jasonpincin

Anivia

Anivia is Walmart's mobile analytics platform. It collects user-interaction metrics from mobile devices -- iPhone, iPad, Android, and mWeb. It also processes logging and other metrics from a bunch of mobile services. Anivia allows the business to have real-time insight and reporting into what is going on in the mobile business and provides vital capabilities for developers and ops folks to monitor the health of their services.

Anivia is built on Node.js, Hapi, RabbitMQ, and a multitude of downstream systems including Splunk and Omniture. Anivia is taking in 7,000 events per second on average (as of this writing), which after some fan-out and demuxing comes out to around 20,000 messages per second in flight. These rates are expected to soar leading up to and including Black Friday. The platform has grown in recent months to over 1,000 node processes spanning multiple data centers, gaining features such as link resiliency in the process.

A few of Anivia's functionalities

• __Timestamp Correc

jasonpincin

Quimby

Quimby is Walmart's service layer for mobile clients' configuration, CMS, a-b testing setup, and a few other sundry related services. It stitches together a constellation of data sources into a concise menu of API calls that mobile clients make to intialize and configure themselves.

Quimby is a REST service layer based upon the Gogo micro-service framework that we in turn built with Node.js, Hapi, Zookeeper, and Redis. Gogo is able to expose an array of web servers as a single host, and offers the ability to isolate tasks into smaller focused processes, emphasizing scalability and failure recovery. For example, a failure in any micro-service will not affect the life cycle of a request. Gogo also offers the additional features required to build distributed services with shared state, such as leader election.

Quimby components

• Penny (part of Gogo) - The micro-service router, responsible for pairing a request with a servicer

kpdecker

Mobile Server Side Rendering

This year marks the first year that we are doing full scale rendering of our SPA application on our mobile.walmart.com Node.js tier, which has provided a number of challenges that are very different from the mostly IO-bound load of our prior #nodebf.

The infrastructure outlined for last year is the same but our Home, Item and a few other pages are prerendered on the server using fruit-loops and hula-hoop to execute an optimized version of our client-side JavaScript and provide a SEO and first-load friendly version of the site.

To support the additional CPU load concerns as peak, which we hope will be unfounded or mitigated by our work, we have also taken a variety of steps to increase cache lifetimes of the pages that are being served in this manner. In order of their impact:

Event Loop Management