What are the reasons for Kafka’s high throughput?

The main reasons for Kafka’s high throughput are as follows:

1. Distributed architecture: Kafka utilizes a distributed architecture to distribute data across multiple nodes for parallel processing, ultimately boosting throughput.
2. Zero-copy technique: Kafka utilizes zero-copy technique to avoid multiple data duplication during data transmission, reducing memory and CPU overhead and improving data transfer efficiency.
3. Batch processing: Kafka supports processing messages in batches, allowing multiple messages to be sent to the server at once, reducing network transmission overhead and increasing throughput.
4. Efficient file system: Kafka utilizes efficient file systems like Linux file systems for storing and managing data, which results in high-speed read and write capabilities, ultimately increasing throughput.
5. Efficient disk storage: Kafka utilizes sequential disk writing to store data, eliminating the cost of random writes and enhancing disk utilization and performance.
6. Compression technology: Kafka supports compressing messages to reduce the amount of data transferred over the network, increasing throughput.
7. Replication mechanism: Kafka utilizes a replication mechanism to ensure data reliability and fault tolerance by copying data to multiple nodes, enhancing data availability and throughput.

Overall, Kafka’s high throughput is mainly attributed to its distributed architecture, zero-copy technology, batch processing, efficient file system and disk storage, compression technology, and replication mechanism.