Amazon EMR on EKS is a managed service that allows users to run Apache Spark on Kubernetes. This service provides a scalable and cost-effective way to process large amounts of data using Spark. In this article, we will discuss how to submit Spark jobs using Spark Operator and spark-submit with Amazon Web Services.
Spark Operator is an open-source project that simplifies the deployment and management of Spark applications on Kubernetes. It provides a custom resource definition (CRD) for SparkApplications, which allows users to define and manage Spark jobs as Kubernetes resources. Spark Operator also provides a set of controllers that monitor the state of SparkApplications and automatically manage their lifecycle.
To submit a Spark job using Spark Operator, you need to create a SparkApplication resource that defines the job’s configuration. The configuration includes the Spark version, the main class or script to run, the input and output paths, and any additional arguments or environment variables. You can also specify the number of driver and executor pods, the memory and CPU resources for each pod, and other Kubernetes-specific settings.
Here is an example of a SparkApplication resource that runs a simple word count job:
“`yaml
apiVersion: “sparkoperator.k8s.io/v1beta2”
kind: SparkApplication
metadata:
name: wordcount
spec:
type: Scala
mode: cluster
image: “spark:3.1.1”
mainClass: “org.apache.spark.examples.JavaWordCount”
mainApplicationFile: “local:///opt/spark/examples/jars/spark-examples_2.12-3.1.1.jar”
arguments:
– “s3://my-bucket/input”
– “s3://my-bucket/output”
driver:
cores: 1
memory: “512m”
labels:
app: spark
role: driver
executor:
cores: 1
instances: 2
memory: “1g”
labels:
app: spark
role: executor
“`
In this example, we specify the Spark version as 3.1.1 and the job type as Scala. We also provide the image name for the Spark container and the main class and application file for the job. The arguments specify the input and output paths for the job. Finally, we define the resources for the driver and executor pods, including the number of cores, memory, and labels.
Once you have created the SparkApplication resource, you can submit it to Kubernetes using kubectl apply command:
“`bash
kubectl apply -f wordcount.yaml
“`
Spark Operator will create a new Spark driver pod and one or more executor pods, depending on the configuration. It will also monitor the job’s progress and update the status of the SparkApplication resource accordingly.
Alternatively, you can submit a Spark job using spark-submit command-line tool. This tool allows you to run Spark jobs on EMR clusters or standalone Spark clusters, as well as on Kubernetes using Spark Operator.
To submit a Spark job using spark-submit with EMR on EKS, you need to provide the following parameters:
– The Spark version and deployment mode (cluster or client)
– The main class or script to run
– The input and output paths
– Any additional arguments or environment variables
– The Kubernetes namespace and service account to use
Here is an example of a spark-submit command that runs the same word count job as before:
“`bash
spark-submit
–master k8s://https://
–deploy-mode cluster
–name wordcount
–class org.apache.spark.examples.JavaWordCount
–conf spark.executor.instances=2
–conf spark.kubernetes.container.image=spark:3.1.1
–conf spark.kubernetes.namespace=my-namespace
–conf spark.kubernetes.authenticate.driver.serviceAccountName=spark
local:///opt/spark/examples/jars/spark-examples_2.12-3.1.1.jar
s3://my-bucket/input
s3://my-bucket/output
“`
In this example, we specify the Kubernetes API server URL and the deployment mode as cluster. We also provide the job name, main class, and application file for the job. The –conf options specify the number of executor pods, the Spark container image, and the Kubernetes namespace and service account to use. Finally, we provide the input and output paths as arguments.
Submitting Spark jobs using Spark Operator and spark-submit with Amazon EMR on EKS provides a flexible and scalable way to process large amounts of data using Spark. With these tools, you can easily define and manage Spark jobs as Kubernetes resources or command-line parameters, and take advantage of the benefits of running Spark on Kubernetes.
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