The Evolution of a Scrappy Startup to a Successful Web Service

The Evolution of a Scrappy Startup to a Successful Web ServicePoornima Vijayashanker – Software Engineer

November 7, 2008

Confidential & Proprietary

What is Mint.com? My Bio Came up with the name and the second employee

Getting Mint.com off the ground Used open source tools: Eclipse, mySQL, Apache Tomcat No prior experience at a startup or in web services

Mint.com today


Creating a prototype

Prototype: a rudimentary working model of a product or information system,

build for demonstration purposes or as part of the development process.


What doesn’t belong in a prototype?

Don’t waste time spec’ing out a complete feature set “Everyone by now presumably knows about the danger of premature

optimization. I think we should be just as worried about premature design - designing too early what a program should do. “

- Paul Graham

Mint.com’s mission statement: “Do more with your money.” Aggregate checking, savings, and credit card accounts Show balances Auto-categorize transactions


What does belong in a prototype.

Started small by focusing on features that differentiated our productFocus on solving critical user problems. Engineering problems arise from their solutions. e.g. Financial data is a sensitive matter, needed a good security model. Handle concurrency amongst our 100+ users, and making sure we

were encrypting stored data. Bare-bones implementation for algorithms is sufficient. Don’t get tied

down to any one particular solution. Refine in subsequent releases and based on product specifications.

Simple unit test framework, no system tests; focus was to get the product out there and have real users test it with real data!


What did our application server look like?

Single Server Web layer and analysis engine, wired together using RMI. UI -> Business Logic -> Data processing engines Single data base that consisted of less than 50 tables


What did our code base look like?

A few key choices during prototyping molded our software’s architecture, and affected the longevity of our code base e.g. Choice in messaging system: JMS, RMI, or none Re-factored code into logical modules to avoid spaghetti code, a task

that would have been much harder to do later on, especially with a growing team and code base.


Web Application to Web Service

Prototype -> Product likened to Web Application to Web Service. Application is a point tool used to complete a simple tasks Web services is more than features Goal is to create a product with growing user base you have to

broaden thinking from features to logistics


Prime mover of Software’s Architecture

User Growth Rate To solve the business problem of becoming profitable you have to

grow the user base

More users -> Growing Pains Need to be addressed in a timely manner in order to continue to grow

and keep the existing users happy.


Computer Architecture 101

Latency: a reasonable response timeThroughput: satisfying many requests without compromising latencyQuality: accurate data and reliable service Address points of failure


How do you achieve each?

Latency: affected by amount of data that needs to be retrieved, in order to satisfy the incoming requests. e.g. computed data and persisted to db, as users increased

reading/writing to the db took forever. Switched to loading user data into a cache upon login and then

computing.


Throughput: handle multiple requests/processes Separate into code into tiers (UI, web, business/service logic, DAO, db)

in parallel data processing/retrieving tier Separate data engine from web engine

• One server for handling data processing• One server to process user requests and serve user pages

Tune each server based on its needs (web vs. analysis)


Quality: low bug count, which is directly proportional to incoming number of customer service requests Testing (unit, load, and system) Beware of the vocal minority, by measuring the number of users

impacted by a bug.


Optimization: improving performance of code at runtime in order to satisfy latency and throughput requirements. “…premature optimization is the root of all evil." – Donald Knuth


How to Measure Created internal tools to measure the

performance of our code base, which helps figure out where to optimize.

Product will continue to evolve in approximately 6 month cycles.

Don't waste time optimizing everything, or before you see a demand for a feature. Remember its a startup; resources are scarce and time is critical.


What to measure

How quickly a data set is going to grow when designing tables, foreign key associations, retrieving data, and frequency with which data is accessedSharding: break up a large database into smaller pieces that contains redundant information or a parent db can map data to separate dbs. Implementation was based on user id lookup Easy to shard because we were dealing with financial data that is

restricted to a single person, each user and their data was limited to a single shard, unless services like Twitter or Facebook that have lots of interactions amongst users


Review Decisions

We didn’t cache user data from the start because synchronizing data across nodes was difficult and had no mechanism for centralized locking, but once this was put in place we switched to loading data on demandWe didn't shard databases from the start because of overhead and more impending issues.In the future we might only show most recent data instead of all data).


Summary

Prototype with limited featuresAddressing CS 101 Basics: Latency, Throughput, and QualityMaking architectural decisions based on the time frameMeasure and Optimize that which is criticalCheck it out: www.mint.comMy blog: www.femgineer.com

http://www.mint.com/

http://www.femgineer.com/

The Evolution of a Scrappy Startup to a Successful Web Service

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