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Community Blog Quick Start and Best Practices of Seata Saga Mode

Quick Start and Best Practices of Seata Saga Mode

This article offers a quick start guide to help users get started with Seata Saga and delves into best practices for its usage.

By Te Wang (Yixia)

Seata is an open-source distributed transaction solution that aims to provide high-performance and user-friendly distributed transaction services in a microservices architecture. Seata offers multiple transaction modes, including AT, TCC, SAGA, and XA, to address transactional consistency issues in various business scenarios.

This article primarily focuses on the usage and best practices of Seata Saga mode. It focuses on three parts.

  • Introduction to Seata Saga
  • Quick start guide
  • Seata Saga Best Practices

This article aims to help users effectively and efficiently utilize Seata Saga mode.

Introduction to Seata Saga

1.1 Saga Mode

The Saga pattern is one of the solutions to distributed transactions, and the concept originated in the Sagas paper published by Hector and Kenneth in 1987. It divides the entire distributed transaction process into multiple stages, with each stage corresponding to a sub-transaction. These sub-transactions are executed by local transactions and committed after execution.

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Saga mode follows a failure-based design. In this mode, each activity or sub-transaction process generally has a corresponding compensation service. In case of an exception in a distributed transaction, a recovery process must be performed. There are two ways to recover: reverse compensation and forward retry. For example, if the execution of T3 fails in the above distributed transaction, reverse compensation will sequentially perform the corresponding C3, C2, and C1 operations to cancel the effect of the transaction activity. Forward compensation is relentless. If the execution of T3 fails, it will continue retrying and then execute T4, T5, and so on.

Based on the design of Saga mode, we can identify the advantages and disadvantages of the Saga transaction mode.

Advantages

  • Sub-transactions (or processes) are committed at the local transaction level and do not require global locks. This avoids long resource locks in long transaction processes. Additionally, the pipeline processing model naturally aligns with the phased signal processing model, enabling higher performance and throughput.
  • Both forward services and compensation services are implemented by business developers, making the Saga model independent of the underlying database protocol. The XA/AT mode may depend on specific database types and versions. For example, MySQL supports XA only after version 5.0.

Disadvantages

  • The implementation of forward and compensation services by business developers incurs development costs and has a higher level of intrusiveness compared to XA/AT, which simply requires an annotation.
  • The Saga model does not guarantee isolation because one-phase sub-transactions commit at the local transaction level. After committing, they may "affect" other distributed transactions or be affected by them. For example, other distributed transactions may read the updates of sub-transactions in the current unfinished distributed transaction, leading to dirty reads. If other distributed transactions update the fields updated by sub-transactions in the current unfinished distributed transaction, the updates of the current transaction will be lost. Non-repeatable read scenarios can also pose problems.

Therefore, it is important to consider the impact when using the Saga mode. Saga mode can be used in the following scenarios:

  • Long transaction process. Long transactions cannot tolerate long-term resource locking, as Saga's characteristics make it suitable for handling long transaction processes.
  • Businesses that can accept or resolve the impact caused by the lack of isolation. For example, Saga mode can be applied to businesses that only require eventual consistency but overlook isolation.
  • Distributed transaction participants that include services from other organizations or third parties, where data resource services are not maintained internally and cannot provide the interfaces required by the TCC mode.

1.2 Seata Saga

Now, let's take a look at the implementation of Seata Saga. The mainstream implementations of Saga are orchestration and coordination. The implementation of Seata Saga is orchestration and is based on a state machine engine. The smallest unit of execution in the state machine is a node, which represents a service call. The corresponding Saga transaction is a sub-transaction activity/process. By configuring the compensation node, you can create a state machine call process by linking nodes together. Currently, Seata describes the calling process using JSON and executes it using the state machine engine. In case of an exception, we can select a compensation policy and trigger transaction compensation through the Seata coordinator.

You may be wondering if this is service orchestration. However, Seata Saga's state machine combines Saga with service orchestration, supporting compensation services and ensuring eventual consistency.

Let's look at a simple state machine process definition:

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The image above is a state machine description with the name of reduceIncentoryAndBalance, which specifies the service call node of the ServiceTask type and the corresponding compensation node CompensateReduceInventory.

Let’s look at some basic properties:

  • Type: the node type. Seata Saga supports multiple types of nodes. For example, ServiceTask is a service invocation node.
  • ServiceName/ServiceMethod: identifies the ServiceTask service and its corresponding method.
  • Input/Output: defines input and output parameters. SPEL expressions are used for the values of input and output parameters.
  • Retry: controls the retry process.
  • Catch/Next: for process control, articulation, and concatenation of the entire state machine process.

For more information about types and syntax, please refer to the Seata official documentations [1]. It is evident that declaring a state machine in JSON can be somewhat challenging. To simplify the compilation of the JSON state machine, we also provide a visualized orchestration interface [2]. This interface, as shown in the following diagram, allows for the orchestration of more complex workflows.

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Now, let's start practicing.

Getting Started with Seata Saga

2.1 Start with Seata's Official Document

Seata has three roles: TC, TM, and RM. TC (server side) is deployed as a separate server, and TM and RM (client side) are integrated by the business system.

The server-side storage mode currently has three types: file, db, and redis (raft and mongodb will be introduced later). The file mode does not need to be changed and can be started directly.

2.1.1 Deploy Seata Server

From the newcomer's document, it can be seen that Seata is still a traditional CS model. First, we need to deploy the Seata server side. The default storage mode on the server side is the file mode. You can directly run the main method of the springboot startup class to start the Seata server without any changes. For convenience, this demonstration uses the file mode. For other modes of the start methods, please refer to the detailed information in the official document.

2.1.2 Create a Client-Side Test Application

At the same time, we need to create a client-side test application named seata-saga-test. Use the springboot framework, configure spring's application name and port, and introduce seata-spring-boot-starter dependencies. That is how we complete the build of the client-side application.

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2.2 Start with the Seata Saga Unit Tests

In general, to understand the function of a framework, it is recommended to start by examining the unit tests of its entry point. In the Seata repository, locate the test module for Seata Saga and begin with the outermost test class, io.seata.saga.engine.StateMachineTests. Typically, this class serves as the entry point for open-source projects.

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As can be seen from the screenshot above, the entry test method is mainly divided into three parts.

The spring configuration file at [1] declares StateMachineEngine Bean and its corresponding attributes. Additionally, at [2], this class is referenced to execute the start method, indicating that it serves as the entry class for our state machine. In fact, the StateMachineEngine class acts as the operational entry point for the Seata Saga state machine, controlling operations such as start and recovery. An important attribute of the StateMachineEngine is resources, which specifies the storage path for the state machine JSON file. Upon starting the Seata Saga state machine engine, the state machine definition located at the corresponding path is loaded for subsequent use. This path can be modified according to our requirements.

At [3], the start method of the StateMachineEngine is called, passing the state machine name and start parameters to initiate a state machine process call. Let's briefly examine the implementation. It can be observed that the state name corresponds to the name attribute in the JSON definition of the state machine under the resource path.

To test the Seata Saga state machine flow, we need to have a state machine JSON definition. Use the Seata Saga StateMachine Designer to define a state machine process where a simple AService#doA method calls the BService#doB method and includes an input parameter. Finally, our class#method and the state machine JSON appear as follows.

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With the basic calling model and state machine JSON definition, according to the test case, we also declare the state machine Bean and the execution entry (note: the state machine name in the start method needs to be consistent with the name in the state machine JSON definition). After executing the main method, we can find that the AService#doA and BService#doB methods are both called.

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At this point, we have completed the quick start of the Seata Saga state machine. Continuing to observe the single test, we find that Seata Saga single test has two modules, DB and Mock.

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Let's take a look at the single test of the DB module first. We can see that the single test class of the DB module is basically similar to the above. The only difference lies in the state machine engine, which specifies DB storage and executes DDL SQL (initializes Seata Saga tables). If the DB storage is specified, the execution process of our state machine will be persisted in the DB storage, which is convenient for transaction execution process query and exception recovery, and is also a practical way in the production environment.

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The Mock module is separated from the Seata Sever through mock translocation and uses only the service orchestration capabilities of Seata Saga. If you are interested, you can practice using DB and Mock modules again.

Seata Saga Best Practices

3.1 Basic Usage

  • At the application level, the Seata Saga state machine mode is used differently from the AT and TCC annotation modes. It is executed by utilizing the state machine API.
  • In the state machine mode, the recovery strategy is divided into forward retry and backward compensation. The appropriate compensation strategy should be selected based on the business scenario.
  • Seata Saga supports asynchronous state execution and asynchronous execution of state machines. Utilizing asynchrony when appropriate can enhance the overall system throughput.

3.2 Saga Services

  • Since the saga service may be invoked multiple times, it is essential for the SAGA service to be idempotent.
  • The compensation service may be executed before the original service, and allowing empty compensation is necessary. At the same time, subsequent requests to the original service should be rejected to enforce anti-suspension control.

3.3 Coping with Isolation

  • Be business-driven. If the business can tolerate the lack of isolation's impact, no action is required.
  • Semantic locks. Apply locks at the semantic level for operating resources.
  • Use a pessimistic process. For example, when transferring funds from A to B, rather than transferring money to B before deducting from A, a pessimistic process dictates that money is deducted from A first before transferring it to B. This prevents cash shortages caused by immediate consumption of B.
  • Other approaches.

3.4 Stability

  • When using the Saga mode with DB storage, note that retrying or compensating will, by default, insert a state execution record. Frequent retries or compensations can cause an explosion of state execution records. This can lead to memory crashes if there is a large Object Storage Service. Seata Saga provides an update mode that uses update records instead of new execution records to mitigate such problems.

3.5 Extension

  • Seata Saga's state machine storage and syntax parsing are designed using SPI, allowing for smooth replacement of corresponding storage or state machine language implementation in business applications. For example, the JSON parsing of the state machine can be replaced with YAML parsing.
  • Seata Saga supports the Mock Transaction method, which utilizes only the service orchestration capability. It also supports dynamic release of state machine definitions (JSON), enabling dynamic orchestration control, which is particularly useful for DSL-based dynamic control.

However, it is important to note that mastering the current implementation of Seata Saga's state machine can still be challenging. On one hand, we are committed to making the Seata Saga state machine mode more user-friendly. On the other hand, we are designing Saga's annotation mode and streaming orchestration mode to provide users with a more product-oriented Seata Saga.

You are very welcome to join the co-construction of Seata Saga state machine.

Related Links

[1] Seata official website
http://seata.io/en-us/index.html
[2] Visual orchestration interface
http://seata.io/saga_designer/index.html#/

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