kindkid's comments

It's built in to MacOS.

Settings > Accessibility > Pointer Control > Alternate Control Methods > Head pointer

For example: "out fox", "loan out", and "crossed out"?

Also, look at their competition. New Relic's product is so painful and broken that despite successfully winning deals with many large companies, they still manage to lose business to Datadog as their end users work extremely hard to convince management to not renew, and switch to Datadog, for the good of the company. Ask any insider.

Cool. I remember having to jump through hoops in order to get the behavior I wanted in Oracle many years ago: https://stackoverflow.com/questions/675398

Very cool! I'm particularly interested in this section:

> SpaceTime database is built on a unique, multidimensional data index that automatically adapts to mutable and possibly highly skewed data distribution (which is usually the case with data coming from moving objects). There is one single multi-columnar index which indexes all associated columns of records at once. There are no secondary indices!

> The physical model intertwines index and record data – as a consequence, records that are logically close (based on id, position and timestamp) are at the same time also physically close. In turn, this maximizes the throughput of disk reads.

> The main ideas for inventing SpaceTime index came from several inspiring scientific papers. It is similar to the kd-tree family of indices, but with two major improvements: first, the index tree in SpaceTime is built using the bottom-up approach (as opposed to the top-down kd-tree construction) and second, the process of index creation adapts to particular space-time distribution of data. Kd-trees work well on a large scale only with static datasets; our bottom-up approach overcomes this.

Can you provide links to the scientific papers that were mentioned?

Are there any plans to publish a more detailed description of SpaceTime's index data structure and algorithm?

"In essence, you can only make money if you are doing hardcore R&D. This strongly incentivizes the creation of new capabilities but also disincentivizes publication of CS research.

"You see this in markets like databases, where open source has captured almost the entire market for undifferentiated capabilities, and there is a lucrative high-end market with unique product capabilities that don't exist in open source or CS literature. The trend toward treating CS research as trade secrets, originally started because algorithm patents were impractical to enforce, turned out to be effective at maintaining profitability in high-end software products if open source can't replicate capability."

https://news.ycombinator.com/item?id=20196610

Ah drat, apparently my fears are confirmed. If you should someday have enough money and not enough fame, I'll be eagerly looking forward to hearing the lessons you're willing to share.

Andrew, everything you reveal publicly has been tantalizing! (See https://www.jandrewrogers.com/ for those who haven't stumbled upon his posts yet)

"There is virtually no literature on practical representations of topological spaces, never mind parallel algorithms using those representations. A thorough exposition of both the theory and practice is on the order of a few hundred pages of dense technical literature that no one has had time to write, despite multiple implementations. Watch this space." - October 2015, J. Andrew Rogers.

I emailed you back in 2016 to inquire about your work and wondered what had become of SpaceCurve. (Thank you for replying!) You mentioned recent work then on a "modality architecture." Is that related to the work you mentioned in your post above?

Obviously, you're a busy man with a desire and the potential to change the world with your creations. But perhaps also a drive to withhold your creations from public display until only after you have them distilled to their purest elegance?

If it is your intention to eventually share, I encourage you to do the world a great favor and just share what you've got so far (with a "no guarantees; no support" reminder in your README), even if some corners are unpolished, inscrutable, or built on shifting ideas. With an appropriate license, you'll at least get the benefit of easily taking bits of your implementations with you between projects, even without supporting anyone else who consumes it.

Do you have any peers who are familiar enough and excited about your work to start writing up some posts laying out the conceptual ground-work? Have there been any relevant research papers or books published that would be foundational to understanding? Maybe start with links to those? I'd devour them!

On the other hand, perhaps you are motivated not to share, while your skills are highly marketable due to near exclusivity? If so, I certainly don't begrudge you that! And like you said, you have no obligations. :)

Very cool.

I noticed that all the write benchmarks in https://apple.github.io/foundationdb/benchmarking.html are for random writes. Is write throughput affected by highly-sequential writes (e.g. - time series) vs random writes? How do you avoid hot-spotting on recent ranges?

How efficient are range deletes?

On https://apple.github.io/foundationdb/performance.html I read "The memory engine is optimized for datasets that entirely fit in memory, with secondary storage used for durable writes but not reads." I'd like some clarification:

(1) Which memory does "entirely fit in memory" refer to? A single machine? Or SingleNodeMemory * Nodes / ReplicationFactor?

(2) If only recently-written data is likely to be queried, and all recently-written data fits entirely in memory, is that sufficient? If so, would an unexpected query of old data cause a huge impact on write throughput?

(3) What is the structure/format of the data stored on disk? How is it updated?

I'm wondering how well this could be used for time series data. I saw mention here that wavefront uses FoundationDB for this, but would like more details if any are available.