Does cosmosdb stores data differently depending on which api you use?

Question

What is the ramification of using each api?

For example if I am using sql api, am I sacrificing ACID, and which part of CAP am I using? How do azure achieve horizontal scalability

If I am using document api or key value api, is the internal data layout different?

Answer 1

The internal storage format for the various database API's in Cosmos DB doesn't have any bearing on ACID or CAP. The API you choose should be driven by the appropriate use case needed and/or your familiarity with it. For instance, both SQL API and Mongo DB API are document databases. But if you have experience using Mongo, then it's probably a better choice rather than say Gremlin which is a graph, Table which is a key/value store, or Cassandra which is a columnar store.

Cosmos DB provides ACID support for data within the same logical partition. With regards to CAP that depends on which consistency level/model you choose. Strong consistency trades "A" availability for "C" consistency. All the other consistency models trade consistency for availability but at varying degrees. For instance, bounded staleness defines an upper bound that data can lag in consistency by time or updates. As that boundary approaches, Cosmos will throttle new writes to allow the replication queue to catch up, ensuring the consistency guarantees are met.

Achieving horizontal scalability is a function of your partitioning strategy. For write heavy workloads, the objective should be choosing a partition key that will allow for writes to be distributed across a wide range of values. For read heavy workloads, you want for queries to be served by one or a bounded number of partitions. If it's both, then using change feed to copy data into 2 or more containers as needed such that the cost of copying data is cheaper than running it in a container that would result in cross-partition queries. At the end of the day, choosing a good partition key requires testing under production levels of load and data.