concurrency-control-techniques.ppt

1
Outline
 Databases Concurrency Control
1. Purpose of Concurrency Control
2. Two-Phase locking
3. Limitations of CCMs
4. Index Locking
5. Lock Compatibility Matrix
6. Lock Granularity

2
Database Concurrency Control
 1 Purpose of Concurrency Control
 To enforce Isolation (through mutual exclusion) among
conflicting transactions.
 To preserve database consistency through consistency
preserving execution of transactions.
 To resolve read-write and write-write conflicts.
 Example:
 In concurrent execution environment if T1 conflicts with T2
over a data item A, then the existing concurrency control
decides if T1 or T2 should get the A and if the other
transaction is rolled-back or waits.

3
Two-Phase Locking Techniques
 Locking is an operation which secures
 (a) permission to Read
 (b) permission to Write a data item for a transaction.
 Example:
 Lock (X). Data item X is locked in behalf of the requesting
transaction.
 Unlocking is an operation which removes these permissions
from the data item.
 Example:
 Unlock (X): Data item X is made available to all other
transactions.
 Lock and Unlock are Atomic operations.

4
Two-Phase Locking Techniques: Essential components
 Two locks modes:
 (a) shared (read) (b) exclusive (write).
 Shared mode: shared lock (X)
 More than one transaction can apply share lock on X for
reading its value but no write lock can be applied on X by any
other transaction.
 Exclusive mode: Write lock (X)
 Only one write lock on X can exist at any time and no shared
lock can be applied by any other transaction on X.
 Conflict matrix Read Write
Read
Write
N
N
N
Y

5
Two-Phase Locking Techniques: Essential
components
 Lock Manager:
 Managing locks on data items.
 Lock table:
 Lock manager uses it to store the identify of
transaction locking a data item, the data item, lock
mode and pointer to the next data item locked. One
simple way to implement a lock table is through
linked list.
T1
Transaction ID Data item id lock mode Ptr to next data item
Next
X1 Read

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components
 Database requires that all transactions should be
well-formed. A transaction is well-formed if:
 It must lock the data item before it reads or writes to
it.
 It must not lock an already locked data items and it
must not try to unlock a free data item.

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 The following code performs the lock operation:
B:if LOCK (X) = 0 (*item is unlocked*)
then LOCK (X)  1 (*lock the item*)
else begin
wait (until lock (X) = 0) and
the lock manager wakes up the transaction);
goto B
end;

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components
 The following code performs the unlock operation:
LOCK (X)  0 (*unlock the item*)
if any transactions are waiting then
wake up one of the waiting the transactions;

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 The following code performs the read operation:
B: if LOCK (X) = “unlocked” then
begin LOCK (X)  “read-locked”;
no_of_reads (X)  1;
end
else if LOCK (X)  “read-locked” then
no_of_reads (X)  no_of_reads (X) +1
else begin wait (until LOCK (X) = “unlocked” and
go to B
end;

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 The following code performs the write lock operation:
B: if LOCK (X) = “unlocked” then
begin LOCK (X)  “read-locked”;
no_of_reads (X)  1;
end
no_of_reads (X)  no_of_reads (X) +1
else begin wait (until LOCK (X) = “unlocked” and
go to B
end;

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 The following code performs the unlock operation:
if LOCK (X) = “write-locked” then
begin LOCK (X)  “unlocked”;
wakes up one of the transactions, if any
end
begin
no_of_reads (X)  no_of_reads (X) -1
if no_of_reads (X) = 0 then
begin
LOCK (X) = “unlocked”;
wake up one of the transactions, if any
end
end;

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 Lock conversion
 Lock upgrade: existing read lock to write lock
if Ti has a read-lock (X) and Tj has no read-lock (X) (i  j) then
convert read-lock (X) to write-lock (X)
else
force Ti to wait until Tj unlocks X
 Lock downgrade: existing write lock to read lock
Ti has a write-lock (X) (*no transaction can have any lock on X*)
convert write-lock (X) to read-lock (X)

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Two-Phase Locking Techniques: The algorithm
 Two Phases:
 (a) Locking (Growing)
 (b) Unlocking (Shrinking).
 Locking (Growing) Phase:
 A transaction applies locks (read or write) on desired data items
one at a time.
 Unlocking (Shrinking) Phase:
 A transaction unlocks its locked data items one at a time.
 Requirement:
 For a transaction these two phases must be mutually exclusively,
that is, during locking phase unlocking phase must not start and
during unlocking phase locking phase must not begin.

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T1 T2 Result
read_lock (Y); read_lock (X); Initial values: X=20; Y=30
read_item (Y); read_item (X); Result of serial execution
unlock (Y); unlock (X); T1 followed by T2
write_lock (X); Write_lock (Y); X=50, Y=80.
read_item (X); read_item (Y); Result of serial execution
X:=X+Y; Y:=X+Y; T2 followed by T1
write_item (X); write_item (Y); X=70, Y=50
unlock (X); unlock (Y);

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T1 T2 Result
read_lock (Y); X=50; Y=50
read_item (Y); Nonserializable because it.
unlock (Y); violated two-phase policy.
read_lock (X);
read_item (X);
unlock (X);
write_lock (Y);
read_item (Y);
Y:=X+Y;
write_item (Y);
unlock (Y);
write_lock (X);
read_item (X);
X:=X+Y;
write_item (X);
unlock (X);
Time

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T’1 T’2
read_lock (Y); read_lock (X); T1 and T2 follow two-phase
read_item (Y); read_item (X); policy but they are subject to
write_lock (X); Write_lock (Y); deadlock, which must be
unlock (Y); unlock (X); dealt with.
read_item (X); read_item (Y);
X:=X+Y; Y:=X+Y;
write_item (X); write_item (Y);
unlock (X); unlock (Y);

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 Two-phase policy generates two locking algorithms
 (a) Basic
 (b) Conservative
 Conservative:
 Prevents deadlock by locking all desired data items before
transaction begins execution.
 Basic:
 Transaction locks data items incrementally. This may cause
deadlock which is dealt with.
 Strict:
 A more stricter version of Basic algorithm where unlocking is
performed after a transaction terminates (commits or aborts and
rolled-back). This is the most commonly used two-phase locking
algorithm.

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Dealing with Deadlock and Starvation
 Deadlock
T’1 T’2
read_lock (Y); T1 and T2 did follow two-phase
read_item (Y); policy but they are deadlock
read_lock (X);
read_item (Y);
write_lock (X);
(waits for X) write_lock (Y);
(waits for Y)
 Deadlock (T’1 and T’2)

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 Deadlock prevention
 A transaction locks all data items it refers to before
it begins execution.
 This way of locking prevents deadlock since a
transaction never waits for a data item.
 The conservative two-phase locking uses this
approach.

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 Deadlock detection and resolution
 In this approach, deadlocks are allowed to happen. The
scheduler maintains a wait-for-graph for detecting cycle. If
a cycle exists, then one transaction involved in the cycle is
selected (victim) and rolled-back.
 A wait-for-graph is created using the lock table. As soon as
a transaction is blocked, it is added to the graph. When a
chain like: Ti waits for Tj waits for Tk waits for Ti or Tj
occurs, then this creates a cycle. One of the transaction o

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 Deadlock avoidance
 There are many variations of two-phase locking algorithm.
 Some avoid deadlock by not letting the cycle to complete.
 That is as soon as the algorithm discovers that blocking a
transaction is likely to create a cycle, it rolls back the
transaction.
 Wound-Wait and Wait-Die algorithms use timestamps to
avoid deadlocks by rolling-back victim.

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 Starvation
 Starvation occurs when a particular transaction consistently
waits or restarted and never gets a chance to proceed
further.
 In a deadlock resolution it is possible that the same
transaction may consistently be selected as victim and
rolled-back.
 This limitation is inherent in all priority based scheduling
mechanisms.
 In Wound-Wait scheme a younger transaction may always
be wounded (aborted) by a long running older transaction
which may create starvation.

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Timestamp based concurrency control algorithm
 Timestamp
 A monotonically increasing variable (integer)
indicating the age of an operation or a transaction.
A larger timestamp value indicates a more recent
event or operation.
 Timestamp based algorithm uses timestamp to
serialize the execution of concurrent transactions.

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 Basic Timestamp Ordering
 1. Transaction T issues a write_item(X) operation:
 If read_TS(X) > TS(T) or if write_TS(X) > TS(T), then an
younger transaction has already read the data item so abort
and roll-back T and reject the operation.
 If the condition in part (a) does not exist, then execute
write_item(X) of T and set write_TS(X) to TS(T).
 2. Transaction T issues a read_item(X) operation:
 If write_TS(X) > TS(T), then an younger transaction has
already written to the data item so abort and roll-back T and
reject the operation.
 If write_TS(X)  TS(T), then execute read_item(X) of T and set
read_TS(X) to the larger of TS(T) and the current read_TS(X).

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 Strict Timestamp Ordering
 1. Transaction T issues a write_item(X) operation:
 If TS(T) > read_TS(X), then delay T until the
transaction T’ that wrote or read X has terminated
(committed or aborted).
 2. Transaction T issues a read_item(X) operation:
 If TS(T) > write_TS(X), then delay T until the
transaction T’ that wrote or read X has terminated
(committed or aborted).

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 Thomas’s Write Rule
 If read_TS(X) > TS(T) then abort and roll-back T
and reject the operation.
 If write_TS(X) > TS(T), then just ignore the write
operation and continue execution. This is because
the most recent writes counts in case of two
consecutive writes.
 If the conditions given in 1 and 2 above do not
occur, then execute write_item(X) of T and set
write_TS(X) to TS(T).

27
Multiversion concurrency control techniques
 This approach maintains a number of versions of a
data item and allocates the right version to a read
operation of a transaction. Thus unlike other
mechanisms a read operation in this mechanism is
never rejected.
 Side effect:
 Significantly more storage (RAM and disk) is
required to maintain multiple versions. To check
unlimited growth of versions, a garbage collection is
run when some criteria is satisfied.

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Multiversion technique based on timestamp
ordering
 This approach maintains a number of versions of a
data item and allocates the right version to a read
operation of a transaction.
 Thus unlike other mechanisms a read operation in
this mechanism is never rejected.
 Side effects: Significantly more storage (RAM and
disk) is required to maintain multiple versions. To
check unlimited growth of versions, a garbage
collection is run when some criteria is satisfied.

29
Multiversion technique based on timestamp ordering
 Assume X1, X2, …, Xn are the version of a data item X
created by a write operation of transactions. With each Xi a
read_TS (read timestamp) and a write_TS (write timestamp)
are associated.
 read_TS(Xi): The read timestamp of Xi is the largest of all
the timestamps of transactions that have successfully read
version Xi.
 write_TS(Xi): The write timestamp of Xi that wrote the
value of version Xi.
 A new version of Xi is created only by a write operation.

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 To ensure serializability, the following two rules are used.
 If transaction T issues write_item (X) and version i of X has
the highest write_TS(Xi) of all versions of X that is also less
than or equal to TS(T), and read _TS(Xi) > TS(T), then
abort and roll-back T; otherwise create a new version Xi and
read_TS(X) = write_TS(Xj) = TS(T).
 If transaction T issues read_item (X), find the version i of X
that has the highest write_TS(Xi) of all versions of X that is
also less than or equal to TS(T), then return the value of Xi
to T, and set the value of read _TS(Xi) to the largest of
TS(T) and the current read_TS(Xi).

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 To ensure serializability, the following two rules are used.
 If transaction T issues write_item (X) and version i of X has the
highest write_TS(Xi) of all versions of X that is also less than
or equal to TS(T), and read _TS(Xi) > TS(T), then abort and
roll-back T; otherwise create a new version Xi and read_TS(X)
= write_TS(Xj) = TS(T).
 If transaction T issues read_item (X), find the version i of X
that has the highest write_TS(Xi) of all versions of X that is
also less than or equal to TS(T), then return the value of Xi to
T, and set the value of read _TS(Xi) to the largest of TS(T) and
the current read_TS(Xi).
 Rule 2 guarantees that a read will never be rejected.

32
Multiversion Two-Phase Locking Using Certify
Locks
 Concept
 Allow a transaction T’ to read a data item X while it
is write locked by a conflicting transaction T.
 This is accomplished by maintaining two versions
of each data item X where one version must
always have been written by some committed
transaction. This means a write operation always
creates a new version of X.

33
Multiversion Two-Phase Locking Using Certify Locks
 Steps
1. X is the committed version of a data item.
2. T creates a second version X’ after obtaining a write lock on X.
3. Other transactions continue to read X.
4. T is ready to commit so it obtains a certify lock on X’.
5. The committed version X becomes X’.
6. T releases its certify lock on X’, which is X now.
read/write locking scheme read/write/certify locking scheme
Compatibility tables for
Read Write
yes no
no no
Read
Write
Read Write Certify
yes no no
no no no
no no no
Read
Write
Certify

34
Multiversion Two-Phase Locking Using Certify
Locks
 Note:
 In multiversion 2PL read and write operations from
conflicting transactions can be processed
concurrently.
 This improves concurrency but it may delay
transaction commit because of obtaining certify
locks on all its writes. It avoids cascading abort
but like strict two phase locking scheme conflicting
transactions may get deadlocked.

35
Validation (Optimistic) Concurrency Control Schemes
 In this technique only at the time of commit serializability
is checked and transactions are aborted in case of non-
serializable schedules.
 Three phases:
1. Read phase
2. Validation phase
3. Write phase
1. Read phase:
 A transaction can read values of committed data items.
However, updates are applied only to local copies
(versions) of the data items (in database cache).

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Validation (Optimistic) Concurrency Control Schemes
2. Validation phase: Serializability is checked before transactions write
their updates to the database.
 This phase for Ti checks that, for each transaction Tj that is either
committed or is in its validation phase, one of the following
conditions holds:
 Tj completes its write phase before Ti starts its read phase.
 Ti starts its write phase after Tj completes its write phase, and the
read_set of Ti has no items in common with the write_set of Tj
 Both the read_set and write_set of Ti have no items in common with
the write_set of Tj, and Tj completes its read phase.
 When validating Ti, the first condition is checked first for each
transaction Tj, since (1) is the simplest condition to check. If (1) is
false then (2) is checked and if (2) is false then (3 ) is checked. If
none of these conditions holds, the validation fails and Ti is aborted.

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Validation (Optimistic) Concurrency Control
Schemes
3. Write phase: On a successful validation
transactions’ updates are applied to the
database; otherwise, transactions are restarted.

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Granularity of data items and Multiple Granularity Locking
 A lockable unit of data defines its granularity. Granularity
can be coarse (entire database) or it can be fine (a tuple
or an attribute of a relation).
 Data item granularity significantly affects concurrency
control performance. Thus, the degree of concurrency is
low for coarse granularity and high for fine granularity.
 Example of data item granularity:
1. A field of a database record (an attribute of a tuple)
2. A database record (a tuple or a relation)
3. A disk block
4. An entire file
5. The entire database

39
Granularity of data items and Multiple Granularity
Locking
 The following diagram illustrates a hierarchy of
granularity from coarse (database) to fine
(record).
DB
f1 f2
p11 p12 ... p1n
r111 ... r11j r111 ... r11j r111 ... r11j r111 ... r11j r111 ... r11j r111 ... r11j
p11 p12 ... p1n

40
 To manage such hierarchy, in addition to read and write,
three additional locking modes, called intention lock
modes are defined:
 Intention-shared (IS): indicates that a shared lock(s) will be
requested on some descendent nodes(s).
 Intention-exclusive (IX): indicates that an exclusive lock(s)
will be requested on some descendent node(s).
 Shared-intention-exclusive (SIX): indicates that the
current node is locked in shared mode but an exclusive
lock(s) will be requested on some descendent nodes(s).

41
Granularity of data items and Multiple Granularity
Locking
 These locks are applied using the following
compatibility matrix:
IS IX S SIX X
yes yes yes yes no
yes yes no no no
yes no yes no no
yes no no no no
no no no no no
IS
IX
S
SIX
X
Intention-shared (IS
Intention-exclusive (IX)
Shared-intention-exclusive
(SIX)

42
 The set of rules which must be followed for producing serializable
schedule are
1. The lock compatibility must adhered to.
2. The root of the tree must be locked first, in any mode..
3. A node N can be locked by a transaction T in S or IX mode only if
the parent node is already locked by T in either IS or IX mode.
4. A node N can be locked by T in X, IX, or SIX mode only if the
parent of N is already locked by T in either IX or SIX mode.
5. T can lock a node only if it has not unlocked any node (to enforce
2PL policy).
6. T can unlock a node, N, only if none of the children of N are
currently locked by T.

43
Granularity of data items and Multiple Granularity Locking: An example of a serializable execution:
T1 T2 T3
IX(db)
IX(f1)
IX(db)
IS(db)
IS(f1)
IS(p11)
IX(p11)
X(r111)
IX(f1)
X(p12)
S(r11j)
IX(f2)
IX(p21)
IX(r211)
Unlock (r211)
Unlock (p21)
Unlock (f2)
S(f2)

44
 Granularity of data items and Multiple Granularity Locking: An
example of a serializable execution (continued):
T1 T2 T3
unlock(p12)
unlock(f1)
unlock(db)
unlock(r111)
unlock(p11)
unlock(f1)
unlock(db)
unlock (r111j)
unlock (p11)
unlock (f1)
unlock(f2)
unlock(db)

45
Summary
 Databases Concurrency Control
1. Purpose of Concurrency Control
2. Two-Phase locking
3. Limitations of CCMs
4. Index Locking
5. Lock Compatibility Matrix
6. Lock Granularity

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