| COMP9315 18s2 | Assignment 2 SIMC Signature Index Files |
DBMS Implementation |
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Aims
This assignment aims to give you an understanding of
- how database files are structured and accessed
- how superimposed codeword (SIMC) signatures are implemented
- how partial-match retrieval searching is implemented using SIMC signatures
The goal is to build a simple implementation of a SIMC signature file, including application to create SIMC files, insert tuples into them, and search for tuples based on partial-match retrieval queries.
Summary
Deadline: 23:59:59pm on Sunday 21 October
Late Penalty: 0.09 marks off the ceiling mark for each hour late
Marks: Contributes 15 marks toward your total mark for this course.
Groups: do this assignment in pairs or individually (you can use the same groups as for Assignment 1)
Submission: Login to Course Web Site > Assignments > Assignment 2 > Submission > upload ass2.tar
The ass2.tar file must contain the Makefile plus all of the *.c and *.h files that are needed to compile the create, insert and select executables. However, you should not change or submit create.c, insert.c and select.c. Details on how to build the ass2.tar file are given below.
Make sure that you read this assignment specification carefully and completely before starting work on the assignment.
Questions which indicate that you haven’t done this will simply get the response “Please read the spec”.
Note: this assignment does not require you to do anything with PostgreSQL.
Introduction
Signatures are a style of indexing where (in its simplest form) each tuple is associated with a compact representation of its values. They are used in the context of partial-match retirieval queries, and are particularly effective for large tuples. Selection is performed by scanning signatures, matching them against a query signature, and then examining tuples that are flagged as potential matches. Efficient signature matching (small signatures, simple bit-comparison) allows for “false matches”, where the query and tuple signatures match, but the tuple is not a valid result for the query.
The kind of signature matching described above uses one signature for each tuple (as in the diagram below). Other kinds of signatures exist, and one goal is to implement them and compare their performance to that of tuple signatures.
- gendata #tuples #attributes [startID] [seed]
- Generates a specified number of n-attribute tuples in the appropriate format to insert into a created relation. All tuples are the same format and look like
UniqID,RandomString,a3-Num,a4-Num,...,an-Num
For example, the following 4-attribute tuples could be generated by a call like gendata 1000 4
7654321,aTwentyCharLongStrng,a3-013,a4-001 3456789,aTwentyChrLongString,a3-042,a4-128
A tuple is a sequence of comma-separated fields. The first field is a unique 7-digit number; the second field is a random 20-char string; the remaining fields have a field identifier followed by a non-unique 3-digit number. The size of each tuple is
7+1 + 20+1 + (n-2)*(6+1) - 1 = 28 + 7*(n-2) bytes
Note that tuples are limited to at most 9 attributes, which means that the maximum tuple size is a modest 77 bytes. (If you wish, you can work with larger tuples by tweaking thegendata and create commands and the newRelation() function, but this not required for the assignment).
- create RelName #tuples #attrs 1/pF
- Creates an empty relation called RelName with all tuples having #attrs attributes. The #tuples parameter give a maximum number of tuples that can be indexed using bit-sliced superimposed codewords. The 1/pF gives the inverse of the false match probability; for example, a value of 1000 gives a false match probability of 1/1000 (0.001).
These parameters are combined using the formulas given in lectures to determine how large tuple- and page-signatures are. Each bit-slice has a number of bits equal to the number of data pages, which is determined from #attrs, #tuples and the page size. (Aside: this is different to what I said in lectures, where I was thinking to have a fixed 4096 bits in each slice; this is slightly more realistic, especially if the relation is approaching full).
- the name of the relation
- the number of attributes
- the false match probability
This gives you storage for one relation/table, and is analogous to making an SQL data definition like:
create table R ( a1 integer, a2 text, ... an text );
Note that internally, attributes are indexed 0..n-1 rather than 1..n.
The following example of using create makes a relation called abc where each tuple has 4 attributes and the indexing has a false match probability of 1/100. The relation can hold up to 10000 tuples (it can actually hold more, but only the first 10000 will be indexed via the bit-sliced signatures).
$ ./create abc 10000 4 100
- insert RelName
- Reads tuples, one per line, from standard input and inserts them into the relation specified on the command line. Tuples all take the form val1,val2,…,valn. The values can be any sequence of alpha-numeric characters and '-'. The characters ',' (field separator) and '?' (query wildcard) are treated specially.
Since all tuples need to be the same length, it is simplest to use gendata to generate them, and pipe the generated tuples into the insert command
- dump RelName
- Writes all tuples from the relation RelName, one per line, to standard output. This is like an inverse of the insert command. Tuples are dumped in a form that could be used by insert to rebuild a database.
- select RelName QueryString SigType
- Takes a “query tuple” on the command line, and finds all tuples in the data pages of the relation RelName that match the query. Queries take the form val1,val2,…,valn, where some of the vali can be '?' (without the quotes). Some examples, and their interpretation are given below. You can find more examples in the lecture slides and course notes.
?,?,? # matches any tuple in the relation 10,?,? # matches any tuple with 10 as the value of attribute 1 ?,abc,? # matches any tuple with abc as the value of attribute 2 10,abc,? # matches any tuple with 10 and abc as the values of attributes 1 and 2
Note that the startQuery() function can return NULL. It should do so only if the query string contains the wrong number of attributes for the relation.
In fact, a SIMC relation R is represented by five physical files:
- R.info containing global information such as
- the number of attributes and size of each tuple
- the number of data pages and number of tuples
- the sizes of the various kinds of signatures
- the number of signatures and signature pages
- etc. etc. etc.
The R.info file contains a copy of the RelnParams structure given in the reln.h file (see below).
- R.data containing data pages, where each data page contains
- a count of the number of tuples in the page
- the tuples (as comma-separated character sequences)
Each data page has a capacity of c tuples. If there are n tuples then there will be b = ⌈n/c⌉ pages in the data file. All pages except the last are full. Tuples are never deleted.
- R.tsig containing tuple signatures, where each page contains
- a count of the number of signatures in the page
- the signatures themselves (as bit strings)
Each tuple signature is formed by superimposing the codewords from each attribute in the tuple. If there are n tuples in the relation, there will be n tuple signatures, in bt pages. All tuple signature pages except the last are full.
- R.psig containing page signatures, where each page contains
- a count of the number of signatures in the page
- the signatures themselves (as bit strings)
Page signatures are much larger than tuple signatures, and are formed by superimposing the codewords of all attribute values in all tuples in the page. There is one page signature for each page in the data file.
- R.bsig containing bit-sliced signatures, where each page contains
- a count of the number of signatures in the page
- the bit-slices themselves (as bit strings)
Bit-slices give an alternate 90o view of page signatures. If there are b data pages, then each bit-slice is b-bits long. If page signatures are pm bits long, then there are pm bit-slices. attribute values in all tuples in the page.
The following diagram gives a very simple example of the correspondence between page signatures and bit-slices:
Setting Up
You should make a working directory for this assignment and put the supplied code there, and start reading to make sure that you understand all of the data types and operations used in the system.
$ mkdir your/ass2/directory $ cd your/ass2/directory $ unzip /web/cs9315/18s2/assignments/ass2/ass2.zip
You should see the following files in the directory:
$ ls Makefile dump.c psig.c stats.c x1.c bits.c gendata.c psig.h tsig.c x2.c bits.h hash.c query.c tsig.h x3.c bsig.c hash.h query.h tuple.c bsig.h insert.c reln.c tuple.h create.c page.c reln.h util.c defs.h page.h select.c util.h
The .h files define data types and function interfaces for the various types used in the system. The corresponding .c files contain the implementation of the functions on the data type. The remaining .c files either provide the commands described above, or are test harnesses for individual types (x1.c, x2.c, x3.c). You can add additional testing files, bu there is no need to submit them.
The above files give you a partial implementation of SIMC indexing. You need to complete the code so that it provides the functionality described above.
You should be able to build the supplied partial implementation via the following:
$ make gcc -std=gnu99 -Wall -Werror -g -c -o query.o query.c gcc -std=gnu99 -Wall -Werror -g -c -o page.o page.c gcc -std=gnu99 -Wall -Werror -g -c -o reln.o reln.c gcc -std=gnu99 -Wall -Werror -g -c -o tuple.o tuple.c gcc -std=gnu99 -Wall -Werror -g -c -o util.o util.c gcc -std=gnu99 -Wall -Werror -g -c -o tsig.o tsig.c gcc -std=gnu99 -Wall -Werror -g -c -o psig.o psig.c gcc -std=gnu99 -Wall -Werror -g -c -o bsig.o bsig.c gcc -std=gnu99 -Wall -Werror -g -c -o hash.o hash.c gcc -std=gnu99 -Wall -Werror -g -c -o bits.o bits.c gcc -std=gnu99 -Wall -Werror -g -c -o create.o create.c gcc -o create create.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o -lm gcc -std=gnu99 -Wall -Werror -g -c -o insert.o insert.c gcc insert.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o -o insert gcc -std=gnu99 -Wall -Werror -g -c -o select.o select.c gcc select.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o -o select gcc -std=gnu99 -Wall -Werror -g -c -o stats.o stats.c gcc stats.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o -o stats gcc -std=gnu99 -Wall -Werror -g -c -o gendata.o gendata.c gcc -o gendata gendata.o util.o -lm gcc -std=gnu99 -Wall -Werror -g -c -o dump.o dump.c gcc dump.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o -o dump gcc -std=gnu99 -Wall -Werror -g -c -o x1.o x1.c gcc -o x1 x1.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o gcc -std=gnu99 -Wall -Werror -g -c -o x2.o x2.c gcc -o x2 x2.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o gcc -std=gnu99 -Wall -Werror -g -c -o x3.o x3.c gcc -o x3 x3.o query.o page.o reln.o tuple.o util.o tsig.o psig.o bsig.o hash.o bits.o
This should not produce any errors on the CSE servers; let me know ASAP if this is not the case.
The gendata command should work completely without change. For example, the following command generates 5 tuples, each of which has 4 attributes. Values in the first attribute are unique; values in the second attribute are highly likely to be unique. Note that the third and fourth attributes cycle through values at different rates, so they won’t always have the same number.
$ ./gendata 5 4 1000000,lrfkQyuQFjKXyQVNRTyS,a3-000,a4-000 1000001,FrzrmzlYGFvEulQfpDBH,a3-001,a4-001 1000002,lqDqrrCRwDnXeuOQqekl,a3-002,a4-002 1000003,AITGDPHCSPIjtHbsFyfv,a3-003,a4-003 1000004,lADzPBfudkKlrwqAOzMi,a3-004,a4-004
The create command itself is complete, but some of the functions it calls are not complete. It will allow you to make an empty relation, although without a complete bit-slice file (you add this as one of the assignment tasks). The stats command is complete and can display information about a relation. Using these commands, you could do the following: use the createcommand to create an empty relation which can hold 4-attribute tuples and able to index up to 5000 tuples (using bit-slices), with a false match probability of 1/1000. The statscommand then displays the generated parameter values.
$ ./create R 5000 4 1000 $ ./stats R Global Info: Dynamic: #items: tuples: 0 tsigs: 0 psigs: 0 bsigs: 0 #pages: tuples: 1 tsigs: 1 psigs: 1 bsigs: 1 Static: tups #attrs: 4 size: 42 bytes max/page: 97 sigs bits/attr: 9 tsigs size: 64 bits (8 bytes) max/page: 511 psigs size: 5584 bits (698 bytes) max/page: 5 bsigs size: 56 bits (7 bytes) max/page: 584 $
You can apply the formulae for calculating the various quantities to check that the above values make sense. Note that the bits for signatures are rounded up to the next multiple of 8 (why waste a few bits?). Note also that all pages are defined to be 4096 bytes. Finally, note that create makes a file with one empty page for each of the files holding tuples, signatures and slices.
As supplied, the insert command inserts tuples into the data pages, but does not generate any signatures. Using gendata is the easiest (and safest) way to add valid tuples. You can then check that the tuples have been inserted via the dump command, and see how the parameters have changed using stats again.
$ ./gendata 5 4 | ./insert R
Inserting: 1000000,lrfkQyuQFjKXyQVNRTyS,a3-000,a4-000
Inserting: 1000001,FrzrmzlYGFvEulQfpDBH,a3-001,a4-001
Inserting: 1000002,lqDqrrCRwDnXeuOQqekl,a3-002,a4-002
Inserting: 1000003,AITGDPHCSPIjtHbsFyfv,a3-003,a4-003
Inserting: 1000004,lADzPBfudkKlrwqAOzMi,a3-004,a4-004
$ ./stats R
Global Info:
Dynamic:
#items: tuples: 5 tsigs: 0 psigs: 0 bsigs: 0
#pages: tuples: 1 tsigs: 1 psigs: 1 bsigs: 1
Static:
tups #attrs: 4 size: 42 bytes max/page: 97
sigs bits/attr: 9
tsigs size: 64 bits (8 bytes) max/page: 512
psigs size: 5584 bits (698 bytes) max/page: 5
bsigs size: 56 bits (7 bytes) max/page: 585
$
Note that the only difference between the above stats and the stats for the newly-created file is the 5 tuples. There are no signatures, no new pages, etc.
The dump command is complete; it simply scans the data file and displays any tuples it finds, e.g.
$ ./dump R 1000000,lrfkQyuQFjKXyQVNRTyS,a3-000,a4-000 1000001,FrzrmzlYGFvEulQfpDBH,a3-001,a4-001 1000002,lqDqrrCRwDnXeuOQqekl,a3-002,a4-002 1000003,AITGDPHCSPIjtHbsFyfv,a3-003,a4-003 1000004,lADzPBfudkKlrwqAOzMi,a3-004,a4-004 $
The select command, as supplied, is not complete. However, once it is working (at least with tuple signatures), you should be able to ask queries like:
$ ./select R 1000001,?,? t # not enough attrs Invalid query: 101,?,? $ ./select R 1000001,?,?,? t Matched Pages:00000001 1000001,FrzrmzlYGFvEulQfpDBH,a3-001,a4-001 Query Stats: # signatures read: 5 # sig pages read: 1 # tuples examined: 5 # data pages read: 1 # false match pages: 0 $ ./select R 1000001,?,a3-002,? t Matched Pages:00000000 Query Stats: # signatures read: 5 # sig pages read: 1 # tuples examined: 0 # data pages read: 0 # false match pages: 0 $ ./select R 1000001,?,a3-002,? x Query Stats: # signatures read: 0 # sig pages read: 0 # tuples examined: 5 # data pages read: 1 # false match pages: 1
Some explanation:
- The second query finds a match because there is a tuple with the value 1000001 for its first attribute. The ? represent “don’t care” or wild-card values.
- The third query fails because the tuple with 1000001 for its first attribute, does not have the value a3-002 for its third attribute.
- The fourth query performs a linear scan of the data file. SInce the query itself is the same as the third query, there are no matching tuples. This query reads every data page (there is only one). Any data page read, which does not contain matching tuples, is counted as a “false page match“.
- The digits after Matched Pages is a bit-string, with one bit for each data page. The bit-string contains a 1 for each page where the indexing thinks that there is a match. In this case, there is only one data page, so there’s only one bit that could be set. There are 8 bits in the “matched pages” string, because we round the size of all bit-strings up to a multiple of 8.
- The t at the end of the query tells the query evaluator to use tuple signatures as a first-pass filter. Other possibilities are p for page signatures or b for bit-sliced signatures. If you don’t specify a signature type, the evaluator uses a liner scan and checks all tuples.
- With all types of signatures, queries run in two phases:
- use the signatures to determine pages which potentially contain matching tuples
- read each of these pages and check all tuples to find real matches
- The query statistics are maintained in a Query data structure while the query is executing.
Data Types
There are four important data types defined in the system:
- Relations (data type Reln)
- Relations are defined by three data types: Reln, RelnRep, RelnParams. Reln is just a pointer to a RelnRep object; this is useful for passing to functions that need to modify some aspect of the relation structure. RelnRep is a representation of an open relation and contains the parameters, plus file descriptors for all of the open files. RelnParams is a list of various properties of the database. See reln.h for details.
- Queries (data type Query)
- Queries are defined via a QueryRep structure which contains fields to represent the current state of the scan for the query, plus a collection of statistics counters. It is essentially like the query iteration structures described in lectures, and is used to control and monitor the query evaluation. The QueryRep structure also contains a reference to the relation being queried, and a copy of the query string. The Query data type is simply a pointer to a QueryRep structure. See query.h for details. The following diagram might also help:
- Pages (data type Page)
- Pages are defined via a PageRep structure which contains a counter for the number of items, and then an array of bytes containing the actual items, whether they are tuples or signatures or slices. The size of each type of item is held in the RelnParams structure, and so Pages are typically considered in conjunction with Relns. The Page data type is simply a pointer to a PageRep structure. See page.h for details. The following diagram might also help:
- Bit-strings (data type Bits)
- Bit-strings are defined via a BitsRep structure which contains two counters (one for the number of bits, and the other for the number of bytes used to represent the bit-string). The BitsRep structure also contains an array of bytes which hold the bits in the string; the array is created when and instance of a Bits data type is created. Note that Bits is an ADT, so the concrete data structure is hidden from its clients; the Bits data type is simply a pointer to a BitsRep structure. See bits.c for details of the data structure, and bits.h for the function interface. The following diagram might help:
Tuple (data type Tuple)
Tuples are just character sequences (like C strings). See tuple.h for details.
There are also a range of (hopefully) self-explanatory data types defined in defs.h. The various signature types are represented as bit-strings (Bits).
Goal
Your goal for this assignment is to complete the implementation of the various components of the system, so that it can handle all three kinds of signatures. This includes inserting/updating signatures when new tuples are added, and using these signatures in answering queries.
The header (.h) files contain definitions of the data types used in the system.
Each of the source code (.c)files contains comments on each function, describing briefly what it should do. Some functions contain //TODO comments to indicate where you need to complete them. You can put all of the code in the indicated function, or you can write new functions that these functions use.
You are free to change any file except create.c, insert.c and select.c. Since you can’t change these files, you also cannot change the interfaces to the data types that they use (Reln, Query, Page, Bits). Basically, all of the functions mentioned in the .h files for these types must exist, with the same interface, but you can implement their internals however you like. You can also add extra functions to each data type (i.e. extend its interface) if that helps.
Task 1: A Bit-string Type (2 marks)
Implement all of the incomplete functions in the bits.c file, to produce a working bit-string data type. The functions to complete are flagged with TODO, and the purpose of each should be clear from the comment at the start of the function and its name. The x1.c file contains some simple test cases for the Bits type.
Task 2: Scanning for Results (2 marks)
After you have Bits working, you can start to implement query evaluation, although without indexing. The startQuery() function parses the query string and then uses the appropriate type of signature to generate a list of pages which potentially contain matching tuples. This list is implemented as a bit-string where a 1 indicates a page which needs to be checked for matches. At this stage, all of the signature types mark all pages as potential matches, so all pages need to be checked.
Before this will work, you need to implement the scanAndDisplayMatchingTuples() function, which performs the check for matching tuples in each of the marked pages. This function, as well as finding and displaying result tuples, maintains the query statistics for number of data pages read, and number of pages that were read but contained no matching tuples.
For this task, you need to complete the scanAndDisplayMatchingTuples() function from the query.c file. This function behaves roughly as follows:
foreach PID in 0 .. npages-1 {
if (PID is not set in MatchingPages)
ignore this page
for each tuple T in page PID {
if (T matches the query string)
display it as a query result
}
if (no tuples in page PID are results)
count it as a false match page
}
Task 3: Tuple Signatures (4 marks)
Implement indexing by using tuple-based signatures (i.e. each tuple has its own signature, stored in the Rel.tsig file). You will need to complete the makeTupleSig() and findPagesUsingTupSigs() functions in the tsig.c file, and add some code to the addToRelation() function in reln.c.
The addToRelation() function inserts a tuple into the next available slot in the data file, but currently does nothing with signatures. You should add code here which generates a tuple signature for the new tuple and inserts it in the next available slot in the Rel.tsig file.
The makeTupleSig() function takes a tuple and returns a bit-string which contains a superimposed codeword signature for that tuple. It behaves roughly as follows:
Tsig = AllZeroBits
for each attribute A in tuple T {
CW = codeword for A
Tsig = Tsig OR CW
}
A method for computing codewords is given in the lecture notes.
The findPagesUsingTupSigs() take a tuple signature and scans the Rel.tsig file, comparing that signature to the stored tuple signatures. It builds a bit-string showing which pages contain at least one “matching” tuple. It behaves roughly as follows:
QuerySig = makeTupleSig(Query)
Pages = AllZeroBits
foreach Tsig in tsigFile {
if (Tsig matches QuerySig) {
PID = data page for tuple corresponding to Tsig
include PID in Pages
}
}
Note that the ith tuple in the data file has its correpsonding signature as the ith signature in the Rel.tsig file. However, since tuples and tuple signatures are different sizes, the page that the signature appears on will not necessarily have the same page ID as the page in which the corresponding tuple is located.
Task 4: Page Signatures (3 marks)
Implement indexing using page-level signatures (psigs).
This is similar to how tuple-level signature indexing is done, except that the sigantures are larger. The functions that you need to complete are makePageSig() andfindPagesUsingPageSigs() in the psig.c file. You will also need to add more code to the addToRelation() function to maintain page signatures when new tuples are inserted.
One major difference between tuple signatures and page signatures is that page signatures are not a one-off insertion. When a new tuple is added, its page-level signature needs to be included page signature for the page where it it is inserted. The process can be described roguhly as follows:
new Tuple is inserted into page PID Psig = makePageSig(Tuple) PPsig = fetch page signature for data page PID from psigFile merge Psig and PPsig giving a new PPsig update page signature for data page PID in psigFile
The makePageSig() function be used to generate a page-level signature for the query, and then used to generate a bit-string of matching pages roughly as follows:
QuerySig = makePageSig(Query)
Pages = AllZeroBits
foreach Psig in psigFile {
if (Psig matches QuerySig) {
PID = data page corresponding to Psig
include PID in Pages
}
}
Task 5: Bit-sliced Signatures (4 marks)
Implement indexing using bit-sliced page signatures.
Each bit-slice is effectively a list of pages that have a specific bit from the page-signature set to 1 (e.g. if a page-level signature has bit 5 set to one, the bit-slice 5 has a 1 bit for every page with a page signature where bit 5 is set). This drives both the updating of bit-slices and their use in indexing.
You will need to modify the functions: newRelation() in reln.c, addToRelation() in reln.c, and findPagesUsingBitSlices() in bsig.c. The modifications to newRelation() are relatively straightforward, but remember to update the relation parameters appropriately.
The addToRelation() should take a tuple, produce a page signature for it, then update all of the bit-slices corresponding to 1-bits in the page signature. This can be described roughly as follows:
PID = data page where new Tuple inserted
Psig = makePageSig(Tuple)
for each i in 0..pm-1 {
if (Psig bit[i] is 1) {
Slice = get i'th bit slice from bsigFile
set the PID'th bit in Slice
write updated Slice back to bsigFile
}
}
The findPagesUsingBitSlices() function computes a page-level signature for query and then takes an intersection of the bit-slices corresponding to the 1-bits in the page signature. This gives a “matching” pages list straight away, and hopefully after reading far less of the Rel.bsig file than would be read using a Rel.psig file. The method can be described roughly as follows:
Qsig = makePageSig(Query)
Pages = AllOneBits
for each i in 0..pm-1 {
if (Psig bit[i] is 1) {
Slice = get i'th bit slice from bsigFile
zero bits in Pages which are zero in Slice
}
}
Task 6: Comparison of SIMC Indexing Methods (1 bonus mark)
For a range of parameter settings:
- size of tuples (determined by number of attributes)
- size of bit-slices (determined by expected number of tuples)
- size of tuple and page signatures (determined by pF)
run a range of queries using the select command:
- not using indexing (use x as the signature type)
- open query (?,?,...,?)
- query with one solution (100001,?,...,?)
- query with many solutions (?,?,a3-001,...,?)
- query with multiple values (100001,?,a3-001,...,?)
Report the total number of page reads (signature pages + data pages), for each combination and make some comments about the effectiveness of each type of indexing under different scenarios.
Put your results in a file called analysis.pdf and submit that with your code files in the ass2.tar file.
Submission
You need to submit a single tar file containing all of the code files that are needed to build the create, insert and select commands, including a new Makefile if you add extra modules.
You should not submit the code for the commands, i.e. do not submit create.c, insert.c or select.c. We will use the original versions of these for testing your code, so you must preserve the module interfaces that these commands use.
When you want to submit your work, do the following:
$ cd your/ass2/directory $ tar cf ass2.tar FilesToSubmit
The FilesToSubmit would typically include:
Makefile bits.c bits.h page.c page.h query.c query.h reln.c reln.h tuple.c tuple.h util.c util.h tsig.c tsig.h psig.c psig.c bsig.c bsig.h
plus any other modules you may have created, along with the modified Makefile to include them in the compilation.
Once you have generated the ass2.tar file, you can submit it via WebCMS.
Have fun, jas