Adventures in DevOps

The episode contains substantive technical insights about graph-based database architecture, multi-tenancy challenges, and pragmatic infrastructure decisions, but is diluted by extended anecdotes (the DDoS story, the RPG game project) and philosophical tangents about AI-assisted coding that add little operational value. A practitioner building databases or operating production systems will learn concrete ideas, but must wade through significant filler.

“Berkeley DB can be embedded into the the there's a Java edition can be embedded into the process. So it's actually all those queries are l are just API calls and within the same process. So it's super fast.”

“we we we are running like thousand thousands of tenants on each instance. So that that's one of the the good things that I talked that I talked about, like they had they can use a shared memory pool on Berkeley D B”

The guest offers genuinely unconventional technical choices - embedding Berkeley DB in-process, building a graph-based system instead of relational, housing thousands of tenants per instance with shared memory pools - that run counter to conventional wisdom. However, the semantic web discussion is dated, and later sections devolve into well-trodden territory (AI limitations, multi-cloud complexity, the DDoS-forced-migration story).

“we we we are under the same assumption that and spreadsheets are kind of the de facto interface that business people like to um interact with data. So in the very beginning, in the very beginning, Ragic is nothing about spreadsheets.”

“it's not based on um SQL. It's not based on database tables. It's not relational. Okay. So it's basically based on graph. Like because it came from semantic web.”

Jeff Quo is a founder-CEO with 20+ years of deep technical and operational experience - he's maintained Oracle systems, built semantic web prototypes, debugged production database corruption at 3am, and managed infrastructure decisions across multiple cloud providers. He is a practitioner, not a pundit. However, much of his commentary in the latter half (AI, gaming side projects) ventures into areas where he's not the primary expert, diluting the caliber slightly.

“I was just a a master student, graduate student. So so it it took me quite a long time to to get it right.”

“I was on vacation. So I was not even in the office. So I was kind of like in a museum. I was sitting there with trying to use the museum Wi Fi and then my kids were just like off off like in visiting in the museum. I was just in in the the lobby and trying to m move everything from Linux to G C P.”

The episode provides concrete details in pockets: Berkeley DB performance improved 10-100x, the 2014 Linode DDoS lasted ~24 hours, thousands of tenants per instance, version 4 Berkeley DB had a critical data deletion bug fixed in version 7. However, many claims lack numbers or specifics: no metrics on customer scale, vague timelines for various pivots, no performance comparisons post-migration, no concrete examples of the 'weird uses' customers find for Ragic.

“the the performance was like ten a hundredfold from the original ones using the the SQL.”

“There's like one day I was just talking to some of my tech friends and he was talking about how great this this this thing, the Berkeley Berkeley D B”

Warren (host) demonstrates strong technical depth and asks sharp follow-ups: he challenges the semantic web discussion directly, probes the multi-tenancy model, pushes back on graph database speed claims with his own data, and questions containerization and load-testing approaches. However, he allows long tangents (the RPG game digression takes 5+ minutes) and doesn't always press for specifics when Jeff remains vague. The conversation has genuine intellectual engagement but lacks consistent rigor in extraction.

“I mean, you basically have the experience now to potentially go out and just write the whole database from scratch. Have you ever considered making that final leap, like using the Berkeley DB as sort of a starting point and reconfiguring it in a way that would be optimal for the the business?”

“Can I can I ask you using like AWS or GCP or something specific to Taiwan?”