1.Kubernetes SIG Big Data 

2.Landscape Introduce SIG Big Data Apache Airflow Apache Spark Future of this SIG Audience Dialogue 

3. SIG Mission1 Serve as a community resource for advising big data and data science related software projects on techniques and best practices for integrating with Kubernetes. Represent the concerns of users from big data communities to Kubernetes for the purposes of driving new features and other enhancements, based on big data use cases. 1: https://github.com/kubernetes/community/pull/2988 

4.Chairs Anirudh Ramanathan (Rockset) Erik Erlandson (Red Hat) Yinan Li (Google) 

5. Provenance Winter 2017: Revived as part of the process of creating a community platform for prototyping development of a Kubernetes scheduler backend for Apache Spark. 

6. Software Projects Kubernetes scheduler backend for Apache Spark HDFS deployments for Kubernetes Apache Airflow operator and executor for Kubernetes 

7.Participating Organizations Bloomberg Google Lightbend Palantir Pepperdata Red Hat 

8.Apache Airflow Task Dependencies Task B Task D Task A Task F Task C Task E Execution 

9. Airflow Operators ● Units of work ● Corresponds to a command or functionality ● Associates with a task ID Operator A Task A ● Contains parameters and other resources to execute 

10.Operator Flavors Run a bash command Invoke a python function Send an HTTP request Execute a SQL query 

11. Airflow Scheduler Task Task B D Task A Task F Task Task E C ● Runs tasks in order ● Tracks success and failure 

12.Airflow Executors Executor Task A ● Local ● Mesos Task B ● Kubernetes Task C 

13. Kubernetes Operator op = KubernetesPodOperator( name="example", container image task_id="Task-A", namespace='default', image=[container_image_name], cmds=["bash", "-cx"], arguments=["echo", "K8S!"], labels={"label": "value"}, secrets=[secret_file,secret_env] volume=[volume], command to run volume_mounts=[volume_mount] affinity=affinity, is_delete_operator_pod=True, hostnetwork=False, tolerations=tolerations) 

14.Airflow On Kubernetes Kubernetes Task Task B D Task A Task F Task Task E C DAG & Task State 

15.Airflow DAG Provisioning GitHub NFS EFS Cinder [Extend via hooks ...] 

16.Apache Spark Compute Model Logical View App 0 1 2 3 4 5 6 7 8 Physical View 0 1 2 3 4 5 6 7 8 Driver Executor Executor Executor 

17.Apache Spark Compute Model 0 2 4 6 8 10 12 14 16 Driver Executor Executor Executor λx: x * 2 λx: x * 2 λx: x * 2 λx: x * 2 

18.Spark on Kubernetes Driver Pod Executor Pod Executor Pod Executor Pod 0 2 4 6 8 10 12 14 16 Driver Executor Executor Executor λx: x * 2 λx: x * 2 λx: x * 2 λx: x * 2 

19.Cluster Mode Kubernetes Cluster Schedule driver pod scheduler Request executor pods Executor pod watch events apiserver mit - sub ar k exe sp Sc tor p cu he du ods le 

20.K8S Scheduler Backend for Spark What we have done so far • Initial release in Spark 2.3.0 with support for cluster mode, Java/Scala, remote dependencies, and limited pod customization. • More features in release 2.4.0: Python, R, and limited client mode support. • New features in upcoming Spark 3.0: Kerberos support and support for pod customization using a pod template. 

21. Client Mode (2.4) • Useful for interactive apps, e.g., notebooks and spark-shell. scheduler • Supports drivers running both inside and outside the cluster apiserver • Garbage collection of executor pods supported for in-cluster • Users are responsible for setting up network connectivity from executors to the driver • E.g., a headless service for in-cluster 

22. Kerberos Support (3.0) • Necessary for secure DT Secret HDFS access. keytab • Needs both a Delegation Token (DT) and Hadoop Submission api Client server configuration Hadoop • Does not yet support Config Hadoop Map configuration delegation token renewal. 

23. Kubernetes Operator for Spark apiVersion: "sparkoperator.k8s.io/v1alpha1" kind: SparkApplication metadata: name: spark-pi namespace: default spec: type: Scala mode: cluster image: "gcr.io/spark-operator/spark:v2.4.0" • Kubernetes CRD + custom controller mainClass: org.apache.spark.examples.SparkPi • Supports extensive pod customization through a mutating mainApplicationFile: "..." driver: admission webhook memory: "512m" serviceAccount: spark • Native Cron support for running scheduled applications executor: instances: 1 • Automatic application restart with a configurable restart policy memory: "512m" monitoring: • Supports exporting application-level metrics and driver/executor exposeDriverMetrics: true metrics to Prometheus exposeExecutorMetrics: true prometheus: • Supports installation with Helm port: 8090 restartPolicy: Never • Comes with a command-line tool sparkctl 

24. Roadmap (3.0 and Beyond) • Support for using a pod template to customize the driver and executor pods. • No more new configuration properties • Dynamic resource allocation and external shuffle service. • New shuffle service work in progress • Better support for local application dependencies on client machines. • Driver resilience for Spark Streaming applications. • Better scheduling support. 

25.Getting Involved • github.com/apache/spark: code under resource-managers/kubernetes • Documentation: http://spark.apache.org/docs/latest/running-on-kubernetes.html • Spark user & dev mailing lists • Jira (use Kubernetes for Component) • Slack sig-big-data: https://kubernetes.slack.com/messages/sig-big-data 

26.Trajectory: “Graduating” Projects Apache Spark K8S Backend Project upstream channels Apache Airflow 

27.Trajectory: Reaching Out To New Communities Flink Operator Demo Hazelcast (IMDG & Jet) 

28. SIG Big Data Charter Charter currently submitted for consideration https://github.com/kubernetes/community/pull/2988 Kubernetes Definition of a SIG: Owns some component, subsystem or other body of Kubernetes code. 

29.Future Directions Acquire ownership of Kubernetes code Working Group SIG sub-project User Community