How to implement distributed transaction processing in Go language?

With the continuous expansion of the scale of Internet applications and the gradual split of vertical services, the development of distributed systems has become increasingly important. The question that arises is how to deal with transaction consistency in such a system. This article will introduce some mainstream distributed transaction processing solutions in the Go language and their implementation principles.

Traditional ACID transactions

In stand-alone systems, applications usually use traditional ACID transactions to ensure data consistency. ACID is the abbreviation of Atomicity, Consistency, Isolation, and Durability, which respectively represent the four key attributes of a transaction:

• Atomicity (Atomicity): A series of operations in a transaction, either all succeed, or all Failure, there is no intermediate state.
• Consistency: The execution of transactions will not violate the integrity constraints in the database.
• Isolation: Multiple transactions executed concurrently are isolated and will not interfere with each other.
• Durability: Once a transaction is committed, its results are permanent.

However, when an application becomes a distributed application, more complexities need to be managed, including network latency, unreliable messaging, data splitting, etc. If still Using traditional ACID transactions will increase the complexity and overhead of the system, create performance bottlenecks, and limit the scalability of the system. Therefore, distributed systems need a new solution that can manage transaction consistency in a distributed environment.

CAP Theory

In distributed systems, CAP theory is used to describe conflicts in three key elements:

• Consistency: in distribution In this environment, all nodes have the same view.
• Availability (Availability): The application limits the appropriate precision when responding to requests.
• Partition Tolerance: The system can continue to work when network partitions occur between machines.

CAP theory holds that in any distributed system, at most two of these elements can be satisfied simultaneously. That is, if we want to achieve consistency and availability in distributed systems, we must tolerate the occurrence of network partitions. If we want to achieve consistency and partition tolerance, we have to give up availability in this case.

BASE transactions

Unlike ACID, distributed systems usually use BASE-style transactions to handle transaction consistency. BASE is the abbreviation of Basically Available, Soft state, Eventually consistent.

• Basically Available: Try to ensure the availability of the system. Even if the system fails, it will not affect the availability of the entire system.
• Soft state: The system will make the state data inconsistent for a period of time, and does not guarantee that the state data will be consistent in real time. Eventually Consistent: The system will eventually reach a consistent state.

BASE transactions do not guarantee strong consistency, but achieve transaction consistency through eventual consistency. BASE transactions are not suitable for applications that need to satisfy constraints, but are suitable for large applications, data warehouses, and other projects that need to process large amounts of data.

Distributed transaction implementation scheme

In Go, there are currently three mainstream distributed transaction implementation schemes:

1. Two-Phase Commit Protocol Protocol)

Two-phase commit protocol is a synchronization protocol used for the management of distributed transactions. It ensures atomicity, consistency, and isolation of distributed transactions when executed across multiple nodes. Among them, the coordinator is responsible for managing the global commit protocol, and each node is responsible for executing the commit/rollback protocol.

In the two-phase commit protocol, there are two phases:

1. Prepare phase: The coordinator asks all participating nodes whether they are ready to commit transactions. If all nodes are ready, enter the submission phase. Otherwise, the transaction is rolled back.

2. Commit phase: All participating nodes issue "submit" or "rollback" instructions to the coordinator. If all nodes submit successfully, the distributed transaction is completed. If at least one node fails to commit, the transaction is rolled back.

However, the two-phase commit protocol will have a problem of being stuck in the preparation phase (the so-called two-phase blocking problem). At this time, some nodes may have submitted while other nodes are stuck after the preparation phase. This results in some of the nodes possibly never getting rollback instructions, leading to data inconsistency. Therefore, the two-phase commit protocol is not a perfect distributed transaction processing solution.

2. Three-Phase Commit Protocol

The three-phase commit protocol is an optimization of the two-phase commit protocol, aiming to reduce the occurrence of two-phase blocking problems. It splits the preparation phase of the two-phase commit protocol into two sub-phases:

1. Ask phase: The coordinator asks the participating nodes whether they are ready to commit the transaction.

2. Prepare phase: Participating nodes confirm whether they are ready to commit transactions.

As the name suggests, the three-phase submission protocol includes three stages:

1. CanCommit phase: The coordinator asks each participating node whether it is ready to commit the transaction.
2. PreCommit phase: If all nodes are ready, enter the pre-commit phase. Otherwise, the coordinator sends a rollback message.
3. DoCommit phase: If each participating node is confident that no errors will occur during the pre-commit and commit process, it sends a commit message. If any participating node encounters an error during the pre-commit or commit process, a rollback message is sent.

The advantage of the three-phase commit protocol is that compared to the two-phase commit protocol, it reduces the possibility of two-phase blocking and can respond to failures (such as failover) faster. However, there is still the issue that it may not be able to handle network partitions.

3.SAGA Pattern (Saga Pattern)

SAGA pattern is a long transaction implementation scheme by dividing the transaction into a series of interdependent steps and converting each step into an atom Operate to achieve the following goals:

• Reduce the scope of the transaction to the greatest extent possible.
• It is not necessary to enforce consistency at all steps.
• You can partially roll back, not all steps.

SAGA mode consists of several stages, each stage performs a part of transaction operations. These operations can be any operation that can obtain idempotence guarantee (that is, the operation itself can be executed repeatedly without will have an impact on the results). If a stage fails, the SAGA mode will roll back the stage forward or backward according to the execution situation, and finally reaches a state in which the operations of all stages have been executed correctly or cannot be rolled back.

Through the SAGA mode, we can achieve independent development, deployment, and expansion of each business module, at the cost of supporting at least partial rollback; the SAGA mode will guarantee the final result, so it is not necessary to ensure that all steps are strictly consistent. , which allows it to function in complex asynchronous/synchronous distributed environments.

Summary

In the Go language, we have multiple ways to handle distributed transaction processing, such as distributed protocols, long transaction schemes, and Saga. Each solution has its own advantages and disadvantages, and is optimally applied in suitable scenarios. In practical applications, we need to make choices based on specific circumstances to better achieve distributed transaction management.

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