from "overnight" to "real-time": a two-year nosql case study
DESCRIPTION
Meteor Solutions integrates site and advertising analytics to provide major publishers and advertisers the ability to identify and reach their influential visitors with advertising, exclusive content and rewards. Eighteen months ago, Meteor was backed by a relational DB and struggling to keep up with volumes in a batch processing environment that was ill-suited to our graph oriented data model. Today, the service is backed by Cloudant, a distributed document store based on CouchDB, and provides deeper analytics in real-time. This transition enabled 10x growth and allowed us to open our technology to a much broader range of applications -- though not without some bumps along the way. This talk will cover: Overview of our services and specific technical challenges Overview of Cloudant/CouchDB, how we leverage it, and its relation to other SQL, NoSQL, and web technologies in our stack Benefits we've seen and tradeoffs we have had to make Operational lessons learned Future plans: how NoSQL's possibilities and limitations influence business, product and operational decisionsTRANSCRIPT
2 years of nosqlbenjamin anderson
@banjiewenmeteor solutions, inc.
about meteor
our data
2009pretty simple
why this was bad
enter couchdb/cloudant(and some redis)
nosql in production
what we’ve learned
solved initial problems
introduced new ones
no documentation
problems
problemsno documentation
problemsyou probably don’t need data model flexibility
problemsad-hoc data analysis is challenging
problemspredictability
problemslibrary support
problemsnot unsolvable!
overall, good stuff
good stuffrapid iteration
good stufffanatical customer support
good stuffgood people want to work on it
good stuffhorizontal scalability is no joke
good stuffsolves real problems
we’ve learned a lot
what we’ve learnedpay for the support
what we’ve learnedexpect tradeoffs
what we’ve learnedyou probably don’t need it
thanksbenjamin anderson
@banjiewenmeteor solutions, inc.