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What are the concurrency control techniques? 


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Concurrency control techniques are used to ensure the consistency and integrity of a database when executing multiple transactions simultaneously. These techniques include Two-Phase Commit (2PC) protocol, Three-Phase Commit (3PC) protocol, Deadlock-Free Cell lock (DFCL) algorithm, locking, timestamp ordering, and optimistic validation method. The 2PC and 3PC protocols are used in distributed database systems to handle conflicts, deadlock, and rolled-back transactions . One technique for concurrency control involves controlling access requests to overlapping data based on transaction priority . Traditional techniques such as locking, timestamp ordering, and optimistic validation have been evaluated and a new technique has been proposed to improve optimistic validation . Various concurrency control techniques have been discussed, compared, and studied for their advantages and disadvantages . These techniques aim to maintain consistency, performance, and correctness of the database, and can be categorized as pessimistic, optimistic, multiversion, and two-phase locking techniques .

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The paper discusses various concurrency control techniques but does not explicitly list them.
The paper discusses various concurrency control techniques, including pessimistic, optimistic, multiversion, and two-phase locking techniques.
The concurrency control techniques mentioned in the paper are locking, timestamp ordering, and optimistic validation.
The provided paper discusses a technique for concurrency control in a database system, but it does not explicitly mention other concurrency control techniques.
The paper discusses the Two-Phase Commit (2PC) protocol, Three-Phase Commit (3PC) protocol, and Deadlock-Free Cell lock (DFCL) algorithm as concurrency control techniques in DBMS.

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What is concurrency control?5 answersConcurrency control is a mechanism provided by database systems to allow multiple transactions to be executed simultaneously while maintaining isolation. Different concurrency control algorithms have different strengths and weaknesses, making each suitable for specific workloads. To address this limitation, a scheme called transaction clustering has been proposed to automatically determine the best isolation mechanism for any given pair of transactions. Based on transaction clustering, the Clustering-based Concurrency Control algorithm (C3) combines pessimistic and optimistic concurrency control algorithms to achieve better performance for high-conflict workloads. Additionally, a technique for concurrency control involves receiving transaction start requests, locking data against access requests not belonging to the transaction, and controlling acceptance of access requests based on transaction priority. Concurrency control methods improve the concurrency throughput capacity of a database system.
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