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Chapter 17: Recovery System
Database System Concepts
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Database System Concepts
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Chapter 1: Introduction
Part 1: Relational databases
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Chapter 2: Relational Model

Chapter 3: SQL
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Chapter 4: Advanced SQL

Chapter 5: Other Relational Languages
Part 2: Database Design
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Chapter 6: Database Design and the E-R Model
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Chapter 7: Relational Database Design

Chapter 8: Application Design and Development
Part 3: Object-based databases and XML
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Chapter 9: Object-Based Databases

Chapter 10: XML
Part 4: Data storage and querying

Chapter 11: Storage and File Structure
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Chapter 12: Indexing and Hashing

Chapter 13: Query Processing

Chapter 14: Query Optimization
Part 5: Transaction management
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Chapter 15: Transactions
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Chapter 16: Concurrency control

Chapter 17: Recovery System
Database System Concepts, 5th Ed.
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Part 6: Data Mining and Information Retrieval
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Chapter 18: Data Analysis and Mining
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Chapter 19: Information Retreival
Part 7: Database system architecture
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Chapter 20: Database-System Architecture
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Chapter 21: Parallel Databases
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Chapter 22: Distributed Databases
Part 8: Other topics

Chapter 23: Advanced Application Development
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Chapter 24: Advanced Data Types and New Applications

Chapter 25: Advanced Transaction Processing
Part 9: Case studies

Chapter 26: PostgreSQL
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Chapter 27: Oracle
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Chapter 28: IBM DB2
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Chapter 29: Microsoft SQL Server
Online Appendices
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Appendix A: Network Model

Appendix B: Hierarchical Model

Appendix C: Advanced Relational Database Model
17.2
©Silberschatz, Korth and Sudarshan
Part 5: Transaction management
(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.
Database System Concepts, 5th Ed.
17.3
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.4
©Silberschatz, Korth and Sudarshan
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 (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
Database System Concepts, 5th Ed.
17.5
©Silberschatz, Korth and Sudarshan
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
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
Database System Concepts, 5th Ed.
17.6
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.7
©Silberschatz, Korth and Sudarshan
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

for log space
Database System Concepts, 5th Ed.
17.8
©Silberschatz, Korth and Sudarshan
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):
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.
Database System Concepts, 5th Ed.
17.9
©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
17.10
©Silberschatz, Korth and Sudarshan
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 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.
Database System Concepts, 5th Ed.
17.11
©Silberschatz, Korth and Sudarshan
Block Storage Operations
Database System Concepts, 5th Ed.
17.12
©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
17.13
©Silberschatz, Korth and Sudarshan
Example of Data Access
buffer
input(A)
Buffer Block A
x
Buffer Block B
Y
A
output(B)
B
read(X)
write(Y)
x2
x1
disk
y1
work area
of T1
work area
of T2
memory
Database System Concepts, 5th Ed.
17.14
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.15
©Silberschatz, Korth and Sudarshan
Recovery 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.
Database System Concepts, 5th Ed.
17.16
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.17
©Silberschatz, Korth and Sudarshan
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.
 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…
Database System Concepts, 5th Ed.
17.18
©Silberschatz, Korth and Sudarshan
Example of Data Access
buffer
input(A)
Buffer Block A
x
Buffer Block B
Y
A
output(B)
Database
B
read(X)
write(Y)
x1
Disk
x2
Log file
y1
work area
of T1
work area
of T2
Log records
Main memory
Database System Concepts, 5th Ed.
17.19
©Silberschatz, Korth and Sudarshan
Deferred 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.
Database System Concepts, 5th Ed.
17.20
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.21
©Silberschatz, Korth and Sudarshan
Deferred Database Modification: State of Log
and Database
 Example transactions T0 and T1 (T0 executes before T1):
T0: read (A)
T1 : read (C)
A = A - 50
C = C - 100
Write (A)
write (C)
read (B)
B = B + 50
write (B)
State of the Log and Database
corresponding to T0 and T1
Database System Concepts, 5th Ed.
17.22
©Silberschatz, Korth and Sudarshan
Deferred Database Modification (Cont.)
T0, T1, T2 …..
 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: commit이 되어도 buffer까지 update이 된것은 알지만 DB에 update가 되었는지
안되었는지 지안된지는 알수 없으므로, buffer에 update값을 살려놔야 안심할수 있다.
Database System Concepts, 5th Ed.
17.23
©Silberschatz, Korth and Sudarshan
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

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.
Database System Concepts, 5th Ed.
17.24
©Silberschatz, Korth and Sudarshan
Immediate Database Modification Example
 Example transactions T0 and T1
Log
(T0 executes before T1):
<T0 start>
T0:
<T0, A, 1000, 950>
<To, B, 2000, 2050>
read (A)
C = C - 100
B = B + 50
<T0 commit>
<T1 start>
<T1, C, 700, 600>
write (B)
T1 : read (C)
A = A – 50
Output
A = 950
B = 2050
Write (A)
read (B)
Write
C = 600
BB, BC
<T1 commit>
BA
write (C)

Database System Concepts, 5th Ed.
Note: BX denotes block containing X.
17.25
©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
17.26
©Silberschatz, Korth and Sudarshan
Immediate DB Modification Recovery Example
Assume T0, T1, T2….
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
* Undo하는 이유: buffer에 반영이 되었었으므로 혹시 DB에도 반영되었을 가능성도 있으므로, 다시 buffer에
예전값을 써주어서 DB에 반영이 되었어도 다시 원상복귀시키기위해.
Database System Concepts, 5th Ed.
17.27
©Silberschatz, Korth and Sudarshan
Checkpoints

Problems in recovery procedure as discussed earlier :
1. searching the entire log is time-consuming
2. 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.
checkpoint
Log records
buffer
log
M1
A
Insert <checkpoint > into log

During the checkpointing, other transactions cannot be processed
Database System Concepts, 5th Ed.
17.28
©Silberschatz, Korth and Sudarshan
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).
Database System Concepts, 5th Ed.
17.29
©Silberschatz, Korth and Sudarshan
Example of Checkpoints
T1, T2, T3, T4….
Tf
Tc
T1
T2
T3
T4
system failure
checkpoint
 T1 can be ignored (updates already output to disk due to checkpoint)
 Undo T4 (remember Undo first, then Redo) // 이유는 clear하지 않다
 Redo T2 and T3.
• Be careful for the order of undo & redo
• Suppose T1 (x =3 ); T2 (x = x + 1); T3 (x = x +1); T4 (x = x * 2)
Database System Concepts, 5th Ed.
17.30
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.31
©Silberschatz, Korth and Sudarshan
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 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
Database System Concepts, 5th Ed.
17.32
©Silberschatz, Korth and Sudarshan
Example of Shadow Paging
Shadow and current page tables after write to page 4
Database System Concepts, 5th Ed.
17.33
©Silberschatz, Korth and Sudarshan
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).
Database System Concepts, 5th Ed.
17.34
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.35
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.36
©Silberschatz, Korth and Sudarshan
Recovery 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
Database System Concepts, 5th Ed.
17.37
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.38
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.39
©Silberschatz, Korth and Sudarshan
Example of Recovery
with concurrent transactions
 Go over the steps of the recovery
algorithm on the following log:
<T0 start>
 In the step 1, 2, and 3
<T0, A, 0, 10>

Undo-list = { T2 , T1}
<T0 commit>

Redo-list = { T3 }
<T1 start>
 In the step 4
<T1, B, 0, 10>

Undo T2 , Undo T1 , then Redo T3
<T2 start>
<T2, C, 0, 10>
T1
<T2, C, 10, 20>
<checkpoint {T1, T2}>
<T3 start>
T2
T3
T0
<T3, A, 10, 20>
<T3, D, 0, 10>
<T3 commit>
/* the end of log */
Crash here
Database System Concepts, 5th Ed.
checkpoint
17.40
crash
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.41
©Silberschatz, Korth and Sudarshan
Log 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 System Concepts, 5th Ed.
17.42
©Silberschatz, Korth and Sudarshan
Database 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.
Database System Concepts, 5th Ed.
17.43
©Silberschatz, Korth and Sudarshan
OS 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 mainmemory 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.
Database System Concepts, 5th Ed.
17.44
©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
17.45
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.46
©Silberschatz, Korth and Sudarshan
Failure 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
Database System Concepts, 5th Ed.
17.47
©Silberschatz, Korth and Sudarshan
예제추가
Database System Concepts, 5th Ed.
17.48
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.49
©Silberschatz, Korth and Sudarshan
Advanced 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
Database System Concepts, 5th Ed.
17.50
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
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
Database System Concepts, 5th Ed.
17.51
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
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,
Database System Concepts, 5th Ed.
17.52
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
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 operationbegin> is found
Database System Concepts, 5th Ed.
17.53
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
Transaction Rollback (2)

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.
Database System Concepts, 5th Ed.
17.54
©Silberschatz, Korth and Sudarshan
Transaction Rollback Example
(under the early lock release)
 Example transactions T0
T0:
Log
read (A)
A = A - 50
write (A)
read (B)
rollback
B = B + 50
write (B)
Rollback
occurs
Log records are
generated by scan
the log backward
after rollback
A = A – 50 에 대한 Undo는 A에 50을
더해주는 것이 되어야지 A = A – 50 을
수행하기 전 A값으로 돌아가는 것으로
충분하지 않다. 다른 transaction이 A값을
변경해 놓을 수도 있다.
Database System Concepts, 5th Ed.
<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>
17.55
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
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 undolist
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.
Database System Concepts, 5th Ed.
17.56
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
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.
Database System Concepts, 5th Ed.
17.57
©Silberschatz, Korth and Sudarshan
Recovery from System Crash Example
Step 1: Scan log forward;
Redo is performed naturally
System
crash!
Log
Undo-list
<checkpoint L>
<T1 start>
…
<T1 commit>
<T2 start>
…
<T3 start>
…
<T2 abort>
{}
{T1}
{}
{T2}
{T2, T3}
{T3}
Step 2: Scan log backward;
Only T3 is rolled back as described earlier (as in 17.55)
Database System Concepts, 5th Ed.
17.58
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
Checkpointing

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
Database System Concepts, 5th Ed.
17.59
©Silberschatz, Korth and Sudarshan
Advanced Recovery Techniques:
Fuzzycheckpointing

Fuzzy checkpointing is done as follows:
1. Temporarily stop all updates by transactions
2. Write a <checkpoint L> log record and force log to stable storage
3. Note list M of modified buffer blocks
4. Now permit transactions to proceed with their actions
5.
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 output
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.
6.

Incomplete checkpoints, where system had crashed while performing
checkpoint, are handled safely
Database System Concepts, 5th Ed.
17.60
©Silberschatz, Korth and Sudarshan
Normal Checkpointing Example
Assume Last_chkpoint L1
(4) End chkpoint L2
(5) Set Last_chkpoint L2
(6) Resume the stopped transactions
(1) Stop all transactions
(2) Begin chkpoint L2
(3) during chkpoint
•
Log force
•
Flush buffer
…
buffer
M1
M2
A
B
Database System Concepts, 5th Ed.
17.61
©Silberschatz, Korth and Sudarshan
Fuzzy Checkpointing Example
Assume Last_chkpoint L1
(4) End chkpoint L2
(5) Resume the stopped transactions
(1) Stop all transactions
(2) Begin chkpoint L2
(7) Set Last_chkpoint L2
…
(6) Flush buffer
buffer
M1
(3) during chkpoint
•
Log force
Database System Concepts, 5th Ed.
M2
A
B
17.62
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
17.63
©Silberschatz, Korth and Sudarshan
ARIES


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 …
Database System Concepts, 5th Ed.
17.64
©Silberschatz, Korth and Sudarshan
ARIES 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
Database System Concepts, 5th Ed.
17.65
©Silberschatz, Korth and Sudarshan
ARIES 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
Database System Concepts, 5th Ed.
17.66
©Silberschatz, Korth and Sudarshan
ARIES Data Structures (Cont.)
 Each log record contains LSN of previous log record of the same transaction
LSN TransId PrevLSN RedoInfo UndoInfo

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 UndoNextLSN RedoInfo
Database System Concepts, 5th Ed.
17.67
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.68
©Silberschatz, Korth and Sudarshan
ARIES Running Example (1)
Active Transaction List
Trans. ID
LastLSN
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
(Page 1 flushed to disk)
Dirty Page Table
PageID RecLSN
PageLSN
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
Database System Concepts, 5th Ed.
17.69
©Silberschatz, Korth and Sudarshan
ARIES Running Example (2)
Active Transaction List
Trans. ID
LastLSN
T1
1
RedoInfo UndoInfo <LSN TransId PrevLSN>
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
< 1, T1, - >
(Page 1 flushed to disk)
Dirty Page Table
Page #
PageLSN
RecLSN
1
1
0
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
Database System Concepts, 5th Ed.
17.70
©Silberschatz, Korth and Sudarshan
ARIES Running Example (3)
Active Transaction List
Trans. ID
LastLSN
T1
1
T2
2
RedoInfo UndoInfo <LSN TransId PrevLSN>
1.
T1 write page 1
< 1, T1, - >
2.
T2 write page 2
< 2, T2, - >
3.
T1 write page 1
4.
T3 write page 4
(Page 1 flushed to disk)
Dirty Page Table
Page #
PageLSN
RecLSN
1
1
0
2
2
1
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
Database System Concepts, 5th Ed.
17.71
©Silberschatz, Korth and Sudarshan
ARIES Running Example (4)
Active Transaction List
Trans. ID
LastLSN
T1
3
T2
2
RedoInfo UndoInfo <LSN TransId PrevLSN>
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)
Dirty Page Table
Page #
PageLSN
RecLSN
1
3
0
2
2
1
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
Database System Concepts, 5th Ed.
17.72
©Silberschatz, Korth and Sudarshan
ARIES Running Example (5)
Active Transaction List
1.
T1 write page 1
< 1, T1, - >
2.
T2 write page 2
< 2, T2, - >
3.
T1 write page 1
< 3, T1, 1 >
2
4.
T3 write page 4
< 4, T3, - >
4
(Page 1 flushed to disk)
Trans. ID
LastLSN
T1
3
T2
T3
Dirty Page Table
Page #
PageLSN
RecLSN
1
3
0
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoInfo UndoInfo <LSN TransId PrevLSN>
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.73
©Silberschatz, Korth and Sudarshan
ARIES Running Example (6)
Active Transaction List
1.
T1 write page 1
< 1, T1, - >
2.
T2 write page 2
< 2, T2, - >
3.
T1 write page 1
< 3, T1, 1 >
2
4.
T3 write page 4
< 4, T3, - >
4
(Page 1 flushed to disk)
Trans. ID
LastLSN
T1
3
T2
T3
Dirty Page Table
Page #
PageLSN
RecLSN
1
3
0
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoInfo UndoInfo <LSN TransId PrevLSN>
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.74
©Silberschatz, Korth and Sudarshan
ARIES Running Example (7)
Active Transaction List
1.
T1 write page 1
< 1, T1, - >
2.
T2 write page 2
< 2, T2, - >
3.
T1 write page 1
< 3, T1, 1 >
2
4.
T3 write page 4
< 4, T3, - >
4
(Page 1 flushed to disk)
Trans. ID
LastLSN
T1
3
T2
T3
Dirty Page Table
Page #
PageLSN
RecLSN
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoInfo UndoInfo <LSN TransId PrevLSN>
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
< 5, T2 commit >
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.75
©Silberschatz, Korth and Sudarshan
ARIES Running Example (8)
Active Transaction List
Trans. ID
LastLSN
T1
3
T3
4
PageLSN
RecLSN
2
2
1
4
4
3
Database System Concepts, 5th Ed.
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)
Dirty Page Table
Page #
RedoInfo UndoInfo <LSN TransId PrevLSN>
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 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.76
©Silberschatz, Korth and Sudarshan
ARIES Running Example (9)
Active Transaction List
Trans. ID
LastLSN
T1
3
RedoInfo UndoInfo <LSN TransId PrevLSN>
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, - >
T3
4
(Page 1 flushed to disk)
T4
8
5.
T2 commits
< 5, T2 commit >
6.
Begin Checkpoint
< begin chkpt >
7.
End Checkpoint
< end chkpt >
8.
T4 write page 3
< 8, T4, - >
Dirty Page Table
Page #
PageLSN
RecLSN
2
2
1
4
4
3
3
8
7
Database System Concepts, 5th Ed.
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.77
©Silberschatz, Korth and Sudarshan
ARIES Running Example (10)
Active Transaction List
Trans. ID
LastLSN
T1
3
RedoInfo UndoInfo <LSN TransId PrevLSN>
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, - >
T3
4
(Page 1 flushed to disk)
T4
8
5.
T2 commits
< 5, T2 commit >
6.
Begin Checkpoint
< begin chkpt >
7.
End Checkpoint
< end chkpt >
8.
T4 write page 3
< 8, T4, - >
Dirty Page Table
Page #
PageLSN
RecLSN
2
2
1
4
4
3
3
8
7
Database System Concepts, 5th Ed.
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
Crash!
17.78
©Silberschatz, Korth and Sudarshan
ARIES Running Example (11)
Active Transaction List
Trans. ID
LastLSN
T1
3
RedoInfo UndoInfo <LSN TransId PrevLSN>
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, - >
T3
9
(Page 1 flushed to disk)
T4
8
5.
T2 commits
< 5, T2 commit >
6.
Begin Checkpoint
< begin chkpt >
7.
End Checkpoint
< end chkpt >
8.
T4 write page 3
< 8, T4, - >
Dirty Page Table
Page #
2
PageLSN
9
RecLSN
1
(Page 4 flushed to disk)
9.
T3 write page 2
10.
T3 commits
11.
T1 writes page 4
< 9, T3, 4 >
Crash!
3
8
Database System Concepts, 5th Ed.
7
17.79
©Silberschatz, Korth and Sudarshan
ARIES Running Example (12)
Active Transaction List
Trans. ID
LastLSN
T1
3
RedoInfo UndoInfo <LSN TransId PrevLSN>
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, - >
T3
9
(Page 1 flushed to disk)
T4
8
5.
T2 commits
< 5, T2 commit >
6.
Begin Checkpoint
< begin chkpt >
7.
End Checkpoint
< end chkpt >
8.
T4 write page 3
< 8, T4, - >
Dirty Page Table
Page #
2
PageLSN
9
RecLSN
1
(Page 4 flushed to disk)
9.
T3 write page 2
< 9, T3, 4 >
10.
T3 commits
< 10, T3 commit >
11.
T1 writes page 4
Crash!
3
8
Database System Concepts, 5th Ed.
7
17.80
©Silberschatz, Korth and Sudarshan
ARIES Running Example (13)
Active Transaction List
Trans. ID
LastLSN
T1
11
RedoInfo UndoInfo <LSN TransId PrevLSN>
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)
T4
8
Dirty Page Table
Page #
PageLSN
RecLSN
4
11
10
2
9
1
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!
3
8
Database System Concepts, 5th Ed.
7
17.81
©Silberschatz, Korth and Sudarshan
ARIES Recovery Algorithm
ARIES 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
Database System Concepts, 5th Ed.
17.82
©Silberschatz, Korth and Sudarshan
ARIES 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
Database System Concepts, 5th Ed.
17.83
©Silberschatz, Korth and Sudarshan
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
Database System Concepts, 5th Ed.
17.84
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Analysis Pass (1)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
3
T3
4
Dirty Page Table
from the last checkpoint
RedoInfo UndoInfo <LSN TransId PrevLSN>
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
2
2
1
RedoLSN = min(1, 3) = 1
4
4
3
Undo-list = {T1, T3}
Database System Concepts, 5th Ed.
17.85
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Analysis Pass (2)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
3
T4
8
T3
4
Dirty Page Table
from the last checkpoint
RedoInfo UndoInfo <LSN TransId PrevLSN>
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
3
8
8
2
2
1
RedoLSN = min(1, 3, 8) = 1
4
4
3
Undo-list = {T1, T3, T4}
Database System Concepts, 5th Ed.
17.86
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Analysis Pass (3)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
3
T4
8
T3
9
Dirty Page Table
from the last checkpoint
RedoInfo UndoInfo <LSN TransId PrevLSN>
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
3
8
8
2
2
1
RedoLSN = min(1, 3, 8) = 1
4
4
3
Undo-list = {T1, T3, T4}
Database System Concepts, 5th Ed.
17.87
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Analysis Pass (4)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
3
T4
8
T3
9
Dirty Page Table
from the last checkpoint
RedoInfo UndoInfo <LSN TransId PrevLSN>
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
3
8
8
2
2
1
RedoLSN = min(1, 3, 8) = 1
4
4
3
Undo-list = {T1, T3, T4}
Database System Concepts, 5th Ed.
17.88
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Analysis Pass (5)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
11
T4
8
Dirty Page Table
from the last checkpoint
RedoInfo UndoInfo <LSN TransId PrevLSN>
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
3
8
8
2
2
1
RedoLSN = min(1, 3, 8) = 1
4
4
3
Undo-list = {T1, T4}
Database System Concepts, 5th Ed.
17.89
©Silberschatz, Korth and Sudarshan
ARIES 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!
Database System Concepts, 5th Ed.
17.90
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Redo Pass (1)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
11
T4
8
Dirty Page Table
from the last checkpoint
Page #
PageLSN
RecLSN
3
8
8
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoLSN = min(1, 3, 8) = 1
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
1 : No redo
page 1 is not in dirty page table
17.91
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Redo Pass (2)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
11
T4
8
Dirty Page Table
from the last checkpoint
Page #
PageLSN
RecLSN
3
8
8
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoLSN = min(1, 3, 8) = 1
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
2 : LSN 2 >= RecLSN 1  read page 2
PageLSN of the fetched page 0 < 2,
thus redo
17.92
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Redo Pass (3)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
11
T4
8
Dirty Page Table
from the last checkpoint
RedoLSN = min(1, 3, 8) = 1
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
Page #
PageLSN
RecLSN
3
8
8
3 : No redo
2
2
1
4
4
3
4 : LSN 4 >= RecLSN 3  read page 4
PageLSN of the fetched page 4 >= 4,
thus no redo
Database System Concepts, 5th Ed.
17.93
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Redo Pass (4)
Active Transaction List
from the last checkpoint
Trans. ID
LastLSN
T1
11
T4
8
Dirty Page Table
from the last checkpoint
Page #
PageLSN
RecLSN
3
8
8
2
2
1
4
4
3
Database System Concepts, 5th Ed.
RedoLSN = min(1, 3, 8) = 1
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
8, 9, 11 : Redo
17.94
©Silberschatz, Korth and Sudarshan
ARIES 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
Database System Concepts, 5th Ed.
4
4'
3'
5
17.95
6
6'
5' 2'
1'
©Silberschatz, Korth and Sudarshan
ARIES: Undo Pass
Undo 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
Database System Concepts, 5th Ed.
17.96
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Undo Pass (1)
Active Transaction List
Trans. ID
LastLSN
T1
11
T4
8
Undo-list = {T1, T4}
Next record to undo
= max(3, 8) = 11
LSN TransID UndoNextLSN RedoInfo
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
12. CLR
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
< 11’, T1, 3 >
Last LSN T1
= prevLSN of record 11 = 3
Database System Concepts, 5th Ed.
17.97
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Undo Pass (2)
Active Transaction List
Trans. ID
LastLSN
T1
3
T4
8
Undo-list = {T1, T4}
Next record to undo
= max(3, 8) = 8
Last LSN T4
= prevLSN of record 8 = “-”
 Remove T4 from undo-list
Database System Concepts, 5th Ed.
LSN TransID UndoNextLSN RedoInfo
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
12. CLR
13. CLR
17.98
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
< 11’, T1, 3 >
< 8’ T4, - >
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Undo Pass (3)
Active Transaction List
Trans. ID
LastLSN
T1
3
Undo-list = {T1}
Next record to undo = 3
Last LSN T1
= prevLSN of record 3 = 1
Database System Concepts, 5th Ed.
LSN TransID UndoNextLSN RedoInfo
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
12. CLR
13. CLR
14. CLR
17.99
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
< 11’, T1, 3 >
< 8’ T4, - >
< 3’, T1, 1 >
©Silberschatz, Korth and Sudarshan
ARIES Recovery – Undo Pass (4)
Active Transaction List
Trans. ID
LastLSN
T1
1
Undo-list = {T1}
Last LSN T1
= prevLSN of record 1 = “-”
 Remove T1 from undo-list
Empty undo-list
 Undo complete
Database System Concepts, 5th Ed.
LSN TransID UndoNextLSN RedoInfo
Log at crash
1.
T1 write page 1
2.
T2 write page 2
3.
T1 write page 1
4.
T3 write page 4
5.
T2 commits
6.
Begin Checkpoint
7.
End Checkpoint
8.
T4 write page 3
9.
T3 write page 2
10. T3 commits
11. T1 writes page 4
12. CLR
13. CLR
14. CLR
15. CLR
17.100
< 1, T1, - >
< 2, T2, - >
< 3, T1, 1 >
< 4, T3, - >
< 5, T2 commit >
< begin chkpt >
< end chkpt >
< 8, T4, - >
< 9, T3, 4 >
< 10, T3 commit >
< 11, T1, 3 >
< 11’, T1, 3 >
< 8’ T4, - >
< 3’, T1, 1 >
< 1’, T1, - >
©Silberschatz, Korth and Sudarshan
Other 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
Database System Concepts, 5th Ed.
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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
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
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Remote 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
Database System Concepts, 5th Ed.
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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.
Database System Concepts, 5th Ed.
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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
Database System Concepts, 5th Ed.
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Hot Spare Configuration Example
LA : <T0, A, 950, 1000>
(a) Non-Hot spare configuration
A=950
LA
backup
network
primary
log
records
Not immediately
processed
A=1000
LA
Process redo log records
when failure of the
primary is detected
log
records
(b) Hot spare configuration
A=950
LA
backup
network
primary
log
records
Database System Concepts, 5th Ed.
immediately
processed
A=950
17.107
LA
Continually process
redo log record
as they arrive
log
records
©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Time to Commit Example
(a) One-safe; T1 can commit even if L1 is not written in the log of the backup
Backup
Primary
network
T1 commit
L1 : <T1 commit>
L1
log
records
log
records
(b) Two-very-safe; T1 cannot commit if L1 is not written in the log of the backup
Backup
Primary
network
T1 commit
L1
Database System Concepts, 5th Ed.
log
records
log
records
17.109
©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
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Ch17. Summary (3)
 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.
Database System Concepts, 5th Ed.
17.113
©Silberschatz, Korth and Sudarshan
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 shadowpaging 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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Ch17. Summary (5)
 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
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Ch17. Summary (7)
 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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Ch18. Summary (8)
 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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
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].
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
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.
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
Chapter 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
Database System Concepts, 5th Ed.
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©Silberschatz, Korth and Sudarshan
End of Chapter
Database System Concepts
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use