BACKGROUND 
     The invention relates generally to computer database systems and more particularly to processing referential integrity constraint violations resulting in cascading deletes during database load operations. The subject matter of the invention is generally related to the following jointly owned and co-pending patent application: “Constraint Processing” by Christopher Y. Blaicher, Kerry C. Tenberg and Randol K. Bright (Ser. No. 10/871,160) which is incorporated herein by reference in its entirety. 
     Virtually all modern DataBase Management Systems (“DBMS”) provide mechanisms that permit users to constrain the value of one database entity based on the value or existence of another database entity. One common constraint type is the referential constraint. Referential constraints require that a value referred to by one database entity is associated with an existing entity in the database. In the context of the Structured Query Language (“SQL”), referential constraints are implemented through the use of Foreign Keys (“FK”), wherein a database entity&#39;s FK value must equate to the Primary Key (“PK”) value of another, existing, database entity. 
     In general, constraint processing is preformed during database update and load operations and may be handled in accordance with one of three policies. In the first, deletion of a referenced entity is prohibited. This policy is often referred to as the “Reject Violating Modifications” policy. In the second, if a referenced entity is deleted or determined to be invalid then all entities that reference it are also deleted (or marked invalid). This policy is often referred to as the “Cascading” policy. In the third, FK values referencing a deleted or invalid PK value are set to NULL. This policy is often referred to as the “Set-Null” policy. 
     Consider, for example, a relational database entity (a table) such as that shown in Table 1. Under the Reject Violating Modifications policy, the entry associated with Employee-1 (row 1) could not be deleted because at least one other entry&#39;s FK references the entry. In the example of Table 1, in fact, two other entries (rows 2 and 3) are referentially constrained to the entry associated with Employee-1. Under the Cascading policy, if the entry associated with Employee-1 (row 1) was deleted or determined to be invalid during a load operation, those entries in rows 2 and 3 would become invalid because their FK would no longer refer to an existing/valid entry. Once the entry in row 2 is invalid, the entries in rows 4 and 5 become invalid because they too would have FK values associated with a non-existing or invalid PK value. Similarly, those entries in rows 77 and 97 become invalid. Thus, under the second policy above, only row 6 remains valid after deletion of the entry in row 1. Under the Set-Null policy, all illustrated entries except that of row 6 would have their FK value set to NULL. 
     
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Illustrative Database Table Entity 
               
             
          
           
               
                   
                   
                 Employee ID 
                 Manager ID 
                 Other 
               
               
                   
                 Row 
                 (PK) 
                 (FK) 
                 Attributes 
               
               
                   
                   
               
               
                   
                 1 
                 1 
                 1 
                 Data 
               
               
                   
                 2 
                 2 
                 1 
                 Data 
               
               
                   
                 3 
                 3 
                 1 
                 Data 
               
               
                   
                 4 
                 4 
                 2 
                 Data 
               
               
                   
                 5 
                 5 
                 2 
                 Data 
               
               
                   
                 6 
                 23  
                 47  
                 Data 
               
               
                   
                 
                           
                 
                 
                           
                 
                 
                           
                 
                 
                           
                 
               
               
                   
                 77  
                 37  
                 5 
                 Data 
               
               
                   
                 
                           
                 
                 
                           
                 
                 
                           
                 
                 
                           
                 
               
               
                   
                 97  
                 47  
                 5 
                 Data 
               
               
                   
                 
                           
                 
                 
                           
                 
                 
                           
                 
                 
                           
                 
               
               
                   
                   
               
             
          
         
       
     
     The situation in which one row (through failure to load or becoming invalid or deleted) causes a chain of one or more additional rows to become invalid is known as the “cascading delete” problem. It will be recognized by those of ordinary skill that the problem of cascading deletes only occur in self-referencing tables (see  FIG. 2A ) or when two or more tables are related through referential constraints to form a cycle (see  FIG. 2B ). 
     As illustrated by Table 1, both the Cascading and Set-Null policies require that when an entry is marked for deletion, or as invalid during a load operation, the effect of this action must be checked against all other entries in the affected entities (e.g., table or tables). It is for this reason that referential constraint checking can consume a larger amount of time, particularly during database load operations and especially for large and/or heavily constrained entities. 
     In the context of a relational database system and referential constraint processing in accordance with a Cascading policy,  FIG. 1  shows prior art load-time referential integrity check operation  100  proceeds generally as follows. A first row of data is obtained (block  105 ) and its data is checked for validity. If all of the row&#39;s data is valid (the “Yes” prong of block  110 ), the data row is loaded into a table (block  115 ). If all of the data is not valid (the “No” prong of block  110 ), the row is not loaded into the table (block  120 ). In a modern DBMS such as DB2, a row rejected as invalid at this stage of the load process is also identified in a system log entry. Following the acts of blocks  115  and  120 , if additional data remains to be checked/verified (the “Yes” prong of block  125 ), processing continues at block  110 . If no additional data remains (the “No” prong of block  125 ), referential integrity check processing begins (block  130 - 155 ). 
     During referential constraint processing a first valid row is obtained from the loaded table (block  130 ) and a check is made to determine if its FK corresponds to a PK in a valid entry. If the entry&#39;s FK does not correspond to a valid PK entry, meaning the entry&#39;s referential constraint is violated (the “Yes” prong of block  135 ), the row is marked for deletion (block  140 ) and the entire table is checked to see if this change further invalidates additional row entries (block  145 ). Typically, as each new invalid row is identified (the “No” prong of block  150 ), it is marked for deletion (block  140 ). As illustrated in  FIG. 1 , the acts of blocks  140  through  150  are recursive in nature such that each entry (data row) marked as invalid or for deletion causes the entire table (or an index thereon) to be searched. Once all entries in the table have been checked (the “Yes” prong of block  150 ), a check is made to determine if additional constraints need to be checked. If additional constraints need to be verified (the “Yes” prong of block  155 ), processing resumes at block  135  where the particular constraint is checked. If all referential constraints have been checked (the “No” prong of block  155 ), a check is made to determine if additional data rows in the table remain to be checked. If additional data rows remain to be checked (the “Yes” prong of block  160 ), processing continues at block  130 . If all valid rows have been checked (the “No” prong of block  160 ), the table is updated to remove all rows designated for deletion and made available to users of the DBMS (block  165 ). 
     One drawback to prior art techniques such as process  100  is the time required to process cascading deletes. As illustrated by blocks  140  and  145 , each time a row is determined to be invalid (marked for deletion), the table is checked to determine what effect this has on other rows. Accordingly, the total time to load a table T(total) is the time required to physically load the data T(data) plus the time required to check a referential integrity constraint T(ref) multiplied by a function that depends upon the number of referential constraints (N), processed f(N):
 
 T (total)= T (data)+[ T ( ref )×ƒ( N )].  EQ. 1
 
In an operational DB2 database system, it has been found that the function f(N) is typically a low-order polynomial. For example, if the value of N is doubled, the value of f(N) may increase by a factor of 1.5 to 1.8.
 
     It is clear from  FIG. 1  that in situations where one invalid row causes additional rows to become invalid through failure to satisfy a referential constraint (creating “cascading deletes”), the prior art makes multiple passes through the table data (see discussion above regarding blocks  140 - 150 )—requiring a processing time given by the [T(ref)×ƒ(N)] term in EQ. 1. As the time to process even a single referential constraint in this manner can significantly increase a table&#39;s load time (especially for large tables and/or tables exhibiting highly “nested” or “layered” referential constraint relationships), it would be beneficial to provide methods and devices to efficiently load database objects in the face of cascading deletes. 
     SUMMARY 
     In one embodiment the invention provides a method to identify and process cascade deletes during relational database load operations. The method includes loading one or more tables into memory (each table having one or more rows, where at least one of the tables is a parent table and at least one of said tables is a child table), building a foreign key index for each loaded child table, logging all primary key errors detected during the act of loading, probing each relevant foreign key index to identify all loaded rows that violate a referential constraint due to a primary key error (where such referential constraint violation is a foreign key error), logging all identified foreign key errors to the error file and using the error file contents to identify, mark and delete loaded table rows that violate a referential constraint. The method may be stored in any media that is readable and executable by a computer system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows, in flowchart format, a prior art database object load operation that incorporates referential constraint checking. 
         FIG. 2  illustrates a self-referencing ( 2 A) and cyclically related series of tables ( 2 B) of the type that can generate cascading deletes. 
         FIG. 3  shows, in flowchart format, a cascading delete processing method in accordance with one embodiment of the invention. 
         FIGS. 4 ,  5  and  6  show, in flowchart format, details of the cascading delete processing method of  FIG. 3 . 
     
    
    
     DETAILED DESCRIPTION 
     Methods, systems and computer program storage devices to efficiently process cascading delete events due to referential constraint violations are described. The following embodiments of the invention, described in the context of a DB2® database system, are illustrative only and are not to be considered limiting in any respect. (“DB2” is a registered trademark of the International Business Machines Corporation of Armonk, N.Y.) Techniques in accordance with the invention use index probes and an error log file to identify and track referential dependencies between data elements (e.g., table rows) such that cascading deletes are identified in a single-pass through the data. Thus, the time to process referential constraints in accordance with the invention is reduced to approximately the time it takes to load the data itself. 
     Referring to  FIG. 3 , cascade delete processing method  300  in accordance with one embodiment of the invention follows table load operations (block  305 ). Table load operations obtain data from storage (e.g., a tape or magnetic disk storage unit), and store or load the data into memory. During the acts of block  305 , each row of data retrieved from storage is validated prior to loading it into memory. If any element of a row&#39;s data fails its data validation check, an error log is generated and the row containing the element is not loaded into memory. For example, if a table&#39;s schema defines column ‘k’ to be of type “date,” and the data retrieved from storage corresponding to column ‘k’ fails to be formatted in an accepted date format, that data is considered invalid and the entire row is rejected—not loaded. Whenever a row&#39;s data fails this type of data validation, DBMS&#39; generate or “log” an error record to a DBMS error file. Error records identify, inter alia, the table (via a table identifier, “TID”), the row (via a row identifier, “RID”) and the type of error that caused the data to be rejected. In the context of the present discussion, rows rejected during initial load operations are referred to as primary key (“PK”) errors. (One of ordinary skill in the art will recognize that other types of PK errors exist. For example, if one row&#39;s PK value is a duplicate of a previously loaded row&#39;s PK value (resulting in duplicate PK values), the second row is rejected as a “duplicate” and a PK error record is generated.) In accordance with the invention, if any child or dependent table being loaded during the acts of block  305  does not have a foreign key (“FK”) index defined for it, a temporary FK index is built. 
     Next, the DBMS error file is checked to determine if any rows were rejected during the load operations of block  305  (block  310 ). If no PK failures were detected (the “No” prong of block  310 ), there is no potential for cascading deletes and method  300  ends (block  315 ). If one or more PK failures were logged during the acts of block  305  (the “Yes” prong of block  310 ), the first such error record is obtained and the PK of the rejected row is determined (block  320 ). The PK is then used to probe the FK index of each table that depends upon the table from which the PK error is associated and, should a referential constraint error be detected, a FK error record is logged to the error file. This process is repeated until all referential constraints of all rows affected by the initial PK error have been checked and logged to the DBMS error file (block  325 ). If all PK error records generated during the acts of block  305  have not been processed (the “No” prong of block  330 ), processing loops to block  320 . If all PK error records generated during the acts of block  305  have been processed (the “Yes” prong of block  330 ), all of the logged FK error records are obtained from the DBMS error file and the rows associated therewith are deleted (block  335 ). The table(s) may then be made available to the DBMS for general access (block  340 ). It is significant to note that during cascade delete processing method  300  the loaded data is only processed once—at the time it is loaded into memory. Thereafter, referential constraint processing is carried out through index probes and error record logging. This is significant because the time required to log, sort and retrieve error records from an error log file can be substantially less than the time required to make multiple passes through the data itself—especially for large tables. 
     Referring to  FIG. 4 , a detailed flowchart of cascade delete processing method  400  in accordance with the invention is shown for a DB2 DBMS. Initially, load operations as discussed above are performed (block  305 ). It is noted that in a DB2 DBMS, load-time error records (hereinafter, PK error records) are written or logged to the SYSERR file—a DBMS-wide flat file. Following the load operations of block  300 , the SYSERR file is sorted so that the PK error records generated during the acts of block  300  are convenient (block  405 ). For example, the SYSERR file may be sorted on error record type and TID and RID. If no unprocessed PK error records exist (the “No” prong of block  410 ), cascade delete processing is complete—any row determined to violate a referential constraint is deleted whereafter the table(s) are made available to the DBMS for general access (block  415 ). 
     If an unprocessed PK error record exists (the “Yes” prong of block  410 ), the first such record is obtained (block  420 ) and its TID and RID values are used to create an entry in a referential constraint error (“RCE”) list (block  425 ). In one embodiment, the RCE is implemented as a stack data structure. The top-most RCE entry is then obtained (block  430 ) and processed to identify all child table rows that are invalid due to the PK error from which the RCE entry is based (block  435 ). That is, acts in accordance with block  435  identify cascading deletes—see discussion below regarding  FIG. 5 . If an unprocessed RCE entry exists (the “Yes” prong of block  440 ), processing loops to block  430 . (As will be discussed below with respect to  FIG. 5 , RCE entry processing (block  435 ) may generate a number of RCE entries.) If no unprocessed RCE entry exists (the “No” prong of block  440 ), a secondary or FK error record is generated and written to the SYSERR log file for each existing RCE entry (block  445 ), whereafter the RCE list is cleared (block  450 ). Each time the acts of blocks  425 - 445  are performed, the RCE list includes an entry for each child table row that must be deleted because the row identified in the PK error record obtained during the acts of block  420  failed to load or was itself determined to be invalid due to a referential constraint violation. Following the acts of block  450 , processing loops to block  410 . 
     Referring to  FIG. 5 , each RCE entry is processed in accordance with block  435  as follows. A check is first made to determine if the table associated which the RCE entry has any dependent or children tables. (One of ordinary skill will recognize that this information is typically provided by the DBMS.) If the RCE has no associated dependent tables (the “No” prong of block  500 ), there can be no cascading deletes and the current RCE&#39;s processing is complete (block  505 ). If the RCE entry is associated with at least one dependent table (the “Yes” prong of block  500 ), a check is made to determine if the first of such one or more dependent tables has been loaded. If the dependent table has not been loaded (the “No” prong of block  510 ), a check is made to determine if another dependent table exists (block  500 ). If a dependent table exists and has been loaded (the “Yes” prong of block  510 ), a check is made to determine if the row identified by the RCE entry has previously been obtained. If the row identified by the RCE entry has not yet been obtained (the “No” prong of block  515 ), it is obtained (block  520 ). If the row identified by the RCE entry has already been obtained (the “Yes” prong of block  515 ), and after the acts of block  520 , the row data is used to construct its PK (block  525 ). The PK is then used to probe the dependent table&#39;s FK index (built during the acts of block  300 , see  FIG. 4 ). If the PK probe indicates no FK was found (the “No” prong of block  530 ), no referential constraint violation for that dependent child table and RCE entry combination exists and processing continues at block  500 . If the PK probe indicates a FK was found (the “Yes” prong of block  530 ), the FK is retrieved from the FK index (block  535 ) and a test is made to determine if the PK and FK are the same. If the PK and FK are not the same (the “No” prong of block  540 ), no referential constraint error exists and cascade delete detection processing for the current dependent table is complete—processing continues at block  500 . If the PK and FK are the same (the “Yes” prong of block  540 ), a new RCE list entry is generated for the row associated with the FK obtained in accordance with block  535  (block  545 ). A test is then made to determine if the PK probe of the dependent table&#39;s FK index in accordance with block  525  identified more than one FK entry. If another FK entry exists and has not yet been processed (the “Yes” prong of block  550 ), the “next” FK is obtained in accordance with block  535  and processing continues at block  540 . If another unprocessed FK entry does not exist (the “No” prong of block  550 ), cascade delete detection processing of the current dependent table is complete and processing continues at block  500 . 
     Referring to  FIG. 6 , an expanded description of the “Delete Rows” action of  FIG. 4  is shown (see block  415 ). In one embodiment, all FK error records generated in accordance with cascade delete processing method  400  are sorted by TID and RID (block  600 ). The sorted list of FK error records facilitate the orderly identification or marking of rows that must be deleted from the data loaded in accordance with block  300  (block  605 ). Rows so marked are physically deleted from the affected table(s) (block  610 ) and the resulting “clean” tables are made available to the DBMS for general access (block  615 ). In another embodiment, all error records associated with cascade delete processing method  400  are sorted by TID and RID in block  605 . While those rows associated with PK error records were not loaded and, therefore, do not need to be deleted, it has been found more time efficient to process all error records rather than to partition out only the FK error records. 
     Various changes in the details of the illustrated operational methods are possible without departing from the scope of the following claims. For instance, following the acts of block  435  and before the acts of block  440  (see  FIG. 4 ), a FK error record could be generated for the “top-most” RCE list entry, whereafter the top-most RCE list entry could be deleted. If this embodiment is pursued, the acts of block  445  could be eliminated. It will be recognized that this alternative approach generates a FK error record as each RCE entry is processed while the method illustrated in  FIG. 4  “batches” the generation of FK error records. Further, RCE processing methods (block  435 ) in accordance with the invention can record which child table rows have been identified for deletion such that if the RCE being currently processed identifies a row (RID) that has already been identified as needing to be deleted, processing of the current row (RID) can be assumed to be complete. It will similarly be understood that FK error records may be created “on the fly” during, or immediately following, the acts of block  545  (see  FIG. 5 ) rather than the “batch” process contemplated by  FIG. 4  at block  445 . It will be recognized by those of ordinary skill in the art that the time to process referential integrity constraints in accordance with the invention is substantially equal to the time required to load the data because the time to sort and process error log files (typically sequential access files) is very small compared to the time to load the table data in accordance with block  305 —this is particularly true for large tables. 
     In addition, acts in accordance with  FIGS. 3-6  may be performed by a programmable control device executing instructions organized into one or more program modules. A programmable control device may be a single computer processor, a special purpose processor (e.g., a digital signal processor, “DSP”), a plurality of processors coupled by a communications link or a custom designed state machine. Custom designed state machines may be embodied in a hardware device such as an integrated circuit including, but not limited to, application specific integrated circuits (“ASICs”) or field programmable gate array (“FPGAs”). Storage devices suitable for tangibly embodying program instructions include, but are not limited to: magnetic disks (fixed, floppy, and removable) and tape; optical media such as CD-ROMs and digital video disks (“DVDs”); and semiconductor memory devices such as Electrically Programmable Read-Only Memory (“EPROM”), Electrically Erasable Programmable Read-Only Memory (“EEPROM”), Programmable Gate Arrays and flash devices. 
     The preceding description is presented to enable any person skilled in the art to make and use the invention as claimed and is provided in the context of the particular examples discussed above, variations of which will be readily apparent to those skilled in the art. Accordingly, the claims appended hereto are not intended to be limited by the disclosed embodiments, but are to be accorded their widest scope consistent with the principles and features disclosed herein.
A time-efficient means for identifying and processing cascading deletes due to referential constraint violations includes: logging, to an error file, all primary key ("PK") errors detected during table load operations; building a foreign key ("FK") index for each child table; recursively probing each relevant FK index to identify all loaded rows that violate a referential constraint due to a PK error; logging all identified FK errors to the error file; and using the (preferably sorted) error file contents to identify, mark and physically delete table rows that violate a referential constraint. The described cascade delete processing methods make only a single pass through the table data, using ordinary computer files to track and organize rows identified for deletion. Use of error files rather than tablescans (multiple passes through the loaded table data) can provide a significant reduction in table load times, especially for large or intricately "related" tables.