In computer science, a timestamp-based concurrency control algorithm is a non-lock concurrency control method. It is used in some databases to safely handle transactions, using timestamps.
Every timestamp value is unique and accurately represents an instant in time.
A higher-valued timestamp occurs later in time than a lower-valued timestamp.
A number of different ways have been used to generate timestamp
Use the value of the system's clock at the start of a transaction as the timestamp.
Use a thread-safe shared counter that is incremented at the start of a transaction as the timestamp.
A combination of the above two methods.
Each transaction () is an ordered list of actions (). Before the transaction performs its first action (), it is marked with the current timestamp, or any other strictly totally ordered sequence: . Every transaction is also given an initially empty set of transactions upon which it depends, , and an initially empty set of old objects which it updated, .
Each object in the database is given two timestamp fields which are not used other than for concurrency control: is the time at which the value of object was last used by a transaction, is the time at which the value of the object was last updated by a transaction.
For all :
For each action :
If wishes to use the value of :
If then abort (a more recent thread has overwritten the value),
Otherwise update the set of dependencies and set ;
If wishes to update the value of :
If then abort (a more recent thread is already relying on the old value),
If then skip (the Thomas Write Rule),
Otherwise store the previous values, , set , and update the value of .
While there is a transaction in that has not ended: wait
If there is a transaction in that aborted then abort
Otherwise: commit.
To abort:
For each in
If equals then restore and
Whenever a transaction begins, it receives a timestamp. This timestamp indicates the order in which the transaction must occur, relative to the other transactions.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
This course provides a deep understanding of the concepts behind data management systems. It covers fundamental data management topics such as system architecture, data models, query processing and op
This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students
In information technology and computer science, especially in the fields of computer programming, operating systems, multiprocessors, and databases, concurrency control ensures that correct results for concurrent operations are generated, while getting those results as quickly as possible. Computer systems, both software and hardware, consist of modules, or components. Each component is designed to operate correctly, i.e., to obey or to meet certain consistency rules.
Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...
The aim of this paper is to serve as a lightweight introduction to concurrency control for database theorists through a uniform presentation of the work on robustness against Multiversion Read Committed and Snapshot Isolation. ...
While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every p ...