KafkaConsumer Groups and Rebalancing in Kafka with Spring Boot
Understand how Kafka divides partitions among consumer groups, when rebalancing occurs, and how to reduce rebalancing impact in Spring Boot applications.
Change Data Capture with Debezium and Kafka
Use Debezium to read PostgreSQL's WAL, push every data change to Kafka and consume them with Spring Boot, without modifying the data writing code.
Event Sourcing with Kafka: Save Events Instead of State
Use Kafka as an event log for Order: record immutable events, rebuild state through replay, build read models using projections. Plus the limitations to know before applying.
Kafka Monitoring: consumer lag and key metrics to track
What is consumer lag, how to measure it with CLI, AdminClient and Micrometer, which broker/producer/consumer metrics to track and how to set up reasonable alerts.
Kafka producer: acks, idempotence, and batching
Configure acks, enable.idempotence, batch.size and linger.ms for Kafka producer to prevent message loss, avoid duplicates while achieving high throughput.
Outbox pattern with Kafka: never lose events on database write
Writing to database and sending to Kafka simultaneously easily loses events. This article guides you through implementing the Outbox pattern with Spring Boot 3 and Kafka to keep both systems consistent.
Saga pattern with Kafka for distributed transactions: tour booking example
Set up Saga pattern with orchestration using Spring Boot and Kafka. Book flights, hotels, process payments, and automatically compensate when a step fails.
Schema Registry and Avro/Protobuf in Kafka with Spring Boot
Use Confluent Schema Registry with Avro and Protobuf in Spring Boot 3 to control Kafka message formats and evolve schemas safely.
Consumer groups and rebalancing in Kafka: why consumers stop
How rebalancing works, why it stops consumers, and ways to minimize it through poll configuration, cooperative rebalancing, and static membership.
Designing partition keys in Kafka: message ordering and load balancing
How Kafka selects partitions from keys, how to choose keys to maintain correct message order without creating hot partitions, and common mistakes with Spring Boot.
Kafka Retry and Dead Letter Topic with Spring Kafka
Configure blocking and non-blocking retry for Kafka consumer in Spring Boot 3, push error messages to dead letter topic, and write tests to verify behavior.
Kafka Producer: Getting acks, retry, and idempotence Right
Learn how acks, retries, and idempotence work together to ensure messages are not lost or duplicated when a producer sends data to Kafka.
Kafka Basics: Topics, Partitions, and Offsets
Explaining three fundamental Kafka concepts — topic, partition, offset — with producer/consumer examples using Spring Boot.