SQL and Modularity: Patterns, Anti-Patterns and the Kitchen Sink

There’s a lot of importance placed on code re-use in the database development world. In traditional procedural programming languages, such as C or Fortran, the value of modular programming and its application to promoting code re-use is well known and understood. When SQL enters the picture, however, the situation becomes less clear, and there is less consensus on how best to apply the traditional concept of modularity.

This article will consider the concept of modularity, and how it may best be applied to SQL, from the perspective of ‘patterns’ and ‘anti-patterns’. Here is a definition of these terms from Wikipedia, Anti-pattern:

An anti-pattern (or antipattern) is a pattern used in social or business operations or software engineering that may be commonly used but is ineffective and/or counterproductive in practice.[1][2]

The term was coined in 1995 by Andrew Koenig,[3] inspired by Gang of Four’s book Design Patterns, which developed the concept of design patterns in the software field, since now a days software is used for any business from banks to small stores, and you can even go online to find Blaze cannabis software in case you’re interested in this product. The term was widely popularized three years later by the book AntiPatterns, which extended the use of the term beyond the field of software design and into general social interaction. According to the authors of the latter, there must be at least two key elements present to formally distinguish an actual anti-pattern from a simple bad habit, bad practice, or bad idea:

  • Some repeated pattern of action, process or structure that initially appears to be beneficial, but ultimately produces more bad consequences than beneficial results, and
  • An alternative solution exists that is clearly documented, proven in actual practice and repeatable

 

Procedural Modularity

Modularity starts from the idea that a complex design can generally be broken down into a set of less complex component modules that is easier to work with. In programming terms, a long main program would be broken down into smaller subroutines, with a much shorter main program that calls the subroutines.

From this starting point emerges the possibility of code re-use, whereby the decomposition into modules aims at identifying common logic that can be placed in generic modules and called in multiple places. A simple example of this would be an error-logging module in PL/SQL that would write any Oracle errors to a table along with call stack information, that could be called wherever such errors need to be trapped. This might be termed an error-logging pattern, and it’s clear that this kind of code re-use can lead to simpler and more maintainable systems.

SQL Modularity: Design Patterns

Transactional APIs

The concept of transactions is important for modular design within a database application.

The Oracle manual, Oracle Database Concepts, defines a transaction thus:
A transaction is a logical unit of work that contains one or more SQL statements. A transaction is an atomic unit. The effects of all the SQL statements in a transaction can be either all committed (applied to the database) or all rolled back (undone from the database).

When a transaction needs to be performed in more than one place, then the code can be placed in a PL/SQL module, sometimes called a ‘transactional API’. This is really just the database-specific version of standard modularity, and is obviously a good design pattern to follow.

Data Access Layers

Transactional APIs are often used to form a Data Access Layer (DAL) for front end applications written in languages such as Java. Where the front end requires a record set from the database, the APIs may return a reference cursor, which is essentially a pointer to the data, and avoids the overhead of passing the whole data set at once. The data access layer pattern has a number of important advantages:

  • performance is enhanced through reduced network traffic between application server and database
  • SQL operates in an efficient set-based fashion for retrieving data in batches
  • the PL/SQL language, highly integrated with SQL, is specifically designed for database processing
  • storing the database processing code in database packages promotes modularity and code re-use

It is considered best practice to use data access layers even for clients, such as Oracle Forms, that have an embedded PL/SQL engine.

Of course, in order to achieve these benefits the data access layer has to be correctly written, avoiding the anti-pattern pitfalls discussed later. In particular, ‘kitchen sink’ style APIs that return far more data than required, to promote re-use, must be avoided; different client programs requiring different data should have separate APIs.

Views

Database views have been available in Oracle SQL from the earliest versions and can be used to avoid duplicating a complex SQL query that might be needed in multiple places.

This approach could be seens as a special case of transactional modularity, where a single query is the transaction, and might be regarded as a design pattern for re-use of SQL statements.

Views may form an alternative kind of data access layer, typically used by reporting tools such as Business Objects, and may also be used in conjumction with an API-based layer, which is a common approach in Oracle Forms applications.

Within-SQL Modularity

SQL is essentially a declarative, rather than procedural language for retrieving (and updating etc.) data from relational databases. The original idea was that the programmer specifies the tables and columns where the data are stored, as well as how the tables are related through key values, but does not specify algorithms for retrieving the data: The retrieval algorithms are performed by the SQL engine ‘under the covers’. There might therefore seem to be little scope for modularity within a SQL select statement. However, this is not quite true for a couple of reasons: First, logical paths have to be specified between the tables, and the same path may need to be specified multiple times from different starting points; for example, the path to billing and shipping addresses on a sales order would typically involve the same sequence of steps from different id columns; second, as SQL has evolved, procedural capabilities have been added, such as analytic functions and recursion.

In Oracle in-line views were introduced in v7.2, and are in a sense a first step in modularising an SQL statement, followed in v9.2 by the ‘WITH’ clause for subquery factoring. Analytic functions were introduced in v8i.

Here is an example based on Oracle’s HR demo schema. Suppose we want a list of employees with their current and previous jobs (if any), the same for their manager, and a count of the number of subordinates they have. To get the previous jobs, we need to find the latest records in the job_history table for the employee and his manager separately. This can be done using subqueries, but that is inefficient and it is normally better to join to aggregation views that use the DENSE_RANK clause to allow the required previous records to be obtained in a single pass each. Similarly, the subordinate count could be done by a scalar subquery, but again performance would usually dictate the use of another aggregation view.

Here is a query using in-line views to achieve this:

SELECT emp.last_name || ', ' || emp.first_name name,
       job.job_title,
       job_p.job_title             job_title_prior,
       emp_m.last_name || ', ' || emp_m.first_name name_mgr,
       job_m.job_title             job_title_mgr,
       job_pm.job_title            job_title_mgr_prior,
       sub.n_sub
  FROM hr.employees                emp
  JOIN hr.jobs                     job
    ON job.job_id                  = emp.job_id
  LEFT JOIN (SELECT employee_id,
                    Max (job_id) KEEP (DENSE_RANK LAST ORDER BY end_date) job_id
               FROM hr.job_history
              GROUP BY employee_id
            )                      jhs
    ON jhs.employee_id             = emp.employee_id
  LEFT JOIN hr.jobs                job_p
    ON job_p.job_id                = jhs.job_id
  LEFT JOIN hr.employees           emp_m
    ON emp_m.employee_id           = emp.manager_id
  LEFT JOIN hr.jobs                job_m
    ON job_m.job_id                = emp_m.job_id
  LEFT JOIN (SELECT employee_id,
                    Max (job_id) KEEP (DENSE_RANK LAST ORDER BY end_date) job_id
               FROM hr.job_history
              GROUP BY employee_id
            )                      jhs_m
    ON jhs_m.employee_id           = emp.manager_id
  LEFT JOIN hr.jobs                job_pm
    ON job_pm.job_id               = jhs_m.job_id
  LEFT JOIN (SELECT manager_id,
                    Count(*)       n_sub
               FROM hr.employees
              GROUP BY manager_id
            )                      sub
    ON sub.manager_id              = emp.employee_id
 WHERE emp.department_id           = 30
 ORDER BY 1

Here is a query using subquery factors to achieve the same:

WITH jhs_f AS (
SELECT employee_id,
       Max (job_id) KEEP (DENSE_RANK LAST ORDER BY end_date) job_id
  FROM hr.job_history
 GROUP BY employee_id
), sub_f AS (
SELECT manager_id,
       Count(*)                    n_sub
  FROM hr.employees
 GROUP BY manager_id
)
SELECT emp.last_name || ', ' || emp.first_name name,
       job.job_title,
       job_p.job_title             job_title_prior,
       emp_m.last_name || ', ' || emp_m.first_name name_mgr,
       job_m.job_title             job_title_mgr,
       job_pm.job_title            job_title_mgr_prior,
       sub.n_sub
  FROM hr.employees                emp
  JOIN hr.jobs                     job
    ON job.job_id                  = emp.job_id
  LEFT JOIN jhs_f                  jhs
    ON jhs.employee_id             = emp.employee_id
  LEFT JOIN hr.jobs                job_p
    ON job_p.job_id                = jhs.job_id
  LEFT JOIN hr.employees           emp_m
    ON emp_m.employee_id           = emp.manager_id
  LEFT JOIN hr.jobs                job_m
    ON job_m.job_id                = emp_m.job_id
  LEFT JOIN jhs_f                  jhs_m
    ON jhs_m.employee_id           = emp.employee_id
  LEFT JOIN hr.jobs                job_pm
    ON job_pm.job_id               = jhs_m.job_id
  LEFT JOIN sub_f                  sub
    ON sub.manager_id              = emp.employee_id
 WHERE emp.department_id           = 30
 ORDER BY 1

The second query, although only a line shorter, could be said to be more modular in two ways:

  1. The more complex processing is placed at the beginning, prior to the main select, which now contains only simple joins. This might be said to parallel the procedural modularity practice of having a simple main program calling subroutines, and may help maintainability
  2. A single subquery factor replaces the two inline views for previous jobs, a more modular design, and one that may be more efficient for larger data sets since Oracle generally materialises subquery factors referenced multiple times

This approach might be regarded as a design pattern for modularity within individual SQL statements.

ANSI Join Syntax

The queries above are written using ANSI join syntax, introduced in v9. Oracle SQL originally used its own proprietary syntax, as shown below for the same query requirement:

WITH jhs_f AS (
SELECT employee_id,
       Max (job_id) KEEP (DENSE_RANK LAST ORDER BY end_date) job_id
  FROM hr.job_history
 GROUP BY employee_id
), sub_f AS (
SELECT manager_id,
       Count(*)                    n_sub
  FROM hr.employees
 GROUP BY manager_id
)
SELECT emp.last_name || ', ' || emp.first_name name,
       job.job_title,
       job_p.job_title             job_title_prior,
       emp_m.last_name || ', ' || emp_m.first_name name_mgr,
       job_m.job_title             job_title_mgr,
       job_pm.job_title            job_title_mgr_prior,
       sub.n_sub
  FROM hr.employees                emp,
       hr.jobs                     job,
       jhs_f                       jhs,
       hr.jobs                     job_p,
       hr.employees                emp_m,
       hr.jobs                     job_m,
       jhs_f                       jhs_m,
       hr.jobs                     job_pm,
       sub_f                       sub
 WHERE emp.department_id           = 30
   AND job.job_id                  = emp.job_id
   AND jhs.employee_id (+)         = emp.employee_id
   AND job_p.job_id (+)            = jhs.job_id
   AND emp_m.employee_id (+)       = emp.manager_id
   AND job_m.job_id (+)            = emp_m.job_id
   AND jhs_m.employee_id (+)       = emp.employee_id
   AND job_pm.job_id (+)           = jhs_m.job_id
   AND sub.manager_id (+)          = emp.employee_id
 ORDER BY 1

The tables are listed together, join clauses are in a single block not separated from constraints, and outer joins are specified using a (+) token against every column in the ‘left’ table. The outer join syntax leads to widespread bugs when developers miss the (+) from one of the columns, which silently converts the join to an inner join.

Some of the advantages of the newer syntax are:

  • Greater functionality is available, including full outer joining
  • Outer joining is much harder to get wrong
  • The syntax follows an ANSI standard
  • Locating the join conditions with the table being joined appears to be more modular and readable

ANSI join syntax might therefore be considered a good design pattern to follow.

SQL Modularity: Design Anti-patterns

everything-but-the-kitchen-sink-IDIOM

Here is a thread from Tom Kyte’s AskTom forum dealing with an SQL anti-pattern that is unfortunately common, and strongly opposed by Tom Kyte: Considering SQL as a Service. The idea behind the anti-pattern seems to be to avoid repeating even simple table joins in SQL by hiding them in a special type of data access layer designed to be called within individual SQL statements. This results in over-complex ‘kitchen-sink’ SQL within the layer itself and performance problems in the ‘client’ SQL. In addition, complex PL/SQL involving object types and arrays tends to be needed to glue it all together; the approach thus achieves the opposite of its intended purpose – simplification – in the manner of a classic anti-pattern.

We’ll illustrate the anti-pattern by extending the HR example used above and working through an example. First we’ll use the PL/SQL packaged procedure variant, then look at an older form based on views.

APIs as SQL Building Blocks Anti-pattern

Let’s suppose that we start from the idea that we should centralise the SQL for employee information in a re-useable API. We could take the SQL above and add in department name, address and manager information to make it more general. We might think initially of making the API a function taking an employee id as input and returning a record. But what if we needed the information for a list of employees? It would be inefficient to call a function for every record in the list, which might lead us to think of making the function take a list as input and return a list of records. We might therefore define object and array types, and a function with the following signature:

FUNCTION Emp_Info_List (p_emp_id_list SYS.ODCINumberList) RETURN emp_info_list_type;

[All code and output referenced is attached to this article.]

Now let’s consider a scenario in which we have to provide an API for a web front end following the design pattern of returning a reference cursor. The data required are the following details for all employees in a given department:

  • employee name
  • manager name
  • list of subordinates

The output for department 30 would be:

NAME                      NAME_MGR               NAME_SUB
------------------------- ---------------------- -------------------
Baida, Shelli             Raphaely, Den
Colmenares, Karen         Raphaely, Den
Himuro, Guy               Raphaely, Den
Khoo, Alexander           Raphaely, Den
Raphaely, Den             King, Steven           Baida, Shelli
Raphaely, Den             King, Steven           Colmenares, Karen
Raphaely, Den             King, Steven           Himuro, Guy
Raphaely, Den             King, Steven           Khoo, Alexander
Raphaely, Den             King, Steven           Tobias, Sigal
Tobias, Sigal             Raphaely, Den

10 rows selected.

Here is a possible procedure implementation:

PROCEDURE Get_Mgr_Subs_KS (p_dept_id PLS_INTEGER, x_mgr_sub_cur OUT SYS_REFCURSOR) IS
  l_emp_id_list SYS.ODCINumberList;
BEGIN

  SELECT employee_id
    BULK COLLECT INTO l_emp_id_list
    FROM hr.employees
   WHERE department_id = p_dept_id;

  OPEN x_mgr_sub_cur FOR
  SELECT t.name,
         t.name_mgr,
         e.last_name || ', ' || e.first_name
    FROM TABLE (KSink_Emp.Emp_Info_List (l_emp_id_list)) t
    LEFT JOIN hr.employees e
      ON e.manager_id = t.employee_id
   ORDER BY 1, 2, 3;

END Get_Mgr_Subs_KS;

The first step is to get the list of employees for the department, which we then pass into the API, wrapped in the TABLE key word, and join the employees table to get the subordinates. Three SQL select statements are executed. You might argue that I have over-complicated this by having the API take a list of employees rather than the department id, but remember that in this design anti-pattern the API can’t be designed for one specific caller, and the list input is more general. It is intended to cater for all calls for employee information so in practice such compromises will happen frequently.

We can compare this to an alternative implementation in which we simply join the tables required:

PROCEDURE Get_Mgr_Subs_SQL (p_dept_id PLS_INTEGER, x_mgr_sub_cur OUT SYS_REFCURSOR) IS
BEGIN

  OPEN x_mgr_sub_cur FOR
  SELECT e.last_name || ', ' || e.first_name,
         m.last_name || ', ' || m.first_name,
         s.last_name || ', ' || s.first_name
    FROM hr.employees e
    LEFT JOIN hr.employees m
      ON m.employee_id = e.manager_id
    LEFT JOIN hr.employees s
      ON s.manager_id = e.employee_id
   WHERE e.department_id = p_dept_id
   ORDER BY 1, 2, 3;

END Get_Mgr_Subs_SQL;

This joins three tables in one statement while the earlier procedure effectively makes a join through PL/SQL using an array, which is arguably slightly more complicated. In any case the real problems become apparent when you compare the execution plans. I have written a test driver program that calls each of the APIs and loops over the returned cursor.

Taking the plan for the straight SQL implementation first:

-------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                              | Name              | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
-------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                       |                   |      1 |        |     10 |00:00:00.01 |      14 |       |       |          |
|   1 |  SORT ORDER BY                         |                   |      1 |     35 |     10 |00:00:00.01 |      14 |  2048 |  2048 | 2048  (0)|
|   2 |   NESTED LOOPS OUTER                   |                   |      1 |     35 |     10 |00:00:00.01 |      14 |       |       |          |
|*  3 |    HASH JOIN OUTER                     |                   |      1 |      6 |      6 |00:00:00.01 |      10 |  1281K|  1281K|  544K (0)|
|   4 |     TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES         |      1 |      6 |      6 |00:00:00.01 |       2 |       |       |          |
|*  5 |      INDEX RANGE SCAN                  | EMP_DEPARTMENT_IX |      1 |      6 |      6 |00:00:00.01 |       1 |       |       |          |
|   6 |     VIEW                               | index$_join$_002  |      1 |    107 |    107 |00:00:00.01 |       8 |       |       |          |
|*  7 |      HASH JOIN                         |                   |      1 |        |    107 |00:00:00.01 |       8 |  1245K|  1245K| 1439K (0)|
|   8 |       INDEX FAST FULL SCAN             | EMP_NAME_IX       |      1 |    107 |    107 |00:00:00.01 |       4 |       |       |          |
|   9 |       INDEX FAST FULL SCAN             | EMP_EMP_ID_PK     |      1 |    107 |    107 |00:00:00.01 |       4 |       |       |          |
|  10 |    TABLE ACCESS BY INDEX ROWID BATCHED | EMPLOYEES         |      6 |      6 |      5 |00:00:00.01 |       4 |       |       |          |
|* 11 |     INDEX RANGE SCAN                   | EMP_MANAGER_IX    |      6 |      6 |      5 |00:00:00.01 |       3 |       |       |          |
-------------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   3 - access("M"."EMPLOYEE_ID"="E"."MANAGER_ID")
   5 - access("E"."DEPARTMENT_ID"=:B1)
   7 - access(ROWID=ROWID)
  11 - access("S"."MANAGER_ID"="E"."EMPLOYEE_ID")

This is a relatively simple plan for the single SQL statement, with 14 buffers read.

For the anti-pattern version there are thee SQL select statements, but we’ll ignore the plan for initial bulk collect SQL and consider the other two execution plans. First, the client API SQL:

---------------------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                           | Name             | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem |
---------------------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                    |                  |      1 |        |     10 |00:00:00.14 |    1195 |     41 |      1 |       |       |          |
|   1 |  SORT ORDER BY                      |                  |      1 |  48554 |     10 |00:00:00.14 |    1195 |     41 |      1 |  2048 |  2048 | 2048  (0)|
|*  2 |   HASH JOIN RIGHT OUTER             |                  |      1 |  48554 |     10 |00:00:00.14 |    1195 |     41 |      1 |  1368K|  1368K| 1322K (0)|
|   3 |    VIEW                             | index$_join$_002 |      1 |    107 |    106 |00:00:00.02 |       8 |     12 |      0 |       |       |          |
|*  4 |     HASH JOIN                       |                  |      1 |        |    106 |00:00:00.02 |       8 |     12 |      0 |  1519K|  1519K| 1575K (0)|
|   5 |      INDEX FAST FULL SCAN           | EMP_MANAGER_IX   |      1 |    107 |    106 |00:00:00.01 |       4 |      6 |      0 |       |       |          |
|   6 |      INDEX FAST FULL SCAN           | EMP_NAME_IX      |      1 |    107 |    107 |00:00:00.01 |       4 |      6 |      0 |       |       |          |
|   7 |    COLLECTION ITERATOR PICKLER FETCH| EMP_INFO_LIST    |      1 |   8168 |      6 |00:00:00.13 |    1187 |     29 |      1 |       |       |          |
---------------------------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("E"."MANAGER_ID"=SYS_OP_ATG(VALUE(KOKBF$),1,2,2))
   4 - access(ROWID=ROWID)

Note the extreme inaccuracy of the cardinality estimates at steps 1 and 2, which originate in the step 7 estimate of 8168, which is a database-level default for an array function call. This is exposing the general problem that joining to arrays prevents accurate cardinality estimates. A total of 1195 buffers were read.

Next, the plan for the inner API SQL:

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                       | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                                |                           |      1 |        |      6 |00:00:00.01 |      94 |      1 |      1 |       |       |          |
|   1 |  TEMP TABLE TRANSFORMATION                      |                           |      1 |        |      6 |00:00:00.01 |      94 |      1 |      1 |       |       |          |
|   2 |   LOAD AS SELECT                                |                           |      1 |        |      0 |00:00:00.01 |       6 |      0 |      1 |  1036K|  1036K|          |
|   3 |    SORT GROUP BY NOSORT                         |                           |      1 |      7 |      7 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|   4 |     TABLE ACCESS BY INDEX ROWID                 | JOB_HISTORY               |      1 |     10 |     10 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|   5 |      INDEX FULL SCAN                            | JHIST_EMP_ID_ST_DATE_PK   |      1 |     10 |     10 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|*  6 |   HASH JOIN OUTER                               |                           |      1 |      6 |      6 |00:00:00.01 |      85 |      1 |      0 |   735K|   735K|  506K (0)|
|*  7 |    HASH JOIN OUTER                              |                           |      1 |      6 |      6 |00:00:00.01 |      78 |      1 |      0 |   736K|   736K|  891K (0)|
|*  8 |     HASH JOIN OUTER                             |                           |      1 |      6 |      6 |00:00:00.01 |      75 |      1 |      0 |   737K|   737K|  550K (0)|
|*  9 |      HASH JOIN OUTER                            |                           |      1 |      6 |      6 |00:00:00.01 |      68 |      1 |      0 |   739K|   739K|  883K (0)|
|* 10 |       HASH JOIN OUTER                           |                           |      1 |      6 |      6 |00:00:00.01 |      62 |      0 |      0 |   740K|   740K|  903K (0)|
|* 11 |        HASH JOIN OUTER                          |                           |      1 |      6 |      6 |00:00:00.01 |      55 |      0 |      0 |   746K|   746K|  541K (0)|
|* 12 |         HASH JOIN OUTER                         |                           |      1 |      6 |      6 |00:00:00.01 |      48 |      0 |      0 |   754K|   754K|  534K (0)|
|* 13 |          HASH JOIN OUTER                        |                           |      1 |      6 |      6 |00:00:00.01 |      41 |      0 |      0 |   766K|   766K|  418K (0)|
|* 14 |           HASH JOIN OUTER                       |                           |      1 |      6 |      6 |00:00:00.01 |      33 |      0 |      0 |   773K|   773K|  414K (0)|
|  15 |            NESTED LOOPS OUTER                   |                           |      1 |      6 |      6 |00:00:00.01 |      26 |      0 |      0 |       |       |          |
|* 16 |             HASH JOIN OUTER                     |                           |      1 |      6 |      6 |00:00:00.01 |      23 |      0 |      0 |   833K|   833K|  414K (0)|
|* 17 |              HASH JOIN OUTER                    |                           |      1 |      6 |      6 |00:00:00.01 |      16 |      0 |      0 |   876K|   876K|  415K (0)|
|* 18 |               HASH JOIN                         |                           |      1 |      6 |      6 |00:00:00.01 |       9 |      0 |      0 |   905K|   905K| 1259K (0)|
|  19 |                MERGE JOIN                       |                           |      1 |    107 |    107 |00:00:00.01 |       9 |      0 |      0 |       |       |          |
|  20 |                 TABLE ACCESS BY INDEX ROWID     | JOBS                      |      1 |     19 |     19 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|  21 |                  INDEX FULL SCAN                | JOB_ID_PK                 |      1 |     19 |     19 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|* 22 |                 SORT JOIN                       |                           |     19 |    107 |    107 |00:00:00.01 |       7 |      0 |      0 | 18432 | 18432 |16384  (0)|
|  23 |                  TABLE ACCESS FULL              | EMPLOYEES                 |      1 |    107 |    107 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  24 |                COLLECTION ITERATOR PICKLER FETCH|                           |      1 |      6 |      6 |00:00:00.01 |       0 |      0 |      0 |       |       |          |
|  25 |               TABLE ACCESS FULL                 | DEPARTMENTS               |      1 |     27 |     27 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  26 |              TABLE ACCESS FULL                  | LOCATIONS                 |      1 |     23 |     23 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|* 27 |             INDEX UNIQUE SCAN                   | COUNTRY_C_ID_PK           |      6 |      1 |      6 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  28 |            TABLE ACCESS FULL                    | REGIONS                   |      1 |      4 |      4 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  29 |           VIEW                                  | index$_join$_024          |      1 |    107 |    107 |00:00:00.01 |       8 |      0 |      0 |       |       |          |
|* 30 |            HASH JOIN                            |                           |      1 |        |    107 |00:00:00.01 |       8 |      0 |      0 |  1245K|  1245K| 1550K (0)|
|  31 |             INDEX FAST FULL SCAN                | EMP_NAME_IX               |      1 |    107 |    107 |00:00:00.01 |       4 |      0 |      0 |       |       |          |
|  32 |             INDEX FAST FULL SCAN                | EMP_EMP_ID_PK             |      1 |    107 |    107 |00:00:00.01 |       4 |      0 |      0 |       |       |          |
|  33 |          TABLE ACCESS FULL                      | EMPLOYEES                 |      1 |    107 |    107 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  34 |         TABLE ACCESS FULL                       | JOBS                      |      1 |     19 |     19 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  35 |        VIEW                                     |                           |      1 |     18 |     19 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  36 |         HASH GROUP BY                           |                           |      1 |     18 |     19 |00:00:00.01 |       7 |      0 |      0 |  1558K|  1558K| 1185K (0)|
|  37 |          TABLE ACCESS FULL                      | EMPLOYEES                 |      1 |    107 |    107 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  38 |       VIEW                                      |                           |      1 |      7 |      7 |00:00:00.01 |       6 |      1 |      0 |       |       |          |
|  39 |        TABLE ACCESS FULL                        | SYS_TEMP_0FD9D6604_5813E5 |      1 |      7 |      7 |00:00:00.01 |       6 |      1 |      0 |       |       |          |
|  40 |      TABLE ACCESS FULL                          | JOBS                      |      1 |     19 |     19 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  41 |     VIEW                                        |                           |      1 |      7 |      7 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  42 |      TABLE ACCESS FULL                          | SYS_TEMP_0FD9D6604_5813E5 |      1 |      7 |      7 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  43 |    TABLE ACCESS FULL                            | JOBS                      |      1 |     19 |     19 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   6 - access("JOB_P"."JOB_ID"="JHS"."JOB_ID")
   7 - access("JHS"."EMPLOYEE_ID"="EMP"."EMPLOYEE_ID")
   8 - access("JOB_MP"."JOB_ID"="JHS_M"."JOB_ID")
   9 - access("JHS_M"."EMPLOYEE_ID"="EMP"."EMPLOYEE_ID")
  10 - access("SUB"."MANAGER_ID"="EMP"."EMPLOYEE_ID")
  11 - access("JOB_M"."JOB_ID"="EMP_M"."JOB_ID")
  12 - access("EMP_M"."EMPLOYEE_ID"="EMP"."MANAGER_ID")
  13 - access("EMP_DM"."EMPLOYEE_ID"="DEP"."MANAGER_ID")
  14 - access("REG"."REGION_ID"="COU"."REGION_ID")
  16 - access("LOC"."LOCATION_ID"="DEP"."LOCATION_ID")
  17 - access("DEP"."DEPARTMENT_ID"="EMP"."DEPARTMENT_ID")
  18 - access("EMP"."EMPLOYEE_ID"=VALUE(KOKBF$))
  22 - access("JOB"."JOB_ID"="EMP"."JOB_ID")
       filter("JOB"."JOB_ID"="EMP"."JOB_ID")
  27 - access("COU"."COUNTRY_ID"="LOC"."COUNTRY_ID")
  30 - access(ROWID=ROWID)

This is pretty complex. We can’t pretend to do a real performance analysis on such a small database (107 employees), but the potential for performance problems in real cases is clear. It’s important to understand that any performance analysis on the client API has to take into account not just the client code, but the full complexity of the centralised SQL, so any apparent simplification from using it is to a great extent illusory.

Database Inter-Schema Data Access Layer Anti-pattern

There is a variant of the anti-pattern above in which data access layers are used to retrieve data across schema boundaries. This variant suffers from exactly the same problems as the first of course, and should equally be avoided.

Views as SQL Building Blocks Anti-Pattern

The same ideas as are behind the APIs as SQL Building Blocks Anti-Pattern can also be implemented through views, and in fact this variant form of the anti-pattern has been around longer I think. We can illustrate it on the same example, by creating a view instead of the central API cursor.

CREATE OR REPLACE VIEW emp_ks_v (
       employee_id,
       name,
       job_title,
       job_title_p,
       name_mgr,
       job_title_mgr,
       job_title_mgr_p,
       n_sub,
       department_id,
       department_name,
       name_d_mgr,
       street_address,
       country_name,
       region_name) AS
WITH jhs_f AS (
SELECT employee_id,
     Max (job_id) KEEP (DENSE_RANK LAST ORDER BY end_date) job_id
  FROM hr.job_history
 GROUP BY employee_id
), sub AS (
SELECT manager_id,
       Count(*)                  n_sub
  FROM hr.employees
 GROUP BY manager_id
)
SELECT emp.employee_id,
       emp.last_name || ', ' || emp.first_name,
       job.job_title,
       job_p.job_title,
       emp_m.last_name || ', ' || emp_m.first_name,
       job_m.job_title,
       job_mp.job_title,
       sub.n_sub,
       dep.department_id,
       dep.department_name,
       emp_dm.last_name || ', ' || emp_dm.first_name,
       loc.street_address,
       cou.country_name,
       reg.region_name
  FROM hr.employees              emp
  JOIN hr.jobs                   job
    ON job.job_id                = emp.job_id
  LEFT JOIN jhs_f                jhs
    ON jhs.employee_id           = emp.employee_id
  LEFT JOIN hr.jobs              job_p
    ON job_p.job_id              = jhs.job_id
  LEFT JOIN hr.employees         emp_m
    ON emp_m.employee_id         = emp.manager_id
  LEFT JOIN hr.jobs              job_m
    ON job_m.job_id              = emp_m.job_id
  LEFT JOIN jhs_f                jhs_m
    ON jhs_m.employee_id         = emp.employee_id
  LEFT JOIN hr.jobs              job_mp
    ON job_mp.job_id             = jhs_m.job_id
  LEFT JOIN sub
    ON sub.manager_id            = emp.employee_id
  LEFT JOIN hr.departments       dep
    ON dep.department_id         = emp.department_id
  LEFT JOIN hr.employees         emp_dm
    ON emp_dm.employee_id        = dep.manager_id
  LEFT JOIN hr.locations         loc
    ON loc.location_id           = dep.location_id
  LEFT JOIN hr.countries         cou
    ON cou.country_id            = loc.country_id
  LEFT JOIN hr.regions           reg
    ON reg.region_id             = cou.region_id

The view can then be called to get the employee details for example for a given department, thus:

SELECT t.name,
       t.name_mgr,
       CASE WHEN e.last_name IS NOT NULL THEN e.last_name || ', ' || e.first_name END name_sub
  FROM emp_ks_v t
  LEFT JOIN hr.employees e
    ON e.manager_id = t.employee_id
 WHERE t.department_id = 30
 ORDER BY 1, 2, 3

This is actually quite a lot better than the API-based approach as it’s much simpler, avoiding the need for object arrays, and allowing use simply by joining. Let’s look at the execution plan though (we’ll just run the query rather than put it into a client API returning a reference cursor):

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                    | Name                      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  | Writes |  OMem |  1Mem | Used-Mem |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                             |                           |      1 |        |     10 |00:00:00.01 |      42 |      1 |      1 |       |       |          |
|   1 |  SORT ORDER BY                               |                           |      1 |     35 |     10 |00:00:00.01 |      42 |      1 |      1 |  2048 |  2048 | 2048  (0)|
|   2 |   NESTED LOOPS OUTER                         |                           |      1 |     35 |     10 |00:00:00.01 |      42 |      1 |      1 |       |       |          |
|   3 |    VIEW                                      | EMP_KS_V                  |      1 |      6 |      6 |00:00:00.01 |      38 |      1 |      1 |       |       |          |
|   4 |     TEMP TABLE TRANSFORMATION                |                           |      1 |        |      6 |00:00:00.01 |      38 |      1 |      1 |       |       |          |
|   5 |      LOAD AS SELECT                          |                           |      1 |        |      0 |00:00:00.01 |       6 |      0 |      1 |  1036K|  1036K|          |
|   6 |       SORT GROUP BY NOSORT                   |                           |      1 |      7 |      7 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|   7 |        TABLE ACCESS BY INDEX ROWID           | JOB_HISTORY               |      1 |     10 |     10 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|   8 |         INDEX FULL SCAN                      | JHIST_EMP_ID_ST_DATE_PK   |      1 |     10 |     10 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|*  9 |      HASH JOIN OUTER                         |                           |      1 |      6 |      6 |00:00:00.01 |      29 |      1 |      0 |   883K|   883K|  517K (0)|
|* 10 |       HASH JOIN OUTER                        |                           |      1 |      6 |      6 |00:00:00.01 |      21 |      1 |      0 |   890K|   890K|  857K (0)|
|* 11 |        HASH JOIN OUTER                       |                           |      1 |      6 |      6 |00:00:00.01 |      14 |      1 |      0 |   895K|   895K|  886K (0)|
|* 12 |         HASH JOIN OUTER                      |                           |      1 |      6 |      6 |00:00:00.01 |      11 |      1 |      0 |   905K|   905K|  893K (0)|
|  13 |          NESTED LOOPS                        |                           |      1 |      6 |      6 |00:00:00.01 |       5 |      0 |      0 |       |       |          |
|  14 |           NESTED LOOPS OUTER                 |                           |      1 |      1 |      1 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  15 |            TABLE ACCESS BY INDEX ROWID       | DEPARTMENTS               |      1 |      1 |      1 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|* 16 |             INDEX UNIQUE SCAN                | DEPT_ID_PK                |      1 |      1 |      1 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|* 17 |            INDEX UNIQUE SCAN                 | LOC_ID_PK                 |      1 |      1 |      1 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|  18 |           TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES                 |      1 |      6 |      6 |00:00:00.01 |       2 |      0 |      0 |       |       |          |
|* 19 |            INDEX RANGE SCAN                  | EMP_DEPARTMENT_IX         |      1 |      6 |      6 |00:00:00.01 |       1 |      0 |      0 |       |       |          |
|  20 |          VIEW                                |                           |      1 |      7 |      7 |00:00:00.01 |       6 |      1 |      0 |       |       |          |
|  21 |           TABLE ACCESS FULL                  | SYS_TEMP_0FD9D6606_5813E5 |      1 |      7 |      7 |00:00:00.01 |       6 |      1 |      0 |       |       |          |
|  22 |         VIEW                                 |                           |      1 |      7 |      7 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  23 |          TABLE ACCESS FULL                   | SYS_TEMP_0FD9D6606_5813E5 |      1 |      7 |      7 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
|  24 |        VIEW                                  |                           |      1 |     18 |     19 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  25 |         HASH GROUP BY                        |                           |      1 |     18 |     19 |00:00:00.01 |       7 |      0 |      0 |  1558K|  1558K| 1211K (0)|
|  26 |          TABLE ACCESS FULL                   | EMPLOYEES                 |      1 |    107 |    107 |00:00:00.01 |       7 |      0 |      0 |       |       |          |
|  27 |       VIEW                                   | index$_join$_013          |      1 |    107 |    107 |00:00:00.01 |       8 |      0 |      0 |       |       |          |
|* 28 |        HASH JOIN                             |                           |      1 |        |    107 |00:00:00.01 |       8 |      0 |      0 |  1245K|  1245K| 1410K (0)|
|  29 |         INDEX FAST FULL SCAN                 | EMP_NAME_IX               |      1 |    107 |    107 |00:00:00.01 |       4 |      0 |      0 |       |       |          |
|  30 |         INDEX FAST FULL SCAN                 | EMP_EMP_ID_PK             |      1 |    107 |    107 |00:00:00.01 |       4 |      0 |      0 |       |       |          |
|  31 |    TABLE ACCESS BY INDEX ROWID BATCHED       | EMPLOYEES                 |      6 |      6 |      5 |00:00:00.01 |       4 |      0 |      0 |       |       |          |
|* 32 |     INDEX RANGE SCAN                         | EMP_MANAGER_IX            |      6 |      6 |      5 |00:00:00.01 |       3 |      0 |      0 |       |       |          |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   9 - access("EMP_M"."EMPLOYEE_ID"="EMP"."MANAGER_ID")
  10 - access("SUB"."MANAGER_ID"="EMP"."EMPLOYEE_ID")
  11 - access("JHS_M"."EMPLOYEE_ID"="EMP"."EMPLOYEE_ID")
  12 - access("JHS"."EMPLOYEE_ID"="EMP"."EMPLOYEE_ID")
  16 - access("DEP"."DEPARTMENT_ID"=30)
  17 - access("LOC"."LOCATION_ID"="DEP"."LOCATION_ID")
  19 - access("EMP"."DEPARTMENT_ID"=30)
  28 - access(ROWID=ROWID)
  32 - access("E"."MANAGER_ID"="T"."EMPLOYEE_ID")

There is only one SQL statement, and the plan is better too, with better cardinality estimates owing to the absence of array processing, and with 42 buffers read. It’s still a bad idea though because the client caller would be executing far more complex SQL than is required and as before performance analysis on the client requires the full complexity of the ‘centralised’ SQL to be included. Using complex views as SQL building blocks is generally considered to have poor performance characteristics.

SQL Modularity: Other Design Options

Splitting Up Long SQL Statements

Oracle’s Cost Based Optimiser (CBO) has been greatly enhanced since it’s introduction in v7, but remains imperfect. The combinatorial nature of the problem that it tries to solve suggests that there will always be larger queries where it makes a bad choice of plan. In some cases splitting a large query into smaller ones and using temporary tables to join them can give better performance. This may arise from new indexing options for the CBO, or by dynamic sampling capabilities on the temporary tables, or just from the CBO algorithms happening to work better on the divided queries.

It’s important to understand though that any such splitting should be done purely on performance grounds: the splitting increases the code complexity and breaking a sequence of declarative joins into several subsequences is not comparable with standard modularisation of programs into subprograms.

Long SQL statements are not necessarily problematic, but obviously should only be as long as necessary, and avoiding the design anti-patterns mentioned helps to ensure this.

Simple Views

Simple views, without joins, are often used in areas such as access control; for example Oracle Applications multi-org features (upto release 11) generally involve transactional tables being referenced by such simple views. These do not cause the performance problems seen with the complex building-block views anti-pattern.

Data Access Layers for Back-end Programs

It is quite possible to use a similar approach for database programs as for front-end programs in terms of a data access layer. One could adopt a standard that stand-alone database PL/SQL programs should access data through packaged APIs rather than directly. This obviously does not have the performance or language advantages of the design pattern in relation to Java front-ends for example, but it may in some circumstances be a preferred method of code organisation. It is therefore not an anti-pattern of the kind we have considered – as long as it is for stand-alone programs only.

Conclusions

The main aim of this article has been to distinguish between good approaches to modularity in SQL (patterns) and bad ones (anti-patterns) based on personal experience of seeing both types applied.

  • The Data Access Layer design pattern is an excellent approach for client applications developed in Java, .net etc. to access a database
  • Using Data Access Layers for internal access within a database is a classic anti-pattern leading to overcomplication and performance problems
  • A good design pattern used in an inappropriate context can become an anti-pattern

SQL Modularity Code






 

Design Patterns for Extracting Relational Data from XML

I recently replicated a very complicated Informatica data load from XML files into two Oracle staging tables in a much simpler way using just three SQL insert statements. I was able to load over 13,000 XML files into the tables in around 3 minutes on a windows PC running Oracle 11.2 XE, populating about 2 million records. This article will focus on the general design patterns involved in selecting relational data from XML using SQL in Oracle 11.2.

XML Selection Scenarios

The hierarchical structure of XML files means that the data may be stored at various different levels, but a relational SELECT statement returns data in a flat format. One way of converting between hierarchical and flat formats is to have separate SELECT statements for each level of data, and have any additional post-processing performed after inserting into staging tables. However, it is also possible to flatten the hierarchy within a single SELECT query clause in the case of a strict linear hierarchy.

It is instructive to consider the general case where any number of entities can occur within a linear hierarchy. If we understand how to deal with the case of two entities in a master-detail relationship, along with global values, then we will know how to deal with the general case, and so I will construct just such a test example.

XML Test Data

Here is the test XML file

<elem-0-body>
    <elem-1-global attr-1-global="id_1_global">
        <field-1-global>val_global</field-1-global>
    </elem-1-global>
    <list-1-master>
        <elem-1-master>
            <field-1-master>val_master_m1</field-1-master>
            <list-2-detail>
                  <elem-2-detail>
                       <field-2-detail>val_detail_m1_d1</field-2-detail>
                  </elem-2-detail>
                  <elem-2-detail>
                       <field-2-detail>val_detail_m1_d2</field-2-detail>
                  </elem-2-detail>
            </list-2-detail>
        </elem-1-master>
        <elem-1-master>
            <field-1-master>val_master_m2</field-1-master>
            <list-2-detail>
                  <elem-2-detail>
                       <field-2-detail>val_detail_m2_d1</field-2-detail>
                  </elem-2-detail>
                  <elem-2-detail>
                       <field-2-detail>val_detail_m2_d2</field-2-detail>
                  </elem-2-detail>
            </list-2-detail>
        </elem-1-master>
        <elem-1-master>
            <field-1-master>val_master_m3</field-1-master>
        </elem-1-master>
    </list-1-master>
</elem-0-body>

The file represents data in a master-detail relationship, plus data that are global to the file. As can be seen, there is a global entity, elem-1-global, that occurs exactly once, a master entity, elem-1-master, that may occur multiple times and that contains a child entity, elem-2-detail, that may occur multiple times. Each entity has been assigned a single sample field. One entity has an XML attribute.

There are three master records, having two, two and zero detail records respectively. We will filter out one of the second master’s detail records.

In an article from January 2012, Data Modelling of XML SOAP Documents, I introduced my own diagram notation for hierarchical data structures, and re-use it below, showing the links to entities in relational form. I have added rounded corners to distinguish attributes from fields.

XMLSQL

SQL
Reading the XML File

CREATE TABLE xml_design_pattern (
       xml_field   XMLTYPE) 
       XMLTYPE COLUMN xml_field STORE AS BINARY XML
/
INSERT INTO xml_design_pattern VALUES (
       XMLTYPE (BFilename ('BRENDAN_IN_DIR', 'DESIGN_PATTERN.xml'),                      
                           NLS_Charset_id ('AL32UTF8'))
)
/

Unfiltered, Outer Join on Detail

SELECT /*+ GATHER_PLAN_STATISTICS XMLOJA */ 
       x1.global_id, x1.global_val, x2.master, x3.detail
  FROM xml_design_pattern t,
     XMLTable ('/elem-0-body'
                   PASSING t.xml_field
                   COLUMNS global_id           VARCHAR2(100) PATH 'elem-1-global/@attr-1-global',
                           global_val          VARCHAR2(100) PATH 'elem-1-global/field-1-global',
                           l2_xml              XMLTYPE       PATH 'list-1-master') x1,
     XMLTable ('/list-1-master/elem-1-master'
                   PASSING x1.l2_xml
                   COLUMNS master              VARCHAR2(100) PATH 'field-1-master',
                           l3_xml              XMLTYPE       PATH 'list-2-detail') x2,
     XMLTable ('/list-2-detail/elem-2-detail'
                   PASSING x2.l3_xml
                   COLUMNS detail              VARCHAR2(100) PATH 'field-2-detail') (+) x3
ORDER BY 1, 2, 3, 4

Output

GLOBAL_ID       GLOBAL_VAL      MASTER          DETAIL
--------------- --------------- --------------- --------------------
id_1_global     val_global      val_master_m1
id_1_global     val_global      val_master_m2
id_1_global     val_global      val_master_m3

The result shows that none of the detail records have been joined, while the expected result would be that all detail records would be joined with null detail only for m3.

Filtered, Outer Join on Detail

SELECT x1.global_id, x1.global_val, x2.master, x3.detail
  FROM xml_design_pattern t,
     XMLTable ('/elem-0-body'
                   PASSING t.xml_field
                   COLUMNS global_id           VARCHAR2(100) PATH 'elem-1-global/@attr-1-global',
                           global_val          VARCHAR2(100) PATH 'elem-1-global/field-1-global',
                           l2_xml              XMLTYPE       PATH 'list-1-master') x1,
     XMLTable ('/list-1-master/elem-1-master'
                   PASSING x1.l2_xml
                   COLUMNS master              VARCHAR2(100) PATH 'field-1-master',
                           l3_xml              XMLTYPE       PATH 'list-2-detail') x2,
     XMLTable ('/list-2-detail/elem-2-detail[field-2-detail != "val_detail_m2_d1"]'
                   PASSING x2.l3_xml
                   COLUMNS detail              VARCHAR2(100) PATH 'field-2-detail') (+) x3
ORDER BY 1, 2, 3, 4

Output

no rows selected

The result shows that no records have been returned, while the expected result would be that all master records would be returned, with detail records joined for all except the filtered out detail, with null detail only for m3.

Unfiltered, Outer Joins on all
SQL

SELECT x1.global_id, x1.global_val, x2.master, x3.detail
  FROM xml_design_pattern t,
     XMLTable ('/elem-0-body'
                   PASSING t.xml_field
                   COLUMNS global_id           VARCHAR2(100) PATH 'elem-1-global/@attr-1-global',
                           global_val          VARCHAR2(100) PATH 'elem-1-global/field-1-global',
                           l2_xml              XMLTYPE       PATH 'list-1-master') (+) x1,
    XMLTable ('/list-1-master/elem-1-master'
                   PASSING x1.l2_xml
                   COLUMNS master              VARCHAR2(100) PATH 'field-1-master',
                           l3_xml              XMLTYPE       PATH 'list-2-detail') (+) x2,
     XMLTable ('/list-2-detail/elem-2-detail'
                   PASSING x2.l3_xml
                   COLUMNS detail              VARCHAR2(100) PATH 'field-2-detail') (+) x3
ORDER BY 1, 2, 3, 4

Output

GLOBAL_ID       GLOBAL_VAL      MASTER          DETAIL
--------------- --------------- --------------- --------------------
id_1_global     val_global      val_master_m1   val_detail_m1_d1
id_1_global     val_global      val_master_m1   val_detail_m1_d2
id_1_global     val_global      val_master_m2   val_detail_m2_d1
id_1_global     val_global      val_master_m2   val_detail_m2_d2
id_1_global     val_global      val_master_m3

The result shows that all records are returned, as expected.

Unfiltered, Inner Joins
The attached file shows that, without the outer joins, records are returned only for the first two master records that have detail records, as expected.

Filtered, Outer Joins on all

SQL

SELECT x1.global_id, x1.global_val, x2.master, x3.detail
  FROM xml_design_pattern t,
     XMLTable ('/elem-0-body'
                   PASSING t.xml_field
                   COLUMNS global_id           VARCHAR2(100) PATH 'elem-1-global/@attr-1-global',
                           global_val          VARCHAR2(100) PATH 'elem-1-global/field-1-global',
                           l2_xml              XMLTYPE       PATH 'list-1-master') (+) x1,
     XMLTable ('/list-1-master/elem-1-master'
                   PASSING x1.l2_xml
                   COLUMNS master              VARCHAR2(100) PATH 'field-1-master',
                           l3_xml              XMLTYPE       PATH 'list-2-detail') (+) x2,
     XMLTable ('/list-2-detail/elem-2-detail[field-2-detail != "val_detail_m2_d1"]'
                   PASSING x2.l3_xml
                   COLUMNS detail              VARCHAR2(100) PATH 'field-2-detail') (+) x3
ORDER BY 1, 2, 3, 4

Output

GLOBAL_ID       GLOBAL_VAL      MASTER          DETAIL
--------------- --------------- --------------- --------------------
id_1_global     val_global      val_master_m1   val_detail_m1_d1
id_1_global     val_global      val_master_m1   val_detail_m1_d2
id_1_global     val_global      val_master_m2   val_detail_m2_d2
id_1_global     val_global      val_master_m3

The result shows that, with all joins outer joins, all records are returned except for the filtered-out detail record, as expected.

Filtered, Ansi Outer Joins on all

SQL

SELECT x1.global_id, x1.global_val, x2.master, x3.detail
  FROM xml_design_pattern t
    LEFT JOIN
     XMLTable ('/elem-0-body'
                   PASSING t.xml_field
                   COLUMNS global_id           VARCHAR2(100) PATH 'elem-1-global/@attr-1-global',
                           global_val          VARCHAR2(100) PATH 'elem-1-global/field-1-global',
                           l2_xml              XMLTYPE       PATH 'list-1-master') x1 ON 1=1
    LEFT JOIN
     XMLTable ('/list-1-master/elem-1-master'
                   PASSING x1.l2_xml
                   COLUMNS master              VARCHAR2(100) PATH 'field-1-master',
                           l3_xml              XMLTYPE       PATH 'list-2-detail') x2 ON 1=1
    LEFT JOIN
     XMLTable ('/list-2-detail/elem-2-detail[field-2-detail != "val_detail_m2_d1"]'
                   PASSING x2.l3_xml
                   COLUMNS detail              VARCHAR2(100) PATH 'field-2-detail') x3 ON 1=1
ORDER BY 1, 2, 3, 4

Output: The result in the attached file shows that, with all joins Ansi outer joins, all records are returned except for the filtered-out detail record, as expected.

Notes on SQL

XMLTable and XPath Syntax
Thh XMLTable function represents a rowset retrieved from an XML fragment. The first string within the call represents an XPath expression for the root element defining the rowset in the XML fragment passed. The number of occurrences in the fragment is the rowset cardinality.

The PASSING clause passes in the XMLTYPE field from a prior table or XMLTable instance from which the XML fragment is extracted. For a detail entity, the field will be passed from a master record in a a prior instance, and the instance order matters.

The COLUMNS clause defines the columns that can be included in the select list, and specifies a field for each column by an XPath expression. The expression is relative to the XMLTable root and must specify a single element instance.

XQuery Error (ORA-19279)
A common error in development is:
‘ORA-19279: XPTY0004 – XQuery dynamic type mismatch: expected singleton sequence – got multi-item sequence’
The error occurs when the XPath expression for a column selects more than one instance.

Filtering
Filtering of the XNL elements is effected using standard XPath notation, with conditions placed in square brackets after the element name.

Fields and Attributes
Elements may contain attributes, which are denoted in standard XML syntax by preceding the attribute name with ‘@’. Further details on XML syntax for Oracle, including additional features such as namespaces, can be found in Oracle XML DB Developer’s Guide

Ansi and Oracle Join Syntaxes
Oracle introduced the Ansi standard join syntax in version 9, and it is generally to be preferred to its older proprietary syntax. However, this is debatable in the special case of SQL incorporating XMLTable because joining occurs in a non-standard way within the PASSING and other clauses of XMLTable, and not in the mandatory (except for cross joins) ON clause. In my examples, I have had to join ON 1=1 as a work-around.

Outer Joins
The examples show that outer-joining only the detail table does not work correctly. This appears to be a bug in Oracle 11.2. We have worked around it by outer-joining all tables.

Also note that the outer-join (+) needs to be after the table instance, unlike in standard SQL, where it goes after the join columns.

XMLTYPE Storage Clause
The XMLTYPE column in the table used to load the XML file has a storage clause that can specify either CLOB or BINARY XML (and maybe others) as the storage mode. It is vital for performance to choose BINARY XML if the table is going to be queried using XMLTable.

XPlan Statistics
I included calls to DBMS_XPlan in some of the queries in the attached files, and it can be seen that the CBO cardinalities are extermely inaccurate. This is perhaps not surprising as there is only one record in the table, which is exploded within the SQL, and the CBO is obviously not designed to optimise this.
SQL XML Design Patterns Files







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PL/SQL Profiling 1 – Overview

This article describes at overview level the use of three PL/SQL profiling methods on two example program structures. The examples are designed to illustrate profiler behaviour over as many different scenarios as possible, while keeping the examples as simple as possible. The first two profilers are Oracle’s hierarchical and flat profiling tools, while the third is the author’s own custom code timing package, Timer_Set. It’s based on an article published in March 2013 on the hierarchical profiler and updated shortly thereafter with the inclusion of Oracle’s older flat profiler and of custom code timing. In June 2020 installation and source code were put onto GitHub, and the article was restructured into an overview article with the detail on the three profiling methods as separate articles. This is the overview and the links to the other three detailed articles are:

PL/SQL Profiling 2: Hierarchical Profiler
PL/SQL Profiling 3: Flat Profiler
PL/SQL Profiling 4: Custom Code Timing

All source code, including installation scripts, and short screen recordings, is available on GitHub:
GitHub: Oracle PL/SQL Profiling

Twitter thread with recordings attached

Scenarios

This phrase is often attributed to Albert Einstein, although the attribution is apparently questionable: Everything Should Be Made as Simple as Possible, But Not Simpler. In any case it’s not a bad approach to follow in testing, whether in ‘closed’ unit testing, where there are known expected results, or in ‘open’ exploratory tesing where we are investigating application behaviour, as here. I discussed the issue of test scenario coverage in a 2018 presentation: Database API Viewed As A Mathematical Function: Insights into Testing – OUG Ireland Conference 2018.

At the time of the original article in 2013, I looked at Oracle’s hierarchical profiler tool with a view to using it in an upcoming project. In order to understand the tool properly, I felt it would be a good idea to start by using it to profile a test program that would be as simple as possible while covering as wide a range of scenarios as possible. I then added a second simple program to cover some additional scenarios, and also tested the use of Oracle’s flat profiler, and of custom code timing using my own Timer_Set package.

This first article explains the scenarios covered by the two example programs, briefly summarises the results of each method on the second and simpler example, and finishes with a comparison of the features of the three methods. Fuller discussions of each profiling method, with application to both example programs can be found in the specific articles linked above.

The test programs consist of two driving scripts that have one or more PL/SQL blocks with calls to various system and custom packaged procedures and functions.

Example 1: General

The test program for example 1 covers the following scenarios:

  • Multiple root calls
  • Recursive procedure calls (procedure calling itself: R_Calls_R)
  • Mutually recursive procedure calls (procedures call each other: A_Calls_B and B_Calls_A)
  • Program unit called by multiple program units (child with multiple parents: Database Function)
  • Procedure ‘inlined’ within PL/SQL (Rest_a_While)
  • Type body code (Object Constructor)
  • Static SQL from Sqlplus (SELECT 1)
  • Static SQL within PL/SQL (SELECT 2)
  • Dynamic SQL within PL/SQL (SELECT 3)
  • CPU-consuming sleep (Rest_a_While)

Call Structure Diagram

Example 2: Sleep

The test program for example 2 covers the following scenarios:

  • External system program unit (DBMS_Lock.Sleep in SYS schema)
  • External custom program unit (Utils.Sleep in LIB schema)
  • Sleep that uses no CPU time, only elapsed time (DBMS_Lock.Sleep)
  • Sleep that uses CPU time (Utils.Sleep)
  • Trigger code (Before Insert trigger SLEEP_BI)
  • Two calls from the same parent (DBMS_Lock.Sleep from anonymous block twice)
  • Program unit as root call and as child call (DBMS_Lock.Sleep)

Call Structure Diagram

Profiling Overview

In this section I show the data model and a summary of the results of each method on the second and simpler example. For fuller discussion with both examples see the specific articles for each method.

Hierarchical Profiler Overview

PL/SQL Profiling 2: Hierarchical Profiler

Hierarchical Profiler: Data Model

Network Diagram for Example 2: Sleep


The extended network diagram with function names and times included shows 9 seconds of time used by the SLEEP function in two root-level calls and 1 second in a third call as the child of the custom Utils Sleep function.

Network Query Output for Example 2: Sleep
Function tree                   Sy Owner Module           Inst.  Subtree MicroS Function MicroS Calls Row
------------------------------ --- ----- ---------------- ------ -------------- --------------- ----- ---
SLEEP                            8 SYS   DBMS_LOCK        1 of 2        8998195         8998195     2   1
__static_sql_exec_line6         10                                      2005266            5660     1   2
  __plsql_vm                     1                                      1999606               4     1   3
    SLEEP_BI                     3 APP   SLEEP_BI                       1999602             327     1   4
      SLEEP                      4 LIB   UTILS                          1999268          999837     1   5
        SLEEP                    8 SYS   DBMS_LOCK        2 of 2         999431          999431     1   6
      __pkg_init                 6 LIB   UTILS                                5               5     1   7
      __pkg_init                 5 LIB   UTILS                                2               2     1   8
STOP_PROFILING                   2 APP   HPROF_UTILS                         87              87     1   9
  STOP_PROFILING                 7 SYS   DBMS_HPROF                           0               0     1  10
__static_sql_exec_line700       11 SYS   DBMS_HPROF                          77              77     1  11
__pkg_init                       9 SYS   DBMS_LOCK                            3               3     1  12

12 rows selected.

Flat Profiler Overview

PL/SQL Profiling 3: Flat Profiler

Flat Profiler: Data Model

Flat Profiler: Results Extract

The records produced in the functions table, PLSQL_PROFILER_DATA, are listed below in order of unit name, then unit number and line number.

Seconds  Microsecs   Min S   Max S    Calls Unit                 Unit# Type            Line# Line Text
------- ---------- ------- ------- -------- -------------------- ----- --------------- ----- ------------------------------------------------------------------
  0.000          0   0.000   0.000        0 <anonymous>              1 ANONYMOUS BLOCK     1
  0.000          2   0.000   0.000        0 <anonymous>              1 ANONYMOUS BLOCK     5
  0.000         23   0.000   0.000        1 <anonymous>              1 ANONYMOUS BLOCK     9
  0.000         10   0.000   0.000        1 <anonymous>              1 ANONYMOUS BLOCK    11
  0.000        199   0.000   0.000        1 <anonymous>              1 ANONYMOUS BLOCK    13
  0.000          3   0.000   0.000        1 <anonymous>              1 ANONYMOUS BLOCK    15
  0.000          5   0.000   0.000        1 <anonymous>              1 ANONYMOUS BLOCK    17
  0.000          0   0.000   0.000        0 <anonymous>              1 ANONYMOUS BLOCK    19
  0.000          2   0.000   0.000        0 SLEEP_BI                 2 TRIGGER             1 TRIGGER sleep_bi
  0.000         20   0.000   0.000        1 SLEEP_BI                 2 TRIGGER             2 BEFORE INSERT
  0.000          1   0.000   0.000        1 SLEEP_BI                 2 TRIGGER             4 FOR EACH ROW

11 rows selected.

Timer Set Overview

PL/SQL Profiling 4: Custom Code Timing

Timer Set: Data Model

Timer Set: Example of Call Structure

Timer Set: Results Extract

Timer Set: Profiling DBMS_Lock.Sleep, Constructed at 27 Jun 2020 07:53:00, written at 07:53:11
==============================================================================================
Timer                                       Elapsed         CPU       Calls       Ela/Call       CPU/Call
---------------------------------------  ----------  ----------  ----------  -------------  -------------
3 second sleep                                 3.00        0.00           1        3.00000        0.00000
INSERT INTO trigger_tab VALUES (2, 0.5)        2.00        1.00           1        1.99900        1.00000
6 second sleep                                 6.00        0.00           1        6.00000        0.00000
(Other)                                        0.00        0.00           1        0.00000        0.00000
---------------------------------------  ----------  ----------  ----------  -------------  -------------
Total                                         11.00        1.00           4        2.74975        0.25000
---------------------------------------  ----------  ----------  ----------  -------------  -------------
[Timer timed (per call in ms): Elapsed: 0.00980, CPU: 0.00980]

Conclusion

Oracle’s hierarchical profiler and flat profiler offer different ways of profiling elapsed time and call usage for PL/SQL programs. After initial setup they perform profiling of code automatically between start and stop API calls, without the need to alter the code being profiled. The level of profiling and organisation of data collection are not configurable. They are intended for one-off use or occasional trouble-shooting, rather than as ongoing instrumentation.

Custom code timing requires insertion of timing code within the profiled code, but offers more flexibility in terms of detail gathered, and also captures CPU time usage. Use of a centralized API such as Timer_Set helps to minimize the code required. It can be used both for trouble-shooting and for ongoing production instrumentation.

Features Summary