SlideShare uma empresa Scribd logo
1 de 81
Database System Concepts
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Chapter 4 Recovery SystemChapter 4 Recovery System
©Silberschatz, Korth and Sudarshan17.2Database System Concepts, 5th
Edition, Oct 5, 2006
Chapter 4: Recovery SystemChapter 4: Recovery System
Failure Classification
Storage Structure
Recovery and Atomicity
Log-Based Recovery
Shadow Paging
Recovery With Concurrent Transactions
Buffer Management
Failure with Loss of Nonvolatile Storage
Advanced Recovery Techniques
ARIES Recovery Algorithm
Remote Backup Systems
©Silberschatz, Korth and Sudarshan17.3Database System Concepts, 5th
Edition, Oct 5, 2006
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.
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
©Silberschatz, Korth and Sudarshan17.4Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.5Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.6Database System Concepts, 5th
Edition, Oct 5, 2006
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.
©Silberschatz, Korth and Sudarshan17.7Database System Concepts, 5th
Edition, Oct 5, 2006
Stable-Storage Implementation
(Cont.)(Cont.)
Protecting storage media from failure during data transfer (cont.):
Copies of a block may differ due to failure during output operation. To recover
from failure:
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
1. 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.
©Silberschatz, Korth and Sudarshan17.8Database System Concepts, 5th
Edition, Oct 5, 2006
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.
©Silberschatz, Korth and Sudarshan17.9Database System Concepts, 5th
Edition, Oct 5, 2006
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.
©Silberschatz, Korth and Sudarshan17.10Database System Concepts, 5th
Edition, Oct 5, 2006
Example of Data AccessExample of Data Access
X
Y
A
B
x1
y1
buffer
Buffer Block A
Buffer Block B
input(A)
output(B)
read(X)
write(Y)
disk
work
area
of T1
work area
of T2
memory
x2
©Silberschatz, Korth and Sudarshan17.11Database System Concepts, 5th
Edition, Oct 5, 2006
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.
©Silberschatz, Korth and Sudarshan17.12Database System Concepts, 5th
Edition, Oct 5, 2006
Recovery and Atomicity (Cont.)Recovery and Atomicity (Cont.)
To ensure atomicity despite failures, we first output information
describing the modifications to stable storage without modifying the
database itself.
We study two approaches:
log-based recovery, and
shadow-paging
We assume (initially) that transactions run serially, that is, one after
the other.
©Silberschatz, Korth and Sudarshan17.13Database System Concepts, 5th
Edition, Oct 5, 2006
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.
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 Xj Xj
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
©Silberschatz, Korth and Sudarshan17.14Database System Concepts, 5th
Edition, Oct 5, 2006
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
The write 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.
©Silberschatz, Korth and Sudarshan17.15Database System Concepts, 5th
Edition, Oct 5, 2006
Deferred Database ModificationDeferred Database Modification
(Cont.)(Cont.)
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
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)
©Silberschatz, Korth and Sudarshan17.16Database System Concepts, 5th
Edition, Oct 5, 2006
Deferred Database ModificationDeferred Database Modification
(Cont.)(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) must be performed followed by redo(T1) since
<T0 commit> and <Ti commit> are present
©Silberschatz, Korth and Sudarshan17.17Database System Concepts, 5th
Edition, Oct 5, 2006
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
Can be extended to postpone log record output, so long as prior to
execution of an output(B) operation for a data block B, all log
records corresponding to items B must be flushed to stable
storage
Output of updated blocks can take place at any time before or after
transaction commit
Order in which blocks are output can be different from the order in
which they are written.
©Silberschatz, Korth and Sudarshan17.18Database System Concepts, 5th
Edition, Oct 5, 2006
Immediate Database ModificationImmediate Database Modification
ExampleExample
Log 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.
x1
©Silberschatz, Korth and Sudarshan17.19Database System Concepts, 5th
Edition, Oct 5, 2006
Immediate Database ModificationImmediate Database Modification
(Cont.)(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
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.
©Silberschatz, Korth and Sudarshan17.20Database System Concepts, 5th
Edition, Oct 5, 2006
Immediate DB Modification RecoveryImmediate DB Modification Recovery
ExampleExample
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
©Silberschatz, Korth and Sudarshan17.21Database System Concepts, 5th
Edition, Oct 5, 2006
CheckpointsCheckpoints
Problems in recovery procedure as discussed earlier :
1. searching the entire log is time-consuming
2. we might unnecessarily redo transactions which have already
3. 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.
©Silberschatz, Korth and Sudarshan17.22Database System Concepts, 5th
Edition, Oct 5, 2006
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.
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).
©Silberschatz, Korth and Sudarshan17.23Database System Concepts, 5th
Edition, Oct 5, 2006
Example of CheckpointsExample of Checkpoints
T1 can be ignored (updates already output to disk due to checkpoint)
T2 and T3 redone.
T4 undone
Tc
Tf
T1
T2
T3
T4
checkpoint system failure
©Silberschatz, Korth and Sudarshan17.24Database System Concepts, 5th
Edition, Oct 5, 2006
Recovery With ConcurrentRecovery With Concurrent
TransactionsTransactions
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.
©Silberschatz, Korth and Sudarshan17.25Database System Concepts, 5th
Edition, Oct 5, 2006
Recovery With Concurrent TransactionsRecovery With Concurrent Transactions
(Cont.)(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
1. For every Ti in L, if Ti is not in redo-list, add Ti to undo-list
©Silberschatz, Korth and Sudarshan17.26Database System Concepts, 5th
Edition, Oct 5, 2006
Recovery With Concurrent TransactionsRecovery With Concurrent Transactions
(Cont.)(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.
Recovery now continues as follows:
1. 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.
1. Locate the most recent <checkpoint L> record.
2. 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
©Silberschatz, Korth and Sudarshan17.27Database System Concepts, 5th
Edition, Oct 5, 2006
Example of RecoveryExample of Recovery
Go over the steps of the recovery algorithm on the following log:
<T0 start>
<T0, A, 0, 10>
<T0 commit>
<T1 start> /* Scan at step 1 comes up to here */
<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>
©Silberschatz, Korth and Sudarshan17.28Database System Concepts, 5th
Edition, Oct 5, 2006
Log Record BufferingLog Record Buffering
Log record buffering: log records are buffered in main memory,
instead of 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.
©Silberschatz, Korth and Sudarshan17.29Database System Concepts, 5th
Edition, Oct 5, 2006
Log Record Buffering (Cont.)Log Record Buffering (Cont.)
The rules below must be followed if log records are buffered:
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.
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.
This rule is called the write-ahead logging or WAL rule
Strictly speaking WAL only requires undo information to be
output
©Silberschatz, Korth and Sudarshan17.30Database System Concepts, 5th
Edition, Oct 5, 2006
Database BufferingDatabase Buffering
Database maintains an in-memory buffer of data blocks
When a new block is needed, if 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
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
(Write ahead logging)
No updates should be in progress on a block when it is output to disk. Can
be ensured as follows.
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.
Before a block is output to disk, the system acquires an exclusive latch
on the block
Ensures no update can be in progress on the block
©Silberschatz, Korth and Sudarshan17.31Database System Concepts, 5th
Edition, Oct 5, 2006
Buffer Management (Cont.)Buffer Management (Cont.)
Database buffer can be implemented either
in an area of real main-memory reserved for the database, or
in virtual memory
Implementing buffer in reserved main-memory has drawbacks:
Memory is partitioned before-hand between database buffer and
applications, limiting flexibility.
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.
©Silberschatz, Korth and Sudarshan17.32Database System Concepts, 5th
Edition, Oct 5, 2006
Buffer Management (Cont.)Buffer Management (Cont.)
Database buffers are generally implemented in virtual memory in spite of
some drawbacks:
When operating system needs to evict a page that has been
modified, 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 OS needs to evict a page from the buffer, it should
pass control to database, which in turn should
1. Output the page to database instead of to swap space (making
sure to output log records first), if it is modified
2. Release the page from the buffer, for the OS to use
Dual paging can thus be avoided, but common operating systems
do not support such functionality.
©Silberschatz, Korth and Sudarshan17.33Database System Concepts, 5th
Edition, Oct 5, 2006
Failure with Loss of NonvolatileFailure with Loss of Nonvolatile
StorageStorage
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.
©Silberschatz, Korth and Sudarshan17.34Database System Concepts, 5th
Edition, Oct 5, 2006
Recovering from Failure of Non-VolatileRecovering from Failure of Non-Volatile
StorageStorage
To recover from disk failure
restore database from 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
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Advanced Recovery AlgorithmAdvanced Recovery Algorithm
©Silberschatz, Korth and Sudarshan17.36Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Key FeaturesAdvanced Recovery: Key Features
Support for high-concurrency locking techniques, such as those used
for B+
-tree concurrency control, which release locks early
Supports “logical undo”
Recovery based on “repeating history”, whereby recovery executes
exactly the same actions as normal processing
including redo of log records of incomplete transactions, followed
by subsequent undo
Key benefits
supports logical undo
easier to understand/show correctness
©Silberschatz, Korth and Sudarshan17.37Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Logical UndoAdvanced Recovery: Logical Undo
LoggingLogging
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
Such logging is called logical undo logging, in contrast to
physical undo logging
Operations are called logical operations
Other examples:
delete of tuple, to undo insert of tuple
allows early lock release on space allocation information
subtract amount deposited, to undo deposit
allows early lock release on bank balance
©Silberschatz, Korth and Sudarshan17.38Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Physical RedoAdvanced Recovery: Physical Redo
Redo information is logged physically (that is, new value for each
write) even for operations with logical undo
Logical redo is very complicated since database state on disk may
not be “operation consistent” when recovery starts
Physical redo logging does not conflict with early lock release
©Silberschatz, Korth and Sudarshan17.39Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: OperationAdvanced Recovery: Operation
LoggingLogging
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.
Example: insert of (key, record-id) pair (K5, RID7) into index I9
<T1, O1, operation-begin>
….
<T1, X, 10, K5>
<T1, Y, 45, RID7>
<T1, O1, operation-end, (delete I9, K5,
RID7)>
Physical redo of steps in insert
©Silberschatz, Korth and Sudarshan17.40Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Operation LoggingAdvanced Recovery: Operation Logging
(Cont.)(Cont.)
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.
©Silberschatz, Korth and Sudarshan17.41Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Txn RollbackAdvanced Recovery: Txn Rollback
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
©Silberschatz, Korth and Sudarshan17.42Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Txn Rollback (Cont.)Advanced Recovery: Txn Rollback (Cont.)
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.
3. Stop the scan when the record <Ti, start> is found
4. 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.
©Silberschatz, Korth and Sudarshan17.43Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Txn RollbackAdvanced Recovery: Txn Rollback
ExampleExample
Example with a complete and an incomplete operation
<T1, start>
<T1, O1, operation-begin>
….
<T1, X, 10, K5>
<T1, Y, 45, RID7>
<T1, O1, operation-end, (delete I9, K5, RID7)>
<T1, O2, operation-begin>
<T1, Z, 45, 70>
 T1 Rollback begins here
<T1, Z, 45>  redo-only log record during physical undo (of
incomplete O2)
<T1, Y, .., ..>  Normal redo records for logical undo of O1
…
<T1, O1, operation-abort>  What if crash occurred immediately
after this?
©Silberschatz, Korth and Sudarshan17.44Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: CrashAdvanced Recovery: Crash
RecoveryRecovery
The following actions are taken when recovering from system crash
1. (Redo phase): Scan log forward from last < checkpoint L> record
till end of log
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
This brings database to state as of crash, with committed as well as
uncommitted transactions having been redone.
Now undo-list contains transactions that are incomplete, that is,
have neither committed nor been fully rolled back.
©Silberschatz, Korth and Sudarshan17.45Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Crash RecoveryAdvanced Recovery: Crash Recovery
(Cont.)(Cont.)
Recovery from system crash (cont.)
2. (Undo phase): Scan log backwards, performing undo on log records
of transactions found in undo-list.
Log records of transactions being rolled back are processed as
described earlier, as they are found
Single shared scan for all transactions being undone
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
This undoes the effects of incomplete transactions (those with neither
commit nor abort log records). Recovery is now complete.
©Silberschatz, Korth and Sudarshan17.46Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: CheckpointingAdvanced Recovery: 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
©Silberschatz, Korth and Sudarshan17.47Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Recovery: Fuzzy CheckpointingAdvanced Recovery: Fuzzy Checkpointing
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
1. Store a pointer to the checkpoint record in a fixed position
last_checkpoint on disk
……
<checkpoint L>
…..
<checkpoint L>
…..
Log
last_checkpoint
©Silberschatz, Korth and Sudarshan17.48Database System Concepts, 5th
Edition, Oct 5, 2006
Advanced Rec: Fuzzy CheckpointingAdvanced Rec: Fuzzy Checkpointing
(Cont.)(Cont.)
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
Database System Concepts
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
ARIES Recovery AlgorithmARIES Recovery Algorithm
©Silberschatz, Korth and Sudarshan17.50Database System Concepts, 5th
Edition, Oct 5, 2006
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
1. Uses log sequence number (LSN) to identify log records
Stores LSNs in pages to identify what updates have already
been applied to a database page
1. Physiological redo
2. Dirty page table to avoid unnecessary redos during recovery
3. 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 …
©Silberschatz, Korth and Sudarshan17.51Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.52Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Data StructuresARIES Data Structures
ARIES uses several 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
Page LSN
Log records of several different types
Dirty page table
©Silberschatz, Korth and Sudarshan17.53Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Data Structures: Page LSNARIES Data Structures: Page LSN
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 page, and write the log record
Update the page
Record the LSN of the log record in PageLSN
Unlock page
To flush page to disk, must first S-latch 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
©Silberschatz, Korth and Sudarshan17.54Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Data Structures: Log RecordARIES Data Structures: Log Record
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
Serves the role of operation-abort log records used in advanced recovery
algorithm
Has 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
1 2 3 4 4
'
3
' 2
'
1
'
©Silberschatz, Korth and Sudarshan17.55Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Data Structures: DirtyPageARIES Data Structures: DirtyPage
TableTable
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
Page PLSN RLSN
P1 25 17
P6 16 15
P23 19 18
25
P1
16
P6
19
P23
DirtyPage Table
9
P15
Buffer Pool
P1 16
…
P6 12
..
P15 9
..
P23 11
Page LSNs
on disk
©Silberschatz, Korth and Sudarshan17.56Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Data Structures: CheckpointARIES Data Structures: Checkpoint
LogLog
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
Dirty pages are not written out at checkpoint time
Instead, they are flushed out continuously, in the background
Checkpoint is thus very low overhead
can be done frequently
©Silberschatz, Korth and Sudarshan17.57Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Recovery AlgorithmARIES 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
©Silberschatz, Korth and Sudarshan17.58Database System Concepts, 5th
Edition, Oct 5, 2006
Aries Recovery: 3 PassesAries Recovery: 3 Passes
Analysis, redo and undo passes
Analysis determines where redo should start
Undo has to go back till start of earliest incomplete transaction
Last checkpoint
Log
Time
End of Log
Analysis pass
Redo pass
Undo pass
©Silberschatz, Korth and Sudarshan17.59Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES Recovery: AnalysisARIES Recovery: Analysis
Analysis pass
Starts from last complete checkpoint log record
Reads 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
Scans forward from checkpoint
.. Cont. on next page …
©Silberschatz, Korth and Sudarshan17.60Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.61Database System Concepts, 5th
Edition, Oct 5, 2006
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!
©Silberschatz, Korth and Sudarshan17.62Database System Concepts, 5th
Edition, Oct 5, 2006
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
'
©Silberschatz, Korth and Sudarshan17.63Database System Concepts, 5th
Edition, Oct 5, 2006
ARIES: Undo PassARIES: 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 undone already
Undos performed as described earlier
©Silberschatz, Korth and Sudarshan17.64Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.65Database System Concepts, 5th
Edition, Oct 5, 2006
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
Database System Concepts
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Remote Backup SystemsRemote Backup Systems
©Silberschatz, Korth and Sudarshan17.67Database System Concepts, 5th
Edition, Oct 5, 2006
Remote Backup SystemsRemote Backup Systems
Remote backup systems provide high availability by allowing transaction
processing to continue even if the primary site is destroyed.
©Silberschatz, Korth and Sudarshan17.68Database System Concepts, 5th
Edition, Oct 5, 2006
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 maintain several
communication links between the primary and the remote backup.
Heart-beat messages
Transfer of control:
To take over control 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 old primary when it recovers, old primary
must receive redo logs from the old backup and apply all updates
locally.
©Silberschatz, Korth and Sudarshan17.69Database System Concepts, 5th
Edition, Oct 5, 2006
Remote Backup Systems (Cont.)Remote Backup Systems (Cont.)
Time to recover: To reduce delay in takeover, backup site periodically
proceses 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:
Backup continually processes redo log record as they arrive,
applying the updates locally.
When failure of the primary is detected the backup rolls back
incomplete transactions, and is ready to process new transactions.
Alternative to remote backup: distributed database with replicated data
Remote backup is faster and cheaper, but less tolerant to failure
more on this in Chapter 19
©Silberschatz, Korth and Sudarshan17.70Database System Concepts, 5th
Edition, Oct 5, 2006
Remote Backup Systems (Cont.)Remote Backup Systems (Cont.)
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 is
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
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
End of ChapterEnd of Chapter
©Silberschatz, Korth and Sudarshan17.72Database System Concepts, 5th
Edition, Oct 5, 2006
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
©Silberschatz, Korth and Sudarshan17.73Database System Concepts, 5th
Edition, Oct 5, 2006
Sample Page TableSample Page Table
©Silberschatz, Korth and Sudarshan17.74Database System Concepts, 5th
Edition, Oct 5, 2006
Example of Shadow PagingExample of Shadow Paging
Shadow and current page tables after write to page 4
©Silberschatz, Korth and Sudarshan17.75Database System Concepts, 5th
Edition, Oct 5, 2006
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.
to make the current page table the new shadow page table, simply
update the pointer to point to current page table on disk
Once pointer to shadow page table has been written, transaction is
committed.
No recovery is needed after a crash — new transactions can start right
away, using the shadow page table.
Pages not pointed to from current/shadow page table should be freed
(garbage collected).
©Silberschatz, Korth and Sudarshan17.76Database System Concepts, 5th
Edition, Oct 5, 2006
Show Paging (Cont.)Show 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
©Silberschatz, Korth and Sudarshan17.77Database System Concepts, 5th
Edition, Oct 5, 2006
Block Storage OperationsBlock Storage Operations
©Silberschatz, Korth and Sudarshan17.78Database System Concepts, 5th
Edition, Oct 5, 2006
Portion of the Database LogPortion of the Database Log
Corresponding toCorresponding to TT00 andand TT11
©Silberschatz, Korth and Sudarshan17.79Database System Concepts, 5th
Edition, Oct 5, 2006
State of the Log and DatabaseState of the Log and Database
Corresponding toCorresponding to TT00 andand TT11
©Silberschatz, Korth and Sudarshan17.80Database System Concepts, 5th
Edition, Oct 5, 2006
Portion of the System Log CorrespondingPortion of the System Log Corresponding
toto TT00 andand TT11
©Silberschatz, Korth and Sudarshan17.81Database System Concepts, 5th
Edition, Oct 5, 2006
State of System Log and DatabaseState of System Log and Database
Corresponding toCorresponding to TT00 andand TT11

Mais conteúdo relacionado

Mais procurados

Query optimization
Query optimizationQuery optimization
Query optimizationPooja Dixit
 
Transaction states and properties
Transaction states and propertiesTransaction states and properties
Transaction states and propertiesChetan Mahawar
 
File Management in Operating Systems
File Management in Operating SystemsFile Management in Operating Systems
File Management in Operating Systemsvampugani
 
Distributed file system
Distributed file systemDistributed file system
Distributed file systemAnamika Singh
 
Distributed Database Management System
Distributed Database Management SystemDistributed Database Management System
Distributed Database Management SystemAAKANKSHA JAIN
 
Concurrency Control in Database Management System
Concurrency Control in Database Management SystemConcurrency Control in Database Management System
Concurrency Control in Database Management SystemJanki Shah
 
Dbms ii mca-ch11-recovery-2013
Dbms ii mca-ch11-recovery-2013Dbms ii mca-ch11-recovery-2013
Dbms ii mca-ch11-recovery-2013Prosanta Ghosh
 
11. Storage and File Structure in DBMS
11. Storage and File Structure in DBMS11. Storage and File Structure in DBMS
11. Storage and File Structure in DBMSkoolkampus
 
15. Transactions in DBMS
15. Transactions in DBMS15. Transactions in DBMS
15. Transactions in DBMSkoolkampus
 
Database , 8 Query Optimization
Database , 8 Query OptimizationDatabase , 8 Query Optimization
Database , 8 Query OptimizationAli Usman
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecturepcherukumalla
 
Log based and Recovery with concurrent transaction
Log based and Recovery with concurrent transactionLog based and Recovery with concurrent transaction
Log based and Recovery with concurrent transactionnikunjandy
 
16. Concurrency Control in DBMS
16. Concurrency Control in DBMS16. Concurrency Control in DBMS
16. Concurrency Control in DBMSkoolkampus
 

Mais procurados (20)

Query optimization
Query optimizationQuery optimization
Query optimization
 
Acid properties
Acid propertiesAcid properties
Acid properties
 
Distributed database
Distributed databaseDistributed database
Distributed database
 
Transaction states and properties
Transaction states and propertiesTransaction states and properties
Transaction states and properties
 
File Management in Operating Systems
File Management in Operating SystemsFile Management in Operating Systems
File Management in Operating Systems
 
Distributed file system
Distributed file systemDistributed file system
Distributed file system
 
operating system structure
operating system structureoperating system structure
operating system structure
 
Distributed Database Management System
Distributed Database Management SystemDistributed Database Management System
Distributed Database Management System
 
Concurrency Control in Database Management System
Concurrency Control in Database Management SystemConcurrency Control in Database Management System
Concurrency Control in Database Management System
 
Dbms ii mca-ch11-recovery-2013
Dbms ii mca-ch11-recovery-2013Dbms ii mca-ch11-recovery-2013
Dbms ii mca-ch11-recovery-2013
 
11. Storage and File Structure in DBMS
11. Storage and File Structure in DBMS11. Storage and File Structure in DBMS
11. Storage and File Structure in DBMS
 
15. Transactions in DBMS
15. Transactions in DBMS15. Transactions in DBMS
15. Transactions in DBMS
 
Recovery
RecoveryRecovery
Recovery
 
Database , 8 Query Optimization
Database , 8 Query OptimizationDatabase , 8 Query Optimization
Database , 8 Query Optimization
 
Recovery techniques
Recovery techniquesRecovery techniques
Recovery techniques
 
Database recovery
Database recoveryDatabase recovery
Database recovery
 
Chapter19
Chapter19Chapter19
Chapter19
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
Log based and Recovery with concurrent transaction
Log based and Recovery with concurrent transactionLog based and Recovery with concurrent transaction
Log based and Recovery with concurrent transaction
 
16. Concurrency Control in DBMS
16. Concurrency Control in DBMS16. Concurrency Control in DBMS
16. Concurrency Control in DBMS
 

Destaque

Automating Disaster Recovery PostgreSQL
Automating Disaster Recovery PostgreSQLAutomating Disaster Recovery PostgreSQL
Automating Disaster Recovery PostgreSQLNina Kaufman
 
Topic 4 database recovery
Topic 4 database recoveryTopic 4 database recovery
Topic 4 database recoveryacap paei
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with HadoopPhilippe Julio
 

Destaque (6)

Automating Disaster Recovery PostgreSQL
Automating Disaster Recovery PostgreSQLAutomating Disaster Recovery PostgreSQL
Automating Disaster Recovery PostgreSQL
 
Topic 4 database recovery
Topic 4 database recoveryTopic 4 database recovery
Topic 4 database recovery
 
What is big data?
What is big data?What is big data?
What is big data?
 
What is Big Data?
What is Big Data?What is Big Data?
What is Big Data?
 
Big Data Analytics with Hadoop
Big Data Analytics with HadoopBig Data Analytics with Hadoop
Big Data Analytics with Hadoop
 
Big data ppt
Big  data pptBig  data ppt
Big data ppt
 

Semelhante a Database recovery

Semelhante a Database recovery (20)

Top 17 Data Recovery System
Top 17 Data Recovery SystemTop 17 Data Recovery System
Top 17 Data Recovery System
 
Ch17
Ch17Ch17
Ch17
 
17 Recovery system.ppt
17 Recovery system.ppt17 Recovery system.ppt
17 Recovery system.ppt
 
Lesson12 recovery architectures
Lesson12 recovery architecturesLesson12 recovery architectures
Lesson12 recovery architectures
 
Dbms
DbmsDbms
Dbms
 
Recovery system
Recovery systemRecovery system
Recovery system
 
Recovery system
Recovery systemRecovery system
Recovery system
 
recovery system
recovery systemrecovery system
recovery system
 
Recovery system
Recovery systemRecovery system
Recovery system
 
Recovery Management
Recovery ManagementRecovery Management
Recovery Management
 
Ch17
Ch17Ch17
Ch17
 
Transaction slide
Transaction slideTransaction slide
Transaction slide
 
Assignment#14
Assignment#14Assignment#14
Assignment#14
 
Recovery & Atom city & Log based Recovery
Recovery & Atom city & Log based RecoveryRecovery & Atom city & Log based Recovery
Recovery & Atom city & Log based Recovery
 
Recovery System.pptx
Recovery System.pptxRecovery System.pptx
Recovery System.pptx
 
Transection management
Transection managementTransection management
Transection management
 
_transaction_processing.pptx
_transaction_processing.pptx_transaction_processing.pptx
_transaction_processing.pptx
 
14261 transaction processing
14261 transaction processing14261 transaction processing
14261 transaction processing
 
Ch17 introduction to transaction processing concepts and theory
Ch17 introduction to transaction processing concepts and theoryCh17 introduction to transaction processing concepts and theory
Ch17 introduction to transaction processing concepts and theory
 
Chapter17
Chapter17Chapter17
Chapter17
 

Mais de Student

Cloud computing
Cloud computingCloud computing
Cloud computingStudent
 
Keyword research
Keyword researchKeyword research
Keyword researchStudent
 
Agile Process models
Agile Process modelsAgile Process models
Agile Process modelsStudent
 
Virtual technology
Virtual technologyVirtual technology
Virtual technologyStudent
 
Student management system
Student management systemStudent management system
Student management systemStudent
 
Ip services
Ip servicesIp services
Ip servicesStudent
 
Process models
Process modelsProcess models
Process modelsStudent
 
Student management system project report c++
Student management system project report c++Student management system project report c++
Student management system project report c++Student
 
Application layer chapter-9
Application layer chapter-9Application layer chapter-9
Application layer chapter-9Student
 
computer networks layers
computer networks layerscomputer networks layers
computer networks layersStudent
 
Stack application
Stack applicationStack application
Stack applicationStudent
 
Uml struct2
Uml struct2Uml struct2
Uml struct2Student
 

Mais de Student (12)

Cloud computing
Cloud computingCloud computing
Cloud computing
 
Keyword research
Keyword researchKeyword research
Keyword research
 
Agile Process models
Agile Process modelsAgile Process models
Agile Process models
 
Virtual technology
Virtual technologyVirtual technology
Virtual technology
 
Student management system
Student management systemStudent management system
Student management system
 
Ip services
Ip servicesIp services
Ip services
 
Process models
Process modelsProcess models
Process models
 
Student management system project report c++
Student management system project report c++Student management system project report c++
Student management system project report c++
 
Application layer chapter-9
Application layer chapter-9Application layer chapter-9
Application layer chapter-9
 
computer networks layers
computer networks layerscomputer networks layers
computer networks layers
 
Stack application
Stack applicationStack application
Stack application
 
Uml struct2
Uml struct2Uml struct2
Uml struct2
 

Último

Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationKnoldus Inc.
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkPixlogix Infotech
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...itnewsafrica
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observabilityitnewsafrica
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...itnewsafrica
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Strongerpanagenda
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Kaya Weers
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 

Último (20)

Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog Presentation
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 

Database recovery

  • 1. Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use Chapter 4 Recovery SystemChapter 4 Recovery System
  • 2. ©Silberschatz, Korth and Sudarshan17.2Database System Concepts, 5th Edition, Oct 5, 2006 Chapter 4: Recovery SystemChapter 4: Recovery System Failure Classification Storage Structure Recovery and Atomicity Log-Based Recovery Shadow Paging Recovery With Concurrent Transactions Buffer Management Failure with Loss of Nonvolatile Storage Advanced Recovery Techniques ARIES Recovery Algorithm Remote Backup Systems
  • 3. ©Silberschatz, Korth and Sudarshan17.3Database System Concepts, 5th Edition, Oct 5, 2006 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. 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
  • 4. ©Silberschatz, Korth and Sudarshan17.4Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 5. ©Silberschatz, Korth and Sudarshan17.5Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 6. ©Silberschatz, Korth and Sudarshan17.6Database System Concepts, 5th Edition, Oct 5, 2006 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.
  • 7. ©Silberschatz, Korth and Sudarshan17.7Database System Concepts, 5th Edition, Oct 5, 2006 Stable-Storage Implementation (Cont.)(Cont.) Protecting storage media from failure during data transfer (cont.): Copies of a block may differ due to failure during output operation. To recover from failure: 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 1. 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.
  • 8. ©Silberschatz, Korth and Sudarshan17.8Database System Concepts, 5th Edition, Oct 5, 2006 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.
  • 9. ©Silberschatz, Korth and Sudarshan17.9Database System Concepts, 5th Edition, Oct 5, 2006 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.
  • 10. ©Silberschatz, Korth and Sudarshan17.10Database System Concepts, 5th Edition, Oct 5, 2006 Example of Data AccessExample of Data Access X Y A B x1 y1 buffer Buffer Block A Buffer Block B input(A) output(B) read(X) write(Y) disk work area of T1 work area of T2 memory x2
  • 11. ©Silberschatz, Korth and Sudarshan17.11Database System Concepts, 5th Edition, Oct 5, 2006 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.
  • 12. ©Silberschatz, Korth and Sudarshan17.12Database System Concepts, 5th Edition, Oct 5, 2006 Recovery and Atomicity (Cont.)Recovery and Atomicity (Cont.) To ensure atomicity despite failures, we first output information describing the modifications to stable storage without modifying the database itself. We study two approaches: log-based recovery, and shadow-paging We assume (initially) that transactions run serially, that is, one after the other.
  • 13. ©Silberschatz, Korth and Sudarshan17.13Database System Concepts, 5th Edition, Oct 5, 2006 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. 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 Xj Xj 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
  • 14. ©Silberschatz, Korth and Sudarshan17.14Database System Concepts, 5th Edition, Oct 5, 2006 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 The write 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.
  • 15. ©Silberschatz, Korth and Sudarshan17.15Database System Concepts, 5th Edition, Oct 5, 2006 Deferred Database ModificationDeferred Database Modification (Cont.)(Cont.) 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 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)
  • 16. ©Silberschatz, Korth and Sudarshan17.16Database System Concepts, 5th Edition, Oct 5, 2006 Deferred Database ModificationDeferred Database Modification (Cont.)(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) must be performed followed by redo(T1) since <T0 commit> and <Ti commit> are present
  • 17. ©Silberschatz, Korth and Sudarshan17.17Database System Concepts, 5th Edition, Oct 5, 2006 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 Can be extended to postpone log record output, so long as prior to execution of an output(B) operation for a data block B, all log records corresponding to items B must be flushed to stable storage Output of updated blocks can take place at any time before or after transaction commit Order in which blocks are output can be different from the order in which they are written.
  • 18. ©Silberschatz, Korth and Sudarshan17.18Database System Concepts, 5th Edition, Oct 5, 2006 Immediate Database ModificationImmediate Database Modification ExampleExample Log 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. x1
  • 19. ©Silberschatz, Korth and Sudarshan17.19Database System Concepts, 5th Edition, Oct 5, 2006 Immediate Database ModificationImmediate Database Modification (Cont.)(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 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.
  • 20. ©Silberschatz, Korth and Sudarshan17.20Database System Concepts, 5th Edition, Oct 5, 2006 Immediate DB Modification RecoveryImmediate DB Modification Recovery ExampleExample 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
  • 21. ©Silberschatz, Korth and Sudarshan17.21Database System Concepts, 5th Edition, Oct 5, 2006 CheckpointsCheckpoints Problems in recovery procedure as discussed earlier : 1. searching the entire log is time-consuming 2. we might unnecessarily redo transactions which have already 3. 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.
  • 22. ©Silberschatz, Korth and Sudarshan17.22Database System Concepts, 5th Edition, Oct 5, 2006 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. 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).
  • 23. ©Silberschatz, Korth and Sudarshan17.23Database System Concepts, 5th Edition, Oct 5, 2006 Example of CheckpointsExample of Checkpoints T1 can be ignored (updates already output to disk due to checkpoint) T2 and T3 redone. T4 undone Tc Tf T1 T2 T3 T4 checkpoint system failure
  • 24. ©Silberschatz, Korth and Sudarshan17.24Database System Concepts, 5th Edition, Oct 5, 2006 Recovery With ConcurrentRecovery With Concurrent TransactionsTransactions 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.
  • 25. ©Silberschatz, Korth and Sudarshan17.25Database System Concepts, 5th Edition, Oct 5, 2006 Recovery With Concurrent TransactionsRecovery With Concurrent Transactions (Cont.)(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 1. For every Ti in L, if Ti is not in redo-list, add Ti to undo-list
  • 26. ©Silberschatz, Korth and Sudarshan17.26Database System Concepts, 5th Edition, Oct 5, 2006 Recovery With Concurrent TransactionsRecovery With Concurrent Transactions (Cont.)(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. Recovery now continues as follows: 1. 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. 1. Locate the most recent <checkpoint L> record. 2. 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
  • 27. ©Silberschatz, Korth and Sudarshan17.27Database System Concepts, 5th Edition, Oct 5, 2006 Example of RecoveryExample of Recovery Go over the steps of the recovery algorithm on the following log: <T0 start> <T0, A, 0, 10> <T0 commit> <T1 start> /* Scan at step 1 comes up to here */ <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>
  • 28. ©Silberschatz, Korth and Sudarshan17.28Database System Concepts, 5th Edition, Oct 5, 2006 Log Record BufferingLog Record Buffering Log record buffering: log records are buffered in main memory, instead of 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.
  • 29. ©Silberschatz, Korth and Sudarshan17.29Database System Concepts, 5th Edition, Oct 5, 2006 Log Record Buffering (Cont.)Log Record Buffering (Cont.) The rules below must be followed if log records are buffered: 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. 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. This rule is called the write-ahead logging or WAL rule Strictly speaking WAL only requires undo information to be output
  • 30. ©Silberschatz, Korth and Sudarshan17.30Database System Concepts, 5th Edition, Oct 5, 2006 Database BufferingDatabase Buffering Database maintains an in-memory buffer of data blocks When a new block is needed, if 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 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 (Write ahead logging) No updates should be in progress on a block when it is output to disk. Can be ensured as follows. 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. Before a block is output to disk, the system acquires an exclusive latch on the block Ensures no update can be in progress on the block
  • 31. ©Silberschatz, Korth and Sudarshan17.31Database System Concepts, 5th Edition, Oct 5, 2006 Buffer Management (Cont.)Buffer Management (Cont.) Database buffer can be implemented either in an area of real main-memory reserved for the database, or in virtual memory Implementing buffer in reserved main-memory has drawbacks: Memory is partitioned before-hand between database buffer and applications, limiting flexibility. 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.
  • 32. ©Silberschatz, Korth and Sudarshan17.32Database System Concepts, 5th Edition, Oct 5, 2006 Buffer Management (Cont.)Buffer Management (Cont.) Database buffers are generally implemented in virtual memory in spite of some drawbacks: When operating system needs to evict a page that has been modified, 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 OS needs to evict a page from the buffer, it should pass control to database, which in turn should 1. Output the page to database instead of to swap space (making sure to output log records first), if it is modified 2. Release the page from the buffer, for the OS to use Dual paging can thus be avoided, but common operating systems do not support such functionality.
  • 33. ©Silberschatz, Korth and Sudarshan17.33Database System Concepts, 5th Edition, Oct 5, 2006 Failure with Loss of NonvolatileFailure with Loss of Nonvolatile StorageStorage 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.
  • 34. ©Silberschatz, Korth and Sudarshan17.34Database System Concepts, 5th Edition, Oct 5, 2006 Recovering from Failure of Non-VolatileRecovering from Failure of Non-Volatile StorageStorage To recover from disk failure restore database from 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
  • 35. Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use Advanced Recovery AlgorithmAdvanced Recovery Algorithm
  • 36. ©Silberschatz, Korth and Sudarshan17.36Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Key FeaturesAdvanced Recovery: Key Features Support for high-concurrency locking techniques, such as those used for B+ -tree concurrency control, which release locks early Supports “logical undo” Recovery based on “repeating history”, whereby recovery executes exactly the same actions as normal processing including redo of log records of incomplete transactions, followed by subsequent undo Key benefits supports logical undo easier to understand/show correctness
  • 37. ©Silberschatz, Korth and Sudarshan17.37Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Logical UndoAdvanced Recovery: Logical Undo LoggingLogging 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 Such logging is called logical undo logging, in contrast to physical undo logging Operations are called logical operations Other examples: delete of tuple, to undo insert of tuple allows early lock release on space allocation information subtract amount deposited, to undo deposit allows early lock release on bank balance
  • 38. ©Silberschatz, Korth and Sudarshan17.38Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Physical RedoAdvanced Recovery: Physical Redo Redo information is logged physically (that is, new value for each write) even for operations with logical undo Logical redo is very complicated since database state on disk may not be “operation consistent” when recovery starts Physical redo logging does not conflict with early lock release
  • 39. ©Silberschatz, Korth and Sudarshan17.39Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: OperationAdvanced Recovery: Operation LoggingLogging 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. Example: insert of (key, record-id) pair (K5, RID7) into index I9 <T1, O1, operation-begin> …. <T1, X, 10, K5> <T1, Y, 45, RID7> <T1, O1, operation-end, (delete I9, K5, RID7)> Physical redo of steps in insert
  • 40. ©Silberschatz, Korth and Sudarshan17.40Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Operation LoggingAdvanced Recovery: Operation Logging (Cont.)(Cont.) 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.
  • 41. ©Silberschatz, Korth and Sudarshan17.41Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Txn RollbackAdvanced Recovery: Txn Rollback 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
  • 42. ©Silberschatz, Korth and Sudarshan17.42Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Txn Rollback (Cont.)Advanced Recovery: Txn Rollback (Cont.) 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. 3. Stop the scan when the record <Ti, start> is found 4. 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.
  • 43. ©Silberschatz, Korth and Sudarshan17.43Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Txn RollbackAdvanced Recovery: Txn Rollback ExampleExample Example with a complete and an incomplete operation <T1, start> <T1, O1, operation-begin> …. <T1, X, 10, K5> <T1, Y, 45, RID7> <T1, O1, operation-end, (delete I9, K5, RID7)> <T1, O2, operation-begin> <T1, Z, 45, 70>  T1 Rollback begins here <T1, Z, 45>  redo-only log record during physical undo (of incomplete O2) <T1, Y, .., ..>  Normal redo records for logical undo of O1 … <T1, O1, operation-abort>  What if crash occurred immediately after this?
  • 44. ©Silberschatz, Korth and Sudarshan17.44Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: CrashAdvanced Recovery: Crash RecoveryRecovery The following actions are taken when recovering from system crash 1. (Redo phase): Scan log forward from last < checkpoint L> record till end of log 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 This brings database to state as of crash, with committed as well as uncommitted transactions having been redone. Now undo-list contains transactions that are incomplete, that is, have neither committed nor been fully rolled back.
  • 45. ©Silberschatz, Korth and Sudarshan17.45Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Crash RecoveryAdvanced Recovery: Crash Recovery (Cont.)(Cont.) Recovery from system crash (cont.) 2. (Undo phase): Scan log backwards, performing undo on log records of transactions found in undo-list. Log records of transactions being rolled back are processed as described earlier, as they are found Single shared scan for all transactions being undone 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 This undoes the effects of incomplete transactions (those with neither commit nor abort log records). Recovery is now complete.
  • 46. ©Silberschatz, Korth and Sudarshan17.46Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: CheckpointingAdvanced Recovery: 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
  • 47. ©Silberschatz, Korth and Sudarshan17.47Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Recovery: Fuzzy CheckpointingAdvanced Recovery: Fuzzy Checkpointing 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 1. Store a pointer to the checkpoint record in a fixed position last_checkpoint on disk …… <checkpoint L> ….. <checkpoint L> ….. Log last_checkpoint
  • 48. ©Silberschatz, Korth and Sudarshan17.48Database System Concepts, 5th Edition, Oct 5, 2006 Advanced Rec: Fuzzy CheckpointingAdvanced Rec: Fuzzy Checkpointing (Cont.)(Cont.) 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
  • 49. Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use ARIES Recovery AlgorithmARIES Recovery Algorithm
  • 50. ©Silberschatz, Korth and Sudarshan17.50Database System Concepts, 5th Edition, Oct 5, 2006 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 1. Uses log sequence number (LSN) to identify log records Stores LSNs in pages to identify what updates have already been applied to a database page 1. Physiological redo 2. Dirty page table to avoid unnecessary redos during recovery 3. 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 …
  • 51. ©Silberschatz, Korth and Sudarshan17.51Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 52. ©Silberschatz, Korth and Sudarshan17.52Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Data StructuresARIES Data Structures ARIES uses several 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 Page LSN Log records of several different types Dirty page table
  • 53. ©Silberschatz, Korth and Sudarshan17.53Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Data Structures: Page LSNARIES Data Structures: Page LSN 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 page, and write the log record Update the page Record the LSN of the log record in PageLSN Unlock page To flush page to disk, must first S-latch 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
  • 54. ©Silberschatz, Korth and Sudarshan17.54Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Data Structures: Log RecordARIES Data Structures: Log Record 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 Serves the role of operation-abort log records used in advanced recovery algorithm Has 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 1 2 3 4 4 ' 3 ' 2 ' 1 '
  • 55. ©Silberschatz, Korth and Sudarshan17.55Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Data Structures: DirtyPageARIES Data Structures: DirtyPage TableTable 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 Page PLSN RLSN P1 25 17 P6 16 15 P23 19 18 25 P1 16 P6 19 P23 DirtyPage Table 9 P15 Buffer Pool P1 16 … P6 12 .. P15 9 .. P23 11 Page LSNs on disk
  • 56. ©Silberschatz, Korth and Sudarshan17.56Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Data Structures: CheckpointARIES Data Structures: Checkpoint LogLog 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 Dirty pages are not written out at checkpoint time Instead, they are flushed out continuously, in the background Checkpoint is thus very low overhead can be done frequently
  • 57. ©Silberschatz, Korth and Sudarshan17.57Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Recovery AlgorithmARIES 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
  • 58. ©Silberschatz, Korth and Sudarshan17.58Database System Concepts, 5th Edition, Oct 5, 2006 Aries Recovery: 3 PassesAries Recovery: 3 Passes Analysis, redo and undo passes Analysis determines where redo should start Undo has to go back till start of earliest incomplete transaction Last checkpoint Log Time End of Log Analysis pass Redo pass Undo pass
  • 59. ©Silberschatz, Korth and Sudarshan17.59Database System Concepts, 5th Edition, Oct 5, 2006 ARIES Recovery: AnalysisARIES Recovery: Analysis Analysis pass Starts from last complete checkpoint log record Reads 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 Scans forward from checkpoint .. Cont. on next page …
  • 60. ©Silberschatz, Korth and Sudarshan17.60Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 61. ©Silberschatz, Korth and Sudarshan17.61Database System Concepts, 5th Edition, Oct 5, 2006 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!
  • 62. ©Silberschatz, Korth and Sudarshan17.62Database System Concepts, 5th Edition, Oct 5, 2006 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 '
  • 63. ©Silberschatz, Korth and Sudarshan17.63Database System Concepts, 5th Edition, Oct 5, 2006 ARIES: Undo PassARIES: 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 undone already Undos performed as described earlier
  • 64. ©Silberschatz, Korth and Sudarshan17.64Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 65. ©Silberschatz, Korth and Sudarshan17.65Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 66. Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use Remote Backup SystemsRemote Backup Systems
  • 67. ©Silberschatz, Korth and Sudarshan17.67Database System Concepts, 5th Edition, Oct 5, 2006 Remote Backup SystemsRemote Backup Systems Remote backup systems provide high availability by allowing transaction processing to continue even if the primary site is destroyed.
  • 68. ©Silberschatz, Korth and Sudarshan17.68Database System Concepts, 5th Edition, Oct 5, 2006 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 maintain several communication links between the primary and the remote backup. Heart-beat messages Transfer of control: To take over control 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 old primary when it recovers, old primary must receive redo logs from the old backup and apply all updates locally.
  • 69. ©Silberschatz, Korth and Sudarshan17.69Database System Concepts, 5th Edition, Oct 5, 2006 Remote Backup Systems (Cont.)Remote Backup Systems (Cont.) Time to recover: To reduce delay in takeover, backup site periodically proceses 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: Backup continually processes redo log record as they arrive, applying the updates locally. When failure of the primary is detected the backup rolls back incomplete transactions, and is ready to process new transactions. Alternative to remote backup: distributed database with replicated data Remote backup is faster and cheaper, but less tolerant to failure more on this in Chapter 19
  • 70. ©Silberschatz, Korth and Sudarshan17.70Database System Concepts, 5th Edition, Oct 5, 2006 Remote Backup Systems (Cont.)Remote Backup Systems (Cont.) 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 is commit log record is written at the primary. Better availability than two-very-safe; avoids problem of lost transactions in one-safe.
  • 71. Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use End of ChapterEnd of Chapter
  • 72. ©Silberschatz, Korth and Sudarshan17.72Database System Concepts, 5th Edition, Oct 5, 2006 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
  • 73. ©Silberschatz, Korth and Sudarshan17.73Database System Concepts, 5th Edition, Oct 5, 2006 Sample Page TableSample Page Table
  • 74. ©Silberschatz, Korth and Sudarshan17.74Database System Concepts, 5th Edition, Oct 5, 2006 Example of Shadow PagingExample of Shadow Paging Shadow and current page tables after write to page 4
  • 75. ©Silberschatz, Korth and Sudarshan17.75Database System Concepts, 5th Edition, Oct 5, 2006 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. to make the current page table the new shadow page table, simply update the pointer to point to current page table on disk Once pointer to shadow page table has been written, transaction is committed. No recovery is needed after a crash — new transactions can start right away, using the shadow page table. Pages not pointed to from current/shadow page table should be freed (garbage collected).
  • 76. ©Silberschatz, Korth and Sudarshan17.76Database System Concepts, 5th Edition, Oct 5, 2006 Show Paging (Cont.)Show 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
  • 77. ©Silberschatz, Korth and Sudarshan17.77Database System Concepts, 5th Edition, Oct 5, 2006 Block Storage OperationsBlock Storage Operations
  • 78. ©Silberschatz, Korth and Sudarshan17.78Database System Concepts, 5th Edition, Oct 5, 2006 Portion of the Database LogPortion of the Database Log Corresponding toCorresponding to TT00 andand TT11
  • 79. ©Silberschatz, Korth and Sudarshan17.79Database System Concepts, 5th Edition, Oct 5, 2006 State of the Log and DatabaseState of the Log and Database Corresponding toCorresponding to TT00 andand TT11
  • 80. ©Silberschatz, Korth and Sudarshan17.80Database System Concepts, 5th Edition, Oct 5, 2006 Portion of the System Log CorrespondingPortion of the System Log Corresponding toto TT00 andand TT11
  • 81. ©Silberschatz, Korth and Sudarshan17.81Database System Concepts, 5th Edition, Oct 5, 2006 State of System Log and DatabaseState of System Log and Database Corresponding toCorresponding to TT00 andand TT11