collapseos VS crux

Something like this is probably a lot more realistic than (as fast) "integrated circuits at home" : http://collapseos.org/

http://collapseos.org/

I know the point of it isn't to run on new hardware, but this would be a way to learn it on a stable platform without having to worry about dealing with constant problems from old hardware before trying to implement it on said.

There is something called Collapse OS I read about here on HN:

http://collapseos.org/

I myself am a collector of old devices, having raised three kids plus being a web dev. Hate throwing them away too I was just think about this today could I extract the CPUs or RAM or something to reuse rather than destory for the metals. I'd like to learn more hardware but no time.

Some hope for Colapse OS [1] perhaps?

[1] http://collapseos.org

http://collapseos.org/

Here is a quick guide to the science for those with the brain worms:

Maybe start with sufficient support for a simple OS that allows you to edit and compile programs. Something like FreeDOS or CollapseOs. Once you have that working you can extend it.

During collapse we'll all be using Dusk OS and post collapse we'll be using cobbled together rugged computers running on Collapse OS. I imagine at that point we can probably put the sub name to a vote. Maybe "r/ordinarylife".

Not Linux....but you could just install Kolibri OS for a very light desktop or consider CollapseOS and DuskOS....think Dusk should run bare metal on now and won't be too bloated, but there's always CollapseOS if you prefer to keep things light

Hi, a couple of my colleagues spent some time working on this integration with our open source database product (https://opencrux.com), and I'm curious to know - has anyone done similar things to connect Corda with a secondary off-the-shelf query engine?

For more details, see the release notes.

The context is that I work on on https://opencrux.com, which offers a bi-temporal Datalog query layer (as well as SQL) that more or less addresses the intersection of the two, since Datalog is great for expressing recursive queries.

I suppose another somewhat important distinction, once again performance related, is that graph databases will typically track index statistics to aid with query planning. For example, Crux uses stored knowledge of attribute-value cardinalities (recently via HyperLogLog) to optimise the join order of a query - this can make a big difference when attempting to traverse large graphs efficiently.

Agreed, recursive querying & bitemporal modelling in SQL are non-trivial problems, and the combination of the two is harder still. For an alternative perspective on tackling such problems I'd suggest looking at Datalog, which makes recursion a breeze, and a database with first-class bitemporality - both of which feature in https://opencrux.com (which I happen to work on :))

I work on Crux so can share a few details about our implementation of Datalog. The query is compiled into a kind of Worst-Case Optimal Join algorithm [0] which means that certain types of queries (e.g. cyclic graph-analytical queries, like counting triangles) are generally more efficient than what is possible with a non-WCOJ query execution strategy. However, the potency of this approach relies on the query planner calculating a good ordering of variables for the join order, and this is a hard problem in itself.

Crux is usually very competent at selecting a sensible variable ordering but when it makes a bad choice your query will take an unnecessary performance hit. The workaround for these situations is to break your query into smaller queries (since we don't wish to support any kind of hinting). Over the longer term we will be continuing to build more intelligent heuristics that make use of advanced population statistics. For instance we are about to merge a PR that uses HyperLogLog to inform attribute selectivity: https://github.com/juxt/crux/pull/1472

[0] https://cs.stanford.edu/people/chrismre/papers/paper49.Ngo.p...