Abstract:
A web crawler system includes a central processing unit for performing computations in accordance with stored procedures and a network interface for accessing remotely located computers via a network. A web crawler module downloads pages from remotely located servers via the network interface. A first link processing module obtains page link information from the downloaded page; the page link information includes for each downloaded page a row of page identifiers of other pages. A second link processing module encodes the rows of page identifies in a space efficient manner. It arranges the rows of page identifiers in a particular order. For each respective row it identifies a prior row, if any, that best matches the respective row in accordance with predefined row match criteria, determines a set of deletes representing page identifiers in the identified prior row not in the respective row, and determines a set of adds representing page identifiers in the respective row not in the identifier prior row. The second link processing module delta encodes the set of deletes and delta encodes the set of adds for each respective row, and then Huffman codes the delta encoded set of deletes and delta encoded set of adds for each respective row.

Description:
The present invention relates generally to web crawlers and other systems that maintain a database of information about web pages, and particularly to a data structure for efficiently representing link information in a web database. 
     BACKGROUND OF THE INVENTION 
     Web crawlers and other systems as well maintain a database of information about web pages or documents accessible via a network. The network for which the database is maintained may be the Internet or may an Intranet or other network, but for convenience we will herein refer to this database as a “web database.” The web database will generally store the address of each known web page, as well as information about the outbound links in the web page to other web pages. The addresses of web pages are often called universal resource locators (URL&#39;s). Some web databases also store for each page information about all the links (herein called inbound links) in other web pages that point to this web page. Of course, the web database can also store additional information about the web pages, such as the last time the page was downloaded, the page&#39;s stated expiration date, a fingerprint or sketch or other representation that allows the page to be efficiently compared with other pages without having to compare the actual contents of the pages, and so on. 
     Referring to  FIG. 1 , there is shown a small portion of a hypothetical set  50  of interlinked pages 51–65 in a network. This figure shows inbound links and outbound links for each page. The present invention is directed to a memory space efficient system and method for storing the outbound and/or inbound link information for a set of pages in a network. 
     If the number of web pages in the network is large, the amount of memory required to store the URL&#39;s and links in the web database will be correspondingly large. In systems in which it is important or desirable to store the entire web database in high speed random access memory, such as in web crawler systems, it is desirable for the link information to be stored efficiently so as to reduce the amount of memory required to store the web database. 
     SUMMARY OF THE INVENTION 
     One aspect of the present invention is a web crawler system that includes a central processing unit for performing computations in accordance with stored procedures and a network interface for accessing remotely located computers via a network. A web crawler module downloads pages from remotely located servers via the network interface. A first link processing module obtains page link information from the downloaded pages; the page link information includes for each downloaded page a row of page identifiers of other pages. A second link processing module encodes the rows of page identifies in a space efficient manner. It arranges the rows of page identifiers in a particular order. For each respective row it identifies a prior row, if any, that best matches the respective row in accordance with predefined row match criteria, determines a set of deletes representing page identifiers in the identified prior row not in the respective row, and determines a set of adds representing page identifiers in the respective row not in the identifier prior row. The second link processing module delta encodes the set of deletes and delta encodes the set of adds for each respective row, and then Huffman codes the delta encoded set of deletes and delta encoded set of adds for each respective row. 
     Another aspect of the present invention is a method and computer program product for compressing and storing web page link information, representing links between web pages. The computer program product comprises the aforementioned first and second link processing modules. The method includes obtaining page link information for a set of pages, the page link information including for each page a row of page identifiers of other pages. The rows of page identifiers are arranged in a particular order. Then, for each respective row, the method identifies a reference row, if any, that best matches the respective row in accordance with predefined row match criteria. The respective row is encoded as an identifier for the identified reference row, if any, a set of deletes representing page identifiers in the identified reference row not in the respective row, and a set of adds representing page identifiers in the respective row not in the identified reference row. The encoding of the respective row includes delta encoding the set of deletes and delta encoding the set of adds for each respective row, and further includes Huffman coding the delta encoded set of deletes and delta encoded set of adds for each respective row. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Additional objects and features of the invention will be more readily apparent from the following detailed description and appended claims when taken in conjunction with the drawings, in which: 
         FIG. 1  is a block diagram depicting a set of linked data sets. 
         FIG. 2  is a block diagram of an exemplary distributed computer system, including a web crawler or other computer for implementing the present invention. 
         FIG. 3  is a block diagram of a data structure representing a list of visited data sets. 
         FIG. 4  is a block diagram of a data structure representing a database of outbound or inbound links. 
         FIG. 5  is a conceptual block diagram of a data structure representing a row of the database of  FIG. 4 , prior to compression. 
         FIG. 6  is a block diagram of a data structure representing a row of the database of  FIG. 4 , after compression. 
         FIG. 7  is a flow chart of a web crawler procedure. 
         FIG. 8  is a flow chart of a compression procedure for compressing a set of row entries in a database of outbound or inbound links. 
         FIG. 9  is a flow chart of a decompression procedure for decompressing a row entry in a database of outbound or inbound links. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       FIG. 2  shows an exemplary embodiment of a distributed computer system  100 . The distributed computer system  100  includes a web crawler  120  connected to a network  110  through the network fabric  110  of the network. The network  110  may be a global communication network, such as the Internet, or a private network, sometimes called an Intranet. Network fabric  112  may include switches, routers, electrical and fibre optic cables, and so on. 
     The network  110  includes web servers  114  that store web pages. The network  110  may optionally include a service known as a domain name system  116 , it may also optionally include a web page indexing system  118 . The domain name system  116  is a distributed database that provides the mapping between Internet Protocol (IP) addresses and host names. The domain name system  116  may be accessed by the web crawler  120  in the process of downloading web pages from the world wide web. 
     In other embodiments, the web crawler system of the present invention may operate in a network environment quite different that shown in  FIG. 2 . For instance, the present invention does not require the use of a domain name service, nor does it require the use of any particular network fabric  112 . 
     Web Crawler System 
     The web crawler  120  includes a communications interface, or network connection,  122 , one or more CPU&#39;s  124 , an operator interface  126  (which may be remotely located on another computer), primary or main memory  128  and secondary (e.g. disk) memory  130 . In an exemplary embodiment, the network connection  122  is able to handle overlapping communication requests. The memory  128  includes:
         a multitasking operating system  140 ;   an Intranet/Internet access procedure  142  for fetching web pages as well as communicating with the domain name system  116 ;   one or more threads  144  for downloading web pages from the servers  114 , and processing the downloaded web pages;   a main web crawler procedure  146  executed by each of the threads  144 ;   a link database compression procedure  148  for compressing data representing links to and from web pages;   a link database decompression procedure  148  for decompressing data representing links to and/or from web pages;   Huffman tables  152 , which are used by the compression and decompression procedures,  148  and  150 , to encode and decode link data; the Huffman tables  152  may be considered to be part of databases  160 ,  162 ;   a set of URL&#39;s called the seed set  154 ;   a list of visited URL&#39;s  156  that identifies the URL&#39;s of pages visited during a crawl of the web pages in a network (see  FIG. 3 );   a list of known URL&#39;s that have not yet been visited  158 ;   a database  160  of outbound links in visited web pages (see  FIG. 4 ); and   a database  162  of inbound link to visited web pages.       

     Page Link Data Structures 
     Referring to  FIG. 3 , in a preferred embodiment the list of visited URL&#39;s  156  is stored in a data structure that includes a primary list of URL entries  170 , each entry including a URL  172  and a pointer  174  to a list  176  of outbound link URL&#39;s. If a visited page contains no outbound links, the pointer  174  in the corresponding entry  170  is given a null value; otherwise it points to a list  176  of outbound link URL&#39;s stored by the web crawler procedure. 
     The information in the list of visited URL&#39;s  156  is compressed by the compression procedure to form the database  160  of outbound links in visited web pages. This same information can be used to form a list of inbound links for each visited web page, which in turn can be compressed to form the database  162  of inbound links to visited web pages. Note that some of the unvisited URL&#39;s from outbound links may be invalid (there is no guarantee that the URL&#39;s in outbound links refer to existing data sets). 
     Using the methodology of the present invention, the average amount of memory occupied by databases  160 ,  162  is 4.5 to 5 bits per link. 
     Referring to  FIGS. 5 ,  6  and  7 , we next describe the data structure used to store the outbound links of a web page. The data structure for inbound links is the same at the data structure for outbound links. Each page is identified by a page ID. In a preferred embodiment, the page ID&#39;s are positive integers densely allocated starting at zero. The page ID of a page is the same as its row index in the link information database. For each page A, we store the set of ID&#39;s of pages for which page A contains outbound links. We call this set the “row” for page A. The row is stored in sorted order (sorted by ID), with duplicates removed (i.e., if page A contains multiple links to a particular page, the page ID of that page is included only once in the row for page A). 
     The rows are encoded using two data structures: a Starts array  190  and a Data array  192 . The Starts array  190  is indexed by page ID and contains indices into the Data array  192 . The data encoding the row for page A is stored in the Data array  192  starting at the location identified by Starts[A] and continuing through the location Starts[A+1]−1, inclusive. 
     The encoding in the data array  192  is the result of three compression steps: reference-row compression, delta compression, and bit compression, each of which is described below. 
     The reference-row compression encodes a given row relative to a reference row. For each row A, we select a reference row ref(A) against which we encode row A. Instead of encoding row A directly, we store for row A a pointer to ref(A), a list of “deletes”, and of “adds”. As shown in  FIG. 5 , each row  194  of the link information database  160  or  162  includes a reference  200  to a reference row, a list of deletes  202 , and a list of adds  204 . The deletes and adds represent the operations required to change the reference row into A&#39;s row. Each delete is an index into the reference row of an ID which is not in A&#39;s row, and each add is an ID which is in A&#39;s row but not in the reference row. During decompression, to obtain row A, we first obtain the reference row ref(A) for A, remove the ID&#39;s from ref(A) specified by the deletes, and then add the ID&#39;s specified by the adds. 
     The reference row for any page A is chosen by selecting a best preceding row, row[A−r], in the database for the window 0&lt;r&lt;K. We choose the “best” of these preceding K−1 rows in accordance with predefined reference row selection criteria. In a preferred embodiment, the best reference row is the one for which the resulting deletes and adds sets are smallest (after estimating the effects of delta compression and bit compression, described below). If none of the preceding K−1 rows provide a good reference row, then an empty reference row is used. This condition is identified by setting r=0. 
     When decompressing a row, we first need to decompress its reference row, if any. Unfortunately, decompressing the reference row may require decompressing its reference row, and so on. In order to put a limit on the decompression time for a row, we put a limit on the length of these “reference chains”. We set a hard limit L on the length of any reference chain, and if a row is already at the end of a chain of length L, we omit it from consideration as a reference row for any other row. This rule ensures that at most L rows need to be decompressed in order to reconstruct any row. 
     In alternate embodiments, other methods may be used for choosing the reference row ref(A) for row A. Such alternate methods include:
         Choose ref(A) to be the union of the previous K rows in the database. This strategy generates large reference rows, which takes advantage of the fact that deletes are cheaper to encode than adds.   Choose ref(A) to be the union of all rows from a particular host, or of groups of N rows from hosts with more than N rows.   Choose ref(A) to include all links that appear in at least X% of the rows from a particular host (or group of N rows from large hosts), with X chosen to balance the resulting sizes of adds and deletes.       

     Many other choices for reference rows are possible. 
     Once a reference row is selected (or constructed from a plurality of prior rows), the resulting deletes and adds lists for page A are then sorted by numeric value and passed through a delta compression step. The encoding is as follows: 
                                                 Dddeletes[0] = deletes[0]               Ddeletes[i] = deletes[i] - deletes[i- 1] - 1   i &gt; 0           Dadds[0] = adds[0] - A           Dadds[i] = adds[i] - adds[i-1] - 1   i &gt; 0                        
where the “D” prefix of “Ddeletes” and “Dadds” identifies the new lists as containing “delta” encoded information. Note that because we sort before delta compressing, only Dadds[0] can be negative. The delta compression changes the distribution of the values that need to be compressed, concentrating the values towards zero.
 
     The reference row selection, r, as well as the two lists Ddeletes and Dadds, are then encoded using Huffman codes. A separate Huffman table is used for each of the following (some tables may be combined to save memory during decoding):
         the reference value r;   the size of the Ddeletes array;   the entries of the Ddeletes array;   the entries of the Dadds array.       

     The size of the Dadds array can be deduced from the total length of the encoded data for the row. 
     Huffman tables are built for all values up to the maximum encountered, or a static limit (currently 2 17  entries). Values larger than the static limit are encoded using an escape value. If the value to be encoded is within the table, it is encoded using its Huffman code. If it is outside the table, an escape is encoded, and then the actual value is encoded with a gamma code (or any other table-free code). The weight of the escape code in the Huffman table is chosen to represent the number of values larger than the static limit. 
     The Huffman tables are built during an initial pass over the data, estimating the frequency of each of the table entries. 
     Alternately, the Huffman tables may be computed based on an initial portion of the data (e.g., the data from ten million visited pages), rather than all the data from a crawl of the network. If the initial sample is sufficiently large, the Huffman tables will still provide good compression. This alternate embodiment is particularly useful when the network being crawled contains very large numbers (e.g., hundreds of millions or billions) of web pages. 
     In another alternate embodiment, if the web crawler will be used to crawl a particular network on a periodic basis, such as once per week or once per day, the Huffman tables may be built once, based on either all the data for an initial crawl or a large sampling thereof (for instance on the first N entries (e.g., ten million entries)), and then retained for future crawls. 
     A diagram of the resulting compressed row data structure is shown in  FIG. 6 . The r field  210  is the encoded reference row value, the “size of Ddeletes list” field  212  indicates the number of entries in the Ddeletes field  214  (which contains the deletes entries of the row), the Dadds field  216  contains the add entries of the row, and the pad field  218  contains as many bits (e.g., zero to three) as may be required to make the row data structure end on a permissible bit boundary (e.g., by making the size of the row data structure an integer multiple of a predefined number of bits, such as 4-bits or 8-bits). 
     If the reference row value r  210  is zero (representing an empty reference row), the size and entries of Ddeletes are omitted. In a preferred embodiment, the “pad” data  218  is used to make each row&#39;s data 4-bit aligned, so the Starts array  190  can use indices representing Data array location with 4-bit granularity. 
     Main Web Crawler Procedure 
     Referring to  FIG. 7 , the web crawler procedure  146  in one embodiment of the present invention works as follows. A URL is selected from the set of known web pages that have yet been visited by the web crawler (step  230 ). Initially, the set of known web pages is a seed set of pages  154  ( FIG. 1 ). The web page at the selected URL is downloaded, step  232 , and processed. The URL of the downloaded page is added a list of visited pages, step  234 . A preferred data structure for the list of visited pages is shown in  FIG. 3 . The links in the downloaded page are identified, the URL&#39;s in those links are compared with the list  156  ( FIG. 1 ) of visited pages to determine which, if any, are for pages not yet visited by the web crawler, and then the URL&#39;s of those pages are added to a list  158  ( FIG. 1 ) of known web pages that have not yet been visited, step  236 . 
     In some embodiments the web crawler is configured to compress the web page link information whenever the number of uncompressed entries in the list of visited pages exceeds a threshold value, such as ten million. In such embodiments, when this threshold is reached ( 238 -yes), the link database compression procedure is invoked to compress the entries in the list of visited pages and to store the resulting compressed rows of information into the compressed database of outbound links, step  240 . After performing the compression ( 240 ), or if the number of uncompressed entries in the list of visited pages does not exceed the threshold value ( 238 -no), processing resumes at step  230  (described above). Either at the end of the procedure, when all known pages have been visited, or just before or after the compression procedure is called, the information in the list of visited pages may be used to construct a list of inbound links for each visited page, and then that information may be compressed and stored in a database of inbound link information (step  242 ), using the same compression procedure as the one used for compressing the outbound links. 
     If the compression step  240  is to be performed just once at the end of the crawl, decision step  238  is eliminated and compression step  240  is performed when the list of known pages not yet visited is empty (or some other crawl end condition is reached). 
     Link Database Compression Procedure 
     Referring to  FIG. 8 , the compression procedure  148  operates as follows in a preferred embodiment. Each “row” corresponds to the outbound links in, or the inbound links to, a page. The rows are processed in same order they were added to the list of visited pages by the web crawler, and thus the procedure begins by selecting a first or next row, step  250 . The selected row is also called the current row. A best reference row is identified, in accordance with predefined selection criteria, step  252 . In the preferred embodiments the best reference row is a reference row that results in a compressed row representation for the current row having the smallest number of bits. 
     In a preferred embodiment, the best reference row is a prior row having a row offset r of 0&lt;r&lt;K, excluding rows having a reference row chain length of L, that results in a compressed row representation for the current row having the smallest number of bits. As discussed above, in other embodiments the best reference row may be a composite of multiple prior rows. If the current row is the first row, and thus there are no prior rows, or all the prior rows in the window 0&lt;r&lt;K have reference row chain lengths of L, or encoding the current row with reference to any of the candidate reference rows produces a longer data structure than encoding the current row with an empty reference row, then the best reference row is an empty reference row. In any of these cases, the identified best reference row is represented by a row offset value of zero. 
     Next, the current row is encoded with respect to the identified best reference row, if any, step  254 . In particular, the set of links associated with the page corresponding to the current row is represented as a set of deletes and adds with respect the set of links represented by the identified best reference row. After the lists of deletes and adds for the current row have been generated, these lists are delta encoded, step  256 . 
     Steps  250  through  256  are repeated until all the rows have been processed. Then Huffman tables are generated for the row offset values, delete list size values, delta delete values and delta add values, step  258 . The Huffman tables are based on the frequency of occurrence of each row offset value, delete list size value, delta delete value and delta add value and provide a coding for each of these values that, on average, occupies the least number of bits. 
     Finally, all the rows are bit compressed by Huffman coding the reference row offset value, delete list size value, delete delta values and add delta values of each respective row, using the corresponding Huffman tables, step  260 . Pad bits are added to each row as needed to make each row&#39;s data 4-bit aligned, and the start position of each row is stored in the Starts array, step  260 . 
     In other embodiments the Huffman tables may be computed in advance, in which case each row is Huffman encoded after the delta encoding step  256 . 
     Link Database Decompression Procedure 
     Referring to  FIG. 9 , the decompression procedure  150 , which decompresses one identified row of the outbound or inbound links database, operates as follows in a preferred embodiment. The identified row, also called the current row, is read, using the Starts array to find its start and end positions in the Data array, step  270 . The reference row offset value in the current row is Huffman decoded using the corresponding Huffman table, step  272 . Similarly, the size of the deletes list, the deletes entries and the adds entries are decoded using the corresponding Huffman tables, step  272 , to produce lists of delta encoded deletes and adds. Next, the row offset value, delete entries and add entries are reverse delta decoded to regenerate the reference row ID, deletes entries and adds entries of the current row, step  274 . 
     At this point, if the reference row is not the empty row, the row decompress procedure makes a recursive call to itself to decode the reference row, step  276 . The row decompress procedure may recursively call itself in this way up to L times, where L is the maximum depth of any reference row chain. When step  276  completes, the reference row is returned in the form of a list of page ID&#39;s for the reference row. Then the deletes and adds for the current row are applied to the reference row list of page ID&#39;s to produce a reconstructed list page ID&#39;s for the current row, step  278 . 
     EXAMPLE 
     Suppose the rows for pages 5–9 are as shown below:
         5: 17, 42, 91   6: 17, 42, 108   7: 17, 18, 91, 93   8: 18, 19, 91, 92, 93   9: 17, 91       

     Assume the reference window size is K=4 and the chain length limit L=3. Then the output of the reference row compression will be:
         5: 0, {}, {17, 42, 91}   6: 1, {2}, {108}   7: 2, {1}, {18, 93}   8: 1, {0}, {19, 92}   9: 2, {1, 3}, {}       

     Applying the delta compression within each list, we get:
         5: 0, {}, {12, 24, 48}   6: 1, {2}, {102}   7: 2, {1}, {11, 74}   8: 1, {0}, {11, 72}   9: 2, {1, 1}, {}       

     The data array contains the Huffman encodings of each of the values below, with P representing possible padding, and pointers from the starts array as indicated. 
     
       
                 
         
             
             
         
      
     
     Based on application of the above described link compression procedure to link data obtained from an Internet web crawler, using the above described data structures, with K=8 (i.e., a backward search limit of 7 entries) and L=4 (i.e., a maximum reference chain length of four), compression of the rows of the outbound links results in a data structure that uses about 4.5 to 5 bits per link, depending on the data set. 
     Alternate Embodiments 
     The present invention can be implemented as a computer program product that includes a computer program mechanism embedded in a computer readable storage medium. For instance, the computer program product could contain the program modules shown in  FIG. 2 . These program modules may be stored on a CD-ROM, magnetic disk storage product, or any other computer readable data or program storage product. The software modules in the computer program product may also be distributed electronically, via the Internet or otherwise, by transmission of a computer data signal (in which the software modules are embedded) on a carrier wave. 
     While the present invention has been described with reference to a few specific embodiments, the description is illustrative of the invention and is not to be construed as limiting the invention. Various modifications may occur to those skilled in the art without departing from the true spirit and scope of the invention as defined by the appended claims.