While Amazon DynamoDB indeed lacks a detailed statement about their choices regarding the CAP theorem (still hoping for a DynamoDB edition of Kyle Kingsbury's most excellent Jepsen series - Call me maybe: Cassandra analyzes a Dynamo inspired database), Jeff Walker Code Ranger's answer to DynamoDB: Conditional writes vs. the CAP theorem confirms the lack of clear information in this area, but asserts that we can make some pretty strong inferences.
The referenced forum post also suggests a strong emphasis on availability too in fact:
DynamoDB does indeed synchronously replicate across multiple
availability zones within the region, and is therefore tolerant to a
zone failure. If a zone becomes unavailable, you will still be able to
use DynamoDB and the service will persist any successful writes that
we have acknowledged (including writes we acknowledged at the time
that the availability zone became unavailable).
The customer experience when a complete availability zone is lost
ranges from no impact at all to delayed processing times in cases
where failure detection and service-side redirection are necessary.
The exact effects in the latter case depend on whether the customer
uses the service's API directly or connects through one of our SDKs.
Other than that, Werner Vogels' posts on Dynamo/DynamoDB provide more insight eventually:
- Amazon's Dynamo - about the original paper
Amazon DynamoDB – a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications - main introductory article including:
- History of NoSQL at Amazon – Dynamo
- Lessons learned from Amazon's Dynamo
- Introducing DynamoDB - this features the most relevant information regarding the subject matter
Durable and Highly Available. Amazon DynamoDB replicates its data over
at least 3 different data centers so that the system can continue to
operate and serve data even under complex failure scenarios.
Flexible. Amazon DynamoDB is an extremely flexible system that does
not force its users into a particular data model or a particular
consistency model. DynamoDB tables do not have a fixed schema but
instead allow each data item to have any number of attributes,
including multi-valued attributes. Developers can optionally use
stronger consistency models when accessing the database, trading off
some performance and availability for a simpler model. They can also
take advantage of the atomic increment/decrement functionality of
DynamoDB for counters. [emphasis mine]
DynamoDB One Year Later: Bigger, Better, and 85% Cheaper… - about improvements
Finally, Aditya Dasgupta's presentation about Amazon's Dynamo DB also analyzes its modus operandi regarding the CAP theorem.
Practical Guidance
In terms of practical guidance for retry handling, the DynamoDB team has meanwhile added a dedicated section about Handling Errors, including Error Retries and Exponential Backoff.
