Spark on Kubernetes

2. • Senior Software Engineer of SK Telecom
• Commercial Products
• Big Data Discovery Solution (~’17)
• Hadoop DW (~’15)
• PaaS(CloudFoundry) (~’13)
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• Mail to : jerryjung@apache.org
2

3. Kubernetes
Spark deployment using Kubernetes
Spark on Kubernetes
Demo
3

4. Open Source
Automation
Framework for
deploying,
managing, and
scaling
applications.
4

5. Kubernetes provides a common API and
self-healing framework which
automatically handles machine failures
and application deployments, logging,
and monitoring.
5

6. 6
https://github.com/kubernetes/kubernetes/blob/master/docs/design/architecture.md

7. https://thenewstack.io/kubernetes-an-overview/
7

8. https://thenewstack.io/kubernetes-an-overview/
8

9. 9
https://thenewstack.io/kubernetes-an-overview/

10. Clusters - set of compute, storage, network
resource
Pods - colocated group of application containers
that share volumes and a networking stack
Replication Controllers - ensure a specific number
of pods, manage pods, status updates
Services - cluster wide service discovery
10

11. Node #1 192.168.0.2
Pod #1
10.0.0.2
Node #5 192.168.0.6
Volume
Network
Pod #2
10.0.0.3
Volume
Network
Pod #8
10.0.0.9
Volume
Network
8080 8080 8080
11

12. Support for Event Stream Processing
Fast Data Queries in Real Time
Improved Programmer Productivity
Fast Batch Processing of Large Data Set
12

13. Driver Process that contains the SparkContext
Executor Process that executes one or more Spark tasks
Master Process that manages applications across the cluster
Worker Process that manages executors on a particular node
13
http://spark.apache.org/docs/latest/cluster-overview.html

14. Driver Program
SparkContext
Cluster Manager
Worker Node
Executor
Worker Node
Executor
Worker Node
Executor
14

15. http://freecontent.manning.com/running-spark-an-overview-of-sparks-runtime-architecture/
15
cluster mode client mode

16. https://www.slideshare.net/grahaindia/new-features-of-kubernetes-v120-beta
DaemonSet
16

17. StatefulSets
http://blog.kubernetes.io/2017/01/running-mongodb-on-kubernetes-with-statefulsets.html
17
$(statefulset name)-$(ordinal)

18. https://github.com/Comcast/kube-yarn
18

19. Node #1 …. #n
HDFS
DN
HDFS
NN
HDFS
DN……………
YARN
NM
YARN
RM
YARN
NM……………
zeppelin
pod
spark submit
19

20. 2020

21. Node #1 …. #n
HDFS
DN
HDFS
NN
HDFS
DN……………
YARN
NM
YARN
RM
YARN
NM……………
zeppelin
X
pod
spark submit
21

22. 22

23. 23

24. 24

25. https://github.com/kubernetes/kubernetes/issues/34377
https://issues.apache.org/jira/browse/SPARK-18278
25

26. https://spark-summit.org/2017/events/apache-spark-on-kubernetes/
26

27. https://spark-summit.org/2017/events/apache-spark-on-kubernetes/
27

28. SPARK-18278 - Spark on Kubernetes Design Proposal.pdf
28

29. 29

30. Node Manager # 1…N
external
shuffle plugin
RDD
(IntermediateFile)
RDD
(IntermediateFile)
External Shuffle
30
Executor
Long-Running ETL jobs
Interactive application or Server
Any application with large shuffles
Executor

31. <property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle,spark_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.aux-services.spark_shuffle.class</name>
<value>org.apache.spark.network.yarn.YarnShuffleService</value>
</property>
1. shuffle plugin add jar
2. yarn-site.xml add plugin
31

32. spark.dynamicAllocation.enabled true
spark.shuffle.service.enabled true
spark.dynamicAllocation.minExecutors 50
spark.dynamicAllocation.maxExecutors 100
spark.dynamicAllocation.initialExecutors 50
spark.dynamicAllocation.schedulerBacklogTimeout 5s
spark.dynamicAllocation.executorIdleTimeout 60
http://spark.apache.org/docs/latest/job-scheduling.html#dynamic-resource-allocation
cf) Mesos - Coarse-Grained Mode
3. edit spark-default.conf
32

33. https://spark-summit.org/2017/events/apache-spark-on-kubernetes/
3333
1
2 3

34. https://www.slideshare.net/cfregly/spark-on-kubernetes-advanced-spark-and-tensorflow-
meetup-jan-19-2017-anirudh-ramanthan-from-google-kubernetes-team
34
1

35. bin/spark-submit
--deploy-mode cluster
--class org.apache.spark.examples.SparkPi
--master k8s://https://{k8s address}
--kubernetes-namespace default
--conf spark.executor.instances=5
--conf spark.app.name=spark-pi
--conf spark.kubernetes.driver.docker.image=kubespark/spark-driver:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.executor.docker.image=kubespark/spark-executor:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.initcontainer.docker.image=kubespark/spark-init:v2.1.0-kubernetes-0.2.0
local:///opt/spark/examples/jars/spark-examples_2.11-2.1.0-k8s-0.2.0-SNAPSHOT.jar
3535

36. https://www.slideshare.net/cfregly/spark-on-kubernetes-advanced-spark-and-tensorflow-
meetup-jan-19-2017-anirudh-ramanthan-from-google-kubernetes-team
36
2

37. apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
labels:
app: spark-shuffle-service
spark-version: 2.1.0
name: shuffle
spec:
template:
metadata:
labels:
app: spark-shuffle-service
spark-version: 2.1.0
spec:
volumes:
- name: temp-volume
hostPath:
path: '/var/tmp' # change this path according to your cluster configuration.
containers:
- name: shuffle
image: kubespark/spark-shuffle:v2.1.0-kubernetes-0.2.0
37

38. bin/spark-submit
--deploy-mode cluster
--class org.apache.spark.examples.GroupByTest
--master k8s://https://{k8s address}
--kubernetes-namespace default
--conf spark.app.name=group-by-test
--conf spark.kubernetes.driver.docker.image=kubespark/spark-driver:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.executor.docker.image=kubespark/spark-executor:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.initcontainer.docker.image=kubespark/spark-init:v2.1.0-kubernetes-0.2.0
--conf spark.dynamicAllocation.enabled=true
--conf spark.shuffle.service.enabled=true
--conf spark.kubernetes.shuffle.namespace=default
--conf spark.kubernetes.shuffle.labels="app=spark-shuffle-service,spark-version=2.1.0"
local:///opt/spark/examples/jars/spark-examples_2.11-2.1.0-k8s-0.2.0-SNAPSHOT.jar 10 40000 2
38

39. https://spark-summit.org/2017/events/apache-spark-on-kubernetes/
39
3

40. ---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: spark-resource-staging-server
spec:
replicas: 1
---
apiVersion: v1
kind: Service
metadata:
name: spark-resource-staging-service
spec:
type: NodePort
selector:
resource-staging-server-instance: default
ports:
- protocol: TCP
port: 10000
targetPort: 10000
nodePort: 31000
40

41. bin/spark-submit
--deploy-mode cluster
--class org.apache.spark.examples.SparkPi
--master k8s://{k8s address}
--kubernetes-namespace default
--conf spark.executor.instances=5
--conf spark.app.name=spark-pi
--conf spark.kubernetes.driver.docker.image=kubespark/spark-driver:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.executor.docker.image=kubespark/spark-executor:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.initcontainer.docker.image=kubespark/spark-init:v2.1.0-kubernetes-0.2.0
--conf spark.kubernetes.resourceStagingServer.uri=http://{node ip}:31000
examples/jars/spark-examples_2.11-2.1.0-k8s-0.2.0-SNAPSHOT.jar
41

42. 4242

43. 43

44. https://spark-summit.org/2017/events/hdfs-on-kubernetes-lessons-learned/
4444

45. https://spark-summit.org/2017/events/hdfs-on-kubernetes-lessons-learned/
4545

46. https://spark-summit.org/2017/events/hdfs-on-kubernetes-lessons-learned/
4646

47. https://spark-summit.org/2017/events/hdfs-on-kubernetes-lessons-learned/
4747
https://github.com/apache-spark-on-k8s/kubernetes-HDFS

48. 48

49. 49

50. 50