Operationalizing YARN based Hadoop Clusters in the Cloud

1. Operationalizing YARN Based Hadoop
Clusters in the Cloud
Abhishek Modi
Lead Developer,
Yarn and Hadoop Team,
Qubole

2. Hadoop at Qubole
● Over 300 Petabytes data processed per month.
● More than 100 customers with more than 1000 active users.
● Over 1 million Hadoop jobs completed per month.
● More than 8,000 Hadoop clusters brought up per month.

3. Qubole Architecture
Qubole UI
Qubole
SaaS
Hadoop
Cluster
Hadoop
Cluster
Hadoop
Cluster
Hadoop
Cluster
Cloud
Storage
Prod
New
Qubole
REST API

4. Ephemeral Hadoop Clusters
Bring up Cluster Perform Jobs Terminate Cluster
Scale
Up
Scale
Down

5. • Use cloud storage for job output and input.
• Needs to auto-scale as per work-load.
• Store job history and logs at persistent location.
• Adapting YARN/HDFS to take into account ephemeral cloud nodes.
Challenges: Ephemeral Hadoop Clusters

6. YARN Auto-scaling

7. Up-scaling for MR jobs
Resource
Manager
Node 1
Node 2
User
Submit Job
Launches
MR AM
NodeManager
MR AppMaster
Container
Request
Allocate
Resources
NodeManager
C1 C2
Task
Progress
Up Scale
Request
Cluster
Manager
Add Node
NodeManager
C3 C4
Node 3

8. Generic Up-scaling
Resource
Manager
Cluster
Manager
MR
AppMaster
Spark
AppMaster
Tez
AppMaster
Up Scale
Request
Add
Node

9. Node 2
Down-scaling
Resource
Manager
NodeManager
C1 C2
C3 C4
NodeManager
C1 C2
C3 C4
NodeManager
C1 C2
C4C3
Status
Update
Evaluates cluster is
being underutilized and
can be down scaled
Selects node whose
estimated task
completion time is
lowest
Graceful
Shutdown
User
Submits
Job
Allocates
container
Job1
Completes
Cluster
Manager
Remove
Node
Job 1
Job 2
Job 3
Decommission
Node
Node 1
Node 3

10. Re-commissioning
NodeManager
C2C1
NodeManager
C1 C2
C4C3
C4C3
NodeManager
C4
C2C1
Resource
Manager
Graceful Shutdown
User
Submit Job
Allocates
Containers
C3
Upscale
Request
Re-commission

11. • Containers contains output of Map tasks.
• Can not be terminated until Map output is consumed.
• Upload Map output to cloud.
• Reducers access Map output directly from cloud.
Further Optimizations in Down-scaling

12. • DFS used and incoming data rate is monitored periodically.
• Upscale if free DFS goes below an absolute threshold.
• Upscale if free DFS is projected to go below absolute threshold in next few
minutes.
HDFS Based Up-Scaling

13. Cost Benefits of Auto-scaling

14. • AWS and Google Cloud provide volatile nodes termed as “Spot Nodes” or “Pre-
emptible Nodes”
• Available at very low price as compared to stable nodes.
• Can be lost at any point of time without any prior notification.
• Hadoop’s failure resilience makes these nodes good candidates for Hadoop.
• Approx. 77% of all Qubole clusters make use of volatile nodes.
Volatile Nodes

15. • While starting cluster, percentage of volatile nodes can be specified.
• A maximum ‘bid’ price for volatile nodes is also specified.
• Qubole Placement Policy:
– Ensures at least one replica of each HDFS block is present on Stable Node.
– No Application Master is scheduled on volatile nodes.
Volatile Nodes at Qubole

16. • While up-scaling, RM tries to maintain volatile node percentage.
• If volatile node are not available, fall back to stable nodes.
• Periodically tries to re-balance the volatile node percentage.
Rebalancing – Volatile Nodes

17. • Show job history for terminated clusters.
• Multi-tenant job history server.
• Clusters are generally running in isolated networks – need a proxy.
• Job History files needs to be stored at cloud storage.
Job History

18. Job History – Running cluster
Qubole
UI
Cluster Proxy
Hadoop
Cluster
Hadoop
Cluster
Hadoop
Cluster
Hadoop
Cluster
User
Clicks
UI link
Authenticates the
request
Find cluster
corresponding to
the request
Proxifies link in html
and js
Sends
Request

19. Job History – Terminated Cluster
Qubole
UI
User
Cluster
Proxy
Job
History
Server
Clicks
UI link
Authenticates
the request
Finds cluster
is down
Fetches jhist
file from cloud
Jhist file
Rendered
JobHist
Proxifies Link

20. • Writing output directly to cloud without storing at temporary location.
• Optimizations in getting file status for large number of files with common prefix.
• Added streaming upload support in NativeS3FileSystem.
• Added bulk delete and move support in NativeS3FileSystem.
Cloud Read/Write Optimizations

21. • Issues with newer version of JetS3t (0.9.4)​
– Seek performance degraded around 10X.​
– Empty files.​
• Deadlock when number of threads reading from S3 exceeds JetS3t’s max number
of connections (HADOOP-12739).​
• Too many queues causes a deadlock in cluster.(YARN-3633)​
• Support for Socks Proxy was missing from HA.​
Open Source Issues

22. Thank You