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As I understand, when you perform a query that doesn't filter by one primary key, you perform a cross-partition query. For this to be executed, the query is sent to all physical partitions of your CDB collection, executed in parallel in each of them, and then returned.

As you scale to tens of thousands of requests per second, that means that each of the tens of thousands of requests is executed on each physical partition.

Does this mean that eventually each partition will reach its limit of requests per second it can serve, and horizontal scaling will no longer give any benefit? Because for every new physical partition CDB adds, it will need to serve all requests coming in, so it's not adding new throughout capacity, only storage.

The downstream implication being that even if at a small scale you're ok with incurring the increased RU cost for cross-partition queries, to truly be able to scale indefinitely your data model should ensure queries hit only one partition (possibly by denormalizing it).

Michael Tontchev
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Yes, cross partition queries will not allow a database like Cosmos DB (or any horizontally scalable database) to scale.

Databases like Cosmos DB provide unlimited scale because it scales horizontally. The objective for your partition strategy should be to answer your high volume queries with one, or at a minimum, a bounded set of partitions. The effort around partition strategy is to chose a property that is nearly always passed in queries. Denormalization is generally more a function of modeling data around requests. It has less to do with partitioning directly.

If you would like to learn more about partitioning and modeling with Cosmos DB I highly recommend watching this video. It presents the topics very well, Data modeling & partitioning: What every relational database dev needs to know

Mark Brown
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