https://github.com/captainirs/hadoop-yarn-k8s

A sandbox for running a Hadoop-YARN cluster on Kubernetes
https://github.com/captainirs/hadoop-yarn-k8s

hadoop kubernetes spark yarn

Last synced: 4 days ago
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A sandbox for running a Hadoop-YARN cluster on Kubernetes

• Host: GitHub
• URL: https://github.com/captainirs/hadoop-yarn-k8s
• Owner: CaptainIRS
• License: mit
• Created: 2022-10-15T07:10:55.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-08T19:10:05.000Z (9 months ago)
• Last Synced: 2024-04-09T14:32:24.747Z (7 months ago)
• Topics: hadoop, kubernetes, spark, yarn
• Language: Makefile
• Homepage:
• Size: 28.3 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Hadoop-YARN-k8s Sandbox

This is a sandbox for running a Hadoop YARN cluster on Kubernetes (using Minikube).

The sandbox can be started with a single command and will bring up a Hadoop YARN cluster with 2 datanodes, 1 namenode and 1 resource manager.

The various web interfaces for the cluster are proxied and exposed on the host machine automatically and can be accessed via the URLs listed below.

> [!WARNING]

> The sandbox is intended to be used for testing and development purposes only.

---

### Prerequisites

* Minikube

* GNU Make

* Docker [`buildx` CLI plugin](https://github.com/docker/buildx?tab=readme-ov-file#installing)

### System Requirements

* Minikube should have at least 8GB of memory and 4 CPUs for the sandbox to run properly (This can be changed in the `Makefile`).

---

### Running

* Run `make deploy` to deploy the system.

* Run `make clean` to bring down the system (All data will be lost!)

#### Running spark jobs

* Run `make spark_exec` to exec into the spark pod.

* The `work` directory is mounted as `/work` in the spark pod. You can copy your spark job to this directory and run it using `spark-submit`. (Use `--master yarn` to run the job on the YARN cluster.)

* Or you can enter the spark shell using `spark-shell --master yarn` and run your spark jobs interactively.

#### Managing the cluster or running MapReduce tasks

* Run `make shell` to exec into the `dfsadmin` pod.

* You can run HDFS commands using `hdfs dfs` or run MapReduce jobs using `yarn jar`.

---

### Important URLs

* Datanodes:

    * datanode-0: http://datanode-0.datanode.hadoop.svc.localho.st:9864

    * datanode-1: http://datanode-1.datanode.hadoop.svc.localho.st:9864

* Node Managers:

    * datanode-0: http://datanode-0.datanode.hadoop.svc.localho.st:8042

    * datanode-1: http://datanode-1.datanode.hadoop.svc.localho.st:8042

* Namenode: http://namenode-0.namenode.hadoop.svc.localho.st:9870

* Resource Manager: http://resourcemanager-0.resourcemanager.hadoop.svc.localho.st:8089

* Yarn UI 2: http://resourcemanager-0.resourcemanager.hadoop.svc.localho.st:8089/ui2/

* Yarn Timeline Server: http://resourcemanager-0.resourcemanager.hadoop.svc.localho.st:8188

* Mapreduce Job History Server: http://resourcemanager-0.resourcemanager.hadoop.svc.localho.st:19888

* Spark History Server: http://spark.hadoop.svc.localho.st:18080

---

### Screenshots

| ![DataNode](https://i.imgur.com/PcLl4f4.png) | ![NameNode](https://i.imgur.com/RSJHLyp.png) |

|:--:|:--:|

| Hadoop Data Node | Hadoop Name Node |

| ![NodeManager](https://i.imgur.com/gR65OSv.png) | ![ResourceManager](https://i.imgur.com/CJ4LXjY.png) |

| YARN Node Manager | YARN Resource Manager |

| ![Spark History Server](https://i.imgur.com/JTQwd0d.png) | ![Spark UI](https://i.imgur.com/PaB6Jze.png) |

| Spark History Server | Spark UI |

---

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.