Distributed transaction

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A distributed transaction operates within a distributed environment, typically involving multiple nodes across a network depending on the location of the data. A key aspect of distributed transactions is atomicity, which ensures that the transaction is completed in its entirety or not executed at all. It's essential to note that distributed transactions are not limited to databases. [1]

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The Open Group, a vendor consortium, proposed the X/Open Distributed Transaction Processing Model (X/Open XA), which became a de facto standard for the behavior of transaction model components.

Databases are common transactional resources and, often, transactions span a couple of such databases. In this case, a distributed transaction can be seen as a database transaction that must be synchronized (or provide ACID properties) among multiple participating databases which are distributed among different physical locations. The isolation property (the I of ACID) poses a special challenge for multi database transactions, since the (global) serializability property could be violated, even if each database provides it (see also global serializability). In practice most commercial database systems use strong strict two-phase locking (SS2PL) for concurrency control, which ensures global serializability, if all the participating databases employ it.

A common algorithm for ensuring correct completion of a distributed transaction is the two-phase commit (2PC). This algorithm is usually applied for updates able to commit in a short period of time, ranging from couple of milliseconds to couple of minutes.

There are also long-lived distributed transactions, for example a transaction to book a trip, which consists of booking a flight, a rental car and a hotel. Since booking the flight might take up to a day to get a confirmation, two-phase commit is not applicable here, it will lock the resources for this long. In this case more sophisticated techniques that involve multiple undo levels are used. The way you can undo the hotel booking by calling a desk and cancelling the reservation, a system can be designed to undo certain operations (unless they are irreversibly finished).

In practice, long-lived distributed transactions are implemented in systems based on web services. Usually these transactions utilize principles of compensating transactions, Optimism and Isolation Without Locking. The X/Open standard does not cover long-lived distributed transactions.[ citation needed ]

Several technologies, including Jakarta Enterprise Beans and Microsoft Transaction Server fully support distributed transaction standards.

Synchronization

In event-driven architectures, distributed transactions can be synchronized through using request–response paradigm and it can be implemented in two ways: [2]

See also

Related Research Articles

The Jakarta Transactions, one of the Jakarta EE APIs, enables distributed transactions to be done across multiple X/Open XA resources in a Java environment. JTA was a specification developed under the Java Community Process as JSR 907. JTA provides for:

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.

In computer science, transaction processing is information processing that is divided into individual, indivisible operations called transactions. Each transaction must succeed or fail as a complete unit; it can never be only partially complete.

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.

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.

A database transaction symbolizes a unit of work, performed within a database management system against a database, that is treated in a coherent and reliable way independent of other transactions. A transaction generally represents any change in a database. Transactions in a database environment have two main purposes:

  1. To provide reliable units of work that allow correct recovery from failures and keep a database consistent even in cases of system failure. For example: when execution prematurely and unexpectedly stops in which case many operations upon a database remain uncompleted, with unclear status.
  2. To provide isolation between programs accessing a database concurrently. If this isolation is not provided, the programs' outcomes are possibly erroneous.

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 exist multiple unique implementations for different applications.

In database systems, durability is the ACID property that guarantees that the effects of transactions that have been committed will survive permanently, even in cases 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 is one of the ACID transaction properties. It 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 the order of executions in a set 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. Often, only a subset of the transaction operation types are included in a schedule.

In computer science, an algorithm is called non-blocking if failure or suspension of any thread cannot cause failure or suspension of another thread; for some operations, these algorithms provide a useful alternative to traditional blocking implementations. A non-blocking algorithm is lock-free if there is guaranteed system-wide progress, and wait-free if there is also guaranteed per-thread progress. "Non-blocking" was used as a synonym for "lock-free" in the literature until the introduction of obstruction-freedom in 2003.

<span class="mw-page-title-main">Two-phase commit protocol</span> Computer science transaction algorithm

In transaction processing, databases, and computer networking, the two-phase commit protocol 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. This 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.

Tuxedo is a middleware platform used to manage distributed transaction processing in distributed computing environments. Tuxedo is a transaction processing system or transaction-oriented middleware, or enterprise application server for a variety of systems and programming languages. Developed by AT&T in the 1980s, it became a software product of Oracle Corporation in 2008 when they acquired BEA Systems. Tuxedo is now part of the Oracle Fusion Middleware.

<span class="mw-page-title-main">Linearizability</span> Property of some operation(s) in concurrent programming

In concurrent programming, an operation is linearizable if it consists of an ordered list of invocation and response events, that may be extended by adding response events such that:

  1. The extended list can be re-expressed as a sequential history.
  2. That sequential history is a subset of the original unextended list.

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.

For transaction processing in computing, the X/Open XA standard is a specification released in 1991 by X/Open for distributed transaction processing (DTP).

In computer science and engineering, transactional memory attempts to simplify concurrent programming by allowing a group of load and store instructions to execute in an atomic way. It is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. Transactional memory systems provide high-level abstraction as an alternative to low-level thread synchronization. This abstraction allows for coordination between concurrent reads and writes of shared data in parallel systems.

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 computer science, synchronization is the task of coordinating multiple processes to join up or handshake at a certain point, in order to reach an agreement or commit to a certain sequence of action.

References

  1. ↑ Gray, Jim. Transaction Processing Concepts and Techniques. Morgan Kaufmann. ISBN   9780080519555.
  2. ↑ Richards, Mark. Fundamentals of Software Architecture: An Engineering Approach. O'Reilly Media. ISBN   978-1492043454.

Further reading

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