DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

18CSC303J
Database Management System
Unit- V
SRM
InstituteofScienceandTechnology

Topics covered in Unit 5
Transaction concepts
Properties of
Transactions
Serializability
Testing for
Serializability
System Recovery
Concurrency control
Two-phase commit
protocol
Recovery and Atomicity
Log based recovery
Concurrency problems
Locking mechanism
Deadlock
Two phase locking
protocol
Isolation
Intent locking

Transaction Concept
A transaction is a unit of program execution that accesses and
possibly updates various data items.
E.g., transaction to transfer $50 from account A to account B:
1. read(A)
2. A := A – 50
3. write(A)
4. read(B)
5. B := B + 50
6. write(B)
Two main issues to deal with:
Failures of various kinds, such as hardware failures and
system crashes
Concurrent execution of multiple transactions

Required Properties of a Transaction
Consider a transaction to transfer $50 from account A to account B:
1. read(A)
2. A := A – 50
3. write(A)
4. read(B)
5. B := B + 50
6. write(B)
Atomicity requirement
If the transaction fails after step 3 and before step 6, money will be
“lost” leading to an inconsistent database state
 Failure could be due to software or hardware
The system should ensure that updates of a partially executed
transaction are not reflected in the database
Durability requirement — once the user has been notified that the
transaction has completed (i.e., the transfer of the $50 has taken place), the
updates to the database by the transaction must persist even if there are
software or hardware failures.

Required Properties of a Transaction (Cont.)
Consistency requirement in above example:
The sum of A and B is unchanged by the execution of the transaction
In general, consistency requirements include
 Explicitly specified integrity constraints such as primary keys and
foreign keys
 Implicit integrity constraints
– e.g., sum of balances of all accounts, minus sum of loan
amounts must equal value of cash-in-hand
A transaction, when starting to execute, must see a consistent database.
During transaction execution the database may be temporarily
inconsistent.
When the transaction completes successfully the database must be
consistent
Erroneous transaction logic can lead to inconsistency

Required Properties of a Transaction (Cont.)
Isolation requirement — if between steps 3 and 6 (of the fund transfer
transaction) , another transaction T2 is allowed to access the partially
updated database, it will see an inconsistent database (the sum A + B
will be less than it should be).
T1 T2
1. read(A)
2. A := A – 50
3. write(A)
read(A), read(B), print(A+B)
4. read(B)
5. B := B + 50
6. write(B)
Isolation can be ensured trivially by running transactions serially
That is, one after the other.
However, executing multiple transactions concurrently has significant
benefits, as we will see later.

ACID Properties
Atomicity. Either all operations of the transaction are properly reflected
in the database or none are.
Consistency. Execution of a transaction in isolation preserves the
consistency of the database.
Isolation. Although multiple transactions may execute concurrently,
each transaction must be unaware of other concurrently executing
transactions. Intermediate transaction results must be hidden from other
concurrently executed transactions.
That is, for every pair of transactions Ti and Tj, it appears to Ti that
either Tj, finished execution before Ti started, or Tj started execution
after Ti finished.
Durability. After a transaction completes successfully, the changes it
has made to the database persist, even if there are system failures.
A transaction is a unit of program execution that accesses and possibly
updates various data items. To preserve the integrity of data the database
system must ensure:

Transaction State
Active – the initial state; the transaction stays in this state while it is
executing
Partially committed – after the final statement has been executed.
Failed -- after the discovery that normal execution can no longer
proceed.
Aborted – after the transaction has been rolled back and the
database restored to its state prior to the start of the transaction.
Two options after it has been aborted:
Restart the transaction
 can be done only if no internal logical error
Kill the transaction
Committed – after successful completion.

Concurrent Executions
Multiple transactions are allowed to run concurrently in the
system. Advantages are:
Increased processor and disk utilization, leading to
better transaction throughput
 E.g. one transaction can be using the CPU while
another is reading from or writing to the disk
Reduced average response time for transactions: short
transactions need not wait behind long ones.
Concurrency control schemes – mechanisms to achieve
isolation
That is, to control the interaction among the concurrent
transactions in order to prevent them from destroying the
consistency of the database

Schedules
Schedule – a sequences of instructions that specify the
chronological order in which instructions of concurrent transactions
are executed
A schedule for a set of transactions must consist of all
instructions of those transactions
Must preserve the order in which the instructions appear in
each individual transaction.
A transaction that successfully completes its execution will have a
commit instructions as the last statement
By default transaction assumed to execute commit instruction
as its last step
A transaction that fails to successfully complete its execution will
have an abort instruction as the last statement

Schedule 1
Let T1 transfer $50 from A to B, and T2 transfer 10% of the balance from A to B.
An example of a serial schedule in which T1 is followed by T2 :

Schedule 2
A serial schedule in which T2 is followed by T1 :

Schedule 3
Let T1 and T2 be the transactions defined previously. The following
schedule is not a serial schedule, but it is equivalent to Schedule 1.
Note -- In schedules 1, 2 and 3, the sum “A + B” is preserved.

Schedule 4
The following concurrent schedule does not preserve the sum
of “A + B”

Serializability
Basic Assumption – Each transaction preserves database
consistency.
Thus, serial execution of a set of transactions preserves
database consistency.
A (possibly concurrent) schedule is serializable if it is
equivalent to a serial schedule. Different forms of schedule
equivalence give rise to the notions of:
1. conflict serializability
2. view serializability

Simplified view of transactions
We ignore operations other than read and write instructions
We assume that transactions may perform arbitrary
computations on data in local buffers in between reads and
writes.
Our simplified schedules consist of only read and write
instructions.

Conflicting Instructions
Let li and lj be two Instructions of transactions Ti and Tj
respectively. Instructions li and lj conflict if and only if there
exists some item Q accessed by both li and lj, and at least one of
these instructions wrote Q.
1. li = read(Q), lj = read(Q). li and lj don’t conflict.
2. li = read(Q), lj = write(Q). They conflict.
3. li = write(Q), lj = read(Q). They conflict
4. li = write(Q), lj = write(Q). They conflict
Intuitively, a conflict between li and lj forces a (logical) temporal
order between them.
If li and lj are consecutive in a schedule and they do not
conflict, their results would remain the same even if they had
been interchanged in the schedule.

Conflict Serializability
If a schedule S can be transformed into a schedule S´
by a series of swaps of non-conflicting instructions, we
say that S and S´ are conflict equivalent.
We say that a schedule S is conflict serializable if it is
conflict equivalent to a serial schedule

Conflict Serializability (Cont.)
Schedule 3 can be transformed into Schedule 6 -- a serial schedule where
T2 follows T1, by a series of swaps of non-conflicting instructions.
Therefore, Schedule 3 is conflict serializable.
Schedule 3 Schedule 6

Conflict Serializability (Cont.)
Example of a schedule that is not conflict serializable:
We are unable to swap instructions in the above schedule to
obtain either the serial schedule < T3, T4 >, or the serial
schedule < T4, T3 >.

Precedence Graph
Consider some schedule of a set of transactions T1, T2, ..., Tn
Precedence graph — a direct graph where the vertices are
the transactions (names).
We draw an arc from Ti to Tj if the two transaction conflict,
and Ti accessed the data item on which the conflict arose
earlier.
We may label the arc by the item that was accessed.
Example

Testing for Conflict Serializability
A schedule is conflict serializable if and only if its
precedence graph is acyclic.
Cycle-detection algorithms exist which take order
n2 time, where n is the number of vertices in the
graph.
(Better algorithms take order n + e where e is
the number of edges.)
If precedence graph is acyclic, the serializability
order can be obtained by a topological sorting of
the graph.
That is, a linear order consistent with the
partial order of the graph.
For example, a serializability order for the
schedule (a) would be one of either (b) or (c)

Recoverable Schedules
Recoverable schedule — if a transaction Tj reads a data item
previously written by a transaction Ti , then the commit operation of Ti
must appear before the commit operation of Tj.
The following schedule is not recoverable if T9 commits immediately
after the read(A) operation.
If T8 should abort, T9 would have read (and possibly shown to the user)
an inconsistent database state. Hence, database must ensure that
schedules are recoverable.

Cascading Rollbacks
Cascading rollback – a single transaction failure leads to a
series of transaction rollbacks. Consider the following schedule
where none of the transactions has yet committed (so the
schedule is recoverable)
If T10 fails, T11 and T12 must also be rolled back.
Can lead to the undoing of a significant amount of work

Cascadeless Schedules
Cascadeless schedules — for each pair of transactions Ti and
Tj such that Tj reads a data item previously written by Ti, the
commit operation of Ti appears before the read operation of Tj.
Every cascadeless schedule is also recoverable
It is desirable to restrict the schedules to those that are
cascadeless
Example of a schedule that is NOT cascadeless

Concurrency Control
A database must provide a mechanism that will ensure that all
possible schedules are both:
Conflict serializable.
Recoverable and preferably cascadeless
A policy in which only one transaction can execute at a time
generates serial schedules, but provides a poor degree of
concurrency
Concurrency-control schemes tradeoff between the amount of
concurrency they allow and the amount of overhead that they incur
Testing a schedule for serializability after it has executed is a little
too late!
Tests for serializability help us understand why a concurrency
control protocol is correct
Goal – to develop concurrency control protocols that will assure
serializability.

Weak Levels of Consistency
Some applications are willing to live with weak levels of
consistency, allowing schedules that are not serializable
E.g., a read-only transaction that wants to get an approximate
total balance of all accounts
E.g., database statistics computed for query optimization can
be approximate (why?)
Such transactions need not be serializable with respect to
other transactions
Tradeoff accuracy for performance

Levels of Consistency in SQL-92
Serializable — default
Repeatable read — only committed records to be read, repeated reads of
same record must return same value. However, a transaction may not be
serializable – it may find some records inserted by a transaction but not
find others.
Read committed — only committed records can be read, but successive
reads of record may return different (but committed) values.
Read uncommitted — even uncommitted records may be read.
Lower degrees of consistency useful for gathering approximate
information about the database
Warning: some database systems do not ensure serializable schedules by
default
E.g., Oracle and PostgreSQL by default support a level of consistency
called snapshot isolation (not part of the SQL standard)

Transaction Definition in SQL
Data manipulation language must include a construct for
specifying the set of actions that comprise a transaction.
In SQL, a transaction begins implicitly.
A transaction in SQL ends by:
Commit work commits current transaction and begins a
new one.
Rollback work causes current transaction to abort.
In almost all database systems, by default, every SQL
statement also commits implicitly if it executes successfully
Implicit commit can be turned off by a database directive
 E.g. in JDBC, connection.setAutoCommit(false);

Other Notions of Serializability

View Serializability
Let S and S´ be two schedules with the same set of transactions. S
and S´ are view equivalent if the following three conditions are met,
for each data item Q,
1. If in schedule S, transaction Ti reads the initial value of Q, then in
schedule S’ also transaction Ti must read the initial value of Q.
2. If in schedule S transaction Ti executes read(Q), and that value
was produced by transaction Tj (if any), then in schedule S’ also
transaction Ti must read the value of Q that was produced by the
same write(Q) operation of transaction Tj .
3. The transaction (if any) that performs the final write(Q) operation
in schedule S must also perform the final write(Q) operation in
schedule S’.
As can be seen, view equivalence is also based purely on reads and
writes alone.

View Serializability (Cont.)
A schedule S is view serializable if it is view equivalent to a serial
schedule.
Every conflict serializable schedule is also view serializable.
Below is a schedule which is view-serializable but not conflict
serializable.
What serial schedule is above equivalent to?
Every view serializable schedule that is not conflict serializable has
blind writes.

Test for View Serializability
The precedence graph test for conflict serializability cannot be used
directly to test for view serializability.
Extension to test for view serializability has cost exponential in the
size of the precedence graph.
The problem of checking if a schedule is view serializable falls in the
class of NP-complete problems.
Thus, existence of an efficient algorithm is extremely unlikely.
However ,practical algorithms that just check some sufficient
conditions for view serializability can still be used.

More Complex Notions of Serializability
The schedule below produces the same outcome as the serial schedule
< T1, T5 >, yet is not conflict equivalent or view equivalent to it.
If we start with A = 1000 and B = 2000, the final result is 960 and 2040
Determining such equivalence requires analysis of operations other
than read and write.

Lock-Based Protocols
A lock is a mechanism to control concurrent access to a data
item
Data items can be locked in two modes :
1. exclusive (X) mode. Data item can be both read as well as
written. X-lock is requested using lock-X instruction.
2. shared (S) mode. Data item can only be read. S-lock is
requested using lock-S instruction.
Lock requests are made to the concurrency-control manager
by the programmer. Transaction can proceed only after
request is granted.

Lock-Based Protocols (Cont.)
Lock-compatibility matrix
A transaction may be granted a lock on an item if the requested
lock is compatible with locks already held on the item by other
transactions
Any number of transactions can hold shared locks on an item,
But if any transaction holds an exclusive on the item no other
transaction may hold any lock on the item.
If a lock cannot be granted, the requesting transaction is made to
wait till all incompatible locks held by other transactions have
been released. The lock is then granted.

Lock-Based Protocols (Cont.)
Example of a transaction performing locking:
T2: lock-S(A);
read (A);
unlock(A);
lock-S(B);
read (B);
unlock(B);
display(A+B)
Locking as above is not sufficient to guarantee serializability
— if A and B get updated in-between the read of A and B,
the displayed sum would be wrong.
A locking protocol is a set of rules followed by all
transactions while requesting and releasing locks. Locking
protocols restrict the set of possible schedules.

The Two-Phase Locking Protocol
This protocol ensures conflict-serializable schedules.
Phase 1: Growing Phase
Transaction may obtain locks
Transaction may not release locks
Phase 2: Shrinking Phase
Transaction may release locks
Transaction may not obtain locks
The protocol assures serializability. It can be proved that the
transactions can be serialized in the order of their lock points
(i.e., the point where a transaction acquired its final lock).

The Two-Phase Locking Protocol (Cont.)
There can be conflict serializable schedules that cannot be
obtained if two-phase locking is used.
However, in the absence of extra information (e.g., ordering of
access to data), two-phase locking is needed for conflict
serializability in the following sense:
Given a transaction Ti that does not follow two-phase
locking, we can find a transaction Tj that uses two-phase
locking, and a schedule for Ti and Tj that is not conflict
serializable.

Lock Conversions
Two-phase locking with lock conversions:
– First Phase:
can acquire a lock-S on item
can acquire a lock-X on item
can convert a lock-S to a lock-X (upgrade)
– Second Phase:
can release a lock-S
can release a lock-X
can convert a lock-X to a lock-S (downgrade)
This protocol assures serializability. But still relies on the
programmer to insert the various locking instructions.

Automatic Acquisition of Locks
A transaction Ti issues the standard read/write instruction,
without explicit locking calls.
The operation read(D) is processed as:
if Ti has a lock on D
then
read(D)
else begin
if necessary wait until no other
transaction has a lock-X on D
grant Ti a lock-S on D;
read(D)
end

Automatic Acquisition of Locks (Cont.)
write(D) is processed as:
if Ti has a lock-X on D
then
write(D)
else begin
if necessary wait until no other transaction has any lock on D,
if Ti has a lock-S on D
then
upgrade lock on D to lock-X
else
grant Ti a lock-X on D
write(D)
end;
All locks are released after commit or abort

Deadlocks
Consider the partial schedule
Neither T3 nor T4 can make progress — executing lock-S(B)
causes T4 to wait for T3 to release its lock on B, while executing
lock-X(A) causes T3 to wait for T4 to release its lock on A.
Such a situation is called a deadlock.
To handle a deadlock one of T3 or T4 must be rolled back
and its locks released.

Deadlocks (Cont.)
Two-phase locking does not ensure freedom from deadlocks.
In addition to deadlocks, there is a possibility of starvation.
Starvation occurs if the concurrency control manager is badly
designed. For example:
A transaction may be waiting for an X-lock on an item,
while a sequence of other transactions request and are
granted an S-lock on the same item.
The same transaction is repeatedly rolled back due to
deadlocks.
Concurrency control manager can be designed to prevent
starvation.

Deadlocks (Cont.)
The potential for deadlock exists in most locking protocols.
Deadlocks are a necessary evil.
When a deadlock occurs there is a possibility of cascading roll-
backs.
Cascading roll-back is possible under two-phase locking. To
avoid this, follow a modified protocol called strict two-phase
locking -- a transaction must hold all its exclusive locks till it
commits/aborts.
Rigorous two-phase locking is even stricter. Here, all locks
are held till commit/abort. In this protocol transactions can be
serialized in the order in which they commit.

Implementation of Locking
A lock manager can be implemented as a separate process to
which transactions send lock and unlock requests
The lock manager replies to a lock request by sending a lock
grant messages (or a message asking the transaction to roll
back, in case of a deadlock)
The requesting transaction waits until its request is answered
The lock manager maintains a data-structure called a lock
table to record granted locks and pending requests
The lock table is usually implemented as an in-memory hash
table indexed on the name of the data item being locked

Lock Table
Dark blue rectangles indicate granted
locks; light blue indicate waiting requests
Lock table also records the type of lock
granted or requested
New request is added to the end of the
queue of requests for the data item, and
granted if it is compatible with all earlier
locks
Unlock requests result in the request
being deleted, and later requests are
checked to see if they can now be
granted
If transaction aborts, all waiting or granted
requests of the transaction are deleted
lock manager may keep a list of locks
held by each transaction, to
implement this efficiently

Deadlock Handling
System is deadlocked if there is a set of transactions such that
every transaction in the set is waiting for another transaction in
the set.
Deadlock prevention protocols ensure that the system will
never enter into a deadlock state. Some prevention strategies :
Require that each transaction locks all its data items before it
begins execution (predeclaration).
Impose partial ordering of all data items and require that a
transaction can lock data items only in the order specified by
the partial order.

More Deadlock Prevention Strategies
Following schemes use transaction timestamps for the sake of
deadlock prevention alone.
wait-die scheme — non-preemptive
older transaction may wait for younger one to release data item.
(older means smaller timestamp) Younger transactions never
Younger transactions never wait for older ones; they are rolled
back instead.
a transaction may die several times before acquiring needed data
item
wound-wait scheme — preemptive
older transaction wounds (forces rollback) of younger transaction
instead of waiting for it. Younger transactions may wait for older
ones.
may be fewer rollbacks than wait-die scheme.

Deadlock prevention (Cont.)
Both in wait-die and in wound-wait schemes, a rolled back
transactions is restarted with its original timestamp. Older transactions
thus have precedence over newer ones, and starvation is hence
avoided.
Timeout-Based Schemes:
a transaction waits for a lock only for a specified amount of time. If
the lock has not been granted within that time, the transaction is
rolled back and restarted,
Thus, deadlocks are not possible
simple to implement; but starvation is possible. Also difficult to
determine good value of the timeout interval.

Deadlock Detection
Deadlocks can be described as a wait-for graph, which consists of a
pair G = (V,E),
V is a set of vertices (all the transactions in the system)
E is a set of edges; each element is an ordered pair Ti Tj.
If Ti  Tj is in E, then there is a directed edge from Ti to Tj, implying
that Ti is waiting for Tj to release a data item.
When Ti requests a data item currently being held by Tj, then the edge
Ti  Tj is inserted in the wait-for graph. This edge is removed only
when Tj is no longer holding a data item needed by Ti.
The system is in a deadlock state if and only if the wait-for graph has a
cycle. Must invoke a deadlock-detection algorithm periodically to look
for cycles.

Deadlock Detection (Cont.)
Wait-for graph without a cycle Wait-for graph with a cycle

Deadlock Recovery
When deadlock is detected :
Some transaction will have to rolled back (made a victim) to
break deadlock. Select that transaction as victim that will incur
minimum cost.
Rollback -- determine how far to roll back transaction
 Total rollback: Abort the transaction and then restart it.
 More effective to roll back transaction only as far as
necessary to break deadlock.
Starvation happens if same transaction is always chosen as
victim. Include the number of rollbacks in the cost factor to
avoid starvation

Multiple Granularity
Allow data items to be of various sizes and define a hierarchy of data
granularities, where the small granularities are nested within larger
ones
Can be represented graphically as a tree.
When a transaction locks a node in the tree explicitly, it implicitly locks
all the node's descendents in the same mode.
Granularity of locking (level in tree where locking is done):
fine granularity (lower in tree): high concurrency, high locking
overhead
coarse granularity (higher in tree): low locking overhead, low
concurrency

Example of Granularity Hierarchy
The levels, starting from the coarsest (top) level are
database
area
file
record

Intention Lock Modes
In addition to S and X lock modes, there are three additional lock
modes with multiple granularity:
intention-shared (IS): indicates explicit locking at a lower level of
the tree but only with shared locks.
intention-exclusive (IX): indicates explicit locking at a lower level
with exclusive or shared locks
shared and intention-exclusive (SIX): the subtree rooted by that
node is locked explicitly in shared mode and explicit locking is
being done at a lower level with exclusive-mode locks.
intention locks allow a higher level node to be locked in S or X mode
without having to check all descendent nodes.

Compatibility Matrix with Intention Lock Modes
The compatibility matrix for all lock modes is:

Multiple Granularity Locking Scheme
Transaction Ti can lock a node Q, using the following rules:
1. The lock compatibility matrix must be observed.
2. The root of the tree must be locked first, and may be locked in any
mode.
3. A node Q can be locked by Ti in S or IS mode only if the parent of Q
is currently locked by Ti in either IX or IS mode.
4. A node Q can be locked by Ti in X, SIX, or IX mode only if the parent
of Q is currently locked by Ti in either IX or SIX mode.
5. Ti can lock a node only if it has not previously unlocked any node
(that is, Ti is two-phase).
6. Ti can unlock a node Q only if none of the children of Q are currently
locked by Ti.
Observe that locks are acquired in root-to-leaf order, whereas they are
released in leaf-to-root order.
Lock granularity escalation: in case there are too many locks at a
particular level, switch to higher granularity S or X lock

Timestamp-Based Protocols
Each transaction is issued a timestamp when it enters the system. If
an old transaction Ti has time-stamp TS(Ti), a new transaction Tj is
assigned time-stamp TS(Tj) such that TS(Ti) <TS(Tj).
The protocol manages concurrent execution such that the time-stamps
determine the serializability order.
In order to assure such behavior, the protocol maintains for each data
Q two timestamp values:
W-timestamp(Q) is the largest time-stamp of any transaction that
executed write(Q) successfully.
R-timestamp(Q) is the largest time-stamp of any transaction that
executed read(Q) successfully.

Timestamp-Based Protocols (Cont.)
The timestamp ordering protocol ensures that any conflicting read
and write operations are executed in timestamp order.
Suppose a transaction Ti issues a read(Q)
1. If TS(Ti)  W-timestamp(Q), then Ti needs to read a value of Q
that was already overwritten.
Hence, the read operation is rejected, and Ti is rolled back.
2. If TS(Ti)  W-timestamp(Q), then the read operation is
executed, and R-timestamp(Q) is set to max(R-timestamp(Q),
TS(Ti)).

Timestamp-Based Protocols (Cont.)
Suppose that transaction Ti issues write(Q).
1. If TS(Ti) < R-timestamp(Q), then the value of Q that Ti is
producing was needed previously, and the system assumed that
that value would never be produced.
Hence, the write operation is rejected, and Ti is rolled back.
2. If TS(Ti) < W-timestamp(Q), then Ti is attempting to write an
obsolete value of Q.
Hence, this write operation is rejected, and Ti is rolled back.
3. Otherwise, the write operation is executed, and W-timestamp(Q)
is set to TS(Ti).

Example Use of the Protocol
A partial schedule for several data items for transactions with
timestamps 1, 2, 3, 4, 5

Correctness of Timestamp-Ordering Protocol
The timestamp-ordering protocol guarantees serializability since all
the arcs in the precedence graph are of the form:
Thus, there will be no cycles in the precedence graph
Timestamp protocol ensures freedom from deadlock as no
transaction ever waits.
But the schedule may not be cascade-free, and may not even be
recoverable.

Recoverability and Cascade Freedom
Problem with timestamp-ordering protocol:
Suppose Ti aborts, but Tj has read a data item written by Ti
Then Tj must abort; if Tj had been allowed to commit earlier, the
schedule is not recoverable.
Further, any transaction that has read a data item written by Tj
must abort
This can lead to cascading rollback --- that is, a chain of rollbacks
Solution 1:
A transaction is structured such that its writes are all performed at
the end of its processing
All writes of a transaction form an atomic action; no transaction
may execute while a transaction is being written
A transaction that aborts is restarted with a new timestamp
Solution 2: Limited form of locking: wait for data to be committed
before reading it
Solution 3: Use commit dependencies to ensure recoverability

Thomas’ Write Rule
Modified version of the timestamp-ordering protocol in which obsolete
write operations may be ignored under certain circumstances.
When Ti attempts to write data item Q, if TS(Ti) < W-timestamp(Q),
then Ti is attempting to write an obsolete value of {Q}.
Rather than rolling back Ti as the timestamp ordering protocol
would have done, this {write} operation can be ignored.
Otherwise this protocol is the same as the timestamp ordering
protocol.
Thomas' Write Rule allows greater potential concurrency.
Allows some view-serializable schedules that are not conflict-
serializable.

Validation-Based Protocol
Execution of transaction Ti is done in three phases.
1. Read and execution phase: Transaction Ti writes only to
temporary local variables
2. Validation phase: Transaction Ti performs a ''validation test''
to determine if local variables can be written without violating
serializability.
3. Write phase: If Ti is validated, the updates are applied to the
database; otherwise, Ti is rolled back.
The three phases of concurrently executing transactions can be
interleaved, but each transaction must go through the three phases in
that order.
Assume for simplicity that the validation and write phase occur
together, atomically and serially
 I.e., only one transaction executes validation/write at a time.
Also called as optimistic concurrency control since transaction
executes fully in the hope that all will go well during validation

Validation-Based Protocol (Cont.)
Each transaction Ti has 3 timestamps
Start(Ti) : the time when Ti started its execution
Validation(Ti): the time when Ti entered its validation phase
Finish(Ti) : the time when Ti finished its write phase
Serializability order is determined by timestamp given at validation
time; this is done to increase concurrency.
Thus, TS(Ti) is given the value of Validation(Ti).
This protocol is useful and gives greater degree of concurrency if
probability of conflicts is low.
because the serializability order is not pre-decided, and
relatively few transactions will have to be rolled back.

Validation Test for Transaction Tj
If for all Ti with TS (Ti) < TS (Tj) either one of the following condition
holds:
finish(Ti) < start(Tj)
start(Tj) < finish(Ti) < validation(Tj) and the set of data items
written by Ti does not intersect with the set of data items read
by Tj.
then validation succeeds and Tj can be committed. Otherwise,
validation fails and Tj is aborted.
Justification: Either the first condition is satisfied, and there is no
overlapped execution, or the second condition is satisfied and
the writes of Tj do not affect reads of Ti since they occur after
Ti has finished its reads.
the writes of Ti do not affect reads of Tj since Tj does not read
any item written by Ti.

Schedule Produced by Validation
Example of schedule produced using validation

Multiversion Schemes
Multiversion schemes keep old versions of data item to increase
concurrency.
Multiversion Timestamp Ordering
Multiversion Two-Phase Locking
Each successful write results in the creation of a new version of the
data item written.
Use timestamps to label versions.
When a read(Q) operation is issued, select an appropriate version of
Q based on the timestamp of the transaction, and return the value of
the selected version.
reads never have to wait as an appropriate version is returned
immediately.

Multiversion Timestamp Ordering
Each data item Q has a sequence of versions <Q1, Q2,...., Qm>. Each
version Qk contains three data fields:
Content -- the value of version Qk.
W-timestamp(Qk) -- timestamp of the transaction that created
(wrote) version Qk
R-timestamp(Qk) -- largest timestamp of a transaction that
successfully read version Qk
When a transaction Ti creates a new version Qk of Q, Qk's W-
timestamp and R-timestamp are initialized to TS(Ti).
R-timestamp of Qk is updated whenever a transaction Tj reads Qk, and
TS(Tj) > R-timestamp(Qk).

Multiversion Timestamp Ordering (Cont)
Suppose that transaction Ti issues a read(Q) or write(Q) operation. Let
Qk denote the version of Q whose write timestamp is the largest write
timestamp less than or equal to TS(Ti).
1. If transaction Ti issues a read(Q), then the value returned is the
content of version Qk.
2. If transaction Ti issues a write(Q)
1. if TS(Ti) < R-timestamp(Qk), then transaction Ti is rolled back.
2. if TS(Ti) = W-timestamp(Qk), the contents of Qk are overwritten
3. else a new version of Q is created.
Observe that
Reads always succeed
A write by Ti is rejected if some other transaction Tj that (in the
serialization order defined by the timestamp values) should read
Ti's write, has already read a version created by a transaction older
than Ti.
Protocol guarantees serializability

Multiversion Two-Phase Locking
Differentiates between read-only transactions and update transactions
Update transactions acquire read and write locks, and hold all locks up
to the end of the transaction. That is, update transactions follow rigorous
two-phase locking.
Each successful write results in the creation of a new version of the
data item written.
Each version of a data item has a single timestamp whose value is
obtained from a counter ts-counter that is incremented during
commit processing.
Read-only transactions are assigned a timestamp by reading the current
value of ts-counter before they start execution; they follow the
multiversion timestamp-ordering protocol for performing reads.

Multiversion Two-Phase Locking (Cont.)
When an update transaction wants to read a data item:
it obtains a shared lock on it, and reads the latest version.
When it wants to write an item
it obtains X lock on; it then creates a new version of the item and
sets this version's timestamp to .
When update transaction Ti completes, commit processing occurs:
Ti sets timestamp on the versions it has created to ts-counter + 1
Ti increments ts-counter by 1
Read-only transactions that start after Ti increments ts-counter will see
the values updated by Ti.
Read-only transactions that start before Ti increments the
ts-counter will see the value before the updates by Ti.
Only serializable schedules are produced.

MVCC: Implementation Issues
Creation of multiple versions increases storage overhead
Extra tuples
Extra space in each tuple for storing version information
Versions can, however, be garbage collected
E.g. if Q has two versions Q5 and Q9, and the oldest active
transaction has timestamp > 9, than Q5 will never be required
again

Snapshot Isolation
Motivation: Decision support queries that read large amounts of data
have concurrency conflicts with OLTP transactions that update a few
rows
Poor performance results
Solution 1: Give logical “snapshot” of database state to read only
transactions, read-write transactions use normal locking
Multiversion 2-phase locking
Works well, but how does system know a transaction is read only?
Solution 2: Give snapshot of database state to every transaction,
updates alone use 2-phase locking to guard against concurrent
updates
Problem: variety of anomalies such as lost update can result
Partial solution: snapshot isolation level (next slide)
 Proposed by Berenson et al, SIGMOD 1995
 Variants implemented in many database systems
– E.g. Oracle, PostgreSQL, SQL Server 2005

Snapshot Isolation
A transaction T1 executing with Snapshot
Isolation
takes snapshot of committed data at
start
always reads/modifies data in its own
snapshot
updates of concurrent transactions are
not visible to T1
writes of T1 complete when it commits
First-committer-wins rule:
 Commits only if no other concurrent
transaction has already written data
that T1 intends to write.
T1 T2 T3
W(Y := 1)
Commit
Start
R(X)  0
R(Y) 1
W(X:=2)
W(Z:=3)
Commit
R(Z)  0
R(Y)  1
W(X:=3)
Commit-Req
Abort
Concurrent updates not visible
Own updates are visible
Not first-committer of X
Serialization error, T2 is rolled back

Snapshot Read
Concurrent updates invisible to snapshot read

Snapshot Write: First Committer Wins
Variant: “First-updater-wins”
 Check for concurrent updates when write occurs by locking item
– But lock should be held till all concurrent transactions have finished
 (Oracle uses this plus some extra features)
 Differs only in when abort occurs, otherwise equivalent

Benefits of Snapshot Isolation
Reading is never blocked,
and also doesn’t block other txns activities
Performance similar to Read Committed
Avoids the usual anomalies
No dirty read
No lost update
No non-repeatable read
Predicate based selects are repeatable (no phantoms)
Problems with SI
SI does not always give serializable executions
 Serializable: among two concurrent txns, one sees the effects
of the other
 In SI: neither sees the effects of the other
Result: Integrity constraints can be violated

Snapshot Isolation
E.g. of problem with SI
T1: x:=y
T2: y:= x
Initially x = 3 and y = 17
 Serial execution: x = ??, y = ??
 if both transactions start at the same time, with snapshot
isolation: x = ?? , y = ??
Called skew write
Skew also occurs with inserts
E.g:
 Find max order number among all orders
 Create a new order with order number = previous max + 1

Snapshot Isolation Anomalies
SI breaks serializability when txns modify different items, each based on a
previous state of the item the other modified
Not very common in practice
 E.g., the TPC-C benchmark runs correctly under SI
 when txns conflict due to modifying different data, there is usually also
a shared item they both modify too (like a total quantity) so SI will abort
one of them
But does occur
 Application developers should be careful about write skew
SI can also cause a read-only transaction anomaly, where read-only
transaction may see an inconsistent state even if updaters are serializable
We omit details
Using snapshots to verify primary/foreign key integrity can lead to
inconsistency
Integrity constraint checking usually done outside of snapshot

SI In Oracle and PostgreSQL
Warning: SI used when isolation level is set to serializable, by Oracle, and
PostgreSQL versions prior to 9.1
PostgreSQL’s implementation of SI (versions prior to 9.1) described in
Section 26.4.1.3
Oracle implements “first updater wins” rule (variant of “first committer
wins”)
 concurrent writer check is done at time of write, not at commit time
 Allows transactions to be rolled back earlier
 Oracle and PostgreSQL < 9.1 do not support true serializable
execution
PostgreSQL 9.1 introduced new protocol called “Serializable Snapshot
Isolation” (SSI)
 Which guarantees true serializabilty including handling predicate
reads (coming up)

SI In Oracle and PostgreSQL
Can sidestep SI for specific queries by using select .. for update in Oracle
and PostgreSQL
E.g.,
1. select max(orderno) from orders for update
2. read value into local variable maxorder
3. insert into orders (maxorder+1, …)
Select for update (SFU) treats all data read by the query as if it were
also updated, preventing concurrent updates
Does not always ensure serializability since phantom phenomena can
occur (coming up)
In PostgreSQL versions < 9.1, SFU locks the data item, but releases locks
when the transaction completes, even if other concurrent transactions are
active
Not quite same as SFU in Oracle, which keeps locks until all
concurrent transactions have completed

Insert and Delete Operations
If two-phase locking is used :
A delete operation may be performed only if the transaction
deleting the tuple has an exclusive lock on the tuple to be deleted.
A transaction that inserts a new tuple into the database is given an
X-mode lock on the tuple
Insertions and deletions can lead to the phantom phenomenon.
A transaction that scans a relation
 (e.g., find sum of balances of all accounts in Perryridge)
and a transaction that inserts a tuple in the relation
 (e.g., insert a new account at Perryridge)
(conceptually) conflict in spite of not accessing any tuple in
common.
If only tuple locks are used, non-serializable schedules can result
 E.g. the scan transaction does not see the new account, but
reads some other tuple written by the update transaction

Insert and Delete Operations (Cont.)
The transaction scanning the relation is reading information that indicates
what tuples the relation contains, while a transaction inserting a tuple
updates the same information.
The conflict should be detected, e.g. by locking the information.
One solution:
Associate a data item with the relation, to represent the information
about what tuples the relation contains.
Transactions scanning the relation acquire a shared lock in the data
item,
Transactions inserting or deleting a tuple acquire an exclusive lock on
the data item. (Note: locks on the data item do not conflict with locks on
individual tuples.)
Above protocol provides very low concurrency for insertions/deletions.
Index locking protocols provide higher concurrency while
preventing the phantom phenomenon, by requiring locks
on certain index buckets.

Index Locking Protocol
Index locking protocol:
Every relation must have at least one index.
A transaction can access tuples only after finding them through one or
more indices on the relation
A transaction Ti that performs a lookup must lock all the index leaf
nodes that it accesses, in S-mode
 Even if the leaf node does not contain any tuple satisfying the index
lookup (e.g. for a range query, no tuple in a leaf is in the range)
A transaction Ti that inserts, updates or deletes a tuple ti in a relation r
 must update all indices to r
 must obtain exclusive locks on all index leaf nodes affected by the
insert/update/delete
The rules of the two-phase locking protocol must be observed
Guarantees that phantom phenomenon won’t occur

Next-Key Locking
Index-locking protocol to prevent phantoms required locking entire leaf
Can result in poor concurrency if there are many inserts
Alternative: for an index lookup
Lock all values that satisfy index lookup (match lookup value, or
fall in lookup range)
Also lock next key value in index
Lock mode: S for lookups, X for insert/delete/update
Ensures that range queries will conflict with inserts/deletes/updates
Regardless of which happens first, as long as both are concurrent

Concurrency in Index Structures
Indices are unlike other database items in that their only job is to help in
accessing data.
Index-structures are typically accessed very often, much more than
other database items.
Treating index-structures like other database items, e.g. by 2-phase
locking of index nodes can lead to low concurrency.
There are several index concurrency protocols where locks on internal
nodes are released early, and not in a two-phase fashion.
It is acceptable to have nonserializable concurrent access to an
index as long as the accuracy of the index is maintained.
 In particular, the exact values read in an internal node of a
B+-tree are irrelevant so long as we land up in the correct leaf
node.

Concurrency in Index Structures (Cont.)
Example of index concurrency protocol:
Use crabbing instead of two-phase locking on the nodes of the B+-tree, as
follows. During search/insertion/deletion:
First lock the root node in shared mode.
After locking all required children of a node in shared mode, release the lock
on the node.
During insertion/deletion, upgrade leaf node locks to exclusive mode.
When splitting or coalescing requires changes to a parent, lock the parent in
exclusive mode.
Above protocol can cause excessive deadlocks
Searches coming down the tree deadlock with updates going up the tree
Can abort and restart search, without affecting transaction
Better protocols are available; see Section 16.9 for one such protocol, the B-link
tree protocol
Intuition: release lock on parent before acquiring lock on child
 And deal with changes that may have happened between lock release
and acquire

Weak Levels of Consistency
Degree-two consistency: differs from two-phase locking in that S-locks
may be released at any time, and locks may be acquired at any time
X-locks must be held till end of transaction
Serializability is not guaranteed, programmer must ensure that no
erroneous database state will occur]
Cursor stability:
For reads, each tuple is locked, read, and lock is immediately
released
X-locks are held till end of transaction
Special case of degree-two consistency

Weak Levels of Consistency in SQL
SQL allows non-serializable executions
Serializable: is the default
Repeatable read: allows only committed records to be read, and
repeating a read should return the same value (so read locks should
be retained)
 However, the phantom phenomenon need not be prevented
– T1 may see some records inserted by T2, but may not see
others inserted by T2
Read committed: same as degree two consistency, but most
systems implement it as cursor-stability
Read uncommitted: allows even uncommitted data to be read
In many database systems, read committed is the default consistency
level
has to be explicitly changed to serializable when required
 set isolation level serializable

Transactions across User Interaction
Many applications need transaction support across user interactions
Can’t use locking
Don’t want to reserve database connection per user
Application level concurrency control
Each tuple has a version number
Transaction notes version number when reading tuple
 select r.balance, r.version into :A, :version
from r where acctId =23
When writing tuple, check that current version number is same as the
version when tuple was read
 update r set r.balance = r.balance + :deposit
where acctId = 23 and r.version = :version
Equivalent to optimistic concurrency control without validating read set
Used internally in Hibernate ORM system, and manually in many applications
Version numbering can also be used to support first committer wins check of
snapshot isolation
Unlike SI, reads are not guaranteed to be from a single snapshot

Deadlocks
Consider the following two transactions:
T1: write (X) T2: write(Y)
write(Y) write(X)
Schedule with deadlock

Recovery System
Failure Classification
Storage Structure
Log-Based Recovery
Remote Backup 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
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 media
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
block):
1. Write the information onto the first physical block.
2. When the first write successfully completes, write the same
information onto the second physical block.
3. The output is completed only after the second write
successfully completes.

Stable-Storage Implementation (Cont.)
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
2. If either copy of an inconsistent block is detected to have an error (bad
checksum), overwrite it by the other copy. If both have no error, but are
different, overwrite the second block by the first block.

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

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

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 kept.
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
commits

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-copy and shadow-paging (brief
details in book)
shadow-copy

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 value).
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 written
We assume that the log record is output directly to stable storage
(Will see later that how to postpone log record output to some
extent)
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
already
Writes performed by a transaction may still be in the buffer when the
transaction commits, and may be output later

Immediate Database Modification Example
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.
BC output before T1
commits
BA output after T0
commits

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
transactions
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
locking)
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

Checkpoints
Redoing/undoing all transactions recorded in the log can be very slow
1. processing the entire log is time-consuming if the system has run
for a long time
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 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.
1. 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 operations
1. 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
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

Recovery Algorithm
So far: we covered key concepts
Now: we present the components of the basic recovery algorithm
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 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:
1. Find last <checkpoint L> record, and set undo-list to L.
2. Scan forward from above <checkpoint L> record
1. Whenever a record <Ti, Xj, V1, V2> or <Ti, Xj, V2> is found,
redo it by writing V2 to Xj
2. Whenever a log record <Ti start> is found, add Ti to undo-list
3. Whenever a log record <Ti commit> or <Ti abort> is found,
remove Ti from undo-list
Recovery Algorithm (Cont.)

Recovery Algorithm (Cont.)
Undo phase:
1. Scan log backwards from end
1. Whenever a log record <Ti, Xj, V1, V2> is found where Ti is in
undo-list perform same actions as for transaction rollback:
1. perform undo by writing V1 to Xj.
2. write a log record <Ti , Xj, V1>
2. 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
3. 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

Log 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.

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

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 commits
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
1. First acquire an exclusive latch on the block
1. Ensures no update can be in progress on the block
2. Then perform a log flush
3. Then output the block to disk
4. 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 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.

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.

Fuzzy Checkpointing
To avoid long interruption of normal processing during
checkpointing, allow updates to happen during 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
6. 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>
…..
Log
last_checkpoint

Failure with Loss of Nonvolatile Storage
So far we assumed no loss of non-volatile storage
Technique similar to checkpointing used to deal with loss of non-
volatile storage
Periodically dump the entire content of the database to stable
storage
No transaction may be active during the dump procedure; a
procedure similar to checkpointing must take place
 Output all log records currently residing in main memory onto
stable storage.
 Output all buffer blocks onto the disk.
 Copy the contents of the database to stable storage.
 Output a record <dump> to log on stable storage.

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 and
Logical Undo Operations

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

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

Operation Logging
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

Operation Logging (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.

Transaction Rollback with Logical Undo
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
al <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

(Cont.)
Transaction rollback, scanning the log backwards (cont.):
3. If a redo-only record is found ignore it
4. If a <Ti, Oj, operation-abort> record is found:
skip all preceding log records for Ti until the record
<Ti, Oj, operation-begin> is found.
5. Stop the scan when the record <Ti, start> is found
6. Add a <Ti, abort> record to the log
Some points to note:
Cases 3 and 4 above can occur only if the database crashes while a
transaction is being rolled back.
Skipping of log records as in case 4 is important to prevent multiple
rollback of the same operation.

Transaction rollback during normal
operation

Failure Recovery with Logical Undo

Transaction Rollback: Another Example
Example with a complete and an incomplete operation
<T1, start>
….
<T1, X, 10, K5>
<T1, Y, 45, RID7>
<T1, O1, operation-end, (delete I9, K5, RID7)>
<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?
<T1, abort>

Recovery Algorithm with Logical Undo
Basically same as earlier algorithm, except for changes described
earlier for transaction rollback
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.

Recovery with Logical Undo (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.

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 recovery algorithm we studied earlier is modeled after
ARIES, but greatly simplified by removing optimizations
Unlike the recovery algorithm described earlier, 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 earlier 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

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

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 undo-list
Keeps track of last log record for each transaction in undo-list
 May be needed for later undo
At end of analysis pass:
RedoLSN determines where to start redo pass
RecLSN for each page in DirtyPageTable used to minimize redo work
All transactions in undo-list need to be rolled back

ARIES Redo Pass
Redo Pass: Repeats history by replaying every action not already
reflected in the page on disk, as follows:
Scans forward from RedoLSN. Whenever an update log record is
found:
1. If the page is not in DirtyPageTable or the LSN of the log record is
less than the RecLSN of the page in DirtyPageTable, then skip
the log record
2. Otherwise fetch the page from disk. If the PageLSN of the page
fetched from disk is less than the LSN of the log record, redo the
log record
NOTE: if either test is negative the effects of the log record have
already appeared on the page. First test avoids even fetching the
page from disk!

ARIES Undo Actions
When an undo is performed for an update log record
Generate a CLR containing the undo action performed (actions
performed during undo are logged physicaly or physiologically).
 CLR for record n noted as n’ in figure below
Set UndoNextLSN of the CLR to the PrevLSN value of the update log
record
 Arrows indicate UndoNextLSN value
ARIES supports partial rollback
Used e.g. to handle deadlocks by rolling back just enough to release
reqd. locks
Figure indicates forward actions after partial rollbacks
 records 3 and 4 initially, later 5 and 6, then full rollback
1 2 3 4 4' 3' 5 6 5' 2' 1'
6'

ARIES: Undo 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

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 algorithm
Recovery optimizations: For example:
Dirty page table can be used to prefetch pages during redo
Out of order redo is possible:
 redo can be postponed on a page being fetched from disk,
and
performed when page is fetched.
 Meanwhile other log records can continue to be processed

Remote Backup 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
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.

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

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.

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