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Unit - 4
Introduction to the
Other
Databases
 Introduction : Today single CPU based architecture is not capable enough
for the modern database that are required to handle more
demanding and complex requirements of the users, for
example, high performance, increase availability, distributed
access to data, analysis of distributed data and so on.
 To meet the complex requirement of users, the modern
database system today operate with the architecture where
multiple CPUs are working parallel to provide the complex
database services.
 In some of the architectures, multiple CPUs are working in
parallel and are physically located in closed environment, in
the same building and communicating at very high speed.
 The databases operating in such a environment are called
Parallel Databases.
 In parallel database system, multiple CPUs work in parallel to
improve performance through parallel implementation of
various operations such as loading data, building indexes and
evaluating queries.
 Parallel processing divides a large task into many smaller task
and execute the smaller tasks concurrently on several CPUs.
 As a result the larger task complete more quickly.
 Parallel database system improve the processing and I/O
speed by using multiple CPUs and disks working in parallel.
 the parallel databases are essentially useful for applications
that have to query large databases and process large number
of transactions per second.
 In parallel processing many operations are performed
simultaneously, as opposed to the centralized processing, in
which serial computation is performed.
 The goal of Parallel Database System : To ensure that the database system can continue to
perform at one acceptable speed, even as the size
of database and the number of transactions
increases.
 And this can be done by increasing the capacity of
the system by increasing the parallelism provides a
smoother path for growth for an enterprise then
does replacing a centralized system by a faster
machine.
The parallel database systems are usually
designed to provide a best cost-performance
and they are quit uniform in site machine
architecture.
The cooperation between site machines is
usually achieved at the level of the transaction
module of the database system.
Parallel database system represent an
attempt
to
construct
a
faster
centralized computer using several
small CPUs.
WHY DO WE NEED THEM?
•
More and More Data!
We have databases that hold a high amount of
data, in the order of 1012 bytes:
10,000,000,000,000 bytes!
• Faster and Faster Access!
We have data applications that need to process
data at very high speeds:
10,000s transactions per second!
SINGLE-PROCESSOR DBMS CANNOT DO THIS JOB.....!
 Advantages of Parallel Database System : Increase Throughput (Scale-Up).
 Increase Response time (Speed-Up)
 Useful to the application to query extremely large
databases and to process an extremely large number of
transactions rate (in order of thousands of transactions per
second).
 Increase availability of the system.
 Grater flexibility.
 Possible to serve a large number of users.
 Disadvantages of Parallel Database System : More Start-Up Cost.
 Interface Problem.
BENEFITS OF A PARALLEL DBMS
 Improves Response Time.
INTERQUERY PARALLELISM
It is possible to process a number of transactions in
parallel with each other.

Improves Throughput.
INTRAQUERY PARALLELISM
It is possible to process ‘sub-tasks’ of a transaction in
parallel with each other.
HOW TO MEASURE THE BENEFITS
 Speed-Up.
As you multiply resources by a certain factor, the time taken
to execute a transaction should be reduced by the same factor:
10 seconds to scan a DB of 10,000 records using 1 CPU
1 second to scan a DB of 10,000 records using 10 CPUs
 Scale-up.
As you multiply resources the size of a task that can be executed
in a given time should be increased by the same factor.
1 second to scan a DB of 1,000 records using 1 CPU
1 second to scan a DB of 10,000 records using 10 CPUs
Number of transactions/second
SPEED-UP
Linear speed-up (ideal)
2000/Sec
1000/Sec
5 CPUs
10 CPUs
Number of CPUs
Number of transactions/second
SCALE-UP
1000/Sec
5 CPUs
1 GB Database
Linear scale-up (ideal)
10 CPUs
2 GB Database
Number of CPUs, Database size
1.) Shared-Memory Multiple CPU :2.) Shared-Disk Multiple CPU :3.) Shared-Nothing Multiple CPU :-
 Shared-Memory Multiple CPU : In this system a computer has multiple
simultaneously active CPUs that are attached to an
interconnected network and can share a single
MAIN MEMORY.
 Thus in this architecture a single copy of a
multithreaded
Operating
System
and
multithreaded DBMS can support multiple CPUs.
 This architecture of Parallel Database System is
closest to the traditional single CPU processer of
centralized database system, but much faster in
performance as compare to the single CPU of the
same power.
Shared Memory Multiple CPU Architecture
 Benefits of Shared-Memory : Communication between CPUs is extremely
efficient. Data can be access by any CPU without
being moved with software. A CPU can send a
message to the other CPU much faster by using
memory writes, which usually takes less then a
microsecond, then by sending a message through a
communication mechanism.
 The communication overhead are low, because of
main memory can be used for this purpose and
operating services can be used to utilize the
additional CPUs.
 Limitations of Shared-Memory : Memory access uses a very high speed mechanism that
is difficult to partition without losing efficiency.
Thus the design must take the special type of different
CPUs have equal access to a common memory.
 Since
the
communication
bus
or
interconnection network is shared by all CPUs,
this architecture is not capable beyond 80 or 100 CPUs
in parallel. The bus and interconnection network
become a bottleneck as the number CPUs increase.
 The addition of more CPUs causes CPUs to spend
time waiting for their turn on the bus to access
memory.
 Shared-Disk Multiple CPU : In this system multiple CPUs are attached to an
interconnection network and each CPU ha its own memory
but all of them have access to the same disk storage or more
commonly to the shared array of disk.
 The scalability of the system is largely determine by the
capacity and the throughput of the interconnection network.
 Since the main memory is not shared among the CPU, each
machine has its own OS and its own DBMS.
 It is possible that with the same data accessible, two or more
nodes want to read or write the same data at the same time.
 Therefore the global locking scheme is require to
preservation of the data integrity.
Shared Disk Multiple CPU Architecture
Benefits of Shared Disk Architecture : Easy to load balance, because data does not have
to be permanently divided among available CPUs.
 Since each CPU has its own memory, the memory
bus is not a bottleneck.
 It offers a low cost solution to provide a degree
of fault tolerance. In this case of a CPU or memory
failure, the other CPUs take over its task; since the
database is resident on disk that are accessible from
all CPUs.
 It has found acceptance in wide applications.
 Limitations of Shared Disk : It is also facing the problems of interface and
memory contention bottleneck as the number of
CPUs increase. As more CPUs are added, the
existing CPUs are slow down because of the
increased contention for memory accesses and
network bandwidth.
 It is also having the problem of scalability. The
interconnection to the disk subsystem become
bottleneck, particularly when the database makes
the large number of access to the disk.
 Shared Nothing Multiple CPU :In this system multiple CPUs are attached
with interconnecting network and each CPU
has a local memory and a local disk storage,
but no two CPU can access the same storage
area.
All communication between CPUs is through
a high-speed interconnection network.
Thus the shared nothing environments
involve no sharing on memory or disk.
Each CPU has its own copy of OS and its own
copy of DBMS and its own copy of a
portion of a data managed by DBMS.
In this type of architecture CPUs sharing
responsibilities for database services usually
split up the data among themselves.
CPUs then perform the transactions and
queries by dividing up the work and
communicating by messages over the high
speed network.
Shared Nothing Multiple CPU Architecture
 Benefits of Shared Nothing Architecture :This
architecture
minimized
the
connection of CPUs by not sharing
resources and therefore offer a high degree of
scalability.
Since local disk references are serviced by
local disk ay each CPU, this architecture
overcomes the limitations of requiring all I/O
to go through a single interconnection
network. Only queries accesses to non-local
disk and result relation pass through the
network.
 The interconnection network for this architecture
are usually designed to be scalable. Thus adding
more CPUs and more disks enables the system
grow in a manner that is divided the power and
the capacity of the newly added component.
 In other words the shared-nothing architecture
provides linear Speed-Up and linear Scale-Up.
 Linear Speed-Up and Scale-Up properties
increase the transmission capacity of sharednothing architecture as more nodes are added
and therefore, it can easily support the large
number of CPUs.
 Limitations of Shared Nothing Architecture : Shared nothing architecture are difficult to load-balance.
In many multi CPU environments, it necessary to split
the system work load in some way so that all system
resources are being used efficiently. Proper splitting or
balancing workload across the shared nothing system
requires an administrator to properly partition or divide
the data across the various disks. In practice this is
difficult to achieve.
 Adding a new CPU and disk to Shared-Nothing
Architecture means the data may needed to be
redistributed in order to make advantage of the new
resources
and
thus
require
more
extensive
reorganization of DBMS.
 The cost of communication and non-local disk
access are higher then in Shared-Disk or SharedMemory architecture because of sending data
involves software interaction at both the ends.
 The high speed network are limited in size, because
of speed-of –light consideration. This leads to the
requirement that a parallel architecture has CPUs
that are physically closed together.
 It requires an OS that is capable of accommodating
the heavy amount of messaging that are require to
support the inter processor communication.
1.) Speed-Up :2.) Scale-Up :3.) Synchronization :4.) Locking :-
1.) Speed-Up : Speed-Up is the property in which the time taken for
performing the task decreases in case of increasing
the number of CPUs.
 In other word Speed-Up is the property of running a
given task in less time by increasing the degree of
parallelism (more number of hardware).
 With additional hardware, Speed-Up holds the
task constant and measure the time saved.
 Thus, Speed-Up enables user to improve the system
response time for their queries, assuming the size of
their database remain the same.
 To = Execution time of a task on the original or
smaller machine (or original processing time)
 Tp = execution time of the same task on parallel or
larger machine (or parallel processing time).
 Here the original processing time To is the time
spent by a centralized system or small system on the
given task.
 And the parallel processing time Tp is the time spent
by large system or Parallel System on the same task.
 Consider a database application running on a parallel system
with a certain number of CPUs and disks.
 Now suppose the size of system is increase by increasing the
number of CPUs, disks and other hardware components.
 The goal is to process the task in time inversely proportional
to the number of CPUs and disk allocated.
 For example, if original system takes 60 seconds to perform
the task and the parallel system (with double capacity) takes
30 seconds to complete the same task then the value of
Speed-Up = 60/30 = 2. the Speed-Up value 2 in indicate the
Linear Speed-Up.
 If the Speed-Up is N when the larger system has N times the
resources of the smaller system.
 If the Speed-Up value is less then N then the system is said to
demonstrate Sub Linear Speed-Up.
Number of transactions/second
SPEED-UP
Linear speed-up (ideal)
2000/Sec
Sub Linear speed-up
1000/Sec
5 CPUs
10 CPUs
Number of CPUs
2.) Scale-Up : Scale-Up is the property in which the performance of the
parallel database is sustained if the number of CPU and
disk are increased in proportional to the amount of
data.
 In other word, Scale-Up is the ability of handling the
large task by increasing the degree of parallelism, in the
same time period as the original system.
 With added hardware the formula for Scale-Up holds
the time constant and measure the increase size of
task.
 Thus the Scale-Up enable users to increase the size of
their database while maintaining the same response
time.
 Vp = Parallel or Large Processing Volume.
 Vo
=Original
or
Small
Processing
Volume.
 Here the Original Processing Volume is the
transaction volume process in the given amount of
time on a smaller system. Parallel Processing
Volume is the transaction volume process in the
given amount of time on a larger system.
 For Example, if the original system can process
3000 transactions in given amount of time and if the
parallel system can process 6000 transactions in the
same
amount
of
time
then
the
Scale-Up = 6000/3000 = 2.
The Scale-Up value 2 is an indication of the
Linear Scale-Up, which means that the
twice as much of hardware can process twice
the data volume in same amount of time.
If the Scale-Up value is less then 2 then it is
called Sub Linear Scale-Up.
That means as much of times we increase the
resources of the parallel system, the value of
Linear Scale-Up will also be increase that
much of times.
Number of transactions/second
SCALE-UP
1000/Sec
Linear scale-up (ideal)
Sub Linear scale-up
5 CPUs
1 GB Database
10 CPUs
2 GB Database
Number of CPUs, Database size
3.) Synchronization : Synchronization is the coordination of the current task.
 For a successful operation of the parallel database
system , the task should be divided such that the
synchronization requirement is less. It is necessary for
the correctness.
 With less synchronization requirement better speed-up
and scale-up can be achieved.
 The amount of synchronization depends on the amount
of resources and the number of users and the task
working on the resources.
 More synchronization is require to coordinate large
number of concurrent tasks.
4.) Locking : Locking is a method of synchronizing current task.,
 Both internal as well as external locking mechanisms are
used for synchronization of tasks that are required by the
parallel database system.
 For external locking, a distributed lock manager (DLM) is
used, which is apart of the OS.
 DLM
coordinate
the
resources
sharing
between
communication nodes running a parallel server.
 The instances of parallel server use the DLM to communicate
with each other and coordinate modification of database
resources. The DLM allows application to synchronize access
to resources such as data, software and devices, so that
current requests for the same resource are coordinate
between applications running on different nodes.
1.) Intra-Query Parallelism :2.) Inter-Query Parallelism :3.) Intra-Operation Parallelism :4.) Inter-Operation Parallelism :5.) Input / Output Parallelism :-
1.) Intra-Query Parallelism : Intra-Query Parallelism refers to the execution of single
query in parallel on multiple CPUs using Shared-Nothing
Architecture Technique.
 It is some times called Parallel Query Processing.
 For example, suppose a table has been partitioned across
multiple disks by range partitioning on some attribute and
now user want to perform SORT on the partitioning attribute.
 The SORT operation can be implemented by sorting each
portion in parallel, then concatenating the sorted portions to
get the final sorted relation.
 Thus a query can be parallelized by parallelizing individual
operations.
CPU 1
CPU 2
CPU 3
CPU ‘N’
Interconnection Network
Query 1
 Advantages : Intra-Query Parallelism Speeds Up long running
queries.
 They are beneficial for decision support
applications that issues complex, read-only
queries, including queries involving multiple
JOINs.
2.) Inter-Query Parallelism : In Inter-Query Parallelism multiple transactions are executed
in parallel, One by each CPU.
 It sometimes also called as Parallel Transaction Processing.
 The primary use of Inter-Query Parallelism is to Scale-Up a
Transaction Processing system to support a large number of
transaction per second.
 To support a Inter-Query Parallelism DBMS generally uses a
task or transaction dispatching.
 Efficient lock management is another method to used by
DBMS to support Inter-Query Parallelism, particularly in
Shared-Disk Architecture.
 Since in Inter-Query Parallelism each query is run
sequentially, it does not help in speeding up in long running
query.
 In such a case DBMS must understand the locks held by
different transactions executing on different CPUs in order to
preserve data integrity.
 Inter-Query Parallelism on Shared-Disk architecture perform
best when transactions that execute in parallel do not access
the same disk.
Transaction 1
Transaction 1
Transaction 1
Transaction N
CPU 1
CPU 2
CPU 3
CPU N
Interconnection Network
 Advantages : Easiest form of parallelism to support in a
database system, particularly in Shared-Disk
Parallel System.
 It Scale-Up a transaction processing system to
support a large number of transactions per second.
 Disadvantages : Response time of individual transaction are no
faster then they would be if the transaction were
run in isolation.
 It is more complicated in Shared-Memory and
Shared-Nothing Architectures.
3.) Intra-Operation Parallelism : In Intra-Query Parallelism of each individual operation of a
task, such as sorting, projection, join and so on.
 Since the number of operations in a typical query small,
compared to the number of tuples processed by each
operation, Intra-Operation Parallelism scales better with
increasing parallelism.
 Advantages : Inter-Operation Parallelism is natural in a Database.
 Degree of Parallelism is potentially enormous.
4.) Inter-Operation Parallelism : In Inter-Operation Parallelism, the different operations in a
query expression are executed in parallel.
 Following two types of Inter-Operation Parallelism are used :
 Pipelined Parallelism : Independent Parallelism :1. Pipelined Parallelism : In this parallelism output tuples of one operation A are
consumed by second operation B, even before the first
operation has produced the entire set of tuples in its output.
 Thus it is possible to run operation A and B simultaneously
in different processors, so that the operation B consumes
tuples in parallel with operation A producing them.
 Advantages : Pipelined parallelism useful with smaller number of CPUs.
 Also pipelined execution avoid writing intermediate result
to disk.
 Disadvantages : It does not Scale-Up well.
 Pipelined chain do not attain sufficient length to provide a
high degree of parallelism.
 It is not possible to pipeline relational operators that do not
produce output until all inputs have been accessed.
 Only marginal Speed-Up is obtained for the frequent case
in which one operation’s cost is much higher then the
others.
2. Independent Parallelism : In an independent parallelism the operations in query
expression that do not depend on one other can be ececute in
parallel.
 Advantages :
It is useful with a lower degree of parallelism.
 Disadvantages :-

Like pipelined parallelism, independent parallelism does
not provide a high degree of parallelism so it is less useful
in highly parallel system.