This guide walks through adding the Elasticsearch sink to a Flink job, configuring connection and bulk settings, implementing custom failure handlers, and enabling checkpoint‑based retry mechanisms to ensure reliable streaming writes.
This article explains the internal workings of Java's Stream API pipeline, covering how intermediate and terminal operations are recorded, combined, and executed via stages and Sink interfaces, illustrating lazy evaluation, stateful vs stateless operations, and the mechanisms behind parallel and sequential processing.
This article explains the concept of retract streams in Apache Flink, detailing how non‑retract Kafka sources and Group‑By aggregations generate delete/insert messages, provides code examples for aggregation and sink operators, and compares retract mechanisms across aggregation and CDC sink scenarios.
This article introduces the ElasticsearchSink for Apache Flink, explains how to add Maven dependencies, implement the sink with configuration and retry settings, details failure handlers, and highlights important considerations such as exception handling and checkpoint requirements for reliable streaming pipelines.
This tutorial explains how to consume Kafka messages in real time using Apache Flink and persist them into a MySQL database by adding the JDBC dependency, implementing a custom RichSinkFunction, and configuring a Flink job with a Kafka source and MySQL sink.
