dead lock



A Computer Database is a structured collection of records or data that is stored in a computer system. The structure is achieved by organizing the data according to a database model. The model in most common use today is the relational model. Other models such as the hierarchical model and the network model use a more explicit representation of relationships (see below for explanation of the various database models).

A computer database relies upon software to organize the storage of data. This software is known as a database management system (DBMS). Database management systems are categorized according to the database model that they support. The model tends to determine the query languages that are available to access the database. A great deal of the internal engineering of a DBMS, however, is independent of the data model, and is concerned with managing factors such as performance, concurrency, integrity, and recovery from hardware failures. In these areas there are large differences between products.

Database models

Various techniques are used to model data structure. Most database systems are built around one particular data model, although it is increasingly common for products to offer support for more than one model. For any one logical model various physical implementations may be possible, and most products will offer the user some level of control in tuning the physical implementation, since the choices that are made have a significant effect on performance. Here are three examples:

Hierarchical model

In a hierarchical model, data is organized into an inverted tree-like structure, implying a multiple downward link in each node to describe the nesting, and a sort field to keep the records in a particular order in each same-level list. This structure arranges the various data elements in a hierarchy and helps to establish logical relationships among data elements of multiple files. Each unit in the model is a record which is also known as a node. In such a model, each record on one level can be related to multiple records on the next lower level. A record that has subsidiary records is called a parent and the subsidiary records are called children. Data elements in this model are well suited for one-to-many relationships with other data elements in the database.

This model is advantageous when the data elements are inherently hierarchical. The disadvantage is that in order to prepare the database it becomes necessary to identify the requisite groups of files that are to be logically integrated. Hence, a hierarchical data model may not always be flexible enough to accommodate the dynamic needs of an organization.

Network model

In the network model, records can participate in any number of named relationships. Each relationship associates a record of one type (called the owner) with multiple records of another type (called the member). These relationships (somewhat confusingly) are called sets. For example a student might be a member of one set whose owner is the course they are studying, and a member of another set whose owner is the college they belong to. At the same time the student might be the owner of a set of email addresses, and owner of another set containing phone numbers. The main difference between the network model and hierarchical model is that in a network model, a child can have a number of parents whereas in a hierarchical model, a child can have only one parent. The hierarchical model is therefore a subset of the network model.

Programmatic access to network databases is traditionally by means of a navigational data manipulation language, in which programmers navigate from a current record to other related records using verbs such as find owner, find next, and find prior. The most common example of such an interface is the COBOL-based Data Manipulation Language defined by CODASYL.

Network databases are traditionally implemented by using chains of pointers between related records. These pointers can be node numbers or disk addresses.

The network model became popular because it provided considerable flexibility in modelling complex data relationships, and also offered high performance by virtue of the fact that the access verbs used by programmers mapped directly to pointer-following in the implementation.

However, the model had several disadvantages. Navigational programming proved error-prone as data models became more complex, and small changes to the data structure could require changes to many programs. Also, because of the use of physical pointers, operations such as database loading and restructuring could be very time-consuming.

The network model tends to store records with links to other records. Each record in the database can have multiple parents, i.e., the relationships among data elements can have a many to many relationship. Associations are tracked via "pointers". These pointers can be node numbers or disk addresses. Most network databases tend to also include some form of hierarchical model. Databases can be translated from hierarchical model to network and vice versa. The main difference between the network model and hierarchical model is that in a network model, a child can have a number of parents whereas in a hierarchical model, a child can have only one parent.

The network model provides greater advantage than the hierarchical model in that promotes greater flexibility and data accessibility, since records at a lower level can be accessed without accessing the records above them. This model is more efficient than hierarchical model, easier to understand and can be applied to many real world problems that require routine transactions. The disadvantages are that: It is a complex process to design and develop a network database; It has to be refined frequently; It requires that the relationships among all the records be defined before development starts, and changes often demand major programming efforts; Operation and maintenance of the network model is expensive and time consuming.

Examples of database engines that have network model capabilities are RDM Embedded and RDM Server.

Relational model

The basic data structure of the relational model is a table where information about a particular entity (say, employees) is represented in columns and rows. The columns enumerate the various attributes of an entity (e.g. employee_name, address, phone_number). Rows (also called records) represent instances of an entity (e.g. specific employees).

The "relation" in "relational database" comes from the mathematical notion of relations from the field of set theory. A relation is a set of tuples, so rows are sometimes called tuples. All tables in a relational database adhere to three basic rules.

  • The ordering of columns is immaterial
  • Identical rows are not allowed in a table
  • Each row has a single (separate) value for each of its columns (each tuple has an atomic value).

If the same value occurs in two different records (from the same table or different tables) it can imply a relationship between those records. Relationships between records are often categorized by their cardinality (1:1, (0), 1:M, M:M).

Tables can have a designated column or set of columns that act as a "key" to select rows from that table with the same or similar key values. A "primary key" is a key that has a unique value for each row in the table. Keys are commonly used to join or combine data from two or more tables. For example, an employee table may contain a column named address which contains a value that matches the key of an address table. Keys are also critical in the creation of indexes, which facilitate fast retrieval of data from large tables. It is not necessary to define all the keys in advance; a column can be used as a key even if it was not originally intended to be one.

Relational operations

Users (or programs) request data from a relational database by sending it a query that is written in a special language, usually a dialect of SQL. Although SQL was originally intended for end-users, it is much more common for SQL queries to be embedded into software that provides an easier user interface. Many web applications, such as Wikipedia, perform SQL queries when generating pages.

In response to a query, the database returns a result set, which is the list of rows constituting the answer. The simplest query is just to return all the rows from a table, but more often, the rows are filtered in some way to return just the answer wanted. Often, data from multiple tables are combined into one, by doing a join. There are a number of relational operations in addition to join.

Normal forms

Relations are classified based upon the types of anomalies to which they're vulnerable. A database that's in the first normal form is vulnerable to all types of anomalies, while a database that's in the Boyce-Codd Normal Form has no modification anomalies. Normal forms are hierarchical in nature. That is, the lowest level is the first normal form, and the database cannot meet the requirements for higher level normal forms without first having met all the requirements of the lesser normal form.

Database management systems

Relational database management systems

An RDBMS implements the features of the relational model outlined above. In this context, Date's Information Principle states:

The entire information content of the database is represented in one and only one way. Namely as explicit values in column positions (attributes) and rows in relations (tuples) Therefore, there are no explicit pointers between related tables.

Post-relational database models

Several products have been identified as post-relational because the data model incorporates relations but is not constrained by the Information Principle, requiring that all information is represented by data values in relations. Products using a post-relational data model typically employ a model that actually pre-dates the relational model. These might be identified as a directed graph with trees on the nodes.

Examples of models that could be classified as post-relational are PICK aka MultiValue, and MUMPS.

Object database models

In recent years, the object-oriented paradigm has been applied to database technology, creating a new programming model known as object databases. These databases attempt to bring the database world and the application programming world closer together, in particular by ensuring that the database uses the same type system as the application program. This aims to avoid the overhead (sometimes referred to as the impedance mismatch) of converting information between its representation in the database (for example as rows in tables) and its representation in the application program (typically as objects). At the same time, object databases attempt to introduce the key ideas of object programming, such as encapsulation and polymorphism, into the world of databases.

A variety of these ways have been tried for storing objects in a database. Some products have approached the problem from the application programming end, by making the objects manipulated by the program persistent. This also typically requires the addition of some kind of query language, since conventional programming languages do not have the ability to find objects based on their information content. Others have attacked the problem from the database end, by defining an object-oriented data model for the database, and defining a database programming language that allows full programming capabilities as well as traditional query facilities.

DBMS internals

Storage and physical database design

Database tables/indexes are typically stored in memory or on hard disk in one of many forms, ordered/unordered flat files, ISAM, heaps, hash buckets or B+ trees. These have various advantages and disadvantages discussed further in the main article on this topic. The most commonly used are B+ trees and ISAM.

Other important design choices relate to the clustering of data by category (such as grouping data by month, or location), creating pre-computed views known as materialized views, partitioning data by range or hash. As well memory management and storage topology can be important design choices for database designers. Just as normalization is used to reduce storage requirements and improve the extensibility of the database, conversely denormalization is often used to reduce join complexity and reduce execution time for queries.


All of these databases can take advantage of indexing to increase their speed. This technology has advanced tremendously since its early uses in the 1960s and 1970s. The most common kind of index is a sorted list of the contents of some particular table column, with pointers to the row associated with the value. An index allows a set of table rows matching some criterion to be located quickly. Typically, indexes are also stored in the various forms of data-structure mentioned above (such as B-trees, hashes, and linked lists). Usually, a specific technique is chosen by the database designer to increase efficiency in the particular case of the type of index required.

Relational DBMS's have the advantage that indexes can be created or dropped without changing existing applications making use of it. The database chooses between many different strategies based on which one it estimates will run the fastest. In other words, indexes are transparent to the application or end-user querying the database; while they affect performance, any SQL command will run with or without index to compute the result of an SQL statement. The RDBMS will produce a plan of how to execute the query, which is generated by analyzing the run times of the different algorithms and selecting the quickest. Some of the key algorithms that deal with joins are nested loop join, sort-merge join and hash join. Which of these is chosen depends on whether an index exists, what type it is, and its cardinality.

An index speeds up access to data, but it has disadvantages as well. First, every index increases the amount of storage on the hard drive necessary for the database file, and second, the index must be updated each time the data are altered, and this costs time. (Thus an index saves time in the reading of data, but it costs time in entering and altering data. It thus depends on the use to which the data are to be put whether an index is on the whole a net plus or minus in the quest for efficiency.)

A special case of an index is a primary index, or primary key, which is distinguished in that the primary index must ensure a unique reference to a record. Often, for this purpose one simply uses a running index number (ID number). Primary indexes play a significant role in relational databases, and they can speed up access to data considerably.

Transactions and concurrency

In addition to their data model, most practical databases ("transactional databases") attempt to enforce a database transaction . Ideally, the database software should enforce the ACID rules, summarized here:

  • Atomicity: Either all the tasks in a transaction must be done, or none of them. The transaction must be completed, or else it must be undone (rolled back).
  • Consistency: Every transaction must preserve the integrity constraints — the declared consistency rules — of the database. It cannot place the data in a contradictory state.
  • Isolation: Two simultaneous transactions cannot interfere with one another. Intermediate results within a transaction are not visible to other transactions.
  • Durability: Completed transactions cannot be aborted later or their results discarded. They must persist through (for instance) restarts of the DBMS after crashes

In practice, many DBMSs allow most of these rules to be selectively relaxed for better performance.

Concurrency control is a method used to ensure that transactions are executed in a safe manner and follow the ACID rules. The DBMS must be able to ensure that only serializable, recoverable schedules are allowed, and that no actions of committed transactions are lost while undoing aborted transactions.


Replication of databases is closely related to transactions. If a database can log its individual actions, it is possible to create a duplicate of the data in real time. The duplicate can be used to improve performance or availability of the whole database system. Common replication concepts include:

  • Master/Slave Replication: All write requests are performed on the master and then replicated to the slaves
  • Quorum: The result of Read and Write requests are calculated by querying a "majority" of replicas.
  • Multimaster: Two or more replicas sync each other via a transaction identifier.

Parallel synchronous replication of databases enables transactions to be replicated on multiple servers simultaneously, which provides a method for backup and security as well as data availability.


Database security denotes the system, processes, and procedures that protect a database from unintended activity.

Security is usually enforced through access control, auditing, and encryption.

  • Access control ensures and restricts who can connect and what can be done to the database.
  • Auditing logs what action or change has been performed, when and by whom.
  • Encryption: Since security has become a major issue in recent years, many commercial database vendors provide built-in encryption mechanism. Data is encoded natively into the tables and deciphered "on the fly" when a query comes in. Connections can also be secured and encrypted if required using DSA, MD5, SSL or legacy encryption standard.

Enforcing security is one of the major tasks of the DBA.

In the United Kingdom, legislation protecting the public from unauthorized disclosure of personal information held on databases falls under the Office of the Information Commissioner. United Kingdom based organizations holding personal data in electronic format (databases for example) are required to register with the Data Commissioner.


Locking is how the database handles multiple concurrent operations. This is how concurrency and some form of basic integrity is managed within the database system. Such locks can be applied on a row level, or on other levels like page (a basic data block), extend (multiple array of pages) or even an entire table. This helps maintain the integrity of the data by ensuring that only one process at a time can modify the same data. Unlike a basic filesystem files or folders, where only one lock at the time can be set, restricting the usage to one process only. A database can set and hold mutiple locks at the same time on the different level of the physical data structure. How locks are set, last is determined by the database engine locking scheme based on the submitted SQL or transactions by the users. Generally speaking, no activity on the database should be translated by no or very light locking.

For most DBMS systems existing on the market, locks are generally shared or exclusive. Exclusive locks mean that no other lock can acquire the current data object as long as the exclusive lock lasts. Exclusive locks are usually set while the database needs to change data, like during an UPDATE or DELETE operation.

Shared locks can take ownership one from the other of the current data structure. Shared locks are usually used while the database is reading data, during a SELECT operation. The number, nature of locks and time the lock holds a data block can have a huge impact on the database performances. Bad locking can lead to disastrous performance response (usually the result of poor SQL requests, or inadequate database physical structure)

Default locking behavior is enforced by the isolation level of the dataserver. Changing the isolation level will affect how shared or exclusive locks must be set on the data for the entire database system. Default isolation is generally 1, where data can not be read while it is modified, forbidding to return "ghost data" to end user.

At some point intensive or inappropriate exclusive locking, can lead to the "dead lock" situation between two locks. Where none of the locks can be released because they try to acquire resources mutually from each other. The Database has a fail safe mechanism and will automatically "sacrifice" one of the locks releasing the resource. Doing so processes or transactions involved in the "dead lock" will be rolled back.

Databases can also be locked for other reasons, like access restrictions for given levels of user. Databases are also locked for routine database maintenance, which prevents changes being made during the maintenance. See "Locking tables and databases" (section in some documentation / explanation from IBM) for more detail.)


Depending on the intended use, there are a number of database architectures in use. Many databases use a combination of strategies. On-line Transaction Processing systems (OLTP) often use a row-oriented datastore architecture, while data-warehouse and other retrieval-focused applications like Google's BigTable, or bibliographic database(library catalogue) systems may use a column-oriented datastore architecture.

Document-Oriented, XML, Knowledgebases, as well as frame databases and rdf-stores (aka Triple-Stores), may also use a combination of these architectures in their implementation.

Finally it should be noted that not all database have or need a database 'schema' (so called schema-less databases).

Also there are other types of database which cannot be classified as relational databases

Applications of databases

Databases are used in many applications, spanning virtually the entire range of computer software. Databases are the preferred method of storage for large multiuser applications, where coordination between many users is needed. Even individual users find them convenient, and many electronic mail programs and personal organizers are based on standard database technology. Software database drivers are available for most database platforms so that application software can use a common Application Programming Interface to retrieve the information stored in a database. Two commonly used database APIs are JDBC and ODBC.

For example suppliers database contains the data relating to suppliers such as;

  • supplier name
  • supplier code
  • supplier address

It is often used by schools to teach students and grade them.

Links to DBMS products

See also



  • Connolly, Thomas, and Caroln Begg. Database Systems. New York: Harlow, 2002.
  • Date, C. J. An Introduction to Database Systems, Eighth Edition, Addison Wesley, 2003.
  • Galindo, J., Urrutia, A., Piattini, M., Fuzzy Databases: Modeling, Design and Implementation (FSQL guide). Idea Group Publishing Hershey, USA, 2006.
  • Galindo, J., Ed. Handbook on Fuzzy Information Processing in Databases. Hershey, PA: Information Science Reference (an imprint of Idea Group Inc.), 2008.
  • Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
  • Kroenke, David M. Database Processing: Fundamentals, Design, and Implementation (1997), Prentice-Hall, Inc., pages 130-144.
  • Kroenke, David M., and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
  • Lightstone, S., T. Teorey, and T. Nadeau, Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more, Morgan Kaufmann Press, 2007. ISBN 0-12369-389-6.
  • Shih, J. " Why Synchronous Parallel Transaction Replication is Hard, But Inevitable?", white paper, 2007.
  • Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5
  • Tukey, John W. Exploratory Data Analysis. Reading, MA: Addison Wesley, 1977.

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