Investing in the development of delta-rs is one of the longer shots I have taken recently and with my upcoming Data and AI Summit talk on the subject, I wanted to share some back story on the project. As I have mentioned before Delta Lake is a key component of Scribd’s data platform. We selected Delta Lake for many reasons, including that it is a open and vendor neutral project. The power of Delta Lake has opened up countless opportunities for data and for the past year I have seen the potential for many more.

Almost a year ago, I emailed some pals at Databricks my thinking on why I needed a native interface to Delta Lake. In this post I will share some of those thoughts and highlight where I was wrong.

From that email, with some slight edits:

For framing this conversation and scope of the native interface, I categorize our compute workloads into three groups:

A: Big offline data processing, requiring a cluster of compute resources where Spark makes a big dent.

B: Lightweight/small offline data processing, workloads needing “fractional compute” resources, basically less than a single machine. Ruby/Python type tasks which move data around, or perform small-scale data accesses make up the majority of these in our current infrastructure. We have discussed using Spark for these in the past, but since the cost to develop/deploy/run these small tasks on Spark clusters doesn’t make sense.

C: Boundary data-processing, where the task might involve a little bit of production “online” data and a little bit of warehouse “offline” data to complete its work. In our environment we have Ruby scripts whose sole job is to sync pre-computed (by Spark) offline data into online data stores for the production Rails application, etc, to access and serve.

I don’t want to burn down our current investment in Ruby for many of the ‘B’ and ‘C’ workloads, not to mention retraining a number of developers in-house to learn how to effectively use Scala or Pyspark.

When I originally proposed a native interface, delta-rs did not exist. My colleague QP created the initial sketch of delta-rs over the course of a few weekends following my original description of the challenges we faced; I think it was kind of a “I bet I can do this” motivation on his part.

What I did not fully appreciate at the time was the resource cost and complexity of data ingestion around Delta Lake. A non-trivial amount of time and resources are spent simply getting data into Delta Lake. Following the framing above, these are typically “lightweight data processing workloads” which take some form of data and append it to a Delta table. Identifying the scope of this challenge led to the creation of kafka-delta-ingest. The kafka-delta-ingest daemon is built on top of delta-rs to allow for scalable and rapid ingestion of data from Kafka topics into Delta tables. The challenges of running this workload in Spark Streams ultimately motivated significant interest and investment in delta-rs.

In my original vision I expected us to develop a Ruby binding, with room to grow to a Python, Node, and Golang binding down the road. I hoped we would develop the Ruby binding first to allow the numerous Sidekiq and other Ruby processes at Scribd to dive into Delta Lake data. Luckily that hasn’t happened yet, but others in the open source community have stepped up to provide a really good Python binding which I am told is already being used in production for some use-cases.

There’s still time to be that amazing contributor to build Node or Golang bindings! ;)

Just over a year on from my initial “here’s a wild idea that I think we could make happen” and delta-rs has taken on a life of its own in ways I did not anticipate. There have been over a dozen different contributors, support using Delta Lake in Rust, Python, and early support for Ruby, and storage backends implemented for the local filesystem, AWS S3, and Azure’s Data Lake Storage (Gen 2). There have been over 130 pull requests merged and as of this writing there are a few more in various states of draft or review. A second implementation of the Delta Lake protocol has also led to a few pull requests to improve our collective definition of what Delta Lake actually is.

I am also quite pleased that to date there has not been a single substantive code contribution by Databricks, the company which birthed Delta Lake. I consider this to be hugely positive for the Delta Lake project as a whole. Foundations and open source ecosystems will always benefit from a diversity of contributors whether at an individual or a corporate level.

Databricks has been a hugely supportive partner in this effort, making time and space for answering questions, making suggestions, and helping to popularize the delta-rs work that has been done. For that I am tremendously grateful. One of these days I’ll convince them to start writing some Rust ;)

To me the story of delta-rs and Scribd’s involvement in its development is why I think it is essential for engineering leaders to invest in open source standards, projects, and communities. Any core platform or infrastructure component needs to last despite an ever-changing technology stack; there’s no way to predict where the road will take you. Working within and around an open source community provides a path forward to grow the technology that is key to the organization’s future success.