Transactions and WrapUp Zachary G Ives University of

Transactions and Wrap-Up Zachary G. Ives University of Pennsylvania CIS 550 – Database & Information Systems December 5, 2007 Some slide content derived from Ramakrishnan & Gehrke

Reminders Please be sure you’re signed up for a project demo § Due by 12/14: 5 -10 page report describing: What your project goals were What you implemented Basic architecture and design Division of labor § And the final code! § Also: please email me an assessment of your group; group members’ contributions; your contributions § Final examination will be available on Monday @ noon (unless otherwise announced) § Cheryl Hickey, Levine 502 § You’ll sign when you pick it up and return it 2

ACID Semantics § Atomicity: each operation looks atomic to the user § Consistency: each operation in isolation keeps the database in a consistent state (this is the responsibility of the user) § Isolation: should be able to understand what’s going on by considering each separate transaction independently § Durability: updates stay in the DBMS!!! § Achieved through transactional storage 3

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 4

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: update Accounts set balance = balance + $50 where account#= ‘ 1234’; § This reads and writes the account’s balance § What if two accountholders make deposits simultaneously from two ATMs? 5

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): Deposit 1 read(X. bal) X. bal : = X. bal + $50 write(X. bal) Deposit 2 read(X. bal) X. bal: = X. bal + $10 write(X. bal) where X is the data item representing the account with account# 1234. 6

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: BAD! Deposit 1 read(X. bal) Deposit 2 read(X. bal) time X. bal : = X. bal + $50 X. bal: = X. bal + $10 write(X. bal) 7

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 2 read(X. bal) GOOD! X. bal : = X. bal + $50 write(X. bal) time read(X. bal) X. bal: = X. bal + $10 write(X. bal) 8

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 ) Deposit 1 Deposit 3 read(X. bal) read(Y. bal) X. bal : = X. bal + $50 Y. bal: = Y. bal + $10 write(X. bal) write(Y. bal) Equivalent to executing Deposit 1 then 3, or vice versa § Why would we want to do this instead? 9

Atomicity Problems can also occur if a crash occurs in the middle of executing a transaction: CRASH Transfer read(X. bal) read(Y. bal) X. bal= X. bal-$100 Y. bal= Y. bal+$100 Need to guarantee that the write to X does not persist (ABORT) § Default assumption if a transaction doesn’t commit 10

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. Transfer UPDATE Accounts SET balance = balance - $100 WHERE account#= ‘ 1234’; UPDATE Accounts SET balance = balance + $100 WHERE account#= ‘ 5678’; COMMIT; 11

Read-Only vs. Read-Write Transactions § We can tell the DBMS that we won’t be performing any updates: SET TRANSACTION READ ONLY; SELECT * FROM Accounts WHERE account#=‘ 1234’; § If we are going to modify the DBMS, we need: SET TRANSACTION READ WRITE; UPDATE Accounts SET balance = balance - $100 WHERE account#= ‘ 1234’; . . . 12

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! 13

“Bad” Dirty Read EXEC 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! 14

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=‘ 23 f’; if (!occ) {EXEC SQL update Flights set occupied=true where Num= ‘ 123’ and date=11 -03 -99 and seat=‘ 23 f’; } else {notify user that seat is unavailable} 15

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 16

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 WRITE ISOLATION LEVEL READ UNCOMMITTED; 17

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 18

Locks Prevent “Bad” Execution If the system used locking, the first “bad” execution could have been avoided: Deposit 1 Deposit 2 xlock(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) 19

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. 20

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 21

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 22

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 23

What to Look for Down the Road Well, no one reallyknows the answer to this… But here are some current directions: § Sensors, networks, 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 § … We have lots of research projects at Penn relating to these and other topics 24

A Plug for Next Year CIS 455/555 (Spring): Internet and Web Systems § Focus: building and interconnecting scalable Web servers and services; information retrieval; integration § Emphasis on implementation, programming-in-the-large, experimentation – need substantial coding experience § Consider it 1. 5 CU – but you will get out of the course what you put into it CIS 650 (Fall): Building Data Management Systems 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 was enjoyable! 25