Here is a minimal setup that works on Windows and Mac (yes I use Emacs on Windows and it works well).
;; using an editor you are comfortable with
;; create c:/Users/USERNAME/AppData/Roaming/.emacs.d/init.el
;; and paste these contents in there:
;; NOTE: depending on how you start up emacs, your home directory may
;; be different than the one above. Your initialization file
;; needs to be saved at ~/.emacs.d/init.el (however that is mapped
Note: I have moved this list to a proper repository. I'll leave this gist up, but it won't be updated. To submit an idea, open a PR on the repo.
Note that I have not tried all of these personally, and cannot and do not vouch for all of the tools listed here. In most cases, the descriptions here are copied directly from their code repos. Some may have been abandoned. Investigate before installing/using.
The ones I use regularly include: bat, dust, fd, fend, hyperfine, miniserve, ripgrep, just, cargo-audit and cargo-wipe.
This is intended as a place to list blog posts about Zig, for anyone who's interested in reading what people have to say! Also check out links at https://ziglang.org/learn/ and https://ziglang.org/news/.
Posts were found by searching on lobste.rs, at https://dev.to/t/zig and https://medium.com/tag/zig.
Please let me know in a comment if there are any posts missing that you'd like me to add here!
This is inspired by https://fasterthanli.me/blog/2020/a-half-hour-to-learn-rust/
the command zig run my_code.zig will compile and immediately run your Zig
program. Each of these cells contains a zig program that you can try to run
(some of them contain compile-time errors that you can comment out to play
with)
CPU Usage :
(1 - avg(irate(node_cpu_seconds_total{mode="idle"}[10m])) by (instance)) * 100
Memory Usage :
100 * (1 - ((avg_over_time(node_memory_MemFree_bytes[10m]) + avg_over_time(node_memory_Cached_bytes[10m]) + avg_over_time(node_memory_Buffers_bytes[10m])) / avg_over_time(node_memory_MemTotal_bytes[10m])))
I've been working with Apache Kafka for over 7 years. I inevitably find myself doing the same set of activities while I'm developing or working with someone else's system. Here's a set of Kafka productivity hacks for doing a few things way faster than you're probably doing them now. 🔥
This is the second article in a series of articles around Rusts new async/await
feature. The first article about interfaces can be found
here.
In this part of the series we want to a look at a mechanism which behaves very
different in Rust than in all other languages which feature async/await
support. This mechanism is Cancellation.
国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。
Dockerized 实践 https://github.com/y0ngb1n/dockerized
In response to this brief blog entry, @antirez tweeted for some documentation on high-performance techniques for Redis. What I present here are general high-performance computing (HPC) techniques. The examples are oriented to Redis. but they work well for any program designed to be single- or worker-threaded and asynchronous (e.g. uses epoll).
The motivation for using these techniques is to maximize performance of our system and services. By isolating work, controlling memory, and other tuning, you can achieve significant reduction in latency and increase in throughput.
My perspective comes from the microcosm of my own bare-metal (vs VM), on-premises deployment. It might not be suitable for all scenarios, especially cloud deployments, as I have little experience with HPC there. After some discussion, maybe this can be adapted as [redis.io documentation](https://redis.io/do