Abstract:
A method, system, and computer program product provides a data dictionary that can represent multiple versions of the schema objects, and which provides improved performance, reduced computing costs, and more accurate results in a variety of applications, such as in a database redo log mining system. A method of providing a data dictionary comprises the steps of determining whether information about the data object is present in a denormalized data dictionary history table, and if the information about the data object is not present in the denormalized data dictionary history table, then querying a normalized data dictionary to obtain the information about the data object, including a version identifier of the data object, and storing the version identifier and the obtained information about the data object including the version identifier in the denormalized data dictionary history table.

Description:
FIELD OF THE INVENTION 
   The present invention relates to a method, system, and computer program product for providing a data dictionary that can represent multiple versions of the schema objects, and which provides improved performance, reduced computing costs, and more accurate results. 
   BACKGROUND OF THE INVENTION 
   In a typical relational database management system (RDBMS), all modifications to the database are logged in a redo stream (made up of redo records) to provide recovery and transaction durability. This redo stream (or redo log) can be used to drive asynchronous applications providing a variety of functionality. For example, the redo stream can be used to provide Logical Standby, in which a standby database shadows a primary database by extracting committed transactions out of the redo stream and applying those transactions. As another example, the redo stream can be used to provide Log-based replication, in which a replica site extracts committed changes made to the tables of interest in the database and applies the changes in order to keep the replica tables synchronized. As yet another example, the redo stream can be used to provide user query functionality, in which the redo stream is queried as though it were a relational table. 
   In one conventional application, the redo stream is analyzed to derive the equivalent data manipulation language (DML) statements that produced the redo stream. DML statements belonging to the same transaction are grouped together and committed transactions are provided to the application. Redo records typically only identify the modified schema objects or the associated columns with numbers generated internally to the database management system (DBMS). In order to perform log analysis and subsequent application of transactions, a data dictionary is needed to provide the mapping from the numbers to user-defined names. For example, SQL statements use column names and table names. 
   The organization of schema objects is not static. For example, columns may be dropped from or added to a table. Each new organization of a schema object defines a new version of the object. Since asynchronous log based applications may process a given portion of the redo stream multiple times and the organization of a schema object may change in the portion of the redo stream that must be reprocessed, the data dictionary required to do log analysis must represent multiple versions of the schema objects. Conventional log analysis application could only process a given portion of the redo stream one time or would allow multiple passes over a given portion of the redo stream either by requesting that the data dictionary be completely reloaded before each pass (very expensive in terms of computing) or by accepting results that were missing some symbolic information. 
   A need arises for a technique by which the data dictionary can represent multiple versions of the schema objects that provides improved performance, reduced computing costs, and more accurate results. 
   SUMMARY OF THE INVENTION 
   The present invention is a method, system, and computer program product for providing a data dictionary that can represent multiple versions of the schema objects, and which provides improved performance, reduced computing costs, and more accurate results. 
   In one embodiment of the present invention, a method of providing a data dictionary comprises the steps of determining whether information about the data object is present in a denormalized data dictionary history table, and if the information about the data object is not present in the denormalized data dictionary history table, then querying a normalized data dictionary to obtain the information about the data object, including a version identifier of the data object, and storing the obtained information about the data object including the version identifier in the denormalized data dictionary history table. The denormalized data dictionary history table may comprise at least one flattened table containing denormalized descriptions of data objects that have been previously referenced or reorganized. The method may further comprise the steps of receiving a stream of redo information from a database management system, the redo information comprising information relating to modifications made to a database of the database management system, and detecting a data dictionary transaction in the stream of redo information. The data dictionary transaction may represent a modification made to a system catalog of the database management system. The normalized data dictionary may comprise a normalized replication of the system catalog of the database management system. The information about the data object may comprise a denormalized description of the data object. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The details of the present invention, both as to its structure and operation, can best be understood by referring to the accompanying drawings, in which like reference numbers and designations refer to like elements. 
       FIG. 1  is an exemplary block diagram of a database management system, in which the present invention may be implemented. 
       FIG. 2  is an exemplary block diagram of a data flow and a data structure of redo mining, according to the present invention. 
       FIG. 3  is an exemplary flow diagram of a process of redo mining using a multi-version redo mining data dictionary shown in  FIG. 2 . 
       FIG. 4  is an exemplary format of a denormalized history table, shown in  FIG. 2 . 
       FIG. 5  is an exemplary block diagram of a database server system, in which the present invention may be implemented. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   An exemplary database management system (DBMS)  102 , in which the present invention may be implemented, is shown in  FIG. 1 . Database management system (DBMS)  102  provides the capability to store, organize, modify, and extract information from one or more databases included in DBMS  102 . From a technical standpoint, DBMSs can differ widely. The terms relational, network, flat, and hierarchical all refer to the way a DBMS organizes information internally. The internal organization can affect how quickly and flexibly information can be extracted. 
   Each database included in DBMS  102  includes a collection of information organized in such a way that computer software can select and retrieve desired pieces of data. Traditional databases are organized by fields, records, and files. A field is a single piece of information; a record is one complete set of fields; and a file is a collection of records. An alternative concept in database design is known as Hypertext. In a Hypertext database, any object, whether it be a piece of text, a picture, or a film, can be linked to any other object. Hypertext databases are particularly useful for organizing large amounts of disparate information, but they are not designed for numerical analysis. 
   Typically, a database includes not only data, but also low-level database management functions, which perform accesses to the database and store or retrieve data from the database. Such functions are often termed queries and are performed by using a database query language, such as Structured Query Language (SQL). SQL is a standardized query language for requesting information from a database. Historically, SQL has been a popular query language for database management systems running on minicomputers and mainframes. Increasingly, however, SQL is being supported by personal computer database systems because it supports distributed databases (databases that are spread out over several computer systems). This enables several users on a local-area network to access the same database simultaneously. 
   Most full-scale database systems are relational database systems. Small database systems, however, use other designs that provide less flexibility in posing queries. Relational databases are powerful because they require few assumptions about how data is related or how it will be extracted from the database. As a result, the same database can be viewed in many different ways. An important feature of relational systems is that a single database can be spread across several tables. This differs from flat-file databases, in which each database is self-contained in a single table. 
   DBMS  102  may also include one or more database applications, which are software that implements a particular set of functions that utilize one or more databases. Examples of database applications include:
         computerized library systems   automated teller machines   flight reservation systems   computerized parts inventory systems       

   Typically, a database application, includes data entry functions and data reporting functions. Data entry functions provide the capability to enter data into a database. Data entry may be performed manually, by data entry personnel, automatically, by data entry processing software that receives data from connected sources of data, or by a combination of manual and automated data entry techniques. Data reporting functions provide the capability to select and retrieve data from a database and to process and format that data for other uses. Typically, retrieved data is used to display information to a user, but retrieved data may also be used for other functions, such as account settlement, automated ordering, numerical machine control, etc. 
   DBMS  102  includes one or more databases, such as database  104 . Database  104  includes one or more data tables. One or more streams of transactions, such as transaction stream  106 , are input to DBMS  102 . A transaction is any database operation that may result in a change to database  104  or to the data stored in database  104 . Each transaction includes one or more Data Manipulation Language (DML) statements  107 , which are used to store, retrieve, modify, and erase data from database  104 . The performance of the DML statements  107  making up each transaction results in changes being made to the data stored in database  104 . These changes are used to generate redo stream  108 , which may be output from DBMS  102 . Redo stream  108  includes a plurality of redo records, in which each redo record specifies one or more changes that were made to the database or to the data stored in the database. 
   Transaction stream  106  includes a plurality of transactions, which include commands and/or statements that cause the performance of database operations that may result in a change to database  104  or to the data stored in database  104 . The commands and/or statements included in transaction stream  106  may be DML statements, or they may be higher-level commands, such as Application Program Interface (API) calls. Where transaction stream  106  includes API calls, these calls typically are converted to DML statements  107 , in order for the transactions to be performed. Where transaction stream  106  includes DML statements, in some embodiments, the DML statements may be performed directly, while in other embodiments, the DML statements may be converted to lower-level DML statements, which are then performed. For example, in some embodiments, transaction stream  106  may include DML statements, such as SQL statements, the SQL statements may be performed directly. In other embodiments, transaction stream  106  may include DML statements, such as SQL statements, but the SQL statements are converted to lower-level DML statements, which are then performed. 
   The redo records included in redo stream  108  may be processed to reconstruct the equivalent DML statement that produced them. DML statements belonging to the same transaction are grouped together and committed transactions are returned to the application. Since redo records identify the database objects affected by the transactions by internally generated numbers, in order to perform log analysis and subsequent application of transactions, a data dictionary is needed to provide the mapping between the internally generated numbers and the corresponding user defined names. For example, Structured Query Language (SQL) statements use column names and table names that typically have meaning to a person, while the internal database schema identifies the corresponding columns and tables with internally generated numbers. 
   An exemplary data flow and data structure of redo mining, according to the present invention, is shown in  FIG. 2 . Database management system (DBMS)  102  includes a plurality of data objects  202 , such as data tables that store data, and system catalog  204 , which stores a description of the data objects  202 . Data objects  202  are typically stored in an internal format in DBMS  102 , and are identified by internally generated identifiers, such as identification numbers. These internally generated identifiers provide efficient access to and processing of the data objects  202  by DBMS  102 , but they are not optimal for use by the users of DBMS  102 . System catalog  204  includes associations between the internally generated identifiers and object identifiers, such as user-defined names for the objects, which are more useful to the users of DBMS  102 . 
   As shown in  FIG. 1 , transactions  106  are performed by DBMS  102 . Transactions  106  cause changes to be made to the data stored in data objects  202 . These changes are captured by redo stream  108  and transmitted to redo mining system  206 . In addition, changes  208  are made to the contents of system catalog  204  and these changes are reflected as structural changes in data objects  202 . For example, data tables may be created or deleted, within data tables columns may be created or deleted, tables or columns may be renamed, etc. The initial state of system catalog  204  and changes reflected in system catalog  204  are also transmitted to redo mining system  206 , preferably as data dictionary language (DDL) transactions. Typically, DDL transactions  210  are transmitted to redo mining system  206  in redo stream  108 , but for clarity, they are shown separately in  FIG. 2 . 
   Redo mining system  206  includes redo mining application  212  and redo mining data dictionary  214 . Redo mining application  212  processes redo stream  108  to provide functionality such as logical standby, log-based replication, query functionality, etc. Data dictionary  214  includes data dictionary data tables  216  and history tables  218 . Data tables  216  do not include the historical versions of the state of system catalog  204 , but they do include the current state, in normalized format. The normalized format is similar to the format in which the information is stored in system catalog  204 . In the normalized format, a set of relational database tables is used to store the information. 
   History tables  218  include information about at least some of the historical versions of the state of system catalog  204  in a denormalized format. Preferably, only a portion of the historical states are captured, such as those bounded by specified starting and ending times. In the denormalized format, the information for each system catalog that is stored in the tables of the normalized format is stored in at least one flattened data table, which is shown further in  FIG. 4 . There may be a plurality of history tables  218  as there may be a plurality of system catalogs in DBMS  102 . 
   Each data object referenced in history tables  218  is identified by an object identifier, such as a number, and an object version. This provides the capability to perform mining of redo stream  108  as data objects  202  change. For example, if a column is added to a data table and redo mining application  212  is processing the redo stream for that data table, the processing of the versions of the data table that existed before the addition of the column will be different than the processing of the versions of the data table that existed after the addition of the column. 
   Version numbers are assigned by the DBMS  102  and written to a system catalog  204  table at the time a relevant object is created or modified. When the initial mining dictionary  214  is created, the version numbers along with other object attributes are communicated from the system catalog  204  to the mining data dictionary data tables  216 . If a DDL event occurs on DBMS  102  that alters an object, such as the addition of a column to a table, the table object&#39;s version number is incremented in the system catalog  204 , redo that shows that the version has been updated is transmitted via redo stream  210  and is applied to the mining dictionary data tables  216 . 
   Typically, only a small percentage of the total number of data objects  202  in a database is required to mine a given set of redo log files. Two different circumstances will cause required mining data from the data tables  216  to be written to a history table  218 . One is the request by a redo mining application for object information while processing a data manipulation language (DML) transaction. The other is the manipulation of the dictionary by the redo mining system  206  while processing a data dictionary language (DDL) transaction. 
     FIG. 3  shows an exemplary flow diagram of a process  300  of redo mining using multi-version redo mining data dictionary  214 . It is best viewed in conjunction with  FIG. 2 . The process begins with step  302 , in which a DML transaction is detected by the redo mining application while processing the redo stream. The redo mining application requests a description of an object that was manipulated by the DML transaction. This request for information includes the object number and the object version, which are the keys to access data dictionary  214 . 
   In step  304 , the request for information from the data dictionary  214  is first checked in a data dictionary history table  218 . The redo mining data dictionary  214  queries the appropriate data dictionary history table  218  to establish whether the current version of the requested object is present. A history table  218  stores denormalized descriptions of data objects that have been previously referenced by a DML event or reorganized by a DDL event. If the requested data object is found in a history table  218 , step  308  will be next. If the requested data object is not found in a history table  218 , this means that the object has never been modified and never been previously referenced by redo mining system  206 , and step  306  will be next. 
   In step  306 , data tables  216  are queried to obtain the information about the requested data object. Data tables  216  are a normalized replication of system catalog  204  of DBMS  102 , which produced the redo stream being processed. Redo mining data dictionary system  214  queries the normalized data tables  216  and obtains a denormalized description of the requested data object. The complexity and cost to query data tables  216  is typically more than to query a history table  218 . 
   In step  308 , the information about the requested object is returned. A version identifier, such as a version number, is included in the returned result. The denormalized description of the data object, including the version identifier, is, if not already present there, saved to the appropriate data dictionary history table  218 . 
   A DDL event in redo stream  108  may also cause data to be written to a history table  218 . A DDL transaction typically includes a special DDL event marker followed by redo information associated with the various manipulations of the relevant system catalog tables. A DDL transaction is inserted into the redo stream  108  when a modification is made to system catalog  204 . 
   Certain DDL events, which are going to cause the contents of the data tables  216  to be altered, will first trigger the fetching of relevant object descriptions from data tables  216  and the writing of that information to a history table  218 . This will happen when the version number of an object changes or when an object is deleted. For example, a DDL event that adds a new column to a partitioned table is an event that changes the version number for that table object. Before data tables  216  are modified to reflect the addition of a new column to the table object, a description of the table object and of each of the table&#39;s partition objects is captured and written to history table  218 . Then the redo information, which resulted from the original manipulations of the system catalog  204 , is transformed and applied to manipulate the corresponding tables of the normalized data tables  216 . The result is that a description of the previous version of the data object is captured in a history table  218  and the current version of the data object is captured in the normalized data tables  216 . 
   An example of a denormalized history table, such as history table  218 , shown in  FIG. 2 , is shown in  FIG. 4 . History table  218  includes several levels of information, such as object attributes  402 , common attributes  404 , table attributes  406 , and partition attributes  407 . Object attributes  402  include key information needed to look up a particular object, such as the object number  408 , which identifies the object, and the object version, which identifies the version of the object. Common attributes  404  include attributes that are common for most types of objects. Example attributes include: base object number  414 , which identifies the object for simple objects or identifies the parent for dependent objects, owner number  420 , which identifies the owner of an object, owner name  422 , which is an alphanumeric name of the owner identified by owner number  420 , table space number  428 , which identifies the table space in which the object is stored in the database, table space name  430 , which is an alphanumeric name of the table space identified by table space number  428 , and property and object flags  436 , which include specific flags defining parameters of the object. Table attributes include number of columns  438 , which indicates the number of columns in the table, table properties  426 , which defines properties of the table, and table flags  444 , which include specific flags defining parameters of the table and the table properties. Some objects described in a history table  218  are table partition objects. Partition Attributes are attributes unique to partition objects. These include: partition name  446 , which is the alphanumeric name of the partition with object number  408  and partition type  448 , which indicates whether a partition is a simple partition or a subpartition. 
   An exemplary block diagram of a database server system  500  is shown in  FIG. 5 . Server  500  is typically a programmed general-purpose computer system, such as a personal computer, workstation, server system, and minicomputer or mainframe computer. Server  500  includes one or more processors (CPUs)  502 A- 502 N, input/output circuitry  504 , network adapter  506 , and memory  508 . CPUs  502 A- 502 N execute program instructions in order to carry out the functions of the present invention. Typically, CPUs  502 A- 502 N are one or more microprocessors, such as an INTEL PENTIUM® processor.  FIG. 5  illustrates an embodiment in which server  500  is implemented as a single multi-processor computer system, in which multiple processors  502 A- 502 N share system resources, such as memory  508 , input/output circuitry  504 , and network adapter  506 . However, the present invention also contemplates embodiments in which server  500  is implemented as a plurality of networked computer systems, which may be single-processor computer systems, multi-processor computer systems, or a mix thereof. 
   Input/output circuitry  504  provides the capability to input data to, or output data from, server  500 . For example, input/output circuitry may include input devices, such as keyboards, mice, touchpads, trackballs, scanners, etc., output devices, such as video adapters, monitors, printers, etc., and input/output devices, such as, modems, etc. Network adapter  506  interfaces server  500  with Internet/intranet  510 . Internet/intranet  510  may include one or more standard local area network (LAN) or wide area network (WAN), such as Ethernet, Token Ring, the Internet, or a private or proprietary LAN/WAN. 
   Memory  508  stores program instructions that are executed by, and data that are used and processed by, CPU  502  to perform the functions of server  500 . Memory  508  may include electronic memory devices, such as random-access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc., and electromechanical memory, such as magnetic disk drives, tape drives, optical disk drives, etc., which may use an integrated drive electronics (IDE) interface, or a variation or enhancement thereof, such as enhanced IDE (EIDE) or ultra direct memory access (UDMA), or a small computer system interface (SCSI) based interface, or a variation or enhancement thereof, such as fast-SCSI, wide-SCSI, fast and wide-SCSI, etc, or a fiber channel-arbitrated loop (FC-AL) interface. 
   In the example shown in  FIG. 5 , memory  508  includes database management system  102 , redo mining system  206 , and operating system  516 . Although in this example, database management system  102  and redo mining system  206  are both shown included in database server  500 , one of skill in the art would recognize that these systems may be implemented together or separately, based on factors such as cost and performance. It is to be noted that the present invention contemplates any and all such implementations. 
   Database management system (DBMS)  102  provides the capability to store, organize, modify, and extract information from one or more databases included in DBMS  102 . Database management system (DBMS)  102  includes a plurality of data objects  202 , such as data tables that store data, and system catalog  204 , which stores a description of the data objects  202 . Data objects  202  are typically stored in an internal format in DBMS  102 , and are identified by internally generated identifiers, such as identification numbers. These internally generated identifiers provide efficient access to and processing of the data objects  202  by DBMS  102 , but they are not optimal for use by the users of DBMS  102 . System catalog  204  includes associations between the internally generated identifiers and object identifiers, such as user-defined names for the objects, which are more useful to the users of DBMS  102 . 
   Redo mining system  206  includes redo mining application  212  and redo mining data dictionary  214 . Redo mining application  212  processes redo stream  108  to provide functionality such as logical standby, log-based replication, query functionality, etc. Data dictionary  214  includes data dictionary data tables  216  and history tables  218 . Data tables  216  include the current state of system catalog  204 , in normalized format. The normalized format is similar to the format in which the information is stored in system catalog  204 . In the normalized format, a set of relational database tables is used to store the information. 
   History tables  218  include information about each historical version of the state of system catalog  204  in a denormalized format. In the denormalized format, the information for each system catalog that is stored in the tables of the normalized format is stored in a single flattened data table, which is shown further in  FIG. 4 . There may be a plurality of history tables  218  as there may be a plurality of system catalogs in DBMS  102 . 
   As shown in  FIG. 5 , the present invention contemplates implementation on a system or systems that provide multi-processor, multi-tasking, multi-process, and/or multi-thread computing, as well as implementation on systems that provide only single processor, single thread computing. Multi-processor computing involves performing computing using more than one processor. Multi-tasking computing involves performing computing using more than one operating system task. A task is an operating system concept that refers to the combination of a program being executed and bookkeeping information used by the operating system. Whenever a program is executed, the operating system creates a new task for it. The task is like an envelope for the program in that it identifies the program with a task number and attaches other bookkeeping information to it. Many operating systems, including UNIX®, OS/2®, and WINDOWS®, are capable of running many tasks at the same time and are called multitasking operating systems. Multi-tasking is the ability of an operating system to execute more than one executable at the same time. Each executable is running in its own address space, meaning that the executables have no way to share any of their memory. This has advantages, because it is impossible for any program to damage the execution of any of the other programs running on the system. However, the programs have no way to exchange any information except through the operating system (or by reading files stored on the file system). Multi-process computing is similar to multi-tasking computing, as the terms task and process are often used interchangeably, although some operating systems make a distinction between the two. 
   It is important to note that while the present invention has been described in the context of a fully functioning data processing system, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies equally regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media such as floppy disc, a hard disk drive, RAM, and CD-ROM&#39;s, as well as transmission-type media, such as digital and analog communications links. 
   Although specific embodiments of the present invention have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the invention is not to be limited by the specific illustrated embodiments, but only by the scope of the appended claims.