Developer Friendly Application Persistence That Is Fast And Scalable With HarperDB

Mar 5, 2022

Ask episode

Chapters

Transcript

Episode notes

Summary

Databases are an important component of application architectures, but they are often difficult to work with. HarperDB was created with the core goal of being a developer friendly database engine. In the process they ended up creating a scalable distributed engine that works across edge and datacenter environments to support a variety of novel use cases. In this episode co-founder and CEO Stephen Goldberg shares the history of the project, how it is architected to achieve their goals, and how you can start using it today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy.
So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now!
Your host is Tobias Macey and today I’m interviewing Stephen Goldberg about HarperDB, a developer-friendly distributed database engine designed to scale across edge and cloud environments

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what HarperDB is and the story behind it?
There has been an explosion of database engines over the past 5 – 10 years, with each entrant offering specific capabilities. What are the use cases that HarperDB is focused on addressing?
What are the issues that you experienced with existing database engines that led to the creation of HarperDB?
- In what ways does HarperDB address those issues?
What are some of the ways that the focus on developers has influenced the interfaces and features of HarperDB?
What is your view on the role of the database in the near to medium future?
Can you describe how HarperDB is implemented?
- How have the design and goals changed from when you first started working on it?
One of the common difficulties in document oriented databases is being able to conduct performant joins. What are the considerations that users need to be aware of as they are designing their data models?
What are some examples of deployment topologies that HarperDB can support given the pub/sub replication model?
What are some of the data modeling/database design strategies that users of HarperDB should know in order to take full advantage of its capabilities?
- With the dynamic schema capabilities allowing developers to add attributes and mutate the table structure at any point, what are the options for schema enforcment? (e.g. add an integer attribute and another record tries to write a string to that attribute location)
What are the most interesting, innovative, or unexpected ways that you have seen HarperDB used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on HarperDB?
When is HarperDB the wrong choice?
What do you have planned for the future of HarperDB?

Contact Info

LinkedIn
@sgoldberg on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers