The company has a great track record of success - since launch in1.
Today eHarmony has 55m registered users, a number that will increase dramatically as the service is rolled out to 20 other countries around the globe in the coming months. Users complete a detailed questionnaire when they sign up for the service.
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Additional research based around machine learning and predictive analytics is added to the algorithms to enhance the matching of prospective partners. Unlike searching for a specific item or term on Google, the matching process used to identify prospective partners is bi-directional, with multiple attributes such as age, location, education, preferences, income, etc.
Running a complete matching analysis of the user base was taking 2 weeks.
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In addition to the problems of scale, as the data models became richer and more complex, adjusting the schema required a full database dump and reload, causing operational eharmony dating service and downtime, as well as inhibiting how quickly the business could evolve.
They wanted a database that could: Support the complex, multi-attribute queries that provide the foundation of the compatibility matching system A flexible data model to seamlessly handle new attributes The ability to scale on commodity hardware, and not add operational overhead to a team already managing over 1, servers eHarmony explored Apache Solr as a possible solution, but it was eliminated as the matching system requires bi-directional searches, rather than just conventional un-directional searches.
Apache Cassandra was also considered but the API was too difficult to match to the data model, and there were imbalances between read and write performance.
After extensive evaluation, eHarmony selected MongoDB. As well as meeting the three requirements above, eHarmony also gained a lot of value from the MongoDB community and from the enterprise support that is part of MongoDB Enterprise Advanced.
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They can provide best practices in data modeling, sharding and deployment productization When testing, use production data and queries. The ROI from the migration has been compelling.
Matching the entire user base has been reduced from 2 weeks to 12 hours. In addition to eHarmony rolling out to 20 new countries, they also plan to bring their data science expertise in relationship matching to the jobs market — matching new hires to potential employers.
The ability to mix multiple storage engines within a MongoDB cluster can provide a foundation to consolidate search, matches and user data.
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