Community

Blog Events Webinars Tutorials Forum
  1. Blog
  2. Events
  3. Webinars
  4. Tutorials
  5. Forum
Create Account
×
Community Blog Running Hadoop YARN with K8s by Koordinator

Running Hadoop YARN with K8s by Koordinator

This article introduces Koordinator's support for running Hadoop YARN jobs by utilizing koord-batch resources alongside other Kubernetes pods.

By zwzhang

Introduction

Koordinator has supported hybrid orchestration workloads on Kubernetes, so that batch jobs can use the requested but unused resource as koord-batch priority and BE QoS class to improve the cluster utilization. However, there are still lots of applications running beyond K8s such as Apache Haddop YARN. As a resource management platform in BigData ecosystem, YARN has supported numbers of computing engines including MapReduce, Spark, Flink, Presto, etc.

In order to extend the co-location scenario of Koordinator, now the community has provided Hadoop YARN extended suits Koordinator YARN Copilot in BigData ecosystem, supporting running Hadoop YARN jobs by koord-batch resources with other K8s pods. The Koordinator YARN Copilot has following characteristics:

  • Open-Source native: implement against open-sourced version of Hadoop YARN; so there is no hack inside YARN modules.
  • Unifed resource priority and QoS strategy: the suits aims to the koord-batch priority of Koordinator, and also managed by QoS strategies of koordlet.
  • Resource sharing on node level: node resources of koord-batch priority can be requested by tasks of YARN or Batch pods both.
  • Adaptive for multiple environments: the suits can be run under any environment, including public cloud or IDC.

Prerequisite

  • Kuberenetes >= 1.18
  • Koordinator >= 1.4
  • Koordinator YARN Copilot >= 0.1
  • Hadoop YARN >= 3.2.1

Installation

All charts can be simply installed by helm v3.5+, which is a simple command-line tool, and you can get it from here.

1

Install Koordinator

Please make sure Koordinator components are correctly installed in your cluster. For more information about install and upgrade, please refer to Installation.

# Firstly add koordinator charts repository if you haven't do this.
$ helm repo add koordinator-sh https://koordinator-sh.github.io/charts/

# [Optional]
$ helm repo update

# Install the latest version.
$ helm install koordinator koordinator-sh/koordinator

Install Hadoop YARN

Haddop YARN is consist of ResourceManger and NodeManager, and currently we recommend users deploy the ResourceManger independently on hosts, while the NodeManager as pod.

Koordinator community provides a demo chart hadoop-yarn with Hadoop YARN ResourceManager and NodeManager, also including HDFS components as optional for running example jobs easily. You can use the demo chart for quick start of YARN co-location, otherwise you can refer to Installation for official guides if you want to build your own YARN cluster.

# Firstly add koordinator charts repository if you haven't do this.
$ helm repo add koordinator-sh https://koordinator-sh.github.io/charts/

# [Optional]
$ helm repo update
# Install the latest version.
$ helm install hadoop-yarn koordinator-sh/hadoop-yarn

# check hadoop yarn pods running status
kubectl get pod -n hadoop-yarn

Some key information should be known before you install YARN:

  • ResourceManager must be accessible in K8s pod, no matter it is deployed as host mode or pod mode.
  • NodeManager must be deployed as pod mode with an annotation yarn.hadoop.apache.org/node-id=${nm-hostname}:8041 to identify node ID in YARN.
  • NodeManager must use CgroupsLCEResourcesHandler as linux container executor, and specifies cgroup hierarchy under k8s best-effort directory.
  • NodeManager pods request resources as koord-batch priority, so Koordinator must be pre-installed with co-location enabled.

These features have already been configured in Haddop YARN chart in koordinator repo, and if you are using self-maintained YARN, please check the Koordinator repo for reference during installation.

Install Koordinator YARN Copilot

Koordinator YARN Copilot is consist of yarn-opeartor and copilot-agent (WIP),

# Firstly add koordinator charts repository if you haven't do this.
$ helm repo add koordinator-sh https://koordinator-sh.github.io/charts/

# [Optional]
$ helm repo update

# Install the latest version.
$ helm install koordinator-yarn-copilot koordinator-sh/koordinator-yarn-copilot

Configuration

1.  configuration of koord-manager
After installing through the helm chart, the ConfigMap slo-controller-config will be created in the koordinator-system namespace. YARN tasks are managed under best-effort cgroup, which should be configured as host level application, and here are the related issue of YARN tasks management under Koordinator.

Create a configmap.yaml file based on the following ConfigMap content:

apiVersion: v1
data:
  colocation-config: |
    {
      "enable": true
    }
  resource-threshold-config: |
    {
      "clusterStrategy": {
        "enable": true
      }
    }
  resource-qos-config: |
    {
      "clusterStrategy": {
        "lsrClass": {
          "cpuQOS": {
            "enable": true
          }
        },
        "lsClass": {
          "cpuQOS": {
            "enable": true
          }
        },
        "beClass": {
          "cpuQOS": {
            "enable": true
          }
        }
      }
    }
  host-application-config: |
    {
      "applications": [
        {
          "name": "yarn-task",
          "priority": "koord-batch",
          "qos": "BE",
          "cgroupPath": {
            "base": "KubepodsBesteffort",
            "relativePath": "hadoop-yarn/"
          }
        }
      ]
    }
kind: ConfigMap
metadata:
  name: slo-controller-config
  namespace: koordinator-system

To avoid changing other settings in the ConfigMap, we commend that you run the kubectl patch command to update the ConfigMap.

$ kubectl patch cm -n koordinator-system slo-controller-config --patch "$(cat configmap.yaml)"

2.  configuration of koord-yarn-copilot communicates with YARN ResourceManager during resource syncing, and the ConfigMap defines YARN related configurations.

apiVersion: v1
data:
  core-site.xml: |
    <configuration>
    </configuration>
  yarn-site.xml: |
    <configuration>
        <property>
            <name>yarn.resourcemanager.admin.address</name>
            <value>resource-manager.hadoop-yarn:8033</value>
        </property>
        <property>
            <name>yarn.resourcemanager.address</name>
            <value>resource-manager.hadoop-yarn:8032</value>
        </property>
    </configuration>
kind: ConfigMap
metadata:
  name: yarn-config
  namespace: koordinator-system

You can change the default address and port at yarnConfiguration.resourceManager in chart values.

(Optional) Advanced Settings

You can check the helm chart hadoop-yarm, and koordinator-yarn-copilot for more advanced settings.

Check YARN Available Resources

1.  Check node allocatable batch resources of Koordinator on node.

$ kubectl get node -o yaml | grep batch-cpu
      kubernetes.io/batch-cpu: "60646"
      kubernetes.io/batch-cpu: "60486"

$ kubectl get node -o yaml | grep batch-memory
      kubernetes.io/batch-memory: "245976973438"
      kubernetes.io/batch-memory: "243254790644"

2.  Check node allocatable resources in YARN Visit YARN ResourceManager web UI address ${hadoop-yarn-rm-addr}:8088/cluster/nodes in browser to get YARN NM status and allocatable resources.

If you are using the hadoop-yarn demo chart in Koordinator repo, please execute the following command to make RM accessible locally.

$ kubectl port-forward -n hadoop-yarn service/resource-manager 8088:8088

Then open the ui in your browser: http://localhost:8088/cluster/nodes

The VCores Avail and Mem Avail will be exactly same with batch resources of K8s nodes.

Submit YARN Jobs

Spark, Flink and other computing engines support submitting jobs to YARN since they were published, check the official manual like Spark and Flink before you start the work.

It is worth noting the hadoop-yarn demo chart in Koordinator repo has already integrated with Spark client, you can execute the following command to submit an example job, and get the running status through web UI of ResourceManager.

$ kubectl exec -n hadoop-yarn -it ${yarn-rm-pod-name} yarn-rm -- /opt/spark/bin/spark-submit --master yarn --deploy-mode cluster --class org.apache.spark.examples.SparkPi /opt/spark/examples/jars/spark-examples_2.12-3.3.3.jar 1000
Hadoop Developers Open Source Kubernetes Tutorials cloud native YARN K8s Koordinator
0 1 0
Share on

Read previous post:

Apache RocketMQ EventBridge: Build the Next Generation of Event-driven Engines

Read next post:

Coordinated sharing of CPU resources in Colocation Scenarios - Fine-grained CPU Orchestration

Alibaba Cloud Native Community

506 posts | 48 followers

You may also like

Comments

Alibaba Cloud Native Community

506 posts | 48 followers

Related Products

More Posts by Alibaba Cloud Native Community

See All