glibc VS crux

I am writing a short paper for school and the topic is Fast Inverse Square Root and alternatives. One of the questions is how the sqrt-function is implemented in glibc. Here is the code of that function.

The code isn't the easiest to read but in glibc it seems that the syscall is only performed if waiter are detected in userspace during an unlock operation

https://github.com/lattera/glibc/blob/master/nptl/pthread_mu...

You might still not realize, but floats are a large topic. Have a look at eg. this implementation here: https://github.com/lattera/glibc/blob/master/stdlib/strtod_l.c

This is a nice write up, thank you. However, I'm stilling interpreting this as a "fun trick" rather than the common sense method for solving the problem. For example, looking at the source code for htonl() from glibc: https://github.com/lattera/glibc/blob/master/inet/htonl.c

Second of all: The horrifying truth is that there is no such thing as a canonical text representation for IPv4 (source) (and yes... I am indeed citing the failed attempt to standardize this as my source for it not being standardized). Authoritatively speaking, all possible (non-binary) representations are equally invalid. In fact, text address resolution is typically delegated to the OS kernel, so what constitutes a "usable" address is liable to vary depending on if you're using Linux, OSX, Windows, or Other.

Optimized code gets really weird. The creators of strlen, for example, decided that iterating over each character to find the end was far too slow. So instead, they convert the character pointer into an int pointer and do bitwise manipulation with the int on two different magic numbers so they can check four/eight characters at once.

If you're using a cross-platform C compiler like gcc or clang, you're usually expected to switch to assembly. Here's the syscall instruction in glibc

Maybe you should include the math part of a libc statically with your code. glibc is one option, or dietlibc if you want it to be as small as possible.

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...