We know that MongoDB uses sharding to provide horizontal scaling. These clusters support deployments with large data sets and high-throughput operations. Sharding allows users to partition a collection within a database to distribute the collection's documents across a number of mongod instances or shards. But how can sharding address scalability on an embedded and normalized collections? Which database( embedded or normalized) is better in terms of scalability?
Which database( embedded or normalized) is better in terms of scalability?
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It can't it will be a nightmare to maintain most likely, depending on your usage.
Most likely the normalized approach will perform much better, each has it's pro's and con's however if your embedded data has updates performed on it I would personally recommend not to use this approach. Especially at scale. I also find that in many cases a structural change of the data can solve many of these issues, worth considering.
Obviously these are very broad statements, and should be considered within context of your needs.