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The world of creating real-time applications can get complex when we have to consider latency, fault tolerance, scalability, and possible data losses. Traditionally, web sockets were the go-to option when it came to real-time applications, but think of a situation whereby there’s server downtime. It means that there is a high risk of data loss but Apache Kafka solves this because it is distributed and can easily scale horizontally and other servers can take over the workload seamlessly.
In this blog, we’ll explore how to harness the power of Kafka to streamline event streaming within a microservices architecture and unlock its full potential for building scalable and responsive systems. Let’s get started! 🚀
In this blog, we will cover:
- Apache Kafka
- Powerful Features of Apache Kafka
- 5 Key Apache Kafka Use Cases in 2023
- Apache Kafka in Microservices
- Hands-on
- Conclusion
Apache Kafka
Apache Kafka is a distributed data stream platform that aims at having unified, high-throughput data pipelines. It offers a unified solution to real-time data needs any organisation might have. Think about it this way, traditionally, all the data needs were being handled by the backend server. So if a user requests a piece of information, the request goes to the Database server.
Let’s assume that there are many users using the system simultaneously, you will realize that due to latency some users may get different results depending on how fast the server will process the request and relay back the response.
A good real-world example will be a taxi app. We want to always have unified data which is broadcasted to all the client applications simultaneously without any latency. This is where Apache Kafka comes in.
Another good application is in payment platforms. Since messages are not lost in Apache Kafka in the event a payment server goes down or experiences latency, things like payment requests(messages) will not fail, they will simply be resumed at a later time.
Last but not least it can be well applied in a microservices architecture whereby the microservices are decoupled; which will be handled in this blog. Kafka can also be used to stream data from IoT devices or sensors.
Powerful Features of Apache Kafka
Apache Kafka, a widely used open-source stream-processing platform, offers an array of powerful features that contribute to its popularity. It boasts high throughput, low latency, and fault tolerance. Let’s explore some of its key capabilities:
- Fault-Tolerant & Durable: Kafka ensures data protection and resilience by distributing partitions and replicating data across multiple servers. In the event of server failure, Kafka can autonomously restart itself, ensuring uninterrupted data flow.
- Highly Scalable with Low Latency: Leveraging a partitioned log model, Kafka efficiently distributes data across multiple servers, enabling seamless scalability beyond the limitations of a single server. By separating data streams, Kafka achieves low latency and high throughput.
- Robust Integrations: Kafka offers extensive support for third-party integrations and provides a range of APIs. This flexibility empowers users to effortlessly incorporate additional features and seamlessly integrate Kafka with popular services like Amazon Redshift, Cassandra, and Spark.
- Detailed Analysis: Kafka is widely adopted for real-time operational data tracking. It enables the collection of data from diverse platforms in real-time, organizing it into consolidated feeds while providing comprehensive metrics for monitoring.
5 Key Apache Kafka Use Cases in 2023
Apache Kafka has emerged as the go-to solution for managing streaming data, earning its reputation as the ultimate guardian of real-time data streams. As a distributed data storage system, Kafka has been meticulously optimized to handle the continuous flow of streaming data generated by numerous sources.
But what exactly can Kafka do, and in what scenarios should it be employed? Let’s delve into the details and explore the top five use cases where Apache Kafka shines the brightest.
- Real-Time Data Streaming and Processing: Apache Kafka is widely used for real-time data streaming and processing. It excels in scenarios where large volumes of data need to be ingested, processed, and analyzed in real-time, such as real-time analytics, fraud detection, and monitoring systems.
- Event-Driven Architectures: Kafka is a natural fit for building event-driven architectures. It serves as a central event hub, allowing different microservices or applications to publish and subscribe to events, enabling loose coupling, scalability, and asynchronous communication.
- Log Aggregation and Analytics: Kafka’s durable and fault-tolerant design makes it an excellent choice for log aggregation. It can efficiently collect logs from various sources, consolidate them, and provide real-time analytics and monitoring capabilities, making it valuable for operational intelligence and troubleshooting.
- Messaging Systems and Queuing: Kafka can be used as a messaging system to enable reliable and scalable communication between different components of a distributed system. It provides persistent message storage and supports pub/sub and queuing patterns, making it a versatile choice for building robust and scalable messaging systems.
- Commit Logs and Stream Processing: Kafka’s log-based storage and replayability make it ideal for stream processing use cases. It enables real-time data processing, transformation, and analysis by integrating with stream processing frameworks like Apache Spark, Apache Flink, or Kafka Streams, making it a powerful tool for building data pipelines and real-time analytics applications.
Apache Kafka in Microservices
Apache Kafka is an excellent choice for decoupled microservices architecture. The microservices do not require any knowledge of each other and this may eliminate the need for any integration tests. Generally, since Apache Kafka is fault-tolerant, your microservices will inherit that attribute as well.
Hands-On
The code used in this blog is found at https://github.com/workfall/nest-microservices/tree/v2.0.0
Folder Structure
Installations and Scripts
package.json:
Edit the start:prod script to appear as shown and also install the dependencies listed in this package.json file.
Microservices
We shall use a monorepo version control for our microservices.
The main.ts file which bootstraps the microservices will appear to be tweaked to appear as shown below:
We shall use Docker to containerize the application and because it will be a multi-container application, we shall use Docker compose to have all the applications in the same network.
Dockerfile:
We shall use a multi-stage build to reduce the final image size.
docker-compose.yml(1):
docker-compose.yml(2):
docker-compose.yml(3):
docker-compose.yml(4):
Kafka Service
The major terms or keywords to note are producers, consumers, and topics. Topics are simply a way in which events are organized and classified.
kafka.module.ts:
kafka.service.ts(1):
kafka.service.ts(2):
kafka-service/index.ts:
acl-service.controller.ts:
We can publish to the topic by injecting the Kafka service into any controller that needs to publish an event.
In this example, we want every time a new user registers on the platform in the acl-service, a welcome email is sent out to the user from the test-service.
This achieves much cleaner code because the concerns are separated because we purely rely on events.
acl-service.module.ts:
Kafka uses the TCP transport layer. We have to specify the broker which is a docker container.
Conclusion
In this blog, we demonstrated how we can introduce Kafka as a message broker into a microservices architecture. We also saw the basics of producers, consumers, and topics. A producer fires an event, events are organized into topics and a consumer subscribes to a topic.
Throughout our exploration, we discovered numerous scenarios where Kafka proves invaluable in achieving reliability, low latency, and fault tolerance. We will come up with more such use cases in our upcoming blogs.
Meanwhile…
If you are an aspiring Microservices enthusiast and want to explore more about the above topics, here are a few of our blogs for your reference:
- How to Deploy NestJS Microservices to AWS Elastic Beanstalk?
- Architect NestJS Microservices with AWS Elastic Beanstalk
- How to orchestrate Queue-based Microservices with AWS Step Functions and Amazon SQS (Part 1)?
- How to orchestrate Queue-based Microservices with AWS Step Functions and Amazon SQS?
Stay tuned to get all the updates about our upcoming blogs on the cloud and the latest technologies.
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