Lesson12 recovery architectures

Lesson 12: Recovery System
DBMS Architectures

Lesson 12 / Page 2AE3B33OSD Silberschatz, Korth, Sudarshan S. ©2007
Contents
 Recovery after transactions failure
 Data access and physical disk operations
 Log-Based Recovery
 Checkpoints
 Recovery With Concurrent Transactions
 Database Buffering
 Database System Architectures
 Client-Server Systems
 Transaction Servers
 Parallel Systems
 Distributed Systems

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
 In the previous lesson we mentioned highly inefficient
shadow-database scheme
 Need for a better approach
 Recovery algorithms are techniques to ensure database
consistency and transaction atomicity and durability
despite failures
 Focus of this chapter
 Recovery algorithms have two parts
1. Actions taken during normal transaction processing to ensure
enough information exists to recover from failures
2. Actions taken after a failure to recover the database contents to a
state that ensures atomicity, consistency and durability

Storage 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
 RAID – different levels of redundancy and fault tolerance

Data Access
 Physical blocks are those blocks residing on the disk.
 Buffer blocks are the blocks residing temporarily in main
memory.
 Block movements between disk and main memory are
initiated through the following two operations:
 input(B) transfers the physical block B to main memory.
 output(B) transfers the buffer block B to the disk, and replaces the
appropriate physical block there.
 Each transaction Ti has its private work-area in which local
copies of all data items accessed and updated by it are
kept.
 Ti's local copy of a data item X is called xi.
 We assume, for simplicity, that each data item fits in, and
is stored inside, a single block.

Data Access (Cont.)
 Transaction transfers data items between system buffer
blocks and its private work-area using the following
operations :
 read(X) assigns the value of data item X to the local variable xi.
 write(X) assigns the value of local variable xi to data item {X} in the
buffer block.
 both these commands may necessitate the issue of an input(BX)
instruction before the assignment, if the block BX in which X resides
is not already in memory.
 Transactions
 Perform read(X) while accessing X for the first time;
 All subsequent accesses are to the local copy.
 After last access, transaction executes write(X).
 output(BX) need not immediately follow write(X). System
can perform the output operation when it deems fit.

Example of Data 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

Recovery and Atomicity
 Modifying the database without ensuring that the transaction
will commit may leave the database in an inconsistent state.
 Consider transaction Ti that transfers $50 from account A to
account B; goal is either to perform all database
modifications made by Ti or none at all.
 Several output operations may be required for Ti (to output
A and B). A failure may occur after one of these
modifications have been made but before all of them are
made
 To ensure atomicity despite failures, we first output
information describing the modifications to stable storage
without modifying the database itself.
 We will study two approaches:
 log-based recovery, and
 shadow-paging (block buffering)
 We assume (initially) that transactions run serially, that is,
one after the other.

Log-Based Recovery
 A log is kept on stable storage.
 The log is a sequence of log records, and maintains a record of
update activities on the database.
 When transaction 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

Deferred Database Modification
 The deferred database modification scheme records all
modifications to the log, but defers all the writes to after
partial commit.
 Assume that transactions execute serially
 Transaction starts by writing <Ti start> record to log.
 A write(X) operation results in a log record <Ti, X, V>
being written, where V is the new value for X
 Note: old value is not needed for this scheme
 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.

Deferred Database Modification (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 (redo Ti) 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)

 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 <T1 commit> are present
Deferred Database Modification (cont.)
T0 start
T0, A, 950
T0, B, 2050
T0 start
T0, A, 950
T0, B, 2050
T0 commit
T1 start
T1, C, 600
T0 start
T0, A, 950
T0, B, 2050
T0 commit
T1 start
T1, C, 600
T1 commit
(a) (b) (c)

Immediate Database Modification
 The immediate database modification scheme allows
database updates of an uncommitted transaction to be
made as the writes are issued
 since undoing may be needed, update logs must have both old
value and new value
 Update log record must be written before database item is
written
 We assume that the log record is output directly to stable storage
 Can be extended to postpone log record output, so long as prior to
execution of an output(B) operation for a data block B, all log
records corresponding to items B must be flushed to stable
storage
 Output of updated blocks can take place at any time
before or after transaction commit
 Order in which blocks are output can be different from the
order in which they are written.

Immediate Database Modification Example
Log Write Output
<T0 start>
<T0, A, 1000, 950>
<T0, 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

Immediate Database Modification (cont.)
 Recovery procedure has two operations instead of one:
 undo(Ti) restores the value of all data items updated by Ti to their
old values, going backwards from the last log record for Ti
 redo(Ti) sets the value of all data items updated by Ti to the new
values, going forward from the first log record for Ti
 Both operations must be idempotent
 That is, even if the operation is executed multiple times, the effect is
the same as if it is executed once
 Needed since operations may get re-executed during recovery
 When recovering after failure:
 Transaction Ti needs to be undone if the log contains the record
<Ti start>, but does not contain the record <Ti commit>.
 Transaction Ti needs to be redone if the log contains both the record
<Ti start> and the record <Ti commit>.
 Undo operations are performed first, then redo operations

Immediate DB Modification Recovery Example
 Below we show the log as it appears at three instances of
time.
 Recovery actions in each case above are:
(a) undo(T0): B is restored to 2000 and A to 1000.
(b) undo(T1) and redo(T0): C is restored to 700, and then A and B are
set to 950 and 2050 respectively.
(c) redo(T0) and redo(T1): A and B are set to 950 and 2050
respectively. Then C is set to 600
T0 start
T0, A, 1000, 950
T0, B, 2000, 2050
T0 start
T0, A, 1000, 950
T0, B, 2000, 2050
T0 commit
T1 start
T1, C, 700, 600
T0 start
T0, A, 1000, 950
T0, B, 2000, 2050
T0 commit
T1 start
T1, C, 700, 600
T1 commit
(a) (b) (c)

Checkpoints
 Problems in recovery procedures:
1. Searching the entire log is time-consuming
2. We might unnecessarily redo transactions which have already
output their updates to the database.
 Streamline recovery procedure by periodically performing
checkpointing
1. Output all log records currently residing in main memory onto
stable storage.
2. Output all modified buffer blocks to the disk.
3. Write a log record <checkpoint> onto stable storage

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).

Example of Checkpoint
 T1 can be ignored
 updates already output to disk as marked by the checkpoint
 T2 and T3 redone.
 T4 undone
tc
tf
T1
T2
T3
T4
checkpoint system failure
time

Recovery With Concurrent Transactions
 We modify the log-based recovery schemes to allow
multiple transactions to execute concurrently.
 All transactions share a single disk buffer and a single log
 A buffer block can have data items updated by one or more
transactions
 We assume concurrency control using strict two-phase
locking;
 i.e. the updates of uncommitted transactions should not be visible
to other transactions
 Otherwise how to perform undo if T1 updates A, then T2 updates A and
commits, and finally T1 has to abort?
 Logging is done as described earlier.
 Log records of different transactions may be interspersed in the log.
 The checkpointing technique and actions taken on
recovery have to be changed
 since several transactions may be active when a checkpoint is
performed.

Recovery With Concurrent Transactions (cont.)
 Checkpoints are performed as before, except that the
checkpoint log record is now of the form
<checkpoint L>
where L is the list of transactions active at the time of the
checkpoint
 We assume no updates are in progress while the checkpoint is
carried out (will relax this later)
 When the system recovers from a crash, it first does the
following:
1. Initialize undo-list and redo-list to empty
2. Scan the log backwards from the end, stopping when the first
<checkpoint L> record is found.
For each record found during the backward scan:
• if the record is <Ti commit>, add Ti to redo-list
• if the record is <Ti start>, then if Ti is not in redo-list, add Ti to undo-list
3. For every Ti in L, if Ti is not in redo-list, add Ti to undo-list

Recovery With Concurrent Transactions (cont.)
 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.
2. Locate the most recent <checkpoint L> record.
3. 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

Log Record Buffering
 Log record buffering for better performance
 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
 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

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
 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

Buffer Management
 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 in advance between database buffer and
applications, limiting flexibility
 Requirements may change in time, 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
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 use by OS
Dual paging can thus be avoided, but common operating systems do not
support such functionality.

ARIES
 ARIES is a state of the art recovery method
 Incorporates numerous optimizations to reduce overheads during
normal processing and to speed up recovery
 The “advanced recovery algorithm” we studied earlier is modeled
after ARIES, but greatly simplified by removing optimizations
 Unlike the advanced recovery algorithm, ARIES
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
2. Physiological redo
3. Dirty page table to avoid unnecessary redos during recovery
4. Fuzzy checkpointing that only records information about dirty
pages, and does not require dirty pages to be written out at
checkpoint time
 More coming up on each of the above …

ARIES Optimizations
 Physiological redo
 Affected page is physically identified, action within page can
be logical
 Used to reduce logging overheads
– e.g. when a record is deleted and all other records have to be moved
to fill hole
» Physiological redo can log just the record deletion
» Physical redo would require logging of old and new values for much of the
page
 Requires page to be output to disk atomically
– Easy to achieve with hardware RAID, also supported by some disk
systems
– Incomplete page output can be detected by checksum techniques,
» But extra actions are required for recovery
» Treated as a media failure

ARIES Data 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 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'

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
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

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
crash
 RedoLSN: LSN from which redo should start
 Redo pass:
 Repeats history, redoing all actions from RedoLSN
 RecLSN and PageLSNs are used to avoid redoing actions
already reflected on page
 Undo pass:
 Rolls back all incomplete transactions
 Transactions whose abort was complete earlier are not undone
– Key idea: no need to undo these transactions: earlier undo actions
were logged, and are redone as required

Aries Recovery: 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

Centralized Systems
 Run on a single computer system and do not interact with
other computer systems
 Centralized databases run on a general-purpose computer
system with one or few CPUs
 Everything in the database is executed locally.
 There is no or very low degree of concurrency
 Database technologies described in this course are used to speed-
up data access, only
 Examples include
 simple single-user accounting systems
 inventory management in a small shop with just one cash desk
 management of CD’s in your home
 Importance of such systems is very low from the DBMS
point of view

Client-Server Systems
 Server systems satisfy requests generated at m client
systems
 Database functionality can be divided into:
 Back-end: manages access structures, query evaluation and
optimization, concurrency control and recovery.
 Front-end: consists of tools such as forms, report-writers, and
graphical user interface facilities.
 The interface between the front-end and the back-end is through
SQL or through an application program interface
 Advantages
 better functionality for the cost
 flexibility in locating resources and expanding facilities
 better user interfaces
 easier maintenance
client client client client...
server
network infrastructure
SQL user
interface
forms
interface
reporting
tool
data mining &
analysis
SQL engine
(server)
interface (SQL + API)
front-end
back-end

Server System Architecture
 Server systems can be broadly categorized into two kinds:
 transaction servers which are widely used in relational database
systems, and
 data servers, used in object-oriented database systems
 We treat here transaction servers, only, as object-oriented
database systems were not discussed in this course

Transaction Servers
 Also called query server systems or SQL server systems
 Clients send requests to the server
 Transactions are executed at the server
 Results are shipped back to the client.
 Requests are specified in SQL, and communicated to the
server through a remote procedure call (RPC) mechanism
 Transactional RPC allows many RPC calls to form a
transaction
 Open Database Connectivity (ODBC) is a C language
application program interface (API) standard from Microsoft
for connecting to a server, sending SQL requests, and
receiving results.
 JDBC standard is similar to ODBC, for Java developed by
Sun Microsystems

Transaction Server Process Structure
 A typical transaction server consists of multiple processes
accessing data in shared memory
 Server processes
 These receive user queries (transactions), execute them and send
results back
 Processes may be multithreaded, allowing a single process to
execute several user queries concurrently
 Typically multiple multithreaded server processes
 Lock manager process (more details later)
 Database writer process
 Outputs modified buffer blocks to disks continually
 Log writer process
 Server processes simply add log records to log record buffer
 Log writer process outputs log records to stable storage.
 Checkpoint process
 Performs periodic checkpoints
 Process monitor process
 Monitors other processes, and takes recovery actions if any of the
other processes fail (e.g., aborting any transactions being executed
by a server process and restarting it)

Transaction System Processes
process
monitoring
other
processes
user
process
user
process
user
process
server
process
server
process
server
process
lock manager
process
data writer
process
ODBC
log writer
process
checkpoint
process
Buffer pool
Query plan cache
Log buffer Lock table
Shared
memory
JDBC
log disks
data disks

Transaction System Processes (cont.)
 Shared memory contains shared data
 Buffer pool
 Lock table
 Log buffer
 Cached query plans (reused if same query submitted again)
 All database processes can access shared memory
 To ensure that no two processes are accessing the same
data structure at the same time, databases systems
implement mutual exclusion using either
 Operating system semaphores
 Atomic instructions such as test-and-set
 To avoid overhead of inter-process communication for
lock request/grant, each database process operates
directly on the lock table
 instead of sending requests to lock manager process
 Lock manager process still used for deadlock detection

Parallel Systems
 Parallel database systems consist of multiple processors
and multiple disks connected by a fast interconnection
network
 A coarse-grain parallel machine consists of a small
number of powerful processors
 A massively parallel or fine grain parallel machine
utilizes thousands of smaller processors
 Two main performance measures:
 throughput – the number of tasks that can be completed in a given
time interval
 response time – the amount of time it takes to complete a single
task from the time it is submitted

Parallel Database Architectures
 Shared memory – processors share a common memory
 Shared disk – processors share a common disk set
 Shared nothing – processors share neither a common
memory nor common disk
 Hierarchical – hybrid of the above architectures

Parallel Database Architectures
P
P
P
P
P
P
M
shared memory
PM
PM
PM
PM
PM
PM
shared disk
P M
PM
PM
PM
P M
shared nothing
P
P
P
P
P
P
M
P
P
P
P
P
P
M
P
P
P
P
P
P
M
hierarchical
organization

Shared Memory
 Processors and disks have access to a common memory,
typically via a bus or through an interconnection network.
 Extremely efficient communication between processors –
data in shared memory can be accessed by any processor
without having to move it using software.
 Downside – architecture is not scalable beyond about 32
processors since the bus or the interconnection network
becomes a bottleneck
 Widely used for lower degrees of parallelism (4 to 8).

Shared Disk
 All processors can directly access all disks via an
interconnection network, but the processors have private
memories.
 The memory bus is not a bottleneck
 Architecture provides a degree of fault-tolerance – if a processor
fails, the other processors can take over its tasks since the
database is resident on disks that are accessible from all
processors.
 Examples: IBM Sysplex and DEC clusters (now part of
Compaq) running Rdb (now Oracle Rdb) were early
commercial users
 Downside: bottleneck now occurs at interconnection to the
disk subsystem.
 Shared-disk systems can scale to a somewhat larger
number of processors, but communication between
processors is slower.

Shared Nothing
 Node consists of a processor, memory, and one or more
disks. Processors at one node communicate with another
processor at another node using an interconnection
network. A node functions as the server for the data on the
disk or disks the node owns.
 Examples: Teradata, Tandem, Oracle-n CUBE
 Data accessed from local disks (and local memory
accesses) do not pass through interconnection network,
thereby minimizing the interference of resource sharing.
 Shared-nothing multiprocessors can be scaled up to
thousands of processors without interference.
 Main drawbacks:
 cost of communication and non-local disk access;
 sending data involves software interaction at both ends.

Hierarchical Organization
 Combines characteristics of shared-memory, shared-disk,
and shared-nothing architectures.
 Top level is a shared-nothing architecture
 nodes connected by an interconnection network, and do not share
disks or memory with each other.
 Each node of the system could be a shared-memory system with a
few processors.
 Alternatively, each node could be a shared-disk system, and each
of the systems sharing a set of disks could be a shared-memory
system.
 Reduce the complexity of programming such systems by
distributed virtual-memory architectures
 Also called non-uniform memory architecture (NUMA)

Distributed Systems
 Data spread over multiple machines
 also referred to as sites or nodes
 Network interconnects the machines
 Data shared by users on multiple machines
Site A Site B
Site CLogical
communication
channel between
processes

Distributed Databases
 Homogeneous distributed databases
 Same software & schema on all sites, data may be partitioned
among sites
 Goal: provide a feeling of a single database, hiding details of
distribution
 Heterogeneous distributed databases
 Different software/schema on different sites
 Goal: integrate existing databases to provide useful functionality
 Differentiate between local and global transactions
 A local transaction accesses data in the single site at which the
transaction was initiated.
 A global transaction either accesses data in a site different from the
one at which the transaction was initiated or accesses data in
several different sites.

Trade-offs in Distributed Systems
 Sharing data – users at one site able to access the data
residing at some other sites.
 Autonomy – each site is able to retain a degree of control
over data stored locally.
 Higher system availability through redundancy – data can
be replicated at remote sites, and system can function
even if a site fails.
 Disadvantage: added complexity required to ensure proper
coordination among sites
 Software development cost.
 Greater potential for bugs.
 Increased processing overhead.

Implementation Issues for Distributed Databases
 Atomicity needed even for transactions that update data
at multiple sites
 The two-phase commit protocol (2PC) is used to ensure
atomicity
 Basic idea: each site executes transaction until just before
commit, and the leaves final decision to a coordinator
 Each site must follow decision of coordinator, even if there is a
failure while waiting for coordinators decision
 2PC is not always appropriate: other transaction models
based on persistent messaging, and workflows, are also
used
 Distributed concurrency control (and deadlock detection)
required
 Data items may be replicated to improve data availability

End of Lesson 12
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