Spark on Yarn allows for dynamic provisioning of resources by allowing the Spark application master to request additional executors from Yarn as needed and release idle executors. This helps optimize resource utilization in the Yarn cluster. Qubole provides interfaces like the command UI, REST APIs, and SDKs to easily submit Spark jobs to Yarn clusters managed in Qubole, and integrates Spark with Hive by configuring Spark programs to access the Hive metastore. Key challenges include ensuring low overhead from Yarn, handling cached data, and network performance between clusters and shared services.

1. Spark on Yarn
Spark Meetup
Oct 17, 2015

2. Agenda
• Autoscaling Spark Apps
• Yarn
• Cluster Management
• Interfaces/APIs for Running Spark Jobs
• JobServer
• Persistent History Server
• Hive Integration with Spark

3. Autoscaling Spark Applications

4. Spark Provisioning: Problems
• Spark Application starts with fixed number of
resources and hold on to them till its alive
• Sometimes its difficult to estimate resources
required by a job since AM is long running
• It becomes limiting spl when Yarn clusters can
autoscale.

5. Dynamic Provisioning
• Speed up spark commands by using free
resources in yarn cluster and also by releasing
resources when free to RM.

6. Spark on Yarn basics
Driver
AM
Executor-1 Executor-n
• Cluster Mode: Driver and AM run in same JVM in
a yarn Executor
• Client Mode: Driver and AM run in separate JVM
• Driver and AM talk using Actors to handle both
cases
Driver AM Executor-1 Executor-n

7. Dynamic Provisioning: Problem
Statement
• Two parts:
– Spark AM has no way to ask for additional
containers and give up free containers
– Automating the process of requesting containers
and releasing containers. Cached data in
containers make this difficult

8. Dynamic Provisioning: Part1

9. Dynamic Provisioning: Part1
• Implementation of 2 new apis:
// Request 5 extra executors
sc.requestExecutors(5)
// Kill executors with IDs 1, 15, and 16
sc.killExecutors(Seq("1", "15", "16"))

10. requestExecutors
AM
Reporter Thread
E1 E2 En
• AM has reporter thread that has count of
number of executors
• Reporter thread was used to restart died
executors
• Driver increments count of number of
executors when sc.requestExecutors is called.
Driver

11. removeExecutors
• To kill executors, one must precisely tell which
executors need to be killed
• Driver maintains list of all executors and can
be obtained by:
sc.executorStorageStatuses.foreach(x => println(x.blockManagerId.executorId))
• Whats cached in each executor is also
available using:
sc.executorStorageStatuses.foreach(x => println(s”memUsed = ${x.memUsed}
diskUsed=${x.diskUsed)”))

12. Removing Executors Tradeoffs
• BlockManager in each executor can have
cached RDDs, shuffle and broadcast data
• Killing an executor with shuffle data will
require the stage to rerun.
• To avoid this use external shuffle service
introduced in spark-1.2

13. Dynamic Provisioning: Part2

14. Upscaling Heuristics
• Request Executors as many pending tasks
• Request Executors in rounds if there are
pending tasks, doubling number of executors
added in each round bounded by some upper
limit
• Request executors by estimating workload
• Introduced –max-executors as extra param

15. Downscaling Heuristics
• Remove Executors when they are idle
• Remove Executors if then are idle for X secs
• Cant downscale executors with shuffle data or
broadcast data.
• --num-executors act as minimum executors

16. Scope
• Kill executors on spot nodes first
• Flag for not killing up executors if they have
shuffle data

17. Yarn

18. Hadoop1

19. Disadvantages of hadoop1
• Limited to only MR
• Separate Map and Reduce slots =>
underutilization
• JT has multiple responsibilities of job
scheduling, monitoring and resource
allocation.

20. Yarn Overview

21. Advantages of Spark on Yarn
• General cluster for running multiple
workflows. AM can have custom logic for
scheduling
• AM can ask for more containers when
required and give up containers when free
• This become even better when yarn clusters
can autoscale
• Get features like spot nodes etc which brings
additional challenges

22. Cloud Cluster Management in
Qubole

23. Cluster management
• Clusters run in customer accounts
• Support for VPC and multiple regions and
multiple clouds
• Various node types supported
• Full ssh access to clusters for customers
• Ability to run custom bootstrap code on node
start

24. Cluster Management Interface

25. Interfaces/APIs to submit Spark
Jobs

26. Using SparkSQL - Command UI

27. Using SparkSQL - Results

28. Using SparkSQL - Notebook
• SQL, Python, Scala code can be input

29. Using SparkSQL - REST api - scala
curl --silent -X POST
-H "X-AUTH-TOKEN: $AUTH_TOKEN"
-H "Content-Type: application/json"
-H "Accept: application/json"
-d '{
"program" : "val s = new org.apache.spark.sql.hive.HiveContext(sc);
s.sql("show tables").collect.foreach(println)",
"language" : "scala",
"command_type" : "SparkCommand"
}'
https://api.qubole.net/api/latest/commands

30. Using SparkSQL - REST api - sql
curl --silent -X POST
-H "X-AUTH-TOKEN: $AUTH_TOKEN"
-H "Content-Type: application/json"
-H "Accept: application/json"
-d '{
"program" : "show tables",
"language" : "sql",
"command_type" : "SparkCommand"
}'
https://api.qubole.net/api/latest/commands
NOT RELEASE YET

31. Using SparkSQL - qds-sdk-py / java
from qds_sdk.commands import SparkCommand
with open(“test_spark.py”) as f:
code = f.read()
cmd = SparkCommand.run(language="python",
label="spark", program=code)
results = cmd.get_results()

32. Using SparkSQL - Cluster config

33. Spark UI container info

34. JobServer

35. Persistent History Server

36. Spark Hive Integration

37. What is involved?
• Spark programs should be able to access hive
metastore
• Other Qubole services can be producers or
consumers of data and metadata(hive, presto,
pig etc)

38. Basic cluster organization
• DB instance in Qubole account
• ssh tunnel from master to metastore DB
• Metastore server running on master on port
10000
• On master and slave nodes, hive-site.xml:-
hive.metastore.uris=thrift://master_ip:10000

39. Hosted metastore

40. Questions

41. Problems
• yarn overhead should be 20% (TPC-H)
• Parquet needs higher PermGen
• cached tables use actual table
• alter table recover partitions not supported
• VPC cluster has slow access to metastore
• SchemaRDD gone - old jars dont run
• hive jars needed on system classpath