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    <p>The balance that I have found is using SQL as the normalized database and Mongo as the denormalized copy. I use a ESB to keep them in sync with each other. I use a concept that I call "prepared documents" and "stored documents". Stored documents are data that is only kept in mongo. Useful for data that isn't relational. The prepared documents contain data that can be rebuilt using the data within the normalized database. They act as living caches in a way - they can be rebuilt from scratch if the data ever falls out of sync (in complicated documents this is an expensive process because these documents require many queries to be rebuilt). They can also be updated one field at a time. This is where the service bus comes in. It responds to events sent after the normalized database has been updated and then updates the relevant mongo prepared documents. </p> <p>Use each database to their strengths. Allow SQL to be the write database that ensures data integrity. Let Mongo be the read-only database that is blazing fast and can contain sub-documents so that you need less queries. </p> <p>** EDIT ** I just re-read your question and realized what you were actually asking for. I'm leaving my original answer in case its helpful at all. </p> <p>The way I would handle the Stackoverflow example you gave is to store the user id in each comment. You would load up the post which would have all of the comments in it. Thats one query. </p> <p>You would then traverse the comment data and pull out an array of user ids that you need to load. Then load those as a batch query (using the Q.In() query operator). Thats two queries total. You would then need to merge the data together into a final form. There is a balance that you need to strike between when to do it like this and when to use something like an ESB to manually update each document. Use what works best for each individual scenario of your data structure.</p>
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