Distributed Transactions and Saga Patterns

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In a Knolx session organized by Knoldus, we discussed the idea of following Saga Patterns. For that to be more accessible, I’d like to share the session with the help of this blog.

Service-oriented architecture has given us enough advantages to be a predominant architecture in our Industry, but it can’t be all sunshine and rainbows. There are use cases where monoliths are not only better but technically the only practically possible option. One such case is the feasibility of transactions. 

A transaction is a logically atomic unit of work which may span multiple database queries. To say the least about it, they ensure locks over resources (Eg: tables it accesses) they acquire in order to maintain the consistency of the database. All this is done with the help of the famous ACID properties. ACID here stands for:
Atomicity: It means that either a transaction happens in full or doesn’t happen at all. At any point, if the transaction feels it can’t process, it’ll rollback.
Consistency: It means that the state of the database remains consistent before a transaction begins and after the transaction ends.
Isolation: It means that multiple transactions can run in parallel without disrupting the consistency of the database.
Durability: It means that any changes made in the database actually persist.
Apart from the ACID properties, the ability to make transactions serializable increases the throughput.

But all this is guaranteed in a single database. What happens when we start using microservices where each service houses a separate database? Do we have any means of performing anything similar to a distributed transaction? Let’s find out.

MicroService Architecture

Distributed Transactions

A distributed transaction would do what a transaction would do but on multiple databases. Think about an e-commerce website. In a monolith design, it would comprise of tables such as CUSTOMER_DETAILS, INVENTORY, PRODUCT_DETAILS, ORDER_DETAILS, PAYMENT_DETAILS, and so on. Thus in order to buy a product, we would select a customer from CUSTOMER_DETAILS, a product from PRODUCT_DETAILS, check it’s status in the INVENTORY and make changes in the table if feasible. After which we will add the order in ORDER_DETAILS and record payments in PAYMENT_DETAILS.
In a distributed scenario the architecture would be split into services like user handling service, payment gateway service etc who would house a respective database, and all the aforementioned actions would be performed in the respective databases.

One way of doing it is the Two-Phase Commit. In a two-phase commit, we have a controlling node which houses most of the logic, and we have a few participating nodes on which the actions would be performed. It works in two phases:

  1. Prepare Phase: In this phase, the controlling node would ask all the participating nodes if they are ready to commit. The participating nodes would then respond in yes or no.
  2. Commit Phase: Then, if all the nodes had replied in affirmative, the controlling node would ask them to commit, else even if one node replied in negative, it’ll ask them to roll back.


This solves our problem to a decent extent but as you can see, it brings in some faults as well:

  1. Whole logic gets concentrated in a single node, and in case that node goes down, the whole system fails.
  2. The whole system is bound by the slowest resource since any ready node will have to wait for confirmation from a slower node which is yet to confirm its status.

Thus, even though we can kind of implement it, it’s not really a feasible option. It’s clear from the two-phase commit that atomicity can hamper our performance. Therefore, we make use of a design pattern which trades this atomicity with resource availability. Something like this was published in a paper titles SAGAS from 1987. Which brings us to Saga pattern.

Saga pattern is one of the ways by which we ensure data consistency in a distributed architecture but it doesn’t really qualify as a standard transaction. A major reason for that being absence of atomicity. So in order to implement this pattern, we perform the constituent operations one after another. For example, in the e-commerce website, we talked about, we first do the transaction pertaining to the selection of customer. Once that is complete, we start selecting a product and so on. So all these constituent transactions together will be known as a Saga.

Screenshot from 2018-07-12 20-03-57

So a successful saga looks something like this :
–  Start Saga
–  Start T1
–  End T1
–  Start T2
–  End T2
–  Start T3
–  End T3
–  End Saga

But things rarely go as straight. Sometimes, we might not be in a position to perform a transaction in the middle of the saga. At that point, the previously successful transactions would’ve already committed. So apart from not continuing with the saga, we also need to undo whatever changes we may have already committed. For this, we apply compensatory transactions. Thus, for each transaction Ti, we implement a compensatory transaction Ci, which tries to semantically nullify Ti. It’s not always possible to get back to the exact same state. For example, if Ti involves sending out an email, we can’t really undo that. So we send a corrective email which semantically undoes Ti. So a failed saga looks something like this:
– Begin Saga
– Start T1
– End T1
– Start T2
– Abort Saga
– Start C2
– End C2
– Start C1
– End C1
– End Saga

There can be various ways for implementation of saga pattern, but to actually implement it in a scalable manner, we can introduce a central process aka Saga Execution Coordinator or SEC. SEC is merely a process that handles the flow of execution of transactions or compensatory transactions. It helps us centrally locate the logic of execution. However, unlike the initiating node in a two-phase commit, we can prevent it from being a single point of failure. This will be explained after we actually understand the full structure and flow.

Another important constituent in order to implement our form of saga pattern in the Saga Log. Just like a database log, it’s a basic, durable but distributed source of information. Every event that SEC executes is documented in a saga log. A good example of that could be a Kafka topic.

So a successful saga could be represented something like this.

Successful saga

Here, when we get a hit on the service it marks the start of the saga on the topic where the SEC is listening. Then SEC logs the start of T1 and hits the service 1 for its transaction. The service responds to the SEC who then logs it as the end of T1. After this, SEC similarly logs transaction T2 and moves in an identical manner. When the last transaction has returned success, the SEC logs it and then logs the end of the Saga.

Now let’s discuss the failure case as well.

Unsuccessful Saga

Everything works in the similar fashion as above till the start of T2, where we encounter some issues and service indicates a failure. The SEC after receiving this message logs to abort the saga. It then undoes the changes made by T2 (if at all) by issuing compensatory transaction C2, which is first logged and then hit on service 2. The response marks the end of C2 which is logged and then the SEC tries to undo T1 using C1. The response of C1 marks the end of the saga which is again logged by the SEC.

One question here could be “What if the compensatory transaction fails?”
Well, simply put, we can’t afford that. We need to undo any changes made by the canceled transaction in order to maintain data consistency. So we try again repeated till the time it succeeds. We make them idempotent.

Now coming back to the SEC. The SEC is not all that special because we CAN actually afford to lose it. There can be two cases in consideration
1. Safe state: Safe state exists when the SEC failed at the point of time where we were already under abort or when we had logged the end of the last transaction and not started a new one. In abort, since compensatory transactions are idempotent, we are already guaranteed a consistent state so we needn’t worry about it. And in the other case, well we can quickly spin up another SEC who could carry on from the point of failure of the previous one.
2. An unsafe state would be when SEC fails at a point of time when we have started the transaction and not yet received any confirmation. It could have persisted with the changes, not even started the execution of the transaction, or even failed the transaction. We could never be certain. Which is why we would spin up another SEC and ask it to abort the current saga.

This probably gives us a fair insight into distributed transactions and saga patterns. Feel free to reach out to us in case of any queries. We appreciate multiple POVs.


Written by 

Ayush Prashar is a software consultant having more than 1 year of experience. He is familiar with programming languages such as Java, Scala, C, C++ and he is currently working on reactive technologies like Lagom, Akka, Spark, and Kafka. His hobbies include playing table tennis, basketball, watching TV series and football.

2 thoughts on “Distributed Transactions and Saga Patterns8 min read

  1. Hi, in the serie:

    – Begin Saga
    – Start T1
    – End T1
    – Start T2
    – Abort Saga
    – Start C2
    – End C2
    – Start C1
    – End C1
    – End Saga

    Why are you compensating T2 with C2 if T2 has not been accomplished? is this a typo?

  2. Hi Ayush,

    Very good article, indeed.

    another question. You said: “Safe state exists when the SEC failed …. …. well we can quickly spin up another SEC who could carry on from the point of failure of the previous one”.
    But, if SEC is stateless (I’m assuming it is stateless because we can afford to lose it and recreate), then how can you be sure where to continue the saga recovery ??
    Where are you storing the point ?
    Are you digging into the SAGA log to search incomplete sagas when you first start the SEC ?

    Thank you!

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