Distibuted Database Management System Notes

Distributed DBMS Syllabus Introduction to DDMBS, Features and Needs, Reference architecture, levels of distribution transparency, replication, and Distributed database design, Fragmentation and allocation criteria, and Storage mechanisms, Translation of global queries, global query optimization, queries execution and access plan. Concurrency control, 2-phase locks, distributed deadlocks, quorum based, time based protocols, comparison, reliability, non-blocking commitment protocols 3-phased), partitioned networks checkpoints and cold starts Management of distributed transactions 2-phase unit protocols, architectural aspects, nodes and link failure recoveries, distributed data dictionary management distributed database administration Heterogeneous database-federated data reference architecture, loosely and tightly coupled alternative architectures, development tasks, operational global task management client server database, SQL Server, Open database connectivity, constructing an application.

Introduction Independent or centralized systems were the norm in the earlier days of information management. There was duplication of hardware and facilities. Incompatible procedures and lack of management control were the consequences of the evolving nature of the field. In a centralized database system the DBMS and the data reside at a single location and control and processing is limited to this location. However many organizations have geographically dispersed operations. The reliability of the system is compromised since loss of messages between sites or failure of the communication links may occurs. The excessive load on the system at the central site would likely cause all accesses to be delayed. An organization located in a single building with quazi – independent operational divisions, each with its own information processing needs and using a centralized database on a local network, would have similar problems. The current trend is toward a distributed system. This is a central system connected to intelligent remote devices, each of which can itself be a computer or interconnected, possibly heterogeneous computers. The distribution of processing power creates a feasible environment for data distribution. All distributed data must still be accessible from each site. Thus, distributed database can be defined as consisting of a collection of data with different parts under the control of separate DBMS, running on independent computer systems. All the computers are interconnected and each system has autonomous processing capability, serving local applications. Each system participates as well in the execution of one or more global applications. Such applications require data from more than one site. Another typical definition of a distributed database is the following: A distributed database is a collection of data which belong logically to the same system but are spread over the sites of a computer network. Third definition emphasizes two equally important aspects of a distributed database. 1. Distribution: The fact that the data are not resident at the same site, so that we can distinguish a distributed database from a single, centralized database.

2. Logical co-relation: The fact that the data have some properties which tie them together, so that we can distinguish a distributed database from a set of local databases or files which are resident at different sites of a computer network. The problem with the above definition is that properties, distribution and logical correlation are too vaguely defined. In order to develop a more specific definition, let us consider an example.

Fig. A distributed database on a geographically dispersed network. Consider a bank that has 3 branches at different locations. At each branch a computer controls the teller terminals of the branch and the account database of that branch. Each computer with its local account database at one branch constitutes one site of the distributed database; computers are connected by a communication network. During normal operations the applications which are requested from the terminals of a branch need only to access the database of that branch. These applications are completely executed by the computer of the branch where they are issued, and will therefore be called local applications.

An example of a local applications is a debit or credit application performed on an account stored at the same branch at which the application is required. Therefore the definition of a distributed database is a collection of data which are distributed over different computers of a computer network. Each site of the network has autonomous processing capability and can perform local applications. Each site also participates in the execution of at least one global applications, which requires the accessing data at several sites using a communication subsystem.

 Need of Distributed DBMS There are several reasons why distributed databases are developed. 1. Organizational and economic reasons: Many organizations are decentralized and a distributed database approach fits more naturally the structure of the organizations. The problems of a distributed organizational structure and of the corresponding into systems are the subject of several books and papers. With the recent developments in computer technology, the economy of scale motivates for having large centralized computer centers is becoming questionable. The organizational and economic reasons are probably the most important reasons for developing distributed databases. 2. Interconnection of existing databases: Distributed databases are the natural solution when several databases already exist in an organization and the necessity of performing global applications arises. In this case, the distributed database is created bottom up from the pre-existing local databases. This process may require a certain degree of local restructuring; however the effort which is required by this restructuring in much less than that needed for the creation of a completely new centralized database. 3. Incremental Growth:

if an organization grows by adding new, relatively autonomous organizational units (new branches, warehouses, etc.) then the distributed database approach supports a smooth incremental growth with a minimum degree of impact on the already existing units. With a centralized approach it is very difficult to add new branches. 4. Reduced Communication Overhead: In a geographically distributed database, any applications are local clearly reduces the communication overhead with respect to a centralized database. 5. Performance considerations: The existence of several autonomous processors results in the increase of performance through a high degree of parallelism. This consideration can be applied to any multiprocessor system and not only to distributed databases. Distributed databases have the advantage in that the decomposition of data reflects application dependent criteria which maximize application locality; in this way the natural interference between different processors is maximized. The load is shared between the different processors, and critical bottlenecks are avoided.

 Features of DDBMS Distributed database can be defined as consisting of a collection of data with different parts under the control of separate DBMSs, running on independent computer systems. All the computers are interconnected and each system has autonomous processing capability, serving local applications. There are various features of DDBMS are as follows: 1. Autonomy: in distributed database it is possible to identify a hierarchical control structure based on a global database administrator, who has the central responsibility of the whole database and on local database administrators, who have the responsibility of their respective local databases. However, it must be emphasized that local database administrator may have a high degree of autonomy, up to the point that a global database administrator is completely missing

and the inter-site coordination is performed by the local administrators themselves. This characteristic is usually called site autonomy. 2. Sharing: Data sharing is also provided by DDBMS, Users at a given site are able to access data stored at other sites and at the same time retain control over the data at their own site. 3. Availability & Reliability: Even when a portion of a system (i.e. a local site) is down, the system remains available. With replicated data, the failure of one site staff allows access to the replicated copy of the data from another site. The remaining site, continue to function. The greater accessibility enhances the reliability of the system. 4. Parallel Evaluation: A query involving data from several sites can be subdivided into sub queries and the parts evaluated in parallel. 5. Distributed data: Data distribution in DBMS with redundant copies can be used to increase system availability and reliability. If data can be obtained from a site other than the one that has failed, then availability improves, and as the system can still run, reliability does too. Data distribution can also be used to decrease communication costs. If most of the data used at a given site is available locally, the communication cost compared with that of a remote centralized system obviously reduced. 6. Data Distribution Transparency: Data independence means that the actual organization of data is transparent to the application programmer. 7. Management of distributed Data with different levels of transparency: ideally, a DBMS should be distribution transparent in the sense of hiding the details of where each file (table, relation) is physically stored within the system. 8. Distribution or Network Transparency: this refers to freedom for the users from the operational details of the network. It may be divided into location transparency and naming transparency. Location transparency refers to the fact that the command issued to perform a task is independent of the location of data and the location of the system where the command was issued. Naming transparency implies that once a name is specified, the named object can be accessed unambiguously without additional specifications.

9. Replication Transparency: copies of data may be stored at multiple sites for better availability, performance and reliability. Replication transparency makes the user unaware of the existence of copies. 10. Fragmentation Transparency: two types of fragmentation are possible. Horizontal fragmentation decomposes all the rows of relation into several subsets that are called fragments of that relation. Vertical fragmentation decomposes attributes of a relation into several subsets that are referred as fragments. A global query by the user must be transformed into several fragment queries. Fragmentation transparency makes the user unaware of the existence of fragments. 11. Improved Performance: A distributed DBMS fragments the database by keeping the data closer to where it is needed most. Data localization reduces the contention for CPU and I/O services and simultaneously reduces access delays involved in WAN. When a large database is distributed over multiple sites smaller databases exist at each site. As a result, local queries and transaction accessing data at a single site have better performance because of the smaller local database. 12. Easier Expansion: In a distributed environment, expansion of the system in terms of adding more data, minimizing databases sizes, or adding more processors much easier.

 Reference Architecture for Distributed Database

Fig: Reference Architecture for Distributed Database. Above figure shows reference architecture for distributed database. This architecture is not explicitly implemented in all distributed data base. However, it levels are conceptually relevant in order to understand the organization of any distributed database. The major components of reference architecture are as follows:

Global Schema :At the top level of the reference architecture is the global schema. The global schema defines all the data which are contained in the distributed database as if the database were not distributed at all. For this reason, the global schema can defined exactly in the same way as in a nondistributed database. And also the global schema consists the definition of a set of global relations.

Fragmentation Schema

“Each global relation can be spilt in to several non overlapping parts which are called fragments”. Fragments are indicated by a global relation name with an index. There are several different ways in which to perform the spitting operation; the mapping between global relations and fragments is defined in the fragmentation schema. This mapping is one to many.

Allocation schema The allocation schema defines at which sites a fragment is located. Fragments are logical portion of global relation which is physically located at one or several sites of the network. All the fragments which correspond to the same global relation R and are located at same site j constitute the physical image of global relation are at site j. This image is indicated by global relation name and a site index. Example: Physical image Rj Where, R is the global relation at site j .

Fig: fragments and physical images for global relation. A global relation R is split into four fragments R 1, R2, R3 & R4 are located redundantly at three sites, and thus building three physical images are R 1, R2& R3. To complete the terminology, we will refer to a copy of a fragment at a given site and denote it using the global relation name and two indexes and two physical images can be identical that is it is a copy of another physical image. Example: - R1 is a copy of R2. The three top level of reference architecture are site independent. at a lower level, it is necessary to map the physical image to the object which are manipulated by the local DBMS, this mapping is called a local mapping schema & depends on the type of local DBMS .

Objectives

The three most important objectives which motivate the features of this architecture are the separation of data fragmentation and allocation, the control of redundancy and the independence from local DBMS. 1) Separating the concept of data fragmentation from the concept of data allocation: This separation allows us to distinguish two different level of distribution transparency these are as follows. a) Fragmentation transparency: It is the highest degree of transparency and consists of fact that the user work on global relation. b) Location transparency It is the lower degree of transparency and requires the user to work on the fragments instead of global relation. 2) Explicit control of redundancy reference architecture provides explicit control of redundancy at fragment level in above fig R 2 & R3 physical images contain common data. The definition of disjoin fragments as a building block of physical images allows us to refer explicitly to overlapping part: replicated fragment R2. 3) Independence from local DBMS or Local mapping transparency It allows us to study several problems of distributed DBMS without having to take into account these specific data models of local DBMS.

 Distribution Transparency Distribution transparency means that program can be written as if the databases were not distributed. Thus the correctness of data from site to, however their speed of execution is affected. There are three types of transparencies, which are as follows: 1) Fragmentation transparency (user need not be aware of the data), 2) Location transparency (user does not know the location of the data), and 3) Allocation transparency. Fragmentation transparency is the highest degree of transparency and consists of the fact that the user or application programmer works on global relations.

Location transparency is lower degree of transparency and requires the user or application programmer to work on fragments instead of global relations; however, he or she Does not know where the fragments are located. The separation between concept of fragmentation and allocation is very convenient in distributed database design, because the determination of relevant portions of the data is thus distinguished from the problem of optimal allocation. To understand the concept of Distribution transparency, here consider a simple e.g. application, called SUPINQUIRY, which consists in accepting a supplier number from a terminal, finding the corresponding supplier name, and displaying it at the terminal. Level 1 Fragmentation Transparency

The way in which the application access the database if the DDBMS provides fragmentation transparency in shown in fig a Read (terminal, $SNUM); Select NAME into $NAME From SUPPLIER Where $NUM=$SNUM; WRITE (terminal, NAME).

First, the application accepts a supplier number from the terminal; then it accesses the database. The whole SQL statement repeats a single distributed database access primitive, which receives the variable $SUPNUM as the input parameter and returns the variable $NAME as the o/p parameter. The DDBMS interprets the primitive by accessing the database at one of the three sites in a way in which is completely determined by the system. From the viewpoint of distribution transparency ,notice that the application refers to the global relation name SUPPLIER, completely ignoring the fact that the database is distributed .in this way, the application is completely not distributed by any change, which is applied to all schemata which are below the global schema in reference architecture. Level 2 Location Transparency If the DDBMS provides locate ion transparency but not fragmentation transparency, the same application can be written as shown in the fig. The request for the supplier with the given number in first issued referring to fragment.SUPPLIER1, and if the DDBMS returns a negative answer in the control variable #FOUND, a similar request is issued with request to fragment SUPPLIER2. At this point, this naïve implementation assumes

Read (terminal, $SNUM); Select Name into $NAME From SUPPLIER2

Where SNUM=$SNUM; Write (terminal, NAME). that the supplier has been found and displays the result. Of course several variations of this solutions are possible, for instance, issuing both requests in parallel in order to exploit to the parallelism of the distributed system; however this does not change the distribution transparency characteristics. This application is clearly independent from the changes in the allocation schema, but not from changes in the fragmentation schema, because the fragmentation structure is incorporated in the application. However location transparency is by itself very useful, because it allows the application to ignore which copies exist of which fragment, therefore allowing copies to be moved from one site to another, and allowing the creation of new copies without affecting the application when allocation transparency is provided without fragmentation transparency, it is very effective to write advantage of knowing the fragmentation structure. Level 3 Local Mapping Transparency

Read(terminal,$SNUM); Select NAME into $NAME From SUPPLIER1AT SITE1 Where SNUM=$SNUM;

If not #FOUND then Read(terminal,$SNUM); Select NAME into $NAME From SUPPLIER1AT SITE1 Where SNUM=$SNUM; At this level we assume that the application will refer to objects using names which are independent from the individual local system; however it has to specify at which site names are indicated in the SQL statements by adding an at clause to the from clause. In this case each database access primitive is routed by the DDBMS to a specific site. However, these primitives use site-independent fragment names. If this mapping were not provided, the application would incorporate directly the filenames, which are not used by the local systems.

 Replication Replication improves the performance of simple read operation in a distributed system and improves it’s reliability. However updates incur greater overhead and the requirement that all replicates of data be updated and consistent adds complexity to a distributed database system. A database said to be: 1. Strictly partitioned: when no replicates of the fragments are allowed 2. Fully redundant: when complete database copies are distributed at all sites 3. Partially redundant: when only certain fragments are replicated Choice 1: for data replication would lead to relatively expensive query evaluation due to the unavailability of the data locally or at some near by site Choice 2: Is very expensive in terms of storage space and the overhead to maintain consistency, it is meaningless to replicate data at nodes where it is unlikely to be accessed Choice 3: Is responsible for allowing reduced access time for frequently reading of local data or that from a near by site. This choice allows for the

higher reliability and availability during site crashes. However because of the replication, updates are expensive. Updates require access to all copies of the data item. Because the copies are distributed over different sites of the network, the sites must reach a consensus on the possibility of an update failed sites may not participate in the agreement and sites may fail after the process has started. These issue are deal with later in this section on con-currency control and recovery. Although a major aim of the database system is to reduce if not eliminate redundancy, planned data redundancy can improve distributed database performance Ex : if a number of copies of a data item are available a read operation can be directed to any one of these copies. A write operation, however must update all copies, otherwise we would have inconsistent data. The system is required to ensure that any update operation is done on all replicates this results in increased overhead a price to be paid in distributed databases.

 Fragmentation Fragment Each global relation can be split into several non-overlapping portions which are called fragments.

Fragmentation Schema The mapping between global relations and fragments is defined in the fragmentation schema this mapping is one to many i.e. several fragments correspond to one global relation but only one relation correspond to one fragment fragments are indicated by a global relation name with an index (fragment index) Fragments are logical portions of global relation which are physically located at or several sites of the network all the fragments which correspond to the same global relation R and are located at the same site j constitute the physical image of global relation R at site j. (i.e. R) There are some rules which must be followed when defining fragments: (a) Completeness Condition: All the data of the global relation must be mapped into the fragments i.e. it must not happen that a data item which belongs to a global relation does not belong to any fragment.

(b) Reconstruction Condition: It must always be possible to reconstruct each global relation from its fragments. the necessity of this condition is obvious, in fact only fragments are stored in the distributed database and global relation have to be built through this reconstruction operation if necessary. (c) Disjointness Condition It is convenient that fragments be disjoint, so that the replication of the data can be controlled explicitly at the allocation level. however this condition is useful mainly with horizontal fragmentation, while for vertical fragmentation we will sometimes allow this condition to be violated the reason for this exception will be discussed when dealing with vertical fragmentation

Types of Fragmentation The decomposition of global relations into fragments can be performed by applying two different types of fragmentation 1. Horizontal fragmentation 2. Vertical fragmentation 3. Mixed fragmentation We will first consider these two types of fragmentation separately and then consider more complex fragmentation which can be obtained by applying combination of both.

1. Horizontal fragmentation Horizontal fragmentation consist of partitioning the tuples of a global relation into subset, this is clearly useful in Database where each subset can contain data which have common geographical properties. It can be defined by expressing each fragment as a selection operation on the global relation Ex : SUPPLIER(SNUM,NAME,CITY) Then the horizontal fragmentation can be defined in the following way: SUPPLIER1=SL CITY = “SF” SUPPLIER SUPPLIER2=SL CITY =”LA” SUPPLIER

The above fragmentation satisfies the completeness condition if “SF” and “LA” are the only possible values of the city attribute otherwise we would not know to which fragment the tuples with other CITY values belong The reconstruction condition is easily verified because it is always possible to reconstruct the SUPPLIER global relation through the following operation: SUPPLIER = SUPPLIER1 UN SUPPLIER2 The Disjointness condition is clearly verified we will call the predicate which is used in the selection operation which defines a fragments its qualification q1: CITY = “SF” q2: CITY = “LA” The above example that in order to satisfy the completeness condition the set of qualifications of all fragments must be complete, at least with respect to the set of allowed values. The reconstruction condition is always satisfied through the union operation and the Disjointness condition requires that qualifications be mutually exclusive. Derived Horizontal Fragmentation: In some cases the horizontal fragmentation of a relation cannot be based on a property of its own attributes but is derived from the horizontal fragmentation of another relation. Ex: SUPPLY (SNUM, PNUM, DEPTNUM, QUAN) SNUM is a supplier number it is meaningful to partition this relation so that a fragment contains the tuples for suppliers which are in a given city. However city is not an attribute of the SUPPLY relation it is an attribute of the SUPPLIER relation considered in the above example. Therefore we need a semi-join operation in order to determine the tuples of supply which corresponds to the suppliers in given city. The derived fragmentation of supply can be therefore defined as follows: SUPPLY1=SUPPLY SJ SNUM=SNUM SUPPLIER1 SUPPLY2=SUPPLY SJ SNUM=SNUM SUPPPLIER2

The effect of the semi-joint operations is to select from SUPPLY the tuples which satisfy the join condition between SUPPLIER1 OR SUPPLIER2 and supply. The reconstruction of the global relation SUPPLY can be performed through the union as shown for SUPPLIER. Q1 : SUPPLY.SNUM=SUPPLIER.NUM AND SUPPLIER.CITY =”SF” Q2 :SUPPLY.SNUM=SUPPLIER.NUM AND SUPPLIER.CITY = “LA”

2. VERTICAL FRAGMENTATION: The vertical fragmentation of a global relation is the subdivision of its attributes into groups. Fragments are obtained by projecting the global relation over each group. This can be useful in distributed database where each group of attributes can contain data which have common geographical properties. The fragmentation is correct if each attributes is mapped into at least one attribute of the fragments moreover it must be possible to reconstruct the original relation by joining the fragments together. Ex: EMP(EMPNUM,NAME,SAL,TAX,MGRNUM,DEPTNUM) A vertical fragmentation of this relation can be defined as EMP1 =PJ EMPNUM, NAME, MGRNUM, DEPTNUM EMP EMP2= PJ EMPNUM, SAL, TAX EMP This fragmentation could for instance reflect an organization in which salaries and taxes are managed separately. The reconstruction of relation EMP can be obtained as: EMP = EMP1 JN EMPNUM = EMPNUM EMP2 Notice that the above formula for the reconstruction of global relation EMP is not complete, because the result of joining EMP1 and EMP2 contains th column EMPNUM twice. This undesired replication can be eliminated by a projection operation that we omit to indicate. Ex: the following vertical fragmentation of relation EMP EMP1 =PJ EMONUM, NAME, MGRNUM, DEPTNUM EMP EMP2 = PJ EMPNUM, NAME, SAL, TAX EMP

The attribute NAME is replicated in both fragments. we can explicitly eliminate this attribute when EMP through an additional projection operation. EMP= EMP1 JN EMPNUM = EMPNUM PJ EMPNUM, SAL, TAX EMP2

3. MIXED FRAGMENTATION: The fragments which are obtained by the above fragmentation operations are relation themselves so that it is possible to apply the fragmentation operation recursively provided that the correctness condition are satisfied each time . The reconstruction can be obtained by applying the reconstruction rules in inverse order. Ex: EMP(EMPNUM,NAME,SAL,TAX,MGRNUM,DEPTNUM) The following is a mixed fragmentation which is obtained by applying the vertical fragmentation , followed by a horizontal fragmentation on DEPTNUM EMP1 = SL DEPTNUM 20 PJ EMPNUM,NAME,MGRNUM,DEPTNUM EMP EMP3 = SL DEPTNUM >20 PJ EMPNUM,NAME,MGRNUM,DEPTNUM EMP EMP4 = PJ RMPNUM,NAME, SAL, TAX EMP The reconstruction of EMP relation EMP is defined by the following expression: EMP = UN( EMP1,EMP2,EMP3) JN EMPNUM =EMPNUM PJ EMPNUM,SAL,TAX EMP4 Mixed fragmentation fragmentation tree

can

be

conveniently

represented

by

a

EMP

v

EMP4

h

EMP1

EMP2

EMP3

In a fragmentation tree the root corresponds to a global relation .the leaves corresponds to the fragments and the intermediate nodes correspond to the intermediate results of the fragment defining expression. The set of nodes which are sons of a given node represent the decomposition of this node by a fragmentation operation (vertical or horizontal) Ex: The fragmentation tree of relation EMP. The root (relation EMP) is vertically fragmented into two portions one portion corresponds to a leaf node of the tree (EMP1) the other portion is horizontally partitioned thus generating the other three leaves corresponding to fragments EMP1, EMP2 and EMP3.

 Distributed Database Design Designing a distributed database has many technical and organizational issues that become more difficult in a multiple site system. The technical problem is the interconnection of sites by a computer network and the optimal distribution of data and applications to the sites for meeting the requirements of applications and for optimizing performances. From the

organizational viewpoint, the issue of decentralization is crucial, since distributed systems typically substitute for large centralized systems. Distributing an application has a major impact on the organization. The mathematical problem of optimally distributing data over a computer network has been widely analyzed in the context of distributed the system and distributed databases. The major outcomes of this research are: 1.

Several design criteria have been established about how data can be conveniently distributed. 2. Mathematical foundation has been given to “design aids” that, in the near future, will help the designer in determining data distribution.

Framework for Distributed Database Design The term “Distributed Database Design” has a very broad and imprecise meaning. The design of a centralized database amounts to: 1.

Designing the “conceptual schema” which describes the integrated database (all the data which are used by the database applications). 2. Designing the “physical database”, i.e. mapping the conceptual schema to storage areas and determining appropriate access methods. In a distributed database these two problems become the design of the global schema and the design of the local physical databases at each site. The techniques which can be applied to these problems are the same as in centralized databases. The distribution of the database adds to the above problems two new ones: 3.

Designing the fragmentation i.e. determining how global relations are subdivided into horizontal, vertical or mixed fragments. 4. Designing the allocation of fragments, i.e. determining how fragments are mapped to physical images; in this way, also the replication of fragments is determined. These two problems fully characterize the design of data distribution, Since fragmentation has been established as a distinguishing feature of distributed databases. The distinction between these two problems is conceptually relevant, since the first one deal with the “logical criteria” which motivate the fragmentation of global relation, while the second one deals with the “physical” placement of data at the various sites.

Although the design of application programs is made after the design of schemata, the knowledge of application requirements influences schema design, since schemata must be able to support applications efficiently. Thus, in the design of a distributed database, sufficiently precise knowledge of application requirements is needed; clearly, this knowledge is required only for the more “important” applications, i.e. those which will be executed frequently or whose performances are critical. In the application requirements we include: 1. 2.

The site from which the application is issued (also called site of origin) The frequency of activation of the application (i.e. the number of activation requests in the unit time) 3. The number, type and the statistical distribution of accesses made by each application to each required data “object”.

Objectives of the design of Data Distribution In the design of data distribution, the following objectives should be taken in to account: 1.

Processing Locality: Distributing data to maximize processing locality corresponds to the simple principle of placing data as close as possible to the applications which use them. The simplest way of characterizing processing locality is to consider two types of references to data: “local” reference and “remote” reference. Clearly, once the sites of origin of applications are known, locality and remoteness of references depend only on data distribution. Designing data distribution for maximizing processing locality can be done by adding the number of local and remote references corresponding to each candidate fragmentation and fragment allocation, and selecting the best solution among them. The term complete locality designates those applications which can be completely executed at their sites of origin. The advantage of complete locality is not only the reduction of remote accesses, but also the increased simplicity in controlling the execution of the applications.

2.

Availability and Reliability of Distributed Data: A high degree of availability is achieved by storing multiple copies of the same information; the system must be able to switch to an alternative copy

when the one that should be accessed under normal conditions is not available. Reliability is also achieved by storing multiple copies of the same information. It is possible to recover from crashes or from the physical destruction of one of the copies by using the other, still available copies. Since physical destruction can be caused by events such as fire, earthquake, or sabotage), it is relevant to store replicated copies in geographically dispersed locations. 3.

Workload Distribution: Distributing the workload over the sites

in an important feature of distributed computer systems. Workload distribution is done in order to take advantage of the utilization of computers at each site, and to maximize the degree of parallelism of execution of applications. 4. Storage Cost and Availability: Database distribution should reflect the cost and availability of storage at the different sites. Typically, the cost of data storage is not relevant, if compared with CPU, I/O, and transmission costs of applications (but the limitation of available storage at each site must be considered. Using all the criteria at the same time is extremely difficult, since this leads to complex optimization models. It is possible to consider some of the above features as constraints, rather than objectives.

Top-Down and Bottom-Up Approaches to the Design of Data Distribution There are two alternative approaches to the design of data distribution, 1. 2.

The top down and The bottom up approach

In the top-down approach, we start by designing the global schema, and we proceed by designing the fragmentation of the database, and then by allocating the fragments to the sites, creating the physical images. The approach is completed by performing, at each site, the “physical design” of the data which are allocated to it. This approach is the most attractive for systems which are developed from scratch, since it allows performing the design rationally.

When the distributed database is developed as the aggregation of existing databases, it is not easy to follow the top-down approach. In this case the global schema is often produced as a compromise between existing data descriptions. It is even possible that each pair of existing databases is independently interfaced using a different translation schema, without the notion of a global schema. This leads to systems which are different in conception from distributed database architecture. When existing databases are aggregated, a bottom-up approach to the design of data distribution can be used. This approach is based on the integration of existing schemata into a single, global schema. By integration, we mean the merging of common data definitions and the resolution of conflicts among different representations given to the same data. It should be noticed that the bottom-up approach lends itself less easily to the development of horizontally fragmented relations. Horizontal fragments of a same global relation must have the same relation schema; this feature is easily enforced in a top-down design, while it is difficult to “discover”. Since horizontal fragments are a relevant and useful feature of a distributed database, the integration process should attempt to modify the definitions of local relations, so that they can be regarded as horizontal fragments of common, global relations. When existing databases are aggregated into a distributed database, it is possible that they use different DBMS. A heterogeneous system adds to the complexity of data integration the need for a translation between different representations. In this case, it is possible to make a one-to-one translation between each pair of different DBMS. However the approach which is mostly used in the prototypes of heterogeneous systems is to select a common data model, and then to translate it into unique representations all the different schemata of the involved DBMS. In summary, the bottom-up design of a distributed database requires: 1.

The selection of a common database model for describing the global schema of the database. 2. The translation of each local schema into the common data model. 3. The integration of the local schemata into a common global schema.

 Concurrency Control

A distributed database (DDB) is a collection of multiple, logically interrelated databases distributed over a computer network, like LAN, WAN ,MAN etc.. A distributed database management system (D–DBMS) is the software that manages the DDB and provides an access mechanism that makes this distribution transparent to the users where transparency is the separation of the higher level semantics of a system from the lower level implementation issues. A concurrency control is defined as, it is a situation where several transactions execute concurrently in the database and there may be possibility of deadlock detection, such situation is controlled by any of the concurrency control schema or protocols and prevents the deadlocks, is known as concurrency control. Example 1) Two-Phase Locking-based (2PL) 2) Timestamp Ordering (TO) A concurrency control deals with the ensuring transactions atomicity in the presence of concurrent execution of transactions, such problem occurs in the synchronization. In distributed systems the synchronization problem is harder than centralized system. An atomicity is a property of transaction where either all the operations of the transactions are reflected properly or none of them operation is performed. A transaction is a collection of actions that make consistent transformations of system states while preserving system consistency. A concurrency control method is responsible for maintaining consistency among the multiple copies of the data items placed in the DDBMS.

2-Phase Locking The 2-phase locking ensures the serializability, the transaction is serializable if it produces the same result as some serial execution of the transaction. In serial execution, each transaction runs for completion before any statements from any other transaction are executed.

A lock is a variable associated with each data item and the manipulation of such variable is called locking. The two phase locking is so called, because it has two phases as follows: 1) Growing Phase: A transaction may obtain locks but may not release any lock. 2) Shrinking Phase: A transaction may not obtain any new locks. Both the phases are monoatomic; the number of locks are only increasing at first phase and decreasing at second phase. All locks request by transaction or its sub transactions should be made in the growing phase and released in the shrinking phase. When a transaction issues and unlock instruction phase starts indicating that all required locks are obtained. Where data is replicated all sub transaction of the transaction which modify the replicated data item would be observed the two phase locking protocol. Therefore subtraction released a lock and subsequently has another sub transaction request another lock which required each sub transaction notify all other sub transactions acquired all its locks. This shrinking phase start once all subtractions acquired there locks. When all sub transactions finished their growing phase the number of massages involved is high. The exchange of massages between the sites are done by using two phase commit protocol. Ex. Consider two transactions T 1 & T2 and sites S1 & S2. Suppose a distributed data A is stored S1 and B at sites S2 then for execution of these two transactions, each transactions generates two local sub transactions as T1s1, T1s2 & T2s1, T2s2 respectively executed at site S1 & S2. Transaction T1

Transaction T2

Lock x (A) A : = 100 Write (A) Unlock (A) Lock x (B) B : = 1000 Write (B) Unlock (B)

lockx (A) A : = 200 Write (A) Unlock (A) Lock x (B) B : = 2000 Write (B) Unlock (B) Fig. (a) Two modifying Transactions.

The execution schedule in shown in following fig. Site S1

Site S2

Transaction T1s1 Lock x(A) A : = 100 Write (A) Unlock (A)

Time t1 t2 t3 t4 t5 t6 t7 t8

T2s1

Transaction T1s2

Lockx(A) A : = 200 Write (A) Unlock (A)

Transaction Transaction T2s2 Lock x(B) B : = 2000 Write (B) Unlock(B) Lockx(B) B : = 1000 Write (B) Unlock(B)

Fig.(b) A schedule for transactions.

 Distributed Deadlocks A system is in deadlock state if there exists several transactions such that every transaction is in the set is waiting for another transaction in the set. Consider there exists a set of existing transactions are T0, T1, T3, ……… , Tn such that T0 is waiting for a data item that hold by T 1, T1 is waiting for a data item that hold by T 2, and ……. , and Tn-1 is waiting for a data item that hold by T0. Therefore none of the transaction gets processed in such situation, & is called a deadlock detection. A deadlock is occurred in distributed database system, when a cycle is produced in the execution of the transaction in Distributed Wait-For-Graph (DWFG), and these transactions are aborted or restarted repeated, such complex situation produced in system is known as distributed deadlock, as shown in following fig. A deadlock occurs when each transaction T from the set of one or more transactions is waiting for some item which is locked by some other transaction T’ in that set. Hence each transaction which is in a waiting queue in that set is waiting for one of the other transaction release the lock. A deadlock determines the cycles in the wait-for-graph, when more than one transactions are executes their operations for the same resource. A distributed deadlock is more difficult than that of centralized database. Site-1

T1 A1

T2 A1

site-2

T1 A2

site-1 T1 A1

T2 A1

T1 A2

T2 A2

T2 A2

Fig a. A distributed wait for graph showing fig b. A local wait for graph. distributed deadlock Labeling:-

T-

Transactions A-

Agenda

A distributed wait for graph as shown in fig (a) consist of a cycle which corresponds to the deadlock detection. Fig. consist of two sites, two transaction T1 & T2 & two agents as A1 & A2. Assume that each transaction has only one agent at each site where it is executed. A directed edge from an agent Ti Aj to an agent Tr As means that Ti Aj is blocked and waiting for Tr As. There are following two reasons to an agent for waiting another one. 1. Agent Ti Aj waits for agent Tr Aj to release a resource which it needs. Here, Ti and Tr are different transactions and two agents are at same site, because agents request only local resources. Such wait is shown in fig. (a) where T1 A1 waiting for T2 A1 to release the resource at site 1. 2. Agent Ti Aj waits for agent Ti As to perform some required function. Here, two agents belongs from same transaction. The agent Ti Aj has requested that agent Ti As perform some function at a different site. As shown in fig (a) with dashed lines. In fig. (b) which shows local wait for graph. The square nodes are called input parts if they have an edge entering the local wait for graph and output ports if they receive an edge existing the LWFG. A deadlock occurs in LWFG by a cycle in it. In a distributed system a deadlock is occurred when a cycle is produced in DWFG. It is detected when and exchange of information takes place the cycle gets produced and deadlock occurs. It is a different task than local deadlocks.

A deadlock resolves the one or more transactions to be aborted or restarted, so that it releases its resources for other transactions. Following are the criteria’s for aborting the transactions. a) Abort the youngest transaction. b) Abort the transaction which owns less resources c) Abort the transaction with smallest abort cost. d) Abort the transaction with the longest excepted time to complete. A redundancy increases the deadlocks probability in distributed DBMS. Consider, two transactions T1 & T2 both of which must lock same data item exclusively, as x. If ‘x’ is replicated i.e. If x 1 & x2 are two copies at site 1 & 2 and both transactions wants to obtain lock then deadlock occurs. AT

Site 1 :

T1 locks x1 ; T2 waits

AT

site 2 :

T2 locks x2 ; T1 waits.

Hence, deadlock occurs.

Methods of deadlock detection 1) Deadlock detection using centralized or hierarchical control. 2) Distributed deadlock detection. 3) Deadlock prevention. 1) Deadlock detection using centralized or Hierarchical

control: In a centralized control method a deadlock detector is placed which has a responsibility to discover or find the cycles in DWFG. The deadlock detector receives the information from all sites in a distributed database. At each local site there is a local deadlock detector which has responsibility to find all potential deadlocks at that particular site. Site 1

Site 1

T1 A1 T2 A1

T1 A2 T5 A1

T3 A1 T4 A1T3

T1 A1

T1 A3

T4 A1

T3 A2

A2

Fig.(a) Lacal wait for graph at site 1 at site 1

Fig.(b) Potential global deadlock

The above fig. shows deviation of a potential global deadlock cycle from a local wait for graph. To determine potential deadlock cycles, the local deadlock detector starts from an input port and searches backward along the local graph until it reaches an output port. Such part is a potential deadlock cycle. Only the initial and final agents of each potential cycle must be transmitted as shown in fig. (b). A deadlock detector collects these massages forms a DWFG, finds cycles and selects the transactions for abort. Drawbacks Of Centralized Deadlock 1. It is unprotected to failures of the site where the centralized detector runs. 2. It requires large communication cost. To reducing a communication cost an hierarchical controllers are used.

Hierarchical controllers NLLDD 0

NLDD NLDD 2

1

Site 1 Site 4

Site 2

Site 3

Site 5 Fig. A tree for deadlock detectors

Labeling : NLDD – Non local deadlock detector. LDD – Local deadlock detector. The local deadlock detectors determine local deadlocks and transmit information to the potential global cycles to non local deadlock detectors. The performance of hierarchical deadlock detection mechanism depends on the choice of hierarchy. Here, site 1 & 2 have a common detector as LDD1 While site 3, 4 & 5 has a common detector as NLDD 2 finally all of them are controlled by NLDD 0.

(2) Distributed Deadlock Detection Here, no distinction between local and non-local deadlocks detectors. Each site has same responsibility while exchanging an information to determine global deadlocks. The potential deadlock cycles detected at local sites are transmitted through an algorithms. In LWFG all the input and output parts are collected into a single node called the external (EX). Main difference between centralized & distributed deadlock is that in centralized all the potential deadlocks cycles are sent to the designated site while in distributed deadlock detectors need a rule for determining to which site the potential deadlock cycles are transmitted. This rule must attempt to minimize amount of transmitted information. The following algorithm consist of distributed deadlock detection.

Site 1

Site 1 T1 T2

T1

Site 1 T2

EX

T1

T2

EX

T1 T2

EX

Site 2

T1

T2

EX

Site 2 Fig (a)

T1

T2

Site 2 Fig(b)

fig(c)

Fig. Distributed Deadlock detection algorithm. A local deadlock detectors performs at each site the following actions. 1) Forms LWFG including EX node. 2) The received massage perform following modifications of LWFG. a) For each transaction in massage add it to LWFG if it does not already exist. b) For each transaction n massage, starting with EX create an edge to the next transaction in the massage. 3) Cycles without EX node in LWFG indicate existence of end lock. 4) Cycles with EX node are the potential deadlock cycles. Which must be transmitted to different site.

(3) Distributed Deadlock Prevention Deadlock prevention eliminates the need for Dead lock detection & resolution. It is done in following ways. If a transaction T1 request a resources which hold by T2 then “prevention test” is applied. If this test indicates a risk of dead lock, then T1 is not allowed to enter wait stats. While either T1 is aborted restarted or T2 is aborted & restarted. Prevention test must insure that if T1 is allowed to wait for T2 then dead lock can never occur. This can be obtain by ordering transaction using lexicographical ordering.

Let Ti is allowed to wit for TJ , Where i S and 2* Qw >S To execute a read operation, enough replicas must be read that their total weight is >= Qr. To execute a write operation, enough replicas must be written so that their total weight is >= Qw. The advantage of this approach is that it can permit the cost of either read or writes to be selectively reduce by appropriately defining the read and write quorums. For example: With a small read quorum, reads need to read fewer replicas, but the write quorum will be higher, hence write can succeed only if correspondingly more replicas are available. Also if higher weights are given to some sites (for e.g. those which less likely to fail), fewer sites need to be accessed for acquiring the lock.

• TIMESTAMPING The basic idea behind this scheme is that each transaction is given a unique timestamp that the system uses in deciding the serialization order. Our first task, in generalizing the centralized scheme to a distributed in terms of their right to change schemas or DBMS software. That software must also cooperate with other sites in exchanging information about transactions, to make transaction processing possible across multiple sites. In contrast, in a homogeneous distributed database, different sites may use different schemas, and different DBMS software. The sites may be aware of one another, and they may provide only limited facilities for cooperation in transaction processing, while the divergence in software becomes difficult for processing transactions that access multiple sites.

 Check Point And Cold Restart Check Point: A check point are used to reduce the number of log record that the system must scan when it recover from a crash. Cold Restart: A cold restart is required if the log information is last at the site of network & effected site is not capable reconstructing it’s most recent state then the previous state is reconstructed where the check point is located .This effect in the loss of some local subtransition. Cold restart is required after some catastrophic (sudden accident with teriable result) failure which has caused the loss of log information on stable storage, so that the current consistent state of the data base can not be reconstructed & a previous consistent sate must be restored. A previous consistence state is marked by a check point. In distributed data base the problem of cold restart is worse than in centralized one because if one site has to establish on earlier state, then all other sites also have to establish earlier states which are consistent. A consistent global state C is characterized by the following two properties. 1) For each transaction T, C contains the update performed by all subtraction of Tat any site or it does not contain any them, in the former case we say that T is contained in C. 2) If transaction T1 contained in C then all conflicting transaction which have preceded T in the serialization.

The property 1 is related to the atomicity of transaction: either all effect of t or none of the m can appear in consistent state. The property 2 is related to serialization: if the effects of T are kept as it is then we must keep all the effect of T1. A global consistent state in a distributed database is to use local dumps, local logs& global check point. A global check point is set of local check points which performed at all site of the network & are synchronized by the following condition.

If a sub-transition of a transition T is contained in the local check point at some site then all other subtraction of T must be contained local check point at other site. If the global check points are available it is very easy to reconstruction. first consider the latest local point which earlier global state has to be reconstructed then all other sites are requested to reestablish the local states of the corresponding the local check point. Site1

C1

Site 2

time

C2

T2

R

C

Site 3 T3

C3

T2& T3 are subtraction of T; T3 is coordinator for 2-phase- commitment. C1, C2&C3 are local check point. “Write check point “measure. Massage of 2-phase commitments protocol. R=READY, C= COMMIT The main problem of global check point synchronization is shown in above fig. the fig consist of two sub transaction T 2,T3 of the same transaction T & local check point C1, C2 & C3 where C2 does not contain subtraction T2 & C3 contain subtraction T3. Which break base requirement of global check point. The transaction T performed two phase commitments which do not eliminate the problem the problem of synchronization of subtraction. There fore to avoid such problem it required that all the site become inactive simultaneously before which one record its local check point.

 Management Of Distributed Transactions

A transaction is a collection of actions that make consistent transformations of system states while preserving system consistency.

Example of transaction:Begin_transaction Reservation begin input(flight_no, date, customer_name); EXEC SQL UPDATE FLIGHT SET STSOLD = STSOLD + 1 WHERE FNO = flight_no AND DATE = date; EXEC SQL INSERT INTO FC(FNO, DATE, CNAME, SPECIAL); VALUES (flight_no, date, customer_name, null); output(“reservation completed”) end . {Reservation} The management of distributed transactions requires dealing with several problems which are strictly interconnected, like reliability concurrency control & the efficient utilizing of the resources of whole system. In this section we consider only the most well-known technique, used in building most commercial systems & research prototypes: 2-phase commitment for recovery, & 2-phase locking for concurrency control Framework for Transaction Management In this section, we define the properties of transaction, state the goals of distributed transaction

Properties OF Transactions 1. Atomicity: either all or none of the transactions operation are performed atomicity requires that if a transactions interrupted by a failure, its partial results are undone.

There are two typical reasons why a transaction is not completed: transaction aborts &system crashes. The abort of a transactional be requested by transaction itself because some of its inputs are wrong or because some conditions are recognized that make transaction completion inappropriate or useless .A transaction abort can also be forced by the system for system dependent reasons, typically system overload &deadlocks. The activity of ensuring atomicity in the presence of transaction abort is called transaction recovery& the activity of ensuring atomicity in presence of system crash is called crash recovery 2. Durability Once a transaction has committed, the system must guarantee that the results of its operation will never be lost, indepent of subsequent failures .since the result of a transaction which must be preserved by the system are stored in database, the activity of providing the transaction durability is called database recovery 3. Serilizability: if several transactions are executed concurrently, the result must be the same as if they were executed serially in some order. The activity of guaranteeing transactions Serializibility is called concurrency control. If a system provides concurrency control, the programmer can write the transaction as if it executed alone. Isolation an incomplete transaction cannot reveal to other transaction before its commitment. This property is needed in order to avoid the problem of cascading aborts.i.e the necessity to abort all transaction which have observed the partial result of transaction which was later aborted.

Goals Of Transaction Management: After having consideration the characteristics of transactions, let us return to the goals of transaction management: the efficient, reliable & concurrent execution of transaction. these three goals are strongly interrelated; moreover, there is a trade-off between them, because the effort which is required in order to implement in a reliable way the properties of transactions causes an obvious performance penalty.

1. CPU & Main Memory Utilization

This aspect is common to both centralized &distributed databases. Although typical database applications are I/O bounded in large system can reveal a bottleneck in main memory or in CPU time. If the operating system has to create a process for each active transaction, most of these processes will be swapped in & out of main memory. Moreover, A lot of context switching will required .In order to reduce this overhead transaction managers apply specialized techniques which take advantage of the typical characteristics of database applications, avoid considering them in same way as the generalized processes which dealt with by general purpose operating system.

2. Control Messages In a distributed database we have to consider also another aspect of efficiency: the number of control message which are exchanged between sites .A control message is not used to transfer data but needed in order to control the execution of the application.

3. Response Time As third important efficiency aspect, we have to consider the response time of each individual transaction. Clearly, obtaining an acceptable response time can be more critical for distributed application than for local application, because of the additional time which is required for communication between different sites

4. Availability Another aspect which must be consider by transaction manager of a distributed database is the availability of whole system.Ofcoures ,in a distributed system is not acceptance for failure of one site to stop the whole systems operation. Therefore, the algorithms implemented by the transaction manager must not block the execution of those transactions which do not strictly need to access a site which is not operational. As a result, the effort of increasing the systems availability in the presence of site failure is significant characteristic of the algorithm which are used for transaction recovery & concurrency control in ddbms

Summary 1) Transaction properties.

have

atomicity,

durability,

serializability,

&

isolation

2) Their cost in term of main memory, CPU,& number of transmitted control messages & their response time are minimized. 3) The availability of the system is maximized

The 2 Phase-Commitment Protocol In the basic 2-phase-commitment protocol, there is one agent which has special role. This agent is called coordinator; all the other agents which must commit together are called participants. The coordinator is responsible for making final commit to abort decision Each participant corresponds to a sub transaction which has performed some write action; it is responsible for performing the write action at its local database. We assume that each participant is at different site. Note that when the transaction performs some write action at the site of coordinator, then the coordinator &one participant are at same site; though they don’t need to communicate using the network, we assume that they follow the protocol as if they were at different sites. In 2 phase-commitment protocol consist of two phases. 1) Goal of first phase of the protocol is to reach a common decision; 2) Goal of second phase is to implement this secession Phase one: there is first phase during which the coordinator asks all the participants to prepare for commitment; each participant answer ready if it is ready to commit &willing to do so. Before sending first prepare for commitment message, the coordinator records on stable storage a log record of a new type, called a “prepare “log record, in which the identifiers of all sub transactions participating to the two –phase-commitment are

recorded. The coordinator also activates a time out mechanism, which will interrupt the coordinator after that a given time interval expired. When a participant answers Ready, it ensures that it will be able to commit the sub transaction even if failures occur at its site. In practice, this means that each participant has to record on stable storage two things: 1) All the information which is required for locally committing the substraction.This means that all the log records of the sub transaction must be recorded on stable storage. 2) The fact that this sub transaction has declared to be ready to commit. This means that a log record of a new type, called a “ready” log record, must be recorded on stable storage. The coordinator decides whether to commit or abort the transaction as a result, of the answer it has received from participants. If all participants answered ready it decides to commit transaction. If instead some participant has answer abort or has not yet answered when time out expires, it decides to abort the transaction. Phase two: The coordinator be gains the second phase of 2pc by recording on stable storage its decision .This corresponds to writing a “global _commit” or “global abort record in log. The fact that the coordinator records its decision on storage table means that the distributed transaction will eventually be committed or aborted, in spite of failures. Then the coordinator informs all participants of it decision, by sending them the command massage. All the participants write a commit or abort record in the log, based on the command massage received from the co-coordinator. From this movement, the local recovery procedure is capable of insuring that effect of the subs transaction will not be losses. Finally, all participants send a final acknowdgement (ACK) massage to coordinator, and perform the actions required for committing or aborting the sub transaction. When coordinator has received an ACK massage from all participants he or she writes a log record of new type called a “complete” record. After having written this record, the coordinator can forget the

outcome of the transaction thus all records related to this transaction can be taken offline after the next check point.

Questions 2007 1. Explain in detail the concept of data definition language, data manipulation language and the various types of data manipulation language. 2. What are the different types of failures occurring in transaction processing? Explain. 3. What is query Optimization? How is it Carried Out? Explain with your own example 4. Explain in detail the timestamp based protocol used in concurrency control 5. What do you mean by a deadlock? Explain any one protocol used for deadlock handling 6. Explain the different factors affecting in designing of distributed database environment 7. Explain in detail the procedure of replication and fragmentation techniques carried out for designing distributed database environment. 8. What do you mean by serializability? Explain its importance in database management 9. Explain in detail the execution of client/ server database technology 10.

Write short note on: a) Need of distributed databases b) Heterogeneous database

2004 1. A) Enlist the features of distribute DBMS and its need. B) Explain the mechanism for storage of data. 2. Write a detailed note on concurrency control 3. What are time-based and quorum based protocols. 4. What are deadlocks? Explain distributed dead-locks. How to avoid

deadlocks 5. What is access planning? Why is it necessary 6. Explain in detail the working of client-server technology in the distributed database system 7. Write a short note on: a) Constructing an application, b) Distributed data dictionary management, c) Checkpoints and cold starts 2003 1. A) What do you mean by compatibility of two lock modes. Explain this compatibility relations using a matrix format. B) What is the need of concurrent execution of transactions? What are the different factors affecting concurrent execution? 2. Explain the two phase locking protocol in detail. What are the main advantages of this protocol? Whether it is able to eliminate the problem of deadlock? Explain in brief. 3. What the different types of failure occurring in the distributed database environment? Explain in detail the cause of all the solution for them. 4. What is the working of client server technology in the distributed database system? Explain in detail. 5. Explain the following architecture used in client-server technology: i) Shared Everything, ii) Share Something, iii) Share Nothing. 6. What is meant by concurrency control? Why for is it important in the management of several transactions over the database. Explain in brief. 7. Write short note on: i) partitioned networks, ii) distributed database dictionary management, iii) check points.