Apache Kafka - Real-time Stream Processing (Master Class) - Kafka Streams Architecture

This lecture covers Apache Kafka Streams, focusing on its parallel processing and fault tolerance capabilities. It explains the difference between single-threaded and multithreaded applications, and how to configure them for vertical and horizontal scaling. The concept of stream tasks and topology is introduced, with an example illustrating task assignment and scaling. Finally, the lecture discusses fault tolerance, explaining how Kafka Streams handles failures by reassigning tasks to running instances.

What is the primary advantage of Kafka Streams being built on top of Kafka client APIs?

• It allows for easier debugging.

• It simplifies the user interface.

• It leverages Kafka's native capabilities for data parallelism.

• It reduces the cost of deployment.

How can you increase the degree of parallelism in a Kafka Streams application?

• By using a different programming language.

• By adding more partitions to the input topic.

• By configuring the number of threads.

• By reducing the number of tasks.

What is a limitation of running multiple threads within a single Kafka Streams application?

• The number of threads is limited by the available resources on a single machine.

• It requires a complex configuration setup.

• It increases the risk of data loss.

• It makes the application slower.

How does Kafka Streams handle task distribution when scaling horizontally?

• Tasks are manually assigned by the user.

• Tasks are duplicated across all instances.

• Tasks are distributed based on the number of available threads.

• Tasks are automatically assigned to new instances.

What determines the number of logical stream tasks created by the Kafka Streams framework?

• The number of input topics.

• The number of partitions in the input topic.

• The number of available machines.

• The size of the data being processed.

What happens if a task runs on a machine that fails in Kafka Streams?

• The task is lost and needs to be restarted manually.

• The task is duplicated to all other instances.

• The task is automatically reassigned to another running instance.

• The task is paused until the machine is back online.

Why might additional instances of a Kafka Streams application remain idle?

• Because there are no tasks to assign to them.

• Because they are running on incompatible hardware.

• Because they are not connected to the network.

• Because they are not configured correctly.