Transactions and Concurrency Control Zachary G Ives University

Transactions and Concurrency Control Zachary G. Ives University of Pennsylvania CIS 550 – Database & Information Systems December 2, 2003 Slide content courtesy of Susan Davidson, Raghu Ramakrishnan & Johannes Gehrke

From Queries to Updates § We’ve spent a lot of time talking about querying data § Yet updates are a really major part of many DBMS applications § Particularly important: ensuring ACID properties 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!!! 2

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 3

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? 4

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. 5

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) 6

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) 7

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? 8

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 9

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; 10

Read-Only Transactions § When a transaction only reads information, we have more freedom to let the transaction execute in parallel with other transactions. § We signal this to the system by stating: SET TRANSACTION READ ONLY; SELECT * FROM Accounts WHERE account#=‘ 1234’; . . . 11

Read-Write Transactions § If we state “read-only”, then the transaction cannot perform any updates. ILLEGAL! SET TRANSACTION READ ONLY; UPDATE Accounts SET balance = balance - $100 WHERE account#= ‘ 1234’; . . . § Instead, we must specify that the transaction may update (the default): 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) § Phantom problem: a transaction retrieves a collection of tuples twice and sees different results 16

Phantom Problem Example § T 1: “find the students with best grades who Take either cis 550 -f 03 or cis 570 -f 02” § T 2: “insert new entries for student #1234 in the Takes relation, with grade A for cis 570 -f 02 and cis 550 -f 03” § Suppose that T 1 consults all students in the Takes relation and finds the best grades for cis 550 -f 03 § Then T 2 executes, inserting the new student at the end of the relation, perhaps on a page not seen by T 1 § T 1 then completes, finding the students with best grades for cis 570 -f 02 and now seeing student #1234 17

Isolation § The problems we’ve seen are all related to isolation § General rules of thumb w. r. t. isolation: § Fully serializable isolation is more expensive than “no isolation” We can’t do as many things concurrently (or we have to undo them frequently) § For performance, we generally want to specify the most relaxed isolation level that’s acceptable Note that we’re “slightly” violating a correctness constraint to get performance! 18

Specifying Acceptable Isolation Levels § The default isolation level is SERIALIZABLE (as for the transfer example). § To signal to the system that a dirty read is acceptable, SET TRANSACTION READ WRITE ISOLATION LEVEL READ UNCOMMITTED; § In addition, there are SET TRANSACTION ISOLATION LEVEL READ COMMITTED; SET TRANSACTION ISOLATION LEVEL REPEATABLE READ; 19

READ COMMITTED § Forbids the reading of dirty (uncommitted) data, but allows a transaction T to issue the same query several times and get different answers § No value written by T can be modified until T completes § For example, the Reservation example could also be READ COMMITTED; the transaction could repeatably poll to see if the seat was available, hoping for a cancellation 20

REPEATABLE READ § What it is NOT: a guarantee that the same query will get the same answer! § However, if a tuple is retrieved once it will be retrieved again if the query is repeated § For example, suppose Reservation were modified to retrieve all available seats § If a tuple were retrieved once, it would be retrieved again (but additional seats may also become available) 21

Implementing Isolation Levels § One approach – use locking at some level (tuple, page, table, etc. ): § 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! § Appropriate locks: § Before a read, a shared lock must be acquired § Before a write, an exclusive lock must be acquired 22

Lock Compatibility Matrix Locks on a data item are granted based on a lock compatibility matrix: Request mode { Shared Exclusive Mode of Data Item None Shared Exclusive Y Y N N When a transaction requests a lock, it must wait (block) until the lock is granted 23

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) 24

Lock Types and Read/Write Modes § When we specify “read-only”, the system only uses shared-mode locks § Any transaction that attempts to update will be illegal § When we specify “read-write”, the system may also acquire locks in exclusive mode § Obviously, we can still query in this mode 25

Isolation Levels and Locking § READ UNCOMMITTED allows queries in the transaction to read data without acquiring any lock § For updates, exclusive locks must be obtained and held to end of transaction § READ COMMITTED requires a read-lock to be obtained for all tuples touched by queries, but it releases the locks immediately after the read § Exclusive locks must be obtained for updates and held to end of transaction 26

Isolation levels and locking, cont. § REPEATABLE READ places shared locks on tuples retrieved by queries, holds them until the end of the transaction § Exclusive locks must be obtained for updates and held to end of transaction § SERIALIZABLE places shared locks on tuples retrieved by queries as well as the index, holds them until the end of the transaction § Exclusive locks must be obtained for updates and held to end of transaction § Holding locks to the end of a transaction is called “strict” 27

Summary of Isolation Levels Level Dirty Read Unrepeatable Read Phantoms READ UNCOMMITTED Maybe READ COMMITTED No Maybe REPEATABLE READ No No Maybe SERIALIZABLE No No No 28

Theory of. Serializability § A schedule of a set of transactions is a linear ordering of their actions § e. g. for the simultaneous deposits example: R 1(X. bal) R 2(X. bal) W 1(X. bal) W 2(X. bal) § A serial schedule is one in which all the steps of each transaction occur consecutively § A serializable schedule is one which is equivalent to some serial schedule (i. e. given any initial state, the final state is the same as one produced by some serial schedule) § The example above is neither serial nor serializable 29

Questions to Address § Given a schedule S, is it serializable? § How can we "restrict" transactions in progress to guarantee that only serializable schedules are produced? 30

Conflicting Actions § Consider a schedule S in which there are two consecutive actions Ii and Ij of transactions Ti and Tj respectively § If Ii and Ij refer to different data items, then swapping Ii and Ij does not matter § If Ii and Ij refer to the same data item Q, then swapping Ii and Ij matters if and only ifone of the actions is a write § Ri(Q) Wj(Q) produces a different final value for Q than Wj(Q) Ri(Q) 31

Testing for. Serializability § Given a schedule S, we can construct a di-graph G=(V, E) called a precedence graph § V : all transactions in S § E : Ti Tj whenever an action of Ti precedes and conflicts with an action of Tj in S § Theorem: A schedule S is conflict serializable if and only if its precedence graph contains no cycles § Note that testing for a cycle in a digraph can be done in time O(|V|2) 32

An Example T 1 T 2 T 3 R(X, Y, Z) R(X) W(X) T 1 R(Y) W(Y) T 2 T 3 Cyclic: Not serializable. R(Y) R(X) W(Z) 33

Another Example T 1 T 2 R(X) W(X) T 3 T 1 R(X) W(X) T 2 T 3 Acyclic: serializable R(Y) W(Y) 34

Producing the Equivalent Serial Schedule § If the precedence graph for a schedule is acyclic, then an equivalent serial schedule can be found by a topological sort of the graph § For the second example, the equivalent serial schedule is: § R 1(Y)W 1(Y)R 2(X)W 2(X) R 2(Y)W 2(Y) R 3(X)W 3(X) 35

Locking and. Serializability § We said that 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. 36

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 37

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. 38

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 39