I have had the good fortune this year to help a number of organizations develop and deploy native data applications in Python and Rust using a project I helped found: delta-rs. At a high level delta-rs is a Rust implementation of the Delta Lake protocol which offers ACID-like transactions for data lake use-cases. One of the big areas of my focus has been in evaluating and improving performance in highly concurrent runtime environments on AWS.

To help others understand the problem domain I spent some time earlier in the week documenting the challenges in AWS on the Buoyant Data blog: Concurrency limitations for Delta Lake on AWS

In the case of AWS S3’s consistency model many operations are strongly consistent, but concurrent operations on the same key are not. AWS encourages application-level object locking, which the delta-rs implements using AWS DynamoDB.

AWS S3 is an incredible piece of technology that washes away a myriad of common storage problems, and has been jokingly referred to as “the 8th wonder of the world” by Corey Quinn. THe lack of a “putIfAbsent” like semantic is however very annoying for the Delta Lake protocol, adding the need for an application-wide lock for Delta users:

The dynamodb-lock approach allows for some sensible cooperation between concurrent writers but the key limitation is that all concurrent operations must synchronize on the table itself. There is no smaller division of concurrency than a table operation

In the blog post I offer some potential approaches to mitigate the weakness of needing a table-level lock for concurrent Delta Lake writers on AWS, but the problem will unfortunately remain until in some form or fashion until S3 introduces a “putIfAbsent” semantic which allows writers to “put” a file only if it doesn’t exist in an atomic way.

For concurrent Delta writers I can offer some advice, but unfortunately effective cooperative distributed concucrrency at scale remains a challenging problem! :)