transactions and wrap-up zachary g. ives university of pennsylvania cis 550 – database &...

Transactions and Wrap-Up

Zachary G. IvesUniversity of Pennsylvania

CIS 550 – Database & Information Systems

December 8, 2005

Some slide content derived from Ramakrishnan & Gehrke

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Reminders

Please be sure you’re signed up for a project demo Due at that time: 5-10 page report describing:

What your project goals were What you implemented Basic architecture and design Division of labor

And the code! Also: please email me an assessment of how well

your group worked; group members’ contributions; your contributions

Final examination will be available on Tuesday

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What is a Transaction?

A transaction is a sequence of read and write operations on data items that logically functions as one unit of work: should either be done entirely or not at all if it succeeds, the effects of write operations

persist (commit); if it fails, no effects of write operations persist (abort)

these guarantees are made despite concurrent activity in the system, and despite failures that may occur

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How Things Can Go Awry

Suppose we have a table of bank accounts which contains the balance of the account

An ATM deposit of $50 to account # 1234 would be written as:

This reads and writes the account’s balance What if two accountholders make deposits

simultaneously from two ATMs?

update Accountsset balance = balance + $50where account#= ‘1234’;

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Concurrent Deposits

This SQL update code is represented as a sequence of read and write operations on “data items” (which for now should be thought of as individual accounts):

where X is the data item representing the account with account# 1234.

Deposit 1 Deposit 2read(X.bal) read(X.bal)X.bal := X.bal + $50 X.bal:= X.bal + $10write(X.bal) write(X.bal)

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A “Bad” Concurrent Execution

Only one “action” (e.g. a read or a write) can actually happen at a time, and we can interleave deposit operations in many ways:

Deposit 1 Deposit 2read(X.bal) read(X.bal)X.bal := X.bal + $50 X.bal:= X.bal + $10write(X.bal) write(X.bal)

time

BAD!

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A “Good” Execution

Previous execution would have been fine if the accounts were different (i.e. one were X and one were Y), i.e., transactions were independent

The following execution is a serial execution, and executes one transaction after the other:

Deposit 1 Deposit 2read(X.bal) X.bal := X.bal + $50 write(X.bal) read(X.bal) X.bal:= X.bal + $10 write(X.bal)

time

GOOD!

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Good Executions

An execution is “good” if it is serial (transactions are executed atomically and consecutively) or serializable (i.e. equivalent to some serial execution)

Equivalent to executing Deposit 1 then 3, or vice versa Why would we want to do this instead?

Deposit 1 Deposit 3read(X.bal) read(Y.bal)X.bal := X.bal + $50 Y.bal:= Y.bal + $10write(X.bal) write(Y.bal)

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Atomicity

Problems can also occur if a crash occurs in the middle of executing a transaction:

Need to guarantee that the write to X does not persist (ABORT)

Default assumption if a transaction doesn’t commit

Transferread(X.bal)read(Y.bal)X.bal= X.bal-$100

Y.bal= Y.bal+$100

CRASH

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Transactions in SQL

A transaction begins when any SQL statement that queries the db begins.

To end a transaction, the user issues a COMMIT or ROLLBACK statement.

TransferUPDATE Accounts SET balance = balance - $100 WHERE account#= ‘1234’;UPDATE Accounts SET balance = balance + $100 WHERE account#= ‘5678’;COMMIT;

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Read-Only vs. Read-Write Transactions

We can tell the DBMS that we won’t be performing any updates:

If we are going to modify the DBMS, we need:

SET TRANSACTION READ ONLY; SELECT * FROM Accounts WHERE account#=‘1234’;

SET TRANSACTION READ WRITE; UPDATE AccountsSET balance = balance - $100WHERE account#= ‘1234’; ...

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Dirty Reads

Dirty data is data written by an uncommitted transaction; a dirty read is a read of dirty data (WR conflict)

Sometimes we can tolerate dirty reads; other times we cannot:

e.g., if we wished to ensure balances never went negative in the transfer example, we should test that there is enough money first!

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“Bad” Dirty ReadEXEC SQL select balance into :bal from Accounts where account#=‘1234’;if (bal > 100) {

EXEC SQL update Accounts set balance = balance - $100 where account#= ‘1234’;

EXEC SQL update Accounts set balance = balance + $100 where account#= ‘5678’;}EXEC SQL COMMIT;

If the initial read (italics) were dirty, the balance could become negative!

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Acceptable Dirty Read

If we are just checking availability of an airline seat, a dirty read might be fine! (Why is that?) Reservation transaction:EXEC SQL select occupied into :occ

from Flights where Num= ‘123’ and date=11-03-99 and seat=‘23f’;if (!occ) {EXEC SQL update Flights set occupied=true where Num= ‘123’ and date=11-03-99 and seat=‘23f’;}else {notify user that seat is unavailable}

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Other Undesirable Phenomena

Unrepeatable read: a transaction reads the same data item twice and gets different values (RW conflict) Why? Someone changed the tuple

Phantom problem: a transaction retrieves a collection of tuples twice and sees different results Why? Someone added or removed a tuple

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Isolation

The problems we’ve seen are all related to isolation

General rules of thumb w.r.t. isolation: Fully serializable isolation is expensive

We can’t do as many things concurrently (or we have to undo them frequently)

For performance, the DBMS lets you relax the isolation level if your application can tolerate it, e.g:

SET TRANSACTION READ WRITEISOLATION LEVEL READ UNCOMMITTED;

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Implementing Isolation Levels

One approach – use locking at some level: each data item is either locked (in some mode, e.g.

shared or exclusive) or is available (no lock) an action on a data item can be executed if the

transaction holds an appropriate lock consider granularity of locks – how big of an item to lock

Larger granularity = fewer locking operations but more contention!

tuple, page, table, etc.

Appropriate locks: Before a read, a shared lock must be acquired Before a write, an exclusive lock must be acquired

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Locks Prevent “Bad” ExecutionIf the system used locking, the first “bad” execution could have been avoided:

Deposit 1 Deposit 2xlock(X)read(X.bal) {xlock(X) is not granted}X.bal := X.bal + $50 write(X.bal) release(X) xlock(X) read(X.bal) X.bal:= X.bal + $10 write(X.bal) release(X)

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Locking and Serializability

A transaction must hold all locks until it terminates (a condition called strict locking)

It turns out that this is crucial to guarantee serializability Note that the first (bad) example could have

been produced if transactions acquired and immediately released locks.

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Well-Formed, Two-Phased Transactions

A transaction is well-formed if it acquires at least a shared lock on Q before reading Q or an exclusive lock on Q before writing Q and doesn’t release the lock until the action is performed Locks are also released by the end of the transaction

A transaction is two-phased if it never acquires a lock after unlocking one i.e., there are two phases: a growing phase in which

the transaction acquires locks, and a shrinking phase in which locks are released

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Two-Phased Locking Theorem

If all transactions are well-formed and two-phase, then any schedule in which conflicting locks are never granted ensures serializability i.e., there is a very simple scheduler!

However, if some transaction is not well-formed or two-phase, then there is some schedule in which conflicting locks are never granted but which fails to be serializable i.e., one bad apple spoils the bunch

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Summary

Transactions are all-or-nothing units of work guaranteed despite concurrency or failures in the system

Theoretically, the “correct” execution of transactions is serializable (i.e. equivalent to some serial execution)

Practically, this may adversely affect throughput isolation levels

With isolation levels, users can specify the level of “incorrectness” they are willing to tolerate

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What to Look for Down the Road

Well, no one really knows the answer to this…

But here are some current directions: Sensors and streaming data Peer-to-peer meets databases and data

integration “The Semantic Web”

Security and privacy – especially as integration becomes more commonplace

Uncertainty and ranked retrieval

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A Plug for Next Semester

CSE 455/CIS 555: Internet and Web Systems Focus: building and interconnecting scalable Web

servers and services; information retrieval; integration

Heavy emphasis on implementation, experimentation – need substantial coding experience

Meanwhile… Best of luck on your projects and exams – and have a wonderful break! I hope you learned a lot in this course and that it – at

least for stretches – was enjoyable!