r4um

A must read would be "What Every Programmer Should Know About Memory"[1], Section 4 covers Virtual Memory.

[1] https://people.freebsd.org/~lstewart/articles/cpumemory.pdf

Another great read on the subject "Into the Cool: Energy Flow, Thermodynamics, and Life - Eric D. Schneider, Dorion Sagan" https://www.goodreads.com/en/book/show/52737

Agreed. Problem isn’t timed rollout but lack of automated rollback. If you don’t have one don’t do this. The automated rollback should look for acceptable error rates, latency etc.

Rochester academy compilation. https://www.rochesterastronomy.org/sn2023/sn2023ixf.html

Some more pictures and animations.

https://www.instagram.com/p/Csc-QilOJ7j/ https://www.instagram.com/p/CsdEX8HMEID/ https://www.instagram.com/reel/CsdmaY0g4yS/ https://www.astrobin.com/7jvafm/0/ https://www.astrobin.com/uokdzt/

Coming from http://www.arameb.com/blog/2022/10/19/time-presentation

I highly recommend The Art of Statistics.

https://www.goodreads.com/book/show/43722897-the-art-of-stat...

A related great read is the book Into the Cool[1]. Highly recommend.

[1] https://press.uchicago.edu/ucp/books/book/chicago/I/bo353393...

You can try the MIT one https://www.youtube.com/playlist?list=PLUl4u3cNGP60jIMmB53zl...

Part 2 https://blog.acolyer.org/2019/05/08/distributed-consensus-re...

https://kernelnewbies.org/LinuxChanges?rev1=371

Another good curation https://web.ma.utexas.edu/users/mks/statmistakes/StatisticsM...

How to Lie with Statistics by Darrell Huff https://en.wikipedia.org/wiki/How_to_Lie_with_Statistics

+1 For the above mentioned book, especially if you are looking at computer systems/networks.

For more pragmatic treatment found "Fundamentals of Queueing Theory"[1] really good.

[1] http://mason.gmu.edu/~jshortle/fqt5th.html

Farnam Street curation is also good https://fs.blog/mental-models/

How far are we from "emergence" in terms of AI ecosystem ? Will quantum computing pave the way for it ?

This is very old, for a more recent version https://github.com/0xAX/linux-insides

Not just the JVM, lot of libraries build thread pools based on number of processors and some of them even hard code the multipliers (X the number of processors etc).

Setting up each one becomes a lot of work fast, so we wrote a LD_PRELOAD[1] hook that overrides sysconf[1] _NC_PROCESSORS* call to get number of processors availabale/online to a certain specified value and is baked in the docker image by default during builds.

[1] http://man7.org/linux/man-pages/man3/sysconf.3.html

BGP Sim during the changes https://stat.ripe.net/widget/bgplay#w.ignoreReannouncements=...

Spanner secondary index link is broken correct one https://cloud.google.com/spanner/docs/secondary-indexes

Another excellent series on JVM perf/internals https://shipilev.net/jvm-anatomy-park/

This is not needed since version 1.6 See https://cbonte.github.io/haproxy-dconv/1.6/configuration.htm...

It can do DNS resolution at runtime, additionally you can override bunch of options as to how DNS resolution behaves in terms of TTL, Failures etc

There is an option added in 1.7 which makes old process exit after a grace period https://cbonte.github.io/haproxy-dconv/1.7/configuration.htm...

Other security controls aside, for the user thing we make it mandatory to have USER directive in the Dockerfile, else the docker builds fail.

These are some good curated books https://msdn.microsoft.com/en-us/library/dn568099.aspx http://shop.oreilly.com/product/0636920023777.do

These are not necessarily container centric.

Primarily performance, HTTP HOL[1] kills latencies and wastes a lot of resources, especially with micro services spanning requests recursively to multiple services.

We have had to force keep alive and even forcefully turn it off in some cases :(.

[1]https://en.wikipedia.org/wiki/Head-of-line_blocking

Yes it does, receiver side advertises window as 0 and persist timer kicks in. The send call then blocks until window recovers.

We haven't dealt with those yet. But in general we are always pushing everyone to log judicially, use metrics wherever possible (numbers are still easier to manage than strings), use sentry for exceptions logging and introducing zipkin for tracing.

Same for us fluentd was very resource hungry, heka brought down utilization significantly. Regarding the buffering we just added kafka in between for graylog/ES scaling/maintenance.

That is the peak we see right now at ~20-30% utilization overall network/cpu wise, it can do much more easily.

Binary logging is a must, because even something like string formatting is simply way too slow, by an order of magnitude.

true, string processing takes most of the resources.

Inbuilt docker + fluentd driver + plain ES falls flat on its face under scale. Not to mention docker daemon keeps dying :(.

Whats working for us now is local docker json logs -> heka -> kafka -> graylog -> ES. We even dockerized graylog to scale the processing up and down on demand.

Does 250k messages/sec at peak easily, More details in this presentation https://www.youtube.com/watch?v=PB8dBnpaP8s

LWN's coverage is pretty good https://lwn.net/Articles/262464/