Apache Kafka - Real-time Stream Processing (Master Class) - Creating your First State Store

The video tutorial covers the implementation of stateful processors in Kafka streams, focusing on transforming invoices into notifications and computing customer rewards. It explains the setup of a project from scratch, including dependencies and configurations. The tutorial details the creation of a Kafka streams application, the use of state stores, and the application of transform values using a rewards transformer. The video concludes with a summary of the process and a note on potential issues when scaling the application.

What change was made to the topology DAG in the customer reward computation example?

• Removed the transform values processor

• Added a compute total rewards node

• Replaced MAP values processor with a transform values processor

• Introduced a new stateful processor

What is the purpose of enabling auto import in the project setup?

• To automatically compile the code

• To pull dependencies as defined in the pom file

• To create a new project from scratch

• To generate schema definitions

What is the first step in creating a Kafka Streams application?

• Define properties

• Add a state store

• Create a stream builder

• Filter for prime customers

Why is a store builder used instead of directly creating a state store?

• To enable multiple tasks to create their own copy of the state store

• To simplify the topology

• To reduce memory usage

• To allow sharing of state stores across tasks

Which type of state store is used in the example?

• Windows store

• In-memory key value store

• Persistent key value store

• Session store

What is the role of the transform values processor in the Kafka Streams application?

• To filter messages based on a condition

• To create a new Kafka topic

• To manage state stores

• To transform invoices into notifications

What is the purpose of the init method in the rewards transformer?

• To compile the project

• To set up the processor context and state store

• To define the main method

• To initialize the Kafka stream

How are loyalty points computed in the rewards transformer?

• By subtracting discounts from the total amount

• By multiplying the total amount by a loyalty factor

• By dividing the total amount by a constant

• By adding a fixed number of points

What happens if the state store returns a null value for total rewards?

• The state store is reinitialized

• The program throws an error

• The earned points are set to zero

• The earned points are used as the total

What is a critical consideration when scaling the Kafka Streams application?

• Reducing the number of state stores

• Ensuring the state store is shared across tasks

• Avoiding unexpected results by addressing topology considerations

• Increasing the number of Kafka topics