6.830 Lab 2: SimpleDB Operators

Assigned: Friday, March 2, 2012
Due: Friday, March 16, 2012

Version History:

In this lab assignment, you will write a set of operators for SimpleDB to implement table modifications (e.g., insert and delete records), selections, joins, and aggregates. These will build on top of the foundation that you wrote in Lab 1 to provide you with a database system that can perform simple queries over multiple tables.

Additionally, we ignored the issue of buffer pool management in Lab 1: we have not dealt with the problem that arises when we reference more pages than we can fit in memory over the lifetime of the database. In Lab 2, you will design an eviction policy to flush stale pages from the buffer pool.

You do not need to implement transactions or locking in this lab.

The remainder of this document gives some suggestions about how to start coding, describes a set of exercises to help you work through the lab, and discusses how to hand in your code. This lab requires you to write a fair amount of code, so we encourage you to start early!

1. Getting started

You should begin with the code you submitted for Lab 1 (if you did not submit code for Lab 1, or your solution didn't work properly, contact us to discuss options). We have provided you with extra test cases for this lab that are not in the original code distribution you received. We reiterate that the unit tests we provide are to help guide your implementation along, but they are not intended to be comprehensive or to establish correctness.

You will need to add these new test cases to your release. The easiest way to do this is to untar the new code in the same directory as your top-level simpledb directory, as follows:

1.3. Implementation hints

As before, we strongly encourage you to read through this entire document to get a feel for the high-level design of SimpleDB before you write code.

We suggest exercises along this document to guide your implementation, but you may find that a different order makes more sense for you. As before, we will grade your assignment by looking at your code and verifying that you have passed the test for the ant targets test and systemtest. See Section 3.4 for a complete discussion of grading and list of the tests you will need to pass.

Here's a rough outline of one way you might proceed with your SimpleDB implementation; more details on the steps in this outline, including exercises, are given in Section 2 below.

At this point you should be able to pass all of the tests in the ant systemtest target, which is the goal of this lab.

You'll also be able to use the provided SQL parser to run SQL queries against your database! See Section 2.7 for a brief tutorial and a description of an optional contest to see who can write the fastest SimpleDB implementation.

Finally, you might notice that the iterators in this lab extend the Operator class instead of implementing the DbIterator interface. Because the implementation of next/hasNext is often repetitive, annoying, and error-prone, Operator implements this logic generically, and only requires that you implement a simpler readNext. Feel free to use this style of implementation, or just implement the DbIterator interface if you prefer. To implement the DbIterator interface, remove extends Operator from iterator classes, and in its place put implements DbIterator.

2. SimpleDB Architecture and Implementation Guide

2.1. Filter and Join

Recall that SimpleDB DbIterator classes implement the operations of the relational algebra. You will now implement two operators that will enable you to perform queries that are slightly more interesting than a table scan.
Exercise 1. Implement the skeleton methods in: At this point, your code should pass the unit tests in PredicateTest, JoinPredicateTest, FilterTest, and JoinTest. Furthermore, you should be able to pass the system tests FilterTest and JoinTest.

2.2. Aggregates

An additional SimpleDB operator implements basic SQL aggregates with a GROUP BY clause. You should implement the five SQL aggregates (COUNT, SUM, AVG, MIN, MAX) and support grouping. You only need to support aggregates over a single field, and grouping by a single field.

In order to calculate aggregates, we use an Aggregator interface which merges a new tuple into the existing calculation of an aggregate. The Aggregator is told during construction what operation it should use for aggregation. Subsequently, the client code should call Aggregator.mergeTupleIntoGroup() for every tuple in the child iterator. After all tuples have been merged, the client can retrieve a DbIterator of aggregation results. Each tuple in the result is a pair of the form (groupValue, aggregateValue), unless the value of the group by field was Aggregator.NO_GROUPING, in which case the result is a single tuple of the form (aggregateValue).

Note that this implementation requires space linear in the number of distinct groups. For the purposes of this lab, you do not need to worry about the situation where the number of groups exceeds available memory.

Exercise 2. Implement the skeleton methods in: At this point, your code should pass the unit tests IntegerAggregatorTest, StringAggregatorTest, and AggregateTest. Furthermore, you should be able to pass the AggregateTest system test.

2.3. HeapFile Mutability

Now, we will begin to implement methods to support modifying tables. We begin at the level of individual pages and files. There are two main sets of operations: adding tuples and removing tuples.

Removing tuples: To remove a tuple, you will need to implement deleteTuple. Tuples contain RecordIDs which allow you to find the page they reside on, so this should be as simple as locating the page a tuple belongs to and modifying the headers of the page appropriately.

Adding tuples: The insertTuple method in HeapFile.java is responsible for adding a tuple to a heap file. To add a new tuple to a HeapFile, you will have to find a page with an empty slot. If no such pages exist in the HeapFile, you need to create a new page and append it to the physical file on disk. You will need to ensure that the RecordID in the tuple is updated correctly.

Exercise 3. Implement the remaining skeleton methods in:

To implement HeapPage, you will need to modify the header bitmap for methods such as insertTuple() and deleteTuple(). You may find that the getNumEmptySlots() and isSlotUsed() methods we asked you to implement in Lab 1 serve as useful abstractions. Note that there is a markSlotUsed method provided as an abstraction to modify the filled or cleared status of a tuple in the page header.

Note that it is important that the HeapFile.insertTuple() and HeapFile.deleteTuple() methods access pages using the BufferPool.getPage() method; otherwise, your implementation of transactions in the next lab will not work properly.

Implement the following skeleton methods in src/simpledb/BufferPool.java:

These methods should call the appropriate methods in the HeapFile that belong to the table being modified (this extra level of indirection is needed to support other types of files — like indices — in the future).

At this point, your code should pass the unit tests in HeapPageWriteTest and HeapFileWriteTest. We have not provided additional unit tests for HeapFile.deleteTuple() or BufferPool.

2.4. Insertion and deletion

Now that you have written all of the HeapFile machinery to add and remove tuples, you will implement the Insert and Delete operators.

For plans that implement insert and delete queries, the top-most operator is a special Insert or Delete operator that modifies the pages on disk. These operators return the number of affected tuples. This is implemented by returning a single tuple with one integer field, containing the count.

Exercise 4. Implement the skeleton methods in: At this point, your code should pass the unit tests in InsertTest. We have not provided unit tests for Delete. Furthermore, you should be able to pass the InsertTest and DeleteTest system tests.

2.5. Page eviction

In Lab 1, we did not correctly observe the limit on the maximum number of pages in the buffer pool defined by the constructor argument numPages. Now, you will choose a page eviction policy and instrument any previous code that reads or creates pages to implement your policy.

When more than numPages pages are in the buffer pool, one page should be evicted from the pool before the next is loaded. The choice of eviction policy is up to you; it is not necessary to do something sophisticated. Describe your policy in the lab writeup.

Notice that BufferPool asks you to implement a flushAllPages() method. This is not something you would ever need in a real implementation of a buffer pool. However, we need this method for testing purposes. You should never call this method from any real code. Because of the way we have implemented ScanTest.cacheTest, you will need to ensure that your flushPage and flushAllPages methods do no evict pages from the buffer pool to properly pass this test. flushAllPages should call flushPage on all pages in the BufferPool, and flushPage should write any dirty page to disk and mark it as not dirty, while leaving it in the BufferPool. The only method which should remove page from the buffer pool is evictPage, which should call flushPage on any dirty page it evicts.

Exercise 5. Fill in the flushPage() method and additional helper methods to implement page eviction in:

If you did not implement writePage() in HeapFile.java above, you will also need to do that here.

At this point, your code should pass the EvictionTest system test.

Since we will not be checking for any particular eviction policy, this test works by creating a BufferPool with 16 pages (NOTE: while DEFAULT_PAGES is 50, we are initializing the BufferPool with less!), scanning a file with many more than 16 pages, and seeing if the memory usage of the JVM increases by more than 5 MB. If you do not implement an eviction policy correctly, you will not evict enough pages, and will go over the size limitation, thus failing the test.

You have now completed this lab. Good work!

2.6. Query walkthrough

The following code implements a simple join query between two tables, each consisting of three columns of integers. (The file some_data_file1.dat and some_data_file2.dat are binary representation of the pages from this file). This code is equivalent to the SQL statement:

SELECT * 
  FROM some_data_file1, some_data_file2 
  WHERE some_data_file1.field1 = some_data_file2.field1
  AND some_data_file1.id > 1
For more extensive examples of query operations, you may find it helpful to browse the unit tests for joins, filters, and aggregates.
package simpledb;
import java.io.*;

public class jointest {

    public static void main(String[] argv) {
        // construct a 3-column table schema
        Type types[] = new Type[]{ Type.INT_TYPE, Type.INT_TYPE, Type.INT_TYPE };
        String names[] = new String[]{ "field0", "field1", "field2" };

        TupleDesc td = new TupleDesc(types, names);

        // create the tables, associate them with the data files
        // and tell the catalog about the schema  the tables.
        HeapFile table1 = new HeapFile(new File("some_data_file1.dat"), td);
        Database.getCatalog().addTable(table1, "t1");

        HeapFile table2 = new HeapFile(new File("some_data_file2.dat"), td);
        Database.getCatalog().addTable(table2, "t2");

        // construct the query: we use two SeqScans, which spoonfeed
        // tuples via iterators into join
        TransactionId tid = new TransactionId();

        SeqScan ss1 = new SeqScan(tid, table1.getId(), "t1");
        SeqScan ss2 = new SeqScan(tid, table2.getId(), "t2");

        // create a filter for the where condition
        Filter sf1 = new Filter(
                                new Predicate(0,
                                Predicate.Op.GREATER_THAN, new IntField(1)),  ss1);

        JoinPredicate p = new JoinPredicate(1, Predicate.Op.EQUALS, 1);
        Join j = new Join(p, sf1, ss2);

        // and run it
        try {
            j.open();
            while (j.hasNext()) {
                Tuple tup = j.next();
                System.out.println(tup);
            }
            j.close();
            Database.getBufferPool().transactionComplete(tid);

        } catch (Exception e) {
            e.printStackTrace();
        }

    }

}

Both tables have three integer fields. To express this, we create a TupleDesc object and pass it an array of Type objects indicating field types and String objects indicating field names. Once we have created this TupleDesc, we initialize two HeapFile objects representing the tables. Once we have created the tables, we add them to the Catalog. (If this were a database server that was already running, we would have this catalog information loaded; we need to load this only for the purposes of this test).

Once we have finished initializing the database system, we create a query plan. Our plan consists of two SeqScan operators that scan the tuples from each file on disk, connected to a Filter operator on the first HeapFile, connected to a Join operator that joins the tuples in the tables according to the JoinPredicate. In general, these operators are instantiated with references to the appropriate table (in the case of SeqScan) or child operator (in the case of e.g., Join). The test program then repeatedly calls next on the Join operator, which in turn pulls tuples from its children. As tuples are output from the Join, they are printed out on the command line.

2.7. Query Parser and Contest

We've provided you with a query parser for SimpleDB that you can use to write and run SQL queries against your database once you have completed the exercises in this lab.

The first step is to create some data tables and a catalog. Suppose you have a file data.txt with the following contents:

1,10
2,20
3,30
4,40
5,50
5,50
You can convert this into a SimpleDB table using the convert command (make sure to type ant first!):
java -jar dist/simpledb.jar convert data.txt 2 "int,int"
This creates a file data.dat. In addition to the table's raw data, the two additional parameters specify that each record has two fields and that their types are int and int.

Next, create a catalog file, catalog.txt, with the follow contents:

data (f1 int, f2 int)
This tells SimpleDB that there is one table, data (stored in data.dat) with two integer fields named f1 and f2.

Finally, invoke the parser. You must run java from the command line (ant doesn't work properly with interactive targets.) From the simpledb/ directory, type:

java -jar dist/simpledb.jar parser catalog.txt
You should see output like:
Added table : data with schema INT(f1), INT(f2), 
SimpleDB> 
Finally, you can run a query:
SimpleDB> select d.f1, d.f2 from data d;
Started a new transaction tid = 1221852405823
 ADDING TABLE d(data) TO tableMap
     TABLE HAS  tupleDesc INT(d.f1), INT(d.f2), 
1       10
2       20
3       30
4       40
5       50
5       50

 6 rows.
----------------
0.16 seconds

SimpleDB> 
The parser is relatively full featured (including support for SELECTs, INSERTs, DELETEs, and transactions), but does have some problems and does not necessarily report completely informative error messages. Here are some limitations to bear in mind:
Contest (Optional)

The first-place winner in the contest will receive a $50 gift certificate to Amazon. The second-place winner will receive a $25 gift certificate to Amazon.

We have built a SimpleDB-encoded version of the DBLP database you used in problem set 1; the needed files are located at: http://db.csail.mit.edu/6.830/assignments/dblp_data.tar.gz

You should download the file and unpack it. It will create four files in the dblp_data directory. Move them into the simpledb directory. The following commands will acomplish this, if you run them from the simpledb directory:

wget http://db.csail.mit.edu/6.830/assignments/dblp_data.tar.gz
tar xvzf dblp_data.tar.gz
mv dblp_data/* .
rm -r dblp_data.tar.gz dblp_data

You can then run the parser with:

java -jar dist/simpledb.jar parser dblp_simpledb.schema

We will give a prize to the submission that has shortest total runtime for the following three queries (where total runtime is the sum of the runtime of each of the individual queries):

  1. SELECT p.title
    FROM papers p
    WHERE p.title LIKE 'selectivity';
    
  2. SELECT p.title, v.name
    FROM papers p, authors a, paperauths pa, venues v
    WHERE a.name = 'E. F. Codd'
    AND pa.authorid = a.id
    AND pa.paperid = p.id
    AND p.venueid = v.id;
     
  3. SELECT a2.name, count(p.id)
    FROM papers p, authors a1, authors a2, paperauths pa1, paperauths pa2
    WHERE a1.name = 'Michael Stonebraker'
    AND pa1.authorid = a1.id 
    AND pa1.paperid = p.id 
    AND pa2.authorid = a2.id 
    AND pa1.paperid = pa2.paperid
    GROUP BY a2.name
    ORDER BY a2.name;
     
    

Note that each query will print out its runtime after it executes.

You may wish to create optimized implementations of some of the operators; in particular, a fast join operator (e.g., not nested loops) will be essential for good performance on queries 2 and 3.

There is currently no optimizer in the parser, so the queries above have been written to cause the parser generate reasonable plans. Here are some helpful hints about how the parser works that you may wish to exploit while running these queries:

Our reference implementation can run Query 1 in about .35 seconds, Query 2 in about 10.5 seconds, and Query 3 in about 40 seconds. We implemented a special-purpose join operator for equality joins but did little else to optimize performance.

3. Logistics

You must submit your code (see below) as well as a short (2 pages, maximum) writeup describing your approach. This writeup should:

3.1. Collaboration

This lab should be manageable for a single person, but if you prefer to work with a partner, this is also OK. Larger groups are not allowed. Please indicate clearly who you worked with, if anyone, on your individual writeup.

3.2. Submitting your assignment

To submit your code, please create a 6.830-lab2.tar.gz tarball (such that, untarred, it creates a 6.830-lab2/src/simpledb directory with your code) and submit it for the Lab 2 assigment on the Stellar Site Homework Section. You may submit your code multiple times; we will use the latest version you submit that arrives before the deadline (before 11:59pm on the due date). If applicable, please indicate your partner in your writeup. Please also submit your individual writeup as a PDF or plain text file (.txt). Please do not submit a .doc or .docx.

Make sure your code is packaged so the instructions outlined in section 3.4 work.

3.3. Submitting a bug

SimpleDB is a relatively complex piece of code. It is very possible you are going to find bugs, inconsistencies, and bad, outdated, or incorrect documentation, etc.

We ask you, therefore, to do this lab with an adventurous mindset. Don't get mad if something is not clear, or even wrong; rather, try to figure it out yourself or send us a friendly email. Please submit (friendly!) bug reports to 6830-staff@nms.csail.mit.edu. When you do, please try to include:

You can also post on the class page on Piazza if you feel you have run into a bug.

3.4 Grading

50% of your grade will be based on whether or not your code passes the system test suite we will run over it. These tests will be a superset of the tests we have provided. Before handing in your code, you should make sure it produces no errors (passes all of the tests) from both ant test and ant systemtest.

Important: before testing, we will replace your build.xml, HeapFileEncoder.java, and the entire contents of the test/ directory with our version of these files! This means you cannot change the format of .dat files! You should therefore be careful changing our APIs. This also means you need to test whether your code compiles with our test programs. In other words, we will untar your tarball, replace the files mentioned above, compile it, and then grade it. It will look roughly like this:

$ gunzip 6.830-lab2.tar.gz
$ tar xvf 6.830-lab2.tar
$ cd ./6.830-lab2
[replace build.xml, HeapFileEncoder.java, and test]
$ ant test
$ ant systemtest
[additional tests]
If any of these commands fail, we'll be unhappy, and, therefore, so will your grade.

An additional 50% of your grade will be based on the quality of your writeup and our subjective evaluation of your code.

We've had a lot of fun designing this assignment, and we hope you enjoy hacking on it!