In computer science and data management, a commit is a behavior that marks the end of a transaction and provides Atomicity, Consistency, Isolation, and Durability (ACID) in transactions. The submission records are stored in the submission log for recovery and consistency in case of failure. In terms of transactions, the opposite of committing is giving up tentative changes to the transaction, which is rolled back.
Due to the rise of distributed computing and the need to ensure data consistency across multiple systems, commit protocols have been evolving since their emergence in the 1970s. The main developments include the Two-Phase Commit (2PC) first proposed by Jim Gray, which is the fundamental core of distributed transaction management. Subsequently, the Three-phase Commit (3PC), Hypothesis Commit (PC), Hypothesis Abort (PA), and Optimistic Commit protocols gradually emerged, solving the problems of blocking and fault recovery.
Today, new fields such as e-commerce payment and blockchain technology are emerging, and submission protocols play a significant role in various business areas. By effectively handling transactions, resolving faults and recovering problems, the commit protocol becomes crucial in ensuring the reliability and consistency of data management.
History
thumb|Jim Gray
The concept of Commit originated in the late 1960s and early 1970s, when computer technology was rapidly advancing and data management was becoming an important requirement in business and finance. Enterprises have gradually replaced the traditional paper records with computers, which has fully improved the work efficiency. The reliability and consistency of data have become a necessary requirement. Transaction management at this stage is relatively simple, limited to using a single computer for processing. It merely effectively records the changes in data to ensure that the data remains stable after the transaction is completed or terminated. In the late 1970s, as database systems moved from a single calculator operation to multiple distributed collaborations, ensuring data consistency and reliability became a new challenge. In 1978, computer scientist Jim Gray proposed the famous two-phase Commit Protocol (2PC), which became an effective solution for distributed transaction management, successfully managing data synchronization problems between multiple nodes. However, this commit protocol has some potential transaction blocking problems when nodes fail.
In the early 1980s, researchers discovered that although the two-step commit protocol was effective at synchronizing data, there could be long waits and even system crashes, with limitations. To improve this problem, people have begun to explore new and effective methods, including enhancing efficiency by reducing message communication during the protocol process. IBM's R* database introduced the Assumed Commit and Assumed abort protocols, which contributed significantly to transaction management efficiency. These two protocols have greatly improved the processing efficiency of distributed transactions by reducing communication overhead and have become an important breakthrough in the technology of transaction commit protocols.
By the early 1990s, with the increase in business demands and the complexity of transactions, enterprises required higher efficiency in distributed transaction processing. In order to adapt to the needs of different environments, the scientific community has gradually developed various variants of commit protocols to provide more flexible transaction management options for different needs. For example, the three-phase commit protocol promotes the commit of transactions more effectively and reduces the occurrence of blocking problems by adding a pre-commit protocol and a timeout mechanism.
In the 21st century, with the popularization of mobile Internet and wireless technology, the commit protocol has been further developed, and researchers have begun to pay attention to how to reduce the blocking in the transaction process to solve the problem of broadband limitation, battery life and network instability in the mobile environment. The proposal of optimistic commit protocol marks the extension of commit technology from traditional database to the emerging mobile data field. This protocol allows transactions to temporarily use unconfirmed data, improving the user experience in cases of poor network conditions.
In recent years, with the rise of blockchain and decentralized technologies, submission protocols and consensus mechanisms have gradually merged. These consensus algorithms play a role in tamper-proofing and preventing malicious attacks on node pairs in a decentralized environment. This enables commit to no longer be confined to the scope of traditional database management, but to become the core technology of trust computing and distributed ledgers, further expanding the application field of commit in the digital age. This integration has brought about extensive application impacts. Each transaction can achieve the effect of tracking global submissions through the verification of the consensus mechanism, becoming an important technical foundation for promoting the circulation of digital assets, the operation of cryptocurrencies and decentralized applications.
Commit Protocol Types
In the world of data management, a transaction is a series of database operations, such as bank transfers and order submission. In order to ensure the accuracy, consistency, and security of the data, transactions are usually completed completely, or cancelled completely, leaving no partially completed results. Commit protocol is the method used to coordinate this process. Different protocols are applicable to different submission scenarios and have their own advantages and disadvantages. There are four major commit protocols.
thumb|Two-Phase Commit Protocol
Two-Phase Commit (2PC)
The two-phase commit protocol is the most classic and broadest approach to distributed transactions, which includes both a preparation phase and a commit phase. This type of commit is suitable for high-contention environments, but if a transaction fails, additional compensatory transactions need to be executed to achieve semantic atomicity to ensure final data consistency. In addition, this combination ensures that the results of transaction execution in the blockchain network are consistent across all nodes, thus becoming an essential infrastructure for the digital economy.