PMIT-6102
Advanced Database Systems
ByJesmin Akhter
Assistant Professor, IIT, Jahangirnagar University
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 1
Lecture -11
Distributed Concurrency
Control
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 2
Outline

Transaction
 Role of the distributed execution monitor Schedule
 Detailed Model of the Distributed Execution Monitor,
Distributed Concurrency Control
 Serializability in Distributed DBMS
 Concurrency Control Algorithms
 time-stamping,
 Deadlock
Centralized Deadlock Detection
 Distributed Deadlock Detection

Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 3
Role of the distributed execution monitor

The distributed execution monitor consists of two
modules:
 a transaction manager (TM) and a scheduler (SC).
 The transaction manager is responsible for coordinating the
execution of the database operations on behalf of an
application.
 The scheduler, on the other hand, is responsible for the
implementation of a specific concurrency control algorithm
for synchronizing access to the database.
 A third component that participates in the management of
distributed transactions is the local recovery managers
(LRM) that exist at each site.

Distributed DBMS
Their function is to implement the local procedures by which
the local database can be recovered to a consistent state
following a failure.
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 4
Role of the distributed execution monitor



Each transaction originates at one site, which we will
call its originating site.
The execution of the database operations of a
transaction is coordinated by the TM at that
transaction’s originating site.
The transaction managers implement an interface for
the application programs which consists of five
commands: begin transaction, read, write,
commit, and abort.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 5
Role of the distributed execution monitor





Begin transaction: This is an indicator to the TM that a new
transaction is starting. The TM does some bookkeeping, such as
recording the transaction’s name, the originating application, and so
on, in coordination with the data processor.
Read: If the data item to be read is stored locally, its value is read
and returned to the transaction. Otherwise, the TM finds where the
data item is stored and requests its value to be returned (after
appropriate concurrency control measures are taken).
Write: If the data item is stored locally, its value is updated (in
coordination with the data processor). Otherwise, the TM finds where
the data item is located and requests the update to be carried out at
that site after appropriate concurrency control measures are taken).
Commit: The TM coordinates the sites involved in updating data
items on behalf of this transaction so that the updates are made
permanent at every site.
Abort The TM makes sure that no effects of the transaction are
reflected in any of the databases at the sites where it updated data
items.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 6
Detailed Model of the Distributed
Execution Monitor
Begin_transaction,
Read, Write,
Commit, Abort
Results
Distributed
Execution Monitor
With other
TMs
Transaction Manager
(TM)
Scheduling/
Descheduling
Requests
Scheduler
(SC)
With other
SCs
To data
processor
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 7
Serializability in Distributed DBMS

Somewhat more involved. Two histories have to be
considered:
 local histories
 global history

For global transactions (i.e., global history) to be
serializable, two conditions are necessary:
 Each local history should be serializable.
 Two conflicting operations should be in the same relative
order in all of the local histories where they appear together.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 8
Global Non-serializability
T1: Read(x)
x x5
Write(x)
Commit
T2: Read(x)
x x15
Write(x)
Commit
The following two local histories are individually
serializable (in fact serial), but the two transactions
are not globally serializable.
LH1={R1(x),W1(x),C1,R2(x),W2(x),C2}
LH2={R2(x),W2(x),C2,R1(x),W1(x),C1}
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 9
Concurrency Control Algorithms

Pessimistic
 Two-Phase Locking-based (2PL)
Centralized (primary site) 2PL
 Primary copy 2PL
 Distributed 2PL

 Timestamp Ordering (TO)
Basic TO
 Multiversion TO
 Conservative TO

 Hybrid

Optimistic
 Locking-based
 Timestamp ordering-based
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 10
Locking-Based Algorithms




Transactions indicate their intentions by
requesting locks from the scheduler (called lock
manager).
Locks are either read lock (rl) [also called shared
lock] or write lock (wl) [also called exclusive lock]
Read locks and write locks conflict (because Read
and Write operations are incompatible
rl
wl
rl
yes no
wl
no
no
Locking works nicely to allow concurrent
processing of transactions.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 11
Two-Phase Locking (2PL)
 A Transaction locks an object before using it.
 When an object is locked by another transaction,
the requesting transaction must wait.
 When a transaction releases a lock, it may not
request another lock.
Lock point
Obtain lock
No. of locks
Release lock
Phase 1
BEGIN
Distributed DBMS
Phase 2
END
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 12
Strict 2PL
Hold locks until the end.
Obtain lock
Release lock
BEGIN
END
period of
data item
use
Distributed DBMS
Transaction
duration
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 13
Testing for Serializability
Consider transactions T1, T2, …, Tk
Create a directed graph (called a conflict graph),
whose nodes are transactions. Consider a history
of transactions.
If T1 unlocks an item and T2 locks it afterwards,
draw an edge from T1 to T2 implying T1 must
precede T2 in any serial history
T1→T2
Repeat this for all unlock and lock actions for
different transactions.
If graph has a cycle, the history is not serializable.
If graph is a cyclic, a topological sorting will give the
serial history.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 14
Example
T1:
T1:
T2:
T2:
T2:
T2:
T3:
T3:
Lock X
Unlock X
Lock X
Lock Y
Unlock X
Unlock Y
Lock Y
Unlock Y
T1→T2
T2→T3
T
T
1
2
T
3
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 15
Theorem
Two phase locking is a sufficient condition to ensure
serializablility.
Proof: By contradiction.
If history is not serializable, a cycle must exist in the
conflict graph. This means the existence of a path
such as
T1→T2→T3 … Tk → T1.
This implies T1 unlocked before T2 and after Tk.
T1 requested a lock again. This violates the condition
of two phase locking.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 16
Centralized 2PL





Only one of the sites has a lock manager;
The transaction managers at the other sites communicate with it
rather than with their own lock managers. This approach is also
known as the primary site 2PL algorithm
The communication between the cooperating sites in executing a
transaction
A centralized 2PL (C2PL) algorithm is depicted in Figure in next
slide.
This communication is between
 the transaction manager at the site where the transaction is
initiated (called the coordinating TM), the lock manager at
the central site, and the data processors (DP) at the other
participating sites.
 The participating sites are those that store the data item and
at which the operation is to be carried out.
 The order of messages is denoted in the figure.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 17
Centralized 2PL


There is only one 2PL scheduler in the distributed system.
Lock requests are issued to the central scheduler.
Data Processors at
participating sites
Distributed DBMS
Coordinating TM
Central Site LM
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 18
Distributed 2PL

2PL schedulers are placed at each site. Each
scheduler handles lock requests for data at that site.

A transaction may read any of the replicated copies
of item x, by obtaining a read lock on one of the
copies of x. Writing into x requires obtaining write
locks for all copies of x.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 19
Distributed 2PL Execution
Coordinating TM
Distributed DBMS
Participating LMs
Participating DPs
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 20
Timestamp Ordering

Transaction (Ti) is assigned a globally unique timestamp ts(Ti).

Transaction manager attaches the timestamp to all operations issued
by the transaction.

Each data item is assigned a write timestamp (wts) and a read
timestamp (rts):
 rts(x) = largest timestamp of any read on x
 wts(x) = largest timestamp of any read on x

Conflicting operations are resolved by timestamp order.
Basic T/O:
for Ri(x)
if ts(Ti) < wts(x)
then reject Ri(x)
else accept Ri(x)
rts(x) ts(Ti)
Distributed DBMS
for Wi(x)
if ts(Ti) < rts(x) and ts(Ti) < wts(x)
then reject Wi(x)
else accept Wi(x)
wts(x) ts(Ti)
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 21
Optimistic Concurrency Control Algorithms

Transaction execution model: divide into
subtransactions each of which execute at a site
 Tij: transaction Ti that executes at site j

Transactions run independently at each site
until they reach the end of their read phases

All subtransactions are assigned a timestamp
at the end of their read phase

Validation test performed during validation
phase. If one fails, all rejected.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 22
Optimistic Concurrency Control Processing
Start
Integrity
Control
&
Local
Validation
Read,
Compute,
And
Write Local
Success
Semi-Commit
On
Initiating
Site
Fail
Commit,
Global Write
Finish
Success
Integrity
Control
&
Local
Validation
Fail
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 23
Optimistic CC Validation Test
If all transactions Tk where ts(Tk) < ts(Tij) have
completed their write phase before Tij has
started its read phase, then validation
succeeds
 Transaction executions in serial order
Tk
R
V
W
Tij
Distributed DBMS
R
V
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
W
Slide 24
Optimistic CC Validation Test
If there is any transaction Tk such that ts(Tk)<ts(Tij)
and which completes its write phase while Tij is in
its read phase, then validation succeeds if
WS(Tk)  RS(Tij) = Ø
 Read and write phases overlap, but Tij does not read data
items written by Tk
Tk
R
V
W
Tij
Distributed DBMS
R
V
W
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 25
Optimistic CC Validation Test
If there is any transaction Tk such that ts(Tk)< ts(Tij)
and which completes its read phase before Tij
completes its read phase, then validation succeeds if
WS(Tk) RS(Tij) = Ø and WS(Tk) WS(Tij) = Ø
 They overlap, but don't access any common data items.
R
Tk
Tij
Distributed DBMS
V
R
W
V
W
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 26
Deadlock

A transaction is deadlocked if it is blocked and will
remain blocked until there is intervention.

Locking-based CC algorithms may cause deadlocks.

TO-based algorithms that involve waiting may cause
deadlocks.

Wait-for graph
 If transaction Ti waits for another transaction Tj to release
a lock on an entity, then Ti  Tj in WFG.
Ti
Distributed DBMS
Tj
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 27
Local versus Global WFG
Assume T1 and T2 run at site 1, T3 and T4 run at site 2.
Also assume T3 waits for a lock held by T4 which waits
for a lock held by T1 which waits for a lock held by T2
which, in turn, waits for a lock held by T3.
Local WFG Site 1
Site 2
T1
T4
T2
T3
Global WFG
Distributed DBMS
T1
T4
T2
T3
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 28
Deadlock Management

Ignore
 Let the application programmer deal with it, or
restart the system

Prevention
 Guaranteeing that deadlocks can never occur in
the first place. Check transaction when it is
initiated. Requires no run time support.

Avoidance
 Detecting potential deadlocks in advance and
taking action to insure that deadlock will not
occur. Requires run time support.

Detection and Recovery
 Allowing deadlocks to form and then finding and
breaking them. As in the avoidance scheme, this
requires run time support.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 29
Deadlock Avoidance

Transactions are not required to request
resources a priori.

Transactions are allowed to proceed unless a
requested resource is unavailable.

In case of conflict, transactions may be
allowed to wait for a fixed time interval.

Order either the data items or the sites and
always request locks in that order.

More attractive than prevention in a
database environment.
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 30
Deadlock Avoidance –
Wait-Die & Wound-Wait Algorithms
WAIT-DIE Rule: If Ti requests a lock on a data item
which is already locked by Tj, then Ti is permitted to
wait iff ts(Ti)<ts(Tj). If ts(Ti)>ts(Tj), then Ti is aborted
and restarted with the same timestamp.
 if ts(Ti)<ts(Tj) then Ti waits else Ti dies
 non-preemptive: Ti never preempts Tj
 prefers younger transactions
WOUND-WAIT Rule: If Ti requests a lock on a data
item which is already locked by Tj , then Ti is
permitted to wait iff ts(Ti)>ts(Tj). If ts(Ti)<ts(Tj), then
Tj is aborted and the lock is granted to Ti.
 if ts(Ti)<ts(Tj) then Tj is wounded else Ti waits
 preemptive: Ti preempts Tj if it is younger
 prefers older transactions
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 31
Deadlock Detection

Transactions are allowed to wait freely.

Wait-for graphs and cycles.

Topologies for deadlock detection
algorithms
 Centralized
 Distributed
 Hierarchical
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 32
Centralized Deadlock Detection

One site is designated as the deadlock detector for
the system. Each scheduler periodically sends its
local WFG to the central site which merges them to
a global WFG to determine cycles.

How often to transmit?
 Too often  higher communication cost but lower delays
due to undetected deadlocks
 Too late  higher delays due to deadlocks, but lower
communication cost

Would be a reasonable choice if the concurrency
control algorithm is also centralized.

Proposed for Distributed INGRES
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 33
Distributed Deadlock Detection


Sites cooperate in detection of deadlocks.
One example:
 The local WFGs are formed at each site and passed on to
other sites. Each local WFG is modified as follows:
 Since each site receives the potential deadlock cycles from
other sites, these edges are added to the local WFGs
 The edges in the local WFG which show that local
transactions are waiting for transactions at other sites are
joined with edges in the local WFGs which show that remote
transactions are waiting for local ones.
 Each local deadlock detector:


Distributed DBMS
looks for a cycle that does not involve the external edge. If it
exists, there is a local deadlock which can be handled locally.
looks for a cycle involving the external edge. If it exists, it
indicates a potential global deadlock. Pass on the information
to the next site.
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 34
Thank you
Distributed DBMS
Lectured by, Jesmin Akhter, Assistant Professor, IIT, JU
Slide 35