Web pages are served through HTTP and viewed in browsers. Crawlers fetch pages to build indexes. They start from seed URLs and recursively fetch new URLs found on pages. Crawlers face challenges like overlapping delays, avoiding duplicates, handling redirects, and preventing crawler traps. Large-scale crawlers employ techniques like multi-threading, non-blocking sockets, distributed storage, and estimating change rates to efficiently crawl billions of pages across the web.

1. Crawling the Web
Web pages
•Few thousand characters long
•Served through the internet using the hypertext
transport protocol (HTTP)
•Viewed at client end using `browsers’
Crawler
•To fetch the pages to the computer
•At the computer
Automatic programs can analyze hypertext
documents

2. HTML
 HyperText Markup Language
 Lets the author
• specify layout and typeface
• embed diagrams
• create hyperlinks.
 expressedas an anchor tag with a HREF attribute
 HREF names another page using a Uniform
Resource Locator (URL),
• URL =
 protocol field (“HTTP”) +
 a server hostname (“www.cse.iitb.ac.in”) +
 file path (/, the `root' of the published file system).
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3. HTTP(hypertext transport
protocol)
 Built on top of the Transport Control Protocol
(TCP)
 Steps(from client end)
• resolve the server host name to an Internet address
(IP)
 Use Domain Name Server (DNS)
 DNS is a distributed database of name-to-IP mappings
maintained at a set of known servers
• contact the server using TCP
 connect to default HTTP port (80) on the server.
 Enter the HTTP requests header (E.g.: GET)
 Fetch the response header
– MIME (Multipurpose Internet Mail Extensions)
– A meta-data standard for email and Web content transfer
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 Fetch the HTML page

4. Crawl “all” Web pages?
 Problem: no catalog of all accessible URLs
on the Web.
 Solution:
• start from a given set of URLs
• Progressively fetch and scan them for new
outlinking URLs
• fetch these pages in turn…..
• Submit the text in page to a text indexing
system
• and so on……….
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5. Crawling procedure
 Simple
• Great deal of engineering goes into industry-
strength crawlers
• Industry crawlers crawl a substantial fraction
of the Web
• E.g.: Alta Vista, Northern Lights, Inktomi
 No guarantee that all accessible Web
pages will be located in this fashion
 Crawler may never halt …….
• pages will be added continually even as it is
running.
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6. Crawling overheads
 Delays involved in
• Resolving the host name in the URL to an IP
address using DNS
• Connecting a socket to the server and sending
the request
• Receiving the requested page in response
 Solution: Overlap the above delays by
• fetching many pages at the same time
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7. Anatomy of a crawler.
 Page fetching threads
• Starts with DNS resolution
• Finishes when the entire page has been
fetched
 Each page
• stored in compressed form to disk/tape
• scanned for outlinks
 Work pool of outlinks
• maintain network utilization without
overloading it
 Dealt with by load manager
 Continue till he crawler has collected a
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8. Typical anatomy of a large-scale crawler.
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9. Large-scale crawlers: performance

and reliability considerations
Need to fetch many pages at same time
• utilize the network bandwidth
• single page fetch may involve several seconds of
network latency
 Highly concurrent and parallelized DNS lookups
 Use of asynchronous sockets
• Explicit encoding of the state of a fetch context in a
data structure
• Polling socket to check for completion of network
transfers
•
Multi-processing or multi-threading: Impractical
 Care in URL extraction
• Eliminating duplicates to reduce redundant fetches
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10. DNS caching, pre-fetching and
resolution
 A customized DNS component with…..
1. Custom client for address resolution
2. Caching server
3. Prefetching client
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11. Custom client for address resolution
 Tailored for concurrent handling of
multiple outstanding requests
 Allows issuing of many resolution requests
together
• polling at a later time for completion of
individual requests
 Facilitates load distribution among many
DNS servers.
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12. Caching server
 With a large cache, persistent across DNS
restarts
 Residing largely in memory if possible.
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13. Prefetching client
• Steps
1. Parse a page that has just been fetched
2. extract host names from HREF targets
3. Make DNS resolution requests to the caching
server
• Usually implemented using UDP
• User Datagram Protocol
• connectionless, packet-based communication
protocol
• does not guarantee packet delivery
• Does not wait for resolution to be
completed.
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14. Multiple concurrent fetches
• Managing multiple concurrent
connections
• A single download may take several seconds
• Open many socket connections to different
HTTP servers simultaneously
• Multi-CPU machines not useful
• crawling performance limited by network
and disk
• Two approaches
1. using multi-threading
2. using non-blocking sockets with event
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15. Multi-threading
• logical threads
• physical thread of control provided by the operating
system (E.g.: pthreads) OR
• concurrent processes
• fixed number of threads allocated in advance
• programming paradigm
• create a client socket
• connect the socket to the HTTP service on a server
• Send the HTTP request header
• read the socket (recv) until
• no more characters are available
• close the socket.
• use blocking system calls
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16. Multi-threading: Problems
• performance penalty
• mutual exclusion
• concurrent access to data structures
• slow disk seeks.
• great deal of interleaved, random input-output
on disk
• Due to concurrent modification of document
repository by multiple threads
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17. Non-blocking sockets and event
handlers
• non-blocking sockets
• connect, send or recv call returns immediately
without waiting for the network operation to
complete.
• poll the status of the network operation separately
• “select” system call
• lets application suspend until more data can be read
from or written to the socket
• timing out after a pre-specified deadline
• Monitor polls several sockets at the same time
• More efficient memory management
• code that completes processing not interrupted by
other completions
• No need for locks and semaphores on the pool
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18. Link extraction and normalization
• Goal: Obtaining a canonical form of URL
• URL processing and filtering
• Avoid multiple fetches of pages known by
different URLs
• many IP addresses
• For load balancing on large sites
• Mirrored contents/contents on same file system
• “Proxy pass“
• Mapping of different host names to a single IP address
• need to publish many logical sites
• Relative URLs
• need to be interpreted w.r.t to a base URL.
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19. Canonical URL
Formed by
• Using a standard string for the protocol
• Canonicalizing the host name
• Adding an explicit port number
• Normalizing and cleaning up the path
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20. Robot exclusion
• Check
• whether the server prohibits crawling a
normalized URL
• In robots.txt file in the HTTP root directory of
the server
• species a list of path prefixes which crawlers should
not attempt to fetch.
• Meant for crawlers only
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21. Eliminating already-visited URLs
 Checking if a URL has already been fetched
• Before adding a new URL to the work pool
• Needs to be very quick.
• Achieved by computing MD5 hash function on the
URL
 Exploiting spatio-temporal locality of access
 Two-level hash function.
– most significant bits (say, 24) derived by hashing the host name
plus port
– lower order bits (say, 40) derived by hashing the path
 concatenated bits use d as a key in a B-tree
 qualifying URLs added to frontier of the crawl.
 hash values added to B-tree.
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22. Spider traps
 Protecting from crashing on
• Ill-formed HTML
 E.g.: page with 68 kB of null characters
• Misleading sites
 indefinite number of pages dynamically generated
by CGI scripts
 paths of arbitrary depth created using soft
directory links and path remapping features in
HTTP server
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23. Spider Traps: Solutions
 No automatic technique can be foolproof
 Check for URL length
 Guards
• Preparing regular crawl statistics
• Adding dominating sites to guard module
• Disable crawling active content such as CGI
form queries
• Eliminate URLs with non-textual data types
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24. Avoiding repeated expansion of
links on duplicate pages
 Reduce redundancy in crawls
 Duplicate detection
• Mirrored Web pages and sites
 Detecting exact duplicates
• Checking against MD5 digests of stored URLs
• Representing a relative link v(relativetoaliasesu1and
u2)as tuples (h(u1);v) and (h(u2);v)
 Detecting near-duplicates
• Even a single altered character will completely change
the digest !
 E.g.: date of update/ name and email of the site
administrator
• Solution : Shingling and Ramakrishnan
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25. Load monitor
 Keeps track of various system statistics
• Recent performance of the wide area
network (WAN) connection
 E.g.: latency and bandwidth estimates.
• Operator-provided/estimated upper bound
on open sockets for a crawler
• Current number of active sockets.
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26. Thread manager
 Responsible for
 Choosing units of work from frontier
 Scheduling issue of network resources
 Distribution of these requests over multiple
ISPs if appropriate.
 Uses statistics from load monitor
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27. Per-server work queues
 Denial of service (DoS) attacks
 limit the speed or frequency of responses to
any fixed client IP address
 Avoiding DOS
 limit the number of active requests to a given
server IP address at any time
 maintain a queue of requests for each server
 Use the HTTP/1.1 persistent socket capability.
 Distribute attention relatively evenly between
a large number of sites
 Access locality vs. politeness dilemma
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28. Text repository
 Crawler’s last task
 Dumping fetched pages into a repository
 Decoupling crawler from other functions
for efficiency and reliability preferred
 Page-related information stored in two
parts
 meta-data
 page contents.
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29. Storage of page-related information
 Meta-data
 relational in nature
 usually managed by custom software to avoid
relation database system overheads
 text index involves bulk updates
 includes fields like content-type, last-modified
date, content-length, HTTP status code, etc.
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30. Page contents storage
 Typical HTML Web page compresses to 2-
4 kB (using zlib)
 File systems have a 4-8 kB file block size
 Too large !!
 Page storage managed by custom storage
manager
 simple access methods for
 crawler to add pages
 Subsequent programs (Indexer etc) to retrieve
documents
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31. Page Storage
 Small-scale systems
 Repository fitting within the disks of a single
machine
 Use of storage manager (E.g.: Berkeley DB)
 Manage disk-based databases within a single file
 configuration as a hash-table/B-tree for URL
access key
 To handle ordered access of pages
 configuration as a sequential log of page records.
 Since Indexer can handle pages in any order
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32. Page Storage
 Large Scale systems
 Repository distributed over a number of
storage servers
 Storage servers
 Connected to the crawler through a fast local
network (E.g.: Ethernet)
 Hashed by URLs
 `T3' grade leased lines.
 To handle 10 million pages (40 GB) per hour
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33. Large-scale crawlers often use multiple ISPs and a bank of local storage
servers to store the pages crawled.
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34. Refreshing crawled pages
 Search engine's index should be fresh
 Web-scale crawler never `completes' its job
 High variance of rate of page changes
 “If-modified-since” request header with
HTTP protocol
 Impractical for a crawler
 Solution
 At commencement of new crawling round
estimate which pages have changed
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35. Determining page changes
 “Expires” HTTP response header
 For page that come with an expiry date
 Otherwise need to guess if revisiting that
page will yield a modified version.
 Score reflecting probability of page being
modified
 Crawler fetches URLs in decreasing order of
score.
 Assumption : recent past predicts the future
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36. Estimating page change rates
 Brewington and Cybenko & Cho
 Algorithms for maintaining a crawl in which
most pages are fresher than a specified epoch.
 Prerequisite
 average interval at which crawler checks for
changes is smaller than the inter-modification
times of a page
 Small scale intermediate crawler runs
 to monitor fast changing sites
 E.g.: current news, weather, etc.
 Patched intermediate indices into master
index
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37. Putting together a crawler
 Reference implementation of the HTTP client
protocol
 World-wide Web Consortium (http://www.w3c.org/
)
 w3c-libwww package
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38. Design of the core components:
Crawler class.
 To copy bytes from network sockets to storage
media
 Three methods to express Crawler's contract
with user
 pushing a URL to be fetched to the Crawler
(fetchPush)
 Termination callback handler (fetchDone) called with
same URL
 Method (start) which starts Crawler's event loop.
 Implementation of Crawler class
 Need for two helper classes called DNS and Fetch
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