Monday, December 18, 2017

Saga implementations comparison

In the previous blog post we have investigated the general motions of the saga pattern and how sagas differ from traditional ACID approach. This article will focus on the current state of applicability of this pattern. We will introduce and compare three frameworks that presently support saga processing, namely Narayana LRA, Axon framework and Narayana LRA Narayana Long Running Actions is a specification developed by the Narayana team in the collaboration with the Eclipse MicroProfile initiative. The main focus is to introduce an API for coordinating long running activities with the assurance of the globally consistent outcome and without any locking mechanisms. [] Axon framework Axon framework is Java based framework for building scalable and highly performant applications. Axon is based on the Command Query Responsibility Segregation (CQRS) pattern. The main motion is the event processing which includes the separated Command bus for updates and the Event bus for queries. [] Eventuate is a platform that provides an event-driven programming model that focus on solving distributed data management in microservices architectures. Similarly to the Axon, it is based upon CQRS principles. The framework stores events in the MySQL database and it distributes them through the Apache Kafka platform. [] NOTE: CQRS is an architectural pattern that splits the domain model into two separated models - the first one is responsible for updates and the containing business logic while the other is taking care of the reads and providing information for the user. Comparisons Even though all of the above frameworks achieve the same outcome there are several areas where we can examine how the handling of the saga processing differ. Developer friendliness The LRA provides for the developer the traditional coordinator oriented architecture. Individual participants can join the LRA by the HTTP call to the LRA coordinator, each providing methods for saga completion and compensation. Narayana provides a LRA client which makes the REST invocations transparent. In the Axon sagas are implemented as aggregates. The aggregate is a logical group of entities and value objects that are treated as a single unit. Axon uses special type of event listener that allows the developer to associate a property in the events with the current saga so that the framework knows which saga should be invoked. The invocation and compensation are executed by separated event handlers and therefore different events. Not a long ago Eventuate presented a new API called Eventuate Tram which handles the saga processing for the platform. It enables applications to send messages as a part of an database transaction. The platform provides abstractions for messaging in form of named channels, events as subscriptions to domain events and commands for asynchronous communication.

Saga specifications In the LRA the specification of how the saga should be performed is specified by the initiator. Initiating service is able to invoke participants on the provided endpoints which allows participant to join the LRA context. The participant can specify whether to join, create new or ignore the corresponding LRA context by the CDI annotation. Axon provides a set of annotation the denote the saga class. A @Saga annotation defines the class as a saga aggregate which allows it to declare a saga event handlers. Saga handler can optionally include the name of the property in the incoming event which has been previously associated with the saga. Additionally Axon provides special annotations to mark the start and end of the saga. In Eventuate the developer is able to specify the saga definition including the participant invocations, compensations and reply handlers in the saga definition. It provides a simple builder which constructs the saga step by step providing the handlers for command and event processing. Failure handling The saga compensating actions are in the LRA defined as designated endpoints. If the initiating service cancels the LRA, the coordinator is able to call the compensation endpoints of the included participants. In Axon application developers need to know which events represents the failure to provide the correct event listeners. Also the tracking of the progress of the execution as well as the one of the compensation is operated by the developer. Eventuate registers the compensation handlers in the saga definitions. Participants are allowed to send a special build-in command which indicates failure a therefore the compensation of the previous actions. Ordering of invocations LRA and Eventuate invoke each participant in strict order. This approach expects the actions to be dependent on each prior action so the compensations are called in reverse order.
NOTE: Narayana LRA allows participants to modify this behavior by returning HTTP 202 Accepted. Axon on the contrary does not control the ordering of invocations so the programmer is allowed to send the compensation commands in any desired ordering. Structure and failure recovery As Axon and Eventuate are primarily CQRS based frameworks they require some additional handling for the saga execution. In Axon saga is still the aggregate which means that it consumes commands and produces events. This may be an unwanted overhead when the application does not follow the CQRS domain pattern. The same applies to Eventuate Tram but as the communication is shadowed through the messaging channels it does not force the programmer to follow CQRS. Both platforms mark every event and command into the distributed event log which warrants the saga completion upon the system failure with the eventual consistency. The LRA on the other hand requires only to implement processing and compensation endpoints. Processing is then handled by the LRA coordinator and it is made transparent to the end users. The failures of the coordinator can be managed by replication and/or transaction log. A participant failure is treated by the custom timeout on the saga invocation after which the coordinator cancels the saga. Conclusion We have discussed the main advantages and drawbacks of each platform respectively. As a part of my thesis I have created a basic example in all of the discussed frameworks. It is a simple ordering saga containing requests for shipment and invoice computation invoked on different services. In the next blog post I plan to describe the execution of this saga in every framework to discuss the main distinctions in grater detail. - - - pure CQRS solution - - - Eventuate Tram
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