Developer Friendly Application Persistence That Is Fast And Scalable With HarperDB

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
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• 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.
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Links

• HarperDB
  • @harperdbio on Twitter
• Mulesoft
• Zapier
• LMDB
• SocketIO
• SocketCluster
• MongoDB
• CouchDB
• PostgreSQL
• VoltDB
• Heroku
• SAP/Hana
• NodeJS
• DynamoDB
• CockroachDB
  • Podcast Episode
• Fastify
• HTAP == Hybrid Transactional Analytical Processing
• Splunk

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA