Harnessing the adoption of Python to build a winning team

So how does a team of six engineers—heavily experienced in web development in languages like ReactJS, NodeJs and Java - adopt Python into their work?

The application development and cloud computing technology landscape is constantly changing and an important part of our role as engineers is to stay updated on those changes. Sometimes it is through solo work—such as learning a new framework or skill. But sometimes it is through team-based work - such as adopting and migrating a whole project to a new language.

Like most other engineers, I’ve experienced this kind of team-based work multiple times in my career.  I recently went through it in my current role as a software engineering manager at Capital One. In my role, I am working on leading a team of engineers to develop highly scalable web applications, including both API and UI layers. As part of our journey to migrate applications to the cloud, we were also involved in rewriting a lot of legacy ETL jobs originally built using licensed tools. Most of my past engineering experience before these re-write efforts for ETL was around NodeJS, Java, and Ruby on Rails; until a year ago I had not worked with Python. The same was true with most of the engineers on my team. 

In fact, our entire team consisted of experienced engineers who have delivered multiple web and distributed applications in the cloud, but limited exposure to ETL or data-driven projects. 

About one month into the rewrite efforts, we were hitting limitations around using Java to migrate legacy system code. We wanted to be able to achieve simple file operations, as well as complicated queries using Spark, but with dynamically typed language and minimal bootstrap code. This was one of our reasons for considering whether it was time to switch languages.

For our project, we faced the huge task of re-writing multiple jobs running on a legacy ETL platform. This involved enterprise API integrations, complex data analysis and refinement.

Due to the nature of these jobs, none of the languages we were most experienced with were a great choice. That’s because they were:

• Static-typed languages like Java

• Involved heavy bootstrap code

• Lacked extensive support for data manipulation libraries such as Pandas

• Lacked extensive external community support for Spark integrations or data analysis

Python seemed to be a good choice for us as it was flexible enough to support a wide array of use cases. It also fit in well as it was:

• Dynamically typed

• Supported re-writing Bash-based or ETL jobs in fewer lines of code

• Had a well-supported REST interface

• Had excellent support for data manipulation libraries such as Pandas and Spark

With these benefits and Python also being one of the most popular programming languages, it made sense to bring it to our team.

There were a few other areas that really cemented our use of Python - these were File Operations and Community Support. 

File operations were key to our project as our process involved reading multiple source files in parquet format, extracting, refining, and producing new files. We needed a language that could easily integrate with SPARK on HDFS for complicated and larger datasets, as well as an equally powerful library for smaller datasets like Pandas. Hence, File Operations were at the center of our re-write.

Python worked well in both cases, including:

• Well suited for simple file manipulations using Pandas

• Worked in complex scenarios using Spark Queries on HDFS

• Needed significantly fewer lines of code to accomplish this than in Java or NodeJS

• Could work for File Operations in memory when source files are few a MBs

• Simple enough to make API calls for various Enterprise Layers

As we assessed adopting a new language, Community Support within Capital One and without was a key for us. We wanted a language that was well supported by an active open source community that:

• Constantly updates security enhancements

• Resolves outstanding questions or issues

• Actively merges new feature requests from engineers

• Python has a much more extensive community of engineers in the Data Analysis space as compared to Java

• PySpark has much better support than Spark Integration with Java

• Capital One has a very active community of Python engineers and experts to help teams get started and maintain their Python projects

• This internal community allowed us to seek guidance, as well as go through multiple code reviews

Like any other programming language, we started by defining our source of truth for standards. We went with PEP8 which is the standard that defines Coding Style Guides for Python.

We went through the below key stages from Planning to Production.

Let’s go through some of the key elements to these stages.

As our team was new to Python, we spent the first initial few days defining standards on how we would code to comply with PEP8. But in addition to adoption, we needed to automate our workflow to comply with these standards.

• Added a Pre-Commit Hook for Black which automatically formats code on local commits.

• Black didn’t catch all violations, which is where Flake8 came in.

• Flake 8 installed as a Pre-Commit hook stopped any code commits with outstanding compliance errors with PEP8.

After automating our workflow to comply with standards and a base repo, we started with the core dev work.

This was key given Python is dynamically typed; logging was our solution to better track problems. As a Team we Decided on a common Logging format:

• We leveraged the ELK stack (Elasticsearch, Logstash, and Kibana) for logging.

• By using Kibana as the Web Application UI for our logs, we could see our execution details as well as trace down exceptions in Kibana.

• Adopting a simple library called requests to handle our API calls.

• Spark v/s Pandas: When you perform operations on a dataframe in Spark, a new dataframe/reference is created which is by design. This works well with large datasets but is a hindrance when the dataset is smaller. Hence for filtered smaller datasets under 5MB, we decided to go with Pandas for quick data frame manipulations.

• Automation for compliance to coding standards was a huge time saver as most of our team was new to Python.

We quickly realized that for us to test along with all the dev work, we needed a TDD approach where pytest came into our workspace. This proved extremely helpful.

Given the nature of our project, Python did fit in very well. It’s dynamically typed and the libraries are feature-rich, working for simple API calls and complex operations around data transformation and filtering. As engineers, we worked as a team to “learn how to learn” and adopt a new language the right way. With the help of Capital One’s Python Gurus, we adopted all the best practices for developing a working production application and delivered this project before the committed deadline. I am lucky to be part of such an awesome team, as well as to work with Capital One Python experts like Steven Lott, which was critical for getting us across the finish line.

• PySpark

• Pandas