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。Docker 官方和国内很多云服务商都提供了国内加速器服务。
Dockerized 实践 https://github.com/y0ngb1n/dockerized
Ubuntu 16.04+、Debian 8+、CentOS 7+
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。Docker 官方和国内很多云服务商都提供了国内加速器服务。
Dockerized 实践 https://github.com/y0ngb1n/dockerized
Ubuntu 16.04+、Debian 8+、CentOS 7+
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)
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.
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
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real
Picking the right architecture = Picking the right battles + Managing trade-offs
#!/usr/bin/env python2 | |
""" | |
Other Repositories of python-ping | |
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
* https://github.com/l4m3rx/python-ping supports Python2 and Python3 | |
* https://bitbucket.org/delroth/python-ping |
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])))
Friend: I tried looking at static linking in Mac OS X and it seems nearly impossible. Take a look at this http://stackoverflow.com/a/3801032
Me: I have no idea what that
-static
flag does, but I'm pretty sure that's not how you link to a library. Let me RTFM a bit.
Minutes later...