Hadoop 0.20.2 to 2.0

Jabir Ahmed

•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 ?

Analytics ReportingData

Streaming via HDFS

Adhoc Querying /Modeling

Real time data

processing

Hadoop Usage @ inmobi

•HDFS & MRV1

•Falcon‣ Active MQ

•HBase

•Conduit‣ Scribe

‣ Conduit Worker

‣ Pintail

Hadoop Eco System

• Zoo-keeper• Oozie• WebHDFS• Pig• Hive • Hcatalog &

Metastore

Cluster Topology

• 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

Cluster topology

Centralized Cluster

Co-located Clusters Co-located Clusters

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

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 Challenges1. 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

How we over came the challenges

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

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/hadoopCoreConfig/capacity-scheduler.xml');

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.

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

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

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

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

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_0000000000358739241

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

•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

•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