Abstract:
Systems and methods for scheduling document crawling are provided in which a list of document identifiers is obtained. Each respective document identifier identifies a corresponding document on a network. For each respective document identifier in the list of document identifiers, a content change frequency of the corresponding document is determined and a first score for the document identifier that is a function of the determined content change frequency of the corresponding document is also determined. The first score is compared against a threshold value. The document is scheduled for crawling based on the result of the comparison. The content change frequency for a respective document identifier is determined by comparing information stored for successive downloads of the document corresponding to the document identifier.

Description:
RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 13/449,228, filed Apr. 17, 2012, now U.S. Pat. No. 8,775,403, which is a continuation of U.S. patent application Ser. No. 12/787,321, filed May 25, 2010, now U.S. Pat. No. 8,161,033, which is a continuation of U.S. patent application Ser. No. 10/853,627, filed May 20, 2004, now U.S. Pat. No. 7,725,452, which is a continuation-in-part of U.S. patent application Ser. No. 10/614,113, filed Jul. 3, 2003, now U.S. Pat. No. 7,308,643, which are incorporated by reference herein in their entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates generally to search engine crawlers for use in computer network systems, and in particular to a scheduler for a search engine crawler. 
       BACKGROUND 
       [0003]    A search engine is a software program designed to help a user access files stored on a computer, for example on the World Wide Web (WWW), by allowing the user to ask for documents meeting certain criteria (e.g., those containing a given word, a set of words, or a phrase) and retrieving files that match those criteria. Web search engines work by storing information about a large number of web pages (hereinafter also referred to as “pages” or “documents”), which they retrieve from the WWW. These documents are retrieved by a web crawler or spider, which is an automated web browser which follows every link it encounters in a crawled document. The contents of each document are indexed, thereby adding data concerning the words or terms in the document to an index database for use in responding to queries. Some search engines, also store all or part of the document itself, in addition to the index entries. When a user makes a search query having one or more terms, the search engine searches the index for documents that satisfy the query, and provides a listing of matching documents, typically including for each listed document the URL, the title of the document, and in some search engines a portion of document&#39;s text deemed relevant to the query. 
         [0004]    While web pages can be manually selected for crawling, such manual assignment becomes impracticable as the number of web pages grows. Moreover, to keep within the capacity limits of the crawler, web pages should be added or removed from crawl cycles to ensure acceptable crawler performance. For instance, as of the end of 2003, the WWW is believed to include well in excess of 10 billion distinct documents or web pages, while a search engine may have a crawling capacity that is less than half as many documents. 
         [0005]    Therefore, what is needed is a system and method of automatically selecting and scheduling documents for crawling based on one or more selection criteria. Such a system and method should be able to assess the stature (e.g., page rank) of a web page and schedule the web page for crawling as appropriate based on its stature. 
       SUMMARY 
       [0006]    A scheduler for a search engine crawler includes a history log containing document identifiers (e.g., URLs) corresponding to documents (e.g., web pages) on a network (e.g., Internet). The scheduler is configured to process each document identifier in a set of the document identifiers by determining a content change frequency of the document corresponding to the document identifier, determining a first score for the document identifier that is a function of the determined content change frequency of the corresponding document, comparing the first score against a threshold value, and scheduling the corresponding document for indexing based on the results of the comparison. The threshold value can be computed from an initial sampling of document identifiers. One or more factors can be used to compute a score, including page rank, crawl history and the like. 
         [0007]    A method of scheduling documents to be downloaded by a search engine crawler includes retrieving a number of document identifiers, each document identifier identifying a corresponding document on a network. For each retrieved document identifier, the method determines a content change frequency of the corresponding document and determines a first score for the document identifier that is a function of the determined content change frequency of the corresponding document. It then compares the first score against a threshold value, and schedules the document for indexing based on the result of the comparison. 
         [0008]    A computer-readable medium has stored thereon instructions which, when executed by a processor, cause the processor to perform the operations of the method described above. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]      FIG. 1  illustrates a data structure for storing URLs, in accordance with some embodiments of the present invention. 
           [0010]      FIG. 2  illustrates a web crawler system for processing crawled web pages, in accordance with some embodiments of the present invention. 
           [0011]      FIG. 3  is illustrates a history log generated by the system shown in  FIG. 2 , in accordance with some embodiments of the present invention. 
           [0012]      FIG. 4  is a block diagram of a URL scheduler computer system, in accordance with some embodiments of the present invention. 
           [0013]      FIG. 5  is a flow diagram of a URL scheduler initialization process, in accordance with some embodiments of the present invention. 
           [0014]      FIG. 6  is flow diagram of a URL scheduler process, in accordance with some embodiments of the present invention. 
           [0015]      FIG. 7  illustrates an schedule output file generated by the URL scheduler shown in  FIG. 4 , in accordance with some embodiments of the present invention. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
     Overview of Crawler System 
       [0016]      FIG. 1  illustrates a data structure  100  for storing document identifiers (e.g., web page URLs) in segments, in accordance with some embodiments of the present invention. In some embodiments, the data structure  100  includes a base layer  102 , a daily crawl layer  104  and an optional real-time layer  106 . The base layer  102  comprises a sequence of segments  112   a , . . . ,  112   n , wherein each segment  112  includes a number of URLs representing a percentage of the web page address space that has been (or will be) used by a web crawler system. Some embodiments do not include a daily crawl layer  104  or a real-time layer  106 . 
         [0017]    The daily crawl layer  104  comprises URLs to be crawled more frequently than the URLs in segments  112 . In addition, daily crawl layer  104  includes high priority URLs that are discovered by the web crawler system during a current epoch. An epoch is a predetermined time period (e.g., a day). 
         [0018]    The real-time layer  106  includes URLs to be crawled multiple times during a given epoch (e.g., multiple times per day). For example, the URLs in the real-time layer  106  are crawled every few minutes, or N times per hour (where N is a value greater than or equal to 1). The real-time layer  106  can also include newly discovered URLs that have not been crawled but should be crawled as soon as possible. 
         [0019]      FIG. 2  is a block diagram of a web crawler system  200 , which crawls URLs stored in the data structure  100 , in accordance with some embodiments of the present invention. There are a number of different URL sources used to populate the data structure  100 , including direct submission  203  of URLs by users of the search engine system  200 , or submissions  203  (e.g., time-based submissions) from third parties who have agreed to provide links as they are published, updated or changed. Another source of URLs is through discovery of outgoing links on crawled pages. 
         [0020]    The URL scheduler  202  determines which URLs will be crawled in each epoch, and stores that information in the data structure  100 . The controller  201  selects a segment  112  from the base layer  102  for crawling. The selected segment  112  is referred to hereinafter as the “active segment.” Typically, at the start of each epoch, the controller  201  selects a different segment  112  from the base layer  102  as the active segment so that, over the course of several epochs, all the segments  112   a , . . . , n are selected for crawling in a round-robin manner. The URL scheduler  202  revises the daily crawl layer  104  and/or the real-time layer  106  by moving URLs to the layers  104  and/or  106  from the base layer  102  or vice versa. Alternately, in some embodiments URLs are scheduled to the daily and real-time layers  104 ,  106  without regard to their absence or inclusion in the base layer  102 . When a document appears in document indexes generated by both the daily and base crawl layers, for instance, the front end of the search engine provides a mechanism for using the most recent version of the document when responding to queries. 
         [0021]    A query-independent score (also called a document score) is computed for each URL by URL page rankers  222 . The page rankers  222  compute a page rank for a given URL by considering not only the number of URLs that reference a given URL but also the page rank of such referencing URLs. Page rank data is provided to URL managers  204 , which pass a page rank value for each URL to URL server  206 , robots  208 , content filters  210 , and other servers in the system  200 . An explanation of the computation of page rank is found in U.S. Pat. No. 6,285,999, which is incorporated by reference herein in its entirety. 
         [0022]    From time to time, the URL server  206  requests URLs from the URL managers  204 . In response, the URL managers  204  provide the URL server  206  with URLs obtained from data structure  100 . The URL server  206  then distributes URLs from the URL managers  204  to crawlers  208  (hereinafter also called “robots” or “bots”) to be crawled. A robot  208  is a server that retrieves documents at the URLs provided by the URL server  206 . The robots  208  use various known protocols to download pages associated with URLs (e.g., HTTP, HTTPS, gopher, FTP, etc.). 
         [0023]    In embodiments where the robots  208  use a calling process that requires domain name system (DNS) resolution, a dedicated local DNS database  250  ( FIG. 2 ) can be used to store IP addresses for URLs that have been crawled in the past. This feature allows previously crawled URLs to be pre-resolved with respect to DNS resolution, thus enabling a high percentage of the system&#39;s DNS resolution operations to be handled locally at high speed. 
         [0024]    To address the handling of URLs that use or are regulated by cookies, a cookie database  260  can be included in system  200  for providing stable storage for cookies sent to robots  208  by cookie servers (not shown) on the Internet. The cookie database  260  is structured so that cookie servers can update the status of cookies upon request. The ability to access cookies acquired by robots  208  on previous crawls provides a number of possible advantages to subsequent robot  208  queries, such as speeding up the login process to the URL on the second crawl, gaining access to preferred web content, and possibly regulating which content is accessed from the URL. Further, the use of the cookie database  260  enables robots  208  to crawl content that is regulated by cookie servers. 
         [0025]    Pages obtained from URLs that have been crawled by robots  208  are delivered to the content filters  210 . In typical embodiments, there is more than one content filter  210  in system  200  because of the computational demands of the content filter  210 . Alternatively, the content filter  210  can be implemented as part of each robot  208 . Each content filter  210  sends the retrieved web pages to Dupserver  224  to determine if they are duplicates of other web pages using, for example, techniques described in co-pending U.S. patent application Ser. No. 10/614,111, filed Jul. 3, 2003, which is hereby incorporated by reference as background information. 
         [0026]    In some embodiments, the content filters  210  write out four or more types of log files, including link logs  214 , RTlogs  226 ,  228 , and  230 , history logs  218 , and status logs  212 . The link log  214  contains one link record per URL document. A URL document is a document obtained from a URL by a robot  208  and passed to a content filter  210 . Each link log  214  record comprises all the links (e.g., URLs, also called outbound links) that are found in the URL document associated with the record and the text that surrounds the link. The log records in an RTlog include the full content of the documents obtained by robots  208 . Each document is coupled with a score (e.g., page rank) that was assigned to the source URL of the document by the page rankers  222 . 
         [0027]    Indexers  232 ,  240  and  242  obtain documents from the RTlogs  226 ,  228  and  230 , on a high throughput basis and make these documents searchable by a front-end querying system (not shown). Global state manager  216  reads link logs  214  and uses the information in the link logs to create link maps  220 . The records in the link map  220  are similar to records in the link log  214  with the exception that text is stripped and the records are keyed by a “fingerprint” of the normalized value of the source URL. In some embodiments, a URL fingerprint is a 64-bit integer determined by applying a hash function or other one way function to a URL. The bit-length of the URL fingerprint may be longer or shorter than 64 bits in other embodiments. The records in each link map  220  may optionally be sorted or keyed by a fingerprint. The link maps  220  are used by the page rankers  222  to adjust the page rank of URLs within data structure  100 . Preferably, such page rankings persist between epochs. 
         [0028]    In addition to creating the link maps  220 , the global state manager  216  creates anchor maps  238 . In contrast to records in a link map  220 , records in an anchor map  238  are keyed (i.e., indexed) by the URL fingerprints of outbound URLs present in the link log  214 . The records in each anchor map  238  may optionally be sorted by outbound URL fingerprints as well as being keyed by outbound URL fingerprints. Thus, each record in an anchor map  238  comprises a fingerprint of an outbound URL and text that corresponds to the URL in the link log  214 . The anchor maps  238  are used by indexers  232 ,  240  and  242  to facilitate the indexing of “anchor text” as well as to facilitate the indexing of URLs that do not contain words. The indexing of anchor text is described more fully in U.S. patent application Ser. No. 10/614,113, filed Jul. 3, 2003. 
       URL Scheduling 
       [0029]    In some embodiments, the URL scheduler  202  determines whether to add or remove URLs from the daily layer  104  and the real-time layer  106  based on information stored in records in the history logs  218 . The history log records include information indicating how frequently the content associated with the URLs is changing (hereinafter also referred to as “URL change frequency” or “content change frequency”) and individual URL page ranks set by the page rankers  222 . Note that the history logs  218  also contain log records for URLs that are not found in data structure  100 . For instance, the history log  218  can contain log records for URLs that no longer exist and/or log records for URLs that exist but are no longer scheduled for crawling (e.g., due to a request by the website owner that the URL not be crawled, due to objectionable content, or for any other reasons). 
       History Log 
       [0030]      FIG. 3  illustrates a history log  218 , in accordance with some embodiments of the present invention. The history log  218  includes a record  300  for each URL that has been crawled by the robot  208 . In some embodiments, each record includes a URL Fingerprint (URL FP)  302 , a Timestamp  304 , a Crawl Status  306 , a Content Checksum  308 , a Link Checksum  310 , a Source ID  312 , a Download Time  314 , an Error Condition  316 , a Segment ID  318  and a Page Rank  320 . Note that this is not an exhaustive list of possible fields for records  300 , and records  300  can include more or less data fields as appropriate. 
         [0031]    The URL fingerprint  302  is, for example, an N-bit number (where N is a value or a bit length) that is generated from the corresponding URL by first normalizing the URL text (e.g., converting host names to lower case) and then passing the normalized URL through a fingerprinting function that is similar to a hash function except the fingerprint function guarantees that the fingerprints are well distributed across the entire space of possible numbers. In some embodiments, the fingerprint modulus S, where S is the number of segments  112  in base layer  102  (e.g., “fingerprint modulus  12 ”, in the case where there are 12 segments  112  in base layer  102 ) is used to select the segment  112  in which to place a given URL. In some embodiments, additional rules are used to partition URLs into a segment  112  of base layer  102 , the daily crawl layer  104  and/or the real-time layer  106 . 
         [0032]    The Timestamp  304  indicates the time the record  300  was recorded. The Crawl Status  306  indicates whether the corresponding URL  302  was successfully crawled (i.e., whether the particular download attempt documented by this history log record  300  was successful). The Content Checksum  308  (also called the content fingerprint) is a numerical value corresponding to the content of the downloaded document, if the download was successful. In some embodiments, this checksum value  308  is generated by computing a predefined checksum on the contents of the downloaded document. The Content Checksum  308  can be used to determine whether the content of a web page has changed. When web pages have identical content, they will also have the same Content Checksum  308 . The URL scheduler  202  can compare these content fingerprints  308  with previous content fingerprints obtained for the corresponding URL (e.g., identified by URL FP  302  in the history log record  300 ) on a previous crawl to ascertain whether the web page has changed since the last crawl. 
         [0033]    Similarly, the Link Checksum  310  is a numerical value corresponding to the values of all the outbound links on the web page associated the URL  302 . In some embodiments, the Link Checksum  310  is generated by computing a predefined checksum on the output links of the downloaded document. In some embodiments, the URL scheduler  202  is configured to use the Link Checksum  310  to determine whether any of the outbound links on the web page associated with the corresponding URL  302  have changed since the last crawl. For example, the URL schedule  202  may be configured to compare the Link Checksum  310  of the downloaded document with the Link Checksum  310  for the most recent prior download of the same URL to see if they are equal. If they are not equal, a change has occurred in the set of outbound links in the document (e.g., at least one outbound link has been added, removed or changed in value). 
         [0034]    The Source ID  312  provides an indication of whether the robot  208  accessed the URL  302  using the Internet (which can be considered to be a first database of documents) or an internal repository of documents (which can be considered to be a second database of documents). 
         [0035]    The Download Time  314  provides an indication of how long it took a robot  208  to download the web page associated with the corresponding URL FP  302 . 
         [0036]    The Error Condition  316  records any errors that were encountered by a robot  208  when attempting to download the web page associated with the URL FP  302 . An example of an error is “HTTP4,” which indicates that the web page does not exist. Other, distinct error types may be used to indicate if an existing web page is unavailable or unreachable. 
         [0037]    The Segment ID  318  identifies the particular crawl segment  112   a , . . . ,  112   n  ( FIG. 1 ) associated with the URL FP  302  at the time that the document download operation represented by this record  300  was performed or attempted. 
         [0038]    Page Rank  320  includes the page rank assigned to the URL FP  302  at the time that the document download operation represented by this record was performed or attempted. The page rank of a URL may change over time, as the set of pages having links to the page corresponding to URL FP  302  changes, and as the page ranks of these referring pages change. The Page Rank  320  included in any particular record for a URL FP  302  represents a snapshot of the corresponding URL&#39;s page rank at the time represented by the timestamp  304 . 
       Scoring Functions 
       [0039]    In some embodiments, the determination as to what URLs are placed in daily crawl layer  104  and/or real-time layer  106  (as opposed to base layer  102 ) is determined by computing a Daily Score, which is a composite score of the form: 
         [0000]      Daily Score= F 1(page rank,change frequency,age)  (Eq. 1A)
 
         [0000]    where F1 is a function of a specified document&#39;s page rank, change frequency and age, or a subset of those parameters. For instance in one embodiment, 
         [0000]      Daily Score=(page rank) 2 *URL change frequency  (Eq. 1B)
 
         [0040]    The mechanism by which URL scheduler  202  obtains URL change frequency data is best understood by reviewing  FIG. 2 . When a URL is accessed by a robot  208 , the information is passed through content filters  210 . Content filters  210 , among other things, determine whether a URL has changed (e.g., by checking Content Checksum  308 ) and when a URL was last accessed by a robot  208 . This information is placed in the history logs  218 , which are passed back to the URL scheduler  202 . By reviewing the log records for a particular URL, each of which indicates whether the content of a URL changed since the immediately previous time the URL was crawled, the URL schedule  202  (or other module) can compute a URL change frequency. This technique is particularly useful for identifying URL&#39;s having content (i.e., the content of the page at the URL) that changes infrequently, or perhaps not at all. Further, the computation of a URL change frequency can include supplemental information about the URL. For instance, the URL scheduler  202  can maintain or access information about web sites (i.e., URLs) whose content is known to change quickly. 
         [0041]    In cases where the URL scheduler  202  determines that a URL should be placed in a segment  112  of base layer  102 , the placement of the URL into a given segment  112   a , . . . ,  112   n  of base layer  102  is random (or pseudo-random), so that the URLs to be crawled are evenly distributed (or approximately evenly distributed) over the segments  112   a , . . .  112   n . In some embodiments, a mathematical function (e.g., a modulo function) is applied to the URL FP to achieve the random selection of a segment  112   a , . . .  112   n  in which to place the URL. 
         [0042]    In some embodiments, it is not possible to crawl all the URLs in an active segment  112 , daily crawl layer  104  and/or real-time layer  106  during a given epoch. In some embodiments, this problem is addressed using two different approaches. In a first approach, a Crawl Score is computed for each URL in an active segment  112 , the daily layer  104  and/or the real-time layer  106 . Only those URLs that receive a high Crawl Score (e.g., above a threshold value) are passed on to the next stage (URL managers  204 ) for downloading. In a second approach, URL scheduler  202  determines an optimum crawl frequency for each such URL and passes the crawl frequency information on to the URL managers  204 . The crawl frequency information is then ultimately used by URL managers  204  to decide which URLs to crawl. These two approaches are not mutually exclusive and a combined methodology for prioritizing the URLs to crawl (based on both the Crawl Score and the optimum crawl frequency) may be used. 
         [0043]    In embodiments where a Crawl Score is computed, the URL scheduler  202  determines which URLs will be crawled (downloaded from the Internet) during the epoch by computing a Crawl Score (or referencing a previously computed Crawl Score) for each URL. Those URLs that receive a high Crawl Score (e.g., above a predefined threshold) are passed on to the next stage (URL managers  204 ), whereas those URLs that receive a low Crawl Score (e.g., below the predefined threshold) are not passed on to the next stage during the given epoch. There are many different factors that can be used to compute a Crawl Score including the current location of the URL (active segment  112 , daily crawl segment  104  or real-time segment  106 ), page rank, and crawl history. The crawl history can be obtained from the history logs  218 . 
         [0044]    Although many possible Crawl Scores are possible, in some embodiments the Crawl Score is a composite score computed as follows: 
         [0000]      Crawl Score= F 2(page rank,change frequency,age)  (Eq. 2A)
 
         [0000]    where F2 is a function of a specified document&#39;s page rank, change frequency and age, or a subset of those parameters. In some embodiments, a document&#39;s age is defined as the time since the last download of the document by a web crawler. In other embodiments, the age of a document (u) is defined as: 
         [0000]      Age( u )=Now−(last_crawl( u )+expected_shelf_life( u ))  (Eq. 2B)
 
         [0000]    where the expected_shelf_life(u) of a document (u) is based on an expiration time provided by the document&#39;s source, or based on other information (e.g., rates of change) known about the document or other documents from the same source, or based on such information known about other documents considered to be similar to document (u). Such information may be statistical information about the rates of change of a set of documents, and such information maybe distilled, using various statistical or mathematical techniques, to produce an “expected shelf life” value for a particular document. In one embodiment: 
         [0000]      Crawl Score=(page rank) 2 *(URL change frequency)*(time since last crawl of URL).  (Eq. 2C)
 
         [0000]      In another embodiment, 
         [0000]      Crawl Score=(page rank)*(URL change frequency)*(time since last crawl of URL).  (Eq. 2D)
 
         [0000]      In yet another embodiment, 
         [0000]      Crawl Score=(page rank) 2 *(age)  (Eq. 2E)
 
         [0000]    where the age of document (u) may be defined or computed using any of a variety of techniques, as mentioned above. In this last embodiment, information about a document&#39;s content change frequency may be incorporated into (or otherwise taken into account in) the “age” parameter of the Crawl Score function. 
         [0045]    Additionally, many modifications to the Crawl Score, including modifications using cutoffs and weights are possible. For example, the Crawl Score of URLs that have not been crawled in a relatively long period of time can be weighted so that the minimum refresh time for a URL is a predetermined period of time (e.g., two months). In some embodiments, the URL change frequency is computed using the Content Checksum  308  stored in the history log  218 . In some embodiments, the Content Checksum  308  is generated by applying the 32-bit Ethernet CRC to the content of the document at the URL, while in other embodiments other checksum functions are used. If the document at a URL is altered, the Content Checksum  308  will have a different value. The “time since last crawl” variable can be computed from the TimeStamp  304  and the current system time derived from a master system clock or the like. 
         [0046]    In embodiments where crawl frequency is used, the URL scheduler  202  sets and refines a URL crawl frequency for each URL in the data structure  100 . The URL crawl frequency for a given URL represents the optimum crawl frequency (or, more generally, a selected or computed crawl frequency) for a URL. The crawl frequency for URLs in the daily crawl layer  104  and the real-time layer  106  will tend to be higher than the crawl frequency for URLs in the base layer  102 . The crawl frequency for any given URL can range from high values (e.g., representing crawl repeat rates of multiple times per hour) to low values (e.g., representing crawl repeat rates of less than once per month). In some embodiments, the optimal crawl frequency for a URL is computed based on the historical change frequency of the URL and the page rank of the URL. 
         [0047]    In addition to other responsibilities, the URL scheduler  202  determines which URLs are deleted from the data structure  100  and therefore dropped from the system  200 . The URLs are removed from the data structure  100  to make room for new URLs to be added to the data structure  100 . In some embodiments, a Keep Score is computed for each URL in data structure  100 . The URLs are then sorted by the Keep Score and the URLs that receive a low Keep Score are eliminated as newly discovered URLs are added to the data structure  100 . In some embodiments, the Keep Score for a document (u) is set equal: 
         [0000]      Keep Score= F 3(page rank,change frequency,age)  (Eq. 3A)
 
         [0000]    where F3 is a function of a specified document&#39;s page rank, change frequency and age, or a subset of those parameters. In one embodiment, the Keep Score for a document (u) is set equal to the page rank of the document, as determined by the page rankers  222  ( FIG. 2 ). 
       URL Scheduler Computer System 
       [0048]      FIG. 4  is a block diagram of a standalone URL scheduler computer system  400 , in accordance with some embodiments of the present invention. The URL scheduler computer system  400  generally includes one or more processing units (CPU&#39;s)  402 , one or more network or other communications interfaces  410 , memory  412 , and one or more communication buses  414  for interconnecting these components. The system  400  may optionally include a user interface  404 , for instance a display  406  and a keyboard  408 . Memory  412  may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices. Memory  412  may include mass storage that is remotely located from the central processing unit(s)  402 . 
         [0049]    The memory  412  stores an operating system  416  (e.g., Linux or Unix), a network communication module  418 , a system initialization module  420  and a URL scheduler module  422 . The operating system  416  generally includes procedures for handling various basic system services and for performing hardware dependent tasks. The network communication module  418  is used for connecting the system  400  to the servers hosting the content filters  210  ( FIG. 2 ) and possibly to other servers or computers via one or more communication networks (wired or wireless), such as the Internet, other wide area networks, local area networks, metropolitan area networks, and the like. The system initialization module  420  initializes other modules and data structures stored in memory  414  required for the appropriate operation of the system  400 . 
         [0050]    The URL scheduler module  422  is used to implement various aspects of the present invention, as described below with respect to  FIGS. 5 and 6 . The memory  412  also includes scoring functions  428  and data structures (e.g., data structure  100 ) used by the URL scheduler  422 . In some embodiments the data structures include a history log  424 , a schedule output file  426 , and thresholds  430 . In some embodiments, the URL scheduler computer system  400  is a runtime system integrated into a search engine crawler system (e.g., URL scheduler  202  in web crawler system  200 ) and the scoring functions  428  and thresholds  430  are computed in one or more context filters  210  ( FIG. 2 ). In other embodiments, the URL scheduler computer system  400  is a standalone system that performs background processing independent of the web crawling system  200 . 
         [0051]      FIG. 5  is a flow diagram of a URL scheduler  422  initialization process, in accordance with some embodiments of the present invention. The process begins by selecting  500  (randomly or pseudo-randomly) a sample set of URLs to be scheduled. A set of scores are computed  502  for each URL in the sample. In some embodiments, three types of scores are computed: Keep Score, Crawl Score and Daily Score. In some embodiments, the Keep Score is set equal to the URL page rank and the Daily Score and Crawl Score are computed using Equations (1A) and (2A). Note that more or less scores can be computed, as needed, depending upon the architecture of the system  200 . 
         [0052]    After the scores are computed  502 , the sample set of URLs is sorted  504  in descending (or ascending) order into three sorted lists based on the computed Keep, Crawl and Daily Scores. For the sorted list associated with the Keep Score, a cutoff score (hereinafter also referred to as a “Keep Score threshold”) is selected  506  based on a target size of a set of URLs to be included in base layer  102 . For the sorted list associated with the Crawl Score, a cut off score (hereinafter also referred to as a “Crawl Score Threshold”) is selected  506  based on a target size of a set of URLs from the base layer  102  to be re-crawled (as opposed to being fetched from a repository). For the sorted list associated with the Daily Score, a cut off score (hereinafter also referred to as a “Daily Score Threshold”) is selected  506  based on a target size of a set of URLs to be moved from the base layer  102  into the daily crawl layer  104 . Any new URLs discovered during a crawl can be placed in the smallest segment in base layer  102 . Each of these URLs will have a record in the history log  218  after being crawled for the first time, and will thereafter become part of the normal scheduling process, as described with respect to  FIGS. 6 and 7 . 
         [0053]    To better illustrate the URL scheduler initialization process, let us assume that we have collected a database of URLs, each URL having an associated page rank, change frequency and a time value indicating a period of time that has transpired since the URL was last crawled. The URLs and associated information can be represented as shown in Table I below. Note that this example has been simplified by using integers to represent the URL FP and page rank. In practice, the crawling system  200  would process several billion URLs and the URL FPs and page ranks could be any N-bit integer or other value. 
         [0000]    
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE I 
               
               
                   
               
               
                   
                   
                 Change Frequency 
                 Time Since Last 
               
               
                 URL Fingerprint 
                 Page Rank 
                 (Changes/Day) 
                 Crawl (Days) 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1 
                 8 
                 2 
                 3 
               
               
                 2 
                 3 
                 1 
                 2 
               
               
                 3 
                 1 
                 1 
                 1 
               
               
                 4 
                 4 
                 2 
                 1 
               
               
                 5 
                 10 
                 3 
                 4 
               
               
                 6 
                 9 
                 2 
                 7 
               
               
                 7 
                 7 
                 1 
                 3 
               
               
                 8 
                 2 
                 3 
                 4 
               
               
                 9 
                 5 
                 1 
                 15 
               
               
                 10 
                 6 
                 2 
                 3 
               
               
                   
               
             
          
         
       
     
         [0054]    Table I includes a randomly selected sample set of URLs resulting from the performance of step  500  in  FIG. 5 . In some embodiments, the number of URLs in the sample set is at least one million (e.g., in one embodiment the number of URLs in the sample set is about ten million). In some other embodiments, the number of URLs in the sample set is at least 50,000. A Keep Score, Crawl Score and Daily Score are computed  502  from the sample set of URLs, then sorted  504  by Score into three sorted lists of URLs, as shown in Table II below. The sorted lists include a Keep List, a Crawl List and a Daily List. Note that in this example the Keep Score is set equal to the Page Rank and the Daily and Crawl Scores are computed using Equations (1B) and (2B). 
         [0000]    
       
         
               
               
               
             
           
               
                 TABLE II 
               
               
                   
               
               
                 Keep List 
                 Crawl List 
                 Daily List 
               
               
                 (URL FP, Keep Score) 
                 (URL FP, Crawl Score) 
                 (URL FP, Daily Score) 
               
               
                   
               
             
             
               
                  5, 10 
                  6, 1134 
                  5, 300 
               
               
                 6, 9 
                  5, 1200 
                  6, 162 
               
               
                 1, 8 
                  1, 384 
                  1, 128 
               
               
                 7, 7 
                  9, 375 
                 10, 72  
               
               
                 10, 6  
                 10, 216 
                 7, 49 
               
               
                 9, 5 
                  7, 147 
                 4, 32 
               
               
                 4, 4 
                 8, 48 
                 9, 25 
               
               
                 2, 3 
                 4, 32 
                 8, 12 
               
               
                 8, 2 
                 2, 18 
                 2, 9  
               
               
                 3, 1 
                 3, 1  
                 3, 1  
               
               
                   
               
             
          
         
       
     
         [0055]    After computing the sorted lists for each Score, cut off scores (i.e., threshold values) are selected  506  as a function of URL target size. In this example, the target sizes are arbitrarily selected based on the respective URL capacities of the base layer  102  and daily layer  104  (or real-time layer  106 ) and the bandwidth of the crawler. 
         [0056]    Thus, if the URL capacity of the base layer  102  is 70% of the entire set of known URLs, the Keep Threshold is 3, since 70% of the URLs in the sample set have a Keep Score that exceeds 3, including URLs 5, 6, 1, 7, 10, 9 and 4 (see column 1 of Table II). 
         [0057]    If the capacity of the crawler is 50% of known URLs, the Crawl Threshold is 147, since there 50% of the URLs in the sample set have a Crawl Score that exceeds 147, including URLs 6, 5, 1, 9 and 10 (see column 2 of Table II). 
         [0058]    If the URL capacity of the daily layer  104  is 20% of known URLs, the Daily Threshold may be set to 128, since 20% of the URLs in the sample set have a Daily Score that exceeds 128, including URLs 5 and 6 (see column 3 of Table II). In practice, where the sample set has thousands or millions of URLs, the differences between Daily Score values between adjacent items in the sorted list will typically be very small. The threshold score may be selected as being equal to lowest Daily Score of the URLs to be included in the selected set of URLs, or the next lowest Daily Score, depending on how the threshold is applied to select URLs for the daily crawl. In particular, if the selection criteria is URLs having a score above the threshold, then the threshold score is the highest score of any URL that is not to be included in the selected set; and if the selection criteria is URLs having a score equal to or above the threshold, then the threshold score is the lowest score of any URL to be included in the selected set. Similar criteria are used for selecting the Keep Threshold and the Crawl Threshold. 
         [0059]    After the cut off scores are selected  506 , they are stored  508  (e.g., in memory  412 ) for use by the URL scheduler  422  in performing a scheduling process on the entire data structure  100  of system  200 , as described below with reference to  FIG. 6 . Note that the initialization process described above assumes that the sample set of URLs is a good statistical representation of the entire data structure  100  of URLs, and therefore the selected threshold values will result in an allocation of URLs to the various segments in data structure  100  without exceeding the capacity constraints of those segments or the capacity of the crawler system  200  to download web pages. 
         [0060]      FIG. 6  is flow diagram of a URL scheduler process, in accordance with some embodiments of the present invention. For each base layer segment  112  (see steps  600 ,  602 ), the Keep, Crawl and Daily Scores for the URLs in that segment are computed  604 . The URLS are then sorted  606  by Keep Score and the URLs having a Keep Score above the Keep Score threshold are selected  606 . A Crawl/Reuse and Daily Flag are then set  610  (or unset) for the selected URLs having Crawl and Daily Scores above the Crawl and Daily Score thresholds. The selected URLs, and the Page Rank, Crawl/Reuse Flag and Daily Flag for the selected URLs are then written  612  to a schedule output file  426 , as shown in  FIG. 7 . This process is repeated ( 600 ) for each segment of the base layer. 
         [0061]      FIG. 7  illustrates a schedule output file  426 , in accordance with some embodiments of the present invention. The schedule output file  426  includes a number of records  700   a , . . . ,  700   n . Each record  700   a , . . . ,  700   n  includes a URL  702 , a Page Rank  704 , a Crawl/Reuse Flag  706  and a Daily Flag  708 . The Crawl/Reuse Flag  706  indicates whether the URL should be crawled and downloaded from the network or retrieved from a repository. The Daily Flag  708  indicates whether the URL should be included in the daily layer  104 . 
         [0062]    In some embodiments, the Crawl/Reuse Flag is a single bit, which can be set to logic “1” to indicate that the URL should be crawled and to logic “0” to indicate that the URL should be fetched from a repository, or vice-versa. Similarly, the Daily Flag can be a single bit that is set to logic “1” to indicate that the URL should be included in the daily layer  104  and to logic “0” to indicate that the URL should not be included in the daily layer  104 , or vice-versa. In some embodiments, the Daily Flag can have more than two values. For example, in one embodiment each Daily Flag has one of three values: crawl (i.e., download from Internet), reuse (i.e., use copy from document repository), and crawl if new (i.e., download if the document has been modified since a specified date and/or time). 
         [0063]    By example, if the threshold values determined using Table II (as discussed above) are applied against the URLs in Table I, the URLs would be allocated as shown in Table III below. Note that a logical “1” in the Crawl Flag or Daily Flag column indicates that the URL will be crawled and included in the Daily Crawl, respectively. 
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                 TABLE III 
               
               
                   
                   
               
               
                   
                 URL FP 
                 Keep Score 
                 Crawl Flag 
                 Daily Flag 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 5 
                 10 
                 1 
                 1 
               
               
                   
                 6 
                 9 
                 1 
                 1 
               
               
                   
                 1 
                 8 
                 1 
                 0 
               
               
                   
                 7 
                 7 
                 0 
                 0 
               
               
                   
                 10 
                 6 
                 1 
                 0 
               
               
                   
                 9 
                 5 
                 1 
                 0 
               
               
                   
                 4 
                 4 
                 0 
                 0 
               
               
                   
                   
               
             
          
         
       
     
         [0064]    Thus, referring to Table III, the base layer  104  will include a total of 7 URLS (6,5,1,7,10,9,4). URLs 5,6,1,10 and 9 will be crawled and URLs 7 and 4 will be retrieved from a repository (i.e., reused). URLs 5 and 6 will be moved from the base layer  102  to the daily crawl layer  104  (or real-time layer  106 ) where they will be crawled more frequently (e.g., once a day), and URLs 1, 7, 10, 9 and 4 will remain in the base layer  102  where they will be crawled less frequently (e.g., every ten days). 
         [0065]    The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.