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.

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

Lean Startup

Questions? Twitter: @nathanmarz Email: nathan.marz@gmail.com
Web: http://nathanmarz.com