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Chapter 10
Transactions in
Distributed and
Grid Databases
10.1 Grid Database Challenges
10.2 Distributed and Multidatabase Systems
10.3 Basic Definitions on Transaction Management
10.4 ACID Properties of Transactions
10.5 Transaction Management in Various DB Systems
10.6 Requirements in Grid Database Systems
10.7 Concurrency Control Protocols
10.8 Atomic Commit Protocols
10.9 Replica Synchronisation Protocols
10.10Summary
10.11 Bibliographical Notes
10.12Exercises
10.1. Grid
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Amount of data being produced and stored has increased during
last three decades
Advanced scientific applications are data-intensive, collaborative
and distributed in nature
Example:
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Database Challenges
Different group of scientists gathering data for any application (e.g. weather
forecast or earth movement) at geographically separated locations.
Data is colleted locally but the experiment must use all these data
Say, all organisations are connected by Grid infrastructure
Data is replicated at multiple sites
One collaborator runs an experiment and forecasts a natural disaster
If the outcome is not strictly serialized between all the replicated sites then
other sites may overwrite or never know the outcome of the experiment
From the above example, it is clear that certain applications need
strict synchronisation and a high level of data consistency within the
replicated copies of the data as well as in the individual data sites
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.1. Grid Database Challenges (cont’d)

Following design challenges are identified from the perspective of
data consistency
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Transactional requirements may vary depending upon the application
requirement. i.e. read queries may be scheduled in any order but write
transactions need to be scheduled carefully
Affect of heterogeneity in scheduling policy of sites and maintaining
correctness of data is a major design challenge
How does traditional distributed transaction scheduling protocols work in
heterogeneous and autonomous Grid environment
How does data replication affect data consistency
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed
and Multidatabase
Systems
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Management of distributed data has evolved with continuously
changing computing infrastructure
Distributed architecture that lead to different transaction models can be
classified as follows:
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Homogeneous distributed architecture: Distributed Database systems
Heterogeneous distributed architecture: Multidatabase systems
Many different protocols have been proposed for each individual
architecture but the underlying architectural assumption is the same for
all protocols in one category
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases

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Distributed database systems store data at geographically
distributed sites, but the distributed sites are typically in the same
administrative domain, i.e. technology and policy decisions lie in
one administrative domain
The design strategy used in bottom-up
Communication among sites are done over a network instead of
shared memory
The concept of a distributed DBMS is best suited to individual
institutions operating at geographically distributed locations, e.g.
banks, universities etc.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Distributed Database Architectural Model

A Distributed database system in general has three major
dimensions:
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Autonomy
Distribution and
Heterogeneity
Autonomy: When a database is developed independently of other
DBMS, it is not aware of design decisions and control structures
adopted at those sites.
Distribution: Distribution dimension deals with physical distribution
of data over multiple sites and still maintain the conceptual integrity
of the data. Two major types of distribution have been identified:
Client/Server distribution and peer-to-peer distribution
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)

Heterogeneity: Heterogeneity may occur at hardware as well as
data/transaction model level. Heterogeneity is one of the important
factors that need careful consideration in a distributed environment
because any transaction that spans more than one database may
need to map one data/transaction model to other

Though theoretically the heterogeneity dimension has been
identified, a lot of research work and applications have only
focussed on homogeneous environment.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Distributed Database Working Model

The figure shows a general Distributed Database architecture
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Transactions (T1, T2, … Tn) from different sites are submitted to
the Global Transaction Monitor (GTM)
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Global Data Dictionary is used to build and execute the distributed
queries
T
1
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Each sub-query is transported to
Local Transaction Monitors
checked for local correctness and
then passed down to Local
Database Management System
The results are sent back to the
GTM. Any potential problem, e.g.
global dead lock, is resolved by
GTM after gathering information
from all the participating sites.
Global Data
Dictionary
T2
…
Tn
Global Transaction
Monitor
Communication Interface
Local Transaction
Monitor 1
Local Transaction
Monitor 2
…
Local Transaction
Monitor n
LDBMS 1
LDBMS 2
…
LDBMS n
LDB
LDB
…
LDB
LDB – Local Database
LDBMS – Local Database Management Systems
– Distributed DBMS boundary
T1, T2, … Tn – Transaction originated at different sites
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
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GTM has the following components:
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Global transaction request module
Global request semantic analyser module
Global query decomposer module
Global query object localiser module
Global query optimiser module
Global transaction scheduler module
Global recovery manager module
Global lock manager module, and
The transaction dispatcher module
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Suitability of Distributed DBMS in Grids

Following challenges are faced while applying distributed DB
concepts in Grid environment
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Distributed DBs have global Data Dictionary and transaction monitor: In Grid
environment, it becomes increasingly difficult to manage such a huge global
information such as global locks, global data dictionary etc.
Assumption of uniform protocols among distributed sites: e.g. concurrency
control protocol assumes that all distributed sites support same protocol (such
as locking, timestamp or optimistic). But this assumption is not valid in Grid
environment because all sites are autonomous and individual administrative
domains choose protocols independently.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Multidatabase Systems (MDMS)
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Multidatabase system can be defined as interconnected collection of
autonomous databases.
Fundamental concept in multidatabase system is autonomy.
Autonomy refers to the distribution of control and indicates the
degree to which individual DBMS can operate independently. Levels
of autonomy are as follows:
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Design Autonomy: Individual DBMS can use the data models and transaction
management techniques without intervention of any other DBMS.
Communication Autonomy: Each DBMS can decide the information it wants
to provide to other databases.
Execution Autonomy: Individual databases are free to execute the
transactions as per their scheduling strategy.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Multidatabase Architecture

Each database in multidatabase
environment has its own transaction
processing components such as a local
transaction manager, local data manager,
local scheduler etc
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Transactions submitted to individual
databases are executed independently
and local DBMS is completely responsible
for its correctness.

MDMS is not aware of any local execution
at the local database.
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A global transaction that needs to access
data from multiple sites is submitted to
MDMS, which in turn forwards the request
to, and collects result from, the local
DBMS on behalf of the global transaction
Global Data
Dictionary
Multidatabase/
Global DBMS
Global
Transaction
Global
Transaction
Manager
Global
Access
Layer
Global
Subtransaction 1
Local
Transactio
Global
Subtransaction 2
Local DBMS 1
Local DBMS n
Local
Access
Layer
Local
Access
Layer
Local
Transaction
Manager
Local
Transaction
Manager
Local
Database 1
...
Local
Database n
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.2. Distributed Databases (cont’d)
Suitability of Multidatabase in Grids

Architecturally, multidatabase systems are close to Grid databases
as individual database systems are autonomous
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But, Local database systems in multidatabase systems are not
designed for sharing the data
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Hence, issues related to efficient sharing of data between sites, e.g.
replication, are not addressed in multidatabase systems

The design strategy of multidatabase is a combination of top-down
and bottom-up strategy. Individual database sites are designed
independently, but the development of MDMS requires the
underlying working knowledge of sites. Thus virtualization of
resources is not possible in multidatabase architecture. Furthermore,
maintaining consistency for global transactions is the responsibility
of MDMS. This is undesirable in Grid setup.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3. Basic
Definitions on Transaction
Management
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Condition (1) states that transactions have read and write operations
followed by a termination condition (commit or abort) operation.
Condition (2) says that a transaction can only have one termination
operation, i.e. either commit or abort, but not both.
Condition (3) defines that the termination operation is the last operation
in the transaction.
Finally, condition (4) defines that if the transaction reads and writes the
same data item, it must be strictly ordered.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3. Basic Definitions on Transaction Management
(cont’d)
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Where T is a set of transactions.
A pair (opi, opj) is called conflicting pair iff (if and only if):
Operations opi and opj belong to different transactions; Two operations
access the same database entity and At least one of them is a write
operation
Condition (1) of the definition 10.2 states that a history H represents the
execution of all operations of the set of submitted transactions.
Condition (2) emphasises that the execution order of the operations of
an individual transaction is respected in the schedule.
Condition (3) is clear by itself.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3. Basic Definitions on Transaction Management
(cont’d)
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The history must follow certain rules that will ensure the
consistency of data being accessed (read or written) by different
transactions.
The theory is popularly known as serializability theory. The basic
idea of serializability theory is that concurrent transactions are
isolated from one another in terms of their effect on the
database.
In theory, all transactions, if executed in a serial manner, i.e. one
after another, will not corrupt the data.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3 Basic Definitions on Transaction Management (Cont’d)
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Definition 10.3 states that if any operation, p, of a transaction Ti
precedes any operation, q, of some other transaction Tj in a serial
history Hs, then all operations of Ti must precede all operations of Tj in
Hs.
Serial execution of transactions is not feasible for performance
reasons, hence the transactions are interleaved.
Serializability theory ensures the correctness of data if the transactions
are interleaved.
A history is serializable if it is equivalent to a serial execution of the
same set of transactions.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3 Basic Definitions on Transaction Management (Cont’d)

Serialization Graph (SG) is the most popular way to examine the
serializability of a history. A history will be serializable if and only if (iff)
the SG is acyclic.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.3 Basic Definitions on Transaction Management (Cont’d)
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Consider following transactions:
T1: r1[x] w1[x] r1[y] w1[y] c1
T2: r2[x] w2[x] c2
Consider the following history:
H = r1[x] r2[x] w1[x] r1[y] w2[x] w1[y]
The SG for the history, H, is shown in the
following Figure:
Conflict r2 [x] w1[x]
T1
T2
Conflict w1[x] w2[x]
Basic Definitions on Transaction Management (Cont’d)
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The SG in the previous slide contains a cycle;
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Hence the history H is not serializable.
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From the above example, it is clear that the outcome of the history only
depends on the conflicting transactions.
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Ordering of non-conflicting operations in either way has the same
computational effect.
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View serializability has also been proposed in addition to conflict
serializability for maintaining correctness of the data. But from a
practical point of view, almost all concurrency control protocols are
conflict-based.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.4 ACID Properties of Transactions
Lost Update Problem
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The problem occurs when two transactions access the same data item
in the database and the operations of the two transactions are
interleaved in such a way that leaves the database with incorrect value.
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Example: Let us assume that D1 is a bank account, with balance of
100 dollars. Transaction T1 withdraws 50 dollars and T2 deposits 50
dollars. After correct execution of transaction the account will have 100
dollars.
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Consider the interleaving of operations
(against time) as shown in the figure.
It is evident that transaction T2 has read
the value of D1 before T1 has updated it
and hence the value written by T1 is lost
and the account balance is 150 dollars
(incorrect)
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.4 ACID Properties of Transactions (Cont’d)

Obtain consistency and reliability aspects a transaction must have
following four properties. The properties are known as ACID properties:
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Atomicity: The Atomicity property is also known as all-or-nothing property. This
ensures that the transaction is executed as one unit of operations, i.e. either all of
the transaction’s operations are completed or none at all
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Consistency: A transaction will preserve the consistency, if complete execution of
the transaction takes the database from one consistent state to another
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Isolation: The Isolation property requires that all transactions see a consistent state
of the database
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Durability: The Durability property of the database is responsible to ensure that
once the transaction commits, its effects are made permanent into the database
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.5 Transaction Management in Various DBMS
Transaction Management in Centralised and Homogeneous Distributed
Database Systems
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Atomicity:
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Consistency of global transactions is responsibility of GTM. GTM is designed in a
bottom-up manner to avoid any anomalies
Isolation:
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2-Phace commit protocol or similar variations are used
all of these commit protocols require the existence of GTM and are consensusbased, and not applicable in Grid database environment.
Consistency:
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Both systems operate under single administrative domain
Lock tables, timestamps, commit/abort decisions etc. can be shared
Central management system is used to maintain the ACID properties, e.g. Global
Transaction Manager (GTM)
Serializability is the most widely used correctness criterion for ensuring transaction
isolation
Global serializability is used as concurrency control criterion
Durability:

Global recovery manager is used to maintain the durability of the system
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.5 Transaction Management in Various DBMS (Cont’d)
Transaction Management in Heterogenous (multidatabase) Distributed
Database Systems
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Atomicity
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No global integrity constraints. and local integrity constraints are taken care by
LTM
Isolation
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Decision to commit local transactions and sub-transactions of global transactions
depend on LTM
Prepare-to-commit operation cannot be implemented in multidatabase systems as
they may hold local resources
Consistency
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Clearly distinguishes between local and global transactions
Local Transaction Manager (LTM) is responsible for local transactions and subtransactions of global transactions executing at its site
GTM manages global transactions to ensure global serializability
‘Heterogeneous DBMS’ and ‘multidatabase systems’ are used interchangeably
GTM keeps record of all active Global transactions, that are executing at multiple
databases
Durability

Recovery process is hierarchical with local and Multi-DBMS executing their
recovery processes separately
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.6 Requirements in Grid Database Systems

Considering the requirement of Grid architecture and the correctness
protocols available for distributed DBMSs, a comparison of traditional
distributed DBMSs and Grid databases for various architectural
properties are shown below
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.6 Requirements in Grid Database Systems (Cont’d)

The above table emphasizes that due to different architectural
requirements traditional DBMS techniques will not suffice for Grid
Environment
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Design philosophy of Grid DB is top down, which is different than
traditional DBMSs
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No global DBMS
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Needs virtualization
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Address Replica synchronization issues at protocol level

High precision applications need strict serializability in Grid
environment
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.7 Concurrency Control Protocols
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Concurrency control protocols are classified based on synchronization
criterion
Broadly, Concurrency control protocols are classified as:
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Pessimistic: Provides mutually exclusive access to shared data and hence does
the synchronization at the beginning of the transaction
Optimistic: Assumes there are few conflicts and does the synchronization towards
the end of the transaction execution
Classification of
concurrency control
protocols is shown in the
figure
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.8 Atomic Commit Protocols (ACP)
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All cohorts of distributed transaction should either commit or abort to
maintain Atomicity
Distributed DBMSs are classified into two categories to study ACPs:
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Homogeneous distributed database systems
An ACP helps the processes/subtransactions to reach decision such
that:
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Homogeneous distributed database systems
Heterogeneous distributed database systems
All subtransactions that reach a decision reach the same one.
A process cannot reverse its decision after it has reached one.
A commit decision can only be reached if all subtransactions are ready to commit.
If there are no failures and all subtransactions are ready to commit, then the
decision will be to commit.
Consider occurrence of only those failures that are taken care by the ACP, then all
subtransactions will eventually reach a decision.
2-Phase commit is the simplest and most common ACP
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.8 Atomic Commit Protocols (Cont’d)
Running
Running
2-Phase Commit (2PC):
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State diagram of the 2PC is as shown in
the diagram
Coordinator sends a vote_request to all
the participating sites and enter wait
state
Wait
Commit
Prepared
Abort
State Diagram of coordinator
Commit
Abort
State Diagram of participant
Participating sites respond by yes (prepared to commit) or no (abort)
If coordinator receives all yes votes then it decides to commit and informs all
participants to commit. Even if a single vote is to abort then the coordinator
sends abort message to all participants
Participants then act accordingly, commit or abort
There are 2 phases in the process: Voting phase and decision phase
Other ACPs are: 3-Phase commit, Implicit Yes Vote, Uniform
reliable broadcast, uniform timed reliable broadcast, Paxos Commit
algorithm etc.
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.8 Atomic Commit Protocols (Cont’d)
Heterogeneous Distributed Database Systems
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Multidatabase systems assume autonomous environment for
transaction execution
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Three main strategies for atomic commitment of distributed transaction
in heterogeneous environment:
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Redo: Even if the global decision is to commit LDBMS may decide to abort the
global sub-transaction (as sites are autonomous). Then these sub-transactions of
global transaction must redo the write operation to maintain the consistency.
Retry: The whole global sub-transaction is retried instead of just redoing the write
operation
Compensate: If the global decision was to abort but the local sub-transaction has
committed then a compensating transaction must be executed to semantically
undo the effects
These strategies are not suitable for Grid environment; as to
compensate a transaction it must be compensatable; or to redo an
operation may have cascading affec; or a transaction may keep retrying
forever
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.9 Replica Synchronisation Protocols
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Grid databases stores large volume of geographically distributed data
Data replication is necessary to achieve:
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Increased availability
Improved performance
Replica synchronization protocols need to be designed carefully else
purpose of replication may be defeated due to increased overhead of
maintaining replicated copies of data
The user has one-copy view of the database and hence the
correctness criterion is known as 1-Copy Serializability (1SR)
Replica synchronization protocols such as Write-All, Write-All-Available,
primary copy etc. have been proposed
Replica control becomes a non-trivial task when the data can be
modified
Recent research in Grid environment has mainly focused on replicated
data in read-only queries
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.9 Replica Synchronisation Protocols (Cont’d)
Network Partitioning

Network partitioning is a phenomenon that prevents communication
between two sets of sites in distributed architecture
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Broadly, the network partitioning can be of two types, depending on the
communicating set of sites:
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simple partitioning: if the network is divided in 2 compartments
multiple partitioning: if the network is divided in more than 2 compartments
Network partitioning doesn’t have much impact for read-only queries
Network partitioning may lead to inconsistent data values if write
transactions are present
Replica synchronization protocols must be equipped to deal with
network partitioning
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.9 Replica Synchronisation Protocols (Cont’d)
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Replica synchronization protocols can be classified in 2 categories:
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Read-One-Write-All (ROWA):
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read operation can be done from any replicated copy but write operation must be
executed at all replicas
Limits availability of the system
ROWA-Available (ROWA-A):
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Pessimistic: eager to replicate all copies to all sites, e.g., ROWA (Read one write
all)
Optimistic: allows to execute any transaction in any partition. Increases availability
but may jeopardize consistency
Provides more flexibility in presence of failures
Reads any replicated copy, but writes all available replicas (if any site is not
available ROWA cannot proceed but ROWA-A will still proceed with write)
Primary Copy:
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Assigns one copy as the primary copy and all read / write is redirected to the
primary copy
Cannot work in network partitioning and single point of failure
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.9 Replica Synchronisation Protocols (Cont’d)
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Quorum-based:
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Every replica is assigned a vote
Read / write thresholds are defined for each data item
The sum of read and write threshold as well as twice of write threshold must be
greater than the total vote assigned to the data. These 2 conditions will ensure that
there is always a non-null intersection between any two quorum sets
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
10.10 Summary
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Following three protocols in traditional distributed databases
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Concurrency Control protocols
Atomic Commitment protocols
Replica synchronization protocols
Protocols from traditional distributed databases cannot be implemented
in Grid databases as is
D. Taniar, C.H.C. Leung, W. Rahayu, S. Goel: High-Performance Parallel Database Processing and Grid Databases, John Wiley & Sons, 2008
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