In this post, I examine the effect of different settings for the tree performance options on how the SQL is generated. It is common, to see different performance options in use on different trees in the same SQL query. It is important to be able to look at a piece of SQL generated by nVision and to be able to work out which performance options have been set on which trees.
- Access Method: join to tree selector –v- use literal values
- Tree Selectors: statics –v- dynamic
- Selector Options: single values –v- inequalities/between
Access Method
This option allows you control how the tree selector table is combined with the ledger table. The choices of interest are whether to join the tree selector, or whether to use literal values where feasible.
Join
Here, two tree selectors are joined to the ledger tableSELECT L4.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L4, … PSTREESELECT10 L
WHERE A.LEDGER='ACTUALS'
AND A.FISCAL_YEAR=2017
…
AND L.SELECTOR_NUM=73130
AND A.CHARTFIELD1=L.RANGE_FROM_10
AND A.CURRENCY_CD='GBP'
GROUP BY L4.TREE_NODE_NUMLiteral Values
When 'use literal values' is selected nVision replaces the join to the tree selector table with a set of literal values generated from the contents of the tree selector table.In this example, the join to the CHARTFIELD1 tree selector has been replaced with a series of literal value predicates. There is one for every selected tree leaf. A simple equality condition for single value leaves, and a BETWEEN condition for ranged leaves.
SELECT L2.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L2
WHERE A.LEDGER='ACTUALS'
AND A.FISCAL_YEAR=2017
AND A.ACCOUNTING_PERIOD=1
…
AND L2.SELECTOR_NUM=101142
AND A.ACCOUNT=L2.RANGE_FROM_10
AND (A.CHARTFIELD1='0070700'
OR A.CHARTFIELD1='0078999'
…
OR A.CHARTFIELD1 BETWEEN '0091100' AND '0091101'
OR A.CHARTFIELD1='0150204')
AND A.CURRENCY_CD='GBP'
GROUP BY L2.TREE_NODE_NUMRecommendations
In general, the literal values option is very beneficial.- It simplifies the queries generated by nVision by removing a table from the from clause and leaves the optimizer with fewer choices when determining the execution plan.
- Data is filtered on the scan of the ledger table rather than when it is joined to the tree selector. On an engineered system these predicates can be pushed down to the storage cells.
- As the number of leaves on a tree increase, so the number of literal predicates in the query increases, and the time taken to parse the SQL increases. As a rough guideline, I would say that the parse time starts to become significant as the tree exceeds 2000 leaves, and you might be better using the Join Option.
- Also, as the number of literal predicates increase the time taken to evaluate them increases. This applies to both conventional and engineered systems.
- Some PeopleSoft customers have found it generally beneficial to the system to raise the value of OPTIMIZER_DYNAMIC_SAMPLING from the default of 2 to 4 so that Oracle collects dynamic statistics if there are expressions in where clause predicates or multiple predicates on the same table (see Database SQL Tuning Guide -> Managing Optimizer Statistics). However, this can significantly increase the parse overhead of nVision SQL using many predicates. If necessary, this parameter can be reset at session level for nVision with a trigger.
- The nVision client also has to do more work to generate the SQL.
- Literal values increase the variability of SQL generated, reports using different tree nodes will generate SQL with different numbers of predicates and there is effectively no chance that any two statements would be similar enough to use any of Oracle's plan stability technologies (SQL baselines, SQL profiles, SQL plan directives).
- In theory, cursor sharing could reduce the parse, but in my experience is that the optimizer does not correctly cost the predicates resulting in different execution plans with poorer performance.
Tree Selectors
This option allows you to choose whether a tree selector is managed in a static or dynamic manner. As you will see, the Tree Selectors performance option is tied in with the Selector Options performance option.Static
Where a tree uses a static selector, the entire tree is extracted into the tree selector. There will be no tree node number criteria in the extract statement.INSERT INTO PSTREESELECT10
(SELECTOR_NUM,TREE_NODE_NUM,RANGE_FROM_10,RANGE_TO_10)
SELECT 78722,L.TREE_NODE_NUM, SUBSTR(L.RANGE_FROM,1,10),SUBSTR(L.RANGE_TO,1,10)
FROM PSTREELEAF L
WHERE L.SETID='GLOBE'
AND L.SETCNTRLVALUE=' '
AND L.TREE_NAME='FUNCTION'
AND L.EFFDT=TO_DATE('1901-01-01','YYYY-MM-DD')Tree node number criteria will appear in the nVision queries to identify the required selectors.
SELECT L4.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L4, PSTREESELECT10 L2, PSTREESELECT10 L
WHERE A.LEDGER='ACTUALS'
AND A.FISCAL_YEAR=2017
AND A.ACCOUNTING_PERIOD=1
AND L4.SELECTOR_NUM=89052
AND A.CHARTFIELD3>= L4.RANGE_FROM_10
AND A.CHARTFIELD3 <= L4.RANGE_TO_10
AND L4.TREE_NODE_NUM BETWEEN 1000000000 AND 2000000000Dynamic
Dynamic selectors are built on-the-fly by nVision as the report runs. They select just the rows from the tree that are required by the report.INSERT INTO PSTREESELECT10
(SELECTOR_NUM,TREE_NODE_NUM,RANGE_FROM_10,RANGE_TO_10)
SELECT DISTINCT 108090,L.TREE_NODE_NUM,D.ACCOUNT,D.ACCOUNT FROM
PS_GL_ACCOUNT_TBL D, PSTREELEAF L
WHERE L.SETID='GLOBE' AND L.SETCNTRLVALUE=' '
AND L.TREE_NAME='ACCOUNT'
AND L.EFFDT=TO_DATE('1901-01-01','YYYY-MM-DD')
AND L.RANGE_FROM<>L.RANGE_TO
AND D.ACCOUNT BETWEEN L.RANGE_FROM AND L.RANGE_TO
AND D.SETID='GLOBE' AND
(L.TREE_NODE_NUM BETWEEN 789473386 AND 793372019
OR L.TREE_NODE_NUM BETWEEN 810915873 AND 812865189
…
OR L.TREE_NODE_NUM BETWEEN 1089668204 AND 1095516154
OR L.TREE_NODE_NUM BETWEEN 1113060008 AND 1115009324)SELECT L4.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L4, PSTREESELECT10 L2, PSTREESELECT10 L
WHERE A.LEDGER='S_USMGT'
AND A.FISCAL_YEAR=2017
AND A.ACCOUNTING_PERIOD=1
…
AND L.SELECTOR_NUM=96774
AND A.CHARTFIELD1=L.RANGE_FROM_10 AND
A.CURRENCY_CD='USD' GROUP BY L4.TREE_NODE_NUMNote that in this example when the selector was extracted, the tree leaf table was joined to the underlying table on which the tree was based (in this case GL_ACCOUNT_TBL). This occurs when the single value join option is selected. The tree selector contains a row for each value rather than for each tree leaf. This will result in larger tree selectors where ranged leaves are used.
PeopleSoft do not provide any way to identify the tree used to create a dynamic selector. From the SQL query, all we can see is the field to which it was joined. The above example is a tree related to CHARTFIELD1.
Recommendations
Although static selectors are easier to manage, the decision to use dynamic selectors is driven by the need to use single value joins.- It is important to maintain up-to-date statistics on the selector tables. As new selectors are extracted, a series of ever-increasing selector numbers are allocated from a sequence maintained on the table PSTREESELNUM. If not maintained, the selector numbers will exceed the high value recorded in the column statistics for column SELECTOR_NUM, and the database will not correctly calculate the number of rows expected, resulting in inappropriate execution plans.
- It is easier to manage optimizer statistics on static selectors because the PSTREESELCTL table records when they have been updated. In fact, it is possible to put a trigger on that table to maintain statistics. However, static selectors imply range predicates that bring other problems that I discuss in the next section.
- Using compound triggers on the selector tables it is possible to maintain a log of selectors, maintain statistics on them and purge selectors left behind by nVision reports that have not cleaned up after themselves. This is also used to identify the trees used in dynamic selectors. I will describe this solution later.
However, this approach is generally not effective for queries that reference more than 3 tree selector, nor for very large trees. The size of the Cartesian product is the product of the number of rows extracted from each tree selectors table. For example, three selectors with 100 rows each will result in 1 million lookups on the ledger table. As the size of the Cartesian product grows the number of index look-ups also grows. There comes a point when it is better to remove the least selective tree from the Cartesian and hash joining it after the looking up ledger. Striking the correct balance can be difficult, and leads to using different performance options in different reports.
From Oracle 11g, and especially on engineered systems, I now prefer dynamic selectors because they permit the use of single value joins that can use Bloom filters and Extended Statistics. Although, where smaller trees are in use that do not significantly cut down the query result set, range joins may still be preferable, in which cases, so might static selectors.
Selector Options
This performance option controls how the tree selector is joined to the ledger table. The choice is between single value equality joins or inequality joins.Ranged Joins
There are two forms of ranged joins, either using the BETWEEN keyword or using a pair of inequalities. There is no difference to Oracle because the optimizer rewrites BETWEEN as a pair of inequalities.NB: If you specify single value joins on a static selector you still get an inequality ranged join because only the tree leaves are extracted to the selector. . nVision still does this even if all of the leaves in the tree are single values and not ranges.
SELECT L4.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L4, PSTREESELECT10 L2, PSTREESELECT10 L
WHERE A.LEDGER='ACTUALS'
AND A.FISCAL_YEAR=2017
AND A.ACCOUNTING_PERIOD=1
AND L4.SELECTOR_NUM=89052
AND A.CHARTFIELD3>= L4.RANGE_FROM_10
AND A.CHARTFIELD3 <= L4.RANGE_TO_10
…
AND A.CURRENCY_CD='GBP'
GROUP BY L4.TREE_NODE_NUMSingle Value Joins
If single value joins are selected, nVision generates an equality join between the tree selector and the ledger table. The tree selector must be dynamic, and nVision extracts all the intermediate values between the tree node ranges on the underlying detail table.SELECT L4.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM PS_LEDGER A, PSTREESELECT10 L4, PSTREESELECT10 L2, PSTREESELECT10 L
WHERE A.LEDGER='ACTUALS'
AND A.FISCAL_YEAR=2017
AND A.ACCOUNTING_PERIOD=1
…
AND L.SELECTOR_NUM=96774
AND A.CHARTFIELD1=L.RANGE_FROM_10
AND A.CURRENCY_CD='GBP'
GROUP BY L4.TREE_NODE_NUMRecommendations
In general, I would recommend using single value joins and therefore also dynamic selectors. The main advantage is that they enable two particular optimisations in Oracle.- Bloom filters only work with equality predicates, and therefore only with single value joins. They enable a filter on one column of one table to be roughly mapped to another column of another table upon which there is an equality join. This filters data earlier in a query, cutting down the size of the eventual join operation. It is a rough match in that it may return some false positives to the subsequent hash operation, but it will not omit any results. The hash join then does an exact match of the rows to produce the correct result set. On an Engineered Oracle system this filter can also be pushed down to the storage cells.
- This is an example of the kind of execution plan that we should see when a Bloom filter is used. A filter has been created on PSTREESELECT10 at line 3, and it is applied to PS_LEDGER at line 6.
-------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 12876 (100)| | | |
| 1 | HASH GROUP BY | | 501 | 35571 | 12876 (42)| 00:00:01 | | |
| 2 | HASH JOIN | | 975K| 66M| 12822 (41)| 00:00:01 | | |
| 3 | JOIN FILTER CREATE | :BF0000 | 2577 | 59271 | 7 (0)| 00:00:01 | | |
| 4 | PARTITION RANGE SINGLE | | 2577 | 59271 | 7 (0)| 00:00:01 | 87968 | 87968 |
| 5 | INDEX FAST FULL SCAN | PS_PSTREESELECT10 | 2577 | 59271 | 7 (0)| 00:00:01 | 87968 | 87968 |
| 6 | JOIN FILTER USE | :BF0000 | 1715K| 78M| 12804 (41)| 00:00:01 | | |
| 7 | PARTITION RANGE ITERATOR | | 1715K| 78M| 12804 (41)| 00:00:01 | 27 | 34 |
| 8 | MAT_VIEW REWRITE ACCESS STORAGE FULL| PS_LEDGER | 1715K| 78M| 12804 (41)| 00:00:01 | 27 | 34 |
-------------------------------------------------------------------------------------------------------------------------------- Extended statistics, (i.e. on groups of columns) also only work with equality predicates. Single value join queries can, therefore, benefit from them.
- Single value joins require the use dynamic selectors. That requires management of statistics on the dynamic selectors, and debris left in the tree selectors table from failed reports that have not cleaned up after themselves.
- Single value joins can result in larger tree selector tables with many more rows than tree leaves. For large trees or trees with many values in leaf ranges, resulting in more than about 2000 rows on the tree selector tables, it can be advantageous to change the Access Method performance option from using literal values to join because the parse overhead becomes significant.
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