Chapter 17: Recovery System

Database System Concepts©Silberschatz, Korth and Sudarshan

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Chapter 17: Recovery SystemChapter 17: Recovery System

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©Silberschatz, Korth and Sudarshan17.2Database System Concepts, 5th Ed.

Chapter 1: Introduction Part 1: Relational databases

Chapter 2: Relational Model Chapter 3: SQL Chapter 4: Advanced SQL Chapter 5: Other Relational Languages

Part 2: Database Design Chapter 6: Database Design and the E-R Model Chapter 7: Relational Database Design Chapter 8: Application Design and Development

Part 3: Object-based databases and XML Chapter 9: Object-Based Databases Chapter 10: XML

Part 4: Data storage and querying Chapter 11: Storage and File Structure Chapter 12: Indexing and Hashing Chapter 13: Query Processing Chapter 14: Query Optimization

Part 5: Transaction management Chapter 15: Transactions Chapter 16: Concurrency control Chapter 17: Recovery System

Database System ConceptsDatabase System Concepts

Part 6: Data Mining and Information Retrieval Chapter 18: Data Analysis and Mining Chapter 19: Information Retreival

Part 7: Database system architecture Chapter 20: Database-System Architecture Chapter 21: Parallel Databases Chapter 22: Distributed Databases

Part 8: Other topics Chapter 23: Advanced Application Development Chapter 24: Advanced Data Types and New Applications Chapter 25: Advanced Transaction Processing

Part 9: Case studies Chapter 26: PostgreSQL Chapter 27: Oracle Chapter 28: IBM DB2 Chapter 29: Microsoft SQL Server

Online Appendices Appendix A: Network Model Appendix B: Hierarchical Model Appendix C: Advanced Relational Database Model


Part 5: Transaction management Part 5: Transaction management (Chapters 15 through 17).(Chapters 15 through 17).

Chapter 15: Transactions focuses on the fundamentals of a transaction-processing system, including

transaction atomicity, consistency, isolation, and durability, as well as the notion of serializability.

Chapter 16: Concurrency control focuses on concurrency control and presents several techniques for

ensuring serializability, including locking, timestamping, and optimistic (validation) techniques. The chapter also covers deadlock issues.

Chapter 17: Recovery System covers the primary techniques for ensuring correct transaction execution

despite system crashes and disk failures. These techniques include logs, checkpoints, and database dumps.


Chapter 17: Recovery SystemChapter 17: Recovery System 17.1 Failure Classification 17.2 Storage Structure 17.3 Recovery and Atomicity 17.4 Log-Based Recovery Aux: Shadow Paging 17.5 Recovery with Concurrent Transactions 17.6 Buffer Management 17.7 Failure with Loss of Nonvolatile Storage 17.8 Advanced Recovery Techniques Aux: ARIES Recovery Algorithm 17.9 Remote Backup Systems 19.10 Summary


Failure ClassificationFailure 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 (halt). 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 Multiple copies or archival tapes are solutions


Recovery AlgorithmsRecovery 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

1. Actions taken during normal transaction processing to ensure enough information exists to recover from failures

2. Actions taken after a failure to recover the database contents to a state that ensures atomicity, consistency and durability




Storage StructureStorage 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 for log space


Stable-Storage ImplementationStable-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):

1. Write the information onto the first physical block.

2. When the first write successfully completes, write the same information onto the second physical block.

3. The output is completed only after the second write successfully completes.


Stable-Storage Implementation (Cont.)Stable-Storage Implementation (Cont.)

Protecting storage media from failure during data transfer (cont.): If copies of a block differ due to failure during output operation To recover

from failure:

1. First find inconsistent blocks:

1. Expensive solution: Compare the two copies of every disk block.

2. 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

2. 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 AccessData 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 Ti has its private work-area in which local copies of all data

items accessed and updated by it are kept. Ti's local copy of a data item X is called xi.

We assume, for simplicity, that each data item fits in, and is stored inside, a single block.


Block Storage OperationsBlock Storage Operations


Data Access (Cont.)Data Access (Cont.)

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 xi.

write(X) assigns the value of local variable xi to data item {X} in the buffer block.

both these commands may necessitate the issue of an input(BX) instruction before the assignment, if the block BX 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(BX) need not immediately follow write(X). System can perform the output operation when it deems fit.


Example of Data AccessExample of Data Access

x

Y A

B

x1

y1

bufferBuffer Block A

Buffer Block B

input(A)

output(B) read(X)

write(Y)

disk

work areaof T1

work areaof T2

memory

x2




Recovery and AtomicityRecovery and Atomicity

Modifying the database without ensuring that the transaction will commit may leave the database in an inconsistent state

Consider transaction Ti that transfers $50 from account A to account B

Goal is either to perform all database modifications made by Ti or none at all.

Several output operations may be required for Ti (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 shadow-paging

We assume (initially) that transactions run serially, that is, one after the other.




Log-Based RecoveryLog-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.

Log Records When transaction Ti starts, it registers itself by writing a <Ti start> log record Before Ti executes write(X), a log record <Ti, X, V1, V2> is written, where

V1 is the value of X before the write, and V2 is the value to be written to X. Log record notes that Ti has performed a write on data item X. X had value V1 before the write, and will have value V2 after the write.

When Ti finishes it last statement, the log record <Ti 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

At the moment, Assume serial execution of Transactions T0, T1, T3…


Example of Data AccessExample of Data Access

x

Y A

B

x1

y1

bufferBuffer Block A

Buffer Block B

input(A)

output(B) read(X)

write(Y)

Disk

work areaof T1

work areaof T2

Main memory

x2

Log records

Log file

Database


Deferred Database ModificationDeferred Database Modification

The deferred database modification scheme records all modifications to the log, but defers all the writes to after partial commit.

Assume that transactions execute serially Transaction starts by writing <Ti start> record to log.

A write(X) operation results in a log record <Ti, X, V> being written

where V is the new value for X Note: old value is not needed for this scheme

Deferred Update Steps “write” operation is not performed on X at this time, but is deferred. When Ti partially commits, <Ti commit> is written to the log Finally, the log records are read and used to actually execute the previously

deferred writes.


Deferred Database Modification (Cont.)Deferred Database Modification (Cont.)

REDO only scheme During recovery after a crash, a transaction needs to be redone if and only

if both <Ti start> and <Ti commit> are there in the log.

Redoing a transaction Ti ( redoTi) 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


State of the Log and Database State of the Log and Database corresponding to corresponding to TT0 0 and and TT11

Example transactions T0 and T1 (T0 executes before T1):

T0: read (A) T1 : read (C)

A = A - 50 C = C - 100Write (A) write (C)

read (B)

B = B + 50write (B)

Deferred Database Modification: State of Log Deferred Database Modification: State of Log and Databaseand Database


Deferred Database Modification (Cont.)Deferred Database Modification (Cont.)

Below we show the log as it appears at three instances of time.

If log on stable storage at time of crash is as in case:(a) No redo actions need to be taken(b) redo(T0) must be performed since <T0 commit> is present (c) { redo(T0) ; redo(T1)} since <T0 commit> and < T1 commit> are present

Redo 하는 이유 : commit 이 되어도 buffer 까지 update 이 된것은 알지만 DB 에 update 가 되었는지 안되었는지 지안된지는 알수 없으므로 , buffer 에 update 값을 살려놔야 안심할수 있다 .

T0, T1, T2 …..


Immediate Database ModificationImmediate 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 Before 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 in the buffer.


Immediate Database Modification ExampleImmediate Database Modification ExampleLog Write Output

<T0 start><T0, A, 1000, 950><To, B, 2000, 2050> A = 950 B = 2050<T0 commit><T1 start><T1, C, 700, 600> C = 600 BB, BC

<T1 commit> BA

Note: BX denotes block containing X.

Example transactions T0 and T1

(T0 executes before T1):

T0: read (A)

C = C - 100Write (A)

read (B)

B = B + 50write (B)

T1 : read (C)

A = A – 50write (C)


Immediate Database Modification (Cont.)Immediate Database Modification (Cont.) Recovery procedure has two operations instead of one:

undo(Ti) restores the value of all data items updated by Ti to their old values, going backwards from the last log record for Ti

redo(Ti) sets the value of all data items updated by Ti to the new values, going forward from the first log record for Ti

Both operations must be idempotent undo(Ti) = undo ( undo(Ti) ) = undo (undo (undo(Ti) ))……. 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 Ti needs to be undone if the log contains the record

<Ti start>, but does not contain the record <Ti commit>.

Transaction Ti needs to be redone if the log contains both the record <Ti start> and the record <Ti commit>.

Undo operations are performed first, then redo operations.


Immediate DB Modification Recovery ExampleImmediate DB Modification Recovery Example

Below we show the log as it appears at three instances of time.

Recovery actions in each case above are:(a) undo (T0): B is restored to 2000 and A to 1000.

(b) undo (T1) and redo (T0): C is restored to 700, and then A and B are

set to 950 and 2050 respectively.

(c) redo (T0) and redo (T1): A and B are set to 950 and 2050 respectively. Then C is set to 600

* Undo 하는 이유 : buffer 에 반영이 되었었으므로 혹시 DB 에도 반영되었을 가능성도 있으므로 , 다시 buffer에 예전값을 써주어서 DB 에 반영이 되었어도 다시 원상복귀시키기위해 .

Assume T0, T1, T2….


CheckpointsCheckpoints Problems in recovery procedure as discussed earlier :

1. searching the entire log is time-consuming2. we might unnecessarily redo transactions which have already output their

updates to the database. Streamline recovery procedure by periodically performing checkpointing

1. Output all log records currently residing in main memory onto stable storage.2. Output all modified buffer blocks to the disk.3. Write a log record < checkpoint> onto stable storage.

During the checkpointing, other transactions cannot be processed

M1

Log records

log

A

buffer

checkpoint

Insert <checkpoint > into log


Checkpoints (Cont.)Checkpoints (Cont.)

During recovery we need to consider only the most recent transaction Ti that started before the checkpoint, and transactions that started after Ti.

1. Scan backwards from end of log to find the most recent <checkpoint> record

2. Continue scanning backwards till a record <Ti start> is found.

3. Need only consider the part of log following above start record.

1. Earlier part of log can be ignored during recovery, and can be erased whenever desired.

4. For all transactions (starting from Ti or later) with no <Ti commit>, execute undo(Ti). (Done only in case of immediate modification.)

5. Scanning forward in the log, for all transactions starting from Ti or later with a <Ti commit>, execute redo(Ti).


Example of CheckpointsExample of Checkpoints

T1 can be ignored (updates already output to disk due to checkpoint)

Undo T4 (remember Undo first, then Redo) // 이유는 clear 하지 않다

Redo T2 and T3.

Tc Tf

T1

T2

T3

T4

checkpoint system failure

T1, T2, T3, T4….

• Be careful for the order of undo & redo

• Suppose T1 (x =3 ); T2 (x = x + 1); T3 (x = x +1); T4 (x = x * 2)


Chapter 17: Recovery SystemChapter 17: Recovery System 17.1 Failure Classification 17.2 Storage Structure 17.3 Recovery and Atomicity 17.4 Log-Based Recovery Aux: Shadow Paging 17.5 Recovery With Concurrent Transactions 17.6 Buffer Management 17.7 Failure with Loss of Nonvolatile Storage 17.8 Advanced Recovery Techniques Aux: ARIES Recovery Algorithm 17.9 Remote Backup Systems 19.10 Summary


Shadow PagingShadow 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 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


Example of Shadow PagingExample of Shadow PagingShadow and current page tables after write to page 4


Shadow Paging (Cont.)Shadow Paging (Cont.) 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. 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).


Shadow Paging (Cont.)Shadow Paging (Cont.) Advantages of shadow-paging over log-based schemes

no overhead of writing log records recovery is trivial

Disadvantages : Copying the entire page table is very expensive

Can be reduced by using a page table structured like a B+-tree– No need to copy entire tree, only need to copy paths in the tree that

lead to updated leaf nodes Commit overhead is high even with above extension

Need to flush every updated page, and page table Data gets fragmented (related pages get separated on disk) After every transaction completion, the database pages containing old

versions of modified data need to be garbage collected Hard to extend algorithm to allow transactions to run concurrently

Easier to extend log based schemes




Recovery with Concurrent TransactionsRecovery with Concurrent Transactions

We modify the log-based recovery schemes to allow multiple transactions to execute concurrently. All transactions share a single disk buffer and a single log A buffer block can have data items updated by one or more transactions

We assume concurrency control using strict two-phase locking; i.e. the updates of uncommitted transactions should not be visible to other

transactions Otherwise how to perform undo if T1 updates A, then T2 updates A and

commits, and finally T1 has to abort? Logging is done as described earlier.

Log records of different transactions may be interspersed in the log. The checkpointing technique and actions taken on recovery have to be changed

since several transactions may be active when a checkpoint is performed.

Intersperse: 산재해 있다


Recovery with Concurrent Transactions (Cont.)Recovery with Concurrent Transactions (Cont.)

Checkpoints are performed as before, except that the checkpoint log record is now of the form

< checkpoint L>where L is the list of transactions active at the time of the checkpoint We assume no updates are in progress while the checkpoint is carried out

(will relax this later) When the system recovers from a crash, it first does the following:

1. Initialize undo-list and redo-list to empty

2. Scan the log backwards from the end, stopping when the first <checkpoint L> record is found. For each record found during the backward scan: if the record is <Ti commit>, add Ti to redo-list

if the record is <Ti start>, then if Ti is not in redo-list, add Ti to undo-list

3. For every Ti in L, if Ti is not in redo-list, add Ti to undo-list


Recovery with Concurrent Transactions (Cont.)Recovery with Concurrent Transactions (Cont.)

At this point undo-list consists of incomplete transactions which must be undone, and redo-list consists of finished transactions that must be redone.

4. Recovery now continues as follows: Scan log backwards from most recent record, stopping when <Ti start>

records have been encountered for every Ti in undo-list. During the scan, perform undo for each log record that belongs to a

transaction in undo-list. Locate the most recent <checkpoint L> record. Scan log forwards from the <checkpoint L> record till the end of the log.

During the scan, perform redo for each log record that belongs to a transaction on redo-list

Undo first, Redo next


Example of Recovery Example of Recovery with concurrent transactionswith concurrent transactions

Go over the steps of the recovery algorithm on the following log:

<T0 start>

<T0, A, 0, 10>

<T0 commit>

<T1 start>

<T1, B, 0, 10>

<T2 start>

<T2, C, 0, 10>

<T2, C, 10, 20>

<checkpoint {T1, T2}>

<T3 start>

<T3, A, 10, 20>

<T3, D, 0, 10>

<T3 commit> /* the end of log */

In the step 1, 2, and 3 Undo-list = { T2 , T1}

Redo-list = { T3 }

In the step 4 Undo T2 , Undo T1 , then Redo T3

Crash here

T0 T3

T1

T2

checkpoint crash




Log Record BufferingLog Record Buffering Log record buffering: log records are buffered in main memory, instead of being

output directly to stable storage. Log records are output to stable storage when a block of log records in the

buffer is full, or a log force operation is executed. Log force is performed to commit a transaction by forcing all its log records

(including the commit record) to stable storage. Several log records can thus be output using a single output operation

reducing the I/O cost.

The rules below must be followed if log records are buffered: Write-ahead logging or WAL rule

Before a block of data in main memory is output to the database, all log records pertaining to data in that block must have been output to stable storage.

Log records are output to stable storage in the order in which they are created. Transaction Ti enters the commit state only when the log record <Ti commit>

has been output to stable storage.


Database BufferingDatabase Buffering Database maintains an in-memory buffer of data blocks

When a new block is needed and the buffer is full, an existing block needs to be removed from buffer

If the block chosen for removal has been updated, it must be output to disk As a result of the WAL rule, if a block with uncommitted updates is output to disk,

log records with undo information for the updates are output to the log on stable storage first.

<T0 start><T0, A, 1000, 950>

Transaction T0 issues read(B)Suppose the block (having A) is chosen to be output to disk for the block (having B) to

come to the memory. <T0, A, 1000, 950> should be output to a stable storage first

No updates should be in progress on a block when it is output to disk. Before writing a data item, transaction acquires exclusive lock on block

containing the data item Lock can be released once the write is completed.

Such locks held for short duration are called latches.


OS Role in Database BufferingOS Role in Database Buffering

Database buffer can be implemented either in an area of real main-memory reserved for the database in virtual memory by OS

Implementing buffer in reserved main-memory has drawbacks: Memory is partitioned before-hand between database buffer and

applications, limiting flexibility. DB applications and Non-DB applications cannot share the unused main-

memory area Needs may change, and although operating system knows best how

memory should be divided up at any time, it cannot change the partitioning of memory.


OS Role in Database Buffering (Cont.)OS Role in Database Buffering (Cont.) Database buffers are generally implemented in virtual memory in spite of some

drawbacks: When OS needs to evict a page that has been modified, to make space for

another page, the page is written to swap space on disk. When database decides to write buffer page to disk, buffer page may be in

swap space, and may have to be read from swap space on disk and output to the database on disk, resulting in extra I/O! Known as dual paging problem.

Ideally when swapping out a database buffer page, OS should pass control to database, which in turn outputs page to database instead of to swap space (making sure to output log records first) Dual paging can thus be avoided, but common OSs do not support

such functionality.




Failure with Loss of Nonvolatile StorageFailure with Loss of Nonvolatile Storage So far we assumed no loss of non-volatile storage Technique similar to checkpointing used to deal with loss of non-volatile storage

Periodically dump the entire content of the database to stable storage No transaction may be active during the dump procedure; a procedure similar

to checkpointing must take place Output all log records currently residing in main memory onto stable

storage. Output all buffer blocks onto the disk. Copy the contents of the database to stable storage. Output a record <dump> to log on stable storage.

To recover from disk failure restore database from the most recent dump. Consult the log and redo all transactions that committed after the dump

Can be extended to allow transactions to be active during dump; known as fuzzy dump or online dump Will study fuzzy checkpointing later


예제추가예제추가




Advanced Recovery TechniquesAdvanced Recovery Techniques The recovery scheme in section 17.5 assumes strict 2PL Strict 2PL is not suitable for B+ tree index pages

Practically and widely B+ tree concurrency control Early lock releases in B+ tree concurrency control algorithm does not honor 2PL Therefore the recovery scheme in section 17.5 cannot be used in the real situations

In 1990, C. Mohan in IBM published the ARIES technique Later most commercial DBMS adopted either ARIES or ARIES variants Advanced Recovery Techniques is the summary of ARIES or ARIES variants


Advanced Recovery Techniques:Advanced Recovery Techniques:Logical Undo Logging (1)Logical Undo Logging (1)

Support high-concurrency locking techniques, such as those used for B+-tree concurrency control

Operations like B+-tree insertions and deletions release locks early. They cannot be undone by restoring old values (physical undo), since once

a lock is released, other transactions may have updated the B+-tree. Instead, insertions (resp. deletions) are undone by executing a deletion

(resp. insertion) operation (known as logical undo). For such operations, undo log records should contain the undo operation to be

executed called logical undo logging, in contrast to physical undo logging.

Redo information is logged physically (that is, new value for each write) even for such operations Logical redo is very complicated since database state on disk may not be

“operation consistent” Instead, physically redoing all updates of all transactions after the last

checkpoint


Advanced Recovery Techniques:Advanced Recovery Techniques:Logical Undo Logging (2)Logical Undo Logging (2)

Operation logging is done as follows:

1. When operation starts, log <Ti, Oj, operation-begin>. Here Oj is a unique identifier of the operation instance.

2. While operation is executing, normal log records with physical redo and physical undo information are logged.

3. When operation completes, <Ti, Oj, operation-end, U > is logged, where U contains information needed to perform a logical undo information.

If crash/rollback occurs before operation completes: the operation-end log record is not found, and the physical undo information is used to undo operation.

If crash/rollback occurs after the operation completes: the operation-end log record is found, and in this case logical undo is performed using U; the physical undo information for the

operation is ignored. Redo of operation (after crash) still uses physical redo information by physically

redoing all updates of all transactions,


Advanced Recovery Techniques:Advanced Recovery Techniques:Transaction Rollback (1)Transaction Rollback (1)

Rollback of transaction Ti is done as follows:

Scan the log backwards

1. If a log record <Ti, X, V1, V2> is found, perform the undo and log a special redo-only log record <Ti, X, V1>.

2. If a <Ti, Oj, operation-end, U> record is found Rollback the operation logically using the undo information U.

– Updates performed during roll back are logged just like during normal operation execution.

– At the end of the operation rollback, instead of logging an operation-end record, generate a record

<Ti, Oj, operation-abort>.

Skip all preceding log records for Ti until the record <Ti, Oj operation-begin> is found


Scan the log backwards (cont.):

3. If a redo-only record is found ignore it

4. If a <Ti, Oj, operation-abort > record is found:

skip all preceding log records for Ti until the record <Ti, Oj, operation-begin > is found.

5. Stop the scan when the record <Ti, start> is found

6. Add a <Ti, abort> record to the log

Some points to note: Cases 3 and 4 above can occur only if the database crashes while a transaction

is being rolled back. Skipping of log records as in case 4 is important to prevent multiple rollback of

the same operation.

Advanced Recovery Techniques:Advanced Recovery Techniques:Transaction Rollback (2)Transaction Rollback (2)


Transaction Rollback ExampleTransaction Rollback Example(under the early lock release)(under the early lock release)

Log

<T0 start><T0, OA, operation-begin>

<T0, A, 950, 1000>

<T0, OA, operation-end, A = A + 50 ><T0, OB, operation-begin>

<To, B, 2050, 2000>

<To, B, 2000>

<T0, OA’, operation-begin>

<T0, A, 1000, 950>

<T0, OA’, operation-end, A = A - 50 ><T0, OA, operation-abort>< T0 abort>

Example transactions T0

T0: read (A)

A = A - 50write (A)

read (B)

B = B + 50write (B) Rollback

occurs

Log records are generated by scan the log backward after rollback

rollback

A = A – 50 에 대한 Undo 는 A 에 50 을 더해주는 것이 되어야지 A = A – 50 을 수행하기 전 A 값으로 돌아가는 것으로 충분하지 않다 . 다른 transaction 이 A값을 변경해 놓을 수도 있다 .


Advanced Recovery Techniques:Advanced Recovery Techniques:Restart Recovery (1)Restart Recovery (1)

The following actions are taken when recovering from system crash

1. Scan log forward from last < checkpoint L> record

1. Repeat history by physically redoing all updates of all transactions,

2. Create an undo-list during the scan as follows undo-list is set to L initially Whenever <Ti start> is found Ti is added to undo-list

Whenever <Ti commit> or <Ti abort> is found, Ti is deleted from undo-list

Step1.1 brings database to state as of crash, with committed as well as uncommitted transactions having been redone. (redo-list is not necessary)

In step 1.2 undo-list contains transactions that are incomplete, that is, have neither committed nor been fully rolled back.


Advanced Recovery Techniques:Advanced Recovery Techniques:Restart Recovery (2)Restart Recovery (2)

Recovery from system crash (cont.)

2. Scan log backwards, performing undo on log records of transactions found in undo-list. Transactions are rolled back as described earlier. When <Ti start> is found for a transaction Ti in undo-list, write a <Ti

abort> log record. Stop scan when <Ti start> records have been found for all Ti in undo-list

Step 2 undoes the effects of incomplete transactions (those with neither commit nor abort log records). Recovery is now complete.


Recovery from System Crash ExampleRecovery from System Crash ExampleLog Undo-list

<checkpoint L> { }<T1 start> {T1}

…<T1 commit> { }

<T2 start> {T2}

…<T3 start> {T2, T3}…<T2 abort> {T3}

Step 1: Scan log forward; Redo is performed naturally

Step 2: Scan log backward; Only T3 is rolled back as described earlier (as in 17.55)

System crash!


Advanced Recovery Techniques:Advanced Recovery Techniques:CheckpointingCheckpointing

Checkpointing is done as follows:

1. Output all log records in memory to stable storage

2. Output to disk all modified buffer blocks

3. Output to log on stable storage a < checkpoint L> record.

Transactions are not allowed to perform any actions while checkpointing is in progress.

Fuzzy checkpointing allows transactions to progress while the most time consuming parts of checkpointing are in progress Performed as described on next slide


Advanced Recovery Techniques:Advanced Recovery Techniques:FuzzycheckpointingFuzzycheckpointing

Fuzzy checkpointing is done as follows:1. Temporarily stop all updates by transactions2. Write a <checkpoint L> log record and force log to stable storage3. Note list M of modified buffer blocks4. Now permit transactions to proceed with their actions5. Output to disk all modified buffer blocks in list M

blocks should not be updated while being output Follow WAL: all log records pertaining to a block must be output before

the block is output6. Store a pointer to the checkpoint record in a fixed position

last_checkpoint on disk When recovering using a fuzzy checkpoint, start scan from the checkpoint

record pointed to by last_checkpoint Log records before last_checkpoint have their updates reflected in

database on disk, and need not be redone. Incomplete checkpoints, where system had crashed while performing

checkpoint, are handled safely


Normal Checkpointing ExampleNormal Checkpointing Example

(3) during chkpoint• Log force• Flush buffer

M1

M2AB

buffer

Assume Last_chkpoint L1

…

(1) Stop all transactions(2) Begin chkpoint L2

(4) End chkpoint L2 (5) Set Last_chkpoint L2

(6) Resume the stopped transactions


Fuzzy Checkpointing ExampleFuzzy Checkpointing Example

(6) Flush buffer

M1

M2AB

buffer

Assume Last_chkpoint L1

…

(1) Stop all transactions(2) Begin chkpoint L2

(4) End chkpoint L2 (5) Resume the stopped transactions

(3) during chkpoint• Log force

(7) Set Last_chkpoint L2




ARIESARIES ARIES is a “state of the art” recovery method

Incorporates numerous optimizations to reduce overheads during normal processing and to speed up recovery

The “advanced recovery algorithm” we studied earlier is modeled after ARIES, but greatly simplified by removing optimizations

Unlike the advanced recovery algorithm, ARIES has

1. Log sequence number (LSN) to identify log records Stores LSNs in pages to identify what updates have already been

applied to a database page

2. Physiological redo

3. Dirty page table to avoid unnecessary redos during recovery

4. Fuzzy checkpointing that only records information about dirty pages, and does not require dirty pages to be written out at checkpoint time More coming up on each of the above …


ARIES OptimizationsARIES Optimizations Physiological redo

Affected page is physically identified, action within page can be logical Used to reduce logging overheads

– e.g. when a record is deleted and all other records have to be moved to fill hole» Physiological redo can log just the record deletion » Physical redo would require logging of old and new values for

much of the page Requires page to be output to disk atomically

– Easy to achieve with hardware RAID, also supported by some disk systems

– Incomplete page output can be detected by checksum techniques, » But extra actions are required for recovery » Treated as a media failure


ARIES Data StructuresARIES Data Structures Log sequence number (LSN) identifies each log record

Must be sequentially increasing Typically an offset from beginning of log file to allow fast access

Easily extended to handle multiple log files Each page contains a PageLSN which is the LSN of the last log record whose

effects are reflected on the page To update a page:

X-latch the pag, and write the log record Update the page Record the LSN of the log record in PageLSN Unlock page

Page flush to disk S-latches page Thus page state on disk is operation consistent

– Required to support physiological redo PageLSN is used during recovery to prevent repeated redo

Thus ensuring idempotence


ARIES Data Structures (Cont.)ARIES Data Structures (Cont.)

Each log record contains LSN of previous log record of the same transaction

LSN in log record may be implicit

Special redo-only log record called compensation log record (CLR) used to log actions taken during recovery that never need to be undone Also serve the role of operation-abort log records used in advanced

recovery algorithm Have a field UndoNextLSN to note next (earlier) record to be undone

Records in between would have already been undone Required to avoid repeated undo of already undone actions

LSN TransId PrevLSN RedoInfo UndoInfo

LSN TransID UndoNextLSN RedoInfo


ARIES Data Structures (Cont.)ARIES Data Structures (Cont.) DirtyPageTable

List of pages in the buffer that have been updated Contains, for each such page

PageLSN of the page RecLSN is an LSN such that log records before this LSN have already

been applied to the page version on disk– Set to current end of log when a page is inserted into dirty page

table (just before being updated)– Recorded in checkpoints, helps to minimize redo work

Checkpoint log record Contains:

DirtyPageTable and list of active transactions For each active transaction, LastLSN, the LSN of the last log record

written by the transaction Fixed position on disk notes LSN of last completed checkpoint log record


ARIES Running Example (1)ARIES Running Example (1)

1. T1 write page 12. T2 write page 23. T1 write page 14. T3 write page 4(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

PageID RecLSN PageLSN

Active Transaction List

Dirty Page Table



1. T1 write page 1 < 1, T1, - >2. T2 write page 23. T1 write page 14. T3 write page 4(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 1

Page # PageLSN RecLSN

1 1 0


Dirty Page Table

RedoInfo UndoInfo <LSN TransId PrevLSN>



1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 14. T3 write page 4(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 1

T2 2


1 1 0

2 2 1


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T2 2


1 3 0

2 2 1


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T2 2

T3 4


1 3 0

2 2 1

4 4 3


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T2 2

T3 4


1 3 02 2 1

4 4 3


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint7. End Checkpoint8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T2 2T3 4


2 2 1

4 4 3


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T3 4


2 2 1

4 4 3


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T3 4

T4 8


2 2 1

4 4 3

3 8 7


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >(Page 4 flushed to disk)9. T3 write page 210. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T3 4

T4 8


2 2 1

4 4 33 8 7


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >(Page 4 flushed to disk)9. T3 write page 2 < 9, T3, 4 >10. T3 commits11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T3 9T4 8


2 9 1

3 8 7


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >(Page 4 flushed to disk)9. T3 write page 2 < 9, T3, 4 >10. T3 commits < 10, T3 commit >11. T1 writes page 4Crash!

Trans. ID LastLSN

T1 3

T3 9T4 8


2 9 1

3 8 7


Dirty Page Table




1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >(Page 1 flushed to disk)5. T2 commits < 5, T2 commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >(Page 4 flushed to disk)9. T3 write page 2 < 9, T3, 4 >10. T3 commits < 10, T3 commit >11. T1 writes page 4 < 11, T1, 3 >Crash!

Trans. ID LastLSN

T1 11

T4 8


4 11 102 9 1

3 8 7


Dirty Page Table



ARIES Recovery AlgorithmARIES Recovery AlgorithmARIES recovery involves three passes Analysis pass: Determines

Which transactions to undo Which pages were dirty (disk version not up to date) at time of crash RedoLSN: LSN from which redo should start

Redo pass: Repeats history, redoing all actions from RedoLSN

RecLSN and PageLSNs are used to avoid redoing actions already reflected on page

Undo pass: Rolls back all incomplete transactions

Transactions whose abort was complete earlier are not undone– Key idea: no need to undo these transactions: earlier undo actions

were logged, and are redone as required


ARIES Recovery: AnalysisARIES Recovery: Analysis

Analysis pass Starts from last complete checkpoint log record

Reads in DirtyPageTable from log record Sets RedoLSN = min of RecLSNs of all pages in DirtyPageTable

In case no pages are dirty, RedoLSN = checkpoint record’s LSN Sets undo-list = list of transactions in checkpoint log record Reads LSN of last log record for each transaction in undo-list from

checkpoint log record


ARIES Recovery: Analysis (Cont.)ARIES Recovery: Analysis (Cont.)Analysis pass (cont.) Scans forward from checkpoint

If any log record found for transaction not in undo-list, adds transaction to undo-list

Whenever an update log record is found If page is not in DirtyPageTable, it is added with RecLSN set to LSN of

the update log record If transaction end log record found, delete transaction from undo-list Keeps track of last log record for each transaction in undo-list

May be needed for later undo At end of analysis pass:

RedoLSN determines where to start redo pass RecLSN for each page in DirtyPageTable used to minimize redo work All transactions in undo-list need to be rolled back


ARIES Recovery – Analysis Pass (1)ARIES Recovery – Analysis Pass (1)Active Transaction List

from the last checkpoint

Dirty Page Tablefrom the last checkpoint

Trans. ID LastLSN

T1 3

T3 4


2 2 14 4 3

RedoLSN = min(1, 3) = 1 Undo-list = {T1, T3}

Log at crash1. T1 write page 1 < 1, T1, - >2. T2 write page 2 < 2, T2, - >3. T1 write page 1 < 3, T1, 1 >4. T3 write page 4 < 4, T3, - >5. T2 commits < 5, T2

commit >6. Begin Checkpoint < begin chkpt >7. End Checkpoint < end chkpt >8. T4 write page 3 < 8, T4, - >9. T3 write page 2 < 9, T3, 4 >10. T3 commits < 10, T3

commit >11. T1 writes page 4 < 11, T1, 3 >






Trans. ID LastLSN

T1 3

T4 8T3 4


3 8 82 2 1

4 4 3

RedoLSN = min(1, 3, 8) = 1 Undo-list = {T1, T3, T4}









Trans. ID LastLSN

T1 3

T4 8

T3 9


3 8 8

2 2 1

4 4 3










Trans. ID LastLSN

T1 3

T4 8

T3 9


3 8 8

2 2 1

4 4 3










Trans. ID LastLSN

T1 11

T4 8


3 8 8

2 2 1

4 4 3

RedoLSN = min(1, 3, 8) = 1 Undo-list = {T1, T4}






ARIES Redo PassARIES Redo Pass

Redo Pass: Repeats history by replaying every action not already reflected in the page on disk, as follows:

Scans forward from RedoLSN.

Whenever an update log record is found:

1. If the page is not in DirtyPageTable or the LSN of the log record is less than the RecLSN of the page in DirtyPageTable, then skip the log record

2. Otherwise fetch the page from disk. If the PageLSN of the page fetched from disk is less than the LSN of the log record, redo the log record

NOTE: if either test is negative the effects of the log record have already appeared on the page. First test avoids even fetching the page from disk!


ARIES Recovery – Redo Pass (1)ARIES Recovery – Redo Pass (1)Active Transaction List



Trans. ID LastLSN

T1 11

T4 8


3 8 8

2 2 1

4 4 3

RedoLSN = min(1, 3, 8) = 1



commit >11. T1 writes page 4 < 11, T1, 3 >1 : No redo page 1 is not in dirty page table





Trans. ID LastLSN

T1 11

T4 8


3 8 8

2 2 1

4 4 3



commit >11. T1 writes page 4 < 11, T1, 3 >2 : LSN 2 >= RecLSN 1 read page 2 PageLSN of the fetched page 0 < 2, thus redo

RedoLSN = min(1, 3, 8) = 1





Trans. ID LastLSN

T1 11

T4 8


3 8 8

2 2 1

4 4 3



commit >11. T1 writes page 4 < 11, T1, 3 >3 : No redo4 : LSN 4 >= RecLSN 3 read page 4 PageLSN of the fetched page 4 >= 4, thus no redo

RedoLSN = min(1, 3, 8) = 1





Trans. ID LastLSN

T1 11

T4 8


3 8 8

2 2 1

4 4 3



commit >11. T1 writes page 4 < 11, T1, 3 >8, 9, 11 : Redo

RedoLSN = min(1, 3, 8) = 1


ARIES Undo ActionsARIES Undo Actions When an undo is performed for an update log record

Generate a CLR containing the undo action performed (actions performed during undo are logged physicaly or physiologically). CLR for record n noted as n’ in figure below

Set UndoNextLSN of the CLR to the PrevLSN value of the update log record Arrows indicate UndoNextLSN value

ARIES supports partial rollback Used e.g. to handle deadlocks by rolling back just enough to release reqd.

locks Figure indicates forward actions after partial rollbacks

records 3 and 4 initially, later 5 and 6, then full rollback

1 2 3 4 4' 3' 5 6 5' 2' 1'6'


ARIES: Undo PassARIES: Undo PassUndo pass Performs backward scan on log undoing all transaction in undo-list

Backward scan optimized by skipping unneeded log records as follows: Next LSN to be undone for each transaction set to LSN of last log record

for transaction found by analysis pass. At each step pick largest of these LSNs to undo, skip back to it and

undo it After undoing a log record

– For ordinary log records, set next LSN to be undone for transaction to PrevLSN noted in the log record

– For compensation log records (CLRs) set next LSN to be undo to UndoNextLSN noted in the log record

» All intervening records are skipped since they would have been undo already

Undos performed as described earlier


ARIES Recovery – Undo Pass (1)ARIES Recovery – Undo Pass (1)

Active Transaction ListTrans. ID LastLSN

T1 11

T4 8



commit >11. T1 writes page 4 < 11, T1, 3 >12. CLR < 11’, T1, 3 >

Undo-list = {T1, T4}


Next record to undo = max(3, 8) = 11 Last LSN T1 = prevLSN of record 11 = 3




T1 3

T4 8



commit >11. T1 writes page 4 < 11, T1, 3 >12. CLR < 11’, T1, 3 >13. CLR < 8’ T4, - >

Undo-list = {T1, T4}


Next record to undo = max(3, 8) = 8 Last LSN T4 = prevLSN of record 8 = “-” Remove T4 from undo-list




T1 3



commit >11. T1 writes page 4 < 11, T1, 3 >12. CLR < 11’, T1, 3 >13. CLR < 8’ T4, - >14. CLR < 3’, T1, 1 >

Undo-list = {T1}


Next record to undo = 3 Last LSN T1 = prevLSN of record 3 = 1




T1 1



commit >11. T1 writes page 4 < 11, T1, 3 >12. CLR < 11’, T1, 3 >13. CLR < 8’ T4, - >14. CLR < 3’, T1, 1 >15. CLR < 1’, T1, - >

Undo-list = {T1}


Last LSN T1 = prevLSN of record 1 = “-” Remove T1 from undo-list

Empty undo-list Undo complete


Other ARIES FeaturesOther ARIES Features Recovery Independence

Pages can be recovered independently of others E.g. if some disk pages fail they can be recovered from a backup

while other pages are being used

Savepoints: Transactions can record savepoints and roll back to a savepoint

Useful for complex transactions Also used to rollback just enough to release locks on deadlock


Other ARIES Features (Cont.)Other ARIES Features (Cont.) Fine-grained locking:

Index concurrency algorithms that permit tuple level locking on indices can be used These require logical undo, rather than physical undo, as in advanced

recovery algorithm

Recovery optimizations:

For example: Dirty page table can be used to prefetch pages during redo Out of order redo is possible:

redo can be postponed on a page being fetched from disk, and performed when page is fetched.

Meanwhile other log records can continue to be processed




Remote Backup SystemsRemote Backup Systems Remote backup systems provide high availability by allowing transaction

processing to continue even if the primary site is destroyed. Primary site and secondary site should be synchronized

by sending all log records from primary to secondary


Remote Backup Systems (Cont.)Remote Backup Systems (Cont.) Detection of failure: Backup site must detect when primary site has failed

To distinguish primary site failure from link failure, several communication links between the primary and the remote backup need to be maintained.

Transfer of control: To take over control: the backup site first perform recovery using its copy

of the database and all the long records it has received from the primary. Thus, completed transactions are redone and incomplete transactions

are rolled back. When the backup site takes over processing it becomes the new primary To transfer control back: to the old primary when it recovers, the old

primary must receive redo logs from the old backup and apply all updates locally.


Remote Backup Systems (Cont.)Remote Backup Systems (Cont.) Time to recover: To reduce delay in takeover, the backup site periodically

processes the redo log records (in effect, performing recovery from previous database state), performs a checkpoint, and can then delete earlier parts of the log.

Hot-Spare configuration permits very fast takeover: The backup site continually processes redo log record as they arrive, applying

the updates locally. When failure of the primary is detected, the backup site rolls back incomplete

transactions, and is immediately ready to process new transactions. Alternative to the remote backup site: distributed database with replicated data

Transactions are required to update all replicas of any data item that they update Transactions are accepted in any database replicas

Remote backup is faster and cheaper, but less tolerant to failure more on this in Chapter 19

Spare: ( 동 ) 용서하다 , 아끼다 ( 형 ) 예비의 , 여벌의


Hot Spare ConfigurationHot Spare Configuration ExampleExample

primary

A=950

network

(a) Non-Hot spare configuration

Process redo log recordswhen failure of the primary is detected

(b) Hot spare configuration

Continually process redo log record as they arrive

backup

LA

logrecords A=1000 LA

Not immediatelyprocessed

primary

A=950

network backup

LA

logrecords A=950 LA

immediatelyprocessed

LA : <T0, A, 950, 1000>

logrecords

logrecords


Remote Backup Systems (Cont.)Remote Backup Systems (Cont.)

Time to Commit Ensure durability of updates by delaying transaction commit until update is

logged at backup Avoid this delay by permitting lower degrees of durability.

One-safe: commit as soon as transaction’s commit log record is written at primary Problem: updates may not arrive at backup before it takes over.

Two-very-safe: commit when transaction’s commit log record is written at primary and backup Reduces availability since transactions cannot commit if either site fails.

Two-safe: proceed as in two-very-safe if both primary and backup are active. If only the primary is active, the transaction commits as soon as its commit log

record is written at the primary. Better availability than two-very-safe; avoids problem of lost transactions in

one-safe.


Time to Commit ExampleTime to Commit Example

(a) One-safe; T1 can commit even if L1 is not written in the log of the backup

(b) Two-very-safe; T1 cannot commit if L1 is not written in the log of the backup

Primary

T1 commitnetwork

Backup

L1

logrecords

L1 : <T1 commit>

Primary

T1 commitnetwork

Backup

L1

logrecords

logrecords

logrecords




Ch.17 Summary (1)Ch.17 Summary (1)

A computer system, like any other mechanical or electrical device, is subject to failure. There are a variety of causes of such failure, including disk crash, power

failure, and software errors. In each of these case, information concerning the database system is lost.

In addition to system failures, transactions may also fail for various reasons, such as violation of integrity constraints or deadlocks.

An integral part of a databases system is a recovery scheme that is responsible for the detection of failures and for the restoration of the database to a state that existed before the occurrence of the failure.

The various types of storage in a computer are volatile storage, nonvolatile storage, and stable storage. Data in volatile storage, such as in RAM, are lost when the computer

crashes. Data in nonvolatile storage, such as disk, are not lost when the computer

crashes, but may occasionally be lost because of failures such as disk crashes.

Data in stable storage are never lost.


Ch17. Summary (2)Ch17. Summary (2)

Stable storage that must be accessible online is approximated with mirrored disks, or other forms of RAID, which provide redundant data storage. Offline, or archival, stable storage may consist of multiple tape copies of

data stored in a physically secure location.

In case of failure, the state of the database system may no longer be consistent; that is, it may not reflect a state of the world that the database is supposed to capture. To preserve consistency, we require that each transaction be atomic. It is the responsibility of the recovery scheme to ensure the atomicity and

durability property. There are basically two different approaches for ensuring atomicity: log-base

schemes and shadow paging.



In log-based schemes, all updates are recorded on a log, which must be kept in stable storage. In the deferred- modifications scheme, during the execution of a

transaction, all the write operations are are deferred until the transaction partially commits, at which time the system sues the information on the log associated with the transaction in execution the deferred writes.

In the immediate-modifications scheme, the system applies all updates directly to the database. If a crash occurs, the system uses the information in the restoring the state of the system to a previous consistent state.

To reduce the overhead of searching the log and redoing transactions, we can use the checkpointing technique.


Ch17. Summary (4) Ch17. Summary (4) In shadow paging, two page tables are maintained during the life of a

transaction: the current page table and the shadow page table. When the transaction starts, both page tables are identical. The shadow page table and page it points to are never changed during the

duration of the transaction. When the transaction partially commits, the shadow page table is

discarded, and the current table becomes the new page table. If the transaction aborts, the current page table is simply discarded.

If multiple transactions are allowed to execute concurrently, then the shadow-paging technique is not applicable, but the log-based technique can be used. No transactions can allowed to update a data item that has already been

updated by an incomplete transaction. We an use strict two- phase locking to ensure this condition.



Transaction processing is based on a storage model in which main memory holds a log buffer, a database buffer, and a system buffer. The system buffer holds pages of system object code and local work areas

of transaction. Efficient implementation of a recovery scheme requires that the number of

writes to the database and to stable storage be minimized. Log records may be kept in volatile log buffer initially, but must be written to

stable storage when one of the following conditions occurs: Before the <Ti commit> log record may be output to stable storage, all log

records pertaining to transaction must have been output to the database (in nonvolatile storage), all log records pertaining to data in that block must have been output to stable storage.

Before a block of data in main memory is output to the database (in nonvolatile storage), all log records pertaining to data in that block must have been output to stable storage



To recover from failures that result in the loss of nonvolatile storage, we must dump the entire contents of the database onto stable storage periodically- say, once per day. If a failure occurs that results in the loss of physical database blocks, we use

the most recent dump in restoring the database to previous consistent state. Once this restoration has been accomplished, we use the log to bring the

databases system to the most recent consistent state.

Advanced recovery techniques support high-concurrency locking techniques, such as those used for B+ tree concurrency control. These techniques are based on logical (operation) undo, and follow the principle of repeating history. When recovering from system failure, the system performs a redo pass

using the log, followed by an undo pass on the log to roll back incomplete transactions.



The ARIES recovery scheme is a state-of-the-art scheme that supports a number of features to provide greater concurrency, reduce logging overheads, and minimize recovery time. It is also based on repeating of history, and allows logical undo operations. The scheme flushes pages on a continuous basis and does not need to

flush all pages at the time of a checkpoint. It uses log sequence numbers (LSNs) to implement a variety of

optimizations that reduce the time taken for recovery.

Remote backup systems provide a high degree of availability, allowing transaction processing to continue even if the primary site is destroyed by a fire, flood, or earthquake.


Ch17. Bibliographical Notes (1)Ch17. Bibliographical Notes (1)

A comprehensive presentation of the principles of recovery is offered by Hearder and Reuter [1983].

Gray and Reuter [1993] is an excellent textbook source of information about recovery, including interesting implementation and historical details.

Bernstein et al. [1987] is an early textbook source of information on concurrency control and recovery.

Two early papers that present initial theoretical work in the area of recovery are Davies[1973] and Bjork[1973].

An overview of the recovery scheme of System R is presented by Gray et al. [1981b].

The shadow-paging mechanism of System R is described by Lorie [1977]. Tutorial and survey papers on various recovery techniques for database systems

include Gray [1978], Lindsay et al [1980], and Verhofstad [1978]. The concepts of fuzzy checkpointing and fuzzy dumps are described in Lindsay

et al [1980].


Ch17. Bibliographical Notes (2)Ch17. Bibliographical Notes (2)

Chandy et al. [1975], which describes analytic models for rollback and recovery strategies in database systems, is another early work in this area.

The state of the art in recovery methods is best illustrated by the ARIES recovery method, described in Mohan et al. [1992] and Mohan [1990b].

Aries and its variants are used in several databases products, including IBM DB2 and Mircrosoft SQL Server.

Recovery in Oracle is described in Lahiri et al. [2001]. Specialized recovery techniques for index structures are described in Mohan

and Levine [1992] and Mohan [1993]; Mohan and Narang [1994] describes recovery techniques for client-server architectures, while Mohan and Narang [1991] and Mohan and Narang [1992] describe recovery techniques for parallel databases architectures.

Remote backup for disaster recovery (loss of an entire computing facility by, for example. Fire, flood, or earthquake) is considered in King et al. [1991] and Polyzois and Garcia-Molina [1994].

Chapter 24 lists references pertaining to long-duration transactions and related recovery issues.



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Chapter 17: Recovery System

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