Open Source Applied - Real World Use Cases

Justin Reock
Senior director, support & professional services
Rogue Wave Software

1. Introduction
2. PupBand: E-commerce website
3. UniTrack: Mobile PaaS
4. HelpingHumans: HPC machine
learning
5. Conclusion
6. Q&A

OSS is everywhere!
• OSS has eaten the world!
• Commonly we see businesses of all sizes utilizing open source
solutions to decrease time to market and reduce development effort
• Anyone with a great idea can build an infrastructure using open
source solutions without the financial barriers that existed a decade
ago
• So how are people in the real world benefitting from open source?
• What technologies are they using, and where are they putting them?

Who’s using OSS?
• Today’s presentation centers around three fictitious companies
• Although the architecture and use cases are inspired by our real customers, we’re going to
protect their privacy
• All of these companies have built a full infrastructure stack using only OSS solutions
• We’ll discuss the chosen architecture, point out some pitfalls, and see how these development
teams flexed the power of OSS to meet the demands of their businesses

E-commerce website
PupBand
The only online store featuring specialty headbands for dogs!
Infrastructure needs
• Website front-end
• Order processing middleware
• Inventory database
Technology stack
• Apache web server
• OpenSSL
• PHP
• Apache ActiveMQ
• Apache Camel
• PostgreSQL

Selecting the stack
Front end
• For the front end, the developers knew they needed a robust web framework to work with and a capable web server technology that
supports SSL
• They looked at Java and PHP and decided on PHP 7. It’s easy to find qualified PHP developers and the storefront didn’t need the
overhead that comes with Java EE technologies.
• PHP 7 brings a number of significant enhancements, including a huge boost to performance
• OpenSSL is the de-facto standard for SSL encryption on the web and compiles easily into Apache Web Server. Since the company
collects sensitive payment data, strong SSL is a must!
• Nginx was considered, but, the development group was familiar with Apache and chose to implement it for the web server stack
To get this project going, PupBand’s IT organization needed to vet out the
best open source projects they could find to build their stack

Selecting the stack
Order processing middleware
• Next, the company needed a reliable way of shuffling data between their PHP
front-end application and a backend inventory management system, customer
communications platform, and fulfillment warehouse
• They chose Apache ActiveMQ and Apache Camel
• ActiveMQ gave them the flexibility and reliability to integrate their PHP front-
end with Camel
• Camel ensured that they would have compatibility with all of the backend
systems needed to orchestrate their orders

Selecting the stack
Inventory database
• Finally, the developers at PupBand needed a way to store inventory and order data
• They knew they needed a flexible and scalable solution, with replication and disaster
recovery functions
• And the data models they would be building might be fairly complex, integrating
customer data, fulfillment data, payment status, and RMA
• In the end, they settled on PostgreSQL, a truly FOSS database solution meeting all
their needs for performance, reliability, and RDBMS SQL compliance

A complete picture

Front end
• The team deployed on CentOS in the cloud but, when they installed OpenSSL and Apache using
the CentOS repository framework, their security scans were alerting them to vulnerabilities
• That’s because the newest version of OpenSSL and the newest version of Apache weren’t
available to them through CentOS’s repo
• They quickly decided to build the newest version of Apache from source, against the newest
version of OpenSSL
• They removed all legacy SSL protocol versions, and settled on TLS1.2, to keep them protected
from Heartbleed and DROWN

Middleware tier
• The middleware tier was complex, it needed to integrate
seamlessly with all the backend systems
• At first, the team decided to use the version of Camel that
was built into ActiveMQ
• ActiveMQ allowed the team to receive events from the PHP
front end and to queue those events for processing
• But ActiveMQ became overwhelmed quickly when using it’s
embedded Camel implementation
• The team decided to deploy the Camel routes standalone in
a separate Java container, and ActiveMQ’s footprint was
reduced significantly

The backend
• The credit card API used RESTful services
• The team built a small webservices layer in front of Postgres, to loosely couple
the database
• And the fulfillment warehouse made use of traditional SOAP webservices
• Using Camel Components, the team was able to find a compatible solution for
all of the various integration endpoints they needed to interact with

• In the end, the development team at PupBand was able to develop their entire E-
commerce solution using community-supported, free, and open source software
• The project was not without its challenges and the team had to research and
experiment to determine the safest, fastest, and most robust ways of
implementing the technology stack they chose
• Challenges around versioning, proper deployment, and performance
optimization took many cycles and hard work to overcome
• But in the end, PupBand was left with an infrastructure free from proprietary
commercial licenses, with room to grow
• And puppies everywhere rejoiced in their new head gear!
Code complete

Mobile PaaS
• API front-end
• Scalable event-processing
middleware
• Location database
• Data visualization platform
Technology stack
• Nginx
• Node.js
• Docker
• Puppet
• Apache Cassandra
• Apache Spark
UniTrack
A crowd-sourced platform tracking the movement and migratory patterns
of unicorns

Selecting the stack
API front end
• The team selected a combination of Nginx as a web server and Node.js as a development
platform
• The API was packaged in Docker containers, to allow for elastic auto-scaling of the system
• Node.js would allow them strong concurrency and the ability to handle thousands of
simultaneous API calls
• Docker would allow the API servers to scale based on demand, and Puppet would assist in
spinning up new containers and deploying the API code
The dev team at UniTrack knew that they needed a lightweight, fast, and
concurrent system for receiving location data from millions of mobile
clients and presenting the data to subscribers

Selecting the stack
Event-processing middleware / location database
• The team needed a highly concurrent and highly redundant solution to store event and
location data that came in through the front-end APIs
• The data would be spread across multiple cloud datacenters to allow the best regional
connectivity possible, along with disaster recovery features
• They chose Apache Cassandra for its easy replication strategy, its ability to store and
provide incredibly large datasets, and its performance optimization capabilities
• Node.js was integrated directly with Cassandra, to shuttle data from the front-end APIs
into the database itself
• Puppet integration ensured easy deployment of additional Cassandra nodes in the cloud,
as system demand increased

Selecting the stack
Data visualization
• Finally, the team needed a way to make sense of all this location and event data
• The large data sets needed to be analyzed
• And data visualization needed to be provided to unicorn enthusiasts everywhere via
their smartphones
• Apache Spark was chosen for its lightning-fast data analytics capabilities
• And the GraphX plugin was implemented to visualize the large sets of unicorn
tracking data coming in from millions of worldwide users through the smartphone
client

A complete picture

Front-end APIs
• The team built Docker containers bundling Nginx with a lightweight node.js
application. The learning curve for Node.js proved to be a bottleneck, as traditional
JavaScript developers needed to rethink the way they developed
• A Puppet master server was built and orchestration for spinning up new containers
on-demand was developed. Security became a major concern, as access to the
Puppet master server by a malicious user could lead to disastrous consequences

Event-processing middleware
• Integration of the Node.js containers with Cassandra proved to be easy,
using Cassandra’s asynchronous connection drivers for Node.js
• The real challenge was building a NoSQL data model that could meet all
of their data processing and visualization needs, while remaining fast
and responsive
• Cassandra’s deployment itself was simple, spinning up nodes in various
data centers using Cassandra’s in-built deployment utilities and native
replication protocol

Visualization and analytics
• Spark made it easy to pull in and analyze the vast amounts of data provided by UniTrack’s
enormous user base
• The biggest challenge lay in determining which of the many client libraries should be used
to power Spark
• The team could pick from Java, Scala, Python, or R
• In the end, Java was selected, and a standalone application was built around the Spark
libraries to provide visualization of data via GraphX
• GraphX’s output was compressed and standardized for mobile platform consumption

• By leveraging the elastic scale and microservice capabilities of Docker and Node.js, the team was able to
build a highly concurrent architecture capable of servicing thousands of requests a second
• The choice for Cassandra as a persistence backend proved wise, as the development team was able to
scale the Cassandra implementation into multiple regional datacenters, and service the huge amount
of traffic input by the front-end APIs
• Puppet worked well in detecting increases in load, and elastically scaling the API containers on demand
• Spark and GraphX allowed the application to make sense of the huge amount of data, and provide
visual analysis to mobile clients
• Though so far all unicorn location claims have proven spurious, the UniTrack community feels confident
that with the powerful platform they have built, it is only a matter of time before a real live unicorn is
discovered and captured
Code complete

HPC machine learning
HelpingHumans
Advancing human society through artificial intelligence and robot
sentience
• Multi-platform data gathering
automation
• Redundant storage and analytics
• Dashboard front-end
Technology stack
• Apache Camel
• Apache Cassandra
• Kubernetes
• TensorFlow
• AngularJS

Selecting the stack
Multi-platform data gathering
• The secret development group responsible for building the platform knew they would
need a framework capable of gathering data from hundreds of potential sources
• The platform needed to be capable of querying CDC data, military intelligence, social
media, human history, and more
• Apache Camel was chosen to act as a central data gathering platform for pulling
unstructured data from multiple, disparate sources
• Camel’s huge component library would allow for this kind of flexibility, while developing
against a standard, domain-specific language and reducing boilerplate code
In order to assist the human race in performing useful tasks, the
machines must be capable of learning as much as they can about our
species

Selecting the stack
Redundant storage and analytics
• Once the data had been gathered, the dev group knew that they would need a
redundant storage system capable of quickly writing and reading a gigantic amount
of data
• And once that data was gathered, a machine learning analytics library was
necessary to normalize all that data and understand human activities, capabilities,
and behavior
• Apache Cassandra would be capable of storing all the data
• And the open source TensorFlow machine learning framework could help in building
learning patterns to make sense of all the disparate data

Selecting the stack
Dashboard front-end
• The team needed a way to take data processed by TensorFlow and pushed back into
Cassandra, and build a useful dashboard to check the progress of various learning
initiatives
• The dashboard needed to be lightweight and cross-browser compatible
• It also needed a relatively low learning curve, since the bulk of the development
work would be spent on the learning and analytics engine
• The team decided on AngularJS, which allows a full MVC framework, reusable
components, and a low learning curve for developers who already understand
JavaScript

Selecting the stack
Container orchestration
• Both the Camel data-gathering components and the TensorFlow analytics needed to be deployed in
such a way that they could be easily scaled up to provide concurrent gathering and processing of
data
• Ultimately, the architecture would contain thousands of individual containers operating in concert
• For this, orchestration of Docker through Puppet was simply not sophisticated enough, so the team
implemented Kubernetes to provide a full management platform for their large container
infrastructure
• Service discovery, monitoring, and deployment would all be provided by the Kubernetes management
engine

A complete picture

Multi-platform data gathering
• Camel allowed the HelpingHumans team to source data from the hundreds of various data sources they wished to
pull from
• Initially, the development team thought it best to deploy Camel inside of a Tomcat container and then contained
inside of Docker
• Ultimately this solution had just too much overhead, and multiple instances of Tomcat were found to be
redundant when deployed in a containerized infrastructure
• The Dev team chose to build standalone Camel applications running per container and the system was much more
efficient

Redundant storage and analytics
• It turned out TensorFlow was a great starting point for the type of machine learning the HelpingHumans Dev team
wanted to implement but there was functionality missing that was critical to their work
• Ultimately they leveraged the power of FOSS by modifying the source code of TensorFlow and spinning off their own
internal branch
• And, in the spirit of their altruistic mission, the dev team offered these enhancements back to the TensorFlow
community

Orchestration and dashboarding
• Kubernetes forced the development team at HelpingHumans to
change the way they thought about containerized deployment and
microservice management
• After the paradigm shift was complete, the team ended up with an
amazing elastically-scaling architecture that was able to meet the
high demands their data gathering and analytics needed
• AngularJS proved easy to learn and highly functional, and
integrated well with Cassandra
• Ultimately a lot of the visualization work was offloaded to backend
servers, though, with Angular acting as a proxy, since the client-side
limitations of Angular limited the analysis of the data

• By leveraging powerful open source frameworks, the HelpingHumans team was
able to develop a sophisticated engine for learning new ways to analyze human
needs and behaviors
• Advanced container management through Kubernetes allowed for the complex
orchestration necessary to elastically scale their Camel and TensorFlow based
applications
• And Cassandra’s high performance big data storage allowed HelpingHumans to
efficiently store all the data they gathered from hundreds of sources
• Human civilization as we know it ended roughly six months after deployment
Code complete

Support from a group like the OSS Support team at Rogue Wave
can ensure that what you implement works well, stays available,
and is scaled for growth
Conclusion
You can safely build an entire IT infrastructure on FOSS
technologies, if you make disciplined decisions surrounding the
choice of technology and the practices of development,
implementation, and deployment
Careful vetting can help you ensure that the packages you
choose truly meet your use case
There is a ton of open source available to you, but certain
applications and projects will rise to the top based on their
maturity, the strength of their communities, and their
reactiveness to bug fixes and vulnerabilities

Open Source Applied - Real World Use Cases

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