By Dan Mesh, Vice President of Engineering at Evite

This post is part of Who's at Google I/O, a series of guest blog posts written by developers who are appearing in the Developer Sandbox at Google I/O.


Evite is one of web's oldest social planning services. Since the launch in 1998, Evite has delivered over a billion party invitations. Although it has served us well, after ten years of operation it became necessary to replace the aging platform, and position Evite for another decade of successful party planning.

The reengineering effort took well over a year. Our development team replaced Java with Python and saw a significant increase in developer productivity. However, dealing with a ten year-old Oracle database remained a challenge. To overcome scalability limitations and inflexibility of a large relational DBMS, we designed and implemented a proprietary data store. We conducted a series of A/B tests and gradually started migrating production traffic to the new system.


Our initial motivation to use Google App Engine was fast provisioning. We introduced App Engine as a temporary solution while we increased scale and optimized our proprietary data store. However, once we imported user profile data and put App Engine backend services in production, we never looked back.

Importing a large data set of user profiles from Oracle RAC into App Engine was challenging at first because we had to perform a bulk import and then keep the data synchronized between the two datastores. As we developed our data synchronization tools we gained better understanding of the API and performance characteristics of the App Engine datastore.

Once we synchronized profile data and enabled production traffic, App Engine really started to impress. We would watch our daily traffic grow and observe App Engine’s automatic scaling in action. Additional server instances would come online to meet increased demand and disappear as traffic lowered, without any sysadmin intervention. Despite our proficiency in the use of cloud computing resources and system automation, it was never this easy to provision new servers. As Grig, Evite’s devops guru, likes to say "It's not in production until it's monitored and graphed." App Engine’s dashboard automatically manages instances and graphs system usage data.


Following our positive experience with Evite profiles, we have continued to use App Engine for other services. Occasional frustrations with elevated error rates on the Master/Slave Datastore disappeared as we switched to the High Replication Datastore. At this point we see no technical obstacles to using App Engine more extensively. Architectural decisions and best practices implemented by the App Engine team are very well aligned with the choices we made in designing Evite’s new platform. This makes it easy to deploy additional services and data sets to App Engine.

The real obstacle to using App Engine exclusively for most Evite services is risk management. As reliable and cost-effective as App Engine has been for us, it is difficult to depend completely on a single vendor/service provider. To ensure availability of our application we use multiple cloud providers. Unfortunately, this strategy prevents us from using certain App Engine features and API's because we do not have adequate replacements for them in other deployment environments.

Evite’s new platform built on Python, NoSQL and App Engine has been a success. Our new web application has been well received by users, and our first iPhone app is receiving positive reviews. (Android version is in the works). We look forward to continued use of App Engine for data warehousing, rapid launches of new services and other projects.


Come see Evite in the Developer Sandbox at Google I/O on May 10-11.

Dan Mesh is a Vice President of Engineering at Evite. His extended responsibilities include espresso deliveries and release day food orders.

Posted by Scott Knaster, Editor