j_kao

Would be curious if this ends up incentivizing more littering.

FYI the mooncake team was acquired by Databricks so it's basically vendors trying to compete on features now :)

1 - I think if we were sticking with the JVM, I do wonder if Lucene would be the right choice in that case

2 - It's a great tool with a lot of tuneability and support!

3 - We've been using it for K8s logs and OTEL (with Jaeger). Seems good so far, though I do wonder how the future of this will play out with the $DDOG acquisition.

These are great projects, we use DuckDB to inspect our data lake and for quick munging.

We will have some more blog posts in the future describing different parts of the system in more detail. We were worried too much density in a single post would make it hard to read.

It's a bit difficult at the moment, given we have a lot of proprietary data at the moment and a lot of the logic follows it. I'm hoping we can get it to a state where it can be indexed and serving OSM data but that is going to take some time.

That being said, we are currently working on getting our Google S2 Rust bindings open-sourced. This is a geo-hashing library that makes it very easy to write a reverse geocoder, even from a point-in-polygon or polygon-intersection perspective.

Author here! We were really motivated to turn a "distributed system" problem into a "monolithic system" from an operations perspective and felt this was achievable with current hardware, which is why we went with in-process, embedded storage systems like RocksDB and Tantivy.

Memory-mapping lets us get pretty far, even with global coverage. We are always able to add more RAM, especially since we're running in the cloud.

Backfills and data updates are also trivial and can be performed in an "immutable" way without having to reason about what's currently in ES/Mongo, we just re-index everything with the same binary in a separate node and ship the final assets to S3.

Companies like Netflix with bigger market caps are still on AWS.

I can imagine the productivity of spinning up elastic cloud resources vs fixed data center resourcing being more important, especially considering how frequently a company like Figma ships new features.

Those are people who would probably steal software, anyways, so for most businesses I think worrying about those folks would be a waste of time

A bit of an aside, I'm a big fan of how Mike Perham managed to start a business around Sidekiq. I think it really gives us Hacker News folks that there's hope starting a viable bootstrapped business around open-source infrastructure.

Perhaps if the fee was more expensive for a single person, but economical for >2 people then the math works out.

To be fair, there are a number of job openings where you can "Apply with LinkedIn", so it's structured data end-to-end.

One area which an external monitor (or high resolution) helps a lot with is UI or frontend programming. It's a huge pain to play with the chrome or Firefox debugger and have to jump between windows to get feedback.

When it comes to backend or non-UI work, a laptop is just fine.

Testing in production will trend upwards among companies because everybody's workload is shifting towards the cloud and/or making use of external SaaS services. There are a number of cloud services that are not open source that can't be run locally, or run at the same scale as production.

It is not a good use of time to mock everything, because you have no control of external systems. The only reason I'd see it being important is if these external systems are tightly coupled to complex local logic that should be tested locally. However, there are a number of strategies to deal with such "tight coupling" in such cases.