Transactions are an essential part of applications. Without them, it would be impossible to maintain data consistency.

One of the most powerful types of transactions is called a Two-Phase Commit, which is in summary when the commit of a first transactions depends on the completion of a second. It is useful especially when you have to update multiples entities at the same time, like confirming an order and updating the stock at once.

However, when you are working with Microservices, for example, things get more complicated. Each service is a system apart with its own database, and you no longer can leverage the simplicity of local two-phase-commits to maintain the consistency of your whole system.

When you lose this ability, RDBMS becomes quite a bad choice for storage, as you could accomplish the same “single entity atomic transaction” but dozens of times faster by just using a NoSQL database like Couchbase. That is why the majority of companies working with microservices are also using NoSQL.

To exemplify this problem, consider the following high-level Microservices architecture of an e-commerce system:

In the example above, one can’t just place an order, charge the customer, update the stock and send it to delivery all in a single ACID transaction. To execute this entire flow consistently, you would be required to create a distributed transaction.

We all know how difficult is to implement anything distributed, and transactions, unfortunately, are not an exception. Dealing with transient states, eventual consistency between services, isolations, and rollbacks are scenarios that should be considered during the design phase.

Fortunately, we already came up with some good patterns for it as we have been implementing distributed transactions for over 20 years now. The one that I would like to talk about today is called Saga pattern.

 

The SAGA Pattern

One of the most well-known patterns for distributed transactions is called Saga. The first paper about it was published back in 1987 and has it been a popular solution since then.

A saga is a sequence of local transactions where each transaction updates data within a single service. The first transaction is initiated by an external request corresponding to the system operation, and then each subsequent step is triggered by the completion of the previous one.

Using our previous e-commerce example, in a very high-level design a saga implementation would look like the following:

There are a couple of different ways to implement a saga transaction, but the two most popular are:

  •    Events/Choreography: When there is no central coordination, each service produces and listen to other service’s events and decides if an action should be taken or not.
  •    Command/Orchestration: when a coordinator service is responsible for centralizing the saga’s decision making and sequencing business logic 


Let’s go a little bit deeper in each implementation to understand how they work.

 

Events/Choreography

In the Events/Choreography approach, the first service executes a transaction and then publishes an event. This event is listened by one or more services which execute local transactions and publish (or not) new events.

The distributed transaction ends when the last service executes its local transaction and does not publish any events or the event published is not heard by any of the saga’s participants.

Let’s see how it would look like in our e-commerce example:

  1. Order Service saves a new order, set the state as pending and publish an event called ORDER_CREATED_EVENT.
  2. The Payment Service listens to ORDER_CREATED_EVENT, charge the client and publish the event BILLED_ORDER_EVENT.
  3. The Stock Service listens to BILLED_ORDER_EVENT, update the stock, prepare the products bought in the order and publish ORDER_PREPARED_EVENT.
  4. Delivery Service listens to ORDER_PREPARED_EVENT and then pick up and deliver the product. At the end, it publishes an ORDER_DELIVERED_EVENT
  5.  Finally, Order Service listens to ORDER_DELIVERED_EVENT and set the state of the order as concluded.

In the case above, if the state of the order needs to be tracked, Order Service could simply listen to all events and update its state.

 

Rollbacks in distributed transactions

Rolling back a distributed transaction does not come for free. Normally you have to implement another operation/transaction to compensate for what has been done before.

Suppose that Stock Service has failed during a transaction. Let’s see what the rollback would look like:

  1. Stock Service produces PRODUCT_OUT_OF_STOCK_EVENT;
  2. Both Order Service and Payment Service listen to the previous message:
    1. Payment Service refund the client
    2. Order Service set the order state as failed     

Note that it is crucial to define a common shared ID for each transaction, so whenever you throw an event, all listeners can know right away which transaction it refers to.

 

Benefits and drawbacks of using Saga’s Event/Choreography design

Events/Choreography is a natural way to implement Saga’s pattern, it is simple, easy to understand, does not require much effort to build, and all participants are loosely coupled as they don’t have direct knowledge of each other. If your transaction involves 2 to 4 steps, it might be a very good fit.

However, this approach can rapidly become confusing if you keep adding extra steps in your transaction as it is difficult to track which services listen to which events. Moreover, it also might add a cyclic dependency between services as they have to subscribe to one another’s events.

Finally, testing would be tricky to implement using this design, in order to simulate the transaction behavior you should have all services running.

 

In the next post, I will explain how to address most of the problems with the Saga’s Events/Choreography approach using another Saga implementation called Command/Orchestration

In the meantime, if you have any questions feel free to ask me at @deniswsrosa

Posted by Denis Rosa, Developer Advocate, Couchbase

Denis Rosa is a Developer Advocate for Couchbase and lives in Munich - Germany. He has a solid experience as a software engineer and speaks fluently Java, Python, Scala and Javascript. Denis likes to write about search, Big Data, AI, Microservices and everything else that would help developers to make a beautiful, faster, stable and scalable app.

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