Apache Kafka and microservices
01
Kafka is a distributed data streaming platform. It enables exchange flows in real time thanks to queue management and its Event-oriented architecture. Initially developed by LinkedIn, it has evolved into Open Source under the Apache license, now widely acclaimed by companies.
Kafka offers a powerful and scalable messaging solution, based on the queuing principle, very used by microservices architectures. They can publish and receive messages in real time, enabling asynchronous communication between services.
With its event-driven microservices architecture, Kafka becomes an essential link in managing scalability. The tool acts as an intermediary to facilitate exchanges between microservices on different nodes of a cluster, thanks to its distributed system. Systems decoupled from multiple business units can thus work together efficiently.
Adopting Kafka means ensuring reliable and efficient communication between microservices to focus on business logic. This makes it possible to quickly design and deploy high-performance applications, in a constantly changing digital environment.
02
Scalability
- Kafka is capable of handling large amounts of data and traffic in real time. It can be deployed on multiple servers and facilitate communication between microservices on different nodes of a Kafka cluster
Easy integration
- Kafka integrates easily with other technologies commonly used in microservices architectures
Reliability
- Messages published in Kafka are stored permanently, which ensures that they will not be lost even in the event of a failure. Microservices can consume messages at their own pace without the risk of loss
Flexibility
- Kafka gives microservices the possibility to use the most appropriate communication model (publish/subscribe, queue, ...)
Performance
- With its distributed design and peak load management, Kafka delivers high performance communication between microservices
03
Architecture
- Audit and recommandations to optimize existing infrastructure, design effective communication schemas between microservices, architecture best practices, ...
Development
- Development of applications communicating effectively via Kafka, error handling, implementation of advanced features
Implementation and integration
- Configuration, deployment of Kafka clusters, integration with various systems and technologies, ...
Integration of Kafka into a BI system
- Implémentation of Kafka as data flow, real-time system monitoring, ...
04
| Kafka | RabbitMQ | |
|---|---|---|
| Performance |
|
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| Storage |
|
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| Availability |
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| Data Flow and Use Cases |
|
|
Kafka
Performance :
- High data throughput and low latency
- Easy and efficient sizing
Storage :
- Storing messages on disk
- High retention capacity
Availability :
- High availability via vertical and/or horizontal scaling as needed
Data Flow and Use Cases :
- Suitable for applications requiring real-time data flow and continuous data analysis
RabbitMQ
Performance :
- Comparable performance, if data persistence is not required
- Resource-intensive, the higher the data throughput
Storage :
- Storage of messages mainly in RAM
- Suitable when messages need to be consumed quickly
Availability :
- Dimensioned mainly horizontally
Data Flow and Use Cases :
- Scales better for applications that require traditional queue management
