{"id":25746,"date":"2025-03-13T10:21:00","date_gmt":"2025-03-13T17:21:00","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=article&p=25746"},"modified":"2025-03-23T22:11:06","modified_gmt":"2025-03-24T05:11:06","slug":"mastering-distributed-transactions-with-tidbs-htap-model","status":"publish","type":"article","link":"https:\/\/www.pingcap.com\/ko\/article\/mastering-distributed-transactions-with-tidbs-htap-model\/","title":{"rendered":"Mastering Distributed Transactions with TiDB’s HTAP Model"},"content":{"rendered":"

Understanding Distributed Transaction Models<\/h2>\n

In the realm of database technologies, distributed transactions<\/a> have emerged as a solution to manage circumstantial complexities associated with data storage and access across multiple locations. Unlike traditional single-node databases where transactions occur in a linear, straightforward manner, distributed transactions take place across various network nodes, presenting unique challenges such as data consistency and fault tolerance.<\/p>\n

Key Concepts and Challenges in Distributed Transactions<\/h3>\n

Distributed transactions allow various processes to execute a set of operations as a single unit of work, ensuring that all operations within the transaction are completed successfully or none at all\u2014a property known as Atomicity. However, distributed systems inherently face challenges like network partitions, distributed concurrency, and system failures. These challenges can lead to issues such as deadlocks, data inconsistencies, and increased complexity in managing transactions.<\/p>\n

One of the primary challenges in distributed databases is the issue of data consistency<\/strong>. Ensuring that all nodes agree on the data’s state even in the face of failures requires advanced algorithms like the Raft consensus algorithm<\/a>. This algorithm is crucial in managing the state across nodes to ensure that changes are atomic, consistent, isolated, and durable (ACID properties).<\/p>\n

Comparing Distributed Transactions with Traditional Models<\/h3>\n

In traditional databases, transactions are typically handled within a single system, relying heavily on ACID properties to manage data integrity. These systems ensure that transactions are processed reliably and can roll back if there is an error during execution. However, the downside is limited scalability as the data grows.<\/p>\n

In contrast, distributed transactions enable each part of the database to handle specific tasks, allowing systems like \ud2f0DB<\/a> to scale horizontally. With a distributed SQL<\/a> approach, TiDB ensures that whether handling OLTP (Online Transactional Processing) or OLAP (Online Analytical Processing) operations, the data remains consistent and available across various locations and can accommodate a significantly larger scale of data and traffic.