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Each different way typically requires different processing time. Processing times of the same query may have large variance, from a fraction of a second to hours, depending on the chosen method. The purpose of query optimization, which is an automated process, is to find the way to process a given query in minimum time.
Typically row locks are necessary to support high volume transaction processing applications at the cost of processing overhead to manage the larger number of locks. [4] Further, some systems ensure that a query sees a time-consistent view of the database by locking data that a query is examining to prevent an update from modifying it, stalling ...
Real-time databases may be modified to improve accuracy and efficiency and to avoid conflict, by providing deadlines and wait periods to insure temporal consistency. Real-time database systems offer a way of monitoring a physical system and representing it in data streams to a database. A data stream, like memory, fades over time.
Thus concurrency control is an essential element for correctness in any system where two database transactions or more, executed with time overlap, can access the same data, e.g., virtually in any general-purpose database system. Consequently, a vast body of related research has been accumulated since database systems emerged in the early 1970s.
Valid time is the time period during or event time at which a fact is true in the real world. Transaction time is the time at which a fact was recorded in the database. Decision time is the time at which the decision was made about the fact. Used to keep a history of decisions about valid times.
A hierarchical query is a type of SQL query that handles hierarchical model data. They are special cases of more general recursive fixpoint queries, which compute transitive closures . In standard SQL:1999 hierarchical queries are implemented by way of recursive common table expressions (CTEs).
In other cases data might be brought into the staging area to be processed at different times; or the staging area may be used to push data to multiple target systems. As an example, daily operational data might be pushed to an operational data store (ODS) while the same data may be sent in a monthly aggregated form to a data warehouse.
It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. [9] [10] The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations.