Hadoop was upgraded from version 0.20.2 to 2.0 across 5 production clusters storing over 1 petabyte of data and processing billions of events daily. Key challenges included managing configurations across heterogeneous clusters, allowing data movement between clusters on different versions, ensuring application compatibility, addressing capacity limitations, and performing the upgrade with minimal downtime. Extensive testing, parallel component upgrades, and rolling restarts were used to successfully complete the upgrade while maintaining over 99% uptime and without any failures.

1. Hadoop 0.20.2 to 2.0
Jabir Ahmed
https://twitter.com/jabirahmed
https://www.linkedin.com/in/jabirahmed

2. • New Features
• HA Namenode
• YARN
• Bug fixes & performance improvements
• keeping pace with the community and to be ready to adapt
technologies that are being built rapidly over Hadoop
Why Hadoop 2.0 ?

3. Analytics Reporting
Data
Streaming
via HDFS
Adhoc
Querying
/Modeling
Real time
data
processing
Hadoop Usage @ inmobi

4. • HDFS & MRV1
• Falcon
‣ Active MQ
• HBase
• Conduit
‣ Scribe
‣ Conduit Worker
‣ Pintail
Hadoop Eco System
• Zoo-keeper
• Oozie
• WebHDFS
• Pig
• Hive
• Hcatalog &
Metastore

5. Cluster Topology

6. • 5 Production Quality Clusters spread across 5 co-locations
• 30 to 150 Node clusters
• Largest is over 1 Peta Byte
• Average 500TB
• 2,00,000+ Jobs Per day
• 6TB of data generated every day
• 10,000,000,000 Events Generated a day (10 Billion)
Clusters In Inmobi

7. Cluster topology
Centralized Cluster
Co-located Clusters Co-located Clusters

8. Upgraded Components
Component Old version New version Other
Changes
HDFS 0.20.2 2.0
Job-tracker 0.20.2 2.0
Oozie 3.3.2 3.3.2 Recompiled
Hbase 0.90.6 0.94.6
Webhdfs -NA- 0.0.3 Re-Compiled
internally
Falcon 0.2.3 0.4.5
Pig 0.8.1 0.11.0
Zookeeper 3.3.4 3.4.5
Conduit Recompiled

9. 1. Configuration management
1. Heterogeneous clusters
2. Host Level Configurations were really hard to manage
2. Data movement had to continue between clusters which could/would run
different version of Hadoop
3. All applications had to be seamlessly migrated with least downtime & NO
failures
4. Capacity Challenges
1. Network Challenges
2. Hardware Limitations
3. Storage & Computation limitations
5. Expected uptime for each of the clusters in over 99%, which meant we
couldn’t keep the cluster down for upgrade for a long time
6. Roll back was not possible
Challenges

10. How we over came the challenges

11. 1. Configuration Management
Problem
‣ We had configurations in debians like
‣ Cluster_Conf_version_1.deb
‣ Cluster_conf_version_2.deb and so on
‣ For 5 cluster and 10 components we would manage a lot of debs each with 15-20 Confs
‣ Changing a property value across the cluster was time consuming
Packages & configurations
‣ Since host specific Configurations were really hard to manage we deprecated debians
‣ Moved entire Package & configuration to puppet
Advantages
‣ Under 10 files to manage
‣ Everything was managed via templates and only host/component/cluster specific variables had to
be set appropritely
‣ Verification & confidence was higher with puppet while deploying changes in production

12. 1.1 Sample puppet configuration
Template
<% mapred_queues.each_pair do
|key,value| -%>
<!-- setting for queue <%= key %> -->
<property>
<name>mapred.capacity-
scheduler.queue.<%= key
%>.capacity</name>
<value><%= value %></value>
</property>
<% end -%>
Actual values
$mapred_queues={
”reporting" => 25,
”analytics" => 12,
”default" => 21,
”Hourly" => 14,
”daily" => 13,
.......
}
Apply template
file {
"${conf_location}/capacity-
scheduler.xml":
ensure => present,
owner => root,
group => root,
mode => 644,
content =>
template('grid/hadoop2.0/hadoopC
oreConfig/capacity-scheduler.xml');

13. 2. Data Movement
All applications had
to change to pull the
data from other
clusters
distcp across
clusters was
not possible
with the
standard hdfs
& hftp
protocols
We had to use
Web-HDFS
• Code was patched
to allow only reads
All applications
& Falcon
feeds/data
replications
had to be
tested &
migrated to
web-hdfs.
Since web-hdfs
was a SPOF, it
had to be
made scalable
& high
available
All clients
reading from
HDFS had to
also upgrade
libraries
• Ensured all stacks
were compatible to
read from
upgraded HDFS
• Some applications like falcon & conduit had to be enhanced to use the webhdfs protocol as a pre-requisite.

14. 3. Application Challenges
• 2 Versions of applications had to be maintained
‣ One for 0.20 and the other for 2.0
‣ To avoid disruption in current business & respective development
• Staging cluster had to be rebuilt to run 2 version of
Hadoop for pre prod testing,validation and sign off
• A lot of applications had to be made compatible since
some functions & classes were deprecated in 2.0
• Few class path changes were identified in pre-prod
testing

15. Capacity was a limitation since our headroom in other co-locations
was only 30% but we were flipping 100% from one region to another
• Network & Infra challenge
‣ N/W bandwidth for latency to avoid delays in data movement.
‣ Other stacks also had to check for capacity while we did a failover for the upgrade
• Ensuring we have enough capacity in other cluster to process data
while meeting SLA’s
‣ Added physical nodes to existing clusters & dependent stacks if it was required.
‣ Added more conduit/scribe agents to handle the increase in traffic during upgrade.
4. Capacity Challenges

16. 5. Deployment & Upgrade
• Rolling upgrade of clusters
• The GSLB was changed to redirect traffic to closest region
• Had to latencies were met as per the business requirement
• Maintenance was scheduled at a time when the impact was least.
• The time chosen was when the # of requests were the least for the specific region to ensure we don’t impact
the performance and also don’t require 100% capacity in the failed over region
• Data was processed on another cluster to have business continuity.
• Since datanode upgrade depends on the number of blocks on the datanode we
cleaned up HDFS to reduce the blocks on the datanodes which eventually helped
in expediting the upgrade process
• Upgrading components in parallel where there was no dependency

17. 5.1 Deployment Sequence
HDFS Datanodes 4 hours
JT Tasktrackers 45 minutes
Zookeeper Hbase Master Region Servers 45 minutes
Oozie 15 minutes
WebHDFS < 15 minutes
Falcon < 10 minutes
Conduit 30 minutes

18. Most of the nagios checks and metrics collected in
ganglia remained the same
New Monitoring
• Monitoring for new services like web-hdfs had to
be added.
Monitoring changes
• Nagios had minor changes to monitor the edits
since the edits had changed from
‣ edits & edits.new to
‣ edits_0000000000357071448-
0000000000357619117
edits_inprogress_000000000035873924
1
Ganglia Metrics
• Ganglia was over whelmed with the new RS
metric so we had to patch it to skip sending some
metric that wasn’t required.
‣ Custom filter was written to filter events that
were not used
6. Monitoring & Metrics

19. • The job tracker had a memory leak and had to be restarted once every 3-4 days
• https://issues.apache.org/jira/browse/MAPREDUCE-5508
• Hbase started emitting 1000’s of metrics per table brining ganglia down & we
had to patch it internally to fix it
Issues / Bugs encountered

20. • One step at a time
• We didn’t want to do a lot of things at one go, so we took small steps and at
the end achieved the goal
• Team work, Works!
• Its really hard to do this as a “One Man Show”, we noticed immense sense of
trust and responsibility among the team during the entire process
• Every Mistake was a learning
• Every mistake that was done in the initial stages was not a reason to blame
each other but we went ahead and fixed it ensuring it didn’t happen again
• Finally
• There were smooth upgrades !
Learning & Best Practices