAIN tables. In this
example, the schema for the
EXPLAIN tables is
call explain_from_activity ('*LOCAL.DB2.100530150552', 20,1,'MYMON','VCORVINE',?,?,?,?,?) Value of output parameters -------------------------- Parameter Name : EXPLAIN_SCHEMA Parameter Value : VCORVINE Parameter Name : EXPLAIN_REQUESTER Parameter Value : VCORVINE Parameter Name : EXPLAIN_TIME Parameter Value : 2010-05-30-126.96.36.199000 Parameter Name : SOURCE_NAME Parameter Value : SQLC2H21 Parameter Name : SOURCE_SCHEMA Parameter Value : NULLID Parameter Name : SOURCE_VERSION Parameter Value : Return Status = 0
Step 6: Run db2exfmt to generate the access plan
db2exfmt –d <dbname> -1 –o ex_activity.out Screen Output: DB2 Universal Database Version 9.7, 5622-044 (c) Copyright IBM Corp. 1991, 2008 Licensed Material - Program Property of IBM IBM DATABASE 2 Explain Table Format Tool Connecting to the Database. Connect to Database Successful. Output is in ex_activity.out. Executing Connect Reset -- Connect Reset was Successful.
Step 7: Examine the output
The following is a part of the access plan from the
db2exfmt output in
Rows Rows Actual RETURN ( 1) Cost I/O | ... 0.0208 <-- estimated number of rows 13 <-- actual number of rows IXSCAN ( 5) 0.0104877 NA | 13 NA INDEX: DB2INST1 IDX89
The access plan shows the estimated and actual number of rows at each
Section Actuals are derived from
Explain, so not all data is
available like there is with a regular
of the query. This is identified in the graph of the access plan using the
NA notation. For example,
IXSCAN(5) does not include the I/O cost value,
and the actual base table cardinality is not collected.
Your ability to analyze access plans to tune queries and improve query execution performance is a valuable skill. You can ensure your access plan is optimal, and improve its performance by using the tools and techniques discussed in this article to identify and correct errors in the optimizer estimated cardinality.
- "Automatic statistics collection in DB2 for Linux, UNIX, and Windows" (developerWorks, June 2007): Learn about auto runstats.
- "Comparing real-time cardinality to the optimizer cardinality estimates" (developerWorks, December 2005): A tool to aid in tuning queries.
- "Understand column group statistics in DB2" (developerWorks, December 2006): Learn all about how to use column group statistics.
- "Further Understand column group statistics in DB2" (developerWorks, September 2008): Leverage the extended use of multi-column statistics to improve cardinality estimates.
- "Best Practices: Writing and Tuning Queries for Optimal Performance" (developerWorks, May 2008): Learn best practices for minimizing the impact of SQL statements on DB2 database performance.
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