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In Computer Science, in the field of databases, non-lock concurrency control is a concurrency control method used in relational databases without using locking.
There are several non-lock concurrency control methods, which involve the use of timestamps on transaction to determine transaction priority:
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In computer science, particularly the field of databases, the Thomas write rule is a rule in timestamp-based concurrency control. It can be summarized as ignore outdated writes.
In computer science, a timestamp-based concurrency control algorithm is a optimistic concurrency control method. It is used in some databases to safely handle transactions, using timestamps.
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.
Multiversion concurrency control, is a non-locking concurrency control method commonly used by database management systems to provide concurrent access to the database and in programming languages to implement transactional memory.
In computer science, multiple granularity locking (MGL) is a locking method used in database management systems (DBMS) and relational databases.
Optimistic concurrency control (OCC), also known as optimistic locking, is a non-locking concurrency control method applied to transactional systems such as relational database management systems and software transactional memory. OCC assumes that multiple transactions can frequently complete without interfering with each other. While running, transactions use data resources without acquiring locks on those resources. Before committing, each transaction verifies that no other transaction has modified the data it has read. If the check reveals conflicting modifications, the committing transaction rolls back and can be restarted. Optimistic concurrency control was first proposed in 1979 by H. T. Kung and John T. Robinson. The two main versions of OCC is timestamp OCC and validation OCC. Validation OCC is similar to the timestamp system except that data is recorded about the actions of transactions rather than data about database objects.
In computer science, a lock or mutex is a synchronization primitive that prevents state from being modified or accessed by multiple threads of execution at once. Locks enforce mutual exclusion concurrency control policies, and with a variety of possible methods there exists multiple unique implementations for different applications.
In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block other transactions from accessing the same data during the transaction's life.
In database systems, durability is the ACID property that guarantees that the effects of transactions that have been committed will survive permanently, even in case of failures, including incidents and catastrophic events. For example, if a flight booking reports that a seat has successfully been booked, then the seat will remain booked even if the system crashes.
In database systems, isolation determines how transaction integrity is visible to other users and systems. A lower isolation level increases the ability of many users to access the same data at the same time, but also increases the number of concurrency effects users might encounter. Conversely, a higher isolation level reduces the types of concurrency effects that users may encounter, but requires more system resources and increases the chances that one transaction will block another.
In the fields of databases and transaction processing, a schedule of a system is an abstract model to describe execution of transactions running in the system. Often it is a list of operations (actions) ordered by time, performed by a set of transactions that are executed together in the system. If the order in time between certain operations is not determined by the system, then a partial order is used. Examples of such operations are requesting a read operation, reading, writing, aborting, committing, requesting a lock, locking, etc. Not all transaction operation types should be included in a schedule, and typically only selected operation types are included, as needed to reason about and describe certain phenomena. Schedules and schedule properties are fundamental concepts in database concurrency control theory.
HSQLDB is a relational database management system written in Java. It has a JDBC driver and supports a large subset of SQL-92, SQL:2008, SQL:2011, and SQL:2016 standards. It offers a fast, small database engine which offers both in-memory and disk-based tables. Both embedded and server modes are available.
Btrieve is a database developed by Pervasive Software. The architecture of Btrieve has been designed with record management in mind. This means that Btrieve only deals with the underlying record creation, data retrieval, record updating and data deletion primitives. Together with the MicroKernel Database Engine it uses ISAM, Indexed Sequential Access Method, as its underlying storage mechanism.
In computer science, software transactional memory (STM) is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. It is an alternative to lock-based synchronization. STM is a strategy implemented in software, rather than as a hardware component. A transaction in this context occurs when a piece of code executes a series of reads and writes to shared memory. These reads and writes logically occur at a single instant in time; intermediate states are not visible to other (successful) transactions. The idea of providing hardware support for transactions originated in a 1986 paper by Tom Knight. The idea was popularized by Maurice Herlihy and J. Eliot B. Moss. In 1995 Nir Shavit and Dan Touitou extended this idea to software-only transactional memory (STM). Since 2005, STM has been the focus of intense research and support for practical implementations is growing.
In databases an index is a data structure, part of the database, used by a database system to efficiently navigate access to user data. Index data are system data distinct from user data, and consist primarily of pointers. Changes in a database, may require indexes to be updated to maintain accurate user data accesses. Index locking is a technique used to maintain index integrity. A portion of an index is locked during a database transaction when this portion is being accessed by the transaction as a result of attempt to access related user data. Additionally, special database system transactions may be invoked to maintain and modify an index, as part of a system's self-maintenance activities. When a portion of an index is locked by a transaction, other transactions may be blocked from accessing this index portion. Index Locking Protocol guarantees that phantom read phenomenon won't occur. Index locking protocol states:
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. The result is a delta-driven dataset.
Commitment ordering (CO) is a class of interoperable serializability techniques in concurrency control of databases, transaction processing, and related applications. It allows optimistic (non-blocking) implementations. With the proliferation of multi-core processors, CO has also been increasingly utilized in concurrent programming, transactional memory, and software transactional memory (STM) to achieve serializability optimistically. CO is also the name of the resulting transaction schedule (history) property, defined in 1988 with the name dynamic atomicity. In a CO compliant schedule, the chronological order of commitment events of transactions is compatible with the precedence order of the respective transactions. CO is a broad special case of conflict serializability and effective means to achieve global serializability across any collection of database systems that possibly use different concurrency control mechanisms.
In concurrency control of databases, transaction processing, and other transactional distributed applications, global serializability is a property of a global schedule of transactions. A global schedule is the unified schedule of all the individual database schedules in a multidatabase environment. Complying with global serializability means that the global schedule is serializable, has the serializability property, while each component database (module) has a serializable schedule as well. In other words, a collection of serializable components provides overall system serializability, which is usually incorrect. A need in correctness across databases in multidatabase systems makes global serializability a major goal for global concurrency control. With the proliferation of the Internet, Cloud computing, Grid computing, and small, portable, powerful computing devices, as well as increase in systems management sophistication, the need for atomic distributed transactions and thus effective global serializability techniques, to ensure correctness in and among distributed transactional applications, seems to increase.
Distributed concurrency control is the concurrency control of a system distributed over a computer network.