SQL for Period Aggregate Reporting

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In financial reporting there is often a requirement to report on sales performance across multiple time periods. For example we might want to know total sales of products for a given month, then for the 3 month period, the year to date, and the 12 month period up to that month, etc.. We might also want to break down associated costs of selling across the same periods. There are several ways to approach implementing such reports in SQL, and in real world applications that may have many columns and periods to report on, different approaches will have different levels of complexity and performance characteristics. In this article I will describe three approaches, starting with perhaps the most obvious one, which is to write a Group By query for each period type and combine them all in a union. This obvious approach involves quite a few repeated scans of the source table, so we will describe two other approaches that are less obvious, but avoid so many scans and should be better performing. We’ll use a simple set of generated test data to illustrate the three queries, and will provide the execution plans associated with each one.

Test Data

We take two products and generate three years of monthly sales and cost values randomly between fixed limits in each case. Here is the generation code, for the table and first product (script c_sales_history.sql):

CREATE TABLE sales_history (
        prod_code               VARCHAR2(30) NOT NULL,
        month_dt                DATE NOT NULL,
        sales_value             NUMBER(10,0),
        sales_cost              NUMBER(10,0),
        CONSTRAINT slh_pk       PRIMARY KEY (prod_code, month_dt)
)
/
PROMPT 3 years random values for PROD_ONE
INSERT INTO sales_history
WITH month_gen AS (
    SELECT LEVEL rn, Add_Months(Trunc(SYSDATE, 'MONTH'), LEVEL - 36) dt
      FROM DUAL
   CONNECT BY LEVEL < 37
)
SELECT 'PROD_ONE', dt,
       DBMS_Random.Value (low => 1000, high => 10000),
       DBMS_Random.Value (low => 100, high => 1000)
  FROM month_gen
/

Here is the table data generated, with running sums of the two measures, that can help in testing the queries:

Sales History Report with Running Sums

Product    Month          Value Value to Date     Cost Cost to Date
---------- ----------- -------- ------------- -------- ------------
PROD_ONE   01-MAR-18      9,004         9,004      800          800
           01-APR-18      8,757        17,761      766        1,566
           01-MAY-18      5,748        23,509      835        2,401
           01-JUN-18      6,931        30,440      436        2,837
           01-JUL-18      4,717        35,157      115        2,952
           01-AUG-18      2,309        37,466      478        3,430
           01-SEP-18      1,415        38,881      253        3,683
           01-OCT-18      8,767        47,648      122        3,805
           01-NOV-18      4,560        52,208      547        4,352
           01-DEC-18      2,656        54,864      783        5,135
           01-JAN-19      9,984        64,848      401        5,536
           01-FEB-19      8,131        72,979      381        5,917
           01-MAR-19      2,063        75,042      191        6,108
           01-APR-19      2,933        77,975      147        6,255
           01-MAY-19      7,058        85,033      997        7,252
           01-JUN-19      7,941        92,974      392        7,644
           01-JUL-19      2,270        95,244      949        8,593
           01-AUG-19      2,586        97,830      921        9,514
           01-SEP-19      8,912       106,742      109        9,623
           01-OCT-19      4,018       110,760      272        9,895
           01-NOV-19      8,056       118,816      807       10,702
           01-DEC-19      8,497       127,313      880       11,582
           01-JAN-20      5,266       132,579      888       12,470
           01-FEB-20      1,636       134,215      532       13,002
           01-MAR-20      5,241       139,456      287       13,289
           01-APR-20      8,519       147,975      579       13,868
           01-MAY-20      4,034       152,009      245       14,113
           01-JUN-20      4,633       156,642      107       14,220
           01-JUL-20      8,906       165,548      169       14,389
           01-AUG-20      8,126       173,674      281       14,670
           01-SEP-20      8,319       181,993      487       15,157
           01-OCT-20      7,273       189,266      599       15,756
           01-NOV-20      9,646       198,912      390       16,146
           01-DEC-20      3,988       202,900      933       17,079
           01-JAN-21      2,626       205,526      278       17,357
           01-FEB-21      2,943       208,469      262       17,619
PROD_TWO   01-MAR-18      3,546         3,546      115          115
           01-APR-18      6,627        10,173      525          640
           01-MAY-18      2,892        13,065      877        1,517
           01-JUN-18      8,132        21,197      630        2,147
           01-JUL-18      3,778        24,975      145        2,292
           01-AUG-18      6,258        31,233      109        2,401
           01-SEP-18      2,425        33,658      257        2,658
           01-OCT-18      3,983        37,641      118        2,776
           01-NOV-18      1,181        38,822      971        3,747
           01-DEC-18      2,015        40,837      723        4,470
           01-JAN-19      4,966        45,803      129        4,599
           01-FEB-19      7,711        53,514      770        5,369
           01-MAR-19      1,966        55,480      249        5,618
           01-APR-19      8,129        63,609      689        6,307
           01-MAY-19      1,669        65,278      358        6,665
           01-JUN-19      6,936        72,214      779        7,444
           01-JUL-19      3,023        75,237      515        7,959
           01-AUG-19      1,039        76,276      593        8,552
           01-SEP-19      1,220        77,496      276        8,828
           01-OCT-19      6,371        83,867      268        9,096
           01-NOV-19      2,142        86,009      438        9,534
           01-DEC-19      2,897        88,906      974       10,508
           01-JAN-20      6,446        95,352      126       10,634
           01-FEB-20      6,163       101,515      983       11,617
           01-MAR-20      3,607       105,122      500       12,117
           01-APR-20      6,558       111,680      659       12,776
           01-MAY-20      3,075       114,755      965       13,741
           01-JUN-20      9,317       124,072      451       14,192
           01-JUL-20      2,639       126,711      476       14,668
           01-AUG-20      2,143       128,854      356       15,024
           01-SEP-20      2,347       131,201      507       15,531
           01-OCT-20      4,090       135,291      580       16,111
           01-NOV-20      5,598       140,889      630       16,741
           01-DEC-20      6,018       146,907      264       17,005
           01-JAN-21      4,339       151,246      935       17,940
           01-FEB-21      2,860       154,106      232       18,172

72 rows selected.

Group By Union Query

Here is the first query:

SELECT /*+ gather_plan_statistics XPLAN_MARKER_UGB */ 
     month_dt, prod_code, 'P1 - 1 Month' per_tp, sales_value, sales_cost
  FROM sales_history
UNION ALL
SELECT drv.month_dt, drv.prod_code, 'P2 - 3 Months', Sum(msr.sales_value), Sum(msr.sales_cost)
  FROM sales_history drv
  JOIN sales_history msr
  ON msr.prod_code = drv.prod_code
   AND msr.month_dt BETWEEN Add_Months (drv.month_dt, -2) AND drv.month_dt
 GROUP BY drv.prod_code, drv.month_dt
UNION ALL
SELECT drv.month_dt, drv.prod_code, 'P3 - YTD', Sum(msr.sales_value), Sum(msr.sales_cost)
  FROM sales_history drv
  JOIN sales_history msr
  ON msr.prod_code = drv.prod_code
   AND msr.month_dt BETWEEN Trunc(drv.month_dt, 'YEAR') AND drv.month_dt
 GROUP BY drv.prod_code, drv.month_dt
UNION ALL
SELECT drv.month_dt, drv.prod_code, 'P4 - 1 Year', Sum(msr.sales_value), Sum(msr.sales_cost)
  FROM sales_history drv
  JOIN sales_history msr
  ON msr.prod_code = drv.prod_code
   AND msr.month_dt BETWEEN Add_Months (drv.month_dt, -11) AND drv.month_dt
 GROUP BY drv.prod_code, drv.month_dt
 ORDER BY 1, 2, 3

Notes on Group By Union Query

  • First union member subquery does not aggregate, and includes the label for the period type
  • The remainig aggregation subqueries drive from one scan of the table and join a second instance
  • The second instance has the date range for the period type in its join condition
  • The gather_plan_statistics hint allows capture of plan statistics
  • The XPLAN_MARKER_UGB is a string used to identify the SQL id to pass to the API for displaying the plan

Here are the results from the first query (script period_agg_report_queries.sql), which are the same for the other two queries.

Periods Report by Union of Group Bys

Month       Product    Period             Value     Cost
----------- ---------- --------------- -------- --------
01-MAR-18   PROD_ONE   P1 - 1 Month       9,004      800
                       P2 - 3 Months      9,004      800
                       P3 - YTD           9,004      800
                       P4 - 1 Year        9,004      800
            PROD_TWO   P1 - 1 Month       3,546      115
                       P2 - 3 Months      3,546      115
                       P3 - YTD           3,546      115
                       P4 - 1 Year        3,546      115
01-APR-18   PROD_ONE   P1 - 1 Month       8,757      766
                       P2 - 3 Months     17,761    1,566
                       P3 - YTD          17,761    1,566
                       P4 - 1 Year       17,761    1,566
            PROD_TWO   P1 - 1 Month       6,627      525
                       P2 - 3 Months     10,173      640
                       P3 - YTD          10,173      640
                       P4 - 1 Year       10,173      640
01-MAY-18   PROD_ONE   P1 - 1 Month       5,748      835
                       P2 - 3 Months     23,509    2,401
                       P3 - YTD          23,509    2,401
                       P4 - 1 Year       23,509    2,401
            PROD_TWO   P1 - 1 Month       2,892      877
                       P2 - 3 Months     13,065    1,517
                       P3 - YTD          13,065    1,517
                       P4 - 1 Year       13,065    1,517
01-JUN-18   PROD_ONE   P1 - 1 Month       6,931      436
                       P2 - 3 Months     21,436    2,037
                       P3 - YTD          30,440    2,837
                       P4 - 1 Year       30,440    2,837
            PROD_TWO   P1 - 1 Month       8,132      630
                       P2 - 3 Months     17,651    2,032
                       P3 - YTD          21,197    2,147
                       P4 - 1 Year       21,197    2,147
01-JUL-18   PROD_ONE   P1 - 1 Month       4,717      115
                       P2 - 3 Months     17,396    1,386
                       P3 - YTD          35,157    2,952
                       P4 - 1 Year       35,157    2,952
            PROD_TWO   P1 - 1 Month       3,778      145
                       P2 - 3 Months     14,802    1,652
                       P3 - YTD          24,975    2,292
                       P4 - 1 Year       24,975    2,292
01-AUG-18   PROD_ONE   P1 - 1 Month       2,309      478
                       P2 - 3 Months     13,957    1,029
                       P3 - YTD          37,466    3,430
                       P4 - 1 Year       37,466    3,430
            PROD_TWO   P1 - 1 Month       6,258      109
                       P2 - 3 Months     18,168      884
                       P3 - YTD          31,233    2,401
                       P4 - 1 Year       31,233    2,401
01-SEP-18   PROD_ONE   P1 - 1 Month       1,415      253
                       P2 - 3 Months      8,441      846
                       P3 - YTD          38,881    3,683
                       P4 - 1 Year       38,881    3,683
            PROD_TWO   P1 - 1 Month       2,425      257
                       P2 - 3 Months     12,461      511
                       P3 - YTD          33,658    2,658
                       P4 - 1 Year       33,658    2,658
01-OCT-18   PROD_ONE   P1 - 1 Month       8,767      122
                       P2 - 3 Months     12,491      853
                       P3 - YTD          47,648    3,805
                       P4 - 1 Year       47,648    3,805
            PROD_TWO   P1 - 1 Month       3,983      118
                       P2 - 3 Months     12,666      484
                       P3 - YTD          37,641    2,776
                       P4 - 1 Year       37,641    2,776
01-NOV-18   PROD_ONE   P1 - 1 Month       4,560      547
                       P2 - 3 Months     14,742      922
                       P3 - YTD          52,208    4,352
                       P4 - 1 Year       52,208    4,352
            PROD_TWO   P1 - 1 Month       1,181      971
                       P2 - 3 Months      7,589    1,346
                       P3 - YTD          38,822    3,747
                       P4 - 1 Year       38,822    3,747
01-DEC-18   PROD_ONE   P1 - 1 Month       2,656      783
                       P2 - 3 Months     15,983    1,452
                       P3 - YTD          54,864    5,135
                       P4 - 1 Year       54,864    5,135
            PROD_TWO   P1 - 1 Month       2,015      723
                       P2 - 3 Months      7,179    1,812
                       P3 - YTD          40,837    4,470
                       P4 - 1 Year       40,837    4,470
01-JAN-19   PROD_ONE   P1 - 1 Month       9,984      401
                       P2 - 3 Months     17,200    1,731
                       P3 - YTD           9,984      401
                       P4 - 1 Year       64,848    5,536
            PROD_TWO   P1 - 1 Month       4,966      129
                       P2 - 3 Months      8,162    1,823
                       P3 - YTD           4,966      129
                       P4 - 1 Year       45,803    4,599
01-FEB-19   PROD_ONE   P1 - 1 Month       8,131      381
                       P2 - 3 Months     20,771    1,565
                       P3 - YTD          18,115      782
                       P4 - 1 Year       72,979    5,917
            PROD_TWO   P1 - 1 Month       7,711      770
                       P2 - 3 Months     14,692    1,622
                       P3 - YTD          12,677      899
                       P4 - 1 Year       53,514    5,369
01-MAR-19   PROD_ONE   P1 - 1 Month       2,063      191
                       P2 - 3 Months     20,178      973
                       P3 - YTD          20,178      973
                       P4 - 1 Year       66,038    5,308
            PROD_TWO   P1 - 1 Month       1,966      249
                       P2 - 3 Months     14,643    1,148
                       P3 - YTD          14,643    1,148
                       P4 - 1 Year       51,934    5,503
01-APR-19   PROD_ONE   P1 - 1 Month       2,933      147
                       P2 - 3 Months     13,127      719
                       P3 - YTD          23,111    1,120
                       P4 - 1 Year       60,214    4,689
            PROD_TWO   P1 - 1 Month       8,129      689
                       P2 - 3 Months     17,806    1,708
                       P3 - YTD          22,772    1,837
                       P4 - 1 Year       53,436    5,667
01-MAY-19   PROD_ONE   P1 - 1 Month       7,058      997
                       P2 - 3 Months     12,054    1,335
                       P3 - YTD          30,169    2,117
                       P4 - 1 Year       61,524    4,851
            PROD_TWO   P1 - 1 Month       1,669      358
                       P2 - 3 Months     11,764    1,296
                       P3 - YTD          24,441    2,195
                       P4 - 1 Year       52,213    5,148
01-JUN-19   PROD_ONE   P1 - 1 Month       7,941      392
                       P2 - 3 Months     17,932    1,536
                       P3 - YTD          38,110    2,509
                       P4 - 1 Year       62,534    4,807
            PROD_TWO   P1 - 1 Month       6,936      779
                       P2 - 3 Months     16,734    1,826
                       P3 - YTD          31,377    2,974
                       P4 - 1 Year       51,017    5,297
01-JUL-19   PROD_ONE   P1 - 1 Month       2,270      949
                       P2 - 3 Months     17,269    2,338
                       P3 - YTD          40,380    3,458
                       P4 - 1 Year       60,087    5,641
            PROD_TWO   P1 - 1 Month       3,023      515
                       P2 - 3 Months     11,628    1,652
                       P3 - YTD          34,400    3,489
                       P4 - 1 Year       50,262    5,667
01-AUG-19   PROD_ONE   P1 - 1 Month       2,586      921
                       P2 - 3 Months     12,797    2,262
                       P3 - YTD          42,966    4,379
                       P4 - 1 Year       60,364    6,084
            PROD_TWO   P1 - 1 Month       1,039      593
                       P2 - 3 Months     10,998    1,887
                       P3 - YTD          35,439    4,082
                       P4 - 1 Year       45,043    6,151
01-SEP-19   PROD_ONE   P1 - 1 Month       8,912      109
                       P2 - 3 Months     13,768    1,979
                       P3 - YTD          51,878    4,488
                       P4 - 1 Year       67,861    5,940
            PROD_TWO   P1 - 1 Month       1,220      276
                       P2 - 3 Months      5,282    1,384
                       P3 - YTD          36,659    4,358
                       P4 - 1 Year       43,838    6,170
01-OCT-19   PROD_ONE   P1 - 1 Month       4,018      272
                       P2 - 3 Months     15,516    1,302
                       P3 - YTD          55,896    4,760
                       P4 - 1 Year       63,112    6,090
            PROD_TWO   P1 - 1 Month       6,371      268
                       P2 - 3 Months      8,630    1,137
                       P3 - YTD          43,030    4,626
                       P4 - 1 Year       46,226    6,320
01-NOV-19   PROD_ONE   P1 - 1 Month       8,056      807
                       P2 - 3 Months     20,986    1,188
                       P3 - YTD          63,952    5,567
                       P4 - 1 Year       66,608    6,350
            PROD_TWO   P1 - 1 Month       2,142      438
                       P2 - 3 Months      9,733      982
                       P3 - YTD          45,172    5,064
                       P4 - 1 Year       47,187    5,787
01-DEC-19   PROD_ONE   P1 - 1 Month       8,497      880
                       P2 - 3 Months     20,571    1,959
                       P3 - YTD          72,449    6,447
                       P4 - 1 Year       72,449    6,447
            PROD_TWO   P1 - 1 Month       2,897      974
                       P2 - 3 Months     11,410    1,680
                       P3 - YTD          48,069    6,038
                       P4 - 1 Year       48,069    6,038
01-JAN-20   PROD_ONE   P1 - 1 Month       5,266      888
                       P2 - 3 Months     21,819    2,575
                       P3 - YTD           5,266      888
                       P4 - 1 Year       67,731    6,934
            PROD_TWO   P1 - 1 Month       6,446      126
                       P2 - 3 Months     11,485    1,538
                       P3 - YTD           6,446      126
                       P4 - 1 Year       49,549    6,035
01-FEB-20   PROD_ONE   P1 - 1 Month       1,636      532
                       P2 - 3 Months     15,399    2,300
                       P3 - YTD           6,902    1,420
                       P4 - 1 Year       61,236    7,085
            PROD_TWO   P1 - 1 Month       6,163      983
                       P2 - 3 Months     15,506    2,083
                       P3 - YTD          12,609    1,109
                       P4 - 1 Year       48,001    6,248
01-MAR-20   PROD_ONE   P1 - 1 Month       5,241      287
                       P2 - 3 Months     12,143    1,707
                       P3 - YTD          12,143    1,707
                       P4 - 1 Year       64,414    7,181
            PROD_TWO   P1 - 1 Month       3,607      500
                       P2 - 3 Months     16,216    1,609
                       P3 - YTD          16,216    1,609
                       P4 - 1 Year       49,642    6,499
01-APR-20   PROD_ONE   P1 - 1 Month       8,519      579
                       P2 - 3 Months     15,396    1,398
                       P3 - YTD          20,662    2,286
                       P4 - 1 Year       70,000    7,613
            PROD_TWO   P1 - 1 Month       6,558      659
                       P2 - 3 Months     16,328    2,142
                       P3 - YTD          22,774    2,268
                       P4 - 1 Year       48,071    6,469
01-MAY-20   PROD_ONE   P1 - 1 Month       4,034      245
                       P2 - 3 Months     17,794    1,111
                       P3 - YTD          24,696    2,531
                       P4 - 1 Year       66,976    6,861
            PROD_TWO   P1 - 1 Month       3,075      965
                       P2 - 3 Months     13,240    2,124
                       P3 - YTD          25,849    3,233
                       P4 - 1 Year       49,477    7,076
01-JUN-20   PROD_ONE   P1 - 1 Month       4,633      107
                       P2 - 3 Months     17,186      931
                       P3 - YTD          29,329    2,638
                       P4 - 1 Year       63,668    6,576
            PROD_TWO   P1 - 1 Month       9,317      451
                       P2 - 3 Months     18,950    2,075
                       P3 - YTD          35,166    3,684
                       P4 - 1 Year       51,858    6,748
01-JUL-20   PROD_ONE   P1 - 1 Month       8,906      169
                       P2 - 3 Months     17,573      521
                       P3 - YTD          38,235    2,807
                       P4 - 1 Year       70,304    5,796
            PROD_TWO   P1 - 1 Month       2,639      476
                       P2 - 3 Months     15,031    1,892
                       P3 - YTD          37,805    4,160
                       P4 - 1 Year       51,474    6,709
01-AUG-20   PROD_ONE   P1 - 1 Month       8,126      281
                       P2 - 3 Months     21,665      557
                       P3 - YTD          46,361    3,088
                       P4 - 1 Year       75,844    5,156
            PROD_TWO   P1 - 1 Month       2,143      356
                       P2 - 3 Months     14,099    1,283
                       P3 - YTD          39,948    4,516
                       P4 - 1 Year       52,578    6,472
01-SEP-20   PROD_ONE   P1 - 1 Month       8,319      487
                       P2 - 3 Months     25,351      937
                       P3 - YTD          54,680    3,575
                       P4 - 1 Year       75,251    5,534
            PROD_TWO   P1 - 1 Month       2,347      507
                       P2 - 3 Months      7,129    1,339
                       P3 - YTD          42,295    5,023
                       P4 - 1 Year       53,705    6,703
01-OCT-20   PROD_ONE   P1 - 1 Month       7,273      599
                       P2 - 3 Months     23,718    1,367
                       P3 - YTD          61,953    4,174
                       P4 - 1 Year       78,506    5,861
            PROD_TWO   P1 - 1 Month       4,090      580
                       P2 - 3 Months      8,580    1,443
                       P3 - YTD          46,385    5,603
                       P4 - 1 Year       51,424    7,015
01-NOV-20   PROD_ONE   P1 - 1 Month       9,646      390
                       P2 - 3 Months     25,238    1,476
                       P3 - YTD          71,599    4,564
                       P4 - 1 Year       80,096    5,444
            PROD_TWO   P1 - 1 Month       5,598      630
                       P2 - 3 Months     12,035    1,717
                       P3 - YTD          51,983    6,233
                       P4 - 1 Year       54,880    7,207
01-DEC-20   PROD_ONE   P1 - 1 Month       3,988      933
                       P2 - 3 Months     20,907    1,922
                       P3 - YTD          75,587    5,497
                       P4 - 1 Year       75,587    5,497
            PROD_TWO   P1 - 1 Month       6,018      264
                       P2 - 3 Months     15,706    1,474
                       P3 - YTD          58,001    6,497
                       P4 - 1 Year       58,001    6,497
01-JAN-21   PROD_ONE   P1 - 1 Month       2,626      278
                       P2 - 3 Months     16,260    1,601
                       P3 - YTD           2,626      278
                       P4 - 1 Year       72,947    4,887
            PROD_TWO   P1 - 1 Month       4,339      935
                       P2 - 3 Months     15,955    1,829
                       P3 - YTD           4,339      935
                       P4 - 1 Year       55,894    7,306
01-FEB-21   PROD_ONE   P1 - 1 Month       2,943      262
                       P2 - 3 Months      9,557    1,473
                       P3 - YTD           5,569      540
                       P4 - 1 Year       74,254    4,617
            PROD_TWO   P1 - 1 Month       2,860      232
                       P2 - 3 Months     13,217    1,431
                       P3 - YTD           7,199    1,167
                       P4 - 1 Year       52,591    6,555

288 rows selected.

Here is the execution plan:

----------------------------------------------------------------------------------------------------------------------------
| Id  | Operation             | Name          | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
----------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |               |      1 |        |    288 |00:00:00.01 |      27 |       |       |          |
|   1 |  SORT ORDER BY        |               |      1 |    225 |    288 |00:00:00.01 |      27 | 27648 | 27648 |24576  (0)|
|   2 |   UNION-ALL           |               |      1 |        |    288 |00:00:00.01 |      27 |       |       |          |
|   3 |    TABLE ACCESS FULL  | SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       6 |       |       |          |
|   4 |    HASH GROUP BY      |               |      1 |     51 |     72 |00:00:00.01 |       7 |   934K|   934K| 1401K (0)|
|*  5 |     HASH JOIN         |               |      1 |     67 |    210 |00:00:00.01 |       7 |  1506K|  1506K|  787K (0)|
|   6 |      INDEX FULL SCAN  | SLH_PK        |      1 |     72 |     72 |00:00:00.01 |       1 |       |       |          |
|   7 |      TABLE ACCESS FULL| SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       6 |       |       |          |
|   8 |    HASH GROUP BY      |               |      1 |     51 |     72 |00:00:00.01 |       7 |   934K|   934K| 1401K (0)|
|*  9 |     HASH JOIN         |               |      1 |     67 |    428 |00:00:00.01 |       7 |  1506K|  1506K|  789K (0)|
|  10 |      INDEX FULL SCAN  | SLH_PK        |      1 |     72 |     72 |00:00:00.01 |       1 |       |       |          |
|  11 |      TABLE ACCESS FULL| SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       6 |       |       |          |
|  12 |    HASH GROUP BY      |               |      1 |     51 |     72 |00:00:00.01 |       7 |   934K|   934K| 1408K (0)|
|* 13 |     HASH JOIN         |               |      1 |     67 |    732 |00:00:00.01 |       7 |  1506K|  1506K|  787K (0)|
|  14 |      INDEX FULL SCAN  | SLH_PK        |      1 |     72 |     72 |00:00:00.01 |       1 |       |       |          |
|  15 |      TABLE ACCESS FULL| SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       6 |       |       |          |
----------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
5 - access("MSR"."PROD_CODE"="DRV"."PROD_CODE")
filter(("MSR"."MONTH_DT"<="DRV"."MONTH_DT" AND "MSR"."MONTH_DT">=ADD_MONTHS(INTERNAL_FUNCTION("DRV"."MONTH_DT
"),-2)))
9 - access("MSR"."PROD_CODE"="DRV"."PROD_CODE")
filter(("MSR"."MONTH_DT"<="DRV"."MONTH_DT" AND "MSR"."MONTH_DT">=TRUNC(INTERNAL_FUNCTION("DRV"."MONTH_DT"),'f
myear')))
13 - access("MSR"."PROD_CODE"="DRV"."PROD_CODE")
filter(("MSR"."MONTH_DT"<="DRV"."MONTH_DT" AND "MSR"."MONTH_DT">=ADD_MONTHS(INTERNAL_FUNCTION("DRV"."MONTH_DT
"),-11)))

Notes on Group By Union Query Execution Plan

  • There are 4 full table scans, and 3 index full scans
  • The Buffers value of 27 is a measure of the work done, in logical I/O operations

Analytic Functions and Unpivot Query

Here is the second query:

WITH period_aggs AS (
  SELECT /*+ gather_plan_statistics XPLAN_MARKER_AAG */ 
       month_dt, prod_code, sales_value, 
       Sum(sales_value) OVER (PARTITION BY prod_code ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '2' MONTH PRECEDING AND CURRENT ROW)     sales_value_3m, 
       Sum(sales_value) OVER (PARTITION BY prod_code, Trunc(month_dt, 'YEAR') ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '11' MONTH PRECEDING AND CURRENT ROW)    sales_value_ytd, 
       Sum(sales_value) OVER (PARTITION BY prod_code ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '11' MONTH PRECEDING AND CURRENT ROW)    sales_value_1y, 
       sales_cost,
       Sum(sales_cost) OVER (PARTITION BY prod_code ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '2' MONTH PRECEDING AND CURRENT ROW)     sales_cost_3m, 
       Sum(sales_cost) OVER (PARTITION BY prod_code, Trunc(month_dt, 'YEAR') ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '11' MONTH PRECEDING AND CURRENT ROW)    sales_cost_ytd, 
       Sum(sales_cost) OVER (PARTITION BY prod_code ORDER BY month_dt
                  RANGE BETWEEN INTERVAL '11' MONTH PRECEDING AND CURRENT ROW)    sales_cost_1y
    FROM sales_history
)
SELECT *
  FROM period_aggs
UNPIVOT (
    (sales_value, sales_cost)
    FOR per_tp IN (
      (sales_value, sales_cost)         AS 'P1 - 1 Month',
      (sales_value_3m, sales_cost_3m)   AS 'P2 - 3 Months',
      (sales_value_ytd, sales_cost_ytd) AS 'P3 - YTD',
      (sales_value_1y, sales_cost_1y)   AS 'P4 - 1 Year'
    )
)
 ORDER BY 1, 2, 3

Notes on Analytic Functions and Unpivot Query

  • For each measure a column is added for each period type to do the aggregation via analytic functions
  • The UNPIVOT clause in the main query converts the period type columns into rows with column pair as specified in the first line
  • The column name pair is specified in the first line for the unpivoted row values
  • The ‘FOR per_tp IN’ clauses specifies the name of the period type column with values given in the rows below

Here is the execution plan:

----------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                         | Name          | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
----------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                  |               |      1 |        |    288 |00:00:00.01 |       2 |       |       |          |
|   1 |  SORT ORDER BY                    |               |      1 |    288 |    288 |00:00:00.01 |       2 | 27648 | 27648 |24576  (0)|
|*  2 |   VIEW                            |               |      1 |    288 |    288 |00:00:00.01 |       2 |       |       |          |
|   3 |    UNPIVOT                        |               |      1 |        |    288 |00:00:00.01 |       2 |       |       |          |
|   4 |     VIEW                          |               |      1 |     72 |     72 |00:00:00.01 |       2 |       |       |          |
|   5 |      WINDOW SORT                  |               |      1 |     72 |     72 |00:00:00.01 |       2 | 13312 | 13312 |12288  (0)|
|   6 |       WINDOW BUFFER               |               |      1 |     72 |     72 |00:00:00.01 |       2 |  6144 |  6144 | 6144  (0)|
|   7 |        TABLE ACCESS BY INDEX ROWID| SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       2 |       |       |          |
|   8 |         INDEX FULL SCAN           | SLH_PK        |      1 |     72 |     72 |00:00:00.01 |       1 |       |       |          |
----------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - filter(("unpivot_view_006"."SALES_VALUE" IS NOT NULL OR "unpivot_view_006"."SALES_COST" IS NOT NULL))

Notes on Execution Plan for Analytic Functions and Unpivot Query

  • There is a single index full scan and a single table access by index rowid
  • The number of buffers is only 2
  • The plan suggests that this query will be a lot more efficient than the first one

Single Group By with CASE Expressions Query

Here is the third query:

WITH period_list AS (
  SELECT month_dt, prod_code, COLUMN_VALUE per_tp
    FROM TABLE(SYS.ODCIVarchar2List(
          'P1 - 1 Month',
          'P2 - 3 Months',
          'P3 - YTD',
          'P4 - 1 Year')
      )
  CROSS JOIN (SELECT month_dt, prod_code FROM sales_history)
)
SELECT /*+ gather_plan_statistics XPLAN_MARKER_GBC */
       drv.month_dt, drv.prod_code, drv.per_tp,
       Sum( CASE WHEN ( per_tp = 'P1 - 1 Month'  AND msr.month_dt = drv.month_dt ) OR 
                      ( per_tp = 'P2 - 3 Months' AND msr.month_dt >= Add_Months (drv.month_dt, -2) ) OR 
                      ( per_tp = 'P3 - YTD'      AND Trunc (msr.month_dt, 'YEAR') = Trunc (drv.month_dt, 'YEAR') ) OR 
                      ( per_tp = 'P4 - 1 Year'   AND msr.month_dt >= Add_Months (drv.month_dt, -11) )
                 THEN msr.sales_value END) sales_value,
       Sum( CASE WHEN ( per_tp = 'P1 - 1 Month'  AND msr.month_dt = drv.month_dt ) OR 
                      ( per_tp = 'P2 - 3 Months' AND msr.month_dt >= Add_Months (drv.month_dt, -2) ) OR 
                      ( per_tp = 'P3 - YTD'      AND Trunc (msr.month_dt, 'YEAR') = Trunc (drv.month_dt, 'YEAR') ) OR 
                      ( per_tp = 'P4 - 1 Year'   AND msr.month_dt >= Add_Months (drv.month_dt, -11) )
                 THEN msr.sales_cost END) sales_cost
  FROM period_list drv
  JOIN sales_history msr
  ON msr.prod_code = drv.prod_code
   AND msr.month_dt <= drv.month_dt
 GROUP BY drv.prod_code, drv.month_dt, drv.per_tp
 ORDER BY 1, 2, 3

Notes on Single Group By with CASE Expressions Query

  • In the first subquery we add in the period type values for each product and month
  • The main query then includes the extra column in its grouping fields
  • The main query drives from the first subquery, joining the table to aggregate over, and including only records not later than the driving record
  • The CASE expressions within the Sums ensure that a measure is counted in the sum only if its date on the joined table falls in the required range for the period type, relative to the date in the driving subquery
------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                 | Name          | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                          |               |      1 |        |    288 |00:00:00.01 |       7 |       |       |          |
|   1 |  SORT GROUP BY                            |               |      1 |     51 |    288 |00:00:00.01 |       7 | 40960 | 40960 |36864  (0)|
|*  2 |   HASH JOIN                               |               |      1 |     10M|   5328 |00:00:00.01 |       7 |  1476K|  1476K|  826K (0)|
|   3 |    INDEX FULL SCAN                        | SLH_PK        |      1 |     72 |     72 |00:00:00.01 |       1 |       |       |          |
|   4 |    MERGE JOIN CARTESIAN                   |               |      1 |    588K|    288 |00:00:00.01 |       6 |       |       |          |
|   5 |     TABLE ACCESS FULL                     | SALES_HISTORY |      1 |     72 |     72 |00:00:00.01 |       6 |       |       |          |
|   6 |     BUFFER SORT                           |               |     72 |   8168 |    288 |00:00:00.01 |       0 |  2048 |  2048 | 2048  (0)|
|   7 |      COLLECTION ITERATOR CONSTRUCTOR FETCH|               |      1 |   8168 |      4 |00:00:00.01 |       0 |       |       |          |
------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("MSR"."PROD_CODE"="PROD_CODE")
filter("MSR"."MONTH_DT"<="MONTH_DT")

Notes on Execution Plan for Single Group By with CASE Expressions Query

  • There is a single index full scan and a single full table access
  • The number of buffers is 7
  • The plan suggests that this query will also be a lot more efficient than the first one
  • The data set is too small to be conclusive regarding performance comparison with the second query

See also:






A Generic Unix Script for Uploading eBusiness Concurrent Programs

I have posted a couple of articles recently on XML Publisher report development within Oracle eBusiness applications (A Design Pattern for Oracle eBusiness Audit Trail Reports with XML Publisher and Design Patterns for Database Reports with XML Publisher and Email Bursting). These reports are of one of several types of batch program, or concurrent program that can be defined within Oracle eBusiness.

Oracle eBusiness uses a number of metadata tables to store information on the programs, and in release 11.5 Oracle introduced a Unix utility called FNDLOAD to download the metadata to formatted text files to allow them to be uploaded via the same utility into downstream environments. At that time batch reports were generally developed in the Oracle Reports tool and typically there might only be two text files (known as LDT files after their extension), for the program and for associated value sets, and maybe one for request groups (which control access to the reports). The executable report file, the RDF, would just be copied to the target environment server directory. I wrote a set of wrapper scripts for the utility to streamline its use and to deal with a number of issues, including handling of audit fields, with a structure consisting of a separate pair of scripts for download and upload of each type of LDT file. I published these on Scribd in July 2009.

In working more recently with Oracle’s successor reporting tool, XML Publisher, I found that the number of objects involved in installation has increased substantially. As well as the LDT files there are also now XML configuration files and RTF (usually) templates, uploaded via a Java utility, XDOLoader. Installation also involves at least one PL/SQL package. For example the email version of my model reports (see the second link above) had 11 configuration files. For this reason, I decided to create a single script that would copy, upload and install all objects required for a concurrent program of any type, and I describe the script in this article.

Here is my original Scribd article, describing the issues mentioned, and with the original individual upload and download scripts.

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The new script is here XX_Install_XMLCP_ksh, and here is the MD120 from the model article that uses it.

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Directory Structure

The script operates on an input TAR file containing all the necessary files. The TAR file is called prog.tar after the program short name prog, and contains a directory also called prog with all the installation files. The installer uses relative paths and assumes that the TAR file, with the installer, is placed in a directory below the custom top directory, say $XX_TOP/rel. All loader files are copied to $XX_TOP/import and SQL files to $XX_TOP/install/sql.

After installation, a new directory will remain with all the installation files for reference, $XX_TOP/rel/prog.

Program Structure

The internal call structure of the Unix script is shown below.
Install CP - CSD

The script operates on an input TAR file containing all the necessary files.

After extracting the TAR file, the script has local subroutines that can be divided into four categories, as above:

  1. Preliminaries – parameter processing, file moving and validation
  2. FNDLOAD uploads – uploading the LDT files
  3. XDOLoader uploads – uploading the data and any bursting XML files, and all layout templates present
  4. SQL installations – installing any SQL script present, and the package spec and body files

The following table gives a few notes on the main program and preliminary subroutines.
Preliminaries Summary
Prelims Table
The following table gives a few notes on the upload and SQL subroutines.
Upload and SQL Summary
Install Uploads Table
The upload subroutines all have SQL queries for verification, and here is a sample log file from the script: XX_ERPXMLCP_EM_log

The remainder of the article lists the queries with diagrams and examples of output.

Upload Subroutines

upload_ag

Validation Query

SELECT app_g.application_short_name "App G", fag.group_name "Group",
      fag.description "Description",
      app_t.application_short_name "App T", ftb.table_name "Table",
      fcl.column_name "Column"
  FROM fnd_audit_groups fag
  JOIN fnd_application app_g
    ON app_g.application_id = fag.application_id
  JOIN fnd_audit_tables fat
    ON fat.audit_group_app_id = fag.application_id
   AND fat.audit_group_id = fag.audit_group_id
  JOIN fnd_application app_t
    ON app_t.application_id = fat.table_app_id
  JOIN fnd_tables ftb
    ON ftb.application_id = fat.table_app_id
   AND ftb.table_id = fat.table_id
  JOIN fnd_audit_columns fac
    ON fac.table_app_id = fat.table_app_id
   AND fac.table_id = fat.table_id
  JOIN fnd_columns fcl
    ON fcl.application_id = fac.table_app_id
   AND fcl.table_id = fac.table_id
   AND fcl.column_id = fac.column_id
 WHERE fag.last_update_date     = To_Date ('$sysdate', 'YYYY/MM/DD')
   AND fac.schema_id            = 900
ORDER BY app_g.application_short_name, fag.group_name, 
         app_t.application_short_name, ftb.table_name,
         fcl.column_name;

QSD

Install CP - AG - 1

Example Output
No example available here, but the headings are:
“App G”, “Group”, “Description”, “App T”, “Table”, “Column”

upload_ms

Validation Query

SELECT mes.message_name "Name", mes.message_text "Text"
  FROM fnd_new_messages mes
 WHERE mes.last_update_date     = To_Date ('$sysdate', 'YYYY/MM/DD')
 ORDER BY 1;

QSD

Install CP - Message

Example Output

Name                     Text
--------------------- ---------------------------------------------
XX_ERPXMLCP_EM_NONXML Non-XML Concurrent Program &PROGAPP

upload_vs

Validation Query

SELECT fvs.flex_value_set_name "Value Set", Count(fvl.flex_value_set_id) "Values"
  FROM fnd_flex_value_sets fvs, fnd_flex_values fvl
 WHERE fvs.last_update_date     = To_Date ('$sysdate', 'YYYY/MM/DD')
   AND fvl.flex_value_set_id(+) = fvs.flex_value_set_id
 GROUP BY fvs.flex_value_set_name;

QSD

Install CP - VS

Example Output

Value Set             Values
--------------------- ------
XX_PROGS                   0
XX_APPNAME_ID              0

upload_cp

Validation Query

SELECT prg.user_concurrent_program_name || ': ' || prg.concurrent_program_name "Program", fcu.column_seq_num || ': ' || fcu.end_user_column_name "Parameter"
  FROM fnd_concurrent_programs_vl               prg
  LEFT JOIN fnd_descr_flex_column_usages      fcu
    ON fcu.descriptive_flexfield_name         = '\$SRS\$.' || prg.concurrent_program_name
   AND fcu.descriptive_flex_context_code      = 'Global Data Elements'
 WHERE prg.concurrent_program_name              = '$cp_name'
 ORDER BY 1, 2;

QSD

Install CP - CP

Example Output

Program                                    Parameter
------------------------------------------ ------------------------
XX Example XML CP (Email): XX_ERPXMLCP_EM  100: Cc Email
                                           10: Application
                                           20: From Program
                                           30: To Program
                                           40: From Date
                                           50: To Date
                                           60: From Parameter Count
                                           70: To Parameter Count
                                           80: Override Email
                                           90: From Email

upload_rga

Validation Query

SELECT rgp.request_group_name "Request Group",
       app.application_short_name "App"
  FROM fnd_concurrent_programs          cpr
  JOIN fnd_request_group_units          rgu
    ON rgu.unit_application_id          = cpr.application_id
   AND rgu.request_unit_id              = cpr.concurrent_program_id
  JOIN fnd_request_groups               rgp
    ON rgp.application_id               = rgu.application_id
   AND rgp.request_group_id             = rgu.request_group_id
  JOIN fnd_application                  app
    ON app.application_id               = rgp.application_id
 WHERE cpr.concurrent_program_name      = '$cp_name'
 ORDER BY 1;

QSD

Install CP - RGA

Example Output

Request Group                  App
------------------------------ ----------
System Administrator Reports   FND

upload_dd

Validation Query

SELECT xdd.data_source_code "Code", xtm.default_language "Lang", xtm.default_territory "Terr"
  FROM xdo_ds_definitions_b xdd
  LEFT JOIN xdo_templates_b xtm
    ON xtm.application_short_name       = xdd.application_short_name
   AND xtm.data_source_code             = xdd.data_source_code
 WHERE xdd.data_source_code             = '$cp_name'
 ORDER BY 1, 2, 3;

QSD

Install CP - DD

Example Output

Code                 Lang Terr
-------------------- ---- ----
XX_ERPXMLCP_EM       en   US

upload_all_temps

Validation Query

SELECT xdd.data_source_code "Code", 
        xlb_d.file_name "Data Template",
        xlb_b.file_name "Bursting File",
        xtm.template_code "Template",
        xlb.language "Lang", 
        xlb.territory "Terr",
        xlb.file_name || 
               CASE
               WHEN xlb.language = xtm.default_language AND
                    xlb.territory = xtm.default_territory
               THEN '*' END "File"
  FROM xdo_ds_definitions_b             xdd
  LEFT JOIN xdo_lobs                    xlb_d
    ON xlb_d.application_short_name     = xdd.application_short_name
   AND xlb_d.lob_code                   = xdd.data_source_code
   AND xlb_d.lob_type                   = 'DATA_TEMPLATE'
  LEFT JOIN xdo_lobs                    xlb_b
    ON xlb_b.application_short_name     = xdd.application_short_name
   AND xlb_b.lob_code                   = xdd.data_source_code
   AND xlb_b.lob_type                   = 'BURSTING_FILE'
  LEFT JOIN xdo_templates_b             xtm
    ON xtm.application_short_name       = xdd.application_short_name
   AND xtm.data_source_code             = xdd.data_source_code
  LEFT JOIN xdo_lobs                    xlb
    ON xlb.application_short_name       = xtm.application_short_name
   AND xlb.lob_code                     = xtm.template_code
   AND xlb.lob_type                     LIKE 'TEMPLATE%'
 WHERE xdd.data_source_code             = '$cp_name'
   AND xdd.application_short_name       = '$app'
 ORDER BY 1, 2, 3, 4;

QSD

Install CP - Template

Example Output

Code            Data Template        Bursting File          Template             Lang Terr File
--------------- -------------------- ---------------------- -------------------- ---- ---- -------------------------
XX_ERPXMLCP_EM  XX_ERPXMLCP_EM.xml   XX_ERPXMLCP_EM_BUR.xml XX_ERPXMLCP_EM       en   US   XX_ERPXMLCP_EM.xsl*
                                                                                           XX_ERPXMLCP_EM.rtf*
                                                            XX_ERPXMLCP_EM_XML   en   US   XX_ERPXMLCP_EM_XML.xsl*
                                                                                           XX_ERPXMLCP_EM_XML.rtf*






Design Patterns for Database Reports with XML Publisher and Email Bursting

In a recent article, A Design Pattern for Oracle eBusiness Audit Trail Reports with XML Publisher, I presented a report that I developed within Oracle eBusiness 12.1, using Oracle XML Publisher. The report was for displaying audit trail data for a particular table, and I proposed that it could be used as a design pattern for audit trail reporting on any eBusiness table. The article focussed on the rather complex SQL, with associated layout structures, needed to render the audit data into a readable format.

In this article, I present a pair of XML Publisher reports intended to serve as design patterns for more general reporting. The reports were again developed within the framework of Oracle’s eBusiness embedded version of XML Publisher, of which there is also a stand-alone version,
Oracle Business Intelligence Publisher, which describes the product (rather ungrammatically) thus:

Oracle BI Publisher is the reporting solution to author, manage, and deliver all your reports and documents easier and faster than traditional reporting tools.

Reports Overview

The reports were developed specifically to serve as models for other reports, from which program constructs could be copied and pasted. For this reason I considered taking for the data sources universally available Oracle metadata such as all_tables or all_objects etc., but found problems with that idea, including:

  • They are not generally ordinary tables, and tend to produce very complex execution plans
  • The possible group structures I found were not quite general enough for my purposes

On the other hand, within Oracle eBusiness I had a number of queries available against the (FND application) metadata tables for concurrent programs that seemed to fit the purpose pretty well, and so decided to use these. As a compromise I created views on the tables with generic column names, so that the reports could be ported to other data sources relatively easily.

Concurrent Programs Data Model
In Oracle eBusiness applications concurrent (i.e. batch) programs have a logical metadata record for the program itself, and (potentially) a set of records for each of the following associated entities:

  • Parameter
  • Request group
  • XML layout template

All of these can have zero records, and for the third entity, only programs of specific type can have any records defined, a structure providing a reasonable degree of generality.

Email Bursting
Business application reports are often used to generate documents for sending to customers, suppliers etc., and increasingly companies prefer to email these out to save costs compared with mailing printed documents. In the past report developers had to code the emailing functionality themselves, usually by means of a co-ordinating program that would loop over the master records and call the reporting tool individually for each one, creating an attachment file that would then be emailed, perhaps by the Unix mailx program. As well as the development effort involved, this approach had the drawback that it scales badly as the number of emails rises owing to the individual calls to the reporting tool (such as Oracle Reports). A printed report will frequently create 10,000 records in little more time than for 1,000 records, whereas for the hand-coded emailing process it would likely take 10 times longer.

The bursting functionality in Oracle XML Publisher solves these problems by allowing the emailing processing to be specified by an XML configuration file, and by creating the files to be emailed in batch just as with printed reports. The data files are created initially in XML format from the data model, and are then merged with layout templates that are emailed by a second program.

The design pattern reports will consist of an email bursting version with a printed version, which would generally be required as a fallback for records with missing or invalid email addresses. We’ll address the obvious security issue with automated emailing programs as part of our design pattern below.

Report Outputs

Examples of complete report outputs for the printed report in .pdf format are included in the attached zip file. Here three pages are given, two showing the listing for the example report programs themselves, and a third showing a non-XML program.

Example Report – XX Example XML CP
This is page 8 of the printed report run with the only the first parameter set, the application.

  • Only the parameters that were actually set are listed after the title
  • The templates region appears because it is an XML report
  • There is only one template for this printed report, with XSL conditionality to include or exclude regions or columns

XX_ERPXMLCP

Example Report – XX Example XML CP (Email)
This is page 9 of the printed report run with the only the first parameter set, the application.

  • Notice that the bursting file column appears because this report has the file attached, using XSL conditionality
  • This email version has two layout templates that are used conditionally for each record depending on whether it’s of XML type or not
  • This record-level choice of template is implemented in the bursting XML file, and is not available in this way for the printed version

XX_ERPXMLCP_EM

Advanced Pricing Report – QP: Java Pricing Engine Warm Up
This is page 44 of the printed report run for the Advanced Pricing application.

  • It shows a non-XML program, and the templates region consequently does not appear
  • Notice that only the summary line appears for the parameters as there are no parameters defined for the program.

QPXJCCWU-2

XML Data Templates and XML Generators

XML Publisher reports require at least one layout template in one of several formats, and also require an executable program to generate the XML data file to merge with the template(s). Oracle supplies a Java executable to generate the data file from an XML data template containing the SQL queries, group structure etc. that the programmer attaches to the concurrent program. This is usually the best approach since it mimimizes the amount of programming required.

It is also possible to use Oracle Reports to generate XML. This can be a reasonable approach for converting legacy reports to XML Publisher to avail of some of the additional features, such as Excel output and email bursting. However, if the data model needs changing it is probably best to extract the SQL and create an XML data template from it.

It’s a really bad idea to write a PL/SQL program to generate the XML data file, being a serious case of ‘reinventing the wheel’ – use Oracle’s Java executable with XML data template instead!

Data Model: Groups, Queries, Parameters, Summaries

As described above, the example report has one master data group and three detail groups. As discussed in my last article Query Query Query, the minimum number of queries required for a group structure hierarchy is the number of groups minus the number of parent groups, which equals three here. The group structure with detail queries linked by bind variables could be depicted as below.

Group Structure Diagram
XML Publisher Model - Groups

A possible query structure for the three queries might then look like the following, where I have included summaries for two of the detail groups, and allowed for report constraints via lexical parameters, to be discussed later.

Query Structure Diagram
XML Publisher Model - Queries

Query Links and Bind Variables
It is possible to link detail queries to their master by XML query links, but according to the manual Oracle® XML Publisher Administration and Developer’s Guide, Release 12:

XML Publisher tests have shown that using bind variables is more efficient than using the link tag.

Linking a detail query to its master by bind variables involves simply referencing the master variable link columns within the detail query preceded by a colon.

Constraints and Parameters
Reports often have constraints depending on input parameters, which may be mandatory or optional. Input parameters are defined in a parameters section within the data template.

Mandatory parameters can be referenced directly as bind variables within the queries (i.e. by preceding the parameter name with a colon).

Optional parameters should normally be referenced indirectly using additional lexical parameters. This allows the exact query required to be executed rather than a composite query with Nvls to represent all possible query combinations. The latter all-purpose queries tend to be more complex and to perform poorly. Lexical parameters are declared and processed in the associated database package, and referenced by preceding them with an ampersand (&).

Note that, confusingly, these lexical parameters are not the same as the lexical tags applicable only within eBusiness that refer to eBusiness flexfields. Like the corresponding user-exits in eBusiness Oracle Reports the lexical tags allow flexfields to be included without their structure being known to the report developer. This is necessary for the standard eBusiness reports but developers of custom reports normally know the structures, and so can avoid these tags altogether (at least for new reports).

Summaries
There are various ways of computing summaries in XML Publisher reports:

  • Within the SQL
  • within the XML group elements
  • within the layout template

SQL Summaries
Often the best way to do the summaries is in the SQL itself. This makes testing simpler because the queries can be run and debugged in SQL Developer or Toad, and also facilitates production support where developers can often run queries in a read-only schema, but can’t change the production code.

In the simple case of summarising detail groups that are defined against a main query, the summaries can be done using analytic functions partitioning by the master key for first level details, and as desired for any subsequent levels. In my example reports, Example Line One is summarised in this way; a subquery factor was used but this is optional here.

Groups defined against additional queries cannot be summarised quite so easily in the SQL. I have summarised the Example Line Two group by adding a subquery factor in the main query purely to do the summaries: Because the line detail is not present in the query we can’t do this by analytic functions, so a grouping summary in a separate subquery is necessary.

XML Group Summaries
Where possible SQL summaries via analytic functions appears best, but in other cases we may wish to consider the non-SQL alternatives. One of these is to define the summaries within the appropriate group as elements in the XML template setting the value attribute to the element in the lower level group to be summarised, and setting the function attribute as desired within a limited set of functions. I have not implemented this method in my examples.

XSL Layout Summaries
A further non-SQL alternative for summaries is to define them within the layout template using the XSL language, and I have implemented the summaries of Example Line Three using this method.

Database Package and Report Triggers

Each XML Publisher has a default package specified in the XML template, that handles parameter processing and implements any triggers required.

Package Spec
The spec declares both input parameters and lexical parameters, as well as the procedures that implement the report triggers (usually three).

Triggers

Before Report

  • This trigger is used to process the lexical parameters, constructing the appropriate where condition depending on which optional parameters have values
  • The example packages show how ranges of character, date and number are processed. I have found it more convenient to pass dates as string parameters.

After Report

  • This trigger can be used to write column headings to the log file for fields that are logged in the group filter

Group Filter

  • This trigger can be used to filter out records depending on some condition, by returning FALSE.
  • I use it in the examples to log selected fields for each record returned.
  • This logging is particularly important for email bursting reports as it enables checking that emails to be sent externally are appropriate before sending in a second step.

Printed Example Report Details

Report Parameters
The report parameters were designed to illustrate the implementation of character, date and number ranges, with an equality join parameter that fits well with the report content, all optional:

  • Application – the eBusiness application id
  • Program name From and To parameters – character range
  • Creation date From and To parameters – date range
  • Number of parameters From and To parameters – number range

Where there is an associated email version it may be worth having an additional Yes/No parameter determining whether to include records that have an email address defined.

Layout Template
There is a single layout template, of .rtf format.

Layout Template Structure
XML Publisher Model - Layout

Layout Template File

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Notes on Layout

  • Body and Margins
    • The page numbers are in the bottom margin (or footer)
    • The title, parameters and header fields are above the body tag, and repeat on each page of a given header record
  • Conditional Blocks and Columns
    • XSL if-blocks are used to conditionally print blocks such as input parameters, or detail tables depending on data values
    • XSL if-blocks with column qualifiers are used to conditionally print table columns: If there is no bursting file, the entire bursting file column will not appear
  • Sections and Page Breaks
    • The XSL for-each field for the header group uses the section qualifier, which, in conjunction with the page break field, specifies a new page after each header record except the last
    • By default, the above section qualifier would specify that page numbers reset with each header record, but this is overriden here by the initial-page-number field in the footer
  • XSL Summary
    • As discussed in the data model section, the line three summary is implemented in XSL – by the XSL count field

Email Bursting Example Report

Email Address Parameters
In addition to the printed report constraint parameters, three email address parameters have been added.

  • Override email address – setting a value here overrides the record level email address and is useful for testing
  • From and CC email addresses are parameters, which can be defaulted, for flexibility
  • The email address data source is hard-coded in this demo report: normally it would be something like a supplier email address at master record level

Two Step Bursting Process
The first step is to run the concurrent program for the report, which results in an XML file containing the report data to be created on the server. In the second step, a standard concurrent program ‘XML Publisher Report Bursting Program’ is run, passing the request id of the earlier custom report, in order to merge the XML data file with the layout template(s) and send the individual reports by email.

It’s sometimes recommended to trigger the running of the second program within the custom report itself, but it’s probably better not to do this, in order to allow validation of the reports and email addresses before sending them out.

Email Report Logging
The example report logs the email address and other fields in the concurrent program log file, including whether an override email address was specified, where the user can check them before executing the second step that sends out the emails.

Layout Template
There are two layout templates, of .rtf format, which are used conditionally depending on the type of record. The structure of the one used for XML type programs (shown below) has conditional header fields and the third lines block that are absent from the other (not shown).

Layout Template Structure
XML Publisher Model - Layout EM

Layout Template File

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Notes on Layout

  • The master group is excluded from the template, although its fields are present in the header. The XML Publisher bursting program performs the looping over this group

Email Bursting XML File

<xapi:requestset xmlns:xapi="http://xmlns.oracle.com/oxp/xapi" type="bursting">
<xapi:request select="/XX_ERPXMLCP_EM/LIST_G_HDR/G_HDR">
<xapi:delivery>
<xapi:email server="127.0.0.1" port="25" from="${FROM_EMAIL}" reply-to ="">
<xapi:message id="123" to="${EMAIL_ADDRESS}" cc="${CC_EMAIL}" 
attachment="true" subject="${SUB}">Dear Sir/Madam

Pleae find attached your concurrent program details. 

[Alternatively, leave blank and use output_type=html in document to have attachment go to body instead]</xapi:message>
</xapi:email>
</xapi:delivery>
<xapi:document output="CP_${PROG_NAME}.pdf" output-type="pdf" delivery="123">
<xapi:template type="rtf" location="XDO://CUSTOM.XX_ERPXMLCP_EM.en.US/?getSource=true" filter="//G_HDR[OUTPUT_TYPE!='XML']"></xapi:template>
<xapi:template type="rtf" location="XDO://CUSTOM.XX_ERPXMLCP_EM_XML.en.US/?getSource=true" filter="//G_HDR[OUTPUT_TYPE='XML']"></xapi:template>
</xapi:document>
</xapi:request>
</xapi:requestset>

Notes on Bursting File

  • The email server is specified in the bursting XML configuration file, along with the email addresses to be used, which can be variables from the selected records or hard-coded
  • The layout template is specified, and more than one can be included, as here, with filter conditions depending on selected data fields
  • Here the output is specified to be sent as a .pdf attachment; changing to output type html results in the the output appearing as body text
  • It can be useful to store some or all of the subject (or body) text on the database; the table fnd_messages is used to store the subject here, as records from the fnd tables can be installed automatically via fndload

Code to Download

The zip file Brendan_Model_XML_Reports contains a root folder and three subfolders, as follows:

  • Root – MD120 installation document for the email version. It references a generic Unix script that installs all objects, see A Generic Unix Script for Uploading Oracle eBusiness Concurrent Programs
  • Output – examples of log files for both printed and email versions and pdf outputs for printed version
  • XX_ERPXMLCP – complete code and metadata for the printed version
  • XX_ERPXMLCP_EM – complete code and metadata for the email version (except requires some printed version objects to be installed first)