INFR11199代做、代写SQL语言编程 发布时间：2024-04-08 来源：互联网

University of Edinburgh
School of Informatics
INFR11199 - Advanced Database Systems (Spring 2024)
Coursework Assignment
Due: Thursday, 28 March 2024 at 12:00 noon
IMPORTANT:
• Plagiarism: Every student has to work individually on this project assignment.
All of the code for this project must be your own. You may not copy source code
from other students or other sources that you find on the web. You may not share
your code with other students. You may not host your code on a public code
repository.
Each submission will be checked using plagiarism detection software.
Plagiarism will be reported to School and College Academic Misconduct Officers.
See the University’s page on Academic Misconduct for additional information.
• Start early and proceed in steps. Read the assignment description carefully before
you start programming.
• The assignment is out of 100 points and counts for 40% of your final mark.
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INFR11199 (Spring 2024) Coursework Assignment Page 2 of 17
1 Goals and Important Points
In this project assignment, you will implement a lightweight database management system
called LightDB. The goals of this assignment are threefold:
• to teach you how to translate SQL queries to relational algebra query plans,
• to familiarize you with the iterator model for relational operator evaluation, and
• to build na¨ıve implementations of the most common operators (e.g., selection, projection, join, sort, aggregation).
The assignment consists of two tasks:
Task 1 (60%): Implementation of the iterator model and common operators.
Task 2 (30%): Optimisation of constructed query plans.
The remaining 10% of your mark is for code style and comments; more details later on.
Task 2 requires elements of independent thinking and creativity, which are necessary for
distinction marks according to the Common Marking Scheme.
You will be starting from a bare-bone project consisting of only the main class
LightDB, which defines the expected command line interface. You are free to modify
this class but must preserve the command line interface. The project is also configured
to use JSqlParser1
, so you do not have to write your own SQL parser (unless you want
to). The main class gives an example of how to parse a SQL string into a Java object.
The rest of this document describes in detail how to complete the assignment. Take
time to read it carefully. Note that some topics will be covered later in the course. Start
working as early as possible. This is not a project that should be left to the last minute.
You may start by reviewing the course material on SQL. Also, brush up your knowledge of the basic principles of object-oriented programming (encapsulation, inheritance,
polymorphism, abstraction) and design patterns; in particular, the singleton pattern and
the visitor pattern might come in handy for this assignment.
1https://github.com/JSQLParser/JSqlParser
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2 Overview
In this project, you will implement a simple interpreter for SQL statements. That is, you
will build a program that takes in a database (a set of files with data) and an input file
containing one SQL query. The program will process and evaluate the SQL query on the
database and write the query result in the specified output file.
2.1 Supported Language Features
Your interpreter will not support all of SQL, but it will handle a lot of relatively complex
queries. Here, we give information about the queries you must support.
Your interpreter will process SELECT-FROM-WHERE queries, which may optionally include a SUM aggregate in the SELECT, a DISTINCT, an ORDER BY, a GROUP BY, or a combination of them. You do not need to support nested subqueries, set operators (e.g.,
UNION), aggregate functions other than SUM (e.g., COUNT, AVG), or any other features. In
addition, we make a few simplifying assumptions as below. When we say a query is valid,
we mean it is a permitted input to your interpreter which you should be able to handle.
When we talk about a base table, we mean a real table that exists in the database.
• You may assume all valid queries follow correct SQL syntax and that they only refer
to tables that exist in the database. Also, when a query refers to a table column
such as Sailors.age, you may assume the column name (age) is valid for that
table (Sailors).
• You may assume there will be at least one table in the FROM clause.
• Valid queries may use aliases such as Sailors S, or they may just use the names of
base tables. If a query does not use aliases, all column references are fully qualified
by the base table name. If a query does use aliases, all tables use aliases and all
column references are qualified by an alias. Here are two examples of valid queries,
the first one without aliases and the second with aliases:
SELECT Sailors.name, Reservations.date FROM Sailors, Reservations
WHERE Sailors.id = Reservations.sid;
SELECT S.name, R.date FROM Sailors S, Reservations R
WHERE S.id = R.sid;
You may assume that any string used as an alias will not also be the name of a
base table.
• Self-joins, i.e., joining a table with itself, are valid and must be supported (and
require the use of aliases).
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• The WHERE clause, if present, is a conjunction (i.e., an AND) of expressions of the
form A op B, where op is one of =, ! =, <, >, <=, >= and A and B are either
integers or column references. Thus, Sailors.id = Reservations.sid, S.id <
3, and 42 = 42 are all valid expressions for the WHERE cause, while for example
Sailors.id < Boats.id - 1 is not a valid expression even though it would be ok
in “real SQL”.
• The SELECT clause will either specify a subset of columns or have the form SELECT
*. In both cases, it can optionally include a SUM aggregate function. For SELECT
*, you should order the columns in your answer following the order of the tables
in the FROM clause. Thus, for SELECT * FROM R, S, each answer row has all the
columns of R followed by all the columns of S. The order of columns in a table is
defined by the table schema.
• The SELECT clause may optionally contain one SUM aggregate function. We restrict
the form of the SUM function to take as argument either one term (integer or column
reference) or a product of terms. Thus, for the purpose of this assignment, SUM(5),
SUM(A), SUM(A*B), SUM(C*C*C) are valid expressions, while for example SUM(A+B)
and SUM(A/2) are not valid expressions.
You can assume that SUM, if present, is always at the end of the SELECT clause. It is
possible that SELECT consists of just one SUM; e.g., SELECT SUM(1) FROM R is valid
and returns one integer representing the number of tuples in R.
• There may be a GROUP BY clause that specifies a subset of columns for grouping.
The SELECT list can only list group-by columns but not necessarily all of them, that
is, some group-by columns can be omitted from the output; e.g., SELECT A FROM
R GROUP BY A, B is a valid query. You may assume that HAVING will not be used.
The SUM function, if present, can reference any columns, including non-group-by
columns; e.g., SELECT A, SUM(A*B) FROM R GROUP BY A is a valid query.
• There may be an ORDER BY clause that specifies a subset of columns for ordering.
You may assume that we only want to sort in ascending order. If two tuples agree
on all sort attributes, you can order them as you prefer. You may assume that no
ASC, DESC, OFFSET, or LIMIT keywords will be used.
You may also assume that the attributes mentioned in the ORDER BY are a subset of
those retained by the SELECT. This allows you to do the sorting last, after projection.
Note that this does not mean that every attribute in ORDER BY must be mentioned
in the SELECT – a query like SELECT * FROM Sailors S ORDER BY S.name is valid.
• There may be a DISTINCT right after the SELECT, and it should be processed appropriately. Yes, SELECT DISTINCT * FROM ... is valid.
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2.2 Data and Output Formats
We have provided you some sample data and some sample queries. Take a look at the
samples directory. It has db, input, and expected output as subdirectories.
• The input directory contains SQL files with some example queries. There is one
SQL query per input file.
• The db directory contains schema.txt specifying the schema for your database as
well as the data directory, where the data itself is stored. The names schema.txt
and data are hard-coded and must exist in a valid database directory. Your program
should support an arbitrary schema defined in schema.txt, not just the schema of
the sample data.
The schema.txt file contains one line per relation (table) in the database. Every
line contains several strings separated by spaces. The first string on each line is the
table name and all the remaining ones are attribute (column) names, in the order
in which they appear in the table.
The data subdirectory contains one file per database table, and the name of the file
is the same as the name of the database table with the added .csv extension. Every
file contains zero or more tuples; a tuple is a line in the file with field (attribute)
values separated by commas. All attribute values are integers. Using integer attributes simplifies your job and allows you to focus on implementing “interesting”
functionality rather than boilerplate code to handle different data types. Also, you
do not have to handle null values, but you do need to handle empty tables.
• The expected output directory contains the expected output files for the queries
we provided. For example, query1.csv contains the expected output for the query
in query1.sql. The format for the output is the same as the format for the data.
2.3 Setting Up Local Development Environment
You are free to use any text editor or IDE to complete the project. We will use Maven
to compile your project. We recommend setting up a local development environment by
installing Java 8 or later and using an IDE such as IntelliJ or Eclipse. To import the
project into IntelliJ or Eclipse, make sure that you import as a Maven project (select the
pom.xml file when importing).
2.4 Compile and Run
Your SQL interpreter is a program that takes three arguments: the path to a database
directory, the path to an input SQL file, and the path to an output file. The program then
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executes the SQL query from the input file on the given database and writes the result to
the given output file. The LightDB.java file provides this command-line interface. Run
the LightDB class from your IDE or command line, providing the required arguments.
We will compile your code from the command line, producing a runnable .jar file:
mvn clean compile assembly:single
This command will produce target/lightdb-1.0.0-jar-with-dependencies.jar. We
can run this file as follows:
$ java -jar target/lightdb-1.0.0-jar-with-dependencies.jar
Usage: LightDB database_dir input_file output_file
The LightDB class requires passing three mandatory arguments.
$ java -jar target/lightdb-1.0.0-jar-with-dependencies.jar
samples/db samples/input/query1.sql samples/output/query1.csv
Read statement: SELECT * FROM Sailors
Select body is SELECT * FROM Sailors
Your code should handle these arguments appropriately (i.e., do not hardcode any paths).
We will test your code using our own test queries and databases with potentially
different schemas, including different table names. The database directory will have the
same structure as described above, with files in the data directory named according to
the database schema with .csv as the file extension. You may assume that prior to
execution the given output file does not exist but the output directory does exist.
After we run your code, we will compare your output files with ours. For queries
without an ORDER BY, it is ok if your answer file has the answer tuples in a different order
to ours; for queries with an ORDER BY, your ordering must match our ordering on sort
attributes, while tied tuples may have a different order to ours. As you can imagine, it
is very important for you to respect the expected input and output format.
Note: We will test your code on a DICE machine with Ubuntu Linux. Remember
that Linux/MacOS environments use ’/’ as path separator. The database directory will
be provided with no final ’/’ symbol, as above. If you use Windows, make sure that when
you form file paths, you use File.separator instead of ’’ as path separator.
2.5 Operators and the Iterator Model
A key abstraction in this project will be the iterator model for relational operators. You
will implement several operators:
• the bag relational algebra select, project and (tuple nested loop) join.
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• sort, duplicate elimination, and group-by aggregation operators, which are not part
of the basic relational algebra but must be added to support ORDER BY, DISTINCT,
GROUP BY, and SUM.
• a scan operator which is the leaf operator for any query plan. This is really a
physical operator rather than something you would add to the relational algebra,
but for now we will put it in the same category as the above.
The standard way to implement all relational operators is to use an iterator API.
You should create an abstract class Operator, and all your operators will extend that.
Certain operators may have one or two child operators. A scan operator has no children,
a join has two children, and the remaining operators have one child. Your end goal is to
build a query plan that is a tree of operators.
Every operator must implement the methods getNextTuple() and reset() (put
these in your abstract Operator class). The idea is that once you create a relational
operator, you can call getNextTuple() repeatedly to get the next tuple of the operator’s
output. This is sometimes called “pulling tuples” from the operator. If the operator still
has some available output, it will return the next tuple, otherwise it should return null.
The reset() method tells the operator to reset its state and start returning its output
again from the beginning; that is, after calling reset() on an operator, a subsequent call
to getNextTuple() returns the first tuple in that operator’s output, even though the
tuple may have been returned before. This functionality is useful if you need to process
an operator’s output multiple times, e.g., repeatedly scan the inner table in a join.
For each of the above operators, you will implement both getNextTuple() and
reset(). Remember that if your operator has a child operator, the getNextTuple()
of your operator can - and probably will - call getNextTuple() on the child operator and
do something useful with the output it receives from the child.
A big advantage of the iterator model, and one of the reasons it is popular, is that it
supports pipelined evaluation of multi-operator plans, i.e., evaluation without materialising (writing to disk) intermediate results.
The bulk of this project involves implementing each of the aforementioned operators,
as well as writing code to translate an SQL query (i.e., a line of text) to a query plan (i.e.,
a suitable tree of operators). Once you have the query plan, you can actually compute
the answer to the query by repeatedly calling getNextTuple() on the root operator and
putting the tuples somewhere as they come out.
We suggest you add a dump() method to your abstract Operator class. This method
repeatedly calls getNextTuple() until the next tuple is null (no more output) and writes
each tuple to a suitable PrintStream. That way you can dump() the results of any
operator – including the root of your query plan – to your favourite PrintStream, whether
it leads to a file or whether it is System.out (for testing).
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3 Task 1: Iterator Model Implementation
We recommend that you implement and test one feature at a time. Our instructions
below are given in suggested implementation order.
We also recommend (but do not require) you set up a test infrastructure early on.
You should do two kinds of testing – unit tests for individual components and end-to-end
tests where you run your interpreter on queries and look at the output files produced to
see if they match a set of expected output files. As you add more features, rerun all your
tests to check that you didn’t introduce bugs that affect earlier functionality.
After you implement and test each feature, make a copy of your code and save it so if
you mess up later you still have a version that works (and that you can submit for partial
credit if all else fails!).
3.1 Setting up JSqlParser
For this project, you do not need to write your own SQL parser. We recommend using JSqlParser, which takes care of parsing your SQL and creating a Java object.
JSqlParser is an open source project:
• The project page https://github.com/JSQLParser/JSqlParser contains a wiki
with examples of how to get started with JSqlParser.
• The online documentation is available at https://javadoc.io/doc/com.github.
jsqlparser/jsqlparser/latest/index.html.
The pom.xml file already has a JSqlParser dependency. You are not required to use
JSqlParser, but you need to correctly parse all valid queries as defined in Section 2.1.
In case you do use JSqlParser, you should play around with it on your own and read
the documentation to understand the structure of the objects that it outputs.
To get you started, we have provided a simple method in LightDB.java that uses
JSqlParser to read a SQL query from a file and print it out. This method illustrates
the use of JSqlParser and some methods to access fields of the Statement object, such
as getSelectBody() if the Statement is a Select.
You may assume all the Statements we will work with are Selects, and have a
PlainSelect as the selectBody. Take a look at the PlainSelect Javadocs; the first
table in the FROM clause will be in the fromItem, the remaining ones in joins. Also of relevance to you is the where field for the WHERE clause, and the distinct,orderByElements,
and groupByElements fields. Write some SQL queries and write code to access and print
out all these objects/fields for your queries to get an idea of “what goes where”.
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The where field of a PlainSelect contains an Expression; take a look at the docs for
that. For this project, you only need to worry about AndExpression, LongValue, Column,
Multiplication (used in the SUM aggregate), EqualsTo, NotEqualsTo, GreaterThan,
GreaterThanEquals, MinorThan, and MinorThanEquals. These capture the recursive
structure of an expression. The last six of the aforementioned expression types are comparison expressions, the AndExpression is a conjunction of two other Expressions, the
LongValue is a numeric literal, and Column is a column reference (such as the S.id in
S.id < 5). Every Column object has a column name, as well as an embedded Table object. Every Table object has a name and an alias (if aliases are used). The SUM aggregate
is an expression of type Function.
JSqlParser also provides a number of Visitor interfaces, which you may or may not
choose to use. In particular, ExpressionVisitor and ExpressionDeParser are highly
recommended to use once you get to the selection operator. Check out also the wiki page
of JSqlParser on how to evaluate expressions.
The above should be enough to get you started, but you should expect to do further
explorations on your own as you implement more and more SQL features.
3.2 Implement Scan
Your first goal is to support queries that are full table scans, e.g., SELECT * FROM
Sailors (for now assume the queries do not use aliases). To achieve this, you will
need to implement your first operator – the scan operator.
Implement a ScanOperator that extends your Operator abstract class. Every instance of ScanOperator knows which base table it is scanning. Upon initialisation, it
opens a file scan on the appropriate data file; when getNextTuple() is called, it reads
the next line from the file and returns the next tuple. You probably want to have a Tuple
class to handle the tuples as objects.
The ScanOperator needs to know where to find the data file for its table. It is
recommended to handle this by implementing a database catalog in a separate class. The
catalog can keep track of information such as where a file for a given table is located,
what the schema of different tables is, and so on. Because the catalog is a global entity
that various components of your system may want to access, you should consider using
the singleton pattern for the catalog.
Once you have written ScanOperator, test it thoroughly to be sure getNextTuple()
and reset() both work as expected. Then, hook up your ScanOperator to your interpreter. Assuming that all your queries are of the from SELECT * FROM MyTable, write
code that grabs MyTable from the fromItem and constructs a ScanOperator from it.
In summary the top-level structure of your code at this point should be:
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• parse the query from the input file
• construct a ScanOperator for the table in the fromItem
• call dump() on your ScanOperator to send the results somewhere helpful, like a file
or console (for testing).
3.3 Implement Selection
The next operator is single-table selection, still with fully specified table names (no
aliases). That is, you are aiming to support queries like SELECT * FROM Boats WHERE
Boats.id = 4.
This means you need to implement a second Operator, which is a SelectOperator.
Your query plan will now have two operators – the SelectOperator as the root and the
ScanOperator as its child. During evaluation, the SelectOperator’s getNextTuple()
method will grab the next tuple from its child (i.e., from the scan), check if that tuple
passes the selection condition, and if so output it. If the tuple does not pass the selection
condition, the selection operator will continue pulling tuples from the scan until either it
finds one that passes or it receives null (i.e., the scan runs out of output).
The tricky part will be implementing the logic to check if a tuple passes the selection
condition. The selection condition is an Expression which you will find in the WHERE
clause of your query. The SelectOperator needs to know that Expression.
You will need to write a class to test whether a Expression holds on a given tuple. For
example, if you have a table R with fields A, B and C, you may encounter a tuple (1, 42, 4)
and an expression R.A < R.C AND R.B = 42, and you need to determine whether the
expression is true or false on this tuple.
This is best achieved using a visitor on the Expression. You should have a class
that extends JSqlParser’s ExpressionDeParser. The class will take as input a tuple
and recursively walk the expression to evaluate it to true or false on that tuple. The
expression may contain column references – in our example R.A < R.C AND R.B = 42
refers to all three columns of R. The visitor class needs some way to resolve the references;
i.e., if our input tuple is (1, 42, 4), it needs a way to determine that R.A is 1, etc. So,
your visitor class also needs to take in some schema information. It is up to you how you
structure your schema information, but obviously it must allow mapping from column
references like R.A to indexes into the tuple.
Once you have written your visitor class, unit-test it thoroughly. Start with simple
expressions that have no column references, like 1 < 2 AND 3 = 17. Then test it with
column references until you are 100% sure it works. Once your expression evaluation
logic is solid, you can plug it into the getNextTuple() method of your SelectOperator.
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3.4 Implement Projection
Your next task is to implement projection, i.e., you will be able to handle queries of the
form SELECT Sailors.id FROM Sailors WHERE Sailors.age = 20. We still assume
that the queries do not use aliases and have no SUM aggregation.
For implementing projection, you need a third Operator that is a ProjectOperator.
Recall that bag projection does not eliminate duplicates. When getNextTuple() is
called, it grabs the next tuple from its child, extracts only desired values into a new
tuple, and returns that tuple. Note that the child could be either a SelectOperator or
a ScanOperator, depending on whether your SQL query has a WHERE clause.
You get the projection columns from the selectItems field of your PlainSelect.
selectItems is a list of SelectItem objects, where each one is either AllColumns
(for a SELECT * ) or a SelectExpressionItem. You may assume the Expression in
a SelectExpressionItem will always be a Column. Once you grab these Columns you
need to translate that information into something useful to the ProjectOperator.
Note that the attribute order in the SELECT does not have to match the attribute
order in the table. The queries SELECT R.A, R.B FROM R and SELECT R.B, R.A FROM
R are both valid and produce different output results.
By this point you should have code takes in a SQL query and produces a query plan
containing:
• an optional projection operator, having as a child
• an optional selection operator, having as a child
• a non-optional scan operator.
Thus your query plan could have one, two or three operators. Make sure you are
supporting all possibilities; try queries with/without a projection/selection. If the query
is SELECT *, do not create a projection operator, and if the query has no WHERE clause,
do not create a selection operator.
You are now producing relatively complex query plans; however, things are about to
get much more exciting and messy as we add joins. This is a good time to pull out the
logic for constructing the query plan into its own class, if you have not done so already.
Thus, you should have a top-level interpreter class that reads the statement from the
query file. You should also have a second class that knows how to construct a query plan
for a Statement, and returns the query plan back to the interpreter so the interpreter
can dump() the results of the query plan somewhere.
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3.5 Implement Join
Next up, the star of the show: joins. Assume that there are still no table aliases, so you
don’t have to worry about self-joins for now.
You need a JoinOperator that has both a left and right child Operator. It also
has an Expression which captures the join condition. This Expression could be a
single comparison such as R.A = S.B (or any other comparison operator, not just =), a
conjunction (AND) of comparisons, or it could be null if the join is a cross product.
Implement the simple (tuple) nested loop join algorithm: the join should scan the left
(outer) child once, and for each tuple in the outer child, it should scan the inner child
completely (finally a use for the reset() method!). Once the operator has obtained a
tuple from the outer and a tuple from the inner, it glues them together. If there is a
non-null join condition, the tuple is only returned if it matches the join condition (so
you will be reusing your expression visitor class from Section 3.3). If the join is a cross
product, all pairs of tuples are returned.
Once you have implemented and unit-tested your JoinOperator, you need to figure
out how to translate an SQL query to a plan that includes joins.
For this project, we require that you construct a left-deep join tree that follows the
order in the FROM clause. That is, a query whose FROM clause is FROM R,S,T produces a
plan with the structure shown below:
R" S"
./ T"
./
The tricky part will be processing the WHERE clause to extract join conditions. The
WHERE clause may contain both selections on a single table as well as join conditions
linking multiple tables. For example WHERE R.A < R.B AND S.C = 1 AND R.D = S.G
contains a selection expression on R, a selection expression on S, and a join condition
on both R and S together. Obviously it is most efficient to evaluate the selections as
early as possible and to evaluate R.D = S.G during computation of the join, rather than
computing a cross product and having a selection later.
While the focus of this part is not optimisation, you do not want to compute cross
products unless you have to as this is grossly inefficient. Therefore, we require that you
have some strategy for extracting join conditions from the WHERE clause and evaluating
them as part of the join. You do not need to be very clever about this, but you may
not simply compute the cross products (unless of course the query actually asks for a
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cross product). You must explain your strategy in comments in your code and in the
README that you submit with your code.
A suggested way to do this is to write another class that extends ExpressionDeParser
and processes the WHERE clause. For every conjunct, the visitor determines which tables
are referenced, and adds the conjunct to an appropriate Expression. If there are k
tables being joined, there could be 2k − 1 running Expressions: k − 1 join conditions
and k selection conditions on the individual tables. Once the whole WHERE clause is
processed, the 2k − 1 Expressions can be integrated into the appropriate selection and
join operators in the query plan. Of course some of these Expressions may turn out to
be null, depending on the query.
For example, if we have SELECT * FROM R, S, T WHERE R.A = 1 AND R.B = S.C
AND T.G < 5 AND T.G = S.H, the above approach would give the following query plan:
S"
R"
T"
R.A=1
T.G<5 ./R.B=S.C
./T.G=S.H
You don’t have to follow exactly this strategy; you can pursue alternative ways for
extracting join conditions.
3.6 Implement Aliases
The next step is to implement aliases to support queries such as SELECT R.A FROM
SomeTable R. These are handy in any case, but they are essential to support self-joins.
The aliases themselves come from the FROM clause, and you can extract them from
the fromItem and the joins of your PlainSelect. Unfortunately, when you reference
columns in the SELECT and WHERE clauses, JSqlParser is not smart enough to know
whether you are using aliases or references. Thus, if you have a SELECT R.A, the term
R.A is returned as a Column, and the embedded Table object has R as the table name
whether or not R is a base table name or an alias. Thus, you will need to keep track of
these things yourself. When you start building your query plan, you need to determine
whether the query uses aliases. If yes, you need to keep track of all the aliases used in
the query, so you can resolve the column references throughout your code.
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Implementing aliases is not conceptually difficult, but you may find it a bit fiddly. It
is a great test of how clean and modular your code is; if you have been structuring it
well, you will have to modify relatively little code.
It may be useful to start with supporting aliases only on single-table queries, then
move on to joins. Be sure to test your code on lots of queries including self-joins, as these
can bring out bugs which are not apparent when each base table is used only once. Also,
be sure that you can still correctly handle all the “old” queries you tested (which do not
use aliases).
3.7 Implement ORDER BY
Next is the ORDER BY operator. You will implement ORDER BY by adding a SortOperator.
This is going to read all of the output from its child, place it into an internal buffer, sort
it, and then return individual tuples when requested. You can use Collections.sort();
you will want a custom Comparator to specify the different sort orders.
You may be alarmed by the above description. Yes, sort is a blocking operator, which
means it really needs to see all of its input before producing any output (think about it
– what if the tuple that comes first in the desired sort order is the last one that the child
operator is going to spit out?). As you imagine, buffering all the tuples in memory will
not work for very large numbers of tuples; for this project assignment, this is fine.
If your query has a ORDER BY clause, you should put the SortOperator as the root,
followed by the rest of your plan. It is good to delay sorting as late as possible, in
particular to do it after the projection(s), because there will be less data to sort that way.
We are making life easier for ourselves by assuming it’s always safe to defer the ORDER BY
after the projections; this is not always the case in full “real” SQL. A query like SELECT
S.A FROM S ORDER BY S.B is valid SQL in the real world, we just choose not to support
them in this project.
3.8 Implement DISTINCT
To add support for duplicate elimination and DISTINCT, you will add a new operator DuplicateEliminationOperator. You are free to choose either a sorting-based or
hashing-based implementation for this operator.
3.9 Implement GROUP BY with SUM Aggregation
The final operator to implement is group-by aggregation with the SUM function. The SUM
function may appear at the end of the SELECT clause, after all the selected attributes,
and can take as argument one term (integer or column) or a product of terms.
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INFR11199 (Spring 2024) Coursework Assignment Page 15 of 17
You will implement one group-by aggregation operator, SumOperator, which reads
all of the output from its child, extracts relevant values into tuples, organizes tuples into
groups, and for each group computes an aggregate value. The sum operator needs to see
all of its input before producing any output (i.e., it is a blocking operator).
4 Task 2: Query Optimisation
The final task is to enrich LightDB with query optimisation. The goal of query optimisation is to transform query plans such that their operators process as few tuples as
possible, thus reducing processing time and avoiding large intermediate results; yet such
optimised query plans must still produce correct query results. To achieve this goal, you
must keep the same join order but you are allowed to transform query plans, for example,
swap operators or introduce new instances of the non-join operators discussed above. You
should not implement any new relational algebra operator.
Properly optimised query plans should avoid processing large intermediate results
whenever possible, thus allowing such plans to be executed under restricted memory
budgets and time limits. We will evaluate the effectiveness of your optimisation rules by
running a set of queries on large databases. Failing to properly optimise input queries
will most likely lead to wrong results, out-of-memory exceptions, or timeouts.
Task 2 is not about improving the performance of individual relational algebra operators but about coming up with optimisation rules for the class of valid SQL aggregate
queries, described in Section 2.1. Some optimisation rules will be covered in lectures, but
some will require a bit of thinking on your side on how to reduce the size of intermediate
results during query processing.
5 Grading
We strongly suggest that you follow the architecture we described. However, we will not
penalize you for making different architectural decisions, with a few exceptions:
• You must have relational Operators that implement getNextTuple() and reset()
methods as outlined above. This is the standard relational algebra evaluation
model, and you need to learn it. Do not hard-wire combinations of operators,
e.g., projection should not assume selection as its child (i.e., enforce abstraction).
• You must construct a tree of Operators and then evaluate it by repeatedly calling
getNextTuple() on the root operator.
• As explained in Section 3.5, you must build a left-deep join tree that follows the
ordering of the tables in the FROM clause. Also, you must have a strategy to identify
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INFR11199 (Spring 2024) Coursework Assignment Page 16 of 17
join conditions and evaluate them as part of the join rather than doing a selection
after a cross product.
Disregarding any of the above three requirements will result in severe point deductions.
Next we give the grading breakdown.
5.1 Code Style and Comments (10 points)
Follow standard guidelines for writing clean and understandable code; e.g., use standard
naming conventions for classes and methods, break up large monolithic blocks of code
into smaller logical pieces, avoid code duplication if possible – the “rule of three” says if
your code is copied more than twice, then it probably needs to be abstracted out.
You must provide comments for every method you implement. At minimum, the comment must include one sentence about the purpose of the method, and @params/@return
annotations for every argument/return value respectively. In addition, every class must
have a comment describing the class and the logic of any algorithm used in the class.
If you follow the above rules and write reasonably clean code that follows our overall
architecture, you are likely to get the full 10 points for code style.
5.2 Test Queries (90 points)
Be sure to read Section 2.2 carefully for information on expected input and output format.
We will run your code on our own queries and database. The queries we provide
with the assignment count for 24 out of the 90 points. You can expect that we will add
additional tables to the database; of course the schema of these tables will be mentioned
in schema.txt and the data files will be found in the data directory.
Task 1 is worth 60 points, while Task 2 is worth 30 points. Any optimisation rules
you implement for Task 2 must be correct, thus leaving them enabled for Task 1 is fine.
We will test with basic queries as well as with arbitrarily complex queries that include
any/all of the features you are to implement. We may also reorder the queries we gave
you and/or intersperse them with our own, so don’t hardcode any functionality on the
assumption that the queries will be run in any particular order.
If you cannot implement one or more operators, that is fine, although obviously you
won’t get full points. In that case, you must clearly tell us in the README what you
have not been able to implement.
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6 Submission Instructions
Double-check that your code compiles and runs as described in Section 2.2. If your code
fails to compile or crashes during execution, we will invest a reasonable effort to fix the
problem; this can cause (severe) point deduction.
You must keep the LightDB class as the top-level main class of your code.
Create a README text file containing the following information.
• For Task 1, an explanation of your logic for extracting join conditions from the
WHERE clause. If this logic is fully explained in comments in your code, your
README does not need to repeat that; however, it must mention exactly where
in the code/comments the description is, so the grader can find it easily.
• For Task 2, an explanation of your optimisation rules, reasons why they are correct,
and how they can reduce the size of intermediate results during query evaluation.
• Any other information you want the grader to know, such as known bugs.
Create a .zip archive containing a README file and your entire project folder so that
we can compile and run your code on the command line. Do not include any .class
files nor large .csv files. Disable all debugging statements before submitting your code to
allow us to test your interpreter with large datasets. Failure to do so may result in point
deduction.
Upload the zip archive to Learn: Assessment → CW1 Programming Assignment
Submission.
Keep in mind that this is an individual assignment and not a group project. Your
work will be reviewed for any signs of plagiarism.
Make sure you start early! Good luck!
End of Coursework Assignment

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