Business Management Human Resources Government Work Regulated Environments Big Data Applications Cloud Infrastructure R in Production What is there to learn? What are the needs? What are the problems? Solutions Engineers!
R recognized as an analytic standard? How do you make R a legitimate part of your organization and get the resources you need to support it? In many organizations, R enters through the back door when analysts download the free software and install it on their local workstations… Some organizations struggle to standardize on R due to a lack of management and governance around open source software. At the same time, organizations may neglect R on user workstations, thereby increasing security, legal, and operational risks. - Nathan Stephens, R Views 2016
Access/Security Performance Tuning Shared Goal: Shorten the distance between development and production Shared Goal: The improvement of daily work Shared Goal: Reduce the risk of deploying a breaking change
Rambling, Cluttered - Parts that work well - Parts that work not-so well Local Development EDA, Prototyping, Iteration The “Lightning-Talk” of Data Products - Targeted - Elegant - Streamlined - Optimized Production Development
Use shinyloadtest to see if app is fast enough 2. If not, use profvis to see what’s making it slow 3. Optimize a. Move work out of shiny (very often) b. Make code faster (very often) c. Use caching (sometimes) d. Use async (occasionally) 4. Repeat!
app • You want to be confident that it will keep running in the future Things that can change/break a Shiny application • Modifying code • Upgrading the shiny package • Upgrading other packages • Upgrading R • External data source changes or fails Shared Goal: Reduce the risk of deploying a breaking change
(test, prod) • Release is when that code (feature) is made available to users Application-based release patterns vs. Environment-based release patterns DevOps Learning: Decouple deployment from release
- Limit Work in Progress (WIP) - Reduce Batch Sizes - Reduce the number of handoﬀs - Continually identify and elevate constraints - Eliminate hardships and waste 2. Utilize Feedback - See problems as they occur - Swarm to solve problems and build new knowledge - Keep pushing quality closer to the source - Enable optimizing for downstream work centers 3. Learn and Experiment - Enable organizational learning and a safety culture - Institutionalize the improvement of daily work - Transform local discoveries into global improvements - Inject resilience patterns into daily work Three principles form the underpinnings of DevOps:
Application? - Who is the audience? - What is your service level agreement definition? (SLA) - What does your analytic architecture look like today? - What are your goals for evolving this architecture? - How will monitoring be handled? - Who is responsible for maintenance? Make work visible, Define shared goals, Build a checklist, Iterate Empathetic Communication is Challenging
that end users are using - An app that is live and available to the end user - Apps on our production server are available to our clients - Client facing Credibility AT SCALE - Scaled to a larger audience - Bulletproof, scalable, fails predictably - Live to 1000 of users with production vehicle data SERVICE LEVEL AGREEMENTS - Required for mission-critical operations; downtime affects the ability to serve customers - Deployed for end users to have continual access without performance issues ENVIRONMENTAL REQUIREMENTS - An area where validated applications are deployed in a locked down environment - The main part of a company that handles all process - Application or system operates effectively without much maintaining effects - A server or environment that runs the “final” applications that your ultimate end-users (often external customers) use to get stuff down DOCUMENTATION - TESTING & MONITORING - Creating apps that can reach a wider audience and are deployed/tested in a consistent manner - Running in a way that is stable to use, documented and monitored
for the R Admin April 3. Championing Analytic Infrastructure July 4. Art of the Feature Toggle 5. Environmental Release Patterns August 6. Shiny in Production: Building bridges from data science to IT September 7. Data Product Delivery: The R user’s journey toward improving daily work 8. The R in Production Handoff: Building bridges from data science to IT October 9. Interactivity in Production 10. Is there a Future for DevOps? speakerdeck.com/kellobri solutions.rstudio.com community.rstudio.com #radmins