AWS Case Study: Fashiolista
challenge
The company originally self-hosted its data, but by early 2011, Fashiolista’s growth had outpaced its existing infrastructure. The company was seeing more than 100 million page views per month. Social media sites have unique challenges in terms of scalability—it’s difficult to forecast for peak usage because of the nature of the site. Some users spend six to eight hours a day on the site, driving page hits up on trending topics and increasing load times in unpredictable ways.
Thierry Schellenbach, Fashiolista’s Founder and Chief Technology Officer, says, “Hosting is not our core business, and we didn’t want to go through the pain of growing from 4 to 60 servers in a data center.” Time was a factor, as well. With such explosive growth, Fashiolista did not have time to undergo a lengthy hardware acquisition and provisioning process. The team needed a solution that would allow them to roll out architectural changes in minutes instead of days.
solution
More than 70 different AWS instances provide Fashiolista with a wide range of functions, including:
- 4 database servers
- 8 Redis servers
- 12 web servers
- 10 task servers
- 2 search servers
- 3 Memcached servers
- 3 static servers
- 4 logging servers
In addition, Fashiolista runs a variety of third-party development tools within AWS.
The team uses Apache SOLR, PostgreSQL, Redis and graphite as data storage layers. The web layer runs on Nginx, Supervisor, Gunicorn, Django and Python. In addition, the team is using Jenkins for continuous testing. For monitoring, the team uses Amazon CloudWatch, NewRelic and DataDog.
Fashiolista maintains the entire infrastructure with the help of developer-level AWS Support, Amazon CloudWatch, AWS CloudFormation, and the third-party Puppet application. “Working with AWS gives massive benefits in time-to-market,” Schellenbach says. “It is relatively easy to test when you can boot a stack of servers similar to your production setup in minutes. You pay based on usage, so in terms of costs, it’s very feasible to test changes on a testing stack with a database, load balancer and web servers. Just make sure you remember to shut them down afterwards. These types of experiments were simply not feasible in a bare metal setup.”
results
Arguzo employees are now more empowered; Arguzo also has the benefit of generating reports instantaneously whenever needed. They can now make decisions on the fly based on the latest real time data.
The effort vastly improved the company’s planning and execution functions, created and implemented a new stock policy that accounted for specific SKUs and key variables, streamlined the order preparation process and reduced distribution transport times.
By the numbers, the effort:
- Reduced lead time by 43%
- Decreased variability by 50%
- Lowered the risk of back-order by 95%
- Increased stock for finished goods by 10%
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