Assigned: Monday, Apr 5, 2021
Due: Thursday, Apr 22, 2021 11:59 PM ET
In this lab, you will implement a simple locking-based transaction system in SimpleDB. You will need to add lock and unlock calls at the appropriate places in your code, as well as code to track the locks held by each transaction and grant locks to transactions as they are needed.
The remainder of this document describes what is involved in adding transaction support and provides a basic outline of how you might add this support to your database.
As with the previous lab, we recommend that you start as early as possible. Locking and transactions can be quite tricky to debug!
You should begin with the code you submitted for Lab 3 (if you did not submit code for Lab 3, or your solution didn't work properly, contact us to discuss options). Additionally, we are providing 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 files to your release. The easiest way to do this is to change to your project directory (probably called simple-db-hw) and pull from the master GitHub repository:
$ cd simple-db-hw
$ git pull upstream master
Before starting, you should make sure you understand what a transaction is and how strict two-phase locking (which you will use to ensure isolation and atomicity of your transactions) works.
In the remainder of this section, we briefly overview these concepts and discuss how they relate to SimpleDB.
A transaction is a group of database actions (e.g., inserts, deletes, and reads) that are executed atomically; that is, either all of the actions complete or none of them do, and it is not apparent to an outside observer of the database that these actions were not completed as a part of a single, indivisible action.
To help you understand how transaction management works in SimpleDB, we briefly review how it ensures that the ACID properties are satisfied:
- Atomicity: Strict two-phase locking and careful buffer management ensure atomicity.
- Consistency: The database is transaction consistent by virtue of atomicity. Other consistency issues (e.g., key constraints) are not addressed in SimpleDB.
- Isolation: Strict two-phase locking provides isolation.
- Durability: A FORCE buffer management policy ensures durability (see Section 2.3 below).
To simplify your job, we recommend that you implement a NO STEAL/FORCE buffer management policy.
As we discussed in class, this means that:
- You shouldn't evict dirty (updated) pages from the buffer pool if they are locked by an uncommitted transaction (this is NO STEAL).
- On transaction commit, you should force dirty pages to disk (e.g., write the pages out) (this is FORCE).
To further simplify your life, you may assume that SimpleDB will not crash
while processing a transactionComplete command. Note that
these three points mean that you do not need to implement log-based
recovery in this lab, since you will never need to undo any work (you never evict
dirty pages) and you will never need to redo any work (you force
updates on commit and will not crash during commit processing).
You will need to add calls to SimpleDB (in BufferPool,
for example), that allow a caller to request or release a (shared or
exclusive) lock on a specific object on behalf of a specific
transaction.
We recommend locking at page granularity; please do not implement table-level locking (even though it is possible) for simplicity of testing. The rest of this document and our unit tests assume page-level locking.
You will need to create data structures that keep track of which locks each transaction holds and check to see if a lock should be granted to a transaction when it is requested.
You will need to implement shared and exclusive locks; recall that these work as follows:
- Before a transaction can read an object, it must have a shared lock on it.
- Before a transaction can write an object, it must have an exclusive lock on it.
- Multiple transactions can have a shared lock on an object.
- Only one transaction may have an exclusive lock on an object.
- If transaction t is the only transaction holding a shared lock on an object o, t may upgrade its lock on o to an exclusive lock.
If a transaction requests a lock that cannot be immediately granted, your code should block, waiting for that lock to become available (i.e., be released by another transaction running in a different thread). Be careful about race conditions in your lock implementation --- think about how concurrent invocations to your lock may affect the behavior. (you way wish to read about Synchronization in Java).
Exercise 1.
Write the methods that acquire and release locks in BufferPool. Assuming you are using page-level locking, you will need to complete the following:
- Modify getPage() to block and acquire the desired lock before returning a page.
- Implement unsafeReleasePage(). This method is primarily used for testing, and at the end of transactions.
- Implement holdsLock() so that logic in Exercise 2 can determine whether a page is already locked by a transaction.
You may find it helpful to define a LockManager class that is responsible for maintaining state about transactions and locks, but the design decision is up to you.
You may need to implement the next exercise before your code passes the unit tests in LockingTest.
You will need to implement strict two-phase locking. This means that transactions should acquire the appropriate type of lock on any object before accessing that object and shouldn't release any locks until after the transaction commits.
Fortunately, the SimpleDB design is such that it is possible to obtain locks on
pages in BufferPool.getPage() before you read or modify them.
So, rather than adding calls to locking routines in each of your operators,
we recommend acquiring locks in getPage(). Depending on your
implementation, it is possible that you may not have to acquire a lock
anywhere else. It is up to you to verify this!
You will need to acquire a shared lock on any page (or tuple)
before you read it, and you will need to acquire an exclusive
lock on any page (or tuple) before you write it. You will notice that
we are already passing around Permissions objects in the
BufferPool; these objects indicate the type of lock that the caller
would like to have on the object being accessed (we have given you the
code for the Permissions class.)
Note that your implementation of HeapFile.insertTuple()
and HeapFile.deleteTuple(), as well as the implementation
of the iterator returned by HeapFile.iterator() should
access pages using BufferPool.getPage(). Double check
that these different uses of getPage() pass the
correct permissions object (e.g., Permissions.READ_WRITE
or Permissions.READ_ONLY). You may also wish to double
check that your implementation of
BufferPool.insertTuple() and
BufferPool.deleteTupe() call markDirty() on
any of the pages they access (you should have done this when you
implemented this code in lab 2, but we did not test for this case.)
After you have acquired locks, you will need to think about when to release them as well. It is clear that you should release all locks associated with a transaction after it has committed or aborted to ensure strict 2PL. However, it is possible for there to be other scenarios in which releasing a lock before a transaction ends might be useful. For instance, you may release a shared lock on a page after scanning it to find empty slots (as described below).
Exercise 2.
Ensure that you acquire and release locks throughout SimpleDB. Some (but not necessarily all) actions that you should verify work properly:
- Reading tuples off of pages during a SeqScan (if you
implemented locking in
BufferPool.getPage(), this should work correctly as long as yourHeapFile.iterator()usesBufferPool.getPage().) - Inserting and deleting tuples through BufferPool and HeapFile
methods (if you
implemented locking in
BufferPool.getPage(), this should work correctly as long asHeapFile.insertTuple()andHeapFile.deleteTuple()useBufferPool.getPage().)
You will also want to think especially hard about acquiring and releasing locks in the following situations:
- Adding a new page to a
HeapFile. When do you physically write the page to disk? Are there race conditions with other transactions (on other threads) that might need special attention at the HeapFile level, regardless of page-level locking? - Looking for an empty slot into which you can insert tuples. Most implementations scan pages looking for an empty slot, and will need a READ_ONLY lock to do this. Surprisingly, however, if a transaction t finds no free slot on a page p, t may immediately release the lock on p. Although this apparently contradicts the rules of two-phase locking, it is ok because t did not use any data from the page, such that a concurrent transaction t' which updated p cannot possibly effect the answer or outcome of t.
At this point, your code should pass the unit tests in LockingTest.
Modifications from a transaction are written to disk only after it commits. This means we can abort a transaction by discarding the dirty pages and rereading them from disk. Thus, we must not evict dirty pages. This policy is called NO STEAL.
You will need to modify the evictPage method in BufferPool. In particular, it must never evict a dirty page. If your eviction policy prefers a dirty page for eviction, you will have to find a way to evict an alternative page. In the case where all pages in the buffer pool are dirty, you should throw a DbException. If your eviction policy evicts a clean page, be mindful of any locks transactions may already hold to the evicted page and handle them appropriately in your implementation.
Exercise 3.
Implement the necessary logic for page eviction without evicting dirty pages in the evictPage method in BufferPool.
In SimpleDB, a TransactionId object is created at the
beginning of each query. This object is passed to each of the operators
involved in the query. When the query is complete, the
BufferPool method transactionComplete is called.
Calling this method either commits or aborts the
transaction, specified by the parameter flag commit. At any point
during its execution, an operator may throw a
TransactionAbortedException exception, which indicates an
internal error or deadlock has occurred. The test cases we have provided
you with create the appropriate TransactionId objects, pass
them to your operators in the appropriate way, and invoke
transactionComplete when a query is finished. We have also
implemented TransactionId.
Exercise 4.
Implement the transactionComplete() method in
BufferPool. Note that there are two versions of
transactionComplete, one which accepts an additional boolean commit argument,
and one which does not. The version without the additional argument should
always commit and so can simply be implemented by calling transactionComplete(tid, true).
When you commit, you should flush dirty pages associated to the transaction to disk. When you abort, you should revert any changes made by the transaction by restoring the page to its on-disk state.
Whether the transaction commits or aborts, you should also release any state the
BufferPool keeps regarding
the transaction, including releasing any locks that the transaction held.
At this point, your code should pass the TransactionTest unit test and the
AbortEvictionTest system test. You may find the TransactionTest system test
illustrative, but it will likely fail until you complete the next exercise.
It is possible for transactions in SimpleDB to deadlock (if you do not
understand why, we recommend reading about deadlocks in Ramakrishnan & Gehrke).
You will need to detect this situation and throw a
TransactionAbortedException.
There are many possible ways to detect deadlock. A strawman example would be to implement a simple timeout policy that aborts a transaction if it has not completed after a given period of time. For a real solution, you may implement cycle-detection in a dependency graph data structure as shown in lecture. In this scheme, you would check for cycles in a dependency graph periodically or whenever you attempt to grant a new lock, and abort something if a cycle exists. After you have detected that a deadlock exists, you must decide how to improve the situation. Assume you have detected a deadlock while transaction t is waiting for a lock. If you're feeling homicidal, you might abort all transactions that t is waiting for; this may result in a large amount of work being undone, but you can guarantee that t will make progress. Alternately, you may decide to abort t to give other transactions a chance to make progress. This means that the end-user will have to retry transaction t.
Another approach is to use global orderings of transactions to avoid building the wait-for graph. This is sometimes preferred for performance reasons, but transactions that could have succeeded can be aborted by mistake under this scheme. Examples include the WAIT-DIE and WOUND-WAIT schemes.
Exercise 5.
Implement deadlock detection or prevention in src/simpledb/BufferPool.java. You have many
design decisions for your deadlock handling system, but it is not necessary to
do something highly sophisticated. We expect you to do better than a simple timeout on each
transaction. A good starting point will be to implement cycle-detection in a wait-for graph
before every lock request, and you will receive full credit for such an implementation.
Please describe your choices in the lab writeup and list the pros and cons of your choice
compared to the alternatives.
You should ensure that your code aborts transactions properly when a
deadlock occurs, by throwing a
TransactionAbortedException exception.
This exception will be caught by the code executing the transaction
(e.g., TransactionTest.java), which should call
transactionComplete() to cleanup after the transaction.
You are not expected to automatically restart a transaction which
fails due to a deadlock -- you can assume that higher level code
will take care of this.
We have provided some (not-so-unit) tests in
test/simpledb/DeadlockTest.java. They are actually a
bit involved, so they may take more than a few seconds to run (depending
on your policy). If they seem to hang indefinitely, then you probably
have an unresolved deadlock. These tests construct simple deadlock
situations that your code should be able to escape.
Note that there are two timing parameters near the top of
DeadLockTest.java; these determine the frequency at which
the test checks if locks have been acquired and the waiting time before
an aborted transaction is restarted. You may observe different
performance characteristics by tweaking these parameters if you use a
timeout-based detection method. The tests will output
TransactionAbortedExceptions corresponding to resolved
deadlocks to the console.
Your code should now should pass the TransactionTest system test (which
may also run for quite a long time depending on your implementation).
At this point, you should have a recoverable database, in the
sense that if the database system crashes (at a point other than
transactionComplete()) or if the user explicitly aborts a
transaction, the effects of any running transaction will not be visible
after the system restarts (or the transaction aborts.) You may wish to
verify this by running some transactions and explicitly killing the
database server.
During the course of this lab, we have identified some substantial design choices that you have to make:
- Locking granularity: page-level versus tuple-level
- Deadlock handling: detection vs. prevention, aborting yourself vs. others.
Bonus Exercise 6. (20% extra credit)
For one or more of these choices, implement both alternatives and experimentally compare their performance charateristics. Include your benchmarking code and a brief evaluation (possibly with graphs) in your writeup.
You have now completed this lab. Good work!
You must submit your code (see below) as well as a short (2 pages, maximum) writeup describing your approach. This writeup should:
- Describe any design decisions you made in deadlock handling, and list the pros and cons of your approach.
- Discuss and justify any changes you made to the API.
- Describe any missing or incomplete elements of your code.
- Describe how long you spent on the lab, and whether there was anything you found particularly difficult or confusing.
- Describe any extra credit implementation you have done.
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/ lab4-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.
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.
New:
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Given that this lab will require you to heavily modify your earlier code, regression testing passing is a prerequisite for grading tests. This means that if your submission fails a test from earlier labs, you will get a 0 for the autograder score until you fix them. If this is an issue for you, contact us to discuss options.
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Given that this lab deals with concurrency, we will rerun the autograder after the due date to discourage trying buggy code until lucky. It is your responsibility to ensure that your code reliably passes the tests.
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This lab has a higher percentage of manual grading at 50% compared to previous labs. Specifically, we will be very unhappy if your concurrency handling is bogus (e.g., inserting Thread.sleep(1000) until a race disappears).
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. An additional 50% 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!