Transaction Management

Transcription

1 Instructional Objectives Upon completion of this Unit, students will be introduced to the following About Transaction Processing Transaction and System Concepts Desirable Properties of Transactions Schedules and Recoverability Serializability of Schedules Transaction Support in SQL Concurrency Control Techniques Locking Mechanisms and Timestamping How there exist relationship to transactions How to get stable storage What are Shadow pages Basic Overview of Logs Database Backup Techniques What it Recovery and when we need it How to get stable storage What are Shadow pages Basic Overview of Logs Able to answer the problems posted below: How Transaction processes take place in DBMS? Explain atomicity in Transaction with example. What are scheduling in Transactions? What are the properties of Transaction? Describe each property briefly. How Concurrency take place in DBMS? What is lock management? What is locking techniques in concurrency control? What are the different types of locks? How two-phase locking is possible? Explain Dependency Graph? According to the theorem when the schedule is conflict serializable? What are the properties of Index Locking and Predicate Locking? What is timestamping in Concurrency Control? What is Deadlock? How it is detected and recovered? What is crash recovery? How is it made possible? Explain Write-Ahead Logging. Briefly explain ARIES recovery algorithm Explain Shadow pages What is Write-ahead Logging? What are Logs? Transaction Management Transaction Concept 1 A transaction is a unit of program execution that accesses and possibly updates various data items. 2 A transaction must see a consistent database. 1

2 3 During transaction execution the database may be inconsistent. 4 When the transaction is committed, the database must be consistent. 5 Two main issues to deal with: Failures of various kinds, such as hardware failures and system crashes Concurrent execution of multiple transactions ACID Properties To preserve integrity of data, the database system must ensure: 1 Atomicity. Either all operations of the transaction are properly reflected in the database or none are. 2 Consistency. Execution of a transaction in isolation preserves the consistency of the database. 3 Isolation. Although multiple transactions may execute concurrently, each transaction must be unaware of other concurrently executing transactions. Intermediate transaction results must be hidden from other concurrently executed transactions. That is, for every pair of transactions Ti and Tj, it appears to Ti that either Tj, finished execution before Ti started, or Tj started execution after Ti finished. 4 Durability. After a transaction completes successfully, the changes it has made to the database persist, even if there are system failures. 1 Example of Fund Transfer Transaction to transfer $50 from account A to account B: 1. read(a) 2. A := A write(a) 4. read(b) 5. B := B write(b) 1 Consistency requirement the sum of A and B is unchanged by the execution of the transaction. 2 Atomicity requirement if the transaction fails after step 3 and before step 6, the system should ensure that its updates are not reflected in the database, else an inconsistency will result. 3 Durability requirement once the user has been notified that the transaction has completed (i.e., the transfer of the $50 has taken place), the updates to the database by the transaction must persist despite failures. 4 Isolation requirement if between steps 3 and 6, another transaction is allowed to access the partially updated database, it will see an inconsistent database (the sum A + B will be less than it should be). Can be ensured trivially by running transactions serially, that is one after the other. However, executing multiple transactions concurrently has significant benefits, as we will see. 5 Transaction State Active, the initial state; the transaction stays in this state while it is executing 2

3 6 Partially committed, after the final statement has been executed. 7 Failed, after the discovery that normal execution can no longer proceed. 8 Aborted, after the transaction has been rolled back and the database restored to its state prior to the start of the transaction. Two options after it has been aborted: o restart the transaction only if no internal logical error o kill the transaction 9 Committed, after successful completion. Implementation of Atomicity and Durability 1 The recovery-management component of a database system implements the support for atomicity and durability. 1 The shadow-database scheme: assume that only one transaction is active at a time. a pointer called db_pointer always points to the current consistent copy of the database. all updates are made on a shadow copy of the database, and db_pointer is made to point to the updated shadow copy only after the transaction reaches partial commit and all updated pages have been flushed to disk. in case transaction fails, old consistent copy pointed to by db_pointer can be used, and the shadow copy can be deleted. 3

4 The shadow-database scheme: 1 Assumes disks to not fail 2 Useful for text editors, but extremely inefficient for large databases: executing a single transaction requires copying the entire database. Concurrent Executions 1 Multiple transactions are allowed to run concurrently in the system. Advantages are: increased processor and disk utilization, leading to better transaction throughput: one transaction can be using the CPU while another is reading from or writing to the disk reduced average response time for transactions: short transactions need not wait behind long ones. 2 Concurrency control schemes mechanisms to achieve isolation, i.e., to control the interaction among the concurrent transactions in order to prevent them from destroying the consistency of the database Schedules 1 Schedules sequences that indicate the chronological order in which instructions of concurrent transactions are executed a schedule for a set of transactions must consist of all instructions of those transactions must preserve the order in which the instructions appear in each individual transaction. 2 Example Schedules 3 Let T1 transfer $50 from A to B, and T2 transfer 10% of the balance from A to B. The following is a serial schedule (Schedule 1 in the text), in which T1 is followed by T2. 4

5 1 The following concurrent schedule (Schedule 4 in the text) does not preserve the value of the the sum A + B. Serializability 1 Basic Assumption Each transaction preserves database consistency. 2 Thus serial execution of a set of transactions preserves database consistency. 3 A (possibly concurrent) schedule is serializable if it is equivalent to a serial schedule. Different forms of schedule equivalence give rise to the notions of: 1. conflict serializability 2. view serializability 1 We ignore operations other than read and write instructions, and we assume that transactions may perform arbitrary computations on data in local buffers in between reads and writes. Our simplified schedules consist of only read and write instructions. Conflict Serializability 1 Instructions li and lj of transactions Ti and Tj respectively, conflict if and only if there exists some item Q accessed by both li and lj, and at least one of these instructions wrote Q. 5

6 1. li = read(q), lj = read(q). li and lj don t conflict. 2. li = read(q), lj = write(q). They conflict. 3. li = write(q), lj = read(q). They conflict 4. li = write(q), lj = write(q). They conflict 1 Intuitively, a conflict between li and lj forces a (logical) temporal order between them. If li and lj are consecutive in a schedule and they do not conflict, their results would remain the same even if they had been interchanged in the schedule. 2 If a schedule S can be transformed into a schedule S by a series of swaps of nonconflicting instructions, we say that S and S are conflict equivalent. 3 We say that a schedule S is conflict serializable if it is conflict equivalent to a serial schedule 4 Example of a schedule that is not conflict serializable: 5 T3 T4 read(q) write(q) write(q) We are unable to swap instructions in the above schedule to obtain either the serial schedule < T3, T4 >, or the serial schedule < T4, T3 >. 1 Schedule 3 below can be transformed into Schedule 1, a serial schedule where T2 follows T1, by series of swaps of non-conflicting instructions. Therefore Schedule 3 is conflict serializable. View Serializability 1 Let S and S be two schedules with the same set of transactions. S and S are view equivalent if the following three conditions are met: 1. For each data item Q, if transaction Ti reads the initial value of Q in schedule S, then transaction Ti must, in schedule S, also read the initial value of Q. 2. For each data item Q if transaction Ti executes read(q) in schedule S, and that value was produced by transaction Tj (if any), then transaction Ti must in schedule S also read the value of Q that was produced by transaction Tj. 3. For each data item Q, the transaction (if any) that performs the final write(q) 6

7 operation in schedule S must perform the final write(q) operation in schedule S. As can be seen, view equivalence is also based purely on reads and writes alone. 1 A schedule S is view serializable it is view equivalent to a serial schedule. 2 Every conflict serializable schedule is also view serializable. 3 Schedule 9 (from text) a schedule which is view-serializable but not conflict serializable. 1 Every view serializable schedule that is not conflict serializable has blind writes. Other Notions of Serializability 1 Schedule 8 (from text) given below produces same outcome as the serial schedule < T1, T5 >, yet is not conflict equivalent or view equivalent to it. 1 Determining such equivalence requires analysis of operations other than read and write. Recoverability Need to address the effect of transaction failures on concurrently running ransactions. 7

8 1 Recoverable schedule if a transaction Tj reads a data items previously written by a transaction Ti, the commit operation of Ti appears before the commit operation of Tj. 2 The following schedule (Schedule 11) is not recoverable if T9 commits immediately after the read 1 If T8 should abort, T9 would have read (and possibly shown to the user) an inconsistent database state. Hence database must ensure that schedules are recoverable. Cascading rollback a single transaction failure leads to a series of transaction rollbacks. Consider the following schedule where none of the transactions has yet committed (so the schedule is recoverable) 1 If T10 fails, T11 and T12 must also be rolled back. 2 Can lead to the undoing of a significant amount of work 3 Cascadeless schedules cascading rollbacks cannot occur; for each pair of transactions Ti and Tj such that Tj reads a data item previously written by Ti, the commit operation of Ti appears before the read operation of Tj. 4 Every cascadeless schedule is also recoverable 5 It is desirable to restrict the schedules to those that are cascadeless Implementation of Isolation 1 Schedules must be conflict or view serializable, and recoverable, for the sake of database consistency, and preferably cascadeless. 2 A policy in which only one transaction can execute at a time generates serial schedules, but provides a poor degree of concurrency.. 3 Concurrency-control schemes tradeoff between the amount of concurrency they allow and the amount of overhead that they incur. 4 Some schemes allow only conflict-serializable schedules to be generated, while 8

9 others allow view-serializable schedules that are not conflict-serializable. Transaction Definition in SQL 1 Data manipulation language must include a construct for specifying the set of actions that comprise a transaction. 2 In SQL, a transaction begins implicitly. 3 A transaction in SQL ends by: Commit work commits current transaction and begins a new one. Rollback work causes current transaction to abort. 4 Levels of consistency specified by SQL-92: Serializable default Repeatable read Read committed Read uncommitted Levels of Consistency in SQL-92 1 Serializable default 2 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. 3 Read committed only committed records can be read, but successive reads of record may return different (but committed) values. 4 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. Testing for Serializability 1 Consider some schedule of a set of transactions T1, T2,..., Tn 2 Precedence graph a direct graph where the vertices are the transactions (names). 3 We draw an arc from Ti to Tj if the two transaction conflict, and Ti accessed the data item on which the conflict arose earlier. 4 We may label the arc by the item that was accessed. 1 Example 1 Example Schedule (Schedule A) T1 T2 T3 T4 T5 read(x) read(y) 9

10 read(z) read(u) read(u) write(u) read(y) write(y) write(z) read(y) write(y) read(z) write(z) read(v) read(w) read(w) Precedence Graph for Schedule A T 1 T 2 T 3 T 4 Test for Conflict Serializability 1 A schedule is conflict serializable if and only if its precedence graph is acyclic. 2 Cycle-detection algorithms exist which take order n2 time, where n is the number of 10

11 vertices in the graph. (Better algorithms take order n + e where e is the number of edges.) 3 If precedence graph is acyclic, the serializability order can be obtained by a topological sorting of the graph. This is a linear order consistent with the partial order of the graph. For example, a serializability order for Schedule A would be T5 T1 T3 T2 T4. Test for View Serializability 1 The precedence graph test for conflict serializability must be modified to apply to a test for view serializability. 2 The problem of checking if a schedule is view serializable falls in the class of NPcomplete problems. Thus existence of an efficient algorithm is unlikely. However practical algorithms that just check some sufficient conditions for view serializability can still be used. Concurrency Control vs. Serializability Tests 1 Testing a schedule for serializability after it has executed is a little too late! 2 Goal to develop concurrency control protocols that will assure serializability. They will generally not examine the precedence graph as it is being created; instead a protocol will impose a discipline that avoids nonseralizable schedules. 3 Tests for serializability help understand why a concurrency control protocol is correct. 11

12 Concurrency Control Lock-Based Protocols 1 A lock is a mechanism to control concurrent access to a data item 2 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. 3 Lock requests are made to concurrency-control manager. Transaction can proceed only after request is granted. 4 Lock-compatibility matrix 5 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 6 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. 7 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. 8 Example of a transaction performing locking: T2: lock-s(a); read (A); unlock(a); lock-s(b); read (B); unlock(b); display(a+b) 9 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. 10 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. 12

13 Pitfalls of Lock-Based Protocols 1 Consider the partial schedule 1 Neither T3 nor T4 can make progress executing lock-s(b) causes T4 to wait for T3 to release its lock on B, while executing lock-x(a) causes T3 to wait for T4 to release its lock on A. 2 Such a situation is called a deadlock. To handle a deadlock one of T3 or T4 must be rolled back and its locks released. 3 The potential for deadlock exists in most locking protocols. Deadlocks are a necessary evil. 4 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. 5 Concurrency control manager can be designed to prevent starvation. The Two-Phase Locking Protocol 1 This is a protocol which ensures conflict-serializable schedules. 2 Phase 1: Growing Phase transaction may obtain locks transaction may not release locks 3 Phase 2: Shrinking Phase transaction may release locks transaction may not obtain locks 4 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). 5 Two-phase locking does not ensure freedom from deadlocks 6 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. 7 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 13

14 they commit. 8 There can be conflict serializable schedules that cannot be obtained if two-phase locking is used. 9 However, in the absence of extra information (e.g., ordering of access to data), twophase locking is needed for conflict serializability in the following sense: Given a transaction Ti that does not follow two-phase locking, we can find a transaction Tj that uses two-phase locking, and a schedule for Ti and Tj that is not conflict serializable. Lock Conversions 1 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) 1 This protocol assures serializability. But still relies on the programmer to insert the various locking instructions. Automatic Acquisition of Locks 1 A transaction Ti issues the standard read/write instruction, without explicit locking calls. 2 The operation read(d) is processed as: if Ti has a lock on D then read(d) else begin if necessary wait until no other transaction has a lock-x on D grant Ti a lock-s on D; read(d) end 1 write(d) is processed as: if Ti has a lock-x on D then write(d) else begin if necessary wait until no other trans. has any lock on D, if Ti has a lock-s on D then upgrade lock on D to lock-x else 14

15 grant Ti a lock-x on D write(d) end; 1 All locks are released after commit or abort Implementation of Locking 1 A Lock manager can be implemented as a separate process to which transactions send lock and unlock requests 2 The lock manager replies to a lock request by sending a lock grant messages (or a message asking the transaction to roll back, in case of a deadlock) 3 The requesting transaction waits until its request is answered 4 The lock manager maintains a datastructure called a lock table to record granted locks and pending requests 5 The lock table is usually implemented as an in-memory hash table indexed on the name of the data item being locked Lock Table 1 Black rectangles indicate granted locks, white ones indicate waiting requests 2 Lock table also records the type of lock granted or requested 3 New request is added to the end of the queue of requests for the data item, and granted if it is compatible with all earlier locks 4 Unlock requests result in the request being deleted, and later requests are checked to see if they can now be granted 5 If transaction aborts, all waiting or granted requests of the transaction are deleted lock manager may keep a list of locks held by each transaction, to implement this efficiently 15

16 Graph-Based Protocols 1 Graph-based protocols are an alternative to two-phase locking 2 Impose a partial ordering on the set D = {d1, d2,..., dh} of all data items. If di dj then any transaction accessing both di and dj must access di before accessing dj. Implies that the set D may now be viewed as a directed acyclic graph, called a database graph. 3 The tree-protocol is a simple kind of graph protocol. 1 The tree protocol ensures conflict serializability as well as freedom from deadlock. 2 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.) 1 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 2 Schedules not possible under two-phase locking are possible under tree protocol, and vice versa. Tree Protocol 1 Only exclusive locks are allowed. 2 The first lock by Ti may be on any data item. Subsequently, a data Q can be locked by Ti only if the parent of Q is currently locked by Ti. 3 Data items may be unlocked at any time. 16

17 Timestamp-Based Protocols 1 Each transaction is issued a timestamp when it enters the system. If an old transaction Ti has time-stamp TS(Ti), a new transaction Tj is assigned time-stamp TS(Tj) such that TS(Ti) <TS(Tj). 2 The protocol manages concurrent execution such that the time-stamps determine the serializability order. 3 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. 4 The timestamp ordering protocol ensures that any conflicting read and write operations are executed in timestamp order. 5 Suppose a transaction Ti issues a read(q) 1. If TS(Ti) W-timestamp(Q), then Ti needs to read a value of Q that was already overwritten. Hence, the read operation is rejected, and Ti is rolled back. 2. If TS(Ti) W-timestamp(Q), then the read operation is executed, and R-timestamp(Q) is set to the maximum of R- timestamp(q) and TS(Ti). 1 Suppose that transaction Ti issues write(q). 2 If TS(Ti) < R-timestamp(Q), then the value of Q that Ti is producing was needed previously, and the system assumed that that value would never be produced. Hence, the write operation is rejected, and Ti is rolled back. 3 If TS(Ti) < W-timestamp(Q), then Ti is attempting to write an obsolete value of Q. Hence, this write operation is rejected, and Ti is rolled back. 4 Otherwise, the write operation is executed, and W-timestamp(Q) is set to TS(Ti). Example Use of the Protocol A partial schedule for several data items for transactions with timestamps 1, 2, 3, 4, 5 Correctness of Timestamp-Ordering Protocol 1 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 1 Timestamp protocol ensures freedom from deadlock as no transaction ever waits. 2 But the schedule may not be cascade-free, and may not even be recoverable. Recoverability and Cascade Freedom 17

18 1 Problem with timestamp-ordering protocol: Suppose Ti aborts, but Tj has read a data item written by Ti Then Tj must abort; if Tj had been allowed to commit earlier, the schedule is not recoverable. Further, any transaction that has read a data item written by Tj must abort This can lead to cascading rollback --- that is, a chain of rollbacks 2 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 Thomas Write Rule 1 Modified version of the timestamp-ordering protocol in which obsolete write operations may be ignored under certain circumstances. 2 When Ti attempts to write data item Q, if TS(Ti) < W-timestamp(Q), then Ti is attempting to write an obsolete value of {Q}. Hence, rather than rolling back Ti as the timestamp ordering protocol would have done, this {write} operation can be ignored. 3 Otherwise this protocol is the same as the timestamp ordering protocol. 4 Thomas' Write Rule allows greater potential concurrency. Unlike previous protocols, it allows some view-serializable schedules that are not conflict-serializable. Validation-Based Protocol 1 Execution of transaction Ti is done in three phases. 1. Read and execution phase: Transaction Ti writes only to temporary local variables 2. Validation phase: Transaction Ti performs a ``validation test'' to determine if local variables can be written without violating serializability. 3. Write phase: If Ti is validated, the updates are applied to the database; otherwise, Ti is rolled back. 1 The three phases of concurrently executing transactions can be interleaved, but each transaction must go through the three phases in that order. 2 Also called as optimistic concurrency control since transaction executes fully in the hope that all will go well during validation 3 Each transaction Ti has 3 timestamps 4 Start(Ti) : the time when Ti started its execution 5 Validation(Ti): the time when Ti entered its validation phase 6 Finish(Ti) : the time when Ti finished its write phase 7 Serializability order is determined by timestamp given at validation time, to increase concurrency. Thus TS(Ti) is given the value of Validation(Ti). 8 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. 18

19 Validation Test for Transaction Tj 1 If for all Ti with TS (Ti) < TS (Tj) either one of the following condition holds: finish(ti) < start(tj) start(tj) < finish(ti) < validation(tj) and the set of data items written by Ti does not intersect with the set of data items read by Tj. then validation succeeds and Tj can be committed. Otherwise, validation fails and Tj is aborted. 1 Justification: Either first condition is satisfied, and there is no overlapped execution, or second condition is satisfied and 1. the writes of Tj do not affect reads of Ti since they occur after Ti has finished its reads. 2. the writes of Ti do not affect reads of Tj since Tj does not read any item written by Ti. Schedule Produced by Validation 1 Example of schedule produced using validation Multiple Granularity 1 Allow data items to be of various sizes and define a hierarchy of data granularities, where the small granularities are nested within larger ones 2 Can be represented graphically as a tree (but don't confuse with tree-locking protocol) 3 When a transaction locks a node in the tree explicitly, it implicitly locks all the node's descendents in the same mode. 4 Granularity of locking (level in tree where locking is done): fine granularity (lower in tree): high concurrency, high locking overhead coarse granularity (higher in tree): low locking overhead, low concurrency Example of Granularity Hierarchy The highest level in the example hierarchy is the entire database. The levels below are of type area, file and record in that order. 19

20 Intention Lock Modes 1 In addition to S and X lock modes, there are three additional lock modes with multiple granularity: intention-shared (IS): indicates explicit locking at a lower level of the tree but only with shared locks. intention-exclusive (IX): indicates explicit locking at a lower level with exclusive or shared locks shared and intention-exclusive (SIX): the subtree rooted by that node is locked explicitly in shared mode and explicit locking is being done at a lower level with exclusive-mode locks. 2 intention locks allow a higher level node to be locked in S or X mode without having to check all descendent nodes. Compatibility Matrix with Intention Lock Modes 1 The compatibility matrix for all lock modes is: Multiple Granularity Locking Scheme 1 Transaction Ti can lock a node Q, using the following rules: 1. The lock compatibility matrix must be observed. 2. The root of the tree must be locked first, and may be locked in any mode. 3. A node Q can be locked by Ti in S or IS mode only if the parent of Q is currently locked by Ti in either IX or IS mode. 4. A node Q can be locked by Ti in X, SIX, or IX mode only if the parent of Q is currently locked by Ti in either IX or SIX mode. 5. Ti can lock a node only if it has not previously unlocked any node (that is, Ti is two-phase). 6. Ti can unlock a node Q only if none of the children of Q are currently locked by Ti. 1 Observe that locks are acquired in root-to-leaf order, whereas they are released in leaf-to-root order. Multiversion Schemes 1 Multiversion schemes keep old versions of data item to increase concurrency. Multiversion Timestamp Ordering Multiversion Two-Phase Locking 2 Each successful write results in the creation of a new version of the data item written. 3 Use timestamps to label versions. 4 When a read(q) operation is issued, select an appropriate version of Q based on the timestamp of the transaction, and return the value of the selected version. 5 reads never have to wait as an appropriate version is returned immediately. 20

21 Multiversion Timestamp Ordering 1 Each data item Q has a sequence of versions <Q1, Q2,..., Qm>. Each version Qk contains three data fields: Content -- the value of version Qk. W-timestamp(Qk) -- timestamp of the transaction that created (wrote) version Qk R-timestamp(Qk) -- largest timestamp of a transaction that successfully read version Qk 2 when a transaction Ti creates a new version Qk of Q, Qk's W-timestamp and R- timestamp are initialized to TS(Ti). 3 R-timestamp of Qk is updated whenever a transaction Tj reads Qk, and TS(Tj) > R- timestamp(qk). 4 The multiversion timestamp scheme presented next ensures serializability. 5 Suppose that transaction Ti issues a read(q) or write(q) operation. Let Qk denote the version of Q whose write timestamp is the largest write timestamp less than or equal to TS(Ti). 1. If transaction Ti issues a read(q), then the value returned is the content of version Qk. 2. If transaction Ti issues a write(q), and if TS(Ti) < R- timestamp(qk), then transaction Ti is rolled back. Otherwise, if TS(Ti) = W-timestamp(Qk), the contents of Qk are overwritten, otherwise a new version of Q is created. 1 Reads always succeed; a write by Ti is rejected if some other transaction Tj that (in the serialization order defined by the timestamp values) should read Ti's write, has already read a version created by a transaction older than Ti. Multiversion Two-Phase Locking 1 Differentiates between read-only transactions and update transactions 2 Update transactions acquire read and write locks, and hold all locks up to the end of the transaction. That is, update transactions follow rigorous two-phase locking. Each successful write results in the creation of a new version of the data item written. each version of a data item has a single timestamp whose value is obtained from a counter ts-counter that is incremented during commit processing. 3 Read-only transactions are assigned a timestamp by reading the current value of tscounter before they start execution; they follow the multiversion timestamp-ordering protocol for performing reads. 4 When an update transaction wants to read a data item, it obtains a shared lock on it, and reads the latest version. 5 When it wants to write an item, it obtains X lock on; it then creates a new version of the item and sets this version's timestamp to. 6 When update transaction Ti completes, commit processing occurs: Ti sets timestamp on the versions it has created to ts-counter + 1 Ti increments ts-counter by 1 7 Read-only transactions that start after Ti increments ts-counter will see the values updated by Ti. 8 Read-only transactions that start before Ti increments the 21

22 ts-counter will see the value before the updates by Ti. 9 Only serializable schedules are produced. Deadlock Handling 1 Consider the following two transactions: T1: write (X) T2: write(y) write(y) write(x) 1 Schedule with deadlock More Deadlock Prevention Strategies 1 Following schemes use transaction timestamps for the sake of deadlock prevention alone. 2 wait-die scheme non-preemptive older transaction may wait for younger one to release data item. Younger transactions never wait for older ones; they are rolled back instead. a transaction may die several times before acquiring needed data item 3 wound-wait scheme preemptive older transaction wounds (forces rollback) of younger transaction instead of waiting for it. Younger transactions may wait for older ones. may be fewer rollbacks than wait-die scheme. 4 Both in wait-die and in wound-wait schemes, a rolled back transactions is restarted with its original timestamp. Older transactions thus have precedence over newer ones, and starvation is hence avoided. 5 Timeout-Based Schemes : a transaction waits for a lock only for a specified amount of time. After that, the wait times out and the transaction is rolled back. thus deadlocks are not possible simple to implement; but starvation is possible. Also difficult to determine good value of the timeout interval. Deadlock Detection 1 Deadlocks can be described as a wait-for graph, which consists of a pair G = (V,E), V is a set of vertices (all the transactions in the system) E is a set of edges; each element is an ordered pair Ti Tj. 2 If Ti Tj is in E, then there is a directed edge from Ti to Tj, implying that Ti is waiting for Tj to release a data item. 3 When Ti requests a data item currently being held by Tj, then the edge Ti Tj is inserted in the wait-for graph. This edge is removed only when Tj is no longer holding a data item needed by Ti. 4 The system is in a deadlock state if and only if the wait-for graph has a cycle. Must invoke a deadlock-detection algorithm periodically to look for cycles. 22

23 Wait-for graph without a cycle Wait-for graph with a cycle Deadlock Recovery 1 When deadlock is detected : Some transaction will have to rolled back (made a victim) to break deadlock. Select that transaction as victim that will incur minimum cost. Rollback -- determine how far to roll back transaction Total rollback: Abort the transaction and then restart it. More effective to roll back transaction only as far as necessary to break deadlock. Starvation happens if same transaction is always chosen as victim. Include the number of rollbacks in the cost factor to avoid starvation Insert and Delete Operations 1 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 2 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. 3 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. 4 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, 23

24 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.) 5 Above protocol provides very low concurrency for insertions/deletions. 6 Index locking protocols provide higher concurrency while preventing the phantom phenomenon, by requiring locks on certain index buckets. Index Locking Protocol 1 Every relation must have at least one index. Access to a relation must be made only through one of the indices on the relation. 2 A transaction Ti that performs a lookup must lock all the index buckets that it accesses, in S-mode. 3 A transaction Ti may not insert a tuple ti into a relation r without updating all indices to r. 4 Ti must perform a lookup on every index to find all index buckets that could have possibly contained a pointer to tuple ti, had it existed already, and obtain locks in X- mode on all these index buckets. Ti must also obtain locks in X-mode on all index buckets that it modifies. 5 The rules of the two-phase locking protocol must be observed. Weak Levels of Consistency 1 Degree-two consistency: differs from two-phase locking in that S-locks may be released at any time, and locks may be acquired at any time X-locks must be held till end of transaction Serializability is not guaranteed, programmer must ensure that no erroneous database state will occur] 2 Cursor stability: For reads, each tuple is locked, read, and lock is immediately released X-locks are held till end of transaction Special case of degree-two consistency Weak Levels of Consistency in SQL 1 SQL allows non-serializable executions Serializable: is the default Repeatable read: allows only committed records to be read, and repeating a read should return the same value (so read locks should be retained) However, the phantom phenomenon need not be prevented T1 may see some records inserted by T2, but may not see others inserted by T2 Read committed: same as degree two consistency, but most systems implement it as cursor-stability Read uncommitted: allows even uncommitted data to be read 24

25 Concurrency in Index Structures 1 Indices are unlike other database items in that their only job is to help in accessing data. 2 Index-structures are typically accessed very often, much more than other database items. 3 Treating index-structures like other database items leads to low concurrency. Twophase locking on an index may result in transactions executing practically one-at-atime. 4 It is acceptable to have nonserializable concurrent access to an index as long as the accuracy of the index is maintained. 5 In particular, the exact values read in an internal node of a B+-tree are irrelevant so long as we land up in the correct leaf node. 6 There are index concurrency protocols where locks on internal nodes are released early, and not in a two-phase fashion. 7 Indices are unlike other database items in that their only job is to help in accessing data. 8 Index-structures are typically accessed very often, much more than other database items. 9 Treating index-structures like other database items leads to low concurrency. Twophase locking on an index may result in transactions executing practically one-at-atime. 10 It is acceptable to have nonserializable concurrent access to an index as long as the accuracy of the index is maintained. 11 In particular, the exact values read in an internal node of a B+-tree are irrelevant so long as we land up in the correct leaf node. 12 There are index concurrency protocols where locks on internal nodes are released early, and not in a two-phase fashion. 13 Example of index concurrency protocol: 14 Use crabbing instead of two-phase locking on the nodes of the B+-tree, as follows. During search/insertion/deletion: First lock the root node in shared mode. After locking all required children of a node in shared mode, release the lock on the node. During insertion/deletion, upgrade leaf node locks to exclusive mode. When splitting or coalescing requires changes to a parent, lock the parent in exclusive mode. 15 Above protocol can cause excessive deadlocks. 25

26 Recovery System Failure Classification Transaction failure: Logical errors: transaction cannot complete due to some internal error condition System errors: the database system must terminate an active transaction due to an error condition (e.g., deadlock) System crash: a power failure or other hardware or software failure causes the system to crash. Fail-stop assumption: non-volatile storage contents are assumed to not be corrupted by system crash Database systems have numerous integrity checks to prevent corruption of disk data Disk failure: a head crash or similar disk failure destroys all or part of disk storage Destruction is assumed to be detectable: disk drives use checksums to detect failures Recovery Algorithms Recovery algorithms are techniques to ensure database consistency and transaction atomicity and durability despite failures Focus of this chapter Recovery algorithms have two parts Actions taken during normal transaction processing to ensure enough information exists to recover from failures Actions taken after a failure to recover the database contents to a state that ensures atomicity, consistency and durability Storage Structure Volatile storage: does not survive system crashes examples: main memory, cache memory Nonvolatile storage: survives system crashes examples: disk, tape, flash memory, non-volatile (battery backed up) RAM Stable storage: a mythical form of storage that survives all failures approximated by maintaining multiple copies on distinct nonvolatile media 26

27 Stable-Storage Implementation Maintain multiple copies of each block on separate disks copies can be at remote sites to protect against disasters such as fire or flooding. Failure during data transfer can still result in inconsistent copies: Block transfer can result in Successful completion Partial failure: destination block has incorrect information Total failure: destination block was never updated Protecting storage media from failure during data transfer (one solution): Execute output operation as follows (assuming two copies of each block): Write the information onto the first physical block. When the first write successfully completes, write the same information onto the second physical block. The output is completed only after the second write successfully completes. Protecting storage media from failure during data transfer (cont.): Copies of a block may differ due to failure during output operation. To recover from failure: First find inconsistent blocks: Expensive solution: Compare the two copies of every disk block. Better solution: Record in-progress disk writes on non-volatile storage (Non-volatile RAM or special area of disk). Use this information during recovery to find blocks that may be inconsistent, and only compare copies of these. Used in hardware RAID systems If either copy of an inconsistent block is detected to have an error (bad checksum), overwrite it by the other copy. If both have no error, but are different, overwrite the second block by the first block. Data Access Physical blocks are those blocks residing on the disk. Buffer blocks are the blocks residing temporarily in main memory. Block movements between disk and main memory are initiated through the following two operations: input(b) transfers the physical block B to main memory. output(b) transfers the buffer block B to the disk, and replaces the appropriate physical block there. Each transaction T i has its private work-area in which local copies of all data items accessed and updated by it are kept. T i 's local copy of a data item X is called x i. We assume, for simplicity, that each data item fits in, and is stored inside, a single block. Transaction transfers data items between system buffer blocks and its private work-area using the following operations : read(x) assigns the value of data item X to the local variable x i. 27

28 write(x) assigns the value of local variable x i to data item {X} in the buffer block. both these commands may necessitate the issue of an input(b X ) instruction before the assignment, if the block B X in which X resides is not already in memory. Transactions Perform read(x) while accessing X for the first time; All subsequent accesses are to the local copy. After last access, transaction executes write(x). output(b X ) need not immediately follow write(x). System can perform the output operation when it deems fit. Recovery and Atomicity Modifying the database without ensuring that the transaction will commit may leave the database in an inconsistent state. Consider transaction T i that transfers $50 from account A to account B; goal is either to perform all database modifications made by T i or none at all. Several output operations may be required for T i (to output A and B). A failure may occur after one of these modifications have been made but before all of them are made. To ensure atomicity despite failures, we first output information describing the modifications to stable storage without modifying the database itself. We study two approaches: log-based recovery, and shadow-paging We assume (initially) that transactions run serially, that is, one after the other. Log-Based Recovery A log is kept on stable storage. The log is a sequence of log records, and maintains a record of update activities on the database. When transaction T i starts, it registers itself by writing a <T i start>log record Before T i executes write(x), a log record <T i, X, V 1, V 2 > is written, where V 1 is the value of X before the write, and V 2 is the value to be written to X. Log record notes that T i has performed a write on data item X j X j had value V 1 before the write, and will have value V 2 after the write. When T i finishes it last statement, the log record <T i commit> is written. We assume for now that log records are written directly to stable storage (that is, they are not buffered) Two approaches using logs Deferred database modification Immediate database modification Deferred Database Modification The deferred database modification scheme records all modifications to the log, but defers 28

29 all the writes to after partial commit. Assume that transactions execute serially Transaction starts by writing <T i start> record to log. A write(x) operation results in a log record <T i, X, V> being written, where V is the new value for X Note: old value is not needed for this scheme The write is not performed on X at this time, but is deferred. When T i partially commits, <T i commit> is written to the log Finally, the log records are read and used to actually execute the previously deferred writes. During recovery after a crash, a transaction needs to be redone if and only if both <T i start> and<t i commit> are there in the log. Redoing a transaction T i ( redot i ) sets the value of all data items updated by the transaction to the new values. Crashes can occur while the transaction is executing the original updates, or while recovery action is being taken example transactions T 0 and T 1 (T 0 executes before T 1 ): T 0 : read (A) T 1 : read (C) A: - A - 50 C:- C- 100 Write (A) write (C) read (B) B:- B + 50 write (B) Below we show the log as it appears at three instances of time. 29

30 If log on stable storage at time of crash is as in case: (a) No redo actions need to be taken (b) redo(t 0 ) must be performed since <T 0 commit> is present (c) redo(t 0 ) must be performed followed by redo(t 1 ) since <T 0 commit> and <T i commit> are present Immediate Database Modification The immediate database modification scheme allows database updates of an uncommitted transaction to be made as the writes are issued since undoing may be needed, update logs must have both old value and new value Update log record must be written before database item is written We assume that the log record is output directly to stable storage Can be extended to postpone log record output, so long as prior to execution of an output(b) operation for a data block B, all log records corresponding to items B must be flushed to stable storage Output of updated blocks can take place at any time before or after transaction commit Order in which blocks are output can be different from the order in which they are written. 30

31 Immediate Database Modification Example Log Write Output <T 0 start> <T 0, A, 1000, 950> T o, B, 2000, 2050 A = 950 B = 2050 <T 0 commit> <T 1 start> <T 1, C, 700, 600> C = 600 B B, B C <T 1 commit> Note: B X denotes block containing X. B A Recovery procedure has two operations instead of one: undo(t i ) restores the value of all data items updated by T i to their old values, going backwards from the last log record for T i redo(t i ) sets the value of all data items updated by T i to the new values, going forward from the first log record for T i Both operations must be idempotent That is, even if the operation is executed multiple times the effect is the same as if it is executed once Needed since operations may get re-executed during recovery When recovering after failure: Transaction T i needs to be undone if the log contains the record <T i start>, but does not contain the record <T i commit>. Transaction T i needs to be redone if the log contains both the record <T i start> and the record <T i commit>. Undo operations are performed first, then redo operations. 31

32 Immediate DB Modification Recovery Example Below we show the log as it appears at three instances of time. 32

33 Recovery actions in each case above are: (a) undo (T 0 ): B is restored to 2000 and A to (b) undo (T 1 ) and redo (T 0 ): C is restored to 700, and then A and B are set to 950 and 2050 respectively. (c) redo (T 0 ) and redo (T 1 ): A and B are set to 950 and 2050 respectively. Then C is set to 600 Checkpoints Problems in recovery procedure as discussed earlier : searching the entire log is time-consuming we might unnecessarily redo transactions which have already output their updates to the database. Streamline recovery procedure by periodically performing checkpointing Output all log records currently residing in main memory onto stable storage. Output all modified buffer blocks to the disk. Write a log record < checkpoint> onto stable storage. During recovery we need to consider only the most recent transaction T i that started before the checkpoint, and transactions that started after T i. Scan backwards from end of log to find the most recent <checkpoint> record Continue scanning backwards till a record <T i start> is found. Need only consider the part of log following above start record. Earlier part of log can be ignored during recovery, and can be erased whenever desired. For all transactions (starting from T i or later) with no <T i commit>, execute undo(t i ). (Done only in case of immediate modification.) Scanning forward in the log, for all transactions starting from T i or later with a <T i commit>, execute redo(t i ). Example of Checkpoints T 1 can be ignored (updates already output to disk due to checkpoint) T 2 and T 3 redone. T 4 undone Shadow Paging Shadow paging is an alternative to log-based recovery; this scheme is useful if transactions execute serially Idea: maintain two page tables during the lifetime of a transaction the current page table, and the shadow page table Store the shadow page table in nonvolatile storage, such that state of the database prior to 33

34 transaction execution may be recovered. Shadow page table is never modified during execution To start with, both the page tables are identical. Only current page table is used for data item accesses during execution of the transaction. Whenever any page is about to be written for the first time A copy of this page is made onto an unused page. The current page table is then made to point to the copy The update is performed on the copy Sample Page Table 34

35 Example of Shadow Paging To commit a transaction : 1. Flush all modified pages in main memory to disk 2. Output current page table to disk 3. Make the current page table the new shadow page table, as follows: keep a pointer to the shadow page table at a fixed (known) location on disk. to make the current page table the new shadow page table, simply update the pointer to point to current page table on disk Once pointer to shadow page table has been written, transaction is committed. No recovery is needed after a crash new transactions can start right away, using the shadow page table. Pages not pointed to from current/shadow page table should be freed (garbage collected). Advantages of shadow-paging over log-based schemes no overhead of writing log records 35