在Kubernetes上运行Spark作业，你需要创建一个Spark的部署和相应的Kubernetes资源。以下是一个简化的例子，展示了如何使用Kubernetes部署Spark Driver和Executor。
首先，确保你有一个运行中的Kubernetes集群，并且kubectl 命令行工具已经配置好可以与集群通信。
创建Spark的配置 ConfigMap:
apiVersion: v1
kind: ConfigMap
metadata:
name: spark-config
data:
spark-defaults.conf: |
spark.kubernetes.driver.pod.name=spark-driver-pod
spark.kubernetes.executor.pod.namespace=default
...
创建Spark Driver的部署:
apiVersion: apps/v1
kind: Deployment
metadata:
name: spark-driver
spec:
replicas: 1
template:
metadata:
labels:
component: spark
node: driver
spec:
containers:
- name: spark-kubernetes-driver
image: gcr.io/spark-operator/spark-driver:v2.4.5
command: "/bin/spark-submit"
args: [
"--master", "k8s",
"--deploy-mode", "cluster",
"--name", "spark-job",
"--class", "org.apache.spark.examples.SparkPi",
"--conf", "spark.kubernetes.driver.pod.name=spark-driver-pod",
...
"local:///path/to/your/spark/job.jar"
]
env:
- name: SPARK_CONF_DIR
value: "/opt/spark/conf"
volumeMounts:
- name: spark-config-volume
mountPath: /opt/spark/conf
volumes:
- name: spark-config-volume
configMap:
name: spark-config
创建Spark Executor的部署:
apiVersion: apps/v1
kind: Deployment
metadata:
name: spark-executors
spec:
replicas: 2
template:
metadata:
labels:
component: spark
node: executor
spec:
containers:
- name: spark-kubernetes-executor
image: gcr.io/spark-operator/spark-executor:v2.4.5
env:
- name: SPARK_K8S_EXECUTOR_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: SPARK_CONF_DIR
value: "/opt/spark/conf"
volumeMounts:
- name: spark-config-volume
mountPath: /opt/spark/conf
volumes:
- name: spark-config-volume
configMap:
name: spark-config
确保替换以上配置中的镜像版本和Spark作业的jar路径以及参数。这些YAML文件定义了Spark作业在Kubernetes上的基本部署，包括配置、驱动器和执行器的部署。
要运行这些部署，只需将这些YAML文件应用到你的Kubernetes集群：
kubectl apply -f spark-config.yaml
kubectl apply -f spark-driver.yaml
kubectl apply -f spark-executors.yaml
这将启动一个Spark作业，其中包括一个Driver和多个Executor。Kubernetes将负责调度和管理这些容器的生命周期。