5-Chapter Five - Concurrenc.ppt

Chapter 5: Concurrency Control
Adama Science and Technology University
School of Electrical Engineering and Computing
Department of CSE
CSEg 2208: Database Systems
(2022)

2
Outline
• Databases Concurrency Control
• Purpose of Concurrency Control
• Concurrency Control Techniques
– Locking
– Timestamp
– Optimistic
– Multiversion
– Lock Granularity

3
Lock
Table
Scheduler
Read and Writes
Requests from
transactions
Buffers
Database Concurrency Control
Transaction Processor is divided into:
• A concurrency-control manager/scheduler, responsible for assuring isolation of
transactions
• A logging and recovery manager, responsible for the durability of transactions.
• The scheduler (concurrency-control manager) must assure that the individual
actions of multiple transactions are executed in such an order that the net effect
is the same as if the transactions had in fact executed one-at-a-time.

4
Example:
A = 500
B = 500
C = 500
Account Balances
Bank database: 3 Accounts
Property: A + B + C = 1500
Money does not leave the system

5
– 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.
• A typical scheduler does its work by maintaining locks on certain pieces of the
database. These locks prevent two transactions from accessing the same piece of
data at the same time. 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.
Purpose of Concurrency Control

6
Example
Transaction T1: Transfer 100 from A to B
Read (A, t)
t = t - 100
Write (A, t)
t = t + 100
Write (B, t)
Read (B, t)
Transaction T2: Transfer 100 from A to C
Read (A, s)
s = s - 100
Write (A, s)
Read (C, s)
s = s + 100
Write (C, s)

7
Read (A, t)
t = t - 100
Write (A, t)
Read (B, t)
t = t + 100
Write (B, t)
Read (A, s)
s = s - 100
Write (A, s)
Read (C, s)
s = s + 100
Write (C, s)
Transaction T1 Transaction T2 A B C
400 600
600
500 500
500
400 500
500
400 500
600
400 + 600 + 600 = 1600
Schedule
400
500
500

8
Read (A, t)
t = t - 100
Write (A, t)
Read (B, t)
t = t + 100
Write (B, t)
Read (A, s)
s = s - 100
Write (A, s)
Read (C, s)
s = s + 100
Write (C, s)
Transaction T1 Transaction T2 A B C
300 600
600
500 500
500
400 500
500
300 500
600
300 + 600 + 600
= 1500
Alternative Schedule
300
500
500
So What ?

9
Concurrency Control Techniques
• Basic concurrency control techniques:
– Locking,
– Timestamping
– Optimistic methods
• The First two are conservative approaches: delay transactions in case they
conflict with other transactions.
• Optimistic methods assume conflict is rare and only check for conflicts at
commit.
2.1. Locking
– Lock is a variable associated with a data item that describes the status of the
data item with respect to the possible operations that can be applied to it.

10
• 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.
• Generally, a transaction must claim a shared (read) or exclusive (write) lock
on a data item before read or write.
• Lock prevents another transaction from modifying item or even reading it, in
the case of a write lock.

11
 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

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 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
 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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• It has two oprerations : Lock_item(X) and unLock_item(X)
• A transaction request access to an item X by first issuing a lock_Item(x) opreation .
• If lock (x)=1, the transaction is forced to wait.
• If lock (X)= 0; it is set to 1 and the transaction is allowed to access x
• When a transaction finished operation on X it issues an Unlock _item operation
which set lock(x) to 0 so that X may be accessed by another transaction
• If transaction has shared lock on item, can read but not update item.
• If transaction has exclusive lock on item, can both read and update item.
• Reads cannot conflict, so more than one transaction can hold shared locks
simultaneously on same item.
• Exclusive lock gives transaction exclusive access to that item.
Locking - Basic Rules

14
• The following code performs the read operation: read_lock(X):
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
the lock manager wakes up the transaction);
go to B
end;

10/22/2023 15
• The following code performs the write lock operation: write_lock(X):
B: if LOCK (X) = “unlocked” then
LOCK (X)  “write-locked”;
else
wait (until LOCK(X) = “unlocked”
and the lock manager wakes up the transaction);
goto B
end;

16
• 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)
• Using such locks in the transaction do not guarantee serializability of schedule
on its own: example

17
Write Lock( X)
Read (X)
X=X+100
Write(X)
Unlock(X)
Write Lock( X)
Read (X)
X= X*1.1
Write(X)
Unlock(X)
Write Lock( Y)
Read (Y)
Y= Y*1.1
Write(Y)
Unlock(Y)
Commit
write_lock(Y)
read(Y)
Y= Y-100
write(Y)
unlock(Y)
commit
T1 T2
Schedule:
Example : Incorrect
Locking Schedule

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• If at start, X = 100, Y = 400, result should be:
– X = 220, y = 330, if T1 executes before T2, or
– X = 210, Y = 340, if T2 executes before T1
• However, result gives X= 220 and Y = 340.
• S is not a serializable schedule. Why?
• Problem is that transactions release locks too soon, resulting in loss of
total isolation and atomicity.
• To guarantee serializability, we need an additional protocol concerning
the positioning of lock and unlock operations in every transaction.

19 ???
2.2 Two-Phase Locking Techniques: The algorithm
 Transaction follows 2PL protocol if all locking operations precede first unlock
operation in the transaction.
• Every transaction can be divided into Two Phases: Locking (Growing) &
Unlocking (Shrinking)
– Locking (Growing) Phase:
• A transaction applies locks (read or write) on desired data items one at a
time.
• acquires all locks but cannot release any locks.
– Unlocking (Shrinking) Phase:
• A transaction unlocks its locked data items one at a time.
• Releases locks but cannot acquire any new locks.

10/22/2023 20
Growing Phase Shrinking Phase Time
# locks
held by
Ti
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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Example
T1 T2
read_lock (Y); read_lock (X);
read_item (Y); read_item (X);
unlock (Y); unlock (X);
write_lock (X); Write_lock (Y);
read_item (X); read_item (Y);
X:=X+Y; Y:=X+Y;
write_item (X); write_item (Y);
unlock (X); unlock (Y);
Result
Initial values: X=20; Y=30
Result of serial execution
T1 followed by T2
X=50, Y=80.
Result of serial execution
T2 followed by T1
X=70, Y=50

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T’1 T’2
read_lock (Y); read_lock (X);
read_item (Y); read_item (X);
write_lock (X); Write_lock (Y);
unlock (Y); unlock (X); .
read_item (X); read_item (Y);
X:=X+Y; Y:=X+Y;
write_item (X); write_item (Y);
unlock (X); unlock (Y);
• T’1 and T’2 follow two-phase policy
but they are subject to deadlock,
which must be dealt with
• If every transaction in the schedule follows the 2PL protocol , the schedule is
guaranteed to be serializabe
• Limitation -It may limit the number of concurrency that can occur in the schedule .
How?
• Remark: The use of this locking can cause two additional problems
• Deadlock and starvation

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Dealing with Deadlock and Starvation
• Deadlock
– It is a state that may result when two or more transaction are each
waiting for locks held by the other to be released
– Example :
T1 T2
read_lock (Y);
read_item (Y);
read_lock (X);
read_item (X);
write_lock (X);
write_lock (Y);

– T1 is in the waiting queue for X which is locked by T2
– T2 is on the waiting queue for Y which is locked by T1
– No transaction can continue until the other transaction completes
– T1 and T2 did follow two-phase policy but they are deadlock
• So the DBMS must either prevent or detect and resolve such deadlock situations
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There are possible solutions : Deadlock prevention, deadlock detection and
avoidance ,and lock timeouts
i. Deadlock prevention protocol: two possibilities
 The conservative two-phase locking
− 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.
− Limitation : It restricts concurrency

25
 Transaction Timestamp( TS(T) )
 We can prevent deadlocks by giving each transaction a priority and
ensuring that lower priority transactions are not allowed to wait for higher
priority transactions (or vice versa ).
 One way to assign priorities is to give each transaction a timestamp when
it starts up.
 it is a unique identifier given to each transaction based on time in which it
is started. i.e if T1 starts before T2 , TS(T1)<TS(T2)
 The lower the timestamp, the higher the transaction's priority, that is, the
oldest transaction has the highest priority.
 If a transaction Ti requests a lock and transaction Tj holds a conflicting
lock, the lock manager can use one of the following two policies: Wait-
die & Would-wait

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• Wait-die
– If Ti has higher priority, it is allowed to wait; otherwise it is aborted.
– An older transaction is allowed to wait on a younger transaction.
– A younger transaction requesting an item held by an older transaction is
aborted
– If TS(Ti) < TS(Tj), then (Ti older than Tj)Ti is allowed to wait.
– Otherwise (Ti younger than Tj)Abort Ti (Ti dies) and restart it later with the
same timestamp
wait
wait
wait
T3 (ts =25) : Z is locked, M is requested
T1(ts =10): M is locked , K is requested
T2 (ts =20): K is locked, Z is requested

27 ??
• Wound-wait
– The opposite of wait-die
– If Ti has higher priority, abort Tj; otherwise Ti waits.
– A younger transaction is allowed to wait on al older one
– An older transaction requesting an item held by a younger transaction preempts the
younger transaction by aborting it.
– If TS(Ti) < TS(Tj), then (Ti older than Tj),Abort Tj (Ti wounds Tj) and restart Tj later
with the same timestamp
– Otherwise (Ti younger than Tj)Ti is allowed to wait
wait
wait
wait
T3 (ts =10)
T1(ts =25)
T2 (ts =20)

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 Remark:
− Both methods ended by aborting the younger of the two transaction that may be
involved in the deadlock
− Limitation:
− Both techniques may cause some transaction to be aborted and restarted
needlessly
ii. Deadlock Detection and resolution
– In this approach, deadlocks are allowed to happen
– The scheduler maintains a wait-for-graph for detecting cycle.
– When a chain like: Ti waits for Tj waits for Tk waits for Ti or Tj occurs, then this
creates a cycle.
– When the system is in the state of deadlock , some of the transaction should be aborted
by selection(victim) and rolled-back
– This can be done by aborting those transaction: that have made the least work, the one
with the lowest locks, and that have the least # of abortion and so on
– Example:

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iii. Timeouts
– It uses the period of time that several transaction have been waiting to lock items
– It has lower overhead cost and it is simple
– If the transaction wait for a longer time than the predefined time out period, the system
assume that may be deadlocked and aborted it
 Starvation
– Starvation occurs when a particular transaction consistently waits or restarted and never
gets a chance to proceed further while other transaction continue normally
– This may occur , if the waiting method for item locking:
• Gave priority for some transaction over others
• Problem in Victim selection algorithm- it is possible that the same transaction
may consistently be selected as victim and rolled-back .example In Wound-Wait
– Solution
• FIFO
• Allow for transaction that wait for a longer time
• Give higher priority for transaction that have been aborted for many times

30
Timestamp based concurrency control algorithm
• Timestamp
– In lock based concurrency control , conflicting actions of different transactions are
ordered by the order in which locks are obtained.
– But here, Timestamp values are assigned based on time in which the transaction are
submitted to the system using the current date & time of the system
– 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.
– It doesn’t use lock, thus deadlock cannot be occurred
– In the timestamp ordering, conflicting operation in the schedule shouldn’t violate
serilazable ordering
– This can be achieved by associating timestamp value (TS) to each database item which
is denoted as follow:

31
a) Read_Ts(x): the read timestamp of x – this is the largest time among all the time
stamps of transactions that have successfully read item X
b) Write_TS(X): the largest of all the timestamps of transactions that have
successfully written item X
 The concurrency control algorithm check whether conflict operation violate the
timestamp ordering in the following manner: three options
i. Basic Timestamp Ordering
 Transaction T issues a write_item(X) operation:
• If read_TS(X) > TS(T) or if write_TS(X) > TS(T), then a younger
transaction has already read/write the values of the data item x before T
had a chance to write X . so abort and roll-back T and restarted with a
new, larger timestamp. Why is with new timestamp?, is there a difference b/n this timestamp
protocol and the 2PL for dead lock prevention?
• If the condition above does not exist, then execute write_item(X) of T
and set write_TS(X) to TS(T).

 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)
– Limitation: cyclic restart/starvation may occur when a transaction is
continuously aborted and restarted
10/22/2023 32

33
4. 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.
– This algorithm uses the concept of view serilazabilty than conflict serialiazabilty
– 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.
– Two schemes : based on time stamped ordering & 2PL

34
i. 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.

35
 To ensure serializability, the following two rules are used:
i. 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 set read_TS(X) = write_TS(Xi)
= TS(T).
ii. 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).
– Note that: Rule two indicates that read request will never be rejected

• ii. Multiversion Two-Phase Locking Using Certify Lock
– 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.
10/22/2023 36
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.

37
read/write locking scheme read/write/certify locking scheme
Compatibility tables for
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.
Read Write Certify
Read Yes Yes No
Write Yes No No
Certify No No No

38
Validation (Optimistic) Concurrency Control Schemes
– This technique allow transaction to proceed asynchronously and only at the
time of commit, serializability is checked & transactions are aborted in case of
non-serializable schedules.
– Good if there is little interference among transaction
– It has three phases: Read, Validation , and Write phase
i.. 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).

10/22/2023 39
ii. Validation phase:.
− If the transaction Ti decides that it wants to commit, the DBMS checks
whether the transaction could possibly have conflicted with any other
concurrently executing transaction.
− While one transaction ,Ti, is being validated , no other transaction can be
allowed to commit
− 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:

40
 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 item 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.
− 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.
iii. Write phase:
 On a successful validation, transactions’ updates are applied to the database;
otherwise, transactions are restarted.

41
6. Multiple Granularity Locking
 A lockable unit of data defines its granularity
 Granularity can be coarse (entire database) or it can be fine (an attribute of a
relation).
 Example of data item granularity:
 A field of a database record
 A database record
 A disk block/ page
 An entire file
 The entire database
 Data item granularity significantly affects concurrency control performance.
 Thus, the degree of concurrency is low for coarse granularity and high for fine
granularity.

• Example:
– A transaction that expects to access most of the pages in a file should
probably set a lock on the entire file , rather than locking individual
pages or records
– If a transaction that requires to access relatively few pages of the file , it
is better to lock those pages
– Similarly , if a transaction access several records on a page , it should
lock the entire page and if it access just a few records , it should lock
some those records.
– This example will hold true , if a lock on the node locks that node and
implicitly all its descendants
10/22/2023 42

• The following diagram illustrates a hierarchy of granularity from coarse
(database) to fine (record).
43
Fig 1: Granularity hierarchy

10/22/2023 44
• To manage such hierarchy, in addition to read(S) and write(X), 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 node(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).
– If a transaction needs to read an entire file and modifiy a few of the
records in it. i.e it needs S lock on a file & IX lock on some of the
contained object

45
• 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.

10/22/2023 46
Summery :
Parent Child can be
locked in locked in
IS IS, S
IX IS, S, IX, X, SIX
SIX X, IX
X None
P
C

To lock a node in S mode, a transaction must first lock all its
ancestors
in IS mode. Thus, if a transaction locks a node in S mode, no
other
transaction can have locked any ancestor in X mode;
 Similarly, if a transaction locks a node in X mode, no other
transaction
can have locked any ancestor in S or X mode.
 These two cases ensures that no other transaction holds a lock
on an
ancestor that conflicts with the requested S or X lock on the
node.
10/22/2023 47

48
– 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
Requestor : T1
Holder: T2
• Consider the following three example based Fig 1 diagram given in slide 40
• T1 wants to update record r111 and r211
• T2 wants to update all records on page p12
• T3 wants to read records r11j and the entire f2 file

49
Multiple Granularity Locking: Lock
operation to show 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)
X(r211)
Unlock (r211)
Unlock (p21)
Unlock (f2)
unlock(db)
Multiple Granularity Locking: Lock operation
to show a serializable execution(continued)
T1 T2 T3

Question & Answer
10/22/2023 50

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