This article details the major memory model changes in Flink 1.11 for JobManager and TaskManager, compares them with Flink 1.9, provides concrete JVM command examples, explains the relationship between memory settings and parallelism, and introduces fine‑grained resource management for streaming workloads.
This article explains the architecture of a Flink cluster, detailing the startup procedures for JobManager and TaskManager, the three deployment modes, and the end‑to‑end flow of a Flink job from client code through StreamGraph, JobGraph, ExecutionGraph to the physical execution on TaskManagers.
This tutorial explains how to run Apache Flink jobs on Kubernetes by building Docker images, deploying JobManager and TaskManager components with Kubernetes manifests, configuring high‑availability with ZooKeeper and HDFS, and using SavePoints and scaling techniques to manage and extend Flink streaming applications.
This article explains Flink’s architecture, detailing the roles of Client, JobManager and TaskManager, walks through a SocketTextStreamWordCount example, and clarifies the four‑layer graph model—StreamGraph, JobGraph, ExecutionGraph, and the physical execution graph—highlighting why each layer exists.
This article explains Flink’s cluster architecture, the roles of Client, JobManager and TaskManager, demonstrates a SocketTextStreamWordCount example, and details the four-layer execution graph (StreamGraph, JobGraph, ExecutionGraph, Physical Execution Graph) to illustrate how Flink schedules and runs streaming jobs.
