Abstract:
A system and method for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic are described. Operations expressed in a data manipulation language are executed against a source database. At least one operation constitutes a commit operation that completes each database transaction. A current rule set is defined. Each rule includes business logic specifying a data selection criteria for records stored in the source database. A log entry is periodically generated in a log for each transaction committed to the source database. Each log entry identifies an affected record and includes transactional data. The transaction identified in each log entry is evaluated against the data selection criteria specified in the current rule set. A new record is built in accordance with metadata describing a destination database. The new record contains select transactional data from the log entry of each transaction meeting the selection criteria. The new record is stored into the destination database. The data stored in the destination database includes at least a partial subset of the source database.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates in general to database object extraction and, in particular, to a system and method for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic.  
         BACKGROUND OF THE INVENTION  
         [0002]    Presently, corporate database management systems fall into two categories: production and informational. Production databases, including operational data stores, function as repositories for real-time or near-real-time data generated by or used in the operation of manufacturing, production, and transactional systems. In contrast, informational databases store data periodically obtained from production databases for use in decision support and on-line analytical processing systems. Informational databases include data warehouses, often structured as enterprise databases and datamarts.  
           [0003]    Typically, data warehouses store both informational data and metadata that describe the database structure. At a minimum, informational databases must maintain a degree of large-grained data concurrency with the data stored in the production databases for trend analyses and data derivation.  
           [0004]    On-line transaction processing systems are major producers of production data. On-line transaction processing systems require a minimum guaranteed response time with uninterrupted availability, particularly in electronic commerce (e-commerce) systems. The high data volume and the need for high availability require the use of transaction servers rather than slower database servers.  
           [0005]    Production data provide the raw grist for decision support and on-line analytical processing systems. These systems analyze data and generate reports for use in the planning and strategic operations of a corporation. The raw production data is transformed into informational data by data mining, replication, and cleansing tools. Decision support and on-line analytical processing systems can tolerate slower response times. Nevertheless, the data needs of these systems must balance against the autonomy required by production systems.  
           [0006]    Frequently updating the informational databases can adversely impact the operation of the production systems. On-line transaction processing systems operate near or at total hardware capacity. For instance, a typical e-commerce site can receive over 500 transactions or “hits” per second. Interrupting production system operation to update the informational databases can exacerbate the problem of maintaining the requisite level of availability and responsiveness.  
           [0007]    Periodically, production data must be transformed into informational data through the application of business logic during the data retrieval process. Often, the business logic required to retrieve and transform production data is complex and computationally intensive. As well, the business logic is relatively inflexible and static. These factors can further affect system responsiveness.  
           [0008]    In the prior art, two solutions for updating informational databases have been proposed. One solution presents a data replication manager that periodically copies production data while transforming the data. Unfortunately, this solution causes extensive data duplication and can be time consuming.  
           [0009]    Another prior art solution introduces a multi-tiered database architecture with periodic updating. Business logic is implemented in queries executed against the production database. Second tiered business logic can utilize the retrieved information to populate and update datamarts using department-specific queries. In a rapidly changing environment, excessive updates can drastically disrupt production system operation.  
           [0010]    Therefore, there is a need for a data manager capable of updating an informational database with high-frequency and low overhead. This approach would minimize resource expenditures by substantially avoiding data duplication and inefficient data retrieval.  
           [0011]    There is a further need for an approach to retrieving informational data with dynamically redefinable parameters. This approach would allow flexible redefinition of business logic for selecting data in an ad hoc fashion.  
           [0012]    There is a further need for an approach to non-intrusively updating an informational database. This approach would have minimal effect on a production system operation and respect autonomous operation.  
         SUMMARY OF THE INVENTION  
         [0013]    The present invention provides a system and method for updating a destination database with data indirectly retrieved from a source database through log-based monitoring. A transaction log file is generated as a by-product of transactions committed to a source database by a transaction server. The log file is monitored and evaluated against a dynamic rule set specifying selection criteria implementing business logic. Those log entries satisfying the selection criteria are converted into updated records using metadata describing the schema of a destination database. The rule set and metadata can be dynamically redefined using a database builder tool. The log monitor automatically modifies the selection criteria and record-generation operations. During the data retrieval, the log monitor utilizes information stored in each log entry to indirectly derive informational data with minimal effect on the transaction server operations.  
           [0014]    An embodiment of the present invention is a system and method for refreshing an informational database through log-based transaction monitoring. A production database is maintained and includes one or more tables. Each table stores records of production data generated by a transaction processing system. Log entries are periodically stored into a log file. At least one log entry is generated for each transaction committed to the production database. An informational database including one or more tables is maintained. Each table stores records of informational data for use by a decision support system. The log entries stored into the log file are dynamically analyzed using a rule set that specifies a data selection criteria. The updated records generated from production data satisfying the data selection criteria are stored into the informational database.  
           [0015]    A further embodiment is a system and method for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic. Operations expressed in a data manipulation language are executed against a source database. At least one operation constitutes a commit operation that completes each database transaction. A current rule set is defined. Each rule includes business logic specifying a data selection criteria for records stored in the source database. A log entry is periodically generated in a log for each transaction committed to the source database. Each log entry identifies an affected record and includes transactional data. The transaction identified in each log entry is evaluated against the data selection criteria specified in the current rule set. A new record is built in accordance with metadata describing a destination database. The new record contains select transactional data from the log entry of each transaction meeting the selection criteria. The new record is stored into the destination database. The data stored in the destination database includes at least a partial subset of the source database.  
           [0016]    One benefit of the present invention is the ability to dynamically redefine business logic implemented as rules interpreted by a transaction log monitor. A further benefit is harnessing the metadata intrinsic to a data warehouse to intelligently populate a database and to allow an additional level of responsiveness to changes in the structure of the database.  
           [0017]    Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein is described embodiments of the invention by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0018]    [0018]FIG. 1 is a block diagram showing a distributed computer environment, including a system for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic, in accordance with the present invention.  
         [0019]    [0019]FIG. 2 is a functional block diagram showing a prior art multi-tiered database architecture.  
         [0020]    [0020]FIG. 3 is a block diagram showing the system for maintaining large-grained database concurrency of FIG. 1.  
         [0021]    [0021]FIG. 4 is a data structure diagram showing a log entry used in the system of FIG. 3.  
         [0022]    [0022]FIG. 5 is a process flow diagram showing informational database updating through log-based transaction monitoring.  
         [0023]    [0023]FIG. 6 is a functional block diagram showing the software modules of the system of FIG. 3.  
         [0024]    [0024]FIG. 7 is a data structure diagram showing a rule entry.  
         [0025]    [0025]FIG. 8 is a flow chart showing a method for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic in accordance with the present invention.  
     
    
     DETAILED DESCRIPTION  
       [0026]    [0026]FIG. 1 is a block diagram showing a distributed computing environment  10 , including a system for maintaining large-grained database concurrency with a log monitor incorporating dynamically redefinable business logic, in accordance with the present invention. An operational data store (ODS)  11  stores production data generated by a production system  12 . The production system  12  can constitute an on-line transaction processing system for transacting electronic commerce (e-commerce), reservations, point of sale transactions, inventory control, factory and manufacturing operations, and similar types of activities. For efficiency, the production system  12  maintains a local production database  13  within which production data is maintained.  
         [0027]    To maintain a high level of responsiveness and availability, the operational data store  11  is coupled to a dedicated transaction server  14  that provides a high throughput interface to the operational data store  11 . The production system  12  and transaction server  14  are interconnected via a network  15 , such as an internetwork or an intranetwork, as are known in the art.  
         [0028]    The production data stored in the operational data store  11  is periodically replicated into an enterprise data warehouse  16 . Unlike the operational data store  11 , which only contains production data, the enterprise data warehouse  16  contains both direct and derivative data values, known as informational data, for use by decision support and on-line analytical processing systems. A database server  17  is coupled to the enterprise data warehouse  16  for executing data manipulation language (DML) queries against the enterprise data warehouse  16 . The database server  17  is also interconnected to the other systems via the network  15 .  
         [0029]    The enterprise data warehouse  16  can be hierarchically structured with secondary databases, such as a workgroup datamart  18 . While the enterprise data warehouse  16  contains informational data pertaining to all aspects of the corporate activities, each work group datamart  18  is a subset presenting a specialized view of the informational data for a specific subject area, such as employee records, sales revenue figures, and the like. The work group datamart  18  could be structured in a local network environment with a dedicated local database server  19  interconnected with a plurality of clients  20  over a local network  21 . In turn, the local network  21  can be interconnected with the corporate network  15 , via a series of hubs  22 .  
         [0030]    Finally, the enterprise data warehouse  16  and the work group datamart  18  can both be remotely accessed by a remote client  23 , for instance via an internetwork  24 , such as the Internet, coupled to the corporate network  15  through a gateway  25 .  
         [0031]    The operational data store  11  is characterized by a high degree of volatility and change with 100% availability and guaranteed response times. Conversely, the enterprise data warehouse  16  performs flexible operations responsive to ad hoc queries posed by the various clients  20  and remote clients  23 .  
         [0032]    The informational data stored in the enterprise data warehouse  16  must be periodically refreshed with production data retrieved from the operational data store  11 . Depending upon the system load on the operational data store  11 , the retrieval of production data may be impractical during peak operational times and at best tolerated during off-peak periods. Consequently, a log monitor  26  can provide large-grained database concurrency between the operational data store  11  and enterprise data warehouse  16  by indirectly updating the informational data.  
         [0033]    The log monitor  26  reads log entries generated by the transaction server  14  as a by-product of transaction processing. Individual log entries are retrieved and analyzed by applying selection criteria implementing business logic into a dynamically redefinable rule set, as further described below with reference to FIG. 3. Updated records are generated from those log entries satisfying the selection criteria for updating the informational data in the enterprise data warehouse  16 .  
         [0034]    While the specific forms of databases, including operational data store  11 , enterprise data warehouse  16 , and workgroup datamart  18 , are referenced in relation to the described embodiment, one skilled in the art would recognize that other forms of structured databases could also be used within the general parameters and characteristics outlined herein. In addition, other networked topologies and system configurations can also be used.  
         [0035]    The individual computer systems, including production system  12 , transaction server  14 , database server  17 , local database server  19 , clients  20  and remote client  23 , are general purpose, programmed digital computing devices consisting of a central processing unit (CPU), random access memory (RAM), non-volatile secondary storage, such as a hard drive or CD-ROM drive, network interfaces, and peripheral devices, including user-interfacing means, such as a keyboard and display. Program code, including software programs, and data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage.  
         [0036]    [0036]FIG. 2 is a functional block diagram showing a prior art multi-tiered database architecture  30 . Production and informational databases are structured into several tiers to distribute the database updating and retrieval workload, such as described in R. Orfali, “Client/Server Survival Guide,” Chs. 12-13, John Wiley &amp; Sons, Inc. (3d ed. 1999), the disclosure of which is incorporated by reference. A plurality of production databases  13  are served by a transaction server  14 . Production data is stored and retrieved using the transaction server  14  into and from an operational data store  11  at high volume with maximum availability (step  31 ).  
         [0037]    Periodically, a database server  17  “taps” informational data from the operational data store  11  (step  32 ). The raw production data copied, purified and cleansed into informational data maintained in an enterprise data warehouse  16 . The retrieval of production data from the operational data store  11  is performed by a scheduled recurring process, such as a cron job, that periodically awakens to refresh the production data from the operational data store  11  into the enterprise data warehouse  16 .  
         [0038]    The process of downloading the production data into the enterprise data warehouse  16  is nontrivial and taxes computational, storage and network interfacing resources. The informational data retrieval and transformation executes business logic for selecting the appropriate data values from the operational data store  11 . Each update can potentially implicate a massive volume of replicated information.  
         [0039]    To mitigate the load on the operational data store  11 , a series of specialized datamarts, such as, by way of example, a promotional datamart  34 , an affinity datamart  35 , and a market basket datamart  36 , are created as a subset of the enterprise data warehouse  16  (step  33 ). Like the enterprise data warehouse  16 , the datamarts  34 - 36  must also be periodically refreshed with informational data by a local data server  19 . The use of the local database server  19  offloads a part of the specialized business logic necessary to further refine the informational data into formats usable by decision support and on-line transaction processing system (not shown).  
         [0040]    This prior art approach attempts to balance the needs of the decision support and on-line analytical processing systems against the autonomous operation of the production systems by distributing and offloading the data replication and processing operations. However, the multi-tiered architecture  30  replicates the information needed in each of the specialized databases. In addition, multiple layers of business logic implementations are required to refresh and update the informational data. These layers can potentially include duplicated queries. A less duplicative solution is needed.  
         [0041]    [0041]FIG. 3 is a block diagram showing the system  50  for maintaining large-grained database concurrency of FIG. 1. The core functionality of the system is performed by log monitor  26 . A source database  51 , typically an operational datastore  11  (shown in FIG. 2), or alternatively an enterprise data warehouse  16  or work group datamart  18 , stores production (or informational) data. The stored data values change as transactions are committed to the source database  51 . Upon commitment, a log writer  53  generates a log entry into a transaction log  54  to journal the transacted event. Each log entry includes transactional data, as further described below with reference to FIG. 4, that identifies the table, record and operations performed. The log writer  53  “flushes” a running series of transaction logs  52  (TL), generated on a continuous basis.  
         [0042]    As log entries are written by the log writer  53  into the log  54 , the log monitor  26  applies selection criteria incorporating business logic to the journaled log entries.  
         [0043]    For each log entry that satisfies the selection criteria, the log monitor  26  retrieves the associated production (or informational) data from the source database  51  to generate updated records  57  for a destination database  58 . The selection criteria utilized by the log monitor  26  is implemented as a rule set  58 . The rule set incorporates business logic for selecting the transactions affecting production (or informational) data of interest to a decision support or on-line analytical processing system. In addition, the log monitor  26  utilizes metadata  56  describing the schema employed by the destination database  58 .  
         [0044]    The rule set  55  and metadata  56  can be dynamically redefined through a database builder tool  59 . An example of a database builder tool  59  suitable for use in the present invention is the Oracle Warehouse Builder product, licensed by Oracle Corporation, Redwood Shores, Calif. A user can redefine the business logic and metadata using the database builder tool  59 . Redefinitions are regularly forwarded to the log monitor  26 . Individual rules in the rule set  58  implement the business logic as data manipulation language (DML) operations. The metadata  56  is used by the log monitor  26  to structure the updated records  57  into a format used by tables stored in the destination database  58 . A structure of the rule set  55  is further described below with reference to FIG. 7.  
         [0045]    Preferably, the metadata  56  includes four components, as follows. First, the metadata  56  describes the architectures of the source database  51  and destination database  58 . The metadata  56  also includes network information describing the database links and aliases to other networks. The log  54  can also chronicle transactions performed on logically joined databases, such as in a distributed database environment. In addition, the metadata  56  includes job control and scheduling information specifying a frequency of execution for the implemented business logic against the journaled transaction entries. In the described embodiment, the job control information is specified in a higher order database manipulation language known as PL/SQL. Finally, the metadata  56  includes the actual source code for executing a selection criteria analysis. Other forms of metadata could also be used, for both production (or informational) data retrieval or recovery.  
         [0046]    As an optimization to performance, the log monitor  26  can also include a cache  60  for staging information, including log entries and updated records  57 . In the described embodiment, the log monitor  26  is implemented as a process separate from the transaction server  14  and database servers  17 ,  19  (shown in FIG. 1). The log monitor  26  operates in accordance with a sequence of process steps, further described below with reference to FIG. 8.  
         [0047]    [0047]FIG. 4 is a data structure diagram showing a log entry  70  used in the system  50  of FIG. 3. Each log entry  70  includes, by way of example, five fields of information. A time stamp  71  chronicles the date and time that the associated transaction was committed to the source database  51 . The table identifier  72  and record identifier  73  specify the source table and record entry or entries against which the transaction was committed. The operation type  74  identifies the committed transactions. Finally, the undo information  75  optionally describes the actual changes applied to the data in a format that allows the committed transaction to be unrolled. In the described embodiment, the undo information  75  identifies the table space contents and storage locations for the associated record. Using the undo information  75 , the committed transaction can be unrolled and the original record entry recovered. The undo information  75  can also be used by the log monitor  26  (shown in FIG. 3) to determine whether the selection criteria of the rule set  55  has been satisfied for the current log entry  70 .  
         [0048]    [0048]FIG. 5 is a process flow diagram showing informational database updating through log-based transaction monitoring. Data is updated as updated records  57  from the source database  51  into the destination database  58 . A committed transaction  91  is stored by the transaction server  92  into the source database  51 . A log entry  93  is generated upon the commitment of each transaction  91  and is stored into the log  54  by a log writer  53  (shown in FIG. 3). The log monitor  26  applies business logic  94 , as implemented in a rules set  55 . Each log entry  93  satisfying the selection criteria is used to generate an updated record  95  that is stored into the destination database  58  by the database server  96 .  
         [0049]    Unlike the informational data update approach used in the prior art multi-tiered database architecture  30  (shown in FIG. 2), the present approach generates updated records  95  by indirectly deriving the informational data through the log entries  93 . The log entries  93  are generated as a by-product of the transaction server  92 . Consequently, the updated records  95  are generated at minimal cost to the transaction server  92  and avoids the data replication and potential duplicity of data and business logic inherent in the prior art multi-tiered database hierarchy.  
         [0050]    [0050]FIG. 6 is a functional block diagram showing the software modules  110  of the system  50  of FIG. 3. The log monitor  26  includes two primary modules: an evaluation module  111  and a record-generation module  112 . The evaluation module  111  receives the log entries in the log  53  as input. The transaction data included in each log entry  70  (shown in FIG. 4) is evaluated against the selection criteria presented by the rules set  55 . Those log entries  113  that satisfy the selection criteria are forwarded to the record generation module  112 . Using the schema description of the destination database  58 , stored in the metadata  56 , the record generation module  112  creates updated records  57  for updating the destination database  58 .  
         [0051]    In the described embodiment, the updated records  57  are sent via a file transfer process, such as in accordance with the File Transfer Protocol (FTP) or similar network transport protocol. Each module of the log monitor is a computer program, procedure or module written as source code in a conventional programming language, such as the C++ programming language, and is presented for execution by the CPU as object or byte code, as is known in the art. The various implementations of the source code and object and byte codes can be held on a computer-readable storage medium or embodied on a transmission medium in a carrier wave.  
         [0052]    [0052]FIG. 7 is a data structure diagram showing a rule entry  130 . By way of example, and at a minimum, each rule  130  identifies the table  131  within the source database  51  to which the selection criteria is to be applied. A set of data manipulation language (DML) statements  132  implements the business logic through which the selection criteria are expressed. As well, a threshold  133  can be included to allow the log monitor  26  (shown in FIG. 3) to quickly and efficiently filter the log entries based on a predefined boundary condition. Other rule formats are feasible.  
         [0053]    [0053]FIG. 8 is a flow diagram of a method  150  for maintaining large-grained database concurrency with a log monitor  26  incorporating dynamically redefinable business logic in accordance with the present invention. Preliminarily, the log file  54  is opened by the log monitor  26  (block  151 ) preparatory to the application of the business logic. Each log entry (shown in FIG. 4) is then iteratively processed as follows.  
         [0054]    During each iteration, each log entry  70  is first read by the log monitor  26  (block  152 ). The log entry  70  is evaluated to determine whether the entry describes a committed transaction  91  (shown in FIG. 5) affecting a table  131  (shown in FIG. 7) to which the selection criteria applies (block  153 ). If the log entry  70  does apply to a listed table  131  (block  153 ), the rule, as implemented in the data manipulation language procedure  132 , is evaluated (block  154 ). If the selection criteria of the rule is satisfied (block  155 ), an updated record  57  is generated by the log monitor  26  using the metadata  56  (block  156 ) and the updated record is sent to the destination database  58  (block  157 ). Iterative processing continues (blocks  152 - 158 ) while there are more log entries  70  (block  158 ). Upon the processing of the last log entry  70  (block  158 ), the log file  54  is closed (block  159 ) and the routine terminates.  
         [0055]    While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.