Observability Done Right: Best Practices and Anti-Patterns for Effective System Monitoring

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  WHAT Observability is a concept that refers to the ability to gain insights into the behavior and performance of complex systems. In the context of software engineering, observability involves the collection, analysis, and visualization of data from software applications, infrastructure, and other components of a system. In the animal kingdom, observability plays a critical role in survival, allowing animals to monitor their surroundings, detect threats, and find food. Dolphins use echolocation to observe their surroundings. They emit high-frequency sounds that bounce off objects, allowing them to create a 3D map of their environment. Thanks for reading Knowledge Cafe! Subscribe for free to receive new posts and support my work. Subscribed WHY In today's era, architectures are becoming increasingly large, complex, and fast-paced due to the faster development and deployment of software by distributed teams with the help of DevOps, continuous delivery, and agile development methodo...

Distributed Transactions in Microservices

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What is a distributed transaction?

Microservices architecture has been very popular architecture pattern in recent time. However, one common problem is how to manage distributed transactions across multiple microservices. 
When a microservice architecture decomposes a monolithic system into self-encapsulated services, it can break transactions. This means a local transaction in the monolithic system is now distributed into multiple services that will be called in a sequence.
Lets try to understand this concept with hypothetical train ticket booking system. Consider below ticket booking monolith application. 


In the train ticket booking example above, if a actor sends a book ticket action to a monolithic system, the system will create a  local database transaction that works over multiple database tables (account table, booking table). If any step fails, the transaction can roll back and data consistency is guaranteed by database's ACID (Atomicity, Consistency, Isolation, Durability) property

To achieve high horizontal scalability we can break this monolith in multiple microservices. When we break down this application in microservices we face 2 major challenges.

  1. How to maintain a transaction’s automicity?
  2. How to manage the transaction isolation level for concurrent requests?

We can address this challenges by two phase commit (2PC) pattern or SAGA pattern. Lets try to understand each in bit more details

Two phase commit (2PC):

Two-phase commit is a well known pattern in RDBMS. This pattern can also be used for microservices architecture to implement distributed transactions. In a two-phase commit, there is a orchestrating service that contains most of the logic and participating service on which the actions are performed. It works in two phases:

Even though 2PC can help provide transaction management in a distributed system, it is considered to be impractical for microservices architecture. Reasons are below:

  • Coordinator can become single point of failure and bottleneck from performance perspective.  
  • If one microservice becomes unavailable in the commit phase, there is not a mechanism to roll back the other transaction.
  • Other services must wait until the first service finishes its confirmation. 
  • The resources used by the services are locked until the whole transaction is complete.
  • Two-phase commits are slow by design due to their dependence on the transaction coordinator. 

Saga pattern

The Saga pattern is another widely used pattern for distributed transactions. It is different from 2pc, which is synchronous. The Saga pattern is asynchronous and reactive. In a Saga pattern, the distributed transaction is fulfilled by asynchronous local transactions on all related microservices. The microservices communicate with each other through an event bus. The Saga guarantees that either all operations complete successfully or the corresponding compensation actions are run for all executed operations to roll back any work previously done.



In above diagram, each service send event about success or failure and respective subscriber of the event will take action, for example of receive payment fails then receive payment service will send failure notification to event hub and unblock seat service will revert the seat blocking. 


There are various ways to implement SAGA patterns like choreography and orchestrated.


Advantages of Saga:

  • Maintain data consistency across multiple microservices without tight coupling.
  • Perform better compared to 2PC.
  • Offer no single point of failure.
  • Keep the overall state of the transaction eventually consistent.


Disadvantages of Saga:

  • The Saga pattern is difficult to debug, especially when many microservices are involved. 
  • The event messages could become difficult to maintain if the system gets complex. 
  • Another disadvantage of the Saga pattern is it does not have read isolation. For example, the customer could see the order being created, but in the next second, the order is removed due to a compensation transaction.


In nutshell, in the microservices world where each microservice has its own data store, it is complex and difficult to keep the consistency of data. We need to think of all possible scenarios while designing the system and make design more extensible and flexible. 


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