Abstract:
A search engine comprising a crawler which crawls the WWW and stores pages found on the WWW in a database. An indexer indexes the pages in the database to produce a primary index. A document mapping section maps pages in the database into a plurality of tiers based on a ranking of the pages. The ranking may be based on portions of the pages which have a relatively higher value context. A processor produces a plurality of sub-indices from the primary index based on the mapping. The sub-indices are stored in a search node cluster. The cluster is a matrix of search nodes logically arranged in a plurality of rows and columns. Search nodes in the same column include the same sub-index. Search nodes in the same row include distinct sub-indices. A search query received by a user is sent to a dispatcher which, in turn, forwards the query to the first tier of search nodes. A fall through algorithm is disclosed which indicates when the dispatcher should forward the search query to other tiers of search nodes.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates to a search engine, and, more particularly, to a search engine which maps crawled documents into tiers and then searches those tiers in a hierarchical manner. 
     2. Description of the Related Art 
     The World Wide Web (“WWW”) is a distributed database including literally billions of pages accessible through the Internet. Searching and indexing these pages to produce useful results in response to user queries is constantly a challenge. The device typically used to search the WWW is a search engine. Maintaining a working search engine is difficult because the WWW is constantly evolving, with millions of pages being added daily and existing pages continually changing. Additionally, the cost of search execution typically corresponds directly to the size of the index searched. To deal with the massive size and amount of data in the WWW, most search engines are distributed and use replication and partitioning techniques (all discussed below) to scale down the number of documents. 
     A typical prior art search engine  50  is shown in  FIG. 1 . Pages from the internet or other source  100  are accessed through the use of a crawler  102 . Crawler  102  aggregates documents from source  100  to ensure that these documents are searchable. Many algorithms exists for crawlers and in most cases these crawlers follow links in known hypertext documents to obtain other documents. The pages retrieved by crawler  102  are stored in a database  108 . Thereafter, these documents are indexed by an indexer  104 . Indexer  104  builds a searchable index of the documents in database  108 . Typical prior art methods for indexing include inverted files, vector spaces, suffix structures, and hybrids thereof. For example, each web page may be broken down into words and respective locations of each word on the page. The pages are then indexed by the words and their respective locations. A primary index of the whole database  108  is then broken down into a plurality of sub-indices (discussed below) and each sub-index is sent to a search node in a search node cluster  106 . 
     In use, a user  112  enters a search query to a dispatcher  110 . Dispatcher  110  complies a list of search nodes in cluster  106  to execute the query and forwards the query to those selected search nodes. The compiled list ensures that each partition is searched once. The search nodes in search node cluster  106  search respective parts of the primary index produced by indexer  104  and return sorted search results along with a document identifier and a score to dispatcher  110 . Dispatcher  110  merges the received results to produce a final list displayed to the users  112  sorted by relevance scores. The relevance score is a function of the query itself and the type of document produced. Factors that are used for relevance include: a static relevance score for the document such as link cardinality and page quality, superior parts of the document such as titles, metadata and document headers, authority of the document such as external references and the “level” of the references, and document statistics such as query term frequency in the document, global term frequency, and term distances within the document. 
     Referring now to  FIG. 2 , a cluster  106  of search nodes is shown. For illustrative purposes, cluster  106  is shown in a matrix grouped in columns  122   a ,  122   b , etc. and rows  124   a ,  124   b , etc. In each column  122  of search nodes, the same set of indices is replicated for each respective search node. For example, the search node in column  122   a , row  124   a , includes the same subset of indices as the search node in column  122   a ,  124   b . In each row  124  of search nodes, a different subset of indices is used. The indices are equally split so as to divide the amount of time for a search. 
     For example, the search node in column  122   a , row  124   a  includes a different subset of indices than the search node in column  122   b , row  124   a . In each search node, “I” represents the index for the entire database  108 , “S” corresponds to a search node, “S n (I n )” indicates that search node n holds sub-index n of the entire index I, and “S n   m (I n )” indicates that replication number m of search node n holds sub-index n of the entire index I. 
     Each query from dispatch  110  is sent to respective search nodes so that a single node in every partition is queried. For example, all the nodes in a row  122   a ,  122   b , etc. are queried as the combination of these nodes represents that total index. That is, each row in cluster  120  is a set of search nodes comprising all the partitions of an entire index. The results are merged by dispatcher  110  and a complete result from the cluster is generated. By partitioning data in this way, the data volume is scaled. For example, if there are n columns, then the search time for each node is reduced basically by a factor of n—excluding the time used for merging results by dispatcher  110 . 
     By replicating the search nodes, the query processing rate for each index is increased. In  FIG. 2 , all search nodes in each column hold the same index. This allows dispatcher  110  to rotate among the nodes in a column for each index partition when selecting a set of search nodes to handle an incoming query. 
     However, the inventors have determined that there is a highly skewed distribution of unique search queries in a typical search engine. For example, the top 25 queries may account for more than 1% of the total query volume. As a consequence, equally dividing a primary index into smaller sub-indices may not provide optimum results. 
     Therefore, there is a need in the art for a search engine that organizes its documents and indices in light of the distribution of search queries. 
     SUMMARY OF THE INVENTION 
     A search engine comprising a crawler which crawls the WWW and stores pages found on the WWW in a database. An indexer indexes the pages in the database to produce a primary index. A document mapping section maps pages in the database into a plurality of tiers based on a ranking of the pages. The ranking may be based on portions of the pages which have a relatively higher value context. A processor produces a plurality of sub-indices from the primary index based on the mapping. The sub-indices are stored in a search node cluster. The cluster is a matrix of search nodes logically arranged in a plurality of rows and columns. Search nodes in the same column include the same sub-index. Search nodes in the same row include distinct sub-indices. A search query received by a user is sent to a dispatcher which, in turn, forwards the query to the first tier of search nodes. A fall through algorithm is disclosed which indicates when the dispatcher should forward the search query to other tiers of search nodes. 
     One aspect of the invention is a method for indexing data items in a database. The method comprises retrieving data items from a database and producing a primary index of the data items. The method further comprises mapping the data items on to at least a first tier and a second tier based on respective rankings of the data items. The method further comprises producing at least a first and a second sub-index from the primary index based on the mapping; and storing the at least a first and second sub-index in different search nodes. 
     Another aspect of the invention is a method for searching a database. The method comprises retrieving data items from a database and producing a primary index of the data items. The method further comprises mapping data items on to at least a first tier and a second tier based on respective rankings of the data items. The method still further comprises producing at least a first and a second sub-index from the primary index based on the mapping. The method further comprises storing the at least a first and second sub-index in different search nodes; receiving a search query; and searching the first tier for result data items relating to the search query. 
     Yet another aspect of the invention is a system for indexing a database. The system comprises a crawler which crawls the database to find data items. An indexer receives the data items and produces a primary index. A document mapping section maps data items on to at least a first and a second tier based on respective rankings of the data items. A processor produces at least a first and a second sub-index from the primary index based on the mapping. A first search node which stores the first sub-index. A second search node which stores the second sub-index. 
     Still yet another aspect of the invention is a search node cluster for enabling a search of a database. The cluster comprises search nodes logically arranged in a plurality of columns and plurality of rows. All search nodes in any one of the columns including substantially the same information. All search nodes in any one of the rows including distinct information. The search nodes in the rows being logically divided into at least a first and a second tier. The search nodes in the first tier including an index for a first portion of the database. The search nodes in the second tier including an index for a second portion of the database. The data in the first and second tier is based on respective rankings of the information in the first and second portion of the database. 
     Yet another aspect of the invention is a search engine comprising a crawler which crawls a database to find data items. An indexer receives the data items and produces a primary index. A document mapping section maps data items on to at least a first and a second tier based on respective rankings of the data items. A processor produces at least a first and a second sub-index from the primary index based on the mapping. A first search node stores the first sub-index. A second search node stores the second sub-index. A dispatch which receives a query and forwards the query to the first search node. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a search engine architecture of the prior art. 
         FIG. 2  is a diagram showing a cluster of nodes in accordance with the prior art. 
         FIG. 3  is a block diagram showing a search engine in accordance with an embodiment of the invention. 
         FIG. 4  is a diagram illustrating the function of mapping documents into tiers in accordance with an embodiment of the invention. 
         FIG. 5  is a diagram illustrating mapping of documents into tiers and the resulting cluster of nodes in accordance with an embodiment of the invention. 
         FIG. 6  is a diagram illustrating mapping of documents into tiers and the resulting cluster of nodes in accordance with an embodiment of the invention. 
         FIG. 7  is a diagram illustrating mapping of documents into tiers and the resulting cluster of nodes in accordance with an embodiment of the invention. 
         FIG. 8  is a table showing the values for various variables of a fall through algorithm in accordance with an embodiment of the invention. 
         FIG. 9  is a flow chart showing the operation of a searching algorithm in accordance with an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring to  FIG. 3 , there is shown a search engine  90  in accordance with an embodiment of the invention. A source of information such as the Internet  100  or other collection of files or documents such as an enterprise or organization network, is crawled by a crawler  102  which, in turn, stores data in a database  108  correspond to the source of information. A document mapping algorithm  114  then maps the documents into tiers as discussed below. An indexer  105 , controlled by a processor  111 , builds a plurality of sub-indices based on the mapped documents in database  108 . A plurality of search nodes in a search node cluster  160  each store respective sub-indices and are each enabled to search their respective sub-indices. A dispatcher  110  sends queries from a user  112  to search node cluster  160  as discussed below. 
     Recent research yields that there is a skewed distribution of the most popular queries for information on the Internet. For example, most queries (50%–80%) are within the top 1 (one) million most frequently requested queries. Similarly, single days in different months realize an overlap of 80–85% of the same queries. Conversely, only 7% of the queries are asked just once in a similar time period. To take advantage of these facts, the engine uses a disjointed tiered architecture where the indices are not necessarily divided equally. 
     Referring now to  FIG. 4 , each piece of data in database  108  is mapped into one of a plurality of tiers—three tiers are shown in the figure—based on a set of properties. For example, documents which are deemed to have a static relevance ranking, independent of the search query, above a first threshold defined by a database administrator, may be mapped on to Tier I. Documents with a second highest ranking based on another threshold may be mapped to Tier II. As another example, portions of each document or web page can be divided into different tiers. In a particular document, as shown in  FIG. 4 , the superior context such as headers and anchors, may be placed in Tier I and the body of the document may be placed in Tier II. The mapping is performed periodically on the data on database  108 . 
     Referring now also to  FIG. 5 , a data structure (not explicitly shown) is stored in dispatch  110  so that search nodes in cluster  160  are logically assigned to particular tiers. After the documents in database  108  are mapped into tiers by document mapping algorithm  114 , indexer  105  produces a plurality of corresponding sub-indices based on the tiers. The sub-indices are stored in respective search nodes in cluster  160 . Cluster  160  includes logical columns  162   a ,  162   b ,  162   c , etc. and logical rows  164   a ,  164   b , etc. of search nodes. While the nodes are shown as being physically disposed in columns and rows, clearly the nodes need not be so physically disposed as long as they are logically arranged in a similar manner. 
     Search nodes in each column  162  include replications of the same sub-indices so that dispatcher  110  may cycle through a plurality of search nodes. Search nodes in each row  164  include different sub-indices. For example, as shown in  FIG. 5 , the search nodes in column  162   a  all include information from Tier I. Thus, documents determined to be mapped to Tier I by algorithm  114 , are so mapped, a sub-index is created in indexer  105 , and this sub-index for Tier I is stored in the search nodes in column  162   a.    
     Similarly, the search nodes in column  162   b  include a portion of the information in Tier II. Search nodes in column  162   c  include the remainder of the information from Tier II that was not included in the search nodes in column  162   b . Two search node columns are shown for Tier II, and the indices may be split equally among these nodes. Clearly, any number of nodes could be used. 
     Similarly, search nodes in column  162   d  include a portion of the information from Tier III. To facilitate the illustration of cluster  160 , the nodes in each column are shown as being equal in size though it is clear that each node may include the same or a different amount of information than other nodes in the same row. For example, the node in column  162   a , row  164   a  will probably have less information than the node in column  162   b , row  164   a  because they are in different tiers. As an example of the shown tiered architecture, 1.5 million documents may be indexed in all of the Tier  1  nodes, 6 million documents indexed in all of the Tier  2  nodes, and  10  million documents indexed in all of the Tier  3  nodes. 
     Each inquiry from dispatch  110  is first searched in the indices of Tier  1  and then the search continues to indices of other tiers based on a fall through algorithm (“FTA”) stored in dispatcher  110 . The FTA determines whether a query should continue to be executed in other tiers and also determines how results from multiple tiers should be merged. Stated another way, the FTA determines the path of the query in the set of tiers based on criteria such as relevance scores and number of hits in a result set. It also determines how many results from each tier can be used before the next tier is consulted. 
     The FTA uses a plurality of variables to determine whether a next tier should be evaluated including hitlimit, percentlimit, ranklimit, termranklimit, and minusablehits. The variable hitlimit is the evaluation of the number of hits to be used from a tier before a fall-through to the next tier may be forced. For example, for a jump from tier  1  to  2 , the hitlimit may be 1000 and for a jump from tier  2  to  3 , the hitlimit may be 8100. Percentlimit is the maximum percentage of hits from a tier that may be used before fall-through to a next tier may be forced. If the number of hits in a given tier is less than the percentlimit of the requested results overall, a fall-through occurs. For example, for a jump from tier  1  to  2 , the percentlimit may be 10 and for a jump from tier  2  to  3 , the percentlimit may be 30. Termranklimit—if the relevance score of a hit being considered is less than another variable Ranklimit plus the termranklimit value times the number of terms in the query, then fall-through to the next tier is forced. For example, for a jump from tier  1  to  2 , the ranklimit may be 200 and the termranklimit 400. For example, in a two-term query, the relevancy score for a hit to pass this criteria would be 200+(2×400)=1000. For a jump from tier  2  to  3 , the ranklimit may be 0 and the termranklimit 0. 
     Minusablehits—The number of hits that should pass the above criteria for the FTA for a given tier for there not to be an immediate fall-through to the next tier. This number is typically the number of results presented to a user on a result page. The idea is that if it is known that fall-through will be needed in order to produce the number of hits most often requested, then the fall-through should be done as soon as possible. This variable should be used with a constant value. For example, for a jump from tier  1  to  2 , minusablehits may be 0 and for a jump from tier  2  to  3 , the minusablehits may be 100. 
     As Tier  2  will only process those queries which pass through Tier  1 , and Tier  3  will only process those queries which pass through both Tiers  1  and  2 , it is desirable that Tier I have the highest performance nodes. Extra capacity at Tiers  2  and  3  may be achieved by replicated columns or by reducing the number of documents at each node. 
     In the embodiment in  FIG. 5 , a 1 dimensional tier-ing configuration is used in that all documents and corresponding indices are distributed using a static relevance score. For instance, the static relevance score may be based on link cardinality, link popularity, or site popularity on the web. 
     For example, in a database of one billion records, the top 30 million documents, based on static relevance, are mapped to Tier  1 , the next 360 million documents are mapped to Tier  2  and the following 610 million documents mapped to Tier  3 . One drawback to this configuration is that using static relevance is only part of the overall formula used for determining a relevant document. 
     Referring now to  FIG. 6 , there is shown another cluster of nodes  170  in accordance with the invention. Cluster  170  could be used in place of cluster  160  and includes nodes in columns  172   a ,  172   b  etc. and rows  174   a ,  174   b , etc. In this embodiment, a 1.5 dimensional configuration is realized. A query log is run for the 1 million most common queries for a period of time. The first 20 hits for each of the one million queries are mapped to Tier  1  as shown at  176  in  FIG. 6 . This may be approximately five million documents. The remaining documents are distributed according to a static relevance score. For example, for a billion document database, the top 30 million documents are mapped to Tier  1  (with 5 million of those documents being locked into this tier), 360 million documents mapped to Tier  2  and 610 million documents mapped to Tier  3 . A FTA is used as discussed above. 
     Referring now to  FIG. 7 , there is shown another cluster of nodes  180  in accordance with the invention. Cluster  180  can be used in place of cluster  160  and includes nodes in columns  182   a ,  182   b  etc. and rows  184   a ,  184   b , etc. In this embodiment, a 2 dimensional configuration is realized. In the embodiment of  FIG. 7 , the same tier distribution as the 1.5 dimensional configuration of  FIG. 6  is optionally used. However, information in high value contexts for all documents is searched first simultaneous with Tier I. These high value contexts are the most important portions of the respective web pages when determining dynamic relevancy of a document. These portions include the title, anchors, etc. 
     If more hits are needed, the full index is continually searched using the multi-tier configuration while removing duplicates from the returned results. For example, the body context of the top 30 million documents (with 5 million locked as discussed above) are mapped to Tier  1 , the body context of the 360 million documents mapped to Tier  2  and the body context of the 610 million documents mapped to Tier  3 . A new Tier  0  is used which includes the superior context of all 1 billion documents. Some values for the variables of the FTA for the architecture of cluster  180 , is shown in  FIG. 8 . An optional tier  4  may be used with low value documents. Such documents may be pure links or spam documents. By searching the high volume contexts of all the tiers in tier  0 , the invention takes advantage of the fact that searching a relatively small subset of the information in the tier  2  and tier  3  nodes is much cheaper than searching the full information indexed in these nodes. 
     Referring now to  FIG. 9 , there is shown a flow chart summarizing some of the operations of the invention. At S 2 , a search engine crawls a data source. At S 4 , documents gathered from the data source are stored in a database. At S 6 , the documents are divided into tiers using one of the algorithms discussed above. At S 8 , the documents are mapped into the determined tiers. At S 10 , sub-indices are produced based on the determined tiers. At S 12 , the sub-indices are stored in respective search nodes in a search node cluster. At S 13 , a search query is received from a user. At S 14 , the search engine searches the indices in Tier I. At S 16 , based on the FTA, the search engine searches Tier II search nodes and any other Tier search nodes. At S 18 , results of the search are provided for a user. 
     Thus, by mapping documents crawled in a database into disjointed tiers, a faster, more cost effective search engine is realized. Further, by providing a fall through algorithm that dynamically determines how many of these tiers are searched, scaling of the database is improved. 
     While the invention has been described and illustrated in connection with preferred embodiments, many variations and modifications as will be evident to those skilled in this art may be made without departing from the spirit and scope of the invention, and the invention is thus not to be limited to the precise details of methodology or construction set forth above as such variations and modification are intended to be included within the scope of the invention.