For homework #2, you will use the LinkedList and HashTable modules that you built in homework #1 in order to finish our implementation of a file system crawler, indexer, and search engine. Homework #2 has three parts to it. In Part A, you will build a module that reads the content of a file into memory, parses it into a series of words, and builds a linked list of (word, position) information. In Part B, you will build modules that convert a series of these linked lists into an in-memory, inverted index. In Part C, you will use this in-memory, inverted index to build a query processor that has a console-based interface.

As before, pease read through this entire document before beginning the assignment, and please start early! There is a fair amount of coding you need to do to complete this assignment, and it will definitely expose any conceptual weaknesses you have with the prior material on C, pointers, malloc/free, and the semantics of the LinkedList and HashTable implementations.

In HW1, we asked you to look for out-of-memory errors and return an error code to the caller. To make life a bit easier, in HW2, you can use Verify333() to test for an out-of-memory error; this way, running out of memory will cause the program to halt, and you don’t need to deal with returning an error code if one occurs.

Part A: finish our fileparser.c

You’re going to write a module that reads the contents of a text file into memory and then parses the text file to look for words within it. As it finds words, it will build up a HashTable that contains one record for each word. Each record will contain a lower-case copy of the word, and also a sorted linked list. Each record of the linked list contains an offset within the file that the word appeared in. (The first character in the file has offset zero.)

Our word parser won’t be very smart. It will treat as a word any non-zero sequence of alphabetic characters separated by non-alphabetic characters.

So, graphically, what your module will take a text file that contains something like this. (Whenever you see a “\n” in the text file, that represents a “newline” control character that appears at the end of each line of the text file.)

My goodness!  I love the course CSE333.\n
I'll recommend this course to my friends.\n

and produces a data structure that looks like this:

Specifically, note a few things:

What to do

You should follow these steps to do this assignment:

$ tar xzf hw2.tar.gz
$ ls
clint.py  gtest  hw1  hw1.tar.gz  hw2  hw2.tar.gz  LICENSE.TXT  projdocs

(It’s ok if you’ve previously deleted the hw1.tar.gz archive with the hw1 starter files, but you still need the hw1 directory itself. hw2 won’t build properly without it.)

Part B: finish our file crawler and indexer

At a high-level, a search engine is really simple. It has three major components: a crawler, an indexer, and a query processor. A crawler explores the world, looking for documents to index. The indexer takes a set of documents found by the crawler, and produces something called an inverted index out of them. A query processor asks a user for a query, and processes it using the inverted index to figure out a list of documents that match the query.

File system crawler: Your file system crawler will be provided with the name of a directory in which it should start crawling. Its job is to look through the directory for documents (text files) and to hand them to the indexer, and to look for subdirectories; it recursively descends into each subdirectory to look for more documents and sub-sub-directories. For each document it encounters, it will assign the document a unique “document ID”, or “docID”. A docID is just a 64-bit unsigned integer.

Your crawler itself will build two hash tables in memory, adding to them each time it discovers a new text file. The two hash tables map from docID to document filename, and from document filename to docID:

For each document the crawler finds, it will make use of your part A code to produce a word hashtable using BuildWordHT().

Indexer: This is the heart of the search engine. The job of the indexer is to take each word hashtable produced by BuildWordHT(), and fold its contents in to an inverted index. An inverted index is easy to understand; it’s just a hash table that maps from a word to a “posting list,” and a posting list is just a list of places that word has been found.

Specifically, the indexer should produce an in-memory hash table that looks roughly like this:

Walking through it, the inverted index is a hash table that maps from a (hash of a) word to a structure. The structure (shown in green) contains the word as a string, and also a HashTable. The HashTable maps from the docID (not the hash of docID) to a LinkedList. The LinkedList contains each position that word appeared in that docID.

So, based on the figure, you can see that the word “course” appeared in a single document with docID 3, at byte offsets 25 and 62 from the start of file. Similarly, the word “love” appears in three documents: docID 1 at positions 7 and 92, docID 3 at position 16, and docID 4 at positions 18, 21, and 55.

What to do

The bulk of the work in this homework is in this step. We’ll tackle it in parts.

Once you’ve passed all of the test suites, re-rerun the test suites under valgrind and make sure you don’t have any memory leaks.

Congrats, you’ve passed part B of the assignment!

Part C: finish our query processor

Now that you have a working inverted index, you’re ready to build your search engine. The job of a search engine is to receive queries from a user, and return a list of matching documents in some rank order.

For us, a query is just a string of words, such as:

course friends my

The goal of our search engine is to find all of the documents that contain all of the words. So, if a document has the words “course” and “my” but not the word “friends,” it shouldn’t match.

The way to execute a query is really simple. First, the query processor must split the query up into a list of words (the strtok() function is useful for this). Next, it looks up in the inverted index the list of documents that match the first word. This is our initial matching list.

Next, for each additional word in the query, the query processor uses the inverted index to get access to the HashTable that contains the set of matching documents. For each document in the matching list, the query processor tests to see if that document is also in the HashTable. If so, it keeps the document in the matching list, and if not, it removes the document from the matching list.

Once the processor has processed all of the words, it’s done. Now, it just has to rank the results, sort the results by rank, and return the sorted result list to the user.

For us, our ranking function is very simple: given a document that matches against a query, we sum up the number of times each query word appears in the document, and that’s our rank. So, if the user provides the query “foo” and that words appears on a document 5 times, the rank for that document given that query is 5. If the user provides a multi-word query, our sum includes the number of times each of the words in the query appears. So, if the user provides the query “foo bar”, the word “foo” appears in the document 5 times, and the word “bar” appears 10 times, the rank of the document for that query is 15. The bigger the sum, the higher the rank, and therefore the better the search result.

What to do

We have provided a mostly empty skeleton file searchshell.c. It is up to you to complete it by implementing a program that uses the Linux console to interact with the user. When you run the processor (called searchshell—you can try a working searchshell in the solution_binaries/ directory), it takes from a command line argument the name of a directory to crawl. After crawling the directory and building the inverted index, it enters a query processing loop, asking the user to type in a search string and printing the results. If the user signals end-of-file when the program asks for a search string (control-D from the linux terminal keyboard), the program should clean up any allocated resources (particularly memory) and shut down.

When you are ready, try running ./searchshell to interact with your program and see if your output matches the transcript from a search session with our solution:

$ ./searchshell ./test_tree
Indexing './test_tree'
enter query:
ulysses
  ./test_tree/books/ulysses.txt (10)
  ./test_tree/books/countofmontecristo.txt (2)
  ./test_tree/books/leavesofgrass.txt (1)
  ./test_tree/books/lesmiserables.txt (1)
  ./test_tree/books/paradiselost.txt (1)
enter query:
cosette
  ./test_tree/books/lesmiserables.txt (1022)
enter query:
sherlock
  ./test_tree/books/sherlockholmes.txt (101)
  ./test_tree/books/ulysses.txt (4)
enter query:
sherlock ulysses
  ./test_tree/books/ulysses.txt (14)
enter query:
ocean
  ./test_tree/books/mobydick.txt (81)
  ./test_tree/books/davincinotebooks.txt (41)
  ./test_tree/books/leavesofgrass.txt (36)
  ./test_tree/books/countofmontecristo.txt (24)
  ./test_tree/books/lesmiserables.txt (18)
  ./test_tree/books/paradiselost.txt (16)
  ./test_tree/books/ulysses.txt (10)
  ./test_tree/books/taleoftwocities.txt (6)
  ./test_tree/books/janeeyre.txt (4)
  ./test_tree/books/huckfinn.txt (4)
  ./test_tree/books/therepublic.txt (4)
  ./test_tree/books/analyzepeopleonsight.txt (1)
  ./test_tree/books/warandpeace.txt (1)
  ./test_tree/books/thetimemachine.txt (1)
  ./test_tree/books/thejunglebook.txt (1)
  ./test_tree/bash-4.2/examples/functions/jdate.bash (1)
  ./test_tree/books/wutheringheights.txt (1)
  ./test_tree/books/dracula.txt (1)
  ./test_tree/books/tomsawyer.txt (1)
enter query:
ocean whale
  ./test_tree/books/mobydick.txt (1313)
  ./test_tree/books/leavesofgrass.txt (41)
  ./test_tree/books/countofmontecristo.txt (25)
  ./test_tree/books/lesmiserables.txt (19)
  ./test_tree/books/ulysses.txt (13)
  ./test_tree/books/thejunglebook.txt (5)
  ./test_tree/books/huckfinn.txt (5)
  ./test_tree/books/dracula.txt (2)
enter query:
ocean whale huckleberry
  ./test_tree/books/huckfinn.txt (17)
enter query:
ocean whale ravenous
  ./test_tree/books/mobydick.txt (1314)
  ./test_tree/books/leavesofgrass.txt (42)
  ./test_tree/books/ulysses.txt (15)
enter query:
ocean whale ravenous huckleberry
enter query:
badarraysub
  ./test_tree/bash-4.2/arrayfunc.c (11)
  ./test_tree/bash-4.2/subst.c (2)
  ./test_tree/bash-4.2/error.c (1)
  ./test_tree/bash-4.2/error.h (1)
enter query:
badarraysub subst
  ./test_tree/bash-4.2/subst.c (45)
  ./test_tree/bash-4.2/arrayfunc.c (12)

Alternatively, compare your searchshell to the searchshell we provided in the solution_binaries/ directory. Note that our ranking function does not specify an ordering for two documents with the same rank. Documents that have the same rank may be listed in any order, and that might be different from the ordering in the sample transcript or produced by the solution_binaries version of searchshell.

Bonus

We’re offering two extra credit tasks. These are purely optional; if you choose not to do either, your grade won’t be negatively affected at all. These are just if you happen to have the time and interest! You can do either or both of the bonus tasks.

  1. When searching text documents for a given query, some words in the query do not add any value to the quality of the search results. These words are known as stop words and include common function words such as the, is, and, at, which, there, on, and so on. Some Web search engines filter these stop words, excluding them from both their indexes and ignoring them in queries; this results in both better search quality and significantly more efficient indexes and query execution.

    For extra credit, implement a stop word filter. The search shell should accept a second, optional argument -s that will act as a flag for turning the filter on. When the flag is not specified, your program should not filter any stop words. It is up to you to decide how you will implement the stop word filter (and where you’ll get your list of stop words), but be sure to explain in your README file what changes you had to make and how your filter works. Stop words that have apostrophes in them should be handled the same way that you’ve handled the words in the documents.

  2. You probably noticed that we went to a lot of trouble to have you include word position information in our inverted index posting lists, but we didn’t make use of it. In this bonus task, you will. In addition to letting you search for words, modern search engines also let you search for phrases. For example, I could specify the following query:

    alice "cool fountains" flowers

    This query would match all documents that contain all of the following: the word alice, the phrase cool fountains (i.e., the words cool and fountains right next to each other, in that order), and the word flowers.

    Using the positions information in the inverted index postings list, implement support for phrase search. You’ll have to also modify the query processor to support phrase syntax; phrases are specified by using quotation marks. Be sure to explain in your README file what changes you had to make to get phrasing to work.

What to turn in

When you’re ready to turn in your assignment, do the following:

$ make clean
$ cd ..
$ tar czf hw2_<username>.tar.gz hw2
$ # make sure the tar file has no compiler output files in it, but
$ # does have all your source
$ tar tzf hw2_<username>.tar.gz

Grading

We will be basing your grade on several elements: