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Distributed Database

A distributed database is a database in which storage devices are not all attached to a common processing unit such as the CPU, controlled by a distributed database management system (together sometimes called a distributed database system). It may be stored in multiple computers, located in the same physical location; or may be dispersed over a network of interconnected computers. Unlike parallel systems, in which the processors are tightly coupled and constitute a single database system, a distributed database system consists of loosely coupled sites that share no physical components.

System administrators can distribute collections of data (e.g. in a database) across multiple physical locations. A distributed database can reside on network servers on the Internet, on corporate intranets or extranets, or on other company networks. Because they store data across multiple computers, distributed databases can improve performance at end-user worksites by allowing transactions to be processed on many machines, instead of being limited to one.

Two processes ensures that the distributed databases remain up-to-date and current: replication and duplication.

  1. Replication involves using specialized software that looks for changes in the distributive database. Once the changes have been identified, the replication process makes all the databases look the same. The replication process can be complex and time-consuming depending on the size and number of the distributed databases. This process can also require a lot of time and computer resources.
  2. Duplication, on the other hand, has less complexity. It basically identifies one database as a master and then duplicates that database. The duplication process is normally done at a set time after hours. This is to ensure that each distributed location has the same data. In the duplication process, users may change only the master database. This ensures that local data will not be overwritten.

Both replication and duplication can keep the data current in all distributive locations.

Besides distributed database replication and fragmentation, there are many other distributed database design technologies. For example, local autonomy, synchronous and asynchronous distributed database technologies. These technologies' implementations can and do depend on the needs of the business and the sensitivity/confidentiality of the data stored in the database, and hence the price the business is willing to spend on ensuring data security, consistency and integrity.

When discussing access to distributed databases, Microsoft favors the term distributed query, which it defines in protocol-specific manner as "[a]ny SELECT, INSERT, UPDATE, or DELETE statement that references tables and rowsets from one or more external OLE DB data sources". Oracle provides a more language-centric view in which distributed queries and distributed transactions form part of distributed SQL.

Today the distributed DBMS market is evolving dramatically, with new, innovative entrants and incumbents supporting the growing use of unstructured data and NoSQL DBMS engines, as well as XML databases and NewSQL databases. These databases are increasingly supporting distributed database architecture that provides high availability and fault tolerance through replication and scale out ability. Some examples are Aerospike, Cassandra, Clusterpoint, ClustrixDB, Couchbase, Druid (open-source data store), FoundationDB, NuoDB, Riak and OrientDB. The block chain technology popularised by bitcoin is an implementation of a distributed database.


A database user accesses the distributed database through:

Local applicationsapplications which do not require data from other sites.Global applicationsapplications which do require data from other sites.

A homogeneous distributed database has identical software and hardware running all databases instances, and may appear through a single interface as if it were a single database. A heterogeneous distributed database may have different hardware, operating systems, database management systems, and even data models for different databases.

Homogeneous Distributed Databases Management System

In homogeneous distributed database all sites have identical software and are aware of each other and agree to cooperate in processing user requests. Each site surrenders part of its autonomy in terms of right to change schema or software. A homogeneous DDBMS appears to the user as a single system. The homogeneous system is much easier to design and manage. The following conditions must be satisfied for homogeneous database:

  • The operating system is used, at each location must be same or compatible.
  • The data structures used at each location must be same or compatible.
  • The database application (or DBMS) used at each location must be same or compatible.

Heterogeneous DDBMS

See also: Heterogeneous database system

In a heterogeneous distributed database, different sites may use different schema and software. Difference in schema is a major problem for query processing and transaction processing. Sites may not be aware of each other and may provide only limited facilities for cooperation in transaction processing. In heterogeneous systems, different nodes may have different hardware & software and data structures at various nodes or locations are also incompatible. Different computers and operating systems, database applications or data models may be used at each of the locations. For example, one location may have the latest relational database management technology, while another location may store data using conventional files or old version of database management system. Similarly, one location may have the Windows NT operating system, while another may have UNIX. Heterogeneous systems are usually used when individual sites use their own hardware and software. On heterogeneous system, translations are required to allow communication between different sites (or DBMS). In this system, the users must be able to make requests in a database language at their local sites. Usually the SQL database language is used for this purpose. If the hardware is different, then the translation is straightforward, in which computer codes and word-length is changed. The heterogeneous system is often not technically or economically feasible. In this system, a user at one location may be able to read but not update the data at another location.

Important considerations

Care with a distributed database must be taken to ensure the following:

  • The distribution is transparent — users must be able to interact with the system as if it were one logical system. This applies to the system's performance, and methods of access among other things.
  • Transactions are transparent — each transaction must maintain database integrity across multiple databases. Transactions must also be divided into sub-transactions, each sub-transaction affecting one database system.

There are two principal approaches to store a relation r in a distributed database system:

A) ReplicationB) Fragmentation/Partitioning

A) Replication: In replication, the system maintains several identical replicas of the same relation r in different sites.

  • Data is more available in this scheme.
  • Parallelism is increased when read request is served.
  • Increases overhead on update operations as each site containing the replica needed to be updated in order to maintain consistency.
  • Multi-datacenter replication provides geographical diversity, like in Clusterpoint or Riak.

B) Fragmentation: The relation r is fragmented into several relations r1, r2, r3....rn in such a way that the actual relation could be reconstructed from the fragments and then the fragments are scattered to different locations. There are basically two schemes of fragmentation:

  • Horizontal fragmentation - splits the relation by assigning each tuple of r to one or more fragments.
  • Vertical fragmentation - splits the relation by decomposing the schema R of relation r.

A distributed database can be run by independent or even competing parties as, for example, in bitcoin or Hasq.


  • Management of distributed data with different levels of transparency like network transparency, fragmentation transparency, replication transparency, etc.
  • Increase reliability and availability
  • Easier expansion
  • Reflects organizational structure — database fragments potentially stored within the departments they relate to
  • Local autonomy or site autonomy — a department can control the data about them (as they are the ones familiar with it)
  • Protection of valuable data — if there were ever a catastrophic event such as a fire, all of the data would not be in one place, but distributed in multiple locations
  • Improved performance — data is located near the site of greatest demand, and the database systems themselves are parallelized, allowing load on the databases to be balanced among servers. (A high load on one module of the database won't affect other modules of the database in a distributed database)
  • Economics — it may cost less to create a network of smaller computers with the power of a single large computer
  • Modularity — systems can be modified, added and removed from the distributed database without affecting other modules (systems)
  • Reliable transactions - due to replication of the database
  • Hardware, operating-system, network, fragmentation, DBMS, replication and location independence
  • Continuous operation, even if some nodes go offline (depending on design)
  • Distributed query processing can improve performance
  • Single-site failure does not affect performance of system.
  • For those systems that support full distributed transactions, operations enjoy the ACID properties:
    • A-atomicity, the transaction takes place as a whole or not at all
    • C-consistency, maps one consistent DB state to another
    • I-isolation, each transaction sees a consistent DB
    • D-durability, the results of a transaction must survive system failures

The Merge Replication Method is popularly used to consolidate the data between databases.


  • Complexity — DBAs may have to do extra work to ensure that the distributed nature of the system is transparent. Extra work must also be done to maintain multiple disparate systems, instead of one big one. Extra database design work must also be done to account for the disconnected nature of the database — for example, joins become prohibitively expensive when performed across multiple systems.
  • Economics — increased complexity and a more extensive infrastructure means extra labour costs
  • Security — remote database fragments must be secured, and they are not centralized so the remote sites must be secured as well. The infrastructure must also be secured (for example, by encrypting the network links between remote sites).
  • Difficult to maintain integrity — but in a distributed database, enforcing integrity over a network may require too much of the network's resources to be feasible
  • Inexperience — distributed databases are difficult to work with, and in such a young field there is not much readily available experience in "proper" practice
  • Lack of standards — there are no tools or methodologies yet to help users convert a centralized DBMS into a distributed DBMS
  • Database design more complex — In addition to traditional database design challenges, the design of a distributed database has to consider fragmentation of data, allocation of fragments to specific sites and data replication
  • Additional software is required
  • Operating system should support distributed environment
  • Concurrency control poses a major issue. It can be solved by locking and timestamping.
  • Distributed access to data
  • Analysis of distributed data

Source: Wikipedia, Google