Patent Publication Number: US-7590645-B2

Title: Performant and scalable merge strategy for text indexing

Description:
REFERENCE TO RELATED APPLICATIONS 
   This application is a continuation application and claims priority to U.S. patent application Ser. No. 10/164,052, filed Jun. 5, 2002, entitled “Performant and Scalable Merge Strategy for Text Indexing”, which is incorporated herein by reference in its entirety. 

   FIELD OF THE INVENTION 
   This invention relates generally to the field of computing and in particular to full-text indexing in a database environment. 
   BACKGROUND 
   Full-text searching of unstructured and semi-structured data is becoming more and more important in the world of computing. For many years, the information-retrieval community has had to deal with the storage of documents and with the retrieval of documents based on one or more keywords. Since the burgeoning of the World Wide Web, and the feasibility of storing documents on-line, retrieval of documents based on keywords has become a thorny problem. A number of software solutions have been developed, which have attempted to address some of these problems. 
   A large portion of digitally stored information is presently stored in the form of unstructured textual data, both in plain text files and in formatted documents. Although the bulk of this textual data is stored in file systems, there are advantages to storing such data in relational databases. By doing so, the advantages of a database, including high-performance access, query capability, simple application-based user interfaces for end users, and secure remote access, are made available. 
   Relational Databases 
   Database management systems (DBMSs) such as SQL Server are widely used to search structured data. It is impractical, however, to search unstructured data (e.g., text documents) the same way structured data is searched because doing so is too expensive. 
   For example, in order to retrieve information from structured data in a database, a user typically provides a query (written in a query language such as SQL), where the query specifies the structured information to be retrieved (the search term or terms), the field in which the search term is to be found and the manner in which the retrieved information is to be manipulated or evaluated in order to provide a useful result, which is typically a relational operator or a function. To process the query, the database system typically converts the query into a relational expression that describes algebraically the result specified by the query. The relational expression is used to produce an execution plan, which describes particular steps to be taken by a computer to produce the requested result. Because the search term and the field where the search term is sought are specified, such results can be returned quickly. Indexes based on key fields, (e.g., an index based on name or social security number for a personnel database), routinely assist in efficient searching. 
   A similarly-conducted search for the same search term in unstructured data would require a word-by-word search of the entire text database and is unworkable. 
   Typically, today, an inverted index for searching documents is created by building a custom data structure external to the database system before a search query is entered. These solutions usually involve pulling data out of the database via bridges or gateways and storing the data as files in the file system so that textual indexing can be applied. Some systems actually store index data in a database but use an external engine to build and query the index. This approach does not provide a seamless way for a user to combine a textual query with a regular structured relational query and limits the extent to which a query can be optimized. 
   Typically, a full-text index is organized as a tree where internal nodes represent keywords and whose external nodes contain document identifiers and occurrences. When searched, the keyword(s) are looked up in the index and the documents containing the keyword(s) are retrieved. Naturally, whenever the collection of documents changes, a new index must be built or the existing index must be updated. 
   Although full text searching is frequently a capability of database management systems, the implementation of full-text search is typically unable to take advantage of the features of the database management system, which is to say, relational database management systems generally are unable to accommodate full-text searching of documents within the structure of the database. Typically, the index created to search the document database is not itself part of the database system (i.e., is separate from the database&#39;s index system). Because the index created is not part of the database system, certain limitations arise and certain highly advantageous aspects of database systems do not apply to typical full-text search systems. 
   Limitations associated with a full-text search system that relies on an external index include the following:
         Integration with existing database search technologies like Microsoft&#39;s SQL SERVER is fairly complex and difficult because the index is a custom index, and typically has its own transactional mechanism and storage mechanism. A significant amount of custom code, therefore, is needed for indexing, querying and administration.   Enhancements to existing or newly added systems that require a change in persistent storage format is difficult because changes in the storage management code of the custom index is required.   Implementation of scaling features such as the distribution of workload and files among multiple resources including clustering, etc., requires a significant amount of development.   Replication, i.e., keeping distributed databases synchronized by copying the entire database or subsets of the database to other servers in the network, is typically of the unsophisticated “full copy and propagate” form with very loose integrity semantics. A more efficient form of replication would require a significant amount of development.   Incorporation of database features such as query caching, keyword buffering, data partitioning etc. is more difficult since any such work frequently impacts the core engine code and sometimes impacts persistent store layout.   Upgrading from one file structure to another is a difficult development task.   A significant amount of code must be maintained to perform a function which is very similar to a function already performed by, for example, a cluster index associated with a relational database system such as SQL Server.   Query optimization cannot be tightly integrated.       

   Similarly, some of the advantages of database management systems are not applicable to a full-text search system based on a custom index. For example, most database systems have excellent facilities for data recovery in the event of database degradation, however, these data recovery systems do not work for the index file because the index file is not a DBMS data store. Hence data corruption can be a frequent problem with a file system index file. If there is a hardware malfunction it is very difficult to efficiently reach a point where the documents database and the documents index are in sync because the two different systems have different recovery protocols. 
   Backup and restore mechanisms for the index file generally do not have the advanced features typically available for database files, as discussed above. 
   Scalability issues exist for the index file. Scalability refers to partitioning one logical table into multiple physical tables on the same machine or on different machines in order to accommodate very large collections of data. For example, instead of storing a large database on a single resource, it is frequently desirable to split or partition the database across a number of resources. Database data stores generally maintain data in tables that can reside locally on a single data store or can be distributed among several data stores in a distributed database environment. 
   Advantages to partitioning are the reduction of processing load on a single resource, faster access to data and if a particular machine experiences a hardware failure, only part of the data is lost. Partitioning, however, is typically not available for a file system index file, because partitioning a file system file requires a separate infrastructure. Thus, typically the index file, although frequently very large, cannot be partitioned, so a single resource must be dedicated to the index. 
   Hence, a need exists in the art to provide a full-text searching system wherein the index is built upon standard database technology. 
   Most of the methods of building text indexes based on keyword, document identifier and occurrence lists share the mechanism of building compressed inverted lists and merging the inverted lists to build merged indexes. For example, when a database is searched, data is typically scanned and then indexed. Each time a crawler finishes crawling a batch of data, an indexer may build an inverted list of keywords with data identifiers and the occurrences of the keyword(s) in the data (an index). Typically the index is persisted. Frequently several (or many) indexes are built per data set because typically the body of data is quite large. Indexes are then merged together. During the merge of an old index into a new index, typically a table lookup must be done for every data identifier in the older index to see if the data has been changed or deleted since the older index was built. For example, if a particular data item was present in the older index but is deleted or changed later, the information about the data from the old index is not included in the new index. Typically, for performance reasons, this table is stored in memory. It would be helpful if the number of table lookups could be reduced, especially if the need for an in-memory data structure for the lookup table could be reduced or eliminated. 
   SUMMARY OF THE INVENTION 
   A full-text indexing system and method is built using standard database technology, (e.g., the Microsoft SQL SERVER storage and query engine). An inverted list is generated comprising keywords contained in data, (e.g., text), an identifier associated with the data and the occurrence(s) of the keyword in the list. An instance of a database index (part of the DBMS indexing system) is created and stored in a priority queue. A scheduling process determines when instances of the index are to be merged. Instances are prioritized by number of rows (keywords) and size. One of three types of merges is selected and the instances are merged. 
   A crawl associated with a temporal indicator, (called herein a “timestamp” and equivalently represented by the variable TS), crawls through (scans) data. Data that has been crawled is also associated with the crawl timestamp. A crawl timestamp is assigned to a crawl at the start of the crawl. A table, (called herein “DocIdMap”), maps a full-text data item identifier to a numeric identifier. The numeric identifier of the data item is also preferably associated with the crawl timestamp of the crawl that scanned the data. 
   A plurality of indexes may be generated by one crawl. Each of the indexes generated by a crawl receives the timestamp of the crawl that generated it so that all of the indexes generated by the same crawl will have the same timestamp. Indexes generated by a subsequent crawl will have a timestamp that is greater (or recognizable as later-occurring) than that of the earlier crawl. A crawl is associated with a timestamp based on the start time of the crawl so that crawls and their resulting indexes are ordered temporally. 
   A set of indexes to be merged is selected. Validity of the set of indexes is determined. Merge transactions merge a set of validated indexes into one comprehensive index. The merge process scans a set of indexes in order of keywords and for each keyword from each index, scans the numeric identifiers in the associated list of numeric identifiers. If a numeric identifier qualifies for the new index the numeric identifier and its associated occurrences are put into the new index. If a numeric identifier does not qualify for the new index, the numeric identifier and its associated occurrence list is skipped and the next numeric identifier is considered. 
   Whether or not a numeric identifier of a particular index qualifies for the new index is determined by comparing the timestamp of the index with the maximum timestamp of the set of indexes to be merged and the timestamp of the identifier. A lookup into the DocidMap table is only required when a data item is deleted or when a timestamp for the numeric identifier is less than the maximum timestamp of the indexes to be merged. This reduces the need for table lookups and in some cases eliminates the lookup completely. Because the need for table lookups is significantly reduced, all of the table does not have to be resident in memory all the time. As a special case, in case of a full crawl, there is no need for a lookup of the mapping table. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing summary, as well as the following detailed description of preferred embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings exemplary constructions of the invention; however, the invention is not limited to the specific methods and instrumentalities disclosed. In the drawings: 
       FIG. 1  is a block diagram of an exemplary computing environment in which aspects of the invention may be implemented; 
       FIG. 2  is a block diagram of a full-text search indexing system as is known in the art; 
       FIG. 3  is a block diagram of a portion of the full-text search indexing system of  FIG. 2 , as is known in the art; 
       FIG. 4  is a block diagram of a full-text search indexing system in accordance with one embodiment of the invention; 
       FIG. 5  is a flow diagram of a portion of a full-text search indexing method in accordance with one embodiment of the invention; 
       FIG. 6  is block diagram of a tree data structure implementation of one aspect of the invention, the shared memory InvertedList; 
       FIG. 7  is a representation of a database structure for a full text index dictionary in accordance with one embodiment of the invention; 
       FIG. 8  is a representation of a database structure for a full text index data store in accordance with one embodiment of the invention; 
       FIG. 9  is a flow diagram of a method for scheduling and selecting indexes to merge in accordance with one embodiment of the invention; 
       FIG. 10   a  is a block diagram of one type of merge in accordance with one embodiment of the invention; 
       FIG. 10   b  is a block diagram of another type of merge in accordance with one embodiment of the invention;  FIG. 10   c  is a block diagram of a third type of merge in accordance with one embodiment of the invention; 
       FIG. 11  is a representation of a database structure for a document identification map in accordance with one embodiment of the invention; 
       FIG. 12  is a representation of a database structure for a crawl dictionary in accordance with one embodiment of the invention; 
       FIG. 13  is a representation of a database structure for a full text index dictionary in accordance with one embodiment of the invention; 
       FIG. 14  is a flow diagram of an exemplary method of indexing in accordance with one embodiment of the invention; 
       FIG. 15  is a block diagram of an exemplary indexing system in accordance with one embodiment of the invention; 
       FIG. 16  is a block diagram of an exemplary indexing system in accordance with one embodiment of the invention; 
       FIG. 17  is a flow diagram of an exemplary method of selecting indexes for merging in accordance with one embodiment of the invention; 
       FIG. 18  is a block diagram of exemplary indexes to be merged and a merged index in accordance with one embodiment of the invention; 
       FIG. 19  is a flow diagram of an exemplary method of merging indexes in accordance with one embodiment of the invention; 
       FIG. 20  is an exemplary view  2000  of the table of  FIG. 11  in accordance with one embodiment of the invention; and 
       FIG. 21  is a flow diagram of an exemplary method of indexing and merging indexes in accordance with one embodiment of the invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   Exemplary Computing Environment 
     FIG. 1  and the following discussion are intended to provide a brief general description of a suitable computing environment in which the invention may be implemented. It should be understood, however, that handheld, portable, and other computing devices of all kinds are contemplated for use in connection with the present invention. While a general purpose computer is described below, this is but one example, and the present invention requires only a thin client having network server interoperability and interaction. Thus, the present invention may be implemented in an environment of networked hosted services in which very little or minimal client resources are implicated, e.g., a networked environment in which the client device serves merely as a browser or interface to the World Wide Web. 
   Although not required, the invention can be implemented via an application programming interface (API), for use by a developer, and/or included within the network browsing software which will be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers, or other devices. Generally, program modules include routines, programs, objects, components, data structures and the like that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations. Other well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers (PCs), automated teller machines, server computers, hand-held or laptop devices, multi-processor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network or other data transmission medium. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. 
     FIG. 1  thus illustrates an example of a suitable computing system environment  100  in which the invention may be implemented, although as made clear above, the computing system environment  100  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  100  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  100 . 
   With reference to  FIG. 1 , an exemplary system for implementing the invention includes a general purpose computing device in the form of a computer  110 . Components of computer  110  may include, but are not limited to, a processing unit  120 , a system memory  130 , and a system bus  121  that couples various system components including the system memory to the processing unit  120 . The system bus  121  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus (also known as Mezzanine bus). 
   Computer  110  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  110  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CDROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  110 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
   The system memory  130  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  131  and random access memory (RAM)  132 . A basic input/output system  133  (BIOS), containing the basic routines that help to transfer information between elements within computer  110 , such as during start-up, is typically stored in ROM  131 . RAM  132  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  120 . By way of example, and not limitation,  FIG. 1  illustrates operating system  134 , application programs  135 , other program modules  136 , and program data  137 . 
   The computer  110  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 1  illustrates a hard disk drive  141  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  151  that reads from or writes to a removable, nonvolatile magnetic disk  152 , and an optical disk drive  155  that reads from or writes to a removable, nonvolatile optical disk  156 , such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  141  is typically connected to the system bus  121  through a non-removable memory interface such as interface  140 , and magnetic disk drive  151  and optical disk drive  155  are typically connected to the system bus  121  by a removable memory interface, such as interface  150 . 
   The drives and their associated computer storage media discussed above and illustrated in  FIG. 1  provide storage of computer readable instructions, data structures, program modules and other data for the computer  110 . In  FIG. 1 , for example, hard disk drive  141  is illustrated as storing operating system  144 , application programs  145 , other program modules  146 , and program data  147 . Note that these components can either be the same as or different from operating system  134 , application programs  135 , other program modules  136 , and program data  137 . Operating system  144 , application programs  145 , other program modules  146 , and program data  147  are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer  110  through input devices such as a keyboard  162  and pointing device  161 , commonly referred to as a mouse, trackball or touch pad. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  120  through a user input interface  160  that is coupled to the system bus  121 , but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). 
   A monitor  191  or other type of display device is also connected to the system bus  121  via an interface, such as a video interface  190 . A graphics interface  182 , such as Northbridge, may also be connected to the system bus  121 . Northbridge is a chipset that communicates with the CPU, or host processing unit  120 , and assumes responsibility for accelerated graphics port (AGP) communications. One or more graphics processing units (GPUs)  184  may communicate with graphics interface  182 . In this regard, GPUs  184  generally include on-chip memory storage, such as register storage and GPUs  184  communicate with a video memory  186 . GPUs  184 , however, are but one example of a coprocessor and thus a variety of coprocessing devices may be included in computer  110 . A monitor  191  or other type of display device is also connected to the system bus  121  via an interface, such as a video interface  190 , which may in turn communicate with video memory  186 . In addition to monitor  191 , computers may also include other peripheral output devices such as speakers  197  and printer  196 , which may be connected through an output peripheral interface  195 . 
   The computer  110  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  180 . The remote computer  180  may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  110 , although only a memory storage device  181  has been illustrated in  FIG. 1 . The logical connections depicted in  FIG. 1  include a local area network (LAN)  171  and a wide area network (WAN)  173 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the computer  110  is connected to the LAN  171  through a network interface or adapter  170 . When used in a WAN networking environment, the computer  110  typically includes a modem  172  or other means for establishing communications over the WAN  173 , such as the Internet. The modem  172 , which may be internal or external, may be connected to the system bus  121  via the user input interface  160 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  110 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 1  illustrates remote application programs  185  as residing on memory device  181 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
   One of ordinary skill in the art can appreciate that a computer  110  or other client device can be deployed as part of a computer network. In this regard, the present invention pertains to any computer system having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units or volumes. The present invention may apply to an environment with server computers and client computers deployed in a network environment, having remote or local storage. The present invention may also apply to a standalone computing device, having programming language functionality, interpretation and execution capabilities. 
   Full-Text Indexing System 
   Full-text search indexing systems utilizing an external index may be implemented as shown in  FIG. 2 . Typically a group of documents  210  includes individual documents  210   a ,  210   b , etc., one or more of which may be loaded into a database basetable of a DBMS  202 . In general, a document  210   a  is loaded into the columns of a row in the basetable, one document per column. The database system  202 , including database management system engine  214  and data stores (databases)  212 , typically associates document  210   a  with a unique document identifier (called herein full-text key) which is used to identify the row in the basetable that contains the document. The text of document  210   a  is provided to word breaker  206  by search engine  204 . Word breaker  206  determines which words of the text of document  210   a  should be keywords (also known as tokens). Word breaker  206  also determines the location(s) of the keyword in the document, as a number representing byte offset from the beginning of the document. In other words, if word breaker  206  determines that a key word of document  210   a  is “innovation” and the word “innovation” is the first, fourteenth and twenty-fifth word in document  210   a , the word breaker  206  would typically return the keyword “innovation” and the byte offsets at which the first, fourteenth and twenty-fifth words are located, to search engine  204 . An index  208  of tokens or keywords is generated by an external executable such as a search engine  204 . For each keyword associated with the text of document  210   a , an entry in index  208  would be created, typically containing the keyword and the occurrences of the keyword in document  210   a  for each keyword in document  210   a.    
   For example, and referring now to  FIG. 3 , suppose a document  210   a  of a collection of documents  210 , including document  210   a ,  210   b , etc., is loaded into a database basetable  216 . Document  210   a  is assigned a full-text key by the database management system  202 . For example, database management system  202  may assign document  210   a  the full-text key “ 1017 ”  224 . The full-text key assigned by the database management system  202  typically is alphanumeric and can be rather large. Assume document  210   a  full-text key  1017   224  contains the text “This innovation is a significant step towards search and indexing technology.”  225  somewhere within the document. When full-text key  1017   224  is loaded into basetable  216 , “This innovation is a significant step towards search and indexing technology” is loaded into basetable  216  of database management system  202 . The words “This innovation is a significant step towards search and indexing technology”, for example, might be loaded into word locations  99  through  110  of a row in the content field  222  of basetable  216 . 
   External search engine  204  may retrieve data associated with full-text key  1017   224  from basetable  216  and send this data to word breaker  206 . Typically a word beaker  206  would be invoked to pick out the keywords or tokens from the text of document  210   a . In the example, word breaker  206  has determined that the keywords contained in document  210   a  include “innovation” and “significant”. Word breaker  206  also typically determines the byte offset from the beginning of the document at which the keywords (also referred to as “tokens”) are found. For example, word breaker  206  may determine that “innovation” can be found in document  210   a  at byte offsets  1000 ,  12104  and  16170 , and that “significant” can be found in document  210   a  at byte offsets  1050 ,  1600 ,  15138  and  17010 . This information may be passed back to search engine  204 . 
   Search engine  204  typically uses this information to create index  218  and may convert byte offset to word offset. In the example described above, index  218  would contain keyword “innovation”  226  followed by full-text key  1017   224  followed by the locations (word offset)  228  at which “innovation” can be found in the document, (i.e.,  100 ,  1513 ,  1617 ). Suppose another document  210   b  (assigned a full-text key of “ 1033 ”) also contains the word “innovation” at word locations  200 ,  205  and  375 . The index  218  in this case would contain an entry for full-text key  1033   230  followed by the locations  236  at which “innovation” can be found (i.e.,  200 ,  205 ,  375 ) in full-text key  1033 . Similar entries  234 ,  238  for keyword “significant” would also be generated for full-text key  1017  and full-text key  1033  ( 234 ,  238 ). Thus a file system index typically is created containing keywords, full-text keys for documents containing each keyword and the locations within each document containing the keyword specifying the location by word offset of the keyword in that document. 
   A user querying the database might ask, for example, for all the documents containing the words “innovation” located near the word “significant”. Depending on the specific query form, a table of results might be returned, listing the document numbers containing the words “innovation” located near “significant”. Additionally, a “rank” for each document is also typically returned, where the rank is based on some proprietary formula, (such as, ranked by number of times the word(s) appeared in the document, whether the requested word(s) appeared in the title of the document, etc.). Rank typically reflects the relevance of the document to the search query. 
   Index  218  is typically a structure built on the file system and is not a database file and therefore is subject to the limitations in recoverability, reliability, management and scalability described above. 
   Full-Text Indexing on Relational Store 
     FIG. 4  illustrates a full-text indexing system built on a relational store in accordance with one embodiment of the invention. Referring now to  FIG. 4 , document set  210 , comprising a plurality of documents  210   a ,  210   b , etc. is loaded into basetable  416  of DBMS  202 . Alternatively, the document set  210  may exist as file system files. Thread pool  406  preferably contains a plurality of processing threads,  406   a ,  406   b , etc. and can access basetable  416 . Text shared memory  402  preferably can be accessed by both word breaker  206  (preferably, an outside process  207 ) and the threads comprising threadpool  406 . Keyword shared memory  404  preferably can be accessed by both word breaker  206  and the threads of threadpool  406 . Threads  406   a ,  406   b  etc. generate instances of index  410 , including  410   a ,  410   b , etc. Instances  410   a ,  410   b , etc. of index  410  are put in priority queue  408  and are merged by merge threads  414   a ,  414   b , etc. of merge thread pool  414  to generate merged index  412 . Merged index  412  may include multiple index instances  412   a ,  412   b , etc. Merged indexes are placed into priority queue  408 . 
     FIG. 5  is a flow diagram of a full-text search indexing method in accordance with one embodiment of the invention. Threads  406   a ,  406   b , etc. from thread pool  406  represent separate activities and process separate batch transactions. Preferably these separate threads can be processed in parallel.  FIG. 5  represents the process each thread  406   a ,  406   b  performs when it processes a batch of document rows. An instance of index  410 , represented by  410   a ,  410   b , etc. is generated by each batch as rows from basetable  416  are crawled by the separate processing threads. 
   At step  502 , a new batch scan transaction is begun. Each batch begins with a new transaction. At step  504 , a row of data is retrieved from basetable  416 . A thread from thread pool  406  scans basetable  416  and stores data (i.e., text) from a row of basetable  416  in text shared memory  402 . For example, thread  406   a  may scan row  1  of basetable  416 , storing the row in chunks in shared text memory  402  at some location in the shared memory associated with thread  406   a.    
   At step  506  a unique numeric document identifier, (referred to as DocId henceforth), is generated, which preferably translates a (sometimes) large full-text key that is not necessarily numeric in nature to a compact numerical identifier, called herein “DocId”. For example, full-text key  1017  may be translated to DocId “1” and full-text key  1033  may be translated to DocId “2”. A table that maps full-text keys to DocIds is maintained, preferably keyed on full-text key. As each row is crawled, the new unique DocId generated is stored as well in text shared memory  402 . 
   Text shared memory  402  preferably can be accessed by word breaker  206 . As the rows are crawled, the full-text columns stored in shared memory  402  are parsed by filters and word breakers, collectively represented by reference numeral  206  of  FIG. 4 . Filters typically are modules that parse formatted documents, (e.g., WORD documents or POWERPOINT documents) and emit chunks of text to be parsed by word breaker  206 . 
   Filters (of  206 ) may retrieve, for example, the data contained in row  1  of basetable  416  stored in shared memory  402 , and emit a text chunk. Word breaker  206  feeds on the emitted text chunks and determines which of the words in the document are keywords. For each of the words determined by word breaker  206  to be a keyword, the location(s) of that keyword in the document is determined. Word breaker  206  writes the keyword and occurrence information for each DocId to keyword shared memory  404 . 
   At step  508 , a thread (e.g., thread  406   a ) picks up the keyword, the DocId and the occurrences of the keyword from keyword shared memory and inserts them into an inverted list, called herein InvertedList, associated with the batch. 
   InvertedList preferably is a short in-memory list of keywords and keyword occurrences. An inverted list is generated for the batch of documents processed by the thread, transaction by transaction. Alternatively, more than one inverted list is generated per batch of documents. 
   InvertedList, as illustrated by  FIG. 6 , is preferably implemented as an in-memory balanced tree,  600  of keywords  602 ,  604 , etc. found in the documents processed in the batch. In one embodiment, for each keyword, a heap of DocIds  606 ,  608  and  610 ,  612  containing that keyword is maintained. For each DocId  606 ,  608  and  610 ,  612  in the heap, a sorted list of locations  614 ,  616  and  618 ,  620  (word position, preferably implemented as word offset, which is the same as the basetable  216  word location) is maintained, where the respective keyword appears in the document. This information is preferably stored in local memory (not shown) associated with thread  406   a.    
   Rows from the basetable  416  are processed until it is determined that the batch is complete (step  510 ). Batches may be determined to be complete when some configurable parameter representing a number of documents processed has been reached. When the batch is complete, at step  512 , thread  406   a  creates an instance of index  410 , (e.g., index  410   a ). Index  410  is a standard database table, (e.g., a SQL B-tree). A database table containing meta-data about instances of index  410  is also updated. In the example, the meta-data table is updated to reflect index instance  410   a . This table is called herein FtIndex_Dictionary. The relevant structure of an exemplary FtIndex_Dictionary is depicted in  FIG. 7 , (i.e., FtIndex_Dictionary may include additional entries). 
   Elements of FtIndex_Dictionary  700  preferably include: 
   IndexId  702 : a unique identifier for an instance of FtIndex, 
   ObjectId  704 : a table identifier for the instance of FtIndex, created by the DBMS, 
   IndState  706 : a state indicator for the instance of FtIndex. 
   Possible values for IndState include StateNew, StateOpen, StateClosed and StateInMerge. IndState for the index instance is set to StateNew when the index object is created but is not yet used. IndState for the index instance is set to StateOpen when the index is in use but does not yet contain any committed data. IndState for the index instance is set to StateClosed when the index instance contains committed data and processing is complete. IndState for the index instance is set to StateInMerge when the index instance is being merged with other indexes. 
   IndRows  708 : number of rows in the index (a count of keywords inserted) 
   IndDataSize  710 : an estimate (approximate) size of the instance of the index. 
   After the inverted list is persisted into an instance of an index and DocIdMap has been updated for the batch of rows processed by the thread, the batch is committed (e.g., SQL commit) at step  514 . The 2-part commit transaction ascertains that all locks and resources are acquired and then performs the disk writes. The commit transaction guarantees that no document in a batch is partially committed. If all locks and resources are not acquired, the database changes are entirely backed out. As soon as an instance of an index is committed the index is queryable. 
     FIG. 8  illustrates the physical schema of an instance of an index fragment (FtIndex  800 ) in accordance with one embodiment of the invention. Keyword  802  represents a normalized keyword from the document as determined by word breaker  206 . Preferably Keyword  802  is a variable-length binary of maximum length 132 bytes. Keyword preferably is normalized for case and diacritical marks, based on a user&#39;s configuration. PropertyId  804  represents the column id of an attribute such as Title, Author, Content, etc. DocIdList  806  is a list of DocIds in which Keyword  802  appears. Preferably, the list of DocIds is compressed bit-wise as a BLOB, a Binary Large Object (i.e., a collection of binary data stored as a single entity). DocCount  808  is the number of documents containing the keyword, DocIdMin  810  is the smallest DocId stored in DocIdList  806 . DocIdMax  812  is the largest DocId stored in DocIdList  806 . OccListList  814  is a list of occurrence lists, one occurrence list for each DocId in DocIdList  806 . OccCountList  816  is the number of occurrences in the corresponding occurrence lists for corresponding DocIds. Index fragments are indexed by a cluster index on (Keyword  802 , PropertyId  804  and DocIdMin  810 ). 
   An InvertedList is persisted by inserting one or more rows for each keyword in InvertedList into a new instance of FtIndex. DocIdList  806  is generated by compressing sorted DocIds from InvertedList into a binary stream. Occurrences of the keyword from InvertedList are also compressed into a binary stream for each DocId containing the keyword. Occurrence lists of two consecutive DocIds stored in OccListList  814  are preferably separated by a sentinel in the compressed stream to delimit the end of one occurrence list from the beginning of the next occurrence list. When the InvertedList is persisted into an instance of FtIndex and FtIndex is committed, the state of that instance of FtIndex becomes StateClosed. The number of DocIdList BLOBs stored in a row in an instance of FtIndex is preferably limited by a parameter. At step  516 , the instance of FtIndex is added to priority queue  408 . 
   Merging 
   Periodically, instances of index  410  (e.g., FtIndex) are merged into fewer instances. Merging instances of the index improves query performance because fewer indexes have to be checked when a query is processed. Merging instances into fewer instances also optimizes the amount of storage and memory required because data repeated in index instances is eliminated (e.g., FtIndex_A and FtIndex_B may both contain the keyword “innovation” so that two rows (one in FtIndex_A and one in FtIndex_B) can be combined into one row in FtIndex_X). Finally, the number of I/O (input/output) operations is reduced because when the merged index is rewritten, related data is physically located closer together. 
     FIG. 9  illustrates a method  900  for selecting when to merge persisted indexes and also which indexes and how many indexes to select to merge. In general, it is desirable to merge as many indexes as possible in one merge so that the same data is not merged and merged again and merged again and so on, etc. On the other hand, in general, it is desirable to have as few open (unmerged) indexes as possible so that queries will have to check fewer indexes. For example, if there are 1000 indexes and a query is received, all 1000 indexes will have to be checked, necessitating a seek through each of the 1000 indexes. After a merge is performed, (perhaps all 1000 indexes have been merged into one index), typically, disk space requirements are reduced because redundant information is reduced, (the one merged index takes less space than the 1000 unmerged indexes), and the number of I/O (input/output) operations required are reduced because a fewer number of indexes exist (a seek is done on one index instead of on 1000 indexes). Typically as well, when the merged index is created, the keyword, document identification data and occurrences data are rewritten so that this information is located physically closer together, reducing access time. One embodiment of the invention balances the amount of query overhead that results from the existence of a large number of indexes with the amount of processing resources required to merge indexes. A soft configurable parameter called herein OpenIndexes determines when a desirable number of open (unmerged) indexes has been exceeded and influences when a merge is initiated. At step  901 , a merge is initiated. At step  902 , a list of indexes selected to merge (called herein MergeList) is initialized to “empty”. 
   It is desirable to merge indexes of approximately the same size for efficiency considerations. The priority queue of indexes is preferably implemented as a heap data structure having the sort order: (IndRows, IndDataSize) so that the smallest index in that order is located at the top of the heap. Hence, as indexes are selected from the priority queue in step  904  for merging, indexes containing less data are selected before those containing more data. It will be appreciated that an alternative to sorting in ascending order and selecting from the top of the queue is sorting in descending order and selecting from the bottom of the queue. At step  904  an index is selected to merge. Indexes are pulled from priority queue  406  until any of the following conditions occur. It should be understood that the order of the steps  906 ,  908  and  910  has been selected for exemplary purposes only and in practice any ordering or combination of these steps may be implemented by the conceived invention. A counter, called herein SelectedIndexCounter, that tracks the number of indexes selected to merge, is incremented. In one embodiment of the invention, a configurable parameter referred to herein as MergeFanout determines the preferred number of indexes to be merged in one merge. At step  906 , SelectedIndexCounter is compared to MergeFanout. If MergeFanout is reached or exceeded, processing continues at step  912 . If MergeFanout is not reached, processing continues at step  908 . 
   It is desirable to merge indexes of approximately the same size; however, even indexes of the same overall size may merge inefficiently. For example, FtIndex — 001 and FtIndex — 002 may be approximately the same size but FtIndex — 001 may have a small number of keywords (rows) with a large number of associated DocIds and associated data while FtIndex — 002 may have a larger number of keywords (rows) with a smaller number of documents associated therewith. Alternatively, FtIndex — 001 and FtIndex — 002 may have approximately the same number of unique keys (rows) but FtIndex — 001 may reflect the indexing of far more documents than does FtIndex — 002. Hence, steps  908  and  910  are performed. At step  908  IndRows  708  for the selected index is compared to the average number of rows in the other selected indexes. If IndRows  708  for the selected index is much greater than the average, processing continues at step  912 . If not, processing continues at step  910 . At step  910 , IndDataSize for the last index selected is compared to the average IndDataSize of the other selected indexes. If IndDataSize for the last index selected is much greater than the average, processing continues at step  912 . If IndDataSize for the last index selected is not much greater than the average, processing continues at step  911  in which the index is added to MergeList and the next index is selected from the merge queue (step  904 ). 
   At step  912 , the last selected index is returned to the priority queue and processing continues at step  904 . At step  914 , the selected indexes (in MergeList) are merged. Merging preferably can occur in one of three ways.  FIG. 10   a  illustrates a first merge strategy in accordance with one aspect of the invention. In MergeOutOfPlace, indexes selected from the priority queue  408  (represented by exemplary indexes I 0    1002 , I 1    1004 , . . . I n-1    1006 ) are merged in the order of (Keyword, PropertyId) to form a merged DocIdList, OccListList and OccCountList and the merged rows are inserted into a new index (e.g., I n    1008 ). This type of merge is preferably performed when most of the rows need to be updated. MergeOutOfPlace is typically very fast because a bulk insert can be performed. 
     FIG. 10   b  illustrates a second merge strategy in accordance with one aspect of the invention. In InsertMerge, rows from all indexes (e.g., I 0    1002 , I 1    1004 , . . . I n-1    1006 , I n    1008 ) except the last index selected (I n    1008 ) are merged in (Keyword, Property Id) order into MergedRows  1010  and the merged rows to form a merged DocIdList, OccListList and OccCountList and the merged rows (rows from I 0    1002 , I 1    1004 , . . . I n-1    1006 ) are inserted into the last index, I n    1008 . InsertMerge is preferably performed when there are few keywords that need to be inserted into the index. 
     FIG. 10   c  illustrates a third merge strategy in accordance with one aspect of the invention. In MergeInPlace all the rows from all the indexes (i.e., I 0    1002 , I 1    1004 , . . . I n-1    1006 , I n    1008 ) are merged in the order of (Keyword, PropertyId) to form a merged DocIdList, OccListList and OccCountList and the merged rows are inserted back into the last index selected, (i.e., I n    1008 ). If a row from the last index, (i.e., I n    1008 ), does not need to be merged with any other row from the other indexes (i.e., I 0    1002 , I 1    1004 , . . . I n-1    1006 ), then no changes are made to that row. If a merge is required with one or more rows from the other indexes, then the original rows participating in merger from the last index are deleted and the merged row is inserted. MergeInPlace is preferable when there are many keywords to be inserted but MergeOutOfPlace is not feasible because of resource (e.g. disk space) limitations. 
   Performant Merge System 
   An indexing module (e.g., thread  406   a  of thread pool  406 ) builds instances of an index and assigns a temporal indicator to the instance. Instances of the index are placed in a priority queue  408 . A merging thread (e.g.,  414   a  of merge pool  414 ) selects instances from priority queue  408  to merge and validates the set of selected instances. Invalid instances are returned to priority queue  408 . Valid instances are merged to generate an instance of a merged index (e.g.,  412   a  of  412 ). The merged index is returned to the priority queue  408 . 
   The following database tables, illustrated in  FIGS. 11 ,  12 ,  13 ,  15 ,  16  and  20  are built and maintained. Preferably the database management system utilized is SQL SERVER but use of any suitable database management system is contemplated by the invention. 
     FIG. 11  represents a table (called herein DocIdMap)  1100  that maps a unique alphanumeric data identifier (called herein DocKey) to a smaller numeric identifier (called herein DocId). DocIdMap  1100  preferably includes DocKey  1102 , DocId  1104  and DocIdTimestamp  1106 . DocKey  1102  (a numeric or alphanumeric identifier, of potentially considerable size) uniquely identifies the source of the data (e.g., the source document if the data comprises a document.) For example, DocKey  1102  preferably is the full-text key in a full-text indexed basetable or a unique identifier of a file in a file system. DocId  1104  is a unique identifier generated for every crawled row in a database identified by DocKey or every crawled file in a file system. Preferably, DocId  1104  is a smaller numeric identifier that is more easily compressible than DocKey  1102 . DocId  1104  is used as the data item identifier in the index. DocIdTimestamp  1106  is the timestamp of the crawl that resulted in the index, as described below. When a data item is deleted, the timestamp of the data item is set to infinity, (i.e., larger than any possible timestamp value). 
   Assume for the sake of clarity of discussion that a view of DocIdMap  1100  is defined for each active DocIdTimestamp T  1106 , denoted as DocIdMap (T) which comprises a list of all DocIds  1104  where the DocIdTimestamp  1106  of the DocId  1104  is T. The view DocIdMap (T) includes a unique clustered index on DocId  1104 . DocidMap (T) for T=infinity thus contains a list of deleted DocIds. 
   An indexed view Partial_DocIdMap is preferably maintained. Partial_DocIdMap includes a subset of all the DocIds in DocIdMap that got updated since the last full crawl. That is, preferably, Partial_DocIdMap includes a list of all DocIds having a DocIdTimestamp later than the DocIdTimestamp of the latest full crawl. The view Partial_DocIdMap includes a unique clustered index on DocId. 
     FIG. 12  illustrates a table of crawls called CrawlDictionary  1200 . CrawlDictionary  1200  comprises meta-data for the active crawls that scan the data to be indexed. CrawlDictionary  1200  includes CrawlId  1202 , CrawlTimestamp  1204 , and CrawlStatus  1206 . CrawlId  1202  identifies the crawl, CrawlTimestamp  1204  is a numerical value that is used to track the temporal relationship of a particular crawl to other crawls within a set of crawls and CrawlStatus  1206  tracks the status of a crawl (e.g., “in progress” or “done”.) 
     FIG. 13  illustrates a FtIndexDictionary  1300  that comprises meta-data for the indexes built and maintained by the indexer. FtIndexDictionary  1300  includes but is not limited to IndexId  702 , a unique Index identifier and Index Timestamp  1302 , which indicates the temporal relationship of a particular index to other indexes within a set of indexes, as discussed below. 
   Performant Index and Merge Process 
     FIG. 21  illustrates a flow diagram of an indexing and merge method in accordance with one embodiment of the invention. A temporal indicator is assigned to instances of an index at step  2102 . At step  2104 , instances are selected for merging. At step  2106 , the indexes are validated and merged. A temporal indicator (timestamp) is assigned to the index when the index is created. If the index is created by an indexing thread, the index is given the timestamp of the crawl that created the index. If the index is created by a merge, the index is given a timestamp consistent with the data snapshot at that time, as discussed below. 
     FIG. 14  is a flow diagram of a full-text search indexing method in accordance with one embodiment of the invention. At step  1402 , a crawl is initiated and CrawlStatus  1206  for the crawl is set to “in-progress”. A crawl preferably includes several transactions, each transaction persisting an inverted list into stable storage. A crawl preferably can scan all the data items in a collection (referred to herein as a “full crawl”) or can scan some subset of the entire data item collection (referred to herein as a “partial crawl”.) 
   In any given crawl, data that has been indexed preferably is not re-indexed, that is, if the data being crawled is a set of documents and Crawl 1  indexes Document  1 , Document  1  will not be crawled again by Crawl 1 , although Document  1  may be crawled by a subsequent crawl. 
   When a crawl is initiated, a crawl identifier (CrawlId  1202 ) is assigned to the crawl. The crawl is also assigned a CrawlTimestamp  1204 . CrawlTimestamp  1204  is a unique, ordered number so that if a second crawl is initiated after a first crawl is initiated, the second crawl will receive a CrawlTimestamp that is greater (or otherwise indicates that the crawl is happening later) than the first crawl timestamp. CrawlStatus  1206  for the crawl is set to “in-progress”. An entry is made in CrawlDictionary  1200  for the crawl, persisting the values for CrawlId  1202 , CrawlTimestamp  1204  and CrawlStatus  1206 . Preferably, timestamps are generated by a random number generator where only increasing numbers can be generated. Periodically the number generator may be reset. 
   In one embodiment the timestamp is reset by allowing all the active crawls to finish, and performing a full merge of all the indexes. After the full merge is performed, only one index with one timestamp is left. The timestamp of this index is updated to a new timestamp. The timestamp of all non-deleted data identifiers are also updated to the new timestamp. The new value is a small value, e.g., 1). The timestamp counter is reset to this small value. Alternatively, any suitable method of resetting the timestamp may be performed. 
   Other suitable methods for generating an ordered unique timestamp are contemplated by the invention. Timestamps are preferably based on a common time reference. Timestamps preferably are assigned based on the starting time of the crawl. If there are two crawls that start at exactly the same time, preferably one crawl will still receive a timestamp that is greater than the other&#39;s timestamp. 
   At step  1404 , data to be indexed is retrieved. The data to be indexed may be retrieved from a database (e.g., from basetable  416 ) or alternatively, may be retrieved from a file system. The data to be indexed is identified by a full-text key (DocKey  1102 ). For example, perhaps the row in basetable  416  full-text key (DocKey  1102 ) “ 1017 axpa 324 ” is retrieved. DocKey  1102  typically is a somewhat large alphanumeric identifier that may not be particularly well-suited to compression. Therefore, preferably DocKey  1102  is mapped to a smaller unique numeric document identifier, DocId  1104 , (e.g., “1”). The data item identified by DocKey  1102  and DocId  1104  is associated with the Timestamp of the crawl that accessed it, so that DocIdTimestamp  1106  is set to CrawlTimestamp  1204 . DocKey  1102 , DocId  1104  and DocIdTimestamp  1106  are updated in DocIdMap  1100 . Indexed View Partial_DocIdMap gets updated likewise. View DocIdMap (T) for the timestamp DocIdTimestamp likewise gets changed. 
   At step  1406  the data retrieved is indexed, (e.g., an inverted list of keywords, DocId and occurrences of keyword(s) in the data item is generated). At step  1408 , if more data is to be scanned by the transaction, processing returns to step  1404 . If all the data to be scanned by the transaction has been processed, step  1410  is performed. At step  1410 , an instance of an index FtIndex  800  is generated. A unique identifier is generated and is associated with the index instance. The index identifier is called herein IndexId  702 . The index instance is also preferably associated with an IndexTimestamp  1302  which is the same as the CrawlTimestamp  1204  of the crawl generating the index which is the same as the DocIdTimestamp  1106  of the data being indexed. Preferably the index includes a plurality of keywords and is in keyword order. Associated with each keyword preferably is a list of DocIds that include the keyword and a list of occurrences (e.g., a list of word locations at which the keyword is found in the document). FtIndexDictionary  1300  is updated for the index instance. 
   At step  1412  the index instance is added to a priority queue. At step  1414  if the batch is complete, the process continues at step  1416 . At step  1416  CrawlStatus  1206  is set to “done”. “Done” means that no additional data will be crawled with the same CrawlId  1202 . Thus there will be no more data with the timestamp of CrawlTimestamp  1204  for that crawl. If the batch is not complete, new data is retrieved for indexing and a new index instance is initiated. 
   If a data item to be indexed has changed between crawls, in one embodiment the data item is preferably flagged for re-indexing. Alternatively, the process controlling the crawl may be notified that a particular data item must be re-indexed. 
   Referring now concurrently to  FIGS. 14 and 15 , assume that CrawlID  1  associated with CrawlTimestamp  1  is initiated at step  1402 . CrawlDictionary  1200  row  1 , column  1  is updated with CrawlID  1 , row  1 , column  2  with CrawlTimestamp  1  and row  1 , column  3  with CrawlStatus (i.e., “in-progress”). It should be understood that the particular rows and columns and identifying names used throughout the examples are exemplary only, and the invention contemplates any suitable table entry convention. It should also be noted that, for clarity, only IndexId  702  and IndexTimestamp  1302  are displayed in FtIndex Dictionary  1300  in  FIG. 15 . 
   At step  1404 , assume that data from basetable  416 , row  1 , column  2  identified by DocKey  1017  of basetable  416  row  1 , column  1  is retrieved. Assume that DocKey  1017  maps to DocId  1 . DocIdMap  1100  row  1 , column  1  is updated with DocKey  1017 , row  1 , column  2  is updated with DocId  1  and row  1 , column  3  with DocIdTimestamp  1 . 
   At step  1406 , the data is indexed (e.g., an inverted list is generated). At step  1408 , assume that the transaction is not done, so another data item, for example from basetable  416 , row  2 , column  2  (e.g., the data associated with DocKey  2017  of basetable  416  row  2 , column  1 ) is retrieved. Assume DocKey  2017  maps to DocId  2 . DocIdMap  1100  row  1 , column  1  is updated with DocKey  2107 , row  1 , column  2  with DocId  2  and row  1 , column  3  with DocIdTimestamp  1 . 
   At step  1408 , assume the transaction is done. At step  1410 , a new index instance (e.g., IndexId=1) is generated by persisting the inverted list. IndexId  1  receives the timestamp of the crawl that generated it (i.e., 1). FtIndexDictionary  1300  row  1 , column  1  is updated with IndexId  1  and row  1 , column  2  with IndexTimestamp  1 . At step  1412 , IndexId  1  is added to the priority queue. 
   Assume that at step  1414  it is determined that the batch is not done. Processing returns to step  1404 . At step  1404 , assume that data from basetable  416  row  3 , column  2 , identified by DocKey  3017  of basetable  416  row  3 , column  1  is retrieved. Assume that DocKey  3017  maps to DocId  3 . DocIdMap  1100  row  3 , column  1  is updated with DocKey  3017 , row  3 , column  2  with DocId  3  and row  3 , column  3  with DocIdTimestamp  1 . At step  1406 , the data is indexed (e.g., an inverted list is generated). 
   At step  1408 , assume that the transaction is done, so processing continues at step  1410 . At step  1410 , a new index instance (e.g., IndexId=2) is generated by persisting the inverted list. Row  2 , column  1  of FtIndexDictionary  1300  is updated with IndexId  2  and row  2 , column  2  with IndexTimestamp  1 . At step  1412 , IndexId  2  is added to the priority queue. Assume that at step  1414  it is determined that the batch is done. At step  1416 , row  1 , column  3  of CrawlDictionary  1200  is updated to “done” ( FIG. 16 , CrawlDictionary  1200 , row  1 , column  3 ). 
   Still referring to  FIG. 16 , assume that a second crawl is now initiated. Assume that CrawlID  2  associated with CrawlTimestamp  2  is initiated at step  1402 . CrawlDictionary  1200  row  2 , column  1  is updated with CrawlID  2 , row  2 , column  2  is updated with CrawlTimestamp  2  and row  2 , column  3  with CrawlStatus (i.e., “in-progress”). Assume further that the data associated with DocKey  1017  has changed since CrawlId  1  and that DocKey  1017  has been flagged for re-indexing. For example, row  1 , column  3  of basetable  416  may contain an “update” flag. Alternatively, other methods of flagging the data item or otherwise sending notification of a need for re-indexing are contemplated by the invention. 
   At step  1404 , the updated data from basetable  416 , row  1 , column  2  identified by DocKey  1017  of basetable  416  row  1 , column  1  is retrieved. DocKey  1017  still maps to DocId  1  but DocIdMap  1100  row  1 , column  3  is updated with DocIdTimestamp  2 . DocIdMap (T) will now contain an entry for DocId  1  for timestamp (T)=1, and an entry for DocId  1  for timestamp (T)=2. 
   At step  1406 , the data is indexed (e.g., an inverted list is generated). At step  1408 , assume that the transaction is not done, so another data item, for example from basetable  416 , row  4 , column  2  (e.g., the data associated with DocKey  4017  of basetable  416  row  4 , column  1 ) is retrieved. Assume DocKey  4017  maps to DocId  4 . DocIdMap  1100  row  4 , column  1  is updated with DocKey  4107 , row  4 , column  2  with DocId  4  and row  4 , column  3  with DocIdTimestamp  2 . 
   At step  1408 , assume the transaction is done. At step  1410 , a new index instance (e.g., IndexId=3) is generated by persisting the inverted list. IndexId receives the timestamp of the crawl that generated it (i.e., 2). FtIndexDictionary  1300  row  3 , column  1  is updated with IndexId  3  and row  3 , column  2  with IndexTimestamp  2 . At step  1412 , IndexId  3  is added to the priority queue. 
   Assume that at step  1414  it is determined that the batch is not done. Processing returns to step  1404 . At step  1404 , assume that data from basetable  416  row  5 , column  2 , identified by DocKey  5017  of basetable  416  row  5 , column  1  is retrieved. Assume that DocKey  5017  maps to DocId  5 . DocidMap  1100  row  5 , column  1  is updated with DocKey  5017 , row  5 , column  2  with DocId  5  and row  5 , column  3  with DocIdTimestamp  2 . At step  1406 , the data is indexed (e.g., an inverted list is generated). 
   At step  1408 , assume that the transaction is done, so processing continues at step  1410 . At step  1410 , a new index instance (e.g., IndexId=4) is generated by persisting the inverted list. Row  4 , column  1  of FtIndexDictionary  1300  is updated with IndexId  4  and row  4 , column  2  with IndexTimestamp  2 . At step  1412 , IndexId  4  is added to the priority queue. Assume that at step  1414  it is determined that the batch is done. At step  1416 , row  2 , column  3  of CrawlDictionary  1200  is updated to “done” (not shown). 
   Merge transactions merge a set of existing indexes into one comprehensive index. Preferably, each index has entries for keywords encountered during the crawl of data indexed in the index. The index preferably is in ascending keyword order. For each keyword, the index includes a list of DocIds for the data in which the keyword has occurred at least once. Associated with each of the DocIds preferably is a list of occurrence locations at which the keyword occurs in the data. The DocId list and the occurrence information preferably are stored in a compressed form in order to optimize storage and to minimize I/O requirements. 
   Indexes to be merged are selected, as described below. The merge process scans a set of indexes. The indexes preferably are in ascending keyword order. Associated with each keyword preferably is a list of DocIds (in ascending order). This DocIdList is scanned for each keyword from every index. If a DocId qualifies for the new index the DocId and its associated occurrences is placed into the new index. If the DocId and its associated occurrences does not qualify for the new index, that DocId is skipped and the next element considered. 
     FIG. 17  illustrates an exemplary index selection process  1700  in accordance with one embodiment of the invention. At step  1702 , a set of indexes {S} are selected from the priority queue. At step  1704 , each of the indexes in set {S} is examined to determine if all the timestamps of the indexes in {S} are the same. If all IndexTimestamp values for the selected set of indexes {S} are identical, the timestamp of the merged index is set to IndexTimestamp and processing continues at step  1714 . If all the values for IndexTimestamp for the set of indexes {S} are not the same, processing continues at step  1706 . 
   At step  1706  the CrawlStatus for the CrawlId that generated the index is examined. If each crawl associated with an index in set {S} has CrawlStatus of “Done”, processing continues at step  1710 . If any index is associated with a Crawl whose CrawlStatus is not “done”, the index is returned to the priority queue and the next index is examined (step  1708 ). 
   At step  1710 , it is determined if the set of indexes {S} includes a set of indexes representing a full crawl. If the set of indexes {S} does include a set of indexes representing a full crawl, all indexes with a timestamp less than the timestamp of the set of indexes representing a full crawl are deleted from {S} at step  1712  and processing continues at step  1714 . 
   If the set of indexes {S} does not include a set of indexes representing a full crawl, the timestamp of each of the indexes in set {S} is compared to the set of indexes in the priority queue {U}. If an index in the priority queue is associated with a timestamp T that is identical to or greater than the timestamp of an index that exists in {U} but does not exist in {S} then the indexes with timestamp T are removed from {S} and returned to the priority queue at step  1718 . If however, every index in {S} has a timestamp which is less than each index in {U} but not in {S}, processing continues at step  1714 . 
   Referring now concurrently to  FIGS. 16 and 17 , assume that IndexIds  1  and  2  (rows  1  and  2  of FtIndexDictionary  1300 ) are selected to be merged. At step  1704 , the IndexTimestamp of IndexId  1  (row  1 , column  2  of FtIndexDictionary  1300 ) is compared to the IndexTimestamp of IndexId  2  (row  2 , column  2  of FtIndexDictionary  1300 ). Since both timestamps are “1”, this is a homogeneous merge, the merge index will receive a timestamp of  1  and processing continues at step  1714 . 
   Assume now that IndexIds  1 ,  2 , and  3  are selected at step  1702  to be merged. At step  1704 , the IndexTimestamp of IndexId  1  (row  1 , column  2  of FtIndexDictionary  1300 ) is compared to the IndexTimestamp of IndexId  2  (row  2 , column  2  of FtIndexDictionary  1300 ) and the IndexTimestamp of IndexId  3  (row  3 , column  2  of FtIndexDictionary  1300 ). Since the IndexTimestamp of IndexIds  1  and  2  are “1”, but IndexTimestamp of IndexId  3  is “2”, the timestamps are not all the same so processing continues at step  1706 . 
   At step  1706 , the CrawlStatus of the crawl associated with IndexTimestamp for each index in {S} is determined. Row  1  column  3  of CrawlDictionary  1200  indicates that CrawlStatus for CrawlTimestamp  1  is “Done” but CrawlStatus for CrawlTimestamp  2  is listed as “In-progress”. Thus, IndexId  3  is removed from {S} and returned to the priority queue at step  1708 . 
   Assume instead, that CrawlStatus for CrawlTimestamp  2  is also “Done”. In this case, processing continues at step  1710 . At step  1710 , it is determined whether {S} includes a set of indexes that represents a full crawl. A full crawl, as used herein, refers to a crawl that has scanned all the data in a data set. For example, assume CrawlId  2  scanned DocKeys  1017  through  8017 , in Basetable  416 , generating IndexIds  2 ,  3  and  4 . In this case, CrawlId  2  would be a full crawl. If all the indexes generated by CrawlId  2  (IndexIds  2 ,  3  and  4 ) were included in set {S}, all the indexes generated by CrawlId  1  (IndexId  1 ) would be deleted from the merge set {S} at step  1712 . In fact, none of the indexes generated by crawls predating a full crawl are required because those indexes were replaced with newer data. Thus, preferably the outdated indexes are removed from the database. 
   Assuming again that {S} contains IndexIds  1 ,  2  and  3 , at step  1716  the timestamp of each of the indexes in set {S} (IndexIds  1 ,  2 , and  3  having respective timestamps of  1 ,  1  and  2 ) is compared to the timestamp of the other indexes in the merge queue (IndexId  4  with timestamp  2 ). Since IndexIds  3  (with timestamp  2 ) does not have a timestamp less than IndexId  4  (with timestamp  2 ), (i.e., 2 is not less than 2), IndexId  3  is removed from {S} and returned to the priority queue at step  1718 . 
   Assume that {S} contains IndexIds  1 ,  2 ,  3 , and  4 . Suppose now that a new crawl with timestamp  3  has re-indexed DocId  1 , generating IndexId  5  with IndexTimestamp  3 . DocId  1  thus has changed for the third time. Now there are no indexes in {U} that are not in {S} with timestamps less than or equal to timestamp  2 , (i.e., the only index is {U} not in {S} is 3 and 3 is greater than 2), thus the set of indexes IndexId  1 ,  2 ,  3  and  4  is a valid set of indexes to be merged and processing continues at step  1714 . 
   To merge indexes, the selected rows from the index rows for the smallest keyword in the set of all keywords in all the indexes is selected. For that keyword the DocId and Occurrence information from all the indexes that contain that keyword is merged. Then the next keyword in ascending order is selected to merge. When multiple rows from one or more indexes having the same keyword is merged, the DocIdList from each of the index rows is retrieved and then the set of DocIdLists in merged order of DocIds is iterated over. For each DocId, if the DocId is the most recent in the index set {S} (the DocId does not have to be the most recent according to DocIdMap), then that DocId and its associated occurrence information is inserted into the DocIdList of the merged index. Otherwise that DocId data is skipped. 
     FIG. 19  is a flow diagram of an index merge  1900  in accordance with one embodiment of the invention. At step  1902 , the set of indexes {S} selected in the index selection process  1700  are input to the index merge process  1900 . At step  1904 , the largest (latest or most recent) timestamp of the set of indexes {S} is determined. At step  1906  a keyword K is selected. At step  1908  the set of indexes {S} is scanned for keyword K. At steps  1910 - 1918  the most recent data for the keyword K for each DocId containing the keyword K is applied to a new MergedIndex M. If the last keyword has not been processed, processing continues at step  1906  and the next keyword is selected. After all keywords have been processed, the timestamp of the merged index is set to the maximum timestamp and FtIndex Dictionary  1300  is updated with MergedIndex M (step  1920 ). 
     FIG. 18  illustrates exemplary index data in accordance with one embodiment of the invention.  FIG. 20  illustrates exemplary view DocIdMap (T) data in accordance with one embodiment of the invention. DocIdMap (T)  2000  includes the DocIds with timestamp  1 ,  2002 ,  2 ,  2004  and infinity,  2006 . Referring now concurrently to  FIGS. 16 ,  18 ,  19 , and  20  assume that the set of selected indexes for merging {S} includes IndexIds  1 ,  2 ,  3  and  4  (step  1902 ). Preferably, {S} is sorted in order of descending value of timestamp so that IndexIds  3  and  4  are processed before IndexIds  1  and  2 . At step  1904 , IndexTimestamp, column  2  of FtIndexDictionary  1300  is scanned to determine the highest or most recent timestamp of the indexes in set {S}. The largest IndexTimestamp of IndexIds  1 ,  2 ,  3  and  4  is timestamp “2”. Thus “2” is the MaxTimestamp. At step  1906 , the smallest (first) keyword in the indexes of set {S} is determined. The smallest (first) keyword is “Innovation” of row  1 , column  1  of IndexId  3   1806 . At step  1908 , IndexIds  2 ,  3 , and  4  are scanned for keyword “Innovation”. Keyword “Innovation” is also found in IndexId  1   1802  (row  1 , column  1 ) for DocIds  1  and  2 . At step  1910 , DocId  1  is selected for processing. IndexTimestamp (row  3 , column  2  of FtIndexDictionary  1300 ) for DocId  1  of IndexId  3  is determined. IndexTimestamp of IndexId  3  is “2”. The timestamp of the IndexId  3  is compared to MaxTimestamp at step  1912 . Because IndexId  3  IndexTimestamp and MaxTimestamp are the same (i.e, 2), IndexId  3  is the most current index in the set {S} and OccListList (IndexId 3 ,  1806 , row  1  column  3 ) contains the most current index information for DocId  1 . Therefore,  105 ,  1518 ,  1629  will appear in MergedIndex M  1810 , row  1 , column  3 , first list for DocId  1  (step  1918 ). 
   Next, DocId  2  is processed (step  1910 ). DocId  2  is present in IndexId  1   1802 . “Innovation” can be found at word locations  200 ,  205 ,  375  for DocId  2  (row  1 , column  3 , second list). At step  1912 , IndexTimestamp (row  3 , column 2  of FtIndexDictionary  1300 ) for IndexId  1  is determined. IndexTimestamp of IndexID  1  is  1  (TS=1). The timestamp of IndexId  1  ( 1 ) is compared to MaxTimestamp ( 2 ). Because IndexId  1  IndexTimestamp and MaxTimestamp are not the same, DocIdMap (T) ( FIG. 20 ,  2000 ) is accessed to see if DocId  2  was recrawled between timestamp  1  (the timestamp of the IndexId  1 ) and timestamp  2  (MaxTimestamp) and DocIdMap (Infinity)  2006  is checked to see if DocId  2  was deleted. 
   At step  1914  Partial_DocIdMap  2000  is checked to see if an entry exists for DocId in DocIdMap where timestamp T is greater than the IndexTimestamp for IndexId  1  ( 1 ) and less than or equal to MaxTimestamp ( 2 ) or timestamp T=infinity. Thus DocIdMap entry  2004  is checked for the presence of DocId  2 . DocId  2  is not found in DocIdMap entry  2004  because DocId  2  was not changed after CrawlId  1  created IndexId  1 . Because DocId  2  was not changed between timestamp  1  and timestamp  2 , IndexId  1  contains the most recent data for DocId 2  and DocIdMap ( 2 )  2004  does not contain DocId  2 . Because DocId  2  was not deleted, DocIdMap (Infinity)  2006  does not contain DocId  2 . IndexId  1  thus contains the most recent data in the merge set for DocId  2  and  200 ,  205 ,  375  is written to MergeIndex M at step  1918 . 
   This process continues for all DocIds in a DocIdList for all keywords. When the last DocId for the last keyword has been processed, the timestamp of the MergeIndex M is set to MaxTimestamp and the index is persisted and placed in the priority queue. 
   Thus, it can be seen that in the case of homogenous merges, no table lookups are required while in the case of heterogeneous merges, the following is true: 
   If a data item such as a document is crawled in the latest crawl participating in a merge, then there is a lookup for that document for every keyword that is deleted from the document from the previous crawls. In this case the cost is proportional to the product of the number of distinct keywords removed from the document and logarithm of the number of documents changed (entries in Partial_DocIdMap). Once a heterogeneous merge with maximum timestamp T is performed, T can be defined as the timestamp of the last full-crawl and thus Partial_DocIdMap can be collapsed. 
   If a document did not get crawled then there is a lookup for every distinct keyword in that document. Cost of the lookup is proportional to the number of distinct keywords in the document and is the logarithm of the number of documents changed. 
   The cost of the lookup in both cases is proportional to the logarithm of the number of documents that got crawled in the latest crawl or got deleted after the previous crawl. 
   Thus the cost of the lookup is smallest if either the newest crawl is a full crawl (in which case, no lookups are done and the cost is 0) or most of the documents have been re-crawled in the new crawl with most of the old keywords still in the text (occurrence might have changed, and any number of new keywords may have been added) or very few documents have been re-crawled. The crawls and merges preferably can be controlled to fit the existing situation to minimize cost and maximize efficiency. 
   CONCLUSION 
   It is noted that the foregoing examples have been provided merely for the purpose of explanation and are in no way to be construed as limiting of the present invention. While the invention has been described with reference to various embodiments, it is understood that the words which have been used herein are words of description and illustration, rather than words of limitation. Further, although the invention has been described herein with reference to particular means, materials and embodiments, the invention is not intended to be limited to the particulars disclosed herein; rather, the invention extends to all functionally equivalent structures, methods and uses, such as are within the scope of the appended claims. Those skilled in the art, having the benefit of the teachings of this specification, may effect numerous modifications thereto and changes may be made without departing from the scope and spirit of the invention in its aspects.