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Become Efficient or Die
    The Story of BackType


                      Nathan Marz
                      @nathanmarz
BackType


BackType helps businesses understand
   social media and make use of it
BackType

Data Services (APIs)

Social Media Analytics
      Dashboard
APIs
• Conversational graph for url
• Comment search
• #Tweets / URL
• Influence scores
• Top sites
• Trending links stream
• etc.
Become Efficient or Die: The Story of BackType
URL Profiles
Site comparisons
Influencer Profiles
Twitter Account
   Analytics
Topic Analysis
Topic Analysis
BackType stats

• >30 TB of data
• 100 to 200 machine cluster
• Process 100M messages per day
• Serve 300 req/sec
BackType stats

• 3 full time employees
• 2 interns
• 1.4M in funding
How?

   Avoid waste

Invest in efficiency
Development
 philosophy
Development
         philosophy
• Waterfall
Development
         philosophy
• Waterfall
• Agile
Development
         philosophy
• Waterfall
• Agile
• Scrum
Development
         philosophy
• Waterfall
• Agile
• Scrum
• Kanban
Development
       philosophy

Suffering-oriented programming
Suffering-oriented
  Programming

Don’t add process until you feel
   the pain of not having it
Example


• Growing from 2 people to 3 people
Example

• Founders were essentially “one brain”
• Cowboy coding led to communication
  mishaps
• Added biweekly meeting to sync up
Example


• Growing from 3 people to 5 people
Example
• Moving through tasks a lot faster with 5
  people
• Needed more frequent prioritization of
  tasks
• Changed biweekly meeting to weekly
  meeting
• Added “chat room standups” to facilitate
  mid-week adjustments
Suffering-oriented
  Programming

Don’t build new technology until
you feel the pain of not having it
Suffering-oriented
  Programming

   First, make it possible.
  Then, make it beautiful.
      Then, make it fast.
Example


• Batch processing
Make it possible


• Hack things out using MapReduce/
  Cascading
• Learn the ins and outs of batch processing
Make it beautiful


• Wrote (and open-sourced) Cascalog
• The “perfect interface” to our data
Make it fast

• Use it in production
• Profile and identify bottlenecks
• Optimize
Overengineering

Attempting to create beautiful software without a
   thorough understanding of problem domain
Premature optimization

Optimizing before creating “beautiful” design,
     creating unnecessary complexity
Knowledge debt

 20

 15

 10

  5

  0
      Your productivity Your potential
Knowledge debt

 20

 15
                                         Knowledge
 10
                                           debt
  5

  0
      Your productivity Your potential
Knowledge debt


Use small, independent projects to
experiment with new technology
Example

• Needed to write a small server to collect
  records into a Distributed Filesystem
• Wrote it using Clojure programming
  language
• Huge win: now we use Clojure for most of
  our systems
Example

• Needed to implement social search
• Wrote it using Neo4j
• Ran into lot of problems with Neo4j and
  rewrote it later using Sphinx
Example

• Needed an automated deploy for a
  distributed stream processing system
• Wrote it using Pallet
• Massive win: anticipate dramatic reduction
  in complexity in administering
  infrastructure
Knowledge debt




   (Crappy job ad)
Knowledge debt

Instead of hiring people who share your skill set, hire
      people with completely different skill sets




                 (food for thought)
Technical debt


Technical debt builds up in a codebase
Technical debt

• W needs to be refactored
• X deploy should be faster
• Y needs more unit tests
• Z needs more documentation
Technical debt


Never high enough priority to work on, but these
       issues built up and slow you down
BackSweep


• Issues are recorded on a wiki page
• We spend one day a month removing items
  from that wiki page
BackSweep

• Keeps our codebase lean
• Gives us a way to defer technical debt
  issues when don’t have time to deal with
  them
• “Garbage collection for the codebase”
What is a startup?

 A startup is a human institution designed to deliver a new
product or service under conditions of extreme uncertainty.
                                               - Eric Ries
How do you decide what to work on?
Don’t want to waste three months
building a feature no one cares about
Don’t want to waste three months
building a feature no one cares about

      This could be fatal!
Product development

                                     Keep
                           Valid?

Form hypothesis   Test hypothesis             Learn


                         Invalid?
                                    Discard
Example
Example


Pro product didn’t actually exist yet
Example


• We tested different feature combinations
  and measured click through rate
• Clicking on “sign up” went to a survey page
Example
Hypothesis #1


Customers want analytics on topics
    being discussed on Twitter
Testing hypothesis #1


• Fake feature -> clicking on topic goes to
  survey page
Testing hypothesis #1


• Do people click on those links?
• If not, need to reconsider hypothesis
Hypothesis #2


Customers want to know how often topics
       are mentioned over time
Testing hypothesis #2

• Build topic mentions over time graph for
  “big topics” our private beta customers are
  interested in (e.g. “nike”, “microsoft”,
  “apple”, “kodak”)
• Talk to customers
Hypothesis #3


• Customers want to see who’s talking about
  a topic on a variety of dimensions: recency,
  influence, num followers, or num retweets
Testing hypothesis #3


• Create search index on last 24 hours of
  data that can sort on all dimensions
Become Efficient or Die: The Story of BackType
Lean Startup
Questions?

   Twitter: @nathanmarz

Email: nathan.marz@gmail.com

 Web: http://nathanmarz.com

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