Chapter 17: Recovery System
Chapter 16: Recovery System
Failure Classification
• Transaction failure :
– Logical errors: transaction cannot complete due to some internal
error condition
– System errors: the database system must terminate an active
transaction due to an error condition (e.g., deadlock)
• System crash: a power failure or other hardware or
software failure causes the system to crash.
– Fail-stop assumption: non-volatile storage contents are assumed
to not be corrupted by system crash
• Database systems have numerous integrity checks to prevent corruption of
disk data
• Disk failure: a head crash or similar disk failure destroys all
or part of disk storage
– Destruction is assumed to be detectable: disk drives use
checksums to detect failures
Recovery Algorithms
■ Consider transaction Ti that transfers $50 from account A to
account B
Two updates: subtract 50 from A and add 50 to B
■ Transaction Ti requires updates to A and B to be output to
the database.
A failure may occur after one of these modifications have been
made but before both of them are made.
Modifying the database without ensuring that the transaction will
commit may leave the database in an inconsistent state
Not modifying the database may result in lost updates if failure
occurs just after transaction commits
■ Recovery algorithms have two parts
1. Actions taken during normal transaction processing to ensure
enough information exists to recover from failures
2. Actions taken after a failure to recover the database contents to a
state that ensures atomicity, consistency and durability
Storage 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
– but may still fail, losing data
• Stable storage:
– a mythical form of storage that survives all failures
– approximated by maintaining multiple copies on distinct nonvolatile
– See book for more details on how to implement stable storage
Stable-Storage Implementation
• Maintain multiple copies of each block on separate disks
copies can be at remote sites to protect against disasters such as
fire or flooding.
• Failure during data transfer can still result in inconsistent
copies: Block transfer can result in
Successful completion
Partial failure: destination block has incorrect information
Total failure: destination block was never updated
• Protecting storage media from failure during data transfer
(one solution):
Execute output operation as follows (assuming two copies of each
Write the information onto the first physical block.
When the first write successfully completes, write the same information
onto the second physical block.
The output is completed only after the second write successfully completes.
Data Access
• Physical blocks are those blocks residing on the disk.
• Buffer blocks are the blocks residing temporarily in main
• 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.
• We assume, for simplicity, that each data item fits in, and is
stored inside, a single block.
Example of Data Access
Buffer Block A
Buffer Block B
work area
of T1
work area
of T2
Data Access (Cont.)
• Each transaction Ti has its private work-area in which local
copies of all data items accessed and updated by it are
– Ti's local copy of a data item X is called xi.
• Transferring data items between system buffer blocks and
its private work-area done by:
– 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.
– Note: output(BX) need not immediately follow write(X). System
can perform the output operation when it deems fit.
• Transactions
– Must perform read(X) before accessing X for the first time
(subsequent reads can be from local copy)
– write(X) can be executed at any time before the transaction
Recovery and Atomicity
• To ensure atomicity despite failures, we first output
information describing the modifications to stable storage
without modifying the database itself.
• We study log-based recovery mechanisms in detail
– We first present key concepts
– And then present the actual recovery algorithm
• Less used alternative: shadow-paging (brief details in
Log-Based Recovery
• A log is kept on stable storage.
– The log is a sequence of log records, and maintains a record of
update activities on the database.
• When transaction 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 (the
old value), and V2 is the value to be written to X (the new
• When Ti finishes it last statement, the log record <Ti
commit> is written.
• Two approaches using logs
– Deferred database modification
– Immediate database modification
Immediate Database Modification
• The immediate-modification scheme allows updates of an
uncommitted transaction to be made to the buffer, or the
disk itself, before the transaction commits
• Update log record must be written before database item is
– We assume that the log record is output directly to stable storage
• Output of updated blocks to stable storage 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.
• The deferred-modification scheme performs updates to
buffer/disk only at the time of transaction commit
– Simplifies some aspects of recovery
– But has overhead of storing local copy
Transaction Commit
• A transaction is said to have committed when its commit log
record is output to stable storage
– all previous log records of the transaction must have been output
• Writes performed by a transaction may still be in the buffer
when the transaction commits, and may be output later
Immediate Database Modification Example
<T0 start>
<T0, A, 1000, 950>
<To, B, 2000, 2050
A = 950
B = 2050
<T0 commit>
<T1 start>
<T1, C, 700, 600>
<T1 commit>
C = 600
BC output before T1
BA output after T0
Concurrency Control and Recovery
• With concurrent transactions, all transactions share a single
disk buffer and a single log
– A buffer block can have data items updated by one or more
• We assume that if a transaction Ti has modified an item, no
other transaction can modify the same item until Ti has
committed or aborted
– 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?
– Can be ensured by obtaining exclusive locks on updated items
and holding the locks till end of transaction (strict two-phase
• Log records of different transactions may be interspersed in
the log.
Undo and Redo Operations
• Undo of a log record <Ti, X, V1, V2> writes the old value V1
to X
• Redo of a log record <Ti, X, V1, V2> writes the new value
V2 to X
• Undo and Redo of Transactions
– 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
• each time a data item X is restored to its old value V a special log record
<Ti , X, V> is written out
• when undo of a transaction is complete, a log record
<Ti abort> is written out.
– 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
• No logging is done in this case
Undo and Redo on Recovering from Failure
• When recovering after failure:
– Transaction Ti needs to be undone if the log
• contains the record <Ti start>,
• but does not contain either the record <Ti commit> or <Ti abort>.
– Transaction Ti needs to be redone if the log
• contains the records <Ti start>
• and contains the record <Ti commit> or <Ti abort>
• Note that If transaction Ti was undone earlier and the <Ti
abort> record written to the log, and then a failure occurs,
on recovery from failure Ti is redone
– such a redo redoes all the original actions including the steps
that restored old values
• Known as repeating history
• Seems wasteful, but simplifies recovery greatly
Immediate DB Modification Recovery Example
Below we show the log as it appears at three instances of time.
Recovery actions in each case above are:
(a) undo (T0): B is restored to 2000 and A to 1000, and log records
<T0, B, 2000>, <T0, A, 1000>, <T0, abort> are written out
(b) redo (T0) and undo (T1): A and B are set to 950 and 2050 and C is restored to 700.
Log records <T1, C, 700>, <T1, abort> are written out.
(c) redo (T0) and redo (T1): A and B are set to 950 and 2050
respectively. Then C is set to 600
■ Redoing/undoing all transactions recorded in the log can be
very slow
processing the entire log is time-consuming if the system has run
for a long time
we might unnecessarily redo transactions which have already
output their updates to the database.
■ Streamline recovery procedure by periodically performing
Output all log records currently residing in main memory onto
stable storage.
Output all modified buffer blocks to the disk.
Write a log record < checkpoint L> onto stable storage where L is
a list of all transactions active at the time of checkpoint.
All updates are stopped while doing checkpointing
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.
Scan backwards from end of log to find the most recent
<checkpoint L> record
Only transactions that are in L or started after the checkpoint need
to be redone or undone
Transactions that committed or aborted before the checkpoint
already have all their updates output to stable storage.
■ Some earlier part of the log may be needed for undo
Continue scanning backwards till a record <Ti start> is found for
every transaction Ti in L.
Parts of log prior to earliest <Ti start> record above are not
needed for recovery, and can be erased whenever desired.
Example of Checkpoints
system failure
• T1 can be ignored (updates already output to disk due
to checkpoint)
• T2 and T3 redone.
• T4 undone
Recovery Algorithm
■ So far: we covered key concepts
■ Now: we present the components of the basic recovery
■ Later: we present extensions to allow more concurrency
Recovery Algorithm
• Logging (during normal operation):
– <Ti start> at transaction start
– <Ti, Xj, V1, V2> for each update, and
– <Ti commit> at transaction end
• Transaction rollback (during normal operation)
– Let Ti be the transaction to be rolled back
– Scan log backwards from the end, and for each log record of Ti of
the form <Ti, Xj, V1, V2>
• perform the undo by writing V1 to Xj,
• write a log record <Ti , Xj, V1>
– such log records are called compensation log records
– Once the record <Ti start> is found stop the scan and write the log
record <Ti abort>
Recovery Algorithm (Cont.)
• Recovery from failure: Two phases
Redo phase: replay updates of all transactions, whether they
committed, aborted, or are incomplete
Undo phase: undo all incomplete transactions
• Redo phase:
Find last <checkpoint L> record, and set undo-list to L.
Scan forward from above <checkpoint L> record
Whenever a record <Ti, Xj, V1, V2> is found, redo it by writing V2 to Xj
Whenever a log record <Ti start> is found, add Ti to undo-list
Whenever a log record <Ti commit> or <Ti abort> is found, remove Ti
from undo-list
Recovery Algorithm (Cont.)
• Undo phase:
Scan log backwards from end
Whenever a log record <Ti, Xj, V1, V2> is found where Ti is in undo-list
perform same actions as for transaction rollback:
perform undo by writing V1 to Xj.
2. write a log record <Ti , Xj, V1>
Whenever a log record <Ti start> is found where Ti is in undo-list,
1. Write a log record <Ti abort>
2. Remove Ti from undo-list
Stop when undo-list is empty
● i.e. <Ti start> has been found for every transaction in undo-list
● After undo phase completes, normal transaction processing
can commence
Example of Recovery
Log Record Buffering
• Log record buffering: log records are buffered in main
memory, instead of of being output directly to stable
– 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
• Several log records can thus be output using a single output
operation, reducing the I/O cost.
Log Record Buffering (Cont.)
• The rules below must be followed if log records are
– 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
Database 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
• The recovery algorithm supports the no-force policy: i.e.,
updated blocks need not be written to disk when transaction
– force policy: requires updated blocks to be written at commit
• More expensive commit
• The recovery algorithm supports the steal policy:i.e.,
blocks containing updates of uncommitted transactions can
be written to disk, even before the transaction commits
Database Buffering (Cont.)
• 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.
• To output a block to disk
First acquire an exclusive latch on the block
Ensures no update can be in progress on the block
Then perform a log flush
Then output the block to disk
Finally release the latch on the block
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
– 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.
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
Output the page to database instead of to swap space (making sure to
output log records first), if it is modified
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.
Fuzzy Checkpointing
■ To avoid long interruption of normal processing during checkpointing, allow
updates to happen during checkpointing
■ Fuzzy checkpointing is done as follows:
Temporarily stop all updates by transactions
Write a <checkpoint L> log record and force log to stable storage
Note list M of modified buffer blocks
Now permit transactions to proceed with their actions
Output to disk all modified buffer blocks in list M
blocks should not be updated while being output
Follow WAL: all log records pertaining to a block must be output
before the block is output
Store a pointer to the checkpoint record in a fixed position
last_checkpoint on disk
Fuzzy Checkpointing (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
<checkpoint L>
<checkpoint L>
Failure with Loss of Nonvolatile Storage
So far we assumed no loss of non-volatile storage
Technique similar to checkpointing used to deal with loss of non-volatile storage
– Periodically dump the entire content of the database to stable storage
– No transaction may be active during the dump procedure; a procedure
similar to checkpointing must take place
• Output all log records currently residing in main memory onto stable
• 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.
Recovering from Failure of Non-Volatile Storage
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
– Similar to fuzzy checkpointing
Recovery with Early Lock Release
• 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
Logical Undo Logging
• 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
ARIES Recovery Algorithm
■ ARIES is a state of the art recovery method
● Incorporates numerous optimizations to reduce overheads during
normal processing and to speed up recovery
● The recovery algorithm we studied earlier is modeled after ARIES,
but greatly simplified by removing optimizations
■ Unlike the recovery algorithm described earlier, ARIES
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
Physiological redo
Dirty page table to avoid unnecessary redos during recovery
Fuzzy checkpointing that only records information about dirty
pages, and does not require dirty pages to be written out at
checkpoint time
More coming up on each of the above …
ARIES Optimizations
■ Physiological redo
● Affected page is physically identified, action within page can be
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
Requires page to be output to disk atomically
Easy to achieve with hardware RAID, also supported by some disk systems
Incomplete page output can be detected by checksum techniques,
But extra actions are required for recovery
Treated as a media failure
ARIES Data 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
ARIES 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
ARIES Data Structures: Log Record
• Each log record contains LSN of previous log record of the
same transaction
LSN TransID PrevLSN RedoInfo
– 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 earlier
recovery algorithm
– Has a field UndoNextLSN to note next (earlier) record to be
• Records in between would have already been undone
• Required to avoid repeated undo of already undone actions
LSN TransID UndoNextLSN RedoInfo
ARIES Data Structures: DirtyPage Table
• 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
ARIES Data Structures
ARIES Data Structures: Checkpoint Log
• 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
ARIES Recovery Algorithm
ARIES recovery involves three passes
• Analysis pass: Determines
– Which transactions to undo
– Which pages were dirty (disk version not up to date) at time of
– RedoLSN: LSN from which redo should start
• Redo pass:
– Repeats history, redoing all actions from RedoLSN
• RecLSN and PageLSNs are used to avoid redoing actions already reflected
on page
• Undo pass:
– Rolls back all incomplete transactions
• Transactions whose abort was complete earlier are not undone
– Key idea: no need to undo these transactions: earlier undo actions were
logged, and are redone as required
Aries Recovery: 3 Passes
• Analysis, redo and undo passes
• Analysis determines where redo should start
• Undo has to go back till start of earliest incomplete
Last checkpoint
End of Log
Redo pass
Analysis pass
Undo pass
ARIES 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 …
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 undolist
– 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
– All transactions in undo-list need to be rolled back
ARIES Redo Pass
Redo Pass: Repeats history by replaying every action not
already reflected in the page on disk, as follows:
■ Scans forward from RedoLSN. Whenever an update log
record is found:
If the page is not in DirtyPageTable or the LSN of the log record is
less than the RecLSN of the page in DirtyPageTable, then skip
the log record
2. Otherwise fetch the page from disk. If the PageLSN of the page
fetched from disk is less than the LSN of the log record, redo the
log record
NOTE: if either test is negative the effects of the log record have
already appeared on the page. First test avoids even fetching the
page from disk!
ARIES 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
ARIES: Undo Pass
Undo pass:
• Performs backward scan on log undoing all transaction in
– Backward scan optimized by skipping unneeded log records as
• 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
• Undos performed as described earlier
Other ARIES Features
• Recovery Independence
– Pages can be recovered independently of others
• E.g. if some disk pages fail they can be recovered from a backup while
other pages are being used
• Savepoints:
– Transactions can record savepoints and roll back to a savepoint
• Useful for complex transactions
• Also used to rollback just enough to release locks on deadlock
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 earlier recovery
• Recovery optimizations: For example:
– Dirty page table can be used to prefetch pages during redo
– Out of order redo is possible:
• redo can be postponed on a page being fetched from disk, and
performed when page is fetched.
• Meanwhile other log records can continue to be processed
Remote Backup Systems
Remote Backup Systems
• Remote backup systems provide high availability by
allowing transaction processing to continue even if the
primary site is destroyed.
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
– 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.
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
• 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
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
■ 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.
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