What are the differences between CosmoDB and DocumentDB
The Azure Cosmos DB team member here.
Azure Cosmos DB started as “Project Florence” in 2010 to address developer pain-points faced by large scale applications inside Microsoft. Observing that the challenges of building globally distributed apps are not a problem unique to Microsoft, in 2015 we made the first generation of this technology available to Azure developers in the form of Azure DocumentDB. Since that time, we’ve added new features and introduced significant new capabilities. Azure Cosmos DB is the result. It is the next big leap in globally distributed, at scale, cloud databases. As a part of this release of Azure Cosmos DB, DocumentDB customers, with their data, are automatically Azure Cosmos DB customers. The transition is seamless and they now have access to the new breakthrough system and capabilities offered by Azure Cosmos DB.
In the evolution of Cosmos DB, we have added significant new capabilities since 2015 (when DocumentDB was made generally available) but only a subset of these capabilities was available in DocumentDB. These capabilities are in the areas of the core database engine as well as, global distribution, elastic scalability and industry-leading, comprehensive SLAs. Specifically, we have evolved the Cosmos DB database engine to be able to efficiently map all popular data models, type systems and APIs to the underlying data model of Cosmos DB. The developer facing manifestation of this work currently will experience it via support for Gremlin and Table Storage APIs. And this is just the beginning… We will be adding other popular APIs and newer data models over time with more advances towards performance and storage at global scale.
We also have extended the foundation for global and elastic scalability of throughput and storage. One of the very first manifestations of it is the RU/m (https://docs.microsoft.com/en-us/azure/cosmos-db/request-units-per-minute) but we have more capabilities that we will be announcing in these areas. The new capabilities will help save cost for our customers for various workloads. We have made several foundational enhancements to the global distribution subsystem. One of the many developer facing manifestations of this work is the consistent prefix consistency model (making in total 5 well-defined consistency models). However, there are many more interesting capabilities we will release as they mature.
It is important to point out that we view Azure Cosmos DB as a constantly evolving database service. Typically, we first validate all new capabilities with the large scale applications inside Microsoft, subsequently expose them to key external customers, and finally, release them to the world.
It is also important to point out that DocumentDB’s SQL dialect has always been just one of the many APIs that the underlying Cosmos DB was capable of supporting. As a developer using a fully managed service like Cosmos DB, the only interface to the service is the APIs exposed by the service. To that end, nothing really changes for a DocumentDB customer. Cosmos DB offers the exactly the same SQL API that DocumentDB did. However, now (and in the future) you can get access to other capabilities which were previously not accessible.
DocumentDB is one of the APIs for CosmosDB. Others include Table Storage, MongoDB, Gremlin.
Think about CosmosDB as the database platform that handles scaling, throughput, consitency, etc and DocumentDB as one of the types of the databases than run on CosmosDB.
Azure Cosmos DB natively supports multiple data models including documents, key-value, graph, and column-family. The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS). Atoms consist of a small set of primitive types like string, bool, and number. Records are structs composed of these types. Sequences are arrays consisting of atoms, records, or sequences.
The database engine can efficiently translate and project different data models onto the ARS-based data model. The core data model of Cosmos DB is natively accessible from dynamically typed programming languages and can be exposed as-is as JSON.
https://docs.microsoft.com/en-us/azure/cosmos-db/introduction