Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-usewww.db-book.com Chapter 16 : Concurrency Control.

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1 Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-usewww.db-book.com Chapter 16 : Concurrency Control

2 ©Silberschatz, Korth and Sudarshan16.2Database System Concepts, 5 th Ed. Chapter 16: Concurrency Control Lock-Based Protocols Timestamp-Based Protocols Validation-Based Protocols Insert and Delete Operations

3 ©Silberschatz, Korth and Sudarshan16.3Database System Concepts, 5 th Ed. Lock-Based Protocols A lock is a mechanism to control concurrent access to a data item Data items can be locked in two modes : 1. exclusive (X) mode. Data item can be both read as well as written. X-lock is requested using lock-X instruction. 2. shared (S) mode. Data item can only be read. S-lock is requested using lock-S instruction. Lock requests are made to concurrency-control manager. Transaction can proceed only after request is granted.

4 ©Silberschatz, Korth and Sudarshan16.4Database System Concepts, 5 th Ed. Lock-Based Protocols (Cont.) Lock-compatibility matrix A transaction may be granted a lock on an item if the requested lock is compatible with locks already held on the item by other transactions Any number of transactions can hold shared locks on an item, but if any transaction holds an exclusive on the item no other transaction may hold any lock on the item. If a lock cannot be granted, the requesting transaction is made to wait till all incompatible locks held by other transactions have been released. The lock is then granted.

5 ©Silberschatz, Korth and Sudarshan16.5Database System Concepts, 5 th Ed. Lock-Based Protocols (Cont.) Example of a transaction performing locking: T 2 : lock-S(A); read (A); unlock(A); lock-S(B); read (B); unlock(B); display(A+B) Locking as above is not sufficient to guarantee serializability — if A and B get updated in-between the read of A and B, the displayed sum would be wrong. A locking protocol is a set of rules followed by all transactions while requesting and releasing locks. Locking protocols restrict the set of possible schedules.

6 ©Silberschatz, Korth and Sudarshan16.6Database System Concepts, 5 th Ed. Pitfalls of Lock-Based Protocols Consider the partial schedule Neither T 3 nor T 4 can make progress — executing lock-S(B) causes T 4 to wait for T 3 to release its lock on B, while executing lock-X(A) causes T 3 to wait for T 4 to release its lock on A. Such a situation is called a deadlock. To handle a deadlock one of T 3 or T 4 must be rolled back and its locks released.

7 ©Silberschatz, Korth and Sudarshan16.7Database System Concepts, 5 th Ed. 7 Um “mau” protocolo de Acesso aos Dados Uma transacção tem que adquirir um lock partilhado (de leitura) antes de tentar de ler o valor de um tuplo. Uma transacção tem que adquirir um lock exclusivo (de escrita) antes de tentar de alterar o valor de um tuplo. Os locks são libertados no COMMIT. Notas: Uma transacção que não consiga adquirir um lock terá que esperar até conseguir (diz-se que fica em wait-state).

8 ©Silberschatz, Korth and Sudarshan16.8Database System Concepts, 5 th Ed. 8 Controlo de Concorrência Este protocolo de acesso aos dados assegura que só se acede aos dados com os Locks respectivoas mas não resolve: O problema do DeadLock. O problema da serialização Esta sequência seria possível com o protocolo definido e não era serializável

9 ©Silberschatz, Korth and Sudarshan16.9Database System Concepts, 5 th Ed. Pitfalls of Lock-Based Protocols (Cont.) The potential for deadlock exists in most locking protocols. Deadlocks are a necessary evil. Starvation is also possible if concurrency control manager is badly designed. For example: A transaction may be waiting for an X-lock on an item, while a sequence of other transactions request and are granted an S-lock on the same item. The same transaction is repeatedly rolled back due to deadlocks. Concurrency control manager can be designed to prevent starvation.

10 ©Silberschatz, Korth and Sudarshan16.10Database System Concepts, 5 th Ed. The Two-Phase Locking Protocol This is a protocol which ensures conflict-serializable schedules. Phase 1: Growing Phase transaction may obtain locks transaction may not release locks Phase 2: Shrinking Phase transaction may release locks transaction may not obtain locks The protocol assures serializability. It can be proved that the transactions can be serialized in the order of their lock points (i.e. the point where a transaction acquired its final lock).

11 ©Silberschatz, Korth and Sudarshan16.11Database System Concepts, 5 th Ed. The Two-Phase Locking Protocol (Cont.) Two-phase locking does not ensure freedom from deadlocks Cascading roll-back is possible under two-phase locking. To avoid this, follow a modified protocol called strict two-phase locking. Here a transaction must hold all its exclusive locks till it commits/aborts. Rigorous two-phase locking is even stricter: here all locks are held till commit/abort. In this protocol transactions can be serialized in the order in which they commit.

12 ©Silberschatz, Korth and Sudarshan16.12Database System Concepts, 5 th Ed. 12 Two-Phase-Locking Aquisição de locks na aplicação Libertação de locks na aplicação Nr de locks possuídos pela transacção Tempo

13 ©Silberschatz, Korth and Sudarshan16.13Database System Concepts, 5 th Ed. 13 Strict Two-Phase-Locking Aquisição de locks na aplicação Libertação de locks na aplicação Nr de locks possuídos pela transacção Tempo Locks Exclusivos Locks Partilhados Ao assegurar os Cascadeless schedules, evita os aborts em cascata das transacções. Assegura o rollback das mesmas.

14 ©Silberschatz, Korth and Sudarshan16.14Database System Concepts, 5 th Ed. 14 Rigorous Two-Phase-Locking Aquisição de locks na aplicação Libertação de locks na aplicação Nr de locks possuídos pela transacção Tempo Locks Exclusivos Locks Partilhados Assegura que os schedules são equivalentes à execução sequencial de transacções pela ordem com que fazem commit.

15 ©Silberschatz, Korth and Sudarshan16.15Database System Concepts, 5 th Ed. 15 Como evitar os deadlocks ? Aquisição de locks na aplicação Libertação de locks na aplicação Nr de locks possuídos pela transacção Tempo Adquirindo todos os locks no início das transacções de forma atómica, evita-se os deadlocks.

16 ©Silberschatz, Korth and Sudarshan16.16Database System Concepts, 5 th Ed. Lock Conversions Two-phase locking with lock conversions: – First Phase: can acquire a lock-S on item can acquire a lock-X on item can convert a lock-S to a lock-X (upgrade) – Second Phase: can release a lock-S can release a lock-X can convert a lock-X to a lock-S (downgrade) This protocol assures serializability. But still relies on the programmer to insert the various locking instructions.

17 ©Silberschatz, Korth and Sudarshan16.17Database System Concepts, 5 th Ed. Graph-Based Protocols Graph-based protocols are an alternative to two-phase locking Impose a partial ordering  on the set D = {d 1, d 2,..., d h } of all data items. If d i  d j then any transaction accessing both d i and d j must access d i before accessing d j. Implies that the set D may now be viewed as a directed acyclic graph, called a database graph. The tree-protocol is a simple kind of graph protocol.

18 ©Silberschatz, Korth and Sudarshan16.18Database System Concepts, 5 th Ed. Tree Protocol Only exclusive locks are allowed. The first lock by T i may be on any data item. Subsequently, a data Q can be locked by T i only if the parent of Q is currently locked by T i. Data items may be unlocked at any time.

19 ©Silberschatz, Korth and Sudarshan16.19Database System Concepts, 5 th Ed. Graph-Based Protocols (Cont.) The tree protocol ensures conflict serializability as well as freedom from deadlock. Unlocking may occur earlier in the tree-locking protocol than in the two- phase locking protocol. shorter waiting times, and increase in concurrency protocol is deadlock-free, no rollbacks are required the abort of a transaction can still lead to cascading rollbacks. (this correction has to be made in the book also.) However, in the tree-locking protocol, a transaction may have to lock data items that it does not access. increased locking overhead, and additional waiting time potential decrease in concurrency Schedules not possible under two-phase locking are possible under tree protocol, and vice versa.

20 ©Silberschatz, Korth and Sudarshan16.20Database System Concepts, 5 th Ed. 20 Controlo de Concorrência Políticas de TimeStamp O TimeStamp é um política de controlo de concorrência alternativa ao Locking. Se a transação T1 iniciar a execução antes de T2, então o sistema deve garantir o resultado da serialização {T1;T2}. Ou seja: T1 não pode ler dados alterados por T2, e T1 não pode alterar dados que T2 já tenha lido. Funcionamento: Cada transacção recebe um timestamp no início Em cada acesso a um dado, compara-se o timestamp da última transacção que lhe acedeu com a que pretende aceder. Se existir um conflito, então a transacção que pretende aceder ao dado pode ser abortada e recomeçada com um novo timestamp.

21 ©Silberschatz, Korth and Sudarshan16.21Database System Concepts, 5 th Ed. Timestamp-Based Protocols Each transaction is issued a timestamp when it enters the system. If an old transaction T i has time-stamp TS(T i ), a new transaction T j is assigned time- stamp TS(T j ) such that TS(T i ) <TS(T j ). The protocol manages concurrent execution such that the time-stamps determine the serializability order. In order to assure such behavior, the protocol maintains for each data Q two timestamp values: W-timestamp(Q) is the largest time-stamp of any transaction that executed write(Q) successfully. R-timestamp(Q) is the largest time-stamp of any transaction that executed read(Q) successfully.

22 ©Silberschatz, Korth and Sudarshan16.22Database System Concepts, 5 th Ed. Timestamp-Based Protocols (Cont.) The timestamp ordering protocol ensures that any conflicting read and write operations are executed in timestamp order. Suppose a transaction T i issues a read(Q) 1. If TS(T i )  W-timestamp(Q), then T i needs to read a value of Q that was already overwritten. Hence, the read operation is rejected, and T i is rolled back. 2. If TS(T i )  W-timestamp(Q), then the read operation is executed, and R-timestamp(Q) is set to the maximum of R- timestamp(Q) and TS(T i ).

23 ©Silberschatz, Korth and Sudarshan16.23Database System Concepts, 5 th Ed. Timestamp-Based Protocols (Cont.) Suppose that transaction T i issues write(Q). If TS(T i ) < R-timestamp(Q), then the value of Q that T i is producing was needed previously, and the system assumed that that value would never be produced. Hence, the write operation is rejected, and T i is rolled back. If TS(T i ) < W-timestamp(Q), then T i is attempting to write an obsolete value of Q. Hence, this write operation is rejected, and T i is rolled back. Otherwise, the write operation is executed, and W-timestamp(Q) is set to TS(T i ).

24 ©Silberschatz, Korth and Sudarshan16.24Database System Concepts, 5 th Ed. Correctness of Timestamp-Ordering Protocol The timestamp-ordering protocol guarantees serializability since all the arcs in the precedence graph are of the form: Thus, there will be no cycles in the precedence graph Timestamp protocol ensures freedom from deadlock as no transaction ever waits. But the schedule may not be cascade-free, and may not even be recoverable. transaction with smaller timestamp transaction with larger timestamp

25 ©Silberschatz, Korth and Sudarshan16.25Database System Concepts, 5 th Ed. Example Use of the Protocol A partial schedule for several data items for transactions with timestamps 1, 2, 3, 4, 5 T1T1 T2T2 T3T3 T4T4 T5T5 read(Y) read(X) read(Y) write(Y) write(Z) read(Z) read(X) abort read(X) write(Z) abort write(Y) write(Z)

26 ©Silberschatz, Korth and Sudarshan16.26Database System Concepts, 5 th Ed. Recoverability and Cascade Freedom Problem with timestamp-ordering protocol: Suppose T i aborts, but T j has read a data item written by T i Then T j must abort; if T j had been allowed to commit earlier, the schedule is not recoverable. Further, any transaction that has read a data item written by T j must abort This can lead to cascading rollback --- that is, a chain of rollbacks Solution: A transaction is structured such that its writes are all performed at the end of its processing All writes of a transaction form an atomic action; no transaction may execute while a transaction is being written A transaction that aborts is restarted with a new timestamp

27 ©Silberschatz, Korth and Sudarshan16.27Database System Concepts, 5 th Ed. Thomas’ Write Rule Modified version of the timestamp-ordering protocol in which obsolete write operations may be ignored under certain circumstances. When T i attempts to write data item Q, if TS(T i ) < W-timestamp(Q), then T i is attempting to write an obsolete value of {Q}. Hence, rather than rolling back T i as the timestamp ordering protocol would have done, this {write} operation can be ignored. Otherwise this protocol is the same as the timestamp ordering protocol. Thomas' Write Rule allows greater potential concurrency. Unlike previous protocols, it allows some view-serializable schedules that are not conflict-serializable.

28 ©Silberschatz, Korth and Sudarshan16.28Database System Concepts, 5 th Ed. Schedule 4

29 ©Silberschatz, Korth and Sudarshan16.29Database System Concepts, 5 th Ed. 29 Controlo de Concorrência Políticas Pessimistas e Optimistas O controlo de concorrência que apresentámos considera que toda a entidade acedida por uma transacção irá ser também usada por outra transacção, sendo melhor fazer o lock para evitar o conflito. Quando é pouco provável que haja conflitos nos dados acedidos pelas transacções, pode-se adoptar políticas de controlo de concorrência mais relaxadas (optimistas): As transacções executam-se sem se sincronizarem nos locks. No COMMIT, verifica-se se os dados acedidos pela transacção foram também acedidos por uma outra. Faz-se ROLLBACK de uma ou de ambas as transacções, conforme necessário.

30 ©Silberschatz, Korth and Sudarshan16.30Database System Concepts, 5 th Ed. 30 Política Pessimista T2 não pode prosseguir enquanto T1 não tiver feito COMMIT. O sistema atrasa T2 até T1 ter feito COMMIT. T1 T2 X X+1 getLock(X) update X=X+1 getLock(X) wait COMMIT X+1 getLock(X) update X=2X X+1 2X+2

31 ©Silberschatz, Korth and Sudarshan16.31Database System Concepts, 5 th Ed. 31 Política Optimista T2 não pode prosseguir depois de T1 ter alterado X e feito COMMIT. O sistema aborta T2 após T1 ter feito COMMIT. T1 T2 X 2X X+1 getLock(X) update X=X+1 getLock(X) update X=2X COMMIT X+1 rollback T2

32 ©Silberschatz, Korth and Sudarshan16.32Database System Concepts, 5 th Ed. Validation-Based Protocol Execution of transaction T i is done in three phases. 1. Read and execution phase: Transaction T i writes only to temporary local variables 2. Validation phase: Transaction T i performs a ``validation test'' to determine if local variables can be written without violating serializability. 3. Write phase: If T i is validated, the updates are applied to the database; otherwise, T i is rolled back. The three phases of concurrently executing transactions can be interleaved, but each transaction must go through the three phases in that order. Also called as optimistic concurrency control since transaction executes fully in the hope that all will go well during validation

33 ©Silberschatz, Korth and Sudarshan16.33Database System Concepts, 5 th Ed. Validation-Based Protocol (Cont.) Each transaction Ti has 3 timestamps Start(Ti) : the time when Ti started its execution Validation(Ti): the time when Ti entered its validation phase Finish(Ti) : the time when Ti finished its write phase Serializability order is determined by timestamp given at validation time, to increase concurrency. Thus TS(Ti) is given the value of Validation(Ti). This protocol is useful and gives greater degree of concurrency if probability of conflicts is low. That is because the serializability order is not pre-decided and relatively less transactions will have to be rolled back.

34 ©Silberschatz, Korth and Sudarshan16.34Database System Concepts, 5 th Ed. Validation Test for Transaction T j If for all T i with TS (T i ) < TS (T j ) either one of the following condition holds: finish(T i ) < start(T j ) start(T j ) < finish(T i ) < validation(T j ) and the set of data items written by T i does not intersect with the set of data items read by T j. then validation succeeds and T j can be committed. Otherwise, validation fails and T j is aborted. Justification: Either first condition is satisfied, and there is no overlapped execution, or second condition is satisfied and 1. the writes of T j do not affect reads of T i since they occur after T i has finished its reads. 2. the writes of T i do not affect reads of T j since T j does not read any item written by T i.

35 ©Silberschatz, Korth and Sudarshan16.35Database System Concepts, 5 th Ed. Schedule Produced by Validation Example of schedule produced using validation T 14 T 15 read(B) B:- B-50 read(A) A:- A+50 read(A) (validate) display (A+B) (validate) write (B) write (A)

36 ©Silberschatz, Korth and Sudarshan16.36Database System Concepts, 5 th Ed. Insert and Delete Operations If two-phase locking is used : A delete operation may be performed only if the transaction deleting the tuple has an exclusive lock on the tuple to be deleted. A transaction that inserts a new tuple into the database is given an X-mode lock on the tuple Insertions and deletions can lead to the phantom phenomenon. A transaction that scans a relation (e.g., find all accounts in Perryridge) and a transaction that inserts a tuple in the relation (e.g., insert a new account at Perryridge) may conflict in spite of not accessing any tuple in common. If only tuple locks are used, non-serializable schedules can result: the scan transaction may not see the new account, yet may be serialized before the insert transaction.

37 ©Silberschatz, Korth and Sudarshan16.37Database System Concepts, 5 th Ed. Insert and Delete Operations If two-phase locking is used : A delete operation may be performed only if the transaction deleting the tuple has an exclusive lock on the tuple to be deleted. A transaction that inserts a new tuple into the database is given an X-mode lock on the tuple If timestamp-ordering protocol is used : Se TS(Ti) < R-timestamp(Q) e Ti faz delete(Q) então Ti é abortada pois estava a apagar Q tendo este sido lido por uma transacção mais recente que Ti. Se TS(Ti) < W-timestamp(Q) e Ti faz delete(Q) e Ti faz delete(Q) então Ti é abortada pois estava a apagar Q tendo este sido escrito por uma transacção mais recente que Ti. Se TS(Ti) < R-timestamp(Q) e Ti faz Insert (Q) então R- timestamp(Q) e W-timestamp(Q) é colocado a TS(Ti).

38 ©Silberschatz, Korth and Sudarshan16.38Database System Concepts, 5 th Ed. Insert and Delete Operations (Cont.) The transaction scanning the relation is reading information that indicates what tuples the relation contains, while a transaction inserting a tuple updates the same information. The information should be locked. One solution: Associate a data item with the relation, to represent the information about what tuples the relation contains. Transactions scanning the relation acquire a shared lock in the data item, Transactions inserting or deleting a tuple acquire an exclusive lock on the data item. (Note: locks on the data item do not conflict with locks on individual tuples.) Above protocol provides very low concurrency for insertions/deletions. Index locking protocols provide higher concurrency while preventing the phantom phenomenon, by requiring locks on certain index buckets.

39 ©Silberschatz, Korth and Sudarshan16.39Database System Concepts, 5 th Ed. Transaction Definition in SQL Data manipulation language must include a construct for specifying the set of actions that comprise a transaction. In SQL, a transaction begins implicitly. A transaction in SQL ends by: Commit work commits current transaction and begins a new one. Rollback work causes current transaction to abort. Levels of consistency specified by SQL-92: Serializable — default Repeatable read Read committed Read uncommitted

40 ©Silberschatz, Korth and Sudarshan16.40Database System Concepts, 5 th Ed. Levels of Consistency in SQL-92 Serializable — default Repeatable read — only committed records to be read, repeated reads of same record must return same value. However, a transaction may not be serializable – it may find some records inserted by a transaction but not find others. Read committed — only committed records can be read, but successive reads of record may return different (but committed) values. Read uncommitted — even uncommitted records may be read. Lower degrees of consistency useful for gathering approximate information about the database, e.g., statistics for query optimizer.

41 ©Silberschatz, Korth and Sudarshan16.41Database System Concepts, 5 th Ed. Tx Isolation Levels Interface Connection void setTransactionIsolation(int level) int getTransactionIsolation() Interface DatabaseMetaData boolean supportsTransactionIsolationLevel(int level) Level: TRANSACTION_NONE TRANSACTION_READ_UNCOMMITTED TRANSACTION_READ_COMMITTED TRANSACTION_REPEATABLE_READ TRANSACTION_SERIALIZABLE

42 ©Silberschatz, Korth and Sudarshan16.42Database System Concepts, 5 th Ed. Dirty Reads A dirty read happens when a transaction reads data that is being modified by another transaction that has not yet committed. Transaction A begins UPDATE employee SET salary = 31650 WHERE empno = '000090' Transaction B begins SELECT * FROM employee Transaction B sees data updated by transaction A. Those updates have not yet been committed.

43 ©Silberschatz, Korth and Sudarshan16.43Database System Concepts, 5 th Ed. Non-Repeatable Reads Non-repeatable reads happen when a query returns data that would be different if the query were repeated within the same transaction. Non- repeatable reads can occur when other transactions are modifying data that a transaction is reading. Transaction A begins SELECT * FROM employee WHERE empno = '000090' Transaction B begins UPDATE employee SET salary = 30100 WHERE empno = '000090' Transaction B updates rows viewed by transaction A before transaction A commits. If Transaction A issues the same SELECT statement, the results will be different.

44 ©Silberschatz, Korth and Sudarshan16.44Database System Concepts, 5 th Ed. Phantom Reads Records that appear in a set being read by another transaction. Phantom reads can occur when other transactions insert rows that would satisfy the WHERE clause of another transaction's statement. Transaction A begins SELECT * FROM employee WHERE salary > 30000 Transaction B begins INSERT INTO employee (empno, firstname, lastname, job, salary) VALUES ('000350', ‘Nick', ‘Green', ‘Counsel', 35000) Transaction B inserts a row that would satisfy the query in Transaction A if it were issued again.

45 ©Silberschatz, Korth and Sudarshan16.45Database System Concepts, 5 th Ed. Tx Isolation Levels  not possible  possible Dirty Reads Non-Repeatable Reads Phantom Reads READ UNCOMMITTED  READ COMMITTED  REPEATABLE READ   (row lock)  (table lock) SERIALIZABLE 