Showing posts with label Tree Selectors. Show all posts
Showing posts with label Tree Selectors. Show all posts

Tuesday, November 28, 2017

nVision Performance Tuning: 10. Maintaining Statistics on Non-Partitioned Tree Selector Tables

This blog post is part of a series that discusses how to get optimal performance from PeopleSoft nVision reporting as used in General Ledger.

If you do not partition your selector tables, and if you predominantly use static selectors then you can maintain the statistics on the selector table as the new static selectors are created or updated.  PeopleTools table PSTREESELCTL is used to track static selectors.  It is keyed by selector number.  A row is inserted when a new selector is created, and the version number is updated when an existing selector is re-extracted.  Therefore, a trigger on this table can be used to submit a job to refresh the statistics on the tree selector.
CREATE OR REPLACE TRIGGER sysadm.pstreeselector_stats
BEFORE INSERT OR UPDATE ON sysadm.pstreeselctl
FOR EACH ROW
DECLARE
  l_jobno      NUMBER;
  l_cmd        VARCHAR2(1000);
  l_table_name VARCHAR2(18);
  l_suffix     VARCHAR2(2);
BEGIN
  l_table_name := 'PSTREESELECT'||LTRIM(TO_CHAR(:new.length,'00'));
  l_suffix     := SUBSTR(l_table_name,-2);
  l_cmd := 'dbms_stats.gather_table_stats(ownname=>user,tabname=>'''||l_table_name||''',force=>TRUE);'
       ||'dbms_stats.set_column_stats(ownname=>user,tabname=>'''||l_table_name||''',colname=>''RANGE_FROM_'||l_suffix||''',density=>1,force=>TRUE);'
       ||'dbms_stats.set_column_stats(ownname=>user,tabname=>'''||l_table_name||''',colname=>''RANGE_TO_'||l_suffix||''',density=>1,force=>TRUE);'
  dbms_output.put_line(l_cmd);
  dbms_job.submit(l_jobno,l_cmd);
EXCEPTION WHEN OTHERS THEN NULL;
END;
/
When all the selectors exist in a single segment, the optimizer will not correctly calculate the cardinality of the expressions in the nVision queries on the tree selector tables.  Therefore, I have found it necessary to manually set the density on the range columns to 1 to get the effective execution plans at least some of the time.
N.B. This trigger should not be used if the selector tables are partitioned.


Monday, November 20, 2017

nVision Performance Tuning: 8. Interval Partitioning and Statistics Maintenance of Tree Selector Tables

This blog post is part of a series that discusses how to get optimal performance from PeopleSoft nVision reporting as used in General Ledger.

The decision to use interval partitioning on the tree selector tables came from the need to have accurate statistics for the optimizer when parsing nVision queries.  It is not possible to introduce hints into nVision SQL. The dynamic nature of the code means it is not viable to consider any of the forms of database plan stability across the whole application, (although it may be possible to use SQL Profiles in limited cases). Therefore, as far as possible the optimizer has to choose the best plan on its own. Without accurate optimizer statistics, I have found that the optimizer will usually not choose to use a Bloom filter.
If the selector tables are not partitioned, then each table will usually contain rows for many different selectors. Even with perfectly up to date statistics, including a histogram on SELECTOR_NUM, and extended statistics on SELECTOR_NUM and RANGE_FROM_nn, I have found that Oracle miscosts the join on RANGE_FROMnn and the attribute on the ledger table.
I propose that the tree selector tables should be interval partition such that each selector goes into its own partition.
CREATE TABLE PSYPSTREESELECT10 
(SELECTOR_NUM INTEGER NOT NULL,
 TREE_NODE_NUM INTEGER NOT NULL,
 RANGE_FROM_10 VARCHAR2(10) NOT NULL,
 RANGE_TO_10   VARCHAR2(10) NOT NULL) 
PARTITION BY RANGE (selector_num) INTERVAL (1)
(PARTITION VALUES LESS THAN(2))
TABLESPACE "PTWORK"
STORAGE(INITIAL 128K NEXT 128K)
/
INSERT INTO PSYPSTREESELECT10 
(       SELECTOR_NUM, TREE_NODE_NUM, RANGE_FROM_10, RANGE_TO_10)
SELECT  SELECTOR_NUM, TREE_NODE_NUM, RANGE_FROM_10, RANGE_TO_10
FROM PSTREESELECT10
/
DROP TABLE PSTREESELECT10 
/
ALTER TABLE PSYPSTREESELECT10 RENAME TO PSTREESELECT10
/
  • nVision queries will reference a single selector with a literal value, and therefore Oracle will eliminate all but that single partition at parse time and will use the statistics on that partition to determine how to join it to other tables.
  • Statistics only have to be maintained at partition level, and not at table level. 
  • Now that there is only a single selector number in any one partition, there is no need for extended statistics. 
  • The need to use dynamic selectors, in order to get equality joins between selectors and ledger tables, in order to make use of the Bloom filter, means that statistics on selector table will inevitably be out of date. The PL/SQL triggers and package that log the selector usage, are also used to maintain statistics on the partition. 
  • Partitions do not have to be created in advance. They will be created automatically by Oracle as they are required by the application. 

Compound Triggers on Tree Selector Tables 

There are a pair of compound DML triggers on each tree selector tables, one for insert and one for delete.
  • The after row section captures the current selector number. The one for insert also counts the number of rows and tracks the minimum and maximum values of the RANGE_FROMnn and RANGE_TOnn columns. 
  • The after statement section updates the selector log. The insert trigger directly updates the statistics on the partition, including the minimum and maximum values of the range columns.
    • It is not possible to collect statistics in a trigger in the conventional manner because dbms_stats includes an implicit commit. If dbms_stats was called within an autonomous transaction it could not see the uncommitted insert into the tree selector that fired the trigger. Hence the trigger calls the XX_NVISION_SELECTORS package that uses dbms_stats.set_table_stats and dbms_stats.set_column_stats to set values directly. 
    • The trigger then submits a job to database job scheduler that will collect statistics on the partition in the conventional way using dbms_job. The job number is recorded on the selector log. The job will be picked up by the scheduler when the insert commits. However, there can be a short lag between scheduling the job, and it running. The first query in the nVision report can be parsed before the statistics are available. 
    • The procedure that directly sets the statistics has to make some sensible assumptions about the data. These mostly lead the optimizer to produce good execution plans. However, testing has shown that performance can be better with conventionally collected statistics. Therefore, the trigger both directly sets statistics and submits the database job to collect the statistics.
    • It is important that table level statistics are not maintained by either technique as this would lead to locking between sessions. Locking during partition statistics maintenance will not occur as no two sessions populate the same selector number, and each selector is in a different partition. A table statistics preference for granularity is set to PARTITION on each partitioned tree selector table. 
The combination of dynamics selectors, single value joins, interval partitioning of selector tables, logging triggers on the selector tables driving timely statistics maintenance on the partitions delivers execution plans that perform well and that make effective use of engineered system features.
However, there are two problems that then have to be worked around. 

Library Cache Contention 

Some data warehouse systems can need new partitions in tables daily or even hourly. If partitions were not created in a timely fashion, the application would either break because the partition was missing, or performance would degrade as data accumulated in a single partition. Oracle intended interval partitions to free the DBA from the need to actively manage such partitioning on a day-to-day basis by creating them automatically as the data was inserted. 
However, on a busy nVision system, this solution could create thousands of new selectors in a single day, and therefore thousands of new partitions. This is certainly not how Oracle intended interval partitioning to be used.  I freely admit that I am abusing the feature.
If you have multiple concurrent nVision reports running, using dynamic selectors, you will have multiple database sessions concurrently inserting rows into the tree selector tables each with a different selector number, and therefore each creating new partitions mostly into the same tables.
The recursive code that creates the new partitions, and maintains the data dictionary, acquires a lock the object handle in library cache to prevent other sessions from changing it at the same time.  As the number of concurrent nVisions increase you will start to see nVision sessions waiting on the library cache lock event during the insert into the tree selector table while the new partition is being created. Perversely, as the performance of the nVision queries improve (as you refine tree selector settings) you may find this contention increases. 
The workaround to this is to create multiple database schemas, each with copies of the partitioned tree selector tables (similarly interval partitioned) and the PSTREESELCTL table (to manage static selectors in those schemas). Synonyms will be required for all other tables referenced by nVision queries. 
Then a trigger on the process scheduler request table PSPRCSRQST will arbitarily set the current schema of the nVision batch processes to one of those schemas. The nVision reports still connect and run with privileges of the Owner ID (usually SYSADM), but the objects tables from the current schema. 
I have used a hash function to distribute nVision processes between schemas. I suggest the number of schemas should be a power of 2 (ie, 2, 4, 8 etc.).
CREATE OR REPLACE TRIGGER sysadm.nvision_current_schema
BEFORE UPDATE OF runstatus ON sysadm.psprcsrqst
FOR EACH ROW
WHEN (new.runstatus IN('7') AND new.prcsname = 'RPTBOOK' AND new.prcstype like 'nVision%')
BEGIN
  EXECUTE IMMEDIATE 'ALTER SESSION SET current_schema = NVEXEC'||LTRIM(TO_CHAR(dbms_utility.get_hash_value(:new.prcsinstance,1,8),'00'));
EXCEPTION WHEN OTHERS THEN NULL; --exception deliberately coded to suppress all exceptions
END;
/
Thus different nVision reports use different tree selector tables in different schemas rather than trying to create partitions in the same tree selector table, thus avoiding the library cache locking.

Limitation on the Maximum Number of Partitions 

In Oracle, it is not possible to have more than 1048576 partitions in a table. That applies to all forms of partitioning.
The tree selector tables are interval partitioned on selector number with an interval of 1 starting with 1. So the highest value of SELECTOR_NUM that they can store is 1048575.
INSERT INTO pstreeselect05 VALUES(1048576,0,' ',' ')
            *
ERROR at line 1:
ORA-14300: partitioning key maps to a partition outside maximum permitted number of partitions
New selector numbers are allocated sequentially from PSTREESELNUM. Left unattended, the selector numbers used by nVision will increase until they eventually hit this limit, and then nVision and ad-hoc queries that use the tree-exists operator will start to fail with this error.
Therefore, a procedure RESET_SELECTOR_NUM has been added to the PL/SQL package to reset the selector number allocation table PSTREESELNUM back to 0, delete any tree selector entries for which there is no logging entry, and then runs the regular selector PURGE procedure in the same
package that will drop unwanted interval partitions.

Recommendation: XX_NVISION_SELECTORS.RESET_SELECTOR_NUM should be scheduled run sufficiently frequently to prevent the selector number reaching the maximum.  

Monday, November 13, 2017

nVision Performance Tuning: 7 Analysis of Tree Usage with the Selector Log

This blog post is part of a series that discusses how to get optimal performance from PeopleSoft nVision reporting as used in General Ledger.

Over time, the selector log will build up a picture of how each tree is used in a system. Here are two examples of how it can be used.

You may look at a piece of SQL generated by nVision, it will have a literal value for the selector number, and you want to know about that particular selector.
REM treeanal.sql
WITH t as (
SELECT DISTINCT d.tree_name, s.dtl_fieldname, d.tree_acc_method, d.tree_acc_Selector, d.tree_acc_sel_opt
FROM pstreedefn d, pstreestrct s
WHERE d.tree_Strct_id = s.tree_strct_id
), l as (
SELECT  *
FROM ps_nvs_treeslctlog l
WHERE l.selector_Num = &selector_num
)
SELECT l.*, t.dtl_fieldname, t.tree_acc_method, t.tree_acc_Selector, t.tree_acc_sel_opt
FROM  t, l
WHERE t.tree_name = l.tree_name
/
Now you can see various pieces of information about the selector and the report in which it was used.
  • Process instance of the report, although it is also in the ACTION string
  • Length of the selector.  Here it was 10 characters so it was in PSTREESELECT10.
  • The number of rows inserted into the selector.  This is useful if you want to recreate the conditions at runtime by populating the selector table manually.
  • Time at which the selector was populated.
  • Session module, usually the process name.  
  • Session action string, which contains the process instance, report ID and business unit.
  • The client info string, containing the operator ID, database name, the name of the host running nVision and the name of the executable.
    • PSNVS is nVision running on Microsoft Excel
    • PSNVSSRV is nVision running in OpenXML
    • psae is a PS/Query using the IN TREE operator that has been scheduled to run on the Process Scheduler.
    • PSQED is the windows query client running in 2-tier mode and using an IN TREE operator.
    • PSSAMSRV indicates either nVision or PS/Query running either through the PIA or on the windows client in 3-tier mode.
  • Status of the selector.  X indicates that the selector has been deleted and the partition has been dropped.
  • Name of the tree.
  • Name of the schema in which the selector table is located.
  • Partition name in the tree selector table in which the data was held.  This will be blank if the partition has been dropped.
  • The current values of the three tree selector flags on the tree definition are also reported.
SELECTOR_NUM PROCESS_INSTANCE Len   NUM_ROWS TIMESTAMP                    MODULE       APPINFO_ACTION
------------ ---------------- --- ---------- ---------------------------- ------------ ----------------------------------------------------------------
CLIENT_INFO                                                      S TREE_NAME          OWNERID  PARTITION_NAME           JOB_NO DTL_FIELDNAME      T T T
---------------------------------------------------------------- - ------------------ -------- -------------------- ---------- ------------------ - - -
       10233          1780069  10        362 10-NOV-17 02.40.50.755038 AM RPTBOOK      PI=1780069:UKGL123I:UK001
GBNVISION,PSFINPRD,UKLONWIN001,,PSNVSSRV.EXE,                    X UKGL_ACCOUNT       SYSADM                             33052 ACCOUNT            J D S

The following query aggregated log entries to report the number of times each tree was used over the last 7 days, and provide various statistics about the numbers of rows extracted from trees into the selector tables, and the current tree performance options.
REM tree_usage.sql
WITH t AS (
SELECT DISTINCT d.tree_name, s.dtl_fieldname, d.tree_acc_method
,      d.tree_acc_Selector, d.tree_acc_sel_opt
FROM   pstreedefn d, pstreestrct s
WHERE  d.tree_Strct_id = s.tree_strct_id
), l AS (
SELECT tree_name, length
, COUNT(*) num_uses
, MIN(num_rows) min_rows
, AVG(num_rows) avg_rows
, MEDIAN(num_Rows) med_rows
, MAX(num_rowS) max_rows
, STDDEV(num_Rows) stddev_rows
, SUM(num_rows) sum_rows
, COUNT(distinct process_instance) processes
FROM ps_nvs_treeslctlog l
WHERE num_rows>0
AND timestamp >= sysdate-7
GROUP BY tree_name, length
)
SELECT l.*, t.dtl_fieldname, t.tree_acc_method, t.tree_acc_Selector, t.tree_acc_sel_opt
FROM   t, l
WHERE  t.tree_name = l.tree_name
ORDER BY sum_rows
/
The default recommendation is that all trees should use:
  • Literal values where possible when working with less than about 2000 rows in the selector.  However, where more than 2000 rows it may be better to join the table due to parse and execution overhead of each criterion.
  • Dynamic selectors 
  • Single Value joins 
This report can help to identify trees where extreme volumes mean that different options should be considered.
                          Num    Min Average Median    Max Std Dev       Sum   Num
TREE_NAME          Len   Uses   Rows    Rows   Rows   Rows    Rows      Rows Procs DTL_FIELDNAME      T T T
------------------ --- ------ ------ ------- ------ ------ ------- --------- ----- ------------------ - - -
…
CORP_ACCT           10      5   1147    2839   2616   6668    2263     14194     1 ACCOUNT            J D S
FUNCTION            10    480      9      32     35     35       8     15474    43 CHARTFIELD2        L D S
INT_SUP              6      7    225    2463   2838   2838     987     17243     1 PRODUCT            L D S
STAT_PRODUCT         6      8   2889    2889   2889   2889       0     23112     1 PRODUCT            J D S
AFFILIATE            5     43    215     576    509    938     223     24789    15 AFFILIATE          L D S
INT_GAAP_CON         5     62     82     486    522    730     225     30153    10 BUSINESS_UNIT      L D S
BU_GAAP_CON          5     96     44     619    614    731     115     59461    48 BUSINESS_UNIT      L D S
STAT_ACCOUNT        10     45     23    4204   6516   6516    2905    189182     6 ACCOUNT            J D S
INT_REP1            10    135    149    1563   1664   1664     379    211005     1 CHARTFIELD1        L D S
COMBO_CODE          10    172     17    1592   1532   2430     809    273846    18 CHARTFIELD1        L D S
UKGL_ACCOUNT        10   2586      2    1713   1147   7797    1793   4430262   110 ACCOUNT            J D S
  • The account trees have been set to join the tree to the ledger table rather than literal values because sometimes in excess of 6000 rows are extracted. A query with 6000 literal terms would be extremely large, take time to generate in nVision, and time on the database to parse and execute each criterion. 
  • STAT_PRODUCT has been set to join partly because it is large, it always extracts 2889 rows, but also because the whole tree is extracted every time so it does not cut down the result set.

Monday, November 06, 2017

nVision Performance Tuning: 6. Logging Selector Usage

This blog post is part of a series that discusses how to get optimal performance from PeopleSoft nVision reporting as used in General Ledger.

Static selectors are tracked by entries in the PSTREESELCTL table.  It is maintained after the tree is extracted to the selector table.  The version number on PSTREESELCTL is compared with the corresponding version number on the PSTREEDEFN to determine whether the extract of the tree to the selector table is still valid, or whether it needs to be reextracted because the tree has been updated.  Selectors that do not have an entry in PSTREESELCTL are therefore dynamic.
Static selectors are left in the PSTREESELECTnn table after the report completes so that they can be reused.  However, many customers periodically create new effective dated versions of many trees, and so static selectors on old effective dated versions of the tree will accumulate as there is nothing to purge them.
Dynamic selectors are extracted every time the report runs.  They should normally be deleted by nVision before the report finishes.  However, if a report crashes dynamic selectors can be left behind.  That creates a number of problems
  • The size of the selector tables will tend to increase over time as old static and dynamic selectors are left behind.
  • That in turn affects the statistics on these tables.  The range of values for SELECTOR_NUM will become wider faster than the number of rows in the table grows.  The minimum value will either be the oldest static selector number, or the oldest dynamic selector left after a crash.  The maximum value will be the last selector number inserted when statistics were collected.
Therefore, it is useful to be able to track creation and deletion of dynamic selectors by the various nVision reports and queries.  I have therefore created a logging table PS_NVS_TREESLCTLOG (see nvision_dynamic_selectors.sql), a PL/SQL package XX_NVISION_SELECTORS and compound DML triggers on every tree selector table.
The column names in the log table have been chosen for compatibility with the PeopleSoft data dictionary, so that a record can be defined in Application Designer.
Column Name
Data Type
Description
SELECTOR_NUM
NUMBER
Unique identifier for tree selector records.
PROCESS_INSTANCE
NUMBER
PeopleSoft process instance number for nVision/Query
LENGTH
NUMBER
Length of tree selector
NUM_ROWS
NUMBER
Number of rows inserted into tree selector. 
Counted by the AFTER ROW part of the triggers.
TIMESTAMP
TIMESTAMP
Time when rows inserted
MODULE
VARCHAR2(64)
Module attribute of session inserting selector rows. 
APPINFO_ACTION
VARCHAR2(64)
Action attribute of session inserting selector rows
CLIENTINFO
VARCHAR2(64)
CLIENT_INFO attribute of session inserting selector rows.  This will include:
  • PeopleSoft Operator ID.
  • Name of the application server or process scheduler domain.
  • Name of the machine where the client process is executing.
  • Name of the client executable process.
STATUS_FLAG
VARCHAR2(1)
I=Selectors Inserted
S=Static Selectors Inserted
D=Selectors Deleted
X=Selectors Deleted and Partition Dropped
TREE_NAME
VARCHAR2(18)
Name of the tree from which selector extracted.
Obtained by querying statement from V$SQL.
OWNER_ID
VARCHAR2(8)
Schema under which nVision report run
PARTITION_NAME
VARCHAR2(128)
Name of partition where selectors stored
JOB_NO
NUMBER
Database Job number to collect statistics on the partition.
All of the logic is kept in the PL/SQL package because it is common to the triggers on all the tree selector tables.  Insert triggers track population of selectors and delete triggers track the successful removal of dynamic selectors.  After row triggers track the selector number and count the number of rows inserted.  After statement triggers call the logging procedures.
CREATE OR REPLACE TRIGGER sysadm.pstreeselect10_insert
FOR INSERT ON sysadm.PSTREESELECT10 compound trigger
  l_err_msg VARCHAR2(100 CHAR);
AFTER EACH ROW IS
BEGIN
  sysadm.xx_nvision_selectors.rowins(:new.selector_num,:new.range_from_10,:new.range_to_10);
EXCEPTION WHEN OTHERS THEN NULL;
END after each row;

AFTER STATEMENT IS
BEGIN
  sysadm.xx_nvision_selectors.logins(10,'SYSADM');
EXCEPTION WHEN OTHERS THEN
  l_err_msg := SUBSTR(SQLERRM,1,100);
  dbms_output.put_line('Error:'||l_err_msg);
END after statement;
END;
/

CREATE OR REPLACE TRIGGER sysadm.pstreeselect10_delete
FOR DELETE ON sysadm.PSTREESELECT10 compound trigger
  l_err_msg VARCHAR2(100 CHAR);
AFTER EACH ROW IS
BEGIN
  sysadm.xx_nvision_selectors.rowdel(:old.selector_num);
EXCEPTION WHEN OTHERS THEN NULL;
END after each row;

AFTER STATEMENT IS
BEGIN
  sysadm.xx_nvision_selectors.logdel(10);
EXCEPTION WHEN OTHERS
THEN
  l_err_msg := SUBSTR(SQLERRM,1,100);
  dbms_output.put_line('Error:'||l_err_msg);
END after statement;
END;
/
Once the decision to bear the overhead of triggers on the selector tables is made, there are then various pieces of additional information that can be captured and stored in the logging table for very little additional overhead.  It is easy to record the current session attributes such as module, action, and client_info.  The process instance number is captured on startup and can then be read by the psftapi package.  The tree is identified by scanning the V$SQL for the SQL that fired the triggers.
It is also possible to maintain statistics on the selector tables.

Purging Selectors

The selector log can be used to drive purging of selectors not cleared by nVisions that failed to complete.  The package includes a purge procedure to clear selectors that are not already marked as having been deleted either when the logged process instance is no longer running, or if there is no known process instance then if it is more than 2 days since the selector was inserted.  If the selector table is also interval partitioned, then the partition will be dropped.
A trigger on PSTREESELCTL (see pstreeselctl.sql) tracks the logs static selector maintenance.
The purge process is invoked by a trigger on PSPRCSRQST that fires when nVision process status is changed away from processing.  The selector purge process should also be scheduled to run daily.
The selector log itself is not purged as it contains useful information about tree usage.

Recommendations

  • If the tree selector tables are not partitioned create a histogram on SELECTOR_NUM.  However, up to Oracle 11g, this will not be effective as the number of distinct values reaches the maximum number of buckets, 254.  Dynamic selectors should be purged before this happens.  From Oracle 12c the maximum number of buckets is 2048, and hybrid histograms can be used.

Tuesday, October 10, 2017

nVision Performance Tuning: Introduction

This blog post is the first in a series that discusses how to get good performance from nVision as used in General Ledger reporting.

PS/nVision is a PeopleTools technology that extracts data from the database and places it in an Excel spreadsheet (see PS/nVision Overview).  Although PS/nVision can be used with any PeopleSoft product, it is most commonly used in Financials General Ledger.

The SQL queries generated by nVision are, at least conceptually, similar to data warehouse queries. The ledger, ledger budget or summary ledger tables are the fact tables.

The ledger tables are analysed by their attribute columns. There are always literal conditions on the fiscal year and accounting period, there is usually a literal condition on currency code.  Then there are criteria on some of the other attributes.  I will take an example that analyses the ledger table in three dimensions: BUSINESS_UNIT, ACCOUNT and CHARTFIELD1, but there are many other attribute columns on the ledger tables.  These attributes are defined in lookup tables in the application, but their hierarchies are defined in trees.

nVision reports use the trees to determine which attribute values to report.  A report might report on a whole tree, or particular nodes, or branches of a tree.  nVision joins the tree definition to the attribute table and produces a list of attributes to be reported.  These are put into working storage tree selector tables (PSTREESELECT01 to 30).  The choice of selector table is controlled by the length of the attribute column.  BUSINESS_UNIT is a 5 character column so it goes into PSTREESELECT05. CHARTFIELD1 and ACCOUNT are 10 character columns so they use PSTREESELECT10.  These selector tables form the dimensions in the queries.

Here is an example of a SQL statement generated by nVision.  The tree selector 'dimension' tables are joined to the ledger 'fact' table.

SELECT L.TREE_NODE_NUM,L2.TREE_NODE_NUM,SUM(A.POSTED_TOTAL_AMT)
FROM   PS_LEDGER A
,      PSTREESELECT05 L1
,      PSTREESELECT10 L
,      PSTREESELECT10 L2
WHERE  A.LEDGER='ACTUALS'
AND    A.FISCAL_YEAR=2016
AND    A.ACCOUNTING_PERIOD BETWEEN 1 AND 11
AND    L1.SELECTOR_NUM=30982 AND A.BUSINESS_UNIT=L1.RANGE_FROM_05
AND    L.SELECTOR_NUM=30985 AND A.CHARTFIELD1=L.RANGE_FROM_10
AND    L2.SELECTOR_NUM=30984 AND A.ACCOUNT=L2.RANGE_FROM_10
AND    A.CURRENCY_CD='GBP'
GROUP BY L.TREE_NODE_NUM,L2.TREE_NODE_NUM
This SQL looks simple enough, but there are various complexities
  • The tree selector tables are populated at runtime.  Many dimensions can be stored in each tree selector table, each keyed by a different SELECTOR_NUM.
  • Selectors can be static or dynamic.  In dynamic selectors, the data is only stored temporarily for the lifetime of the report and will be deleted when it completes.  So immediately, there is a challenge of keeping statistics up to date, and even then Oracle doesn't always manage to find an effective execution plan.
  • Different selectors will have different numbers of rows, so the statistics have to describe that skew.
  • Different nVision reports and even different parts of the same report generate different statements that can use different combinations of attribute columns.  The number of dimensions can vary, I have seen systems that use as many as five different trees in a single query.
  • Then the database needs to find the relevant rows on the ledger table for the dimensions specified as efficiently as possible.
This very quickly becomes a difficult and complex problem.  This series articles works through the various challenges and describe methods to overcome them.  Not all of them are applicable to all systems, in some cases, it will be necessary to choose between approaches depending on circumstances.