In computer networking and databases, the three-phase commit protocol (3PC)is a distributed algorithm which lets all nodes in a distributed system agree to commit a transaction. It is a more failure-resilient refinement of the two-phase commit protocol (2PC).
A two-phase commit protocol cannot dependably recover from a failure of both the coordinator and a cohort member during the Commit phase. If only the coordinator had failed, and no cohort members had received a commit message, it could safely be inferred that no commit had happened. If, however, both the coordinator and a cohort member failed, it is possible that the failed cohort member was the first to be notified, and had actually done the commit. Even if a new coordinator is selected, it cannot confidently proceed with the operation until it has received an agreement from all cohort members, and hence must block until all cohort members respond.
The three-phase commit protocol eliminates this problem by introducing the Prepared to commit state. If the coordinator fails before sending preCommit messages, the cohort will unanimously agree that the operation was aborted. The coordinator will not send out a doCommit message until all cohort members have ACKed that they are Prepared to commit. This eliminates the possibility that any cohort member actually completed the transaction before all cohort members were aware of the decision to do so (an ambiguity that necessitated indefinite blocking in the two-phase commit protocol).
The pre-commit phase introduced above helps the system to recover from the case when a participant failure or both coordinator and participant node failure during commit phase. When the recovery coordinator takes over after coordinator failure during commit phase of two-phase commit, the new pre-commit comes handy as follows: On querying participants, if it learns that some nodes are in commit phase then it assumes that previous coordinator before crashing has made the decision to commit. Hence it can shepherd the protocol to commit. Similarly, if a participant says that it doesn’t receive PrepareToCommit message, then the new coordinator can assume that the previous coordinator failed even before it completed the PrepareToCommit phase. Hence it can safely assume no other participant would have committed the changes and hence safely abort the transaction.
Using Skeen's original three-phase commit protocol, it is possible that a quorum becomes connected without being able to make progress (this is not a deadlock situation; the system will still progress if the network partitioning is resolved). Keidar and Dolev's E3PCrefines Skeen's three-phase commit protocol and solves this problem in a way which *always* allows a quorum to make progress.
Three-phase commit assumes a network with bounded delay and nodes with bounded response times; In most practical systems with unbounded network delay and process pauses, it cannot guarantee atomicity. The other drawback of the protocol is it requires at least three round trips to complete, needing a minimum of three round trip times (RTTs). This is potentially a long latency to complete each transaction.
Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another from any system. The components interact with one another in order to achieve a common goal. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.
In computer science, ACID is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps. In the context of databases, a sequence of database operations that satisfies the ACID properties is called a transaction. For example, a transfer of funds from one bank account to another, even involving multiple changes such as debiting one account and crediting another, is a single transaction.
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In databases and transaction processing, two-phase locking (2PL) is a concurrency control method that guarantees serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol utilizes locks, applied by a transaction to data, which may block other transactions from accessing the same data during the transaction's life.
In transaction processing, databases, and computer networking, the two-phase commit protocol (2PC) is a type of atomic commitment protocol (ACP). It is a distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or abort the transaction. The protocol achieves its goal even in many cases of temporary system failure, and is thus widely used. However, it is not resilient to all possible failure configurations, and in rare cases, manual intervention is needed to remedy an outcome. To accommodate recovery from failure the protocol's participants use logging of the protocol's states. Log records, which are typically slow to generate but survive failures, are used by the protocol's recovery procedures. Many protocol variants exist that primarily differ in logging strategies and recovery mechanisms. Though usually intended to be used infrequently, recovery procedures compose a substantial portion of the protocol, due to many possible failure scenarios to be considered and supported by the protocol.
In the field of computer science, an atomic commit is an operation that applies a set of distinct changes as a single operation. If the changes are applied, then the atomic commit is said to have succeeded. If there is a failure before the atomic commit can be completed, then all of the changes completed in the atomic commit are reversed. This ensures that the system is always left in a consistent state. The other key property of isolation comes from their nature as atomic operations. Isolation ensures that only one atomic commit is processed at a time. The most common uses of atomic commits are in database systems and version control systems.
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.
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In concurrency control of databases, transaction processing, and various transactional applications, both centralized and distributed, a transaction schedule is serializable if its outcome is equal to the outcome of its transactions executed serially, i.e. without overlapping in time. Transactions are normally executed concurrently, since this is the most efficient way. Serializability is the major correctness criterion for concurrent transactions' executions. It is considered the highest level of isolation between transactions, and plays an essential role in concurrency control. As such it is supported in all general purpose database systems. Strong strict two-phase locking (SS2PL) is a popular serializability mechanism utilized in most of the database systems since their early days in the 1970s.
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M. Dale Skeen is an American computer scientist. He specializes in designing and implementing large-scale computing systems, distributed computing and database management systems.
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