What is the difference between KStream and KTable in this skill?

A KStream represents an unbounded, append-only stream of independent events, whereas a KTable represents a changelog stream where only the latest state for a specific key is maintained, acting like a table.

Can this skill help with stream windowing?

Yes, it provides implementation patterns for Tumbling, Hopping, Session, and Sliding windows to handle various temporal aggregations and time-based analysis.

Does it support exactly-once semantics (EOS)?

Absolutely. It includes best practices for enabling EOS v2 (the recommended standard) and configuring properties to ensure high data integrity in production.

How does it handle topology optimization?

It suggests strategies like combining operations to reduce intermediate topic writes and reusing KTables across multiple joins to minimize resource consumption and latency.

Kafka Streams Topology Design

Name: Kafka Streams Topology Design
Author: anton-abyzov

byanton-abyzov

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API開発

Designs and implements high-performance event-driven stream processing topologies using Java and Kotlin.

概要

This skill provides expert-level guidance for building complex Kafka Streams applications, helping developers navigate the nuances of KStream, KTable, and GlobalKTable abstractions. It excels at designing robust stream processing workflows, implementing stateful operations like windowed joins and aggregations, and ensuring data integrity through exactly-once semantics (EOS). Whether you are building real-time analytics dashboards, data enrichment pipelines, or event-driven microservices, this skill optimizes your topology for scalability, fault tolerance, and efficient state management.

主な機能

13 GitHub stars
Configuration of advanced windowing strategies like Tumbling, Hopping, and Session windows
Topology optimization through state store management and repartitioning reduction
Expert differentiation between KStream, KTable, and GlobalKTable patterns
Data consistency guidance with Exactly-Once Semantics (EOS v2) setup
Implementation of complex join strategies including Stream-Stream and Stream-Table joins

ユースケース

Implementing event-driven microservices that require complex temporal joins
Enriching real-time event streams with reference data from external look-up tables
Building stateful aggregations for real-time monitoring and anomaly detection

概要

主な機能

13 GitHub stars
Configuration of advanced windowing strategies like Tumbling, Hopping, and Session windows
Topology optimization through state store management and repartitioning reduction
Expert differentiation between KStream, KTable, and GlobalKTable patterns
Data consistency guidance with Exactly-Once Semantics (EOS v2) setup
Implementation of complex join strategies including Stream-Stream and Stream-Table joins

ユースケース

Implementing event-driven microservices that require complex temporal joins
Enriching real-time event streams with reference data from external look-up tables
Building stateful aggregations for real-time monitoring and anomaly detection