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Distributed database system (DDBS) technology is the union of what appear to be two diametrically opposed approaches to data processing: database system and computer network technologies. Database systems have taken us from a paradigm of data processing in which each application defined and maintained its own data to one in which the data are defined and administered centrally. This new orientation results in data independence.
The working definition we use for a distributed computing system or a distuributed processing database states that it is a number of autonomous processing elements (not necessarily homogeneous) that are interconnected by a computer network and that cooperate in performing their assigned tasks. The “processing element” referred to in this definition is a computing device that can execute a program on its own.
What is a Distributed Database System?
processing logic or processing elements are distributed. Another possible distribution is according to function. Various functions of a computer system could be delegated to various pieces of hardware or software. A third possible mode of distribution is according to data. Data used by a number of applications may be distributed to a number of processing sites. Finally, control can be distributed. The control of the execution of various tasks might be distributed instead of being performed by one computer system.
a distributed database as a collection of multiple, logically interrelated databases distributed over a computer network. A distributed database management system (distributed DBMS) is then defined as the software system that permits the management of the distributed database and makes the distribution transparent to the users.
1.3 Data Delivery Alternatives
In distributed databases, data are “delivered” from the sites where they are stored to where the query is posed. We characterize the data delivery alternatives along three orthogonal dimensions: delivery modes, frequency and communication methods. The combinations of alternatives along each of these dimensions (that we discuss next) provide a rich design space.
The alternative delivery modes are pull-only, push-only and hybrid. In the pullonly mode of data delivery, the transfer of data from servers to clients is initiated by a client pull.
In the push-only mode of data delivery, the transfer of data from servers to clients is initiated by a server push in the absence of any specific request from clients.
The hybrid mode of data delivery combines the client-pull and server-push mechanisms.
In periodic delivery, data are sent from the server to clients at regular intervals.
In conditional delivery, data are sent from servers whenever certain conditions installed by clients in their profiles are satisfied.
Ad-hoc delivery is irregular and is performed mostly in a pure pull-based system. Data are pulled from servers to clients in an ad-hoc fashion
1.4 Promises of DDBSs
The third component of the design space of information delivery alternatives is the communication method. These methods determine the various ways in which servers and clients communicate for delivering information to clients. The alternatives are unicast and one-to-many. In unicast, the communication from a server to a client is one-to-one: the server sends data to one client using a particular delivery mode with some frequency. In one-to-many, as the name implies, the server sends data to a number of clients.
1.4.1 Transparent Management of Distributed and Replicated Data
Transparency refers to separation of the higher-level semantics of a system from lower-level implementation issues. In other words, a transparent system “hides” the implementation details from users. The advantage of a fully transparent DBMS is the high level of support that it provides for the development of complex applications.
Data independence is a fundamental form of transparency that we look for within a DBMS. It is also the only type that is important within the context of a centralized DBMS. It refers to the immunity of user applications to changes in the definition and organization of data, and vice versa.
As is well-known, data definition occurs at two levels. At one level the logical structure of the data are specified, and at the other level its physical structure. The former is commonly known as the schema definition, whereas the latter is referred to as the physical data description.
Logical data independence refers to the immunity of user applications to changes in the logical structure (i.e., schema) of the database. Physical data independence, on the other hand, deals with hiding the details of the storage structure from user applications.
188.8.131.52 Network Transparency
In centralized database systems, the only available resource that needs to be shielded from the user is the data (i.e., the storage system). In a distributed database environment, however, there is a second resource that needs to be managed in much the same manner: the network. Preferably, the user should be protected from the operational details of the network; possibly even hiding the existence of the network. Then there would be no difference between database applications that would run on a centralized database and those that would run on a distributed database. This type of transparency is referred to as network transparency or distribution transparency.
transparency requires that users do not have to specify where data are located. Sometimes two types of distribution transparency are identified:
location transparency and naming transparency. Location transparency refercs to the fact that the command used to perform a task is independent of both the location of the data and the system on which an operation is carried out. Naming transparency means that a unique name is provided for each object in the database. In the absence of naming transparency, users are required to embed the location name (or an identifier) as part of the object name.
184.108.40.206 Replication Transparency
that data are replicated, the transparency issue is whether the users should be aware of the existence of copies or whether the system should handle the management of copies and the user should act as if there is a single copy of the data (note that we are not referring to the placement of copies, only their existence). replication transparency refers only to the existence of replicas, not to their actual location. Note also that distributing these replicas across the network in a transparent manner is the domain of network transparency.
220.127.116.11 Fragmentation Transparency
it is commonly desirable to divide each database relation into smaller fragments and treat each fragment as a separate database object (i.e., another relation). This is commonly done for reasons of performance, availability, and reliability. Furthermore, fragmentation can reduce the negative effects of replication. Each replica is not the full relation but only a subset of it; thus less space is required and fewer data items need be managed.
There are two general types of fragmentation alternatives. In one case, called horizontal fragmentation, a relation is partitioned into a set of sub-relations each of which have a subset of the tuples (rows) of the original relation. The second alternative is vertical fragmentation where each sub-relation is defined on a subset of the attributes (columns) of the original relation.
1.4.2 Reliability Through Distributed Transactions
Distributed DBMSs are intended to improve reliability since they have replicated components and, thereby eliminate single points of failure. The failure of a single site, or the failure of a communication link which makes one or more sites unreachable, is not sufficient to bring down the entire system. In the case of a distributed database, this means that some of the data may be unreachable, but with proper care, users may be permitted to access other parts of the distributed database. The “proper care” comes in the form of support for distributed transactions and application protocols. A transaction is a basic unit of consistent and reliable computing, consisting of a sequence of database operations executed as an atomic action. It transforms a consistent database state to another consistent database state even when a number of such transactions are executed concurrently (sometimes called concurrency transparency), and even when failures occur (also called failure atomicity).
1.4.3 Improved Performance
The case for the improved performance of distributed DBMSs is typically made based on two points. First, a distributed DBMS fragments the conceptual database, enabling data to be stored in close proximity to its points of use (also called data localization). This has two potential advantages:
1. Since each site handles only a portion of the database, contention for CPU and I/O services is not as severe as for centralized databases. 2. Localization reduces remote access delays that are usually involved in wide area networks (for example, the minimum round-trip message propagation delay in satellite-based systems is about 1 second).
Most distributed DBMSs are structured to gain maximum benefit from data localization. Full benefits of reduced contention and reduced communication overhead can be obtained only by a proper fragmentation and distribution of the database.
Latency is inherent in the distributed environments and there are physical limits to how fast we can send data over computer networks.
1.4.4 Easier System Expansion
In a distributed environment, it is much easier to accommodate increasing database sizes. Major system overhauls are seldom necessary; expansion can usually be handled by adding processing and storage power to the network. One aspect of easier system expansion is economics. It normally costs much less to put together a system of “smaller” computers with the equivalent power of a single big machine.
1.5 Complications Introduced by Distribution
First, data may be replicated in a distributed environment. A distributed database can be designed so that the entire database, or portions of it, reside at different sites of a computer network. It is not essential that every site on the network contain the database; it is only essential that there be more than one site where the database resides. The possible duplication of data items is mainly due to reliability and efficiency considerations. Consequently, the distributed database system is responsible for (1) choosing one of the stored copies of the requested data for access in case of retrievals, and (2) making sure that the effect of an update is reflected on each and every copy of that data item.
Second, if some sites fail (e.g., by either hardware or software malfunction), or if some communication links fail (making some of the sites unreachable) while an update is being executed, the system must make sure that the effects will be reflected on the data residing at the failing or unreachable sites as soon as the system can recover from the failure.
The third point is that since each site cannot have instantaneous information on the actions currently being carried out at the other sites, the synchronization of transactions on multiple sites is considerably harder than for a centralized system.
1.6 Design Issues
1.6.1 Distributed Database Design
There are two basic alternatives to placing data: partitioned (or non-replicated) and replicated. In the partitioned scheme the database is divided into a number of disjoint partitions each of which is placed at a different site. Replicated designs can be either fully replicated (also called fully duplicated) where the entire database is stored at each site, or partially replicated (or partially duplicated) where each partition of the database is stored at more than one site, but not at all the sites. The two fundamental design issues are fragmentation, the separation of the database into partitions called fragments, and distribution, the optimum distribution of fragments.
1.6.2 Distributed Directory Management
A directory contains information (such as descriptions and locations) about data items in the database. Problems related to directory management are similar in nature to the database placement problem discussed in the preceding section. A directory may be global to the entire DDBS or local to each site; it can be centralized at one site or distributed over several sites; there can be a single copy or multiple copies.
1.6.3 Distributed Query Processing
Query processing deals with designing algorithms that analyze queries and convert them into a series of data manipulation operations.
1.6.4 Distributed Concurrency Control
Concurrency control involves the synchronization of accesses to the distributed database, such that the integrity of the database is maintained. It is, without any doubt, one of the most extensively studied problems in the DDBS field. The condition that requires all the values of multiple copies of every data item to converge to the same value is called mutual consistency. Two fundamental primitives that can be used with both approaches are locking, which is based on the mutual exclusion of accesses to data items, and timestamping, where the transaction executions are ordered based on timestamps.
1.6.5 Distributed Deadlock Management
The deadlock problem in DDBSs is similar in nature to that encountered in operating systems. The competition among users for access to a set of resources (data, in this case) can result in a deadlock if the synchronization mechanism is based on locking. The well-known alternatives of prevention, avoidance, and detection/recovery also apply to DDBSs.
1.6.6 Reliability of Distributed DBMS
for DDBSs is that when a failure occurs and various sites become either inoperable or inaccessible, the databases at the operational sites remain consistent and up to date. Furthermore, when the computer system or network recovers from the failure, the DDBSs should be able to recover and bring the databases at the failed sites up-to-date. This may be especially difficult in the case of network partitioning, where the sites are divided into two or more groups with no communication among them.
If the distributed database is (partially or fully) replicated, it is necessary to implement protocols that ensure the consistency of the replicas,i.e., copies of the same data item have the same value. These protocols can be eager in that they force the updates to be applied to all the replicas before the transaction completes, or they may be lazy so that the transaction updates one copy (called the master) from which updates are propagated to the others after the transaction completes.
1.6.8 Relationship among Problems
The relationship among the components is shown in Figure 1.7. The design of distributed databases affects many areas. It affects directory management, because the definition of fragments and their placement determine the contents of the directory (or directories) as well as the strategies that may be employed to manage them. The same information (i.e., fragment structure and placement) is used by the query processor to determine the query evaluation strategy. On the other hand, the access and usage patterns that are determined by the query processor are used as inputs to the data distribution and fragmentation algorithms. Similarly, directory placement and contents influence the processing of queries.
There is a strong relationship among the concurrency control problem, the deadlock management problem, and reliability issues. This is to be expected, since together they are usually called the transaction management problem. The concurrency control algorithm that is employed will determine whether or not a separate deadlock management facility is required. If a locking-based algorithm is used, deadlocks will occur, whereas they will not if timestamping is the chosen alternative.
1.7 Distributed DBMS Architecture
The architecture of a system defines its structure. This means that the components of the system are identified, the function of each component is specified, and the interrelationships and interactions among these components are defined. The specification of the architecture of a system requires identification of the various modules, with their interfaces and interrelationships, in terms of the data and control flow through the system.
1.7.2 A Generic Centralized DBMS Architecture
A DBMS is a reentrant program shared by multiple processes (transactions), that run database programs. When running on a general purpose computer, a DBMS is interfaced with two other components: the communication subsystem and the operating system. The communication subsystem permits interfacing the DBMS with other subsystems in order to communicate with applications. For example, the terminal monitor needs to communicate with the DBMS to run interactive transactions. The operating system provides the interface
The interface layer manages the interface to the applications. There can be several interfaces
The control layer controls the query by adding semantic integrity predicates and authorization predicates. The query processing (or compilation) layer maps the query into an optimized sequence of lower-level operations.
The execution layer directs the execution of the access plans, including transaction management (commit, restart) and synchronization of algebra operations. It interprets the relational operations by calling the data access layer through the retrieval and update requests.
The data access layer manages the data structures that implement the files, indices, etc. It also manages the buffers by caching the most frequently accessed data. Careful use of this layer minimizes the access to disks to get or write data. Finally, the consistency layer manages concurrency control and logging for update requests. This layer allows transaction, system, and media recovery after failure.
Autonomy is a function of a number of factors such as whether the component systems (i.e., individual DBMSs) exchange information, whether they can independently execute transactions, and whether one is allowed to modify them.
1. Design autonomy: Individual DBMSs are free to use the data models and transaction management techniques that they prefer.
2. Communication autonomy: Each of the individual DBMSs is free to make its own decision as to what type of information it wants to provide to the other DBMSs or to the software that controls their global execution.
3. Execution autonomy: Each DBMS can execute the transactions that are submitted to it in any way that it wants to.
1.7 Distributed DBMS Architecture 27
Whereas autonomy refers to the distribution (or decentralization) of control, the distribution dimension of the taxonomy deals with data.
The client/server distribution concentrates data management duties at servers while the clients focus on providing the application environment including the user interface. The communication duties are shared between the client machines and servers.
In peer-to-peer systems, there is no distinction of client machines versus servers. Each machine has full DBMS functionality and can communicate with other machines to execute queries and transactions.
Heterogeneity may occur in various forms in distributed systems, ranging from hardware heterogeneity and differences in networking protocols to variations in data managers.
1.7.8 Client/Server Systems
The database server approach, as an extension of the classical client/server architecture, has several potential advantages. First, the single focus on data management makes possible the development of specific techniques for increasing data reliability and availability, e.g. using parallelism. Second, the overall performance of database management can be significantly enhanced by the tight integration of the database system and a dedicated database operating system. Finally, a database server can also exploit recent hardware architectures, such as multiprocessors or clusters of PC servers to enhance both performance and data availability.
The application server approach (indeed, a n-tier distributed approach) can be extended by the introduction of multiple database servers and multiple application servers
1.7.9 Peer-to-Peer Systems
The detailed components of a distributed DBMS are shown in Figure 1.15. One component handles the interaction with users, and another deals with the storage. The first major component, which we call the user processor, consists of four elements: 1. The user interface handler is responsible for interpreting user commands as they come in, and formatting the result data as it is sent to the user. 2. The semantic data controller uses the integrity constraints and authorizations that are defined as part of the global conceptual schema to check if the user query can be processed.
This component, which is studied in detail in Chapter 5, is also responsible for authorization and other functions. 3. The global query optimizer and decomposer determines an execution strategy to minimize a cost function, and translates the global queries into local ones using the global and local conceptual schemas as well as the global directory. The global query optimizer is responsible, among other things, for generating the best strategy to execute distributed join operations. These issues are discussed in Chapters 6 through 8.
4. The distributed execution monitor coordinates the distributed execution of the user request. The execution monitor is also called the distributed transaction manager.
1.7 Distributed DBMS Architecture 35
1. The local query optimizer, which actually acts as the access path selector, is responsible for choosing the best access path5 to access any data item (touched upon briefly in Chapter 8).
2. The local recovery manager is responsible for making sure that the local database remains consistent even when failures occur (Chapter 12). 3. The run-time support processor physically accesses the database according to the physical commands in the schedule generated by the query optimizer. The run-time support processor is the interface to the operating system and contains the database buffer (or cache) manager, which is responsible for maintaining the main memory buffers and managing the data accesses.
1.7.10 Multidatabase System Architecture
Multidatabase systems (MDBS) represent the case where individual DBMSs (whether distributed or not) are fully autonomous and have no concept of cooperation; they may not even “know” of each other’s existence or how to talk to each other. Our focus is, naturally, on distributed MDBSs, which is what the term will refer to in the remainder.
A mediator “is a software module that
exploits encoded knowledge about certain sets or subsets of data to create
information for a higher layer of applications.
Distributed Database Design
The design of a distributed computer system involves making decisions on the placement of data and programs across the sites of a computer network, as well as possibly designing the network itself.
1. Level of sharing
2. Behavior of access patterns
3. Level of knowledge on access pattern behavior
In terms of the level of sharing, there are three possibilities. First, there is no sharing: each application and its data execute at one site, and there is no communication with any other program or access to any data file at other sites. This characterizes the very early days of networking and is probably not very common today. We then find the level of data sharing; all the programs are replicated at all the sites, but data files are not. Accordingly, user requests are handled at the site where they originate and the necessary data files are moved around the network. Finally, in data-plus-program sharing, both data and programs may be shared, meaning that a program at a given site can request a service from another program at a second site, which, in turn, may have to access a data file located at a third site.
3.1 Top-Down Design Process
A framework for top-down design process is shown in Figure 3.2. The activity begins with a requirements analysis that defines the environment of the system and “elicits both the data and processing needs of all potential database users” [Yao et al., 1982a]. The requirements study also specifies where the final system is expected to stand with respect to the objectives of a distributed DBMS as identified in Section 1.4. These objectives are defined with respect to performance, reliability and availability, economics, and expandability (flexibility).
The requirements document is input to two parallel activities: view design and conceptual design. The view design activity deals with defining the interfaces for end users. The conceptual design, on the other hand, is the process by which the enterprise is examined to determine entity types and relationships among these entities. One can possibly divide this process into two related activity groups [Davenport, 1981]: entity analysis and functional analysis. Entity analysis is concerned with determining the entities, their attributes, and the relationships among them. Functional analysis, on the other hand, is concerned with determining the fundamental functions with which the modeled enterprise is involved. The results of these two steps need to be cross-referenced to get a better understanding of which functions deal with which entities.
There is a relationship between the conceptual design and the view design. In one sense, the conceptual design can be interpreted as being an integration of user views. Even though this view integration activity is very important, the conceptual model should support not only the existing applications, but also future applications. View integration should be used to ensure that entity and relationship requirements for all the views are covered in the conceptual schema.
In conceptual design and view design activities the user needs to specify the data entities and must determine the applications that will run on the database as well as statistical information about these applications.
The global conceptual schema (GCS) and access pattern information collected as a result of view design are inputs to the distribution design step. The objective at this stage, which is the focus of this chapter, is to design the local conceptual schemas (LCSs) by distributing the entities over the sites of the distributed system. It is possible, of course, to treat each entity as a unit of distribution.
University/College: University of Chicago
Type of paper: Thesis/Dissertation Chapter
Date: 11 October 2016
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