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A 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.
Database topics
Architecture
Depending on the intended use, there are a number of database architectures in use.
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Encyclopedia
A 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.
Database topics
Architecture
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 DBMS 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 databases have or need a database 'schema' (so called schema-less databases).
Over many years the database industry has been dominated by General Purpose database systems, which offer a wide range of functions that are applicable to many, if not most circumstances in modern data processing. These have been enhanced with extensible datatypes, pioneered in the PostgreSQL project, to allow a very wide range of applications to be developed.
There are also other types of database which cannot be classified as relational databases.
Database management systems
A computer database relies on 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.
A Relational Database Management System (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."
Database models
Post-relational database models
Products offering a more general data model than the relational model are sometimes classified as post-relational. The data model in such products incorporates relations but is not constrained by the Information Principle, which requires that all information is represented by data values in relations.
Some of these extensions to the relational model actually integrate concepts from technologies that pre-date the relational model. For example, they allow representation of a directed graph with trees on the nodes.
Some products implementing such models have been built by extending relational database systems with non-relational features. Others, however, have arrived in much the same place by adding relational features to pre-relational systems. Paradoxically, this allows products that are historically pre-relational, such as PICK and MUMPS, to make a plausible claim to be post-relational in their current architecture.
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.
Database storage structures
Relational 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.
Object databases use a range of storage mechanisms. Some use virtual memory mapped files to make the native language (C++, Java etc.) objects persistent. This can be highly efficient but it can make multi-language access more difficult. Others break the objects down into fixed and varying length components that are then clustered tightly together in fixed sized blocks on disk and reassembled into the appropriate format either for the client or in the client address space. Another popular technique is to store the objects in tuples, much like a relational database, which the database server then reassembles for the client.
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. Memory management and storage topology can be important design choices for database designers as well. 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.
Indexing
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.
Most relational DBMS's and some object DBMSs 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
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.
Security
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 mechanisms. 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
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), extent (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.
In basic filesystem files or folders, only one lock at a time can be set, restricting the usage to one process only. Databases, on the other hand, 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.
Some databases are also locked for routine database maintenance, which prevents changes being made during the maintenance. See for more detail.) However, many modern databases don't lock the database during routine maintenance. e.g. for PostgreSQL.
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.
See also
Further reading
- Connolly, Thomas, and Carolyn 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. "", 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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