why we can not build kafka like system using nodejs streaming api

Building a Kafka-like system using Node.js streaming API would be a significant undertaking due to several key challenges and differences in design philosophy and capabilities between Node.js and Kafka. Here are the main reasons why it's impractical to build a system with the same robustness, scalability, and features as Kafka using only the Node.js streaming API:

1. Scalability and Performance

• Kafka: Kafka is designed to handle thousands to millions of messages per second with high throughput and low latency. It achieves this through a distributed architecture with multiple brokers, partitions, and replicas.
• Node.js: While Node.js is capable of handling real-time data streams efficiently, it is not inherently designed for distributed, high-throughput systems. Node.js would struggle to achieve the same level of performance and scalability as Kafka, particularly when dealing with large volumes of data across distributed nodes.

2. Fault Tolerance and Data Durability

• Kafka: Provides robust fault tolerance through data replication across multiple brokers. If one broker fails, another can take over without data loss. Kafka ensures data durability by persisting messages to disk and replicating them across multiple nodes.
• Node.js: Building such fault tolerance and data durability mechanisms from scratch in Node.js would be complex and error-prone. Node.js does not provide built-in support for data replication, broker failover, and persistent storage, which are critical for a Kafka-like system.

3. Distributed Coordination

• Kafka: Utilizes Apache ZooKeeper for distributed coordination, managing broker metadata, and leader election for partitions. This coordination is essential for maintaining consistency and reliability in a distributed system.
• Node.js: Implementing a similar distributed coordination mechanism in Node.js would require significant effort. Node.js lacks native support for such complex distributed coordination, and building it from scratch would involve considerable development and testing.

4. Partitioning and Replication

• Kafka: Provides partitioning of data streams, which allows parallel processing and improves scalability. Kafka's partitioning and replication mechanisms ensure that data is evenly distributed across brokers and that there are multiple copies of data for redundancy.
• Node.js: While it's possible to implement partitioning and replication, it would require substantial custom development. Ensuring that these mechanisms are as efficient and reliable as Kafka’s would be a major challenge.

5. Consumer Group Management

• Kafka: Manages consumer groups to allow multiple consumers to read from the same topic in a coordinated manner, with automatic load balancing and failover.
• Node.js: Building similar consumer group management in Node.js would involve creating complex logic to handle consumer coordination, load balancing, and failover.

6. Ecosystem and Integration

• Kafka: Has a rich ecosystem with numerous tools and integrations for monitoring, management, and extending its functionality (e.g., Kafka Connect, Kafka Streams).
• Node.js: While Node.js has a vast ecosystem, it does not specifically cater to distributed streaming data management and lacks the specialized tools and integrations that Kafka offers.

7. Operational Complexity

• Kafka: Although Kafka requires setup and management, it is optimized for distributed environments and includes features that facilitate its operation at scale.
• Node.js: Managing a distributed Node.js-based system with comparable features would be far more complex and less efficient, requiring extensive custom tooling and operational practices.