Bojan

GitHub - howardjohn/gateway-api-bench: Gateway API Benchmarks provides a common set of tests to evaluate a Gateway API implementation. Gateway API Benchmarks provides a common set of tests to evaluate a Gateway API implementation. - howardjohn/gateway-api-bench

Interesting Kubernetes Gateway API implementation comparison and benchmarks.

github.com/howardjohn/g...

The Architecture of Serverless Data Systems — Jack Vanlightly I recently blogged about why I believe the future of cloud data services is large-scale and multi-tenant, citing, among others, S3.  “ Top tier SaaS services like S3 are able to deliver amazing ...

Interesting survey of serverless multi-tenant data architectures.

jack-vanlightly.com/blog/2023/11...

Oopsie! This will be a fun one.

issues.apache.org/jira/browse/...

discussion: spec: reduce error handling boilerplate using ? · golang go · Discussion #71460 This is a discussion about a new syntax that may be used to handle errors. This is issue #71203 converted into a discussion. I've written a tool that converts ordinary Go code into code that uses t...

Go proposal discussion: Reduce error handling boilerplate using ?

github.com/golang/go/di...

YouTube video by CNCF [Cloud Native Computing Foundation] Reimagining OSS Licensing and Commercialization with Fair Source - Adam Jacob, System Initiative

I've been thinking about and working to understand Free Software, Open Source, and business for a very long time. Thanks to @kelseyhightower.com, I tried to distill all of that into a single talk: youtu.be/rmhYHzJpkuo - I'm very proud of it, and I hope it helps you. :)

memory regions · golang go · Discussion #70257 I'm starting this discussion to collect early feedback on a draft design for a kind of region-based memory management in Go. There is no prototype yet, only a design and a preliminary evaluation. P...

Will be interesting to see where this goes

github.com/golang/go/di...

How Uber Serves Over 40 Million Reads Per Second from Online Storage Using an Integrated Cache Docstore is Uber's in-house, distributed database built on top of MySQL®. Storing tens of PBs of data and serving tens of millions of requests/second, it is one of the largest database engines at Uber used by microservices from all business verticals. Since its inception in 2020, Docstore users and use cases are growing, and so are the request volume and data footprint. The growing number of demands from business verticals and offerings introduces complex microservices and dependency call graphs. As a result, applications demand low latency, higher performance, and scalability from the database, while simultaneously generating higher workloads.

Interesting read

How Uber Serves Over 40 Million Reads Per Second from Online Storage Using an Integrated Cache

www.uber.com/en-CA/blog/h...

Hello world.