CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to and is a continuation of U.S. application Ser. No. 12/509,507 filed on 2009 Jul. 27, the content of which is incorporated by reference herein, in its entirety. 
    
    
     COPYRIGHT &amp; TRADEMARK NOTICES 
     A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The owner has no objection to the facsimile reproduction by any one of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyrights whatsoever. 
     Certain marks referenced herein may be common law or registered trademarks of third parties affiliated or unaffiliated with the applicant or the assignee. Use of these marks is for providing an enabling disclosure by way of example and shall not be construed to limit the scope of the claimed subject matter to material associated with such marks. 
     TECHNICAL FIELD 
     The claimed subject matter relates generally to data processing environments and, more particularly, to duplicate filtering in a data processing environment. 
     BACKGROUND 
     One or more devices in a network may generate a stream (i.e., a continuous flow) of data records, which are processed and stored in a repository. Typically, each data record associates values with data fields, and the values of one or more fields are used to identify the record. Such fields are referred to as keys. The record is a duplicate copy, if there is another record that associates the same values with the keys. 
     In existing implementations, a single index is utilized to filter duplicate copies for the entire stream of records. Unfortunately, such implementations are inefficient, resulting in significant cost with respect to time and other resources without serving any purpose beyond the duplicate filtering. Moreover, the repository is usually partitioned into multiple parts, which may make constructing a single index for the entire stream of records difficult. 
     SUMMARY 
     The present disclosure is directed to systems and corresponding methods that facilitate efficient duplicate filtering in a data processing system. 
     For purposes of summarizing, certain aspects, advantages, and novel features have been described herein. It is to be understood that not all such advantages may be achieved in accordance with any one particular embodiment. Thus, the claimed subject matter may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages without achieving all advantages as may be taught or suggested herein. 
     In accordance with one embodiment, a data processing method is provided. The data processing method comprises collecting a stream of data records received from one or more data sources connected in a communications network; dividing the stream of data records into sets of data records for parallel processing by a plurality of concurrently running tasks, wherein a first task loads a persistent index associated with a first set of data records into memory to generate an in-memory version of the first persistent index for the first set of data records; identifying duplicate and non-duplicate data records in the first set of data records, based on searching the in-memory version of the first persistent index; storing, in a repository, the identified non-duplicate data records and maintaining, in the repository, a single copy of the identified duplicate data records in the first set of data records. In a first data record in the stream of data records is uniquely identifiable by way of a first identifier that comprises a first part and a second part, such that the first identifier is utilized to find whether or not the first data record has a duplicate in the repository. 
     In accordance with another embodiment, a system comprising one or more logic units is provided. The one or more logic units are configured to perform the functions and operations associated with the above-disclosed methods. In accordance with yet another embodiment, a computer program product comprising a computer useable medium having a computer readable program is provided. The computer readable program when executed on a computer causes the computer to perform the functions and operations associated with the above-disclosed methods. 
     One or more of the above-disclosed embodiments in addition to certain alternatives are provided in further detail below with reference to the attached figures. The claimed subject matter is not, however, limited to any particular embodiment disclosed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the claimed subject matter are understood by referring to the figures in the attached drawings, as provided below. 
         FIG. 1  illustrates an exemplary data processing environment, in accordance with one or more embodiments. 
         FIG. 2  is a flow diagram of a method for processing a stream of data records, in accordance with one embodiment. 
         FIG. 3  is a flow diagram of a method for processing a set of data records, in accordance with one embodiment. 
         FIG. 4  is a flow diagram of a first method for synchronizing access to an arbitrary collection of index files, in accordance with one embodiment. 
         FIG. 5  is a flow diagram of a second method for synchronizing access to an arbitrary collection of index files, in accordance with one embodiment. 
         FIG. 6  is a flow diagram of a method for identifying a data record as a duplicate copy, in accordance with one embodiment. 
         FIG. 7  is a flow diagram of a method for updating index files, in accordance with one embodiment. 
         FIGS. 8 and 9  are block diagrams of hardware and software environments in which a system of the present invention may operate, in accordance with one or more embodiments. 
     
    
    
     Features, elements, and aspects that are referenced by the same numerals in different figures represent the same, equivalent, or similar features, elements, or aspects, in accordance with one or more embodiments. 
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     In the following, numerous specific details are set forth to provide a thorough description of various embodiments of the claimed subject matter. Certain embodiments may be practiced without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects of the disclosed embodiments. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others. 
     Referring to  FIG. 1 , in accordance with one embodiment, an exemplary data processing environment  100  comprises a network of one or more devices  110  that generate a stream of data records, a repository  120  for storing the records, a parallel processing system (PPS)  130  for processing the records, and an index system  140  for identifying records stored in the repository  120 . In one implementation, for example, the repository  120  may be a database, and the PPS  130  may be an application executed by the database management system (DBMS). 
     In one embodiment, if there are a large number of data records arriving in the stream, the PPS  130  executes a scheduler  132  that divides the stream into one or more sets of records. Each set of records is processed by a task  134  executed in parallel with other tasks  134  by the PPS  130 . For example, if a system receives about one billion records each day and each task executed by the system processes about a 1000 records per second, the system may execute at least 12 tasks in parallel to continuously sustain the stream of records. Desirably, as the number of records increases, the degree of independence between the tasks  134  may be increased proportionally to allow more tasks to be executed concurrently. 
     Each data record in the stream may be identified according to values associated with one or more fields, or keys. In one embodiment, one or more of the keys may be used to provide a higher-order value, and the rest of the keys may be used to provide a lower-order value. The higher-order value may be shared by one or more data records, while the lower-order value may be used in combination with the higher-order value to identify a particular record. 
     For example, a data record of a transaction may be identified by an identification (ID) of a device that performed the transaction, a time slot during which the transaction occurred, the time of the transaction, and an ID for the user who requested the transaction. In such a scenario, the higher-order value may be a combination of the device ID and the time slot, and the lower-order value may be a combination of the transaction time and the user ID. 
     In some embodiments, the scheduler  132  divides the stream of records into sets according to the higher-order values (e.g., device IDs and time slots) provided by the records. For example, a first set of records may comprise records that share a first higher-order value (e.g., device ID=“001” and time slot=“morning”), and a second set of records may comprise records that share a second higher-order value (e.g., device ID=“002” and time slot=“afternoon”). In one implementation, if a set of records is saved as an input file, the name of the input file may provide the higher-order value (e.g., file name=“morning/001”. In another implementation, the higher-order value may be provided in a separate metadata file that accompanies the input file. 
     Each higher-order value may be registered with a persistent control structure  142  included in the index system  140 . Persistence refers to the ability to retain data after completing the execution of the program or process that generated the data. In one exemplary embodiment, the control structure  142  may be implemented as a table and a higher-order value may be registered with the control structure  142  by adding a row to the table. It is noteworthy, however, that the control structure  142  may be implemented in form of other types of data structures such as arrays and hash maps, for example. 
     A persistent index file  144  may be generated in the index system  140  for each higher-order value (e.g., for each device ID and time slot combination) that is registered with the control structure  142 . Each index file  144  may save a lower-order value (e.g., the transaction time and user ID) provided by each record that shares the higher-order value for which the index file  144  was generated. 
     Referring to  FIGS. 1 and 2 , in accordance with one embodiment, the PPS  130  collects a stream of data records from one or more devices in the network  110  (P 200 ). As the records are collected, the scheduler  132  divides the records into one or more sets and assigns each set to a task  134  for processing (P 210 ). 
     Each task  134  loads one or more index files  144  into memory, as provided in more detail below with reference to  FIGS. 3 through 5 , and uses the in-memory index files  144  to identify duplicate records (P 220 ) and update the repository  120  accordingly, as provided in more detail below with reference to  FIG. 6 . Once the records are processed, the task  134  updates the index system  140  (P 230 ), as provided in more detail below with reference to  FIG. 7 . 
     Referring to  FIGS. 1 and 3 , in accordance with one embodiment, a task  134  is executed to process a set of data records. Upon being executed, the task  134  determines whether any higher-order values (e.g., device ID and time slot) provided by the records is not registered with the control structure  142  (P 300 ). For each higher-order value that is not registered, the task  134  registers the higher-order value with the control structure  142  and generates an index file  144  (P 310 ). 
     Once each higher-order value is registered with the control structure  142 , the task  134  loads the index files  144  generated for the higher-order values into memory according to a synchronization mechanism (P 320 ). 
     Referring to  FIGS. 1 and 4 , in accordance with one embodiment, the task  134  loads each index file  144  into memory according to a first synchronization mechanism. The task  134  accesses the control structure  142  and attempts to acquire exclusive ownership of each index file  144  (P 400 ). In one implementation, for example, the task  134  acquires ownership of an index file  144  by assigning its unique task ID to an owner field of the control structure  142  entry corresponding to the index file  144 . 
     The task  134  may fail to acquire ownership of an index file  144  if the index  144  is owned by another task  134  or if there is a system failure caused by a rollback or a deadlock exception. If the task  134  fails to successfully acquire ownership of each index file  144  (P 410 ), the task  134  releases ownership of the index files  144  for which ownership was successfully acquired and reattempts to acquire ownership of each index file  144  after a certain period of time (P 420 ). Upon successfully acquiring ownership of each index file  144 , the task  134  loads each index file  144  into memory (P 430 ). 
     Referring to  FIGS. 1 and 5 , in accordance with one embodiment, the task  134  loads each index file  144  into memory according to a second synchronization mechanism. The task  134  accesses the control structure  142  and attempts to acquire exclusive ownership of each index file  144  (P 500 ). If the task  134  fails to acquire at least one of the index files  144  (P 510 ), the task  134  suspends activity for a certain amount of time before re-attempting to acquire each index file  144  (P 520 ). Once the certain amount of time elapses, the task  134  re-attempts to acquire each index file  144  (P 500 ). If each index file  144  cannot be acquired successfully (P 530 ), records whose processing uses the index files  144  that were not acquired successfully are re-assigned to another task  134  (P 540 ). Upon successfully acquiring at least one index file  144  (P 510  and P 530 ), the task  134  loads the successfully acquired index files  144  into memory (P 550 ). 
     Referring to  FIGS. 1 and 6 , in accordance with one embodiment, the task  134  may identify a data record as a duplicate copy using an in-memory index file. The task  134  accesses the in-memory index file generated for the higher-order value (e.g., device ID and time slot) provided by the record (P 600 ). If the lower-order value (e.g., transaction time and user ID) is saved in the in-memory index file (P 610 ), the record is identified as a duplicate copy and may not be stored in the repository  120  (P 620 ). Otherwise, the lower-order value is saved in the in-memory index file (P 630 ) and the record is stored in the repository  120  (P 640 ). 
     Referring to  FIGS. 1 and 7 , in accordance with one embodiment, the task  134  finishes processing the records (P 700 ) and determines whether the in-memory index files have been modified (P 710 ). If any in-memory index file has been modified, the task  134  updates the corresponding index file  144  (P 720 ). In one implementation, the task  134  may also store a log of the update in a different storage subsystem for backup purposes (P 730 ). 
     In another implementation, the index system  140  may index records for a limited period of time That is, higher-order values that are older than a certain time limit may be de-registered from the control structure  142 , and index files  144  that are older than the certain time limit may be removed (P 740 ). For example, a record that is no longer indexed by the index system  140  may be associated with a tag that indicates that the record is not being checked during duplicate filtering. Indexing records for a limited period of time increases efficiency with respect to storage space since the possibility of finding duplicate copies decreases with the passage of time. 
     Advantageously, the systems and methods described above provide scalability with respect to growing repositories. The size of the index system  140  increases constantly with the size of the repository  120 , instead of proportionally. 
     Further, the systems and method described above provide higher performance in terms of both delay time and system throughput. With respect to delay time, searching for duplicate copies using particular in-memory index files for a set of records is much faster than doing so using a single index file for the entire stream of records. With respect to system throughput, multiple sets of records are processed in parallel, which is possible since each set accesses different index files. 
     In different embodiments, the claimed subject matter may be implemented either entirely in the form of hardware or entirely in the form of software, or a combination of both hardware and software elements. For example, the data processing environment  100  may comprise a controlled computing system environment that may be presented largely in terms of hardware components and software code executed to perform processes that achieve the results contemplated by the system of the claimed subject matter. 
     Referring to  FIGS. 8 and 9 , a computing system environment in accordance with an exemplary embodiment is composed of a hardware environment  1110  and a software environment  1120 . The hardware environment  1110  comprises the machinery and equipment that provide an execution environment for the software; and the software environment  1120  provides the execution instructions for the hardware as provided below. 
     As provided here, software elements that are executed on the illustrated hardware elements are described in terms of specific logical/functional relationships. It should be noted, however, that the respective methods implemented in software may be also implemented in hardware by way of configured and programmed processors, ASICs (application specific integrated circuits), FPGAs (Field Programmable Gate Arrays) and DSPs (digital signal processors), for example. 
     Software environment  1120  is divided into two major classes comprising system software  1121  and application software  1122 . In one embodiment, the PPS  130  may be implemented as system software  1121  or application software  1122  executed on one or more hardware environments to facilitate efficient duplicate filtering in a data processing system. 
     System software  1121  may comprise control programs, such as the operating system (OS) and information management systems that instruct the hardware how to function and process information. Application software  1122  may comprise but is not limited to program code, data structures, firmware, resident software, microcode or any other form of information or routine that may be read, analyzed or executed by a microcontroller. 
     In an alternative embodiment, the claimed subject matter may be implemented as computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable medium may be any apparatus that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus or device. 
     The computer-readable medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk read only memory (CD-ROM), compact disk read/write (CD-R/W) and digital video disk (DVD). 
     Referring to  FIG. 8 , an embodiment of the application software  1122  may be implemented as computer software in the form of computer readable code executed on a data processing system such as hardware environment  1110  that comprises a processor  1101  coupled to one or more memory elements by way of a system bus  1100 . The memory elements, for example, may comprise local memory  1102 , storage media  1106 , and cache memory  1104 . Processor  1101  loads executable code from storage media  1106  to local memory  1102 . Cache memory  1104  provides temporary storage to reduce the number of times code is loaded from storage media  1106  for execution. 
     A user interface device  1105  (e.g., keyboard, pointing device, etc.) and a display screen  1107  can be coupled to the computing system either directly or through an intervening I/O controller  1103 , for example. A communication interface unit  1108 , such as a network adapter, may be also coupled to the computing system to enable the data processing system to communicate with other data processing systems or remote printers or storage devices through intervening private or public networks. Wired or wireless modems and Ethernet cards are a few of the exemplary types of network adapters. 
     In one or more embodiments, hardware environment  1110  may not include all the above components, or may comprise other components for additional functionality or utility. For example, hardware environment  1110  can be a laptop computer or other portable computing device embodied in an embedded system such as a set-top box, a personal data assistant (PDA), a mobile communication unit (e.g., a wireless phone), or other similar hardware platforms that have information processing and/or data storage and communication capabilities. 
     In some embodiments of the system, communication interface  1108  communicates with other systems by sending and receiving electrical, electromagnetic or optical signals that carry digital data streams representing various types of information including program code. The communication may be established by way of a remote network (e.g., the Internet), or alternatively by way of transmission over a carrier wave. 
     Referring to  FIG. 9 , application software  1122  may comprise one or more computer programs that are executed on top of system software  1121  after being loaded from storage media  1106  into local memory  1102 . In a client-server architecture, application software  1122  may comprise client software and server software. For example, in one embodiment, client software is executed on general computing system (not shown) and server software is executed on a server system (not shown). 
     Software environment  1120  may also comprise browser software  1126  for accessing data available over local or remote computing networks. Further, software environment  1120  may comprise a user interface  1124  (e.g., a Graphical User Interface (GUI)) for receiving user commands and data. Please note that the hardware and software architectures and environments described above are for purposes of example, and one or more embodiments of the invention may be implemented over any type of system architecture or processing environment. 
     It should also be understood that the logic code, programs, modules, processes, methods and the order in which the respective processes of each method are performed are purely exemplary. Depending on implementation, the processes can be performed in any order or in parallel, unless indicated otherwise in the present disclosure. Further, the logic code is not related, or limited to any particular programming language, and may comprise of one or more modules that execute on one or more processors in a distributed, non-distributed or multiprocessing environment. 
     The claimed subject matter has been described above with reference to one or more features or embodiments. Those skilled in the art will recognize, however, that changes and modifications may be made to these embodiments without departing from the scope of the claimed subject matter. These and various other adaptations and combinations of the embodiments disclosed are within the scope of the claimed subject matter as defined by the claims and their full scope of equivalents.
A data processing method is provided. The method comprises collecting a stream of data records received from one or more data sources connected in a communications network; dividing the stream of data records into sets of data records for parallel processing by a plurality of concurrently running tasks, wherein a first task loads a persistent index associated with a first set of data records into memory to generate an in-memory version of the first persistent index for the first set of data records; and identifying duplicate and non-duplicate data records in the first set of data records, based on searching the in-memory version of the first persistent index.