Database Systems II Concurrency Control

Database Systems II Concurrency Control
The slides for this text are organized into chapters. This lecture covers Chapter 5. Chapter 1: Introduction to Database Systems Chapter 2: The Entity-Relationship Model Chapter 3: The Relational Model Chapter 4 (Part A): Relational Algebra Chapter 4 (Part B): Relational Calculus Chapter 5: SQL: Queries, Programming, Triggers Chapter 6: Query-by-Example (QBE) Chapter 7: Storing Data: Disks and Files Chapter 8: File Organizations and Indexing Chapter 9: Tree-Structured Indexing Chapter 10: Hash-Based Indexing Chapter 11: External Sorting Chapter 12 (Part A): Evaluation of Relational Operators Chapter 12 (Part B): Evaluation of Relational Operators: Other Techniques Chapter 13: Introduction to Query Optimization Chapter 14: A Typical Relational Optimizer Chapter 15: Schema Refinement and Normal Forms Chapter 16 (Part A): Physical Database Design Chapter 16 (Part B): Database Tuning Chapter 17: Security Chapter 18: Transaction Management Overview Chapter 19: Concurrency Control Chapter 20: Crash Recovery Chapter 21: Parallel and Distributed Databases Chapter 22: Internet Databases Chapter 23: Decision Support Chapter 24: Data Mining Chapter 25: Object-Database Systems Chapter 26: Spatial Data Management Chapter 27: Deductive Databases Chapter 28: Additional Topics

Introduction The consistency property requires that a transaction transforms a consistent DB state into another consistent DB state. The isolation property requires that concurrent transactions are executed as if they were executed in isolation. More specifically, concurrent transactions are executed in a way that is equivalent to executing the same transactions serially in some order.

Introduction A schedule is a sequence of actions of one or more transactions. The actions that we consider in this chapter are read and write operations in the buffer (not on disk). Need to ensure that schedules are serializable. At the same time, want to execute as many transactions as possible at the same time in order to maximize the throughput of the system and to minimize the response time.

Introduction Example T1: Read(A, t) T2: Read(A,s) t  t+100 s  s2
Write(A,t) Write(A,s) Read(B,t) Read(B,s) t  t s  s2 Write(B,t) Write(B,s) Constraint: A=B

Serial Schedules A schedule is serial, if actions of different transactions are not interleaved, otherwise it is non-serial. A serial schedule executes one transaction at a time. Serial schedules can be denoted by the sequence of their transactions: e.g., (T1,T2) or (T2,T1). For a serial schedule, isolation is trivially satisfied. But the throughput of the DBS is very low, and the response times are very high.

Serial Schedules Schedule A (serial) Constraint: A=B A B 25 25 125 250
25 25 125 250 T1 T2 Read(A,t); t  t+100 Write(A,t); Read(B,t); t  t+100; Write(B,t); Read(A,s); s  s2; Write(A,s); Read(B,s); s  s2; Write(B,s); Constraint: A=B

Serial Schedules Schedule B (serial) A B 25 25 50 150 150 150
25 25 50 150 T1 T2 Read(A,s); s  s2; Write(A,s); Read(B,s); s  s2; Write(B,s); Read(A,t); t  t+100 Write(A,t); Read(B,t); t  t+100; Write(B,t);  resulting DB state different from schedule A but both results satisfy A = B

Serializable Schedules
A schedule S is serializable, if there is a serial schedule S’ (of the same actions) such that - for every initial DB state, and - for every semantics of the transactions, the effects of S and S’ are the same. The order of transactions in the serial schedule is undefined (T1 before T2 or T2 before T1). A serializable schedule transforms a consistent DB state into another consistent DB state.

Schedule C (non-serial) A B 25 25 125 250 T1 T2 Read(A,t); t  t+100 Write(A,t); Read(A,s); s  s2; Write(A,s); Read(B,t); t  t+100; Write(B,t); Read(B,s); s  s2; Write(B,s); schedule equivalent to serial schedule (T1,T2) schedule is serializable

Schedule D (non-serial) A B 25 25 125 250 50 150 T1 T2 Read(A,t); t  t+100 Write(A,t); Read(A,s); s  s2; Write(A,s); Read(B,s); s  s2; Write(B,s); Read(B,t); t  t+100; Write(B,t); resulting DB state inconsistent with A = B schedule is not serializable

Schedule E (non-serial) A B 25 25 125 25 T1 T2’ Read(A,t); t  t+100 Write(A,t); Read(A,s); s  s1; Write(A,s); Read(B,s); s  s1; Write(B,s); Read(B,t); t  t+100; Write(B,t); same as schedule D, but changed semantics of T2 resulting DB state consistent with A = B

Semantics of a transaction: “function” to be computed, defined by the transaction code. In general, it is too hard to analyze the semantics of a transaction automatically. Therefore, the scheduler ignores the semantics of the transactions and considers only the sequence of read and write operations. We assume the worst case: if there is something that T can do to make the DB state inconsistent, then T will do that.

We adopt the following notations: rT(X): transaction T reads database element X, wT(X): transaction T writes database element X. We use r1(X) or w1(X) as shorthand for rT1(X) or wT1(X), resp. An action is of the form rT(X) or wT(X). A transaction Ti is a sequence of actions with subscript i.

A schedule S of a set of transactions Trans is a sequence of actions that contains all actions of all transactions T in Trans in the same order in which they appear in the definition of T. Example T1=r1(A) w1(A) r1(B) w1(B) T2=r2(A) w2(A) r2(B) w2(B) S = r1(A) w1(A) r2(A) w2(A) r1(B) w1(B) r2(B) w2(B)

Conflict-Serializability
Conflict-serializability is stronger than serializability, but easier to enforce. Most commercial DBMS enforce conflict-serializability. It is based on the notion of a conflict. A pair of consecutive actions in a schedule constitutes a conflict if swapping these actions may change the effect of at least one of the transactions involved.

Most pairs of actions do not cause a conflict. ri (X) and rj (Y) never cause a conflict, even if X = Y, since they do not modify the DB state. ri(X) and wj(Y) do not cause a conflict if . wi(X) and rj(Y) do not cause a conflict if . wi(X) and wj(Y) do not cause a conflict if

The following three situations do cause a conflict: Actions of the same transaction, i.e. i = j. Two writes of the same database element by different transactions, i.e. wi(X) and wj(X), Depending on the schedule, the results of either wi(X) or wj(X) survive, which may be different. A read and a write of the same database element by different transactions, i.e. ri(X) and wj(X), . ri(X) may read a different version of X.

Any two actions of different transactions may be swapped, unless they involve the same database element and at least one of them is a write. If there is a sequence of non-conflicting swaps that transforms schedule S into a serial schedule S’, then S is serializable. Schedules S1, S2 are conflict equivalent, if S1 can be transformed into S2 by a series of swaps on non-conflicting actions.

A schedule is conflict serializable if it is conflict equivalent to some serial schedule. Example S=r1(A)w1(A)r2(A)w2(A)r1(B)w1(B)r2(B)w2(B) is conflict equivalent to the serial schedule S’=r1(A) w1(A) r1(B)w1(B) r2(A) w2(A) r2(B) w2(B)  operations on critical DB elements are always first performed by T1, then by T2

If transactions Ti and Tj contain at least two pairs of conflicting actions, then for each of these pairs the action of Ti has to be performed before that of Tj (or always Tj before Ti). Given a schedule S, Ti takes precendence over Tj, denoted by Ti <S Tj, if there are actions Ai of Ti and Aj of Tj such that - Ai is ahead of Aj in S, - both Ai and Aj involve the same database element, and at least one of them is a write.

If Ti takes precendence over Tj, then a schedule S’ that is conflict equivalent to S must have Ai before Aj. Precedence graph: directed graph with nodes representing the transactions of S, i.e. node label i for transaction Ti, edges representing precedence relationships, i.e. edge from node i to j if Ti <S Tj. Notation: P(S)

Example S = w3(A) w2(C) r1(A) w1(B) r1(C) w2(A) r4(A) w4(D) P(S) based on A based on C

Lemma 1 S1, S2 conflict equivalent  P(S1) = P(S2) Proof Assume P(S1)  P(S2)   Ti, Tj: Ti  Tj in P(S1) and not in P(S2)  S1 = …pi(A)... qj(A)… pi, qj S2 = …qj(A)…pi(A)... in conflict  S1, S2 not conflict equivalent

Note P(S1)=P(S2)  S1, S2 conflict equivalent Counter example S1=w1(A) r2(A) w2(B) r1(B) S2=r2(A) w1(A) r1(B) w2(B) P(S1)=P(S2)= 1 2 S1 not conflict equivalent to S2, since w1(A) and r2(A) cannot be swapped

Theorem 2 P(S) acyclic  S conflict serializable Proof (i)  Assume S is conflict serializable.   S’: S’ is serial, S conflict equivalent to S’.  P(S’) = P(S) according to Lemma P(S’) is acyclic because S’ is serial.  P(S) is acyclic.

Proof (ii)  Assume P(S) is acyclic. Transform S as follows: (1) Take T1 to be transaction with no incoming edges. T1 exists, since P(S) is acyclic. (2) Move all T1 actions to the front: S = ……. qj(A)…….p1(A)….. This does not create any conflicts, since there is no Tj with Tj  T1. (3) We now have S’ = < T1 actions ><... rest ...>. (4) Repeat above steps to serialize rest. T1 T2 T3 T4 P(S)

How to enforce that only conflict-serializable schedules are executed? There are two alternative approaches: - pessimistic concurrency control Lock data elements to prevent P(S) cycles from occurring. - optimistic concurrency control Detect P(S) cycles and undo participating transactions, if necessary.

Enforcing Serializability by Locks
Before accessing a database element, a transaction requests a lock on that element in order to prevent other transactions from accessing the same database element at the “same” time. Typically, different types of locks are used for different types of access operations, but we first introduce a simplified lock protocol with only one type of lock.

We introduce two new actions: li (X): lock database element X ui (X): unlock database element X, i.e. release lock. A locking protocol must guarantee the consistency of transactions: - A transaction can only read or write database X element if it currently holds a lock on X. - A transaction must unlock all database elements that is has locked at some later time. A consistent transaction is also called well-formed.

A locking protocol must also guarantee the legality of schedules: At most one transaction can hold a lock on database element X at a given point of time. If there are actions li (X) followed by lj (X) in some schedule, then there must be an action ui(X) somewhere between these two actions.

Example S1 = l1(A)l1(B)r1(A)w1(B)l2(B)u1(A)u1(B) r2(B)w2(B)u2(B)l3(B)r3(B)u3(B)  S1 illegal, because T2 locks B before T1 has unlocked it S2 = l1(A)r1(A)w1(B)u1(A)u1(B) l2(B)r2(B)w2(B)l3(B)r3(B)u3(B)  T1 inconsistent, because T1 writes B before locking it S3 = l1(A)r1(A)u1(A)l1(B)w1(B)u1(B) l2(B)r2(B)w2(B)u2(B)l3(B)r3(B)u3(B)  schedule legal and all transactions consistent

Schedule F Schedule F is legal, but not serializable. A B T T l1(A);Read(A) A A+100;Write(A);u1(A) l2(A);Read(A) A Ax2;Write(A);u2(A) 250 l2(B);Read(B) B Bx2;Write(B);u2(B) 50 l1(B);Read(B) B B+100;Write(B);u1(B)

Two-Phase Locking A legal schedule of consistent transactions is not necessarily conflict-serializable. However, a legal schedule with the following locking protocol is conflict-serializable. Two-phase locking (2PL) In every transaction, all lock actions precede all unlock actions. Growing phase: acquire locks, no unlocks. Shrink phase: release locks, no locks.

Two-Phase Locking Example # locks held by Ti time Growing Shrinking
Phase Phase

Two-Phase Locking Schedule G Schedule G is serializable. T1 T2
l1(A);Read(A) A A+100;Write(A) l1(B); u1(A) l2(A);Read(A) A Ax2;Write(A);l2(B) Read(B);B B+100 Write(B); u1(B) l2(B); u2(A);Read(B) B Bx2;Write(B);u2(B); Schedule G is serializable. delayed changed order!

Two-Phase Locking In 2PL, each transaction may be thought of as executing all of its actions when issuing the first unlock action. Thus, the order according to the first unlock action defines a conflict-equivalent serial schedule. Theorem 3 (1) legality of schedule, and (2) consistency of transactions and (3) 2PL  conflict-serializability.

Two-Phase Locking Lemma 4 Ti  Tj in S  SH(Ti) <S SH(Tj) where Shrink(Ti) = SH(Ti) = first unlock action of Ti Proof Ti  Tj means that S = … pi(A) … qj(A) … and pi,qj conflict According to (1), (2): S = … pi(A) … ui(A) … lj(A) ... qj(A) … According to (3): Therefore, SH(Ti) <S SH(Tj). SH(Ti) SH(Tj)

Two-Phase Locking Proof of theorem 3
Given a schedule S. Assume P(S) has cycle T1  T2 …. Tn  T1 By lemma 4: SH(T1) < SH(T2) < ... < SH(T1). Contradiction, so P(S) acyclic. By theorem 2, S is conflict serializable. 2PL allows only serializable schedules.

Two-Phase Locking Not all serializable schedules are allowed by 2PL.
Example S1: w1(x) w3(x) w2(y) w1(y) The lock by T1 for y must occur after w2(y), so the unlock by T1 for x must also occur after w2(y) (according to 2PL). Because of the schedule legality, w3(x) cannot occur where shown in S1 because T1 holds the x lock at that point. However, S1 serializable (equivalent to T2, T1, T3).

Two-Phase Locking Deadlocks may happen under 2PL, when two or more transactions have got a lock and are waiting for another lock currently held by one of the other transactions. Example (T2 reversed) T1: Read(A, t) T2: Read(B,s) t  t s  s2 Write(A,t) Write(B,s) Read(B,t) Read(A,s) t  t s  s2 Write(B,t) Write(A,s)

Two-Phase Locking Possible schedule
Deadlock cannot be avoided, but can be detected (cycle in wait graph). At least one of the participating transactions needs to be aborted by the DBMS. T1 T2 l1(A); Read(A) l2(B);Read(B) A A+100;Write(A) B Bx2;Write(B) l1(B) l2(A) delayed, wait for T1 delayed, wait for T2

Two-Phase Locking So far, we have introduced the simplest possible 2PL protocol and showed that it works. There are many approaches for improving its performance, i.e. allowing a higher degree of concurrency: - shared locks, - increment locks, - multiple granularity locks, - tree-based locks.

Shared and Exclusive Locks
In principle, several transactions can read database element A at the same time, as long as none is allowed to write A. In order to enable more concurrency, we distinguish two different types of locks: shared (S) lock: there can be multiple shared locks on X, permission only to read A. exclusive (X) lock: there can be only one exclusive lock on A, permission to read and write A.

We introduce the following lock actions for database element A and transaction i: sl-i(A): lock A in S mode xl-i(A): lock A in X mode u-i(A): unlock whatever modes Ti has locked A Modify consistency of transactions as follows: A read action ri(A) must be preceded by sl-i(A) or xl-i(A) with no intervening ui(A). A write action ri(A) must be preceded by xl-i(A) with no intervening ui(A).

Typically, a transaction does not know its needs for locks in advance. What if transaction Ti reads and writes the same database element A? Ti will request both shared and exclusive locks on A at different times. Example Ti=... sl-1(A) … r1(A) ... xl-1(A) …w1(A) ...u(A)… If Ti knows lock needs, request X lock right away.

Modify legality of schedules as follows: If xl-i(A) appears in a schedule, then there cannot follow an xl-j(A) or sl-j(A), without an intervening ui(A). If sl-i(A) appears in a schedule, then an xl-j(A) cannot follow without an intervening ui(A). All other consistency and legality as well as the 2PL requirements remain unchanged. The proof of Theorem 3 still works.

A compatibility matrix is a convenient way to specify a locking protocol. Rows correspond to lock already held by another transaction, columns correspond to a lock being requested by current transaction. Lock requested S X Lock held S Yes No in mode X No No

If a transaction first reads A and later writes A, it has to upgrade its S lock to an X lock. Upgrading is a frequent source of deadlocks. T1 T2 sl-1(A) sl-2(A) r1(A) r2(A) xl-1(A) xl-2(A) w1(A)

Update Locks In order to avoid such deadlocks (as far as possible), we introduce another type of lock. An update lock ul-i(A) gives transaction i the privilege to - read database element A and to - upgrade its lock on A to an X lock. An update lock is not shared. Read locks cannot be upgraded.

Update Locks Compatibility matrix Lock requested S X U
Lock held S Yes No Yes in mode X No No No U No No No Example T1 T2 ul-1(A) ul-2(A) r1(A) xl-1(A) w1(A) U is not symmetric!

Locks With Multiple Granularity
Database elements can be tuples, blocks or entire relations. At which level of granularity shall we lock? There is a trade-off: the lower the level of granularity, the more concurrency, but the more locks and the higher the locking overhead. Best trade-off depends on application: e.g., lock blocks or tuples in bank database, and entire documents in document database.

Even within the same application, there may be a need for locks at multiple levels of granularity. Database elements are organized in a hierarchy: relations R1 blocks B1 B2 B3 B4 tuples t1 t2 t t4 t5 contained in

The warning protocol manages locks on a hierarchy of database elements. We introduce two new types of locks: IS: intention to request an S lock and IX: intention to request an X lock. An IS (IX) lock expresses the intention to request an S (X) lock for a subelement further down in the hierarchy.

To request an S (or X) lock on some database element A, we traverse a path from the root of the hierarchy to element A. If we have reached A, we request the S (X) lock. Otherwise, we request an IS (IX) lock. As soon as we have obtained the requested lock, we proceed to the corresponding child (if necessary).

Compatibility matrix Requester IS IX S X IS Yes Yes Yes No Holder IX Yes Yes No No S Yes No Yes No X No No No No If two transactions intend to read / write a subelement, we can grant both of them an I lock and resolve the potential conflict at a lower level.

An I lock for a superelement constrains the locks that the same transaction can obtain at a subelement. If Ti has locked the parent element P in IS, then Ti can lock child element C in IS, S. If Ti has locked the parent element P in IX, then Ti can lock child element C in IS, S, IX, X. P C

Example T2 wants to request an X lock on tuple t3 T1(IX) T2(IX) R1 B1 B4 T1(IX) B2 T2(IX) B3 t2 t3 t4 t5 T1(X) T2(X)

Example T2 wants to request an S lock on block B2 T1(IX) T2(IS) R1 B1 B4 T1(IX) B2 T2(S) not granted! B3 t2 t3 t4 t5 T1(X)

Optimistic Concurrency Control
Optimistic approaches to concurrency control assume that unserializable schedules are infrequent. Unlike in pessimistic approaches (locking), unserializable schedules are not prevented, but detected and some of the transactions aborted. The two main optimistic approaches are timestamping (not covered in class) and validation (next section).

Concurrency Control by Validation
We allow transactions to proceed without locking. All DB modifications are made on a local copy. At the appropriate time, we check whether the transaction schedule is serializable. If so, the modifications of the local copy are applied to the global DB. Otherwise, the local modifications are discarded, and the transaction is re-started.

For each transaction T, the scheduler maintains two sets of relevant database elements: RS(T), the read set of T: the set of all database elements read by T. WS(T), the write set of T: the set of all database elements written by T. This information is crucial to determine whether some schedule that has already been executed was indeed serializable.

Transaction T is executed in three phases: Read: transaction reads all elements in its read set from DB and is executes all its actions in its local address space. Validate: the serializability of the schedule is checked by comparing RS(T) and WS(T) to the read / write sets of the concurrent transactions. If validation is unsuccessful, skip phase 3. Write: write the new values of the elements in WS(T) back to the DB.

At any time, the scheduler maintains three sets of transactions and some relevant information. START: set of transactions that have started, but have not yet completed their validation phase. For each element T of START, keep START(T). VAL: set of transactions that have completed validation, but not yet their write phase. For elements T of VAL, record VAL(T). FIN: set of transactions that have completed all three phases. For T in FIN, keep FIN(T).

Make validation an atomic operation. If T1, T2, T3, … is validation order, then the resulting schedule will be conflict equivalent to serial schedule S = T1, T2, T3. Can think of each transaction that successfully validates as executing entirely at the moment that it validates.

Example It is possible that T1 wrote database element B after T2 has read it. Schedule is not conflict-equivalent to T1,T2. =  RS(T1)={B} RS(T2)={A,B} WS(T1)={B,D} WS(T2)={C}  T1 start T2 start T1 validated T2 validated T2 reads B T1 writes B time

Example New value of B written by T1 must have been written back to the DB before T2 has read B. Schedule is conflict-equivalent to T1, T2. RS(T1)={B} RS(T2)={A,B} WS(T1)={B,D} WS(T2)={C}  =  T1 start T2 start T1 validated T2 validated T1 writes B T2 start T2 reads B T1 finish phase 3 time

Example The new value of D written by T1 may be output to the DB later than the new value written by T2. Schedule is not conflict-equivalent to T1, T2. RS(T1)={A} RS(T2)={A,B} WS(T1)={D,E} WS(T2)={C,D}  =  T1 validated T2 validated time T2 output D T1 output D T1 finish phase 3

Example The new value of D written by T1 must be output to the DB earlier than the new value of D written by T2. Schedule is conflict-equivalent to T1, T2. RS(T1)={A} RS(T2)={A,B} WS(T1)={D,E} WS(T2)={C,D}  =  T1 validated T2 validated time T1 output D T2 output D T1 finish phase 3 T1 finish phase 3

The above examples motivate the following two validation rules for a given transaction T2. We consider all transactions T1 that have validated before T2. For all T1 with FIN(T1) > START(T2): For all T1 with FIN(T1) > VAL(T2): If T2 does successfully validate, if the two validation rules are satisfied for all these T1.

U: RS(U)={B} WS(U)={D} W: RS(W)={A,D} WS(W)={A,C} U validates successfully, since there are no other transactions that have validated before U. start validate finish T: RS(T)={A,B} WS(T)={A,C} V: RS(V)={B} WS(V)={D,E}

U: RS(U)={B} WS(U)={D} W: RS(W)={A,D} WS(W)={A,C} T validates successfully, since RS(T) and WS(T) have no intersection with WS(U). start validate finish T: RS(T)={A,B} WS(T)={A,C} V: RS(V)={B} WS(V)={D,E}

U: RS(U)={B} WS(U)={D} W: RS(W)={A,D} WS(W)={A,C} V validates successfully, since RS(V) has no intersection with WS(U) and FIN(U) < VAL(V) and neither RS(V) nor WS(V) have intersection with WS(T). start validate finish T: RS(T)={A,B} WS(T)={A,C} V: RS(V)={B} WS(V)={D,E}

U: RS(U)={B} WS(U)={D} W: RS(W)={A,D} WS(W)={A,C} W validates unsuccessfully, since RS(W) has intersection with WS(V) and FIN(V) > START(W). start validate finish T: RS(T)={A,B} WS(T)={A,C} V: RS(V)={B} WS(V)={D,E}

Concurrency Control Mechanisms
We conclude by comparing pessimistic and optimistic concurrency control mechanisms. Locking delays transactions, but avoids rollbacks. Validation does not delay transactions, but can cause a rollback (and re-start). Rollbacks may waste a lot of resources. If interactions between transactions are infrequent, then there will be few rollbacks, and validation will be more efficient.

Database Systems II Concurrency Control

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