Assigned: Wednesday, April 21, 2021
Due: Tuesday, May 4, 2021 11:59 PM EDT
In this lab you will implement a B+ tree index for efficient lookups and range scans. We supply you with all of the low-level code you will need to implement the tree structure. You will implement searching, splitting pages, redistributing tuples between pages, and merging pages.
You may find it helpful to review sections 10.3--10.7 in the textbook, which provide detailed information about the structure of B+ trees as well as pseudocode for searches, inserts and deletes.
As described by the textbook and discussed in class, the internal nodes in B+ trees contain multiple entries, each consisting of a key value and a left and a right child pointer. Adjacent keys share a child pointer, so internal nodes containing m keys have m+1 child pointers. Leaf nodes can either contain data entries or pointers to data entries in other database files. For simplicity, we will implement a B+tree in which the leaf pages actually contain the data entries. Adjacent leaf pages are linked together with right and left sibling pointers, so range scans only require one initial search through the root and internal nodes to find the first leaf page. Subsequent leaf pages are found by following right (or left) sibling pointers.
You should begin with the code you submitted for Lab 4 (if you did not submit code for Lab 4, or your solution didn't work properly, contact us to discuss options). Additionally, we are providing extra source and test files for this lab that are not in the original code distribution you received.
You will need to add these new files to your release and set up your lab4 branch. The easiest way to do this is to change to your project directory (probably called simple-db-hw), set up the branch, and pull from the master GitHub repository:
$ cd simple-db-hw $ git pull upstream master
Take a look at index/ and BTreeFile.java. This is the core file for the implementation of
the B+Tree and where you will write all your code for this lab. Unlike the
HeapFile, the BTreeFile consists of four different kinds of pages. As you would
expect, there are two different kinds of pages for the nodes of the tree:
internal pages and leaf pages. Internal pages are implemented in
BTreeInternalPage.java, and leaf pages are implemented in
BTreeLeafPage.java. For convenience, we have created an abstract class in
BTreePage.java which contains code that is common to both leaf and internal
pages. In addition, header pages are implemented in BTreeHeaderPage.java and
keep track of which pages in the file are in use. Lastly, there is one page at
the beginning of every BTreeFile which points to the root page of the tree and
the first header page. This singleton page is implemented in
BTreeRootPtrPage.java. Familiarize yourself with the interfaces of these
classes, especially BTreePage, BTreeInternalPage and BTreeLeafPage. You
will need to use these classes in your implementation of the B+Tree.
Your first job is to implement the findLeafPage() function in
BTreeFile.java. This function is used to find the appropriate leaf page given
a particular key value, and is used for both searches and inserts. For example,
suppose we have a B+Tree with two leaf pages (See Figure 1). The root node is an
internal page with one entry containing one key (6, in this case) and two child
pointers. Given a value of 1, this function should return the first leaf page.
Likewise, given a value of 8, this function should return the second page. The
less obvious case is if we are given a key value of 6. There may be duplicate
keys, so there could be 6's on both leaf pages. In this case, the function
should return the first (left) leaf page.
Figure 1: A
simple B+ Tree with duplicate keys
Your findLeafPage() function should recursively search through internal nodes
until it reaches the leaf page corresponding to the provided key value. In order
to find the appropriate child page at each step, you should iterate through the
entries in the internal page and compare the entry value to the provided key
value. BTreeInternalPage.iterator() provides access to the entries in the
internal page using the interface defined in BTreeEntry.java. This iterator
allows you to iterate through the key values in the internal page and access the
left and right child page ids for each key. The base case of your recursion
happens when the passed-in BTreePageId has pgcateg() equal to
BTreePageId.LEAF, indicating that it is a leaf page. In this case, you should
just fetch the page from the buffer pool and return it. You do not need to
confirm that it actually contains the provided key value f.
Your findLeafPage() code must also handle the case when the provided key value
f is null. If the provided value is null, recurse on the left-most child every
time in order to find the left-most leaf page. Finding the left-most leaf page
is useful for scanning the entire file. Once the correct leaf page is found, you
should return it. As mentioned above, you can check the type of page using the
pgcateg() function in BTreePageId.java. You can assume that only leaf and
internal pages will be passed to this function.
Instead of directly calling BufferPool.getPage() to get each internal page and
leaf page, we recommend calling the wrapper function we have provided,
BTreeFile.getPage(). It works exactly like BufferPool.getPage(), but takes
an extra argument to track the list of dirty pages. This function will be
important for the next two exercises in which you will actually update the data
and therefore need to keep track of dirty pages.
Every internal (non-leaf) page your findLeafPage() implementation visits
should be fetched with READ_ONLY permission, except the returned leaf page,
which should be fetched with the permission provided as an argument to the
function. These permission levels will not matter for this lab, but they will
be important for the code to function correctly in future labs.
Exercise 1: BTreeFile.findLeafPage()
Implement BTreeFile.findLeafPage().
After completing this exercise, you should be able to pass all the unit tests
in BTreeFileReadTest.java and the system tests in BTreeScanTest.java.
In order to keep the tuples of the B+Tree in sorted order and maintain the
integrity of the tree, we must insert tuples into the leaf page with the
enclosing key range. As was mentioned above, findLeafPage() can be used to
find the correct leaf page into which we should insert the tuple. However, each
page has a limited number of slots and we need to be able to insert tuples even
if the corresponding leaf page is full.
As described in the textbook, attempting to insert a tuple into a full leaf page should cause that page to split so that the tuples are evenly distributed between the two new pages. Each time a leaf page splits, a new entry corresponding to the first tuple in the second page will need to be added to the parent node. Occasionally, the internal node may also be full and unable to accept new entries. In that case, the parent should split and add a new entry to its parent. This may cause recursive splits and ultimately the creation of a new root node.
In this exercise you will implement splitLeafPage() and splitInternalPage()
in BTreeFile.java. If the page being split is the root page, you will need to
create a new internal node to become the new root page, and update the
BTreeRootPtrPage. Otherwise, you will need to fetch the parent page with
READ_WRITE permissions, recursively split it if necessary, and add a new entry.
You will find the function getParentWithEmptySlots() extremely useful for
handling these different cases. In splitLeafPage() you should "copy" the key
up to the parent page, while in splitInternalPage() you should "push" the key
up to the parent page. See Figure 2 and review section 10.5 in the text book if
this is confusing. Remember to update the parent pointers of the new pages as
needed (for simplicity, we do not show parent pointers in the figures). When an
internal node is split, you will need to update the parent pointers of all the
children that were moved. You may find the function updateParentPointers()
useful for this task. Additionally, remember to update the sibling pointers of
any leaf pages that were split. Finally, return the page into which the new
tuple or entry should be inserted, as indicated by the provided key field.
(Hint: You do not need to worry about the fact that the provided key may
actually fall in the exact center of the tuples/entries to be split. You should
ignore the key during the split, and only use it to determine which of the two
pages to return.)
Figure 2: Splitting pages
Whenever you create a new page, either because of splitting a page or creating a
new root page, call getEmptyPage() to get the new page. This function is an
abstraction which will allow us to reuse pages that have been deleted due to
merging (covered in the next section).
We expect that you will interact with leaf and internal pages using
BTreeLeafPage.iterator() and BTreeInternalPage.iterator() to iterate through
the tuples/entries in each page. For convenience, we have also provided reverse
iterators for both types of pages: BTreeLeafPage.reverseIterator() and
BTreeInternalPage.reverseIterator(). These reverse iterators will be
especially useful for moving a subset of tuples/entries from a page to its right
sibling.
As mentioned above, the internal page iterators use the interface defined in
BTreeEntry.java, which has one key and two child pointers. It also has a
recordId, which identifies the location of the key and child pointers on the
underlying page. We think working with one entry at a time is a natural way to
interact with internal pages, but it is important to keep in mind that the
underlying page does not actually store a list of entries, but stores ordered
lists of m keys and m+1 child pointers. Since the BTreeEntry is just an
interface and not an object actually stored on the page, updating the fields of
BTreeEntry will not modify the underlying page. In order to change the data
on the page, you need to call BTreeInternalPage.updateEntry(). Furthermore,
deleting an entry actually deletes only a key and a single child pointer, so we
provide the funtions BTreeInternalPage.deleteKeyAndLeftChild() and
BTreeInternalPage.deleteKeyAndRightChild() to make this explicit. The entry's
recordId is used to find the key and child pointer to be deleted. Inserting an
entry also only inserts a key and single child pointer (unless it's the first
entry), so BTreeInternalPage.insertEntry() checks that one of the child
pointers in the provided entry overlaps an existing child pointer on the page,
and that inserting the entry at that location will keep the keys in sorted
order.
In both splitLeafPage() and splitInternalPage(), you will need to update the
set of dirtypages with any newly created pages as well as any pages modified
due to new pointers or new data. This is where BTreeFile.getPage() will come
in handy. Each time you fetch a page, BTreeFile.getPage() will check to see
if the page is already stored in the local cache (dirtypages), and if it can't
find the requested page there, it fetches it from the buffer pool.
BTreeFile.getPage() also adds pages to the dirtypages cache if they are
fetched with read-write permission, since presumably they will soon be dirtied.
One advantage of this approach is that it prevents loss of updates if the same
pages are accessed multiple times during a single tuple insertion or deletion.
Note that in a major departure from HeapFile.insertTuple(),
BTreeFile.insertTuple() could return a large set of dirty pages, especially if
any internal pages are split. As you may remember from previous labs, the set of
dirty pages is returned to prevent the buffer pool from evicting dirty pages
before they have been flushed.
Warning: as the B+Tree is a complex data structure, it is helpful to understand the properties necessary of every legal B+Tree before modifying it. Here is an informal list:
- If a parent node points to a child node, the child nodes must point back to those same parents.
- If a leaf node points to a right sibling, then the right sibling points back to that leaf node as a left sibling.
- The first and last leaves must point to null left and right siblings respectively.
- Record Id's must match the page they are actually in.
- A
keyin a node with non-leaf children must be larger than any key in the left child, and smaller than any key in the right child. - A
keyin a node with leaf children must be larger or equal than any key in the left child, and smaller or equal than any key in the right child. - A node has either all non-leaf children, or all leaf children.
- A non-root node cannot be less than half full.
We have implemented a mechanized check for all these properties in the file
BTreeChecker.java. This method is also used to test your B+Tree implementation
in the systemtest/BTreeFileDeleteTest.java. Feel free to add calls to this
function to help debug your implementation, like we did in
BTreeFileDeleteTest.java.
N.B.
-
The checker method should always pass after initialization of the tree and before starting and after completing a full call to key insertion or deletion, but not necessarily within internal methods.
-
A tree may be well formed (and therefore pass
checkRep()) but still incorrect. For example, the empty tree will always passcheckRep(), but may not always be correct (if you just inserted a tuple, the tree should not be empty). ***
Exercise 2: Splitting Pages
Implement BTreeFile.splitLeafPage() and BTreeFile.splitInternalPage().
After completing this exercise, you should be able to pass the unit tests in
BTreeFileInsertTest.java. You should also be able to pass the system tests
in systemtest/BTreeFileInsertTest.java. Some of the system test cases may
take a few seconds to complete. These files will test that your code inserts
tuples and splits pages correcty, and also handles duplicate tuples.
In order to keep the tree balanced and not waste unnecessary space, deletions in a B+Tree may cause pages to redistribute tuples (Figure 3) or, eventually, to merge (see Figure 4). You may find it useful to review section 10.6 in the textbook.
Figure 3: Redistributing pages
Figure 4: Merging pages
In this exercise you will implement stealFromLeafPage(),
stealFromLeftInternalPage(), stealFromRightInternalPage(),
mergeLeafPages() and mergeInternalPages() in BTreeFile.java. In the first
three functions you will implement code to evenly redistribute tuples/entries if
the siblings have tuples/entries to spare. Remember to update the corresponding
key field in the parent (look carefully at how this is done in Figure 3 - keys
are effectively "rotated" through the parent). In
stealFromLeftInternalPage()/stealFromRightInternalPage(), you will also need
to update the parent pointers of the children that were moved. You should be
able to reuse the function updateParentPointers() for this purpose.
In mergeLeafPages() and mergeInternalPages() you will implement code to
merge pages, effectively performing the inverse of splitLeafPage() and
splitInternalPage(). You will find the function deleteParentEntry()
extremely useful for handling all the different recursive cases. Be sure to
call setEmptyPage() on deleted pages to make them available for reuse. As
with the previous exercises, we recommend using BTreeFile.getPage() to
encapsulate the process of fetching pages and keeping the list of dirty pages up
to date.
Exercise 3: Redistributing pages
Implement BTreeFile.stealFromLeafPage(),
BTreeFile.stealFromLeftInternalPage(),
BTreeFile.stealFromRightInternalPage().
After completing this exercise, you should be able to pass some of the unit
tests in BTreeFileDeleteTest.java (such as testStealFromLeftLeafPage and
testStealFromRightLeafPage). The system tests may take several seconds to
complete since they create a large B+ tree in order to fully test the system.
Exercise 4: Merging pages
Implement BTreeFile.mergeLeafPages() and BTreeFile.mergeInternalPages().
Now you should be able to pass all unit tests in BTreeFileDeleteTest.java
and the system tests in systemtest/BTreeFileDeleteTest.java.
You may remember that B+ trees can prevent phantom tuples from showing up
between two consecutive range scans by using next-key locking. Since SimpleDB
uses page-level, strict two-phase locking, protection against phantoms
effectively comes for free if the B+ tree is implemented correctly. Thus, at
this point you should also be able to pass BTreeNextKeyLockingTest.
Additionally, you should be able to pass the tests in
test/simpledb/BTreeDeadlockTest.java if you have implemented locking correctly
inside of your B+ tree code.
If everything is implemented correctly, you should also be able to pass the
BTreeTest system test. We expect many people to find BTreeTest difficult, so
it's not required, but we'll give extra credit to anyone who can run it
successfully. Please note that this test may take up to a minute to complete.
Bonus Exercise 5: (10% extra credit)
Create and implement a class called BTreeReverseScan which scans the
BTreeFile in reverse, given an optional IndexPredicate.
You can use BTreeScan as a starting point, but you will probably need to
implement a reverse iterator in BTreeFile. You will also likely need to
implement a separate version of BTreeFile.findLeafPage(). We have provided
reverse iterators on BTreeLeafPage and BTreeInternalPage which you may
find useful. You should also write code to test that your implementation
works correctly. BTreeScanTest.java is a good place to look for ideas.
You must submit your code (see below) as well as a short (1 page, maximum) writeup describing your approach. This writeup should:
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Describe any design decisions you made, including anything that was difficult or unexpected.
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Discuss and justify any changes you made outside of BTreeFile.java.
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How long did this lab take you? Do you have any suggestions for ways to improve it?
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Optional: If you did the extra credit exercise, explain your implementation and show us that you thoroughly tested it.
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 writeup.
We will be using gradescope to autograde all programming assignments. You should have all been invited to the class instance; if not, please let us know and we can help you set up. You may submit your code multiple times before the deadline; we will use the latest version as determined by gradescope. Place the write-up in a file called lab3-writeup.txt with your submission. You also need to explicitly add any other files you create, such as new *.java files.
The easiest way to submit to gradescope is with .zip files containing your code. On Linux/MacOS, you can do so by
running the following command:
$ zip -r submission.zip src/ lab5-writeup.txtSimpleDB 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 6.830-staff@mit.edu. When you do, please try to include:
- A description of the bug.
- A .java file we can drop in the
test/simpledbdirectory, compile, and run. - A .txt file with the data that reproduces the bug. We should be
able to convert it to a .dat file using
HeapFileEncoder.
You can also post on the class page on Piazza if you feel you have run into a bug.
75% 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, gradescope 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 also be careful changing our APIs. You should test that your code compiles the unmodified tests.
You should get immediate feedback and error outputs for failed tests (if any) from gradescope after submission. The score given will be your grade for the autograded portion of the assignment. An additional 25% of your grade will be based on the quality of your writeup and our subjective evaluation of your code. This part will also be published on gradescope after we finish grading your assignment.
We had a lot of fun designing this assignment, and we hope you enjoy hacking on it!