How to build fault-tolerant Microservices Architecture using Kafka

We are already living in a digital world, which is moving swiftly in this new virtual reality. With this, enterprises are looking for ways to build applications and architecture that can evolve with changing customer experience.

To achieve this, tech leaders are looking for ways like serverless architecture, data-driven applications, microservices, hyper automation, and more. Amongst all this, the one thing that is constant is data. Whatever applications and systems are being built, enterprises require systems that can help in achieving scale in processing, communications, storing, and provide real-time data insights. This must be further combined with easy to maintain application architectures.

This is where Microservices (a new way of building applications) and Kafka (a distributed streaming platform) come in. They are already helping many organizations to build fault-tolerant application architectures. The innate ability of Microservices to decouple huge applications into small and manageable services makes it highly popular. However, microservices’ communication usually tends to get more complex because of their distributed nature.

As microservices combine various services and sub-services, the communication between these services must be highly intact, otherwise resulting in application collapse. The independently dependent services communicate with each other using light weight protocols like REST or messaging broker systems like Apache Kafka. Kafka is a distributed streaming platform that is becoming increasingly popular for building microservices applications.

In this blog, we will discuss how to build fault-tolerant microservices architecture using Kafka. Let’s start by introducing Kafka and its features, Microservices and what is driving them, Kafka implementation in Microservices, and some of the best practices to build fault-tolerant microservices architecture using Kafka.

Apache Kafka and its Features

Apache  Kafka, was originally developed by LinkedIn, and was open sourced later. Apache Kafka is a distributed messaging platform that can handle multiple messages sent across systems and services. Apache Kafka goes well with the data intensive applications and services that require constant messaging support from each other. It can publish, subscribe, store, and process streams of records in real time.

Some of its features that makes Kafka highly popular are –

1. Distributed Architecture – Kafka is the first choice for building fault-tolerant and scalable applications because of its highly distributed architecture. At any given point in time, Kafka can scale horizontally across multiple servers, giving it the ability to handle large volumes of data even in real-time production environments.
2. High-throughput Messaging – The distributed architecture makes Kafka highly scalable and flexible to handle high-throughput messaging. It can handle volumes of data coming from multiple servers, services, or other work modules. Kafka also has a messaging persistence nature which allows messages to be stored and consumed later.
3. Stream Processing – Kafka can process multiple (zillions) streams of data in real-time while handling the processing, storage, subscribe, and publishing them. Kafka streams can be processed using various programming languages and frameworks, including Java, Scala, and Python.
4. Connectors and Integrations – Kafka has built a good ecosystem of connectors and integrations with tools like Elasticsearch, Hadoop, and Spark allowing easy integrations with data pipelines, warehouses, and tools.
5. Security and Authentication – Kafka provides security features like SSL/TLS encryption, authentication, and authorization. By extending its integration with LDAP and Kerberos, Kafka is also providing support with external authentication providers.

What are Microservices and why are they highly popular?

Microservices is a way of building application architecture where a large-sized application is broken down into smaller services and sub-services, independent of each other. This makes the application highly resistant to failures, scalable, and easy to maintain and update.

There are several reasons that make Microservices popular –

1. Highly Scalable – Microservices runs on cloud. It can also be developed and managed on in-house data centers and private servers. But cloud’s ability to scale resources up and down gives microservices its magic of scaling up and down services based on their requirements. During peak times, when services require more resources to run efficiently, more capacity can be added without any lag. Similarly, during non-peak times, if the resources are lying idle, they can be scaled down to save costs.
2. Faster Resolution of Issues – In contrast to monolithic architectures, where a single bug can cause the entire system to fail, microservices are fault-tolerant by nature. It is possible for each of them to be deployed independently and to function independently. During any issues, specific services can be restored by working on them. As a result, issues can be resolved more quickly since they are isolated and can be addressed without affecting other systems.
3. Easy Deployments – Microservices are easy to deploy because of their independent nature. Each service can be handled in singularity and hence it doesn’t affect other application modules. With practices like CI- CD pipelines and Infrastructure as Code, the ability to provision new resources, reduce maintenance, scale up and down easily, and deliver new features, resolve issues, get easy.
4. Resilience and Reusability – Microservices promote reusability because of robust communication systems between services. Instead of building services every time, developers can reuse existing services for various functionalities. This also promotes a lot of interoperability.

Microservices are also resilient when compared to monoliths because if one service fails, the entire application won’t be impacted. Developers can investigate the specific service and restore the operations. The ability to identify issues gets easy and quick as developers are notified which service is causing trouble, while the overall system remains operational.

5. Improved Collaboration – Microservices promote collaboration between developers and cross-functional teams. Microservices are also easy to understand for non-technical users as each service is dedicated to one application functionality. This helps in bringing in more collaboration and decrease size of feedback loops in the application architecture.

Implementing Kafka for Fault-Tolerant Microservices Architecture

With its reliable, scalable, and fault-tolerant messaging system, Kafka helps in establishing asynchronous messaging between microservices. As a distributed streaming platform, there are several ways of implementing Kafka in microservices architecture.

A very straightforward approach is to integrate Kafka as a messaging layer into microservices architecture. The Kafka client library handles the messaging between Kafka and microservices. This approach establishes direct connection between microservices and provide a greater control. However, the tight coupling as a message exchange layer with microservices can pose a challenge in future, when the underlying messaging system wants to be changed in future.

In another approach, Kafka acts as event bus. Kafka event bus acts as publisher and subscriber to messaging events. Microservices can publish events to Kafka event bus and other microservices can consume events based on their subscription. In this architecture, microservices are loosely coupled as compared to straightforward messaging layer approach. However, it is very necessary to have a careful management of event types and versions to avoid any over-writing, compatibility, and redundancy issues.

Kafka can also be used as service mesh layer to establish reliable messaging between two microservices. The service mesh routes all communication between microservices using Kafka layer as a seamless message exchange platform. This approach establishes very loosely coupled architecture frameworks between microservices to communicate seamlessly using Kafka. Kafka as a mediator enables data sharing, event-driven processing and communication between various microservices.

Kafka, as a technology has immense potential to implement fault-tolerant microservices architecture. Kafka can be used in various ways depending on the specific needs of the system or applications.

Best Practices for Implementing Kafka to build Fault-Tolerant Microservices

Implementing and managing Kafka can be a daunting task but taking care of few best practices can help in using Kafka as a point of success in your Microservices architecture. Here are some of the best practices for implementing Kafka in Microservices architecture –

1. Use a Kafka cluster for fault intolerance – A Kafka cluster consists of multiple brokers who can host one or more partitions. It is highly recommended to replicate data across multiple brokers so that the data is not lost in case of broker failure. It also helps in creating multiple datasets and validating the entries whenever required.
2. Use service mesh integration, wherever possible – In microservices architecture, use asynchronous communication between services by using Kafka as a service mesh intercept. This helps in establishing seamless communication between two microservices. This allows services to use publish-subscribe feature of the Kafka messaging system and microservices can continue to interact with each other without knowing the origin and consumption of messages. Even if the services become slow or unavailable, Kafka can act as a messaging layer to keep the communication intact, till the time services are restored.
3. Use Kafka idempotent producers – While using Kafka messaging layer, it is important to ensure that multiple messages are not hitting services. Kafka provides at-least-once message delivery semantics, that ensures that messages are not lost or duplicated. If the messages are duplicates in some cases, idempotent producers make sure only one message is delivered and other remain unsent.
4. Use a partitioning strategy – Kafka distributes data across multiple brokers. Sometimes, this leads to uneven distribution of data leading to non-uniform messaging. Make sure that you work with experts like Enhops who has built and managed multiple Kafka architectures and instance to create a good partitioning strategy.
5. Use a consumer group – In a microservices architecture, multiple services consume messages from the same topic. A consumer group ensures that messages are not duplicated and hey work together to obtain messages from a topic. Each consumer in a consumer group is assigned one or more partitions to consume messages from.
6. Implement a dead-letter topic – In a microservices architecture, services can break sometimes or fail to communicate with each other. Using a dead-letter topic makes sure that messages are not lost and are stored in dead-letter topic. Failed messages can be analysed later and processed.
7. Use a circuit breaker pattern – Whenever services break or become unavailable in microservices, it is important to ensure that these failures doesn’t trickle down to all messaging layers and services. Hence, it is important to introduce a circuit breaker pattern that prevents a service from continuously trying to call another service that is failing. Instead, the circuit breaker returns a fallback response or takes other action to prevent a cascading failure.
8. Use monitoring and alerting – In a microservices architecture, it is important to monitor the health of services and the Kafka cluster. This allows for proactive management and can prevent failures before they occur. It is also important to set up alerting for critical events, such as broker failures or service outages. Managing Kafka 24 by 7 can help in preventing outages and services failures. Usually, it is highly advisable to build a Kafka managed services center and if you don’t have one, consider outsourcing it.

Our Kafka Managed Service Engineers provide round the clock support for Kafka to report real-time anomalies and provide fast resolution. Our no gimmick and clear cut pricing plans help our client’s make informed decisions and maintain their Kafka instances within budget.

Achieving fault-tolerance in your applications using Kafka

Building a fault-tolerant microservices architecture using Kafka is not a straight-line and there’s no fixed recipe for the same. It depends on application usage and business objectives intended with the application. While we have provided a general outline of Kafka best practices, we can help you in building a fool proof microservices architecture using Kafka messaging system.

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