© Hortonworks Inc. 2011
Hadoop Engineering Best Practices
Raja Aluri, Release Eng
Deepesh Khandelwal, Quality Eng
Ramya Sunil, Quality Eng
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© Hortonworks Inc. 2011
Agenda
• Source Mechanics
• Why do System Testing?
• Test Matrix
• Automated Testing Flow
• Test Planning
• Planning your own System Testing
• Q & A
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
Apache Hortonworks Partner Source
Mechanics
• Hortonworks Open Source Philosophy
• How we do Apache first development
• How we incorporate fixes or features that did not make into apache yet
• How we integrate our partner contributions to the source code
• Bookkeeping of the delta between apache and Hortonworks
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
Apache-Hortonworks-Partner Source flow
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Architecting the Future of Big Data
Partner
ApacheRef
HDPRef
Partner
HWX
ApacheRef
HDP
Apache Git
Hadoopbranch-2
Hadoopbranch-2.4
Issue Type Course of Action
Normal Issue Patch in Apache first
Urgent Issue Patch in HWX Repo first
Read-Write Repository
Read-Only Repository
Continuous
Merges
Continuous
Merges
HDP Build
CI
HDP
Package
Repo
HDP
Maven
Repository
Publish
Releases
QE Workflow
for Testing

© Hortonworks Inc. 2011
Unit Testing
• Test individual parts of the program in isolation, white-box testing
• Homogeneous cluster, usually in-memory
• One configuration, usually 1 operating system and unsecure
• Limited dataset, usually few kilobytes
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Architecting the Future of Big Data
Unit testing
component A
Unit testing
component
C
Unit testing
component
B
?? ??
??
??
DB
Interaction
Concurrent
user
interaction
Third party
connectors
??
??
??

© Hortonworks Inc. 2011
System Testing
• Mimics production environment
– Multiple nodes in the cluster
– Multiple concurrent users
– Different workloads
• Multiple configurations to test
• Large dataset, more complex and richer
• Encompasses different types of testing
– Functional
– Performance, Stress and Reliability
– High Availability
– Backwards Compatibility
– Integration testing
– Third party connectors
– Upgrade testing
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
System Testing cont...
• Heterogeneous testing
– Cross version testing
– Cross operating system testing
– Hardware configs like Disk and CPU
– Security settings, level of encryption
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
Test Matrix
• Total of ~15000+ configurations to test!
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Architecting the Future of Big Data
OS
•CentOS
•SuSE
•Debian
•Ubuntu
•Windows
JDK
•Oracle JDK
•OpenJDK
•Different version - 1.6.x, 1.7.x,
1.8.x
Security
•Disabled
•Enabled – MIT-only, AD-only,
MIT-AD
•Ranger - enabled/disabled
Encryption
•Wire encryption –
enabled/disabled
•Transparent Data Encryption
– enabled/disabled
DB
•Mysql
•Oracle
•Postgres
•MSSQL
File system
•HDFS
•WASB
•Other vendor specific FSs
Others
•Tez – enabled/disabled
•Slider apps v/s standalone

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Automated Testing Flow
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Architecting the Future of Big Data
Build Job
Apache
Repos
Internal
Commits
Staging
Repo
QE Deploy
Trigger
Provision VMs
Deploy HDP Stack
Test Setup & Execution
Test analysis
Continuous Integration
Publishing Builds to staging
repo
Installer deploying bits from
staging repo to test cluster
Bug tracking system

© Hortonworks Inc. 2011
Test Planning
20+ components in the HDP stack and growing!
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Architecting the Future of Big Data
Test
plan
Internal
developers
Apache jiras
and
community
forums
Product
Management
Support
tickets

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Planning your own QATS
Architecting the Future of Big Data
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Typical user scenarios
• Fresh install
• Upgrade stack, going from an earlier release to a newer one
• Migration, changing distributions
• Applying changes to an existing cluster
– Upgrading hardware in regards to CPU, memory, disks
– Changing dependent software pieces like OS, JDK
– Changing security settings like turning ON Kerberos, Encryption
– Changing component configs in *-site.xml, enabling HA
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Architecting the Future of Big Data

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Planning your own QATS
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Architecting the Future of Big Data
E2E automation
Preparation
phase
• Collect
requirements
on the stack
and workload
• Identify
appropriate
hardware
CI development
phase
• Build in-
house CI
system for
deployment
and testing
Testing phase
• Build basic
acceptance
tests
• End to end
automation
for your
application

© Hortonworks Inc. 2011
Preparation Phase
• Collect the stack requirements
– Identify all the stack components that will be installed including the third-party
applications, connectors
– Identify the installer
– Identify configs
• Hardware selection
– Should be scaled appropriately to mimic production environment
– Prefer multi-node than single-node with component services distributed
• Collect workload information
– Use actual workload whenever possible
– If not, simulate the workload, some tools available
– Use rumen to obtain jobtrace from existing clusters
– Use gridmix to generate workload
– Data set size and complexity
– Number of concurrent users
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
CI Development phase
• Implement a CI system
– Modularize CI system, eg individual Jenkins jobs for provision, deploy and test
• Determine the cadence of testing
• Establish reporting
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Architecting the Future of Big Data
Provision
cluster
Deploy Test

© Hortonworks Inc. 2011
Testing Phase
• Basic Acceptance Tests
– Basic service check for individual deployed components
– Basic acceptance tests to validate integrations
• Establish baseline – to track performance of pipeline components in
future
• Compatibility tests (including apps, third party connectors, dashboards
etc)
• E2E automation to simulate production workloads
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Architecting the Future of Big Data

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Q & A
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Architecting the Future of Big Data

© Hortonworks Inc. 2011
Thank You!
Architecting the Future of Big Data
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