A: Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. It’s horizontally scalable, fault-tolerant, and incredibly fast.
A: Kafka operates on a publish-subscribe model. Producers publish messages into topics, and consumers subscribe to those topics. Messages are stored across Kafka brokers.
A: Topics are categories for records. Partitions allow a topic to be parallelized, splitting data across multiple brokers.
A: Through replication. Each partition can be replicated across brokers, ensuring messages are still available if one broker fails.
A: Producers publish messages to topics. Consumers read messages from topics. Both can be scaled to handle varying loads.
A: Yes, Kafka’s low-latency design is suitable for real-time data processing, working with Kafka Streams or other stream processing systems.
A: Kafka’s speed isn’t just about an abstract notion of being “fast.” It’s about high throughput - moving a large volume of data efficiently, like a large pipe moving liquid. Two key design decisions contribute to Kafka’s performance:
Sequential I/O: Unlike random disk access, which is slow, Kafka relies on sequential access. By using an append-only log as its primary data structure, it takes advantage of this sequential access pattern. Sequential writes on modern hardware reach hundreds of megabytes per second, orders of magnitude faster than random writes.
Zero Copy Principle: Kafka’s efficiency in moving data between disk and network is boosted by the zero copy principle. It minimizes unnecessary copying, using system calls like sendfile() to directly transfer data from the OS cache to the network card buffer. This optimized path, often assisted by Direct Memory Access (DMA), is far more efficient.
These design choices, focusing on the way data is read and transferred, are cornerstones to Kafka’s high performance. They enable it to handle large data volumes quickly and cost-effectively.
A: Kafka is designed for distributed high throughput environments, with parallel processing, large data handling, and integration with big data technologies.
A: Real-time analytics, data lakes, data aggregation, handling burst data loads, etc.
A: It can be complex due to its distributed nature and many configurations, but managed services and community support can assist.
A: Kafka is like the postal service of the data world - it always delivers, rain or shine, and you can always add more mail carriers if the packages keep piling up!
https://kafka.apache.org/intro
https://www.youtube.com/watch?v=UNUz1-msbOM