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After extensive evaluation, e Harmony selected Mongo DB.

As well as meeting the three requirements above, e Harmony also gained a lot of value from the Mongo DB community and from the enterprise support that is part of Mongo DB Enterprise Advanced.

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 complexity and downtime, as well as inhibiting how quickly the business could evolve. They wanted a database that could: e Harmony 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.

e Harmony are also excited by the prospect of pluggable storage engines delivered in Mongo DB 3.0.

The ability to mix multiple storage engines within a Mongo DB cluster can provide a foundation to consolidate search, matches and user data.e Harmony split out the matches into a distributed Postgres database, which bought them some headroom, but as the number of potential matches grew to 3 billion per day, generating 25TB of data, they could only scale so far.Running a complete matching analysis of the user base was taking 2 weeks.e Harmony currently operates in North America, Australia and the UK.The company has a great track record of success - since launch in 2000, 1.2 million couples have married after being introduced by the service.Securely add new data sources, access your corporate data platforms and integrate your data ― without relying on heavy-handed IT infrastructure tools and support.