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[#]: collector: (lujun9972)
[#]: translator: (chen-ni)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: reviewer: (wxy)
[#]: publisher: (wxy)
[#]: url: (https://linux.cn/article-11076-1.html)
[#]: subject: (Can schools be agile?)
[#]: via: (https://opensource.com/open-organization/19/4/education-culture-agile)
[#]: author: (Ben Owens https://opensource.com/users/engineerteacher/users/ke4qqq/users/n8chz/users/don-watkins)
学校可以变得敏捷吗？
======
我们一定不会希望用商业的方式运作我们的学校 —— 但是更加注重持续改进的教育机构是可以让我们受益的。
> 我们一定不会希望用商业的方式运作我们的学校 —— 但是更加注重持续改进的教育机构是可以让我们受益的。
![][1]
我们都有过那种感觉一件事情“似曾相识”的经历。在 1980 年代末期我经常会有这种感觉，那时候我刚刚进入工业领域不久。当时正赶上一波组织变革的热潮，美国制造业在尝试各种各样不同的模型，让企业领导、经理人和像我这样的工程师重新思考我们应该如何处理质量、成本、创新以及股东价值这样的问题。我们似乎每一年（有时候更加频繁）都需要通过学习一本书来找到让我们更精简、更扁平、更灵活以及更加能满足顾客需求的“最佳方案”。
@@ -24,7 +25,7 @@
这种不断改进的文化氛围遇到的第一个阻碍是，教育界普遍不愿意从其它行业借鉴可以为自己所用的思想 —— 特别是来自商界的思想。第二个阻碍是，主导教育界的仍然是一种自上而下的、等级制度森严的领导模式。人们往往只能在小范围内讨论这种系统性的、持续的改进方案，比如包括校长、助理校长、学校监管人（LCTT 译注：美国地方政府下设的一种官职，每个学校监管人管理一定数量的学校，接受学校校长的汇报）等等在内的学校领导和区域领袖。但是一小群人的参与是远远不足以带来整个组织层面的文化改革的。
在进一步展开观点之前，我想强调一下，上面所做的概括一定是存在例外情况 的（我自己就见到过很多），不过我觉得任何一个教育界的利益相关者都应该会同意以下两点基本假设：
在进一步展开观点之前，我想强调一下，上面所做的概括一定是存在例外情况的（我自己就见到过很多），不过我觉得任何一个教育界的利益相关者都应该会同意以下两点基本假设：
1. 为学生提供高质量的、公平的教育和教学系统的工作所涉及到的任何人都应该将持续不断的改进作为思维方式里的重要部分；
2. 如果学校领导在做决策的时候可以更多地参考那些离学生最近的工作者的意见，那么学生以及学生所在的社区都将更加受益；
@@ -37,12 +38,11 @@
我并不是要呼吁大家像经营商业一样经营我们的学校。我所主张的是，用一种清晰而客观的态度去看待任何行业的任何思想，只要它们有可能帮助我们更好地迎合学生个体的需求。不过，如果想有效率地实现这个目标，我们需要仔细研究这个 100 多年来都停滞不前的领导结构。
### 把不断改进作为努力的目标
有一种说法认为教育和其它行业之间存在着巨大的差异，我虽然赞同这种说法，但同时也相信“重新思考组织和领导结构”这件事情对于任何一个希望对利益相关者负责（并且可以及时作出响应）的主体来说都是适用的。大多数其它行业都已经在重新审视它们传统的、封闭的、等级森严的结构，并且采用可以鼓励员工基于共有的优秀目标发挥自主性的组织结构 —— 这种组织结构对于不断改进来说十分关键。我们的学校和行政区是时候放开眼界了，而不应该拘泥于只听到来自内部的声音，因为它们的用意虽然是好的，但都没有脱离现有的范式。
对于任何希望开始或者加速这个转变过程的学校，我推荐一本很好的书：Jim Whitehurst 的《开放的组织》（这不应该让你感到意外）。这本书不仅可以帮助我们理解教育者如何创造更加开放、覆盖面更广的领导领导结构 —— 在这样的结构下，互相尊重让人们可以基于实时数据作出更加灵活的决策 —— 并且它所使用的语言风格也和教育者们所习惯使用的奇怪的词汇库非常契合（这种词汇库简直是教育者们第二天性)。任何组织都可以借鉴开放组织的思维提供的实用主义方法让组织成员更加开放：分享想法和资源、拥抱以共同协作为核心的文化、通过快速制作原型来开发创新思维、基于价值（而不是提出者的职级）来评估一个想法，以及创造一种融入到组织 DNA 里的很强的社区观念。通过众包的方式，这样的开放组织不仅可以从组织内部，也能够从组织外部收集想法，创造一种可以让本地化的、以学生为中心的创新蓬勃发展的环境。
对于任何希望开始或者加速这个转变过程的学校，我推荐一本很好的书：Jim Whitehurst 的《开放组织》（这不应该让你感到意外）。这本书不仅可以帮助我们理解教育者如何创造更加开放、覆盖面更广的领导领导结构 —— 在这样的结构下，互相尊重让人们可以基于实时数据作出更加灵活的决策 —— 并且它所使用的语言风格也和教育者们所习惯使用的奇怪的词汇库非常契合（这种词汇库简直是教育者们第二天性)。任何组织都可以借鉴开放组织的思维提供的实用主义方法让组织成员更加开放：分享想法和资源、拥抱以共同协作为核心的文化、通过快速制作原型来开发创新思维、基于价值（而不是提出者的职级）来评估一个想法，以及创造一种融入到组织 DNA 里的很强的社区观念。通过众包的方式，这样的开放组织不仅可以从组织内部，也能够从组织外部收集想法，创造一种可以让本地化的、以学生为中心的创新蓬勃发展的环境。
最重要的事情是：在快速变化的未来，我们在过去所做的事情不一定仍然适用了 —— 认清楚这一点对于创造一个不断改进的文化氛围是十分关键的。对于教育者来说，这意味着我们不能只是简单地依赖在针对工厂模型发展出来的解决方案和实践方式了。我们必须从其它行业（比如说非营利组织、军事、医疗以及商业 —— 没错，甚至是商业）里借鉴数不清的最佳方案，这样至少应该能让我们 *知道* 如何找到让学生受益最大的办法。从教育界传统的陈词滥调里超脱出来，才有机会拥有更广阔的视角。我们可以更好地顾全大局，用更客观地视角看待我们遇到的问题，同时也知道我们在什么方面已经做得很不错。
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坚持不懈地追求不断改进这件事情，不应该只局限于那种努力在一个全球化的、创新的经济环境中争取竞争力的机构，或者是负责运营学校的少数几个人。当机构里的每一个人都能不断思考怎样才能让今天比昨天做得更好的时候，这就是一个拥有优秀的文化氛围的机构。这种非常有注重协作性和创新的文化氛围，正是我们希望在这些负责改变年轻人命运的机构身上看到的。
我非常期待，有朝一日我能在学校里感受到这种精神，然后微笑着对自己说：“这种感觉多么似曾相识啊。”
--------------------------------------------------------------------------------
@@ -60,7 +59,7 @@ via: https://opensource.com/open-organization/19/4/education-culture-agile
作者：[Ben Owens][a]
选题：[lujun9972][b]
译者：[chen-ni](https://github.com/chen-ni)
校对：[校对者ID](https://github.com/校对者ID)
校对：[wxy](https://github.com/wxy)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Data Still Dominates)
[#]: via: (https://theartofmachinery.com/2019/06/30/data_still_dominates.html)
[#]: author: (Simon Arneaud https://theartofmachinery.com)
Data Still Dominates
======
Here’s [a quote from Linus Torvalds in 2006][1]:
> I’m a huge proponent of designing your code around the data, rather than the other way around, and I think it’s one of the reasons git has been fairly successful… I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.
Which sounds a lot like [Eric Raymond’s “Rule of Representation” from 2003][2]:
> Fold knowledge into data, so program logic can be stupid and robust.
Which was just his summary of ideas like [this one from Rob Pike in 1989][3]:
> Data dominates. If you’ve chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
Which cites [Fred Brooks from 1975][4]:
> ### Representation is the Essence of Programming
>
> Beyond craftmanship lies invention, and it is here that lean, spare, fast programs are born. Almost always these are the result of strategic breakthrough rather than tactical cleverness. Sometimes the strategic breakthrough will be a new algorithm, such as the Cooley-Tukey Fast Fourier Transform or the substitution of an n log n sort for an n2 set of comparisons.
>
> Much more often, strategic breakthrough will come from redoing the representation of the data or tables. This is where the heart of your program lies. Show me your flowcharts and conceal your tables, and I shall be continued to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.
So, smart people have been saying this again and again for nearly half a century: focus on the data first. But sometimes it feels like the most famous piece of smart programming advice that everyone forgets.
Let me give some real examples.
### The Highly Scalable System that Couldn’t
This system was designed from the start to handle CPU-intensive loads with incredible scalability. Nothing was synchronous. Everything was done with callbacks, task queues and worker pools.
But there were two problems: The first was that the “CPU-intensive load” turned out not to be that CPU-intensive after all — a single task took a few milliseconds at worst. So most of the architecture was doing more harm than good. The second problem was that although it sounded like a highly scalable distributed system, it wasn’t one — it only ran on one machine. Why? Because all communication between asynchronous components was done using files on the local filesystem, which was now the bottleneck for any scaling. The original design didn’t say much about data at all, except to advocate local files in the name of “simplicity”. Most of the document was about all the extra architecture that was “obviously” needed to handle the “CPU-intensiveness” of the load.
### The Service-Oriented Architecture that was Still Data-Oriented
This system followed a microservices design, made up of single-purpose apps with REST-style APIs. One component was a database that stored documents (basically responses to standard forms, and other electronic paperwork). Naturally it exposed an API for basic storage and retrieval, but pretty quickly there was a need for more complex search functionality. The designers felt that adding this search functionality to the existing document API would have gone against the principles of microservices design. They could talk about “search” as being a different kind of service from “get/put”, so their architecture shouldn’t couple them together. Besides, the tool they were planning to use for search indexing was separate from the database itself, so creating a new service made sense for implementation, too.
In the end, a search API was created containing a search index that was essentially a duplicate of the data in the main database. This data was being updated dynamically, so any component that mutated document data through the main database API had to also update the search API. It’s impossible to do this with REST APIs without race conditions, so the two sets of data kept going out of sync every now and then, anyway.
Despite what the architecture diagram promised, the two APIs were tightly coupled through their data dependencies. Later on it was recognised that the search index should be an implementation detail of a unified document service, and this made the system much more maintainable. “Do one thing” works at the data level, not the verb level.
### The Fantastically Modular and Configurable Ball of Mud
This system was a kind of automated deployment pipeline. The original designers wanted to make a tool that was flexible enough to solve deployment problems across the company. It was written as a set of pluggable components, with a configuration file system that not only configured the components, but acted as a [DSL][5] for programming how the components fitted into the pipeline.
Fast forward a few years and it’s turned into “that program”. There was a long list of known bugs that no one was ever fixing. No one wanted to touch the code out of fear of breaking things. No one used any of the flexibility of the DSL. Everyone who used the program copy-pasted the same known-working configuration that everyone else used.
What had gone wrong? Although the original design document used words like “modular”, “decoupled”, “extensible” and “configurable” a lot, it never said anything about data. So, data dependencies between components ended up being handled in an ad-hoc way using a globally shared blob of JSON. Over time, components made more and more undocumented assumptions about what was in or not in the JSON blob. Sure, the DSL allowed rearranging components into any order, but most configurations didn’t work.
### Lessons
I chose these three examples because they’re easy to explain, not to pick on others. I once tried to build a website, and failed trying to instead build some cringe-worthy XML database that didn’t even solve the data problems I had. Then there’s the project that turned into a broken mockery of half the functionality of `make`, again because I didn’t think about what I really needed. I wrote a post before based on a time I wrote [a castle-in-the-sky OOP class hierarchy that should have been encoded in data instead][6].
Update:
Apparently many people still thought I wrote this to make fun of others. People who’ve actually worked with me will know I’m much more interested in the things I’m fixing than in blaming the people who did most of the work building them, but, okay, here’s what I think of the engineers involved.
Honestly, the first example obviously happened because the designer was more interested in bringing a science project to work than in solving the problem at hand. Most of us have done that (mea culpa), but it’s really annoying to our colleagues who’ll probably have to help maintain them when we’re bored of them. If this sounds like you, please don’t get offended; please just stop. (I’d still rather work on the single-node distributed system than anything built around my “XML database”.)
There’s nothing personal in the second example. Sometimes it feels like everyone is talking about how wonderful it is to split up services, but no one is talking about exactly when not to. People are learning the hard way all the time.
The third example was actually from some of the smartest people I’ve ever had the chance to work with.
(End update.)
“Does this talk about the problems created by data?” turns out to be a pretty useful litmus test for good systems design. It’s also pretty handy for detecting false expert advice. The hard, messy systems design problems are data problems, so false experts love to ignore them. They’ll show you a wonderfully beautiful architecture, but without talking about what kind of data it’s appropriate for, and (crucially) what kind of data it isn’t.
For example, a false expert might tell you that you should use a pub/sub system because pub/sub systems are loosely coupled, and loosely coupled components are more maintainable. That sounds nice and results in pretty diagrams, but it’s backwards thinking. Pub/sub doesn’t _make_ your components loosely coupled; pub/sub _is_ loosely coupled, which may or may not match your data needs.
On the flip side, a well-designed data-oriented architecture goes a long way. Functional programming, service meshes, RPCs, design patterns, event loops, whatever, all have their merits, but personally I’ve seen tools like [boring old databases][7] be responsible for a lot more successfully shipped software.
--------------------------------------------------------------------------------
via: https://theartofmachinery.com/2019/06/30/data_still_dominates.html
作者：[Simon Arneaud][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://theartofmachinery.com
[b]: https://github.com/lujun9972
[1]: https://lwn.net/Articles/193245/
[2]: http://www.catb.org/~esr/writings/taoup/html/ch01s06.html
[3]: http://doc.cat-v.org/bell_labs/pikestyle
[4]: https://archive.org/stream/mythicalmanmonth00fred/mythicalmanmonth00fred_djvu.txt
[5]: https://martinfowler.com/books/dsl.html
[6]: https://theartofmachinery.com/2016/06/21/code_vs_data.html
[7]: https://theartofmachinery.com/2017/10/28/rdbs_considered_useful.html

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[#]: collector: (lujun9972)
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[#]: translator: (Scoutydren)
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[#]: subject: (The cost of JavaScript in 2019 · V8)
[#]: via: (https://v8.dev/blog/cost-of-javascript-2019)
[#]: author: (Addy Osmani https://twitter.com/addyosmani)
The cost of JavaScript in 2019 · V8
======
**Note:** If you prefer watching a presentation over reading articles, then enjoy the video below! If not, skip the video and read on.
[“The cost of JavaScript”][1] as presented by Addy Osmani at #PerfMatters Conference 2019.
One large change to [the cost of JavaScript][2] over the last few years has been an improvement in how fast browsers can parse and compile script. **In 2019, the dominant costs of processing scripts are now download and CPU execution time.**
User interaction can be delayed if the browser’s main thread is busy executing JavaScript, so optimizing bottlenecks with script execution time and network can be impactful.
### Actionable high-level guidance #
What does this mean for web developers? Parse & compile costs are **no longer as slow** as we once thought. The three things to focus on for JavaScript bundles are:
* **Improve download time**
* Keep your JavaScript bundles small, especially for mobile devices. Small bundles improve download speeds, lower memory usage, and reduce CPU costs.
* Avoid having just a single large bundle; if a bundle exceeds ~50–100 kB, split it up into separate smaller bundles. (With HTTP/2 multiplexing, multiple request and response messages can be in flight at the same time, reducing the overhead of additional requests.)
* On mobile you’ll want to ship much less especially because of network speeds, but also to keep plain memory usage low.
* **Improve execution time**
* Avoid [Long Tasks][3] that can keep the main thread busy and can push out how soon pages are interactive. Post-download, script execution time is now a dominant cost.
* **Avoid large inline scripts** (as they’re still parsed and compiled on the main thread). A good rule of thumb is: if the script is over 1 kB, avoid inlining it (also because 1 kB is when [code caching][4] kicks in for external scripts).
### Why does download and execution time matter? #
Why is it important to optimize download and execution times? Download times are critical for low-end networks. Despite the growth in 4G (and even 5G) across the world, our [effective connection types][5] remain inconsistent with many of us running into speeds that feel like 3G (or worse) when we’re on the go.
JavaScript execution time is important for phones with slow CPUs. Due to differences in CPU, GPU, and thermal throttling, there are huge disparities between the performance of high-end and low-end phones. This matters for the performance of JavaScript, as execution is CPU-bound.
In fact, of the total time a page spends loading in a browser like Chrome, anywhere up to 30% of that time can be spent in JavaScript execution. Below is a page load from a site with a pretty typical workload (Reddit.com) on a high-end desktop machine:![][6]JavaScript processing represents 10–30% of time spent in V8 during page load.
On mobile, it takes 3–4× longer for a median phone (Moto G4) to execute Reddit’s JavaScript compared to a high-end device (Pixel 3), and over 6× as long on a low-end device (the <$100 Alcatel 1X):![][7]The cost of Reddit’s JavaScript across a few different device classes (low-end, average, and high-end)
**Note:** Reddit has different experiences for desktop and mobile web, and so the MacBook Pro results cannot be compared to the other results.
When you’re trying to optimize JavaScript execution time, keep an eye out for [Long Tasks][8] that might be monopolizing the UI thread for long periods of time. These can block critical tasks from executing even if the page looks visually ready. Break these up into smaller tasks. By splitting up your code and prioritizing the order in which it is loaded, you can get pages interactive faster and hopefully have lower input latency.![][9]Long tasks monopolize the main thread. You should break them up.
### What has V8 done to improve parse/compile? #
Raw JavaScript parsing speed in V8 has increased 2× since Chrome 60. At the same time, raw parse (and compile) cost has become less visible/important due to other optimization work in Chrome that parallelizes it.
V8 has reduced the amount of parsing and compilation work on the main thread by an average of 40% (e.g. 46% on Facebook, 62% on Pinterest) with the highest improvement being 81% (YouTube), by parsing and compiling on a worker thread. This is in addition to the existing off-main-thread streaming parse/compile.![][10]V8 parse times across different versions
We can also visualize the CPU time impact of these changes across different versions of V8 across Chrome releases. In the same amount of time it took Chrome 61 to parse Facebook’s JS, Chrome 75 can now parse both Facebook’s JS AND 6 times Twitter’s JS.![][11]In the time it took Chrome 61 to parse Facebook’s JS, Chrome 75 can now parse both Facebook’s JS and 6 times Twitter’s JS.
Let’s dive into how these changes were unlocked. In short, script resources can be streaming-parsed and-compiled on a worker thread, meaning:
* V8 can parse+compile JavaScript without blocking the main thread.
* Streaming starts once the full HTML parser encounters a `<script>` tag. For parser-blocking scripts, the HTML parser yields, while for async scripts it continues.
* For most real-world connection speeds, V8 parses faster than download, so V8 is done parsing+compiling a few milliseconds after the last script bytes are downloaded.
The not-so-short explanation is… Much older versions of Chrome would download a script in full before beginning to parse it, which is a straightforward approach but it doesn’t fully utilize the CPU. Between versions 41 and 68, Chrome started parsing async and deferred scripts on a separate thread as soon as the download begins.![][12]Scripts arrive in multiple chunks. V8 starts streaming once it’s seen at least 30 kB.
In Chrome 71, we moved to a task-based setup where the scheduler could parse multiple async/deferred scripts at once. The impact of this change was a ~20% reduction in main thread parse time, yielding an overall ~2% improvement in TTI/FID as measured on real-world websites.![][13]Chrome 71 moved to a task-based setup where the scheduler could parse multiple async/deferred scripts at once.
In Chrome 72, we switched to using streaming as the main way to parse: now also regular synchronous scripts are parsed that way (not inline scripts though). We also stopped canceling task-based parsing if the main thread needs it, since that just unnecessarily duplicates any work already done.
[Previous versions of Chrome][14] supported streaming parsing and compilation where the script source data coming in from the network had to make its way to Chrome’s main thread before it would be forwarded to the streamer.
This often resulted in the streaming parser waiting for data that arrived from the network already, but had not yet been forwarded to the streaming task as it was blocked by other work on the main thread (like HTML parsing, layout, or JavaScript execution).
We are now experimenting with starting parsing on preload, and the main-thread-bounce was a blocker for this beforehand.
Leszek Swirski’s BlinkOn presentation goes into more detail:
[“Parsing JavaScript in zero* time”][15] as presented by Leszek Swirski at BlinkOn 10.
In addition to the above, there was [an issue in DevTools][16] that rendered the entire parser task in a way that hints that it’s using CPU (full block). However, the parser blocks whenever it’s starved for data (that needs to go over the main thread). Since we moved from a single streamer thread to streaming tasks, this became really obvious. Here’s what you’d use to see in Chrome 69:![][17]The DevTools issue that rendered the entire parser task in a way that hints that it’s using CPU (full block)
The “parse script” task is shown to take 1.08 seconds. However, parsing JavaScript isn’t really that slow! Most of that time is spent doing nothing except waiting for data to go over the main thread.
Chrome 76 paints a different picture:![][18]In Chrome 76, parsing is broken up into multiple smaller streaming tasks.
In general, the DevTools performance pane is great for getting a high-level overview of what’s happening on your page. For detailed V8-specific metrics such as JavaScript parse and compile times, we recommend [using Chrome Tracing with Runtime Call Stats (RCS)][19]. In RCS results, `Parse-Background` and `Compile-Background` tell you how much time was spent parsing and compiling JavaScript off the main thread, whereas `Parse` and `Compile` captures the main thread metrics.![][20]
### What is the real-world impact of these changes? #
Let’s look at some examples of real-world sites and how script streaming applies.![][21]Main thread vs. worker thread time spent parsing and compiling Reddit’s JS on a MacBook Pro
Reddit.com has several 100 kB+ bundles which are wrapped in outer functions causing lots of [lazy compilation][22] on the main thread. In the above chart, the main thread time is all that really matters because keeping the main thread busy can delay interactivity. Reddit spends most of its time on the main thread with minimum usage of the Worker/Background thread.
They’d benefit from splitting up some of their larger bundles into smaller ones (e.g 50 kB each) without the wrapping to maximize parallelization — so that each bundle could be streaming-parsed + compiled separately and reduce main thread parse/compile during start-up.![][23]Main thread vs. worker thread time spent parsing and compiling Facebook’s JS on a MacBook Pro
We can also look at a site like Facebook.com. Facebook loads ~6MB of compressed JS across ~292 requests, some of it async, some preloaded, and some fetched with a lower priority. A lot of their scripts are very small and granular — this can help with overall parallelization on the Background/Worker thread as these smaller scripts can be streaming-parsed/compiled at the same time.
Note, you’re probably not Facebook and likely don’t have a long-lived app like Facebook or Gmail where this much script may be justifiable on desktop. However, in general, keep your bundles coarse and only load what you need.
Although most JavaScript parsing and compilation work can happen in a streaming fashion on a background thread, some work still has to happen on the main thread. When the main thread is busy, the page can’t respond to user input. Do keep an eye on the impact both downloading and executing code has on your UX.
**Note:** Currently, not all JavaScript engines and browsers implement script streaming as a loading optimization. We still believe the overall guidance here leads to good user experiences across the board.
### The cost of parsing JSON #
Because the JSON grammar is much simpler than JavaScript’s grammar, JSON can be parsed more efficiently than JavaScript. This knowledge can be applied to improve start-up performance for web apps that ship large JSON-like configuration object literals (such as inline Redux stores). Instead of inlining the data as a JavaScript object literal, like so:
```
const data = { foo: 42, bar: 1337 };
```
…it can be represented in JSON-stringified form, and then JSON-parsed at runtime:
```
const data = JSON.parse('{"foo":42,"bar":1337}');
```
As long as the JSON string is only evaluated once, the `JSON.parse` approach is much faster compared to the JavaScript object literal, especially for cold loads. A good rule of thumb is to apply this technique for objects of 10 kB or larger — but as always with performance advice, measure the actual impact before making any changes.
There’s an additional risk when using plain object literals for large amounts of data: they could be parsed twice!
1. The first pass happens when the literal gets preparsed.
2. The second pass happens when the literal gets lazy-parsed.
The first pass can’t be avoided. Luckily, the second pass can be avoided by placing the object literal at the top-level, or within a [PIFE][24].
### What about parse/compile on repeat visits? #
V8’s (byte)code-caching optimization can help. When a script is first requested, Chrome downloads it and gives it to V8 to compile. It also stores the file in the browser’s on-disk cache. When the JS file is requested a second time, Chrome takes the file from the browser cache and once again gives it to V8 to compile. This time, however, the compiled code is serialized, and is attached to the cached script file as metadata.![][25]Visualization of how code caching works in V8
The third time, Chrome takes both the file and the file’s metadata from the cache, and hands both to V8. V8 deserializes the metadata and can skip compilation. Code caching kicks in if the first two visits happen within 72 hours. Chrome also has eager code caching if a service worker is used to cache scripts. You can read more about code caching in [code caching for web developers][4].
Download and execution time are the primary bottlenecks for loading scripts in 2019. Aim for a small bundle of synchronous (inline) scripts for your above-the-fold content with one or more deferred scripts for the rest of the page. Break down your large bundles so you focus on only shipping code the user needs when they need it. This maximizes parallelization in V8.
On mobile, you’ll want to ship a lot less script because of network, memory consumption and execution time for slower CPUs. Balance latency with cacheability to maximize the amount of parsing and compilation work that can happen off the main thread.
### Further reading #
--------------------------------------------------------------------------------
via: https://v8.dev/blog/cost-of-javascript-2019
作者：[Addy Osmani][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://twitter.com/addyosmani
[b]: https://github.com/lujun9972
[1]: https://www.youtube.com/watch?v=X9eRLElSW1c
[2]: https://medium.com/@addyosmani/the-cost-of-javascript-in-2018-7d8950fbb5d4
[3]: https://w3c.github.io/longtasks/
[4]: https://v8.dev/blog/code-caching-for-devs
[5]: https://developer.mozilla.org/en-US/docs/Web/API/NetworkInformation/effectiveType
[6]: https://v8.dev/_img/cost-of-javascript-2019/reddit-js-processing.svg
[7]: https://v8.dev/_img/cost-of-javascript-2019/reddit-js-processing-devices.svg
[8]: https://web.dev/long-tasks-devtools/
[9]: https://v8.dev/_img/cost-of-javascript-2019/long-tasks.png
[10]: https://v8.dev/_img/cost-of-javascript-2019/chrome-js-parse-times.svg
[11]: https://v8.dev/_img/cost-of-javascript-2019/js-parse-times-websites.svg
[12]: https://v8.dev/_img/cost-of-javascript-2019/script-streaming-1.svg
[13]: https://v8.dev/_img/cost-of-javascript-2019/script-streaming-2.svg
[14]: https://v8.dev/blog/v8-release-75#script-streaming-directly-from-network
[15]: https://www.youtube.com/watch?v=D1UJgiG4_NI
[16]: https://bugs.chromium.org/p/chromium/issues/detail?id=939275
[17]: https://v8.dev/_img/cost-of-javascript-2019/devtools-69.png
[18]: https://v8.dev/_img/cost-of-javascript-2019/devtools-76.png
[19]: https://v8.dev/docs/rcs
[20]: https://v8.dev/_img/cost-of-javascript-2019/rcs.png
[21]: https://v8.dev/_img/cost-of-javascript-2019/reddit-main-thread.svg
[22]: https://v8.dev/blog/preparser
[23]: https://v8.dev/_img/cost-of-javascript-2019/facebook-main-thread.svg
[24]: https://v8.dev/blog/preparser#pife
[25]: https://v8.dev/_img/cost-of-javascript-2019/code-caching.png

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
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[#]: subject: (Computational Photography)
[#]: via: (https://vas3k.com/blog/computational_photography/)
[#]: author: (vas3k https://vas3k.com/)
Computational Photography
======
![](https://i.vas3k.ru/full/853.png)
It's impossible to imagine a smartphone presentation today without dancing around its camera. Google makes Pixel shoot in the dark, Huawei zooms like a telescope, Samsung puts lidars inside, and Apple presents the new world's roundest corners. Illegal level of innovations happening here.
DSLRs, on the other hand, seems half dead. Sony showers everybody with a new sensor-megapixel rain every year, while manufacturers lazily update the minor version number and keep lying on piles of cash from movie makers. I have a $3000 Nikon on my desk, but I take an iPhone on my travels. Why?
I went online with this question. There, I saw a lot of debates about "algorithms" and "neural networks", though no one could explain how exactly they affect a photo. Journalists are loudly reading the number of megapixels from press releases, bloggers are shitting down the Internet with more unboxings, and the camera-nerds are overflowing it with "sensual perception of the sensor color palette". Ah, Internet. You gave us access to all the information. Love you.
Thus, I spent half of my life to understand the whole thing on my own. I'll try to explain everything I found in this article, otherwise I'll forget it in a month.
[📔 Download pdf, epub, mobi
convenient for reading offline][1] [❤️ Support me][2]
This article in other languages: [Russian][3]
### What is Computational Photography?
Everywhere, including [wikipedia][4], you get a definition like this: computational photography is a digital image capture and processing techniques that use digital computation instead of optical processes. Everything is fine with it except that's bullshit. It includes even an autofocus, but not plenoptic, which has already brought a lot of good stuff to us. The fuzziness of the official definitions kinda indicates that we still have no idea what are we doing.
Stanford Professor and pioneer of computational photography Marc Levoy (he's also in charge of Google Pixel's camera now) [gives][5] another definition - computational imaging techniques that enhance or extend the capabilities of digital photography in which the output is an ordinary photograph, but one that could not have been taken by a traditional camera. I like it more, and in the article, I will follow this definition.
So, the smartphones were to blame for everything.
> Smartphones had no choice but to give life to a new kind of photography — computational
They had little noisy sensors and tiny slow lenses. According to all the laws of physics, they could only bring us pain and suffering. And they did. Until some devs figured out how to use their strengths to overcome the weaknesses: fast electronic shutters, powerful processors, and software.
<https://i.vas3k.ru/88h.jpg>
Most of the significant research in computational photography field was done in 2005-2015, that counts yesterday in science. Means, right now, just in front of our eyes and inside our pockets, there's a new field of knowledge and technology is rising, that never existed before.
<https://i.vas3k.ru/87c.jpg>
Computational photography isn't just about the bokeh on selfies. A recent photograph of a black hole would not have been taken without using computational photography methods. To take such picture with a standard telescope, we would have to make it the size of the Earth. However, by combining the data of eight radio telescopes at different locations of our Earth-ball and writing [some cool Python scripts][6], we got the world's first picture of the event horizon.
It's still good for selfies though, don't worry.
📝 [Computational Photography: Principles and Practice][7]
📝 [Marc Levoy: New Techniques in Computational photography][8]
I'm going to insert such links in the course of the story. They will lead you to the rare brilliant articles 📝 or videos 🎥, that I found, and allow you to dive deeper into the topic if you suddenly became interested. Because I physically can't tell you everything in one blog post.
### The Beginning: Digital Processing
Let's get back to 2010. Justin Bieber released his first album, Burj Khalifa just opened in Dubai, but we couldn't even capture these two great universe events, because our photos were noisy 2-megapixel JPEGs. We got the first irresistible desire to hide the worthlessness of mobile cameras by using "vintage" presets. Instagram cames out.
<https://i.vas3k.ru/88i.jpg>
### Math and Instagram
With the release of Instagram, everyone got obsessed with filters. As the man who reverse engineered the X-Pro II, Lo-Fi, and Valencia for, of course, research (hehe) purposes, I still remember that they comprised three components:
<https://i.vas3k.ru/85k.jpg>
* Color settings (Hue, Saturation, Lightness, Contrast, Levels, etc.) are simple coefficients, just like in any presets that photographers used since ancient times.
<https://i.vas3k.ru/85i.jpg>
* Tone Mapping is a vector of values, each tells us that "red with a hue of 128 should be turned into a hue of 240". It often represented as a single-pixel picture, like [this one][9]. This is an example for the X-Pro II filter.
<https://i.vas3k.ru/85t.jpg>
* Overlay — translucent picture with dust, grain, vignette, and everything else that can be applied from above to get (not at all, yeah) the banal effect of the old film. Used rarely.
Modern filters have not gone far from these three, but have become a little more complicated from the math perspective. With the advent of hardware shaders and [OpenCL][10] on smartphones, they were quickly rewritten under the GPU, and it was considered insanely cool. For 2012, of course. Today any kid can do the same thing [on CSS][11], but he still won't invite a girl to prom.
However, the progress in the area of filters has not stopped there. Guys from [Dehanсer][12], for example, are getting very hands-on with non-linear filters. Instead of poor-human tone-mapping, they use more posh and complex non-linear transformations, which opens up much more opportunities, according to them.
You can do a lot of things with non-linear transformations, but they are incredibly complex, and we humans are incredibly stupid. As soon as it comes to non-linear transformations, we prefer to go with numerical methods or run neural networks to do our job. The same thing happens here.
### Automation and Dreams of a "Masterpiece" Button
When everybody got used to filters, we started to integrate them right into our cameras. It's hidden in history whoever was the first manufacturer to implement this, but just to understand how long ago it was, think, that in iOS 5.0 released in 2011 we already had a public API for [Auto Enhancing Images][13]. Only Steve Jobs knows how long it was in use before it opened to the public.
The automation was doing the same thing that any of us does by opening the photo editor — it fixed the lights and shadows, increased the brightness, took away the red eyes, and fixed the face color. Users didn't even know that "dramatically improved camera" was just the merit of a couple of new lines of code.
<https://i.vas3k.ru/865.jpg>ML Enhance in Pixelmator
Today, the battles for the Masterpiece button have moved to the machine learning field. Tired of playing with tone-mapping everyone rushed to the hype train [CNN's and GAN's][14] and started, forcing computers to move the sliders for us. In other words, to use an input image to determine a set of optimal parameters that will bring the given image closer to a particular subjective understanding of "good photography". Check out how it's implemented in [Pixelmator Pro][15] and other editors who's luring you with their fancy "ML" features stated on a landing page. It doesn't always work well, as you can guess. But you can always take the datasets and train your own network to beat these guys, using the links below. Or not.
📝 [Image Enhancement Papers][16]
📝 [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks][17]
### [Stacking 90% success of mobile cameras](#scroll50)
True computational photography began with stacking — a method of combining several photos on top of each other. It's not a big deal for a smartphone to shoot a dozen pics in half a second. There're no slow mechanical parts in their cameras: the aperture is fixed, and there is an electronic shutter instead of the "moving curtain". The processor simply tells the sensor how many microseconds it should catch the wild photons, and reads the result.
Technically, the phone can shoot photos at a speed of the video, and it can shoot video in a photo resolution, but all that is slowed down to the speed of the bus and processor. Therefore, there is always a software limitation.
Stacking has been with us for a while. Even the founders' fathers used plugins for Photoshop 7.0 to gather some crazy-sharpened HDR photos or to make a panorama of 18000x600 pixels, and… no one figured out what to do with them next. Good wild times.
Now, as grown-ups, we call it "[epsilon photography][18]", which means changing one of the camera parameters (exposure, focus, or position) and putting images together to get something that couldn't be captured in one shot. Although, in practice, we call it stacking. Nowadays, 90% of all mobile camera innovations are based on it.
<https://i.vas3k.ru/85d.jpeg>
There's a thing many people don't care, but it's crucial for understanding the entire mobile photography: **Modern smartphone camera starts taking photos as soon as you open it**. Which is logical, since it should show the image on screen somehow. But in addition to that, it saves high-resolution images to its cyclic buffer and stores them for a couple more seconds. No, not only for NSA.
> When you tap "take a photo" button, the photo has actually already been taken, and the camera is just using the last picture from the buffer
That's how any mobile camera works today. At least the top ones. Buffering allows implementing not only zero [shutter lag][19], which photographers begged for so long, but even a negative one. By pressing the button, the smartphone looks in the past, unloads 5-10 last photos from the buffer and starts to analyze and combine them furiously. No longer need to wait till phone snaps shots for HDR or a night mode — let's simply pick them up from the buffer, the user won't even realize.
In fact, that's how Live Photo implemented in iPhones, and HTC had it back in 2013 under a strange name [Zoe][20].
#### [Exposure Stacking HDR and brightness control](#scroll60)
<https://i.vas3k.ru/85x.jpg>
The old and hot topic is whether the camera sensors [can capture the entire brightness range available to our eyes][21]. Some people say no, as the eye can see up to 25 [f-stops][22] and even the top full-frame sensor can be stretched out to a maximum of 14. Others call the comparison incorrect, since our eyes are assisted by the brain, which automatically adjusts your pupils and completes the image with its neural networks. So the instantaneous dynamic range of the eye is actually no more than 10-14 f-stops. Too hard. Let's leave these disputes to scientists.
The fact remains — taking pictures of friends against a bright sky, without HDR, with any mobile camera, you get either a natural sky and dark faces of friends, or natural faces, but completely burned sky.
The solution was found a long time ago — to expand the brightness range using HDR (High-dynamic-range) process. When we can't get a wide range of brightness right away, we can do it in three steps (or more). We can shoot several pictures with different exposure — "normal" one, brighter, and darker one. Then we can fill in the shady spots using the bright photo, and restore overexposed spots from the dark one.
One last thing needs to be done here is solving the problem of automatic bracketing. How far do we shift the exposure of each photo so as not to overdo it? However, any second-year tech student can do it today using some Python libraries.
<https://i.vas3k.ru/86t.jpg>
The latest iPhone, Pixel and Galaxy turn on HDR mode automatically when a simple algorithm inside their cameras detects you're shooting on a sunny day. You can even see how the phone switches to buffer mode to save shifted images — fps drops down, and the picture on the screen becomes juicier. That moment of switching is every time clearly visible on my iPhone X. Take a closer look at your smartphone next time.
<https://i.vas3k.ru/87u.png>
The main disadvantage of HDR with exposure bracketing is its incredible uselessness in poor lighting. Even in the light of a home lamp, the images come out so dark that even the machine cannot level and stack them together. To solve the problem, Google announced a different approach to HDR in a Nexus smartphone back to 2013. It was using time stacking.
#### [Time Stacking Long exposure and time lapse](#scroll70)
<https://i.vas3k.ru/85v.jpg>
Time stacking allows you to get a long exposure look with a series of short shots. This approach pioneered by the guys, who liked to take pictures of star trails in the night sky. Even with a tripod, it was impossible to shot such pictures by opening the shutter once for two hours. You had to calculate all the settings beforehand, and the slightest shaking would spoil the whole shot. So they decided to divide the process into a few minute intervals and stack the pictures together later in Photoshop.
<https://i.vas3k.ru/86u.jpg>These star patterns are always glued together from a series of photos. That make it easier to control exposure
Thus, the camera never was shooting with a long exposure; we simulated the effect by combining several consecutive shots. Smartphones have a lot of apps using this trick for a long time, but now almost every manufacturer added it to standard camera tools.
<https://i.vas3k.ru/86f.jpg>A long exposure made of iPhone's Live Photo in 3 clicks
Let's get back to Google and its night-time HDR. It turned out that using time bracketing you can create a decent HDR in the dark. This technology appeared in Nexus 5 for the first time and was called HDR+. The technology is still so popular that [it is even praised][23] in the latest Pixel presentation.
HDR+ works quite simple: once the camera detects that you're shooting in the dark, it takes the last 8-15 RAW photos out of the buffer out and stacks them on top of each other. This way, the algorithm collects more information about the dark areas of the shot to minimize the noise — pixels, when due to some reasons the camera screwed up and failed to catch some photons on each particular frame.
Imagine that: you have no idea how [capybara][24] looks like, so you decided to ask five people about it. Their stories would be roughly the same, but each will mention any unique detail, and so you'd gather more information than if asking only one person. Same happens with pixels on photo. More information — more clarity and less noise.
📝 [HDR+: Low Light and High Dynamic Range photography in the Google Camera App][25]
Combining the images captured from the same point gives the same fake long exposure effect as in the example with the stars above. Exposure of dozens of pictures is summarized, and errors on one picture are minimized on the other. Imagine how many times you would have to slam the shutter in your DSLR to achieve this.
<https://i.vas3k.ru/86g.jpg>Pixel ad that glorifies HDR+ and Night Sight
Only one thing left, and this is an automatic color casting. Shots taken in the dark usually have broken color balance (yellowish or greenish), so we need to fix it manually. In earlier versions of HDR+, the issue was resolved by simple auto-toning fix, à la Instagram filters. Later, they brought a neural network to the rescue.
That's how [Night Sight][26] was born — "the night photography" technology in Pixel 2, 3, and later. The description says "machine learning techniques built on top of HDR+ that make Night Sight work". In fact, it's just a fancy name for a neural network and all the HDR+ post-processing steps. The machine was trained on "before" and "after" dataset of photos to make one beautiful image out of a set of dark and dirty ones.
<https://i.vas3k.ru/88k.jpg>
This dataset, by the way, was made public. Maybe Apple guys will take it and finally teach their "world-best cameras" to shoot in the dark?
Also, Night Sight calculates the [motion vector][27] of the objects in the shot to normalize the blurring, that's for sure will appear in a long exposure. Thus, the smartphone can take sharp parts from other shots and stack them.
📝 [Night Sight: Seeing in the Dark on Pixel Phones][28]
📝 [Introducing the HDR+ Burst Photography Dataset][29]
#### [Focus Stacking DoF and refocus in post-production](#scroll90)
<https://i.vas3k.ru/85y.jpg>
The method came from macro photography, where the depth of field has always been a problem. To keep the entire object in focus, you had to take several shots, moving focus back and forth, and combine them later into one sharp shot in photoshop. The same method is often used by landscape photographers to make the foreground and background sharp as shark.
<https://i.vas3k.ru/86c.jpg>Focus stacking in macro. DoF is too small and you can't shoot it one go
Of course, it all migrated to smartphones. With no hype, though. Nokia released Lumia 1020 with "Refocus App" in 2013, and Samsung Galaxy S5 did the same in 2014 with "[Selective Focus][30]". Both used the same approach — they quickly took 3 photos: focused one, focus shifted forth and shifted back. The camera then aligned the images and allowed you to choose one of them, which was introduced as a "real" focus control in the post-production.
There was no further processing, as even this simple hack was enough to hammer another nail in the coffin of Lytro and analogs that used a fair refocus. Let's talk about them, by the way (topic change master 80 lvl).
### [Computational Sensor Plenoptic and Light Fields](#scroll100)
Well, our sensors are shit. We simply got used to it and trying to do our best with them. They haven't changed much in their design from the beginning of time. Technical process was the only thing that improved — we reduced the distance between pixels, fought noise, and added specific pixels for [phase-detection autofocus system][31]. But even if we take the most expensive camera to try to photograph a running cat in the indoor light, the cat will win.
<https://i.vas3k.ru/88p.jpg>
🎥 [The Science of Camera Sensors][32]
<https://i.vas3k.ru/881.jpg>
We've been trying to invent a better sensor for a long time. You can google a lot of researches in this field by "computational sensor" or "non-Bayer sensor" queries. Even the Pixel Shifting example can be referred to as an attempt to improve sensors with calculations.
The most promising stories of the last twenty years, though, come to us from plenoptic cameras.
To calm your sense of impending boring math, I'll throw in the insider's note — the last Google Pixel camera is a little bit plenoptic. With only two pixels in one, there's still enough to calculate a fair optical depth of field map without having a second camera like everyone else.
Plenoptics is a powerful weapon that hasn't fired yet.
#### [Light Field More than a photo, less than VR](#scroll190)
Usually, the explanation of plenoptic starts from light fields. And yes, from the science perspective, the plenoptic camera captures the light field, not just the photo. Plenus comes from the Latin "full", i.e., collecting all the information about the rays of light. Just like a Parliament plenary session.
Let's get to the bottom of this to understand what is a light field is and why do we need it.
Traditional photo is two-dimensional. There, where ray hit a sensor will be a pixel on a photo. The camera doesn't give a shit where the ray came from, whether it accidentally fell from aside or was reflected by a lovely lady's ass. The photo captures only the point of intersection of the ray with the surface of the sensor. So it's kinda 2D.
Light field image is the same, but with a new component — the origin of the ray. Means, it captures the ray vector in 3D space. Like calculating the lighting of a video game, but the other way around — we're trying to catch the scene, not create it. The light field is a set of all the light rays in our scene — both coming from the light sources and reflected.
<https://i.vas3k.ru/86h.png>There are a lot of mathematical models of light fields. Here's one of the most representative
The light field is essentially a visual model of the space around it. We can easily compute any photo within this space mathematically. Point of view, depth of field, aperture — all these are also computable.
I love to draw an analogy with a city here. Photography is like your favourite path from your home to the bar you always remember, while the light field is a map of the whole town. Using the map, you can calculate any route from point A to B. In the same way, knowing the light field, we can calculate any photo.
For an ordinary photo it's an overkill, I agree. But here comes the VR, where the light fields there are one of the most promising areas.
Having a light field model of an object or a room allows you to see this object or a room from any point in space as if everything around is virtual reality. It's no longer necessary to build a 3D-model of the room if we want to walk through it. We can "simply" capture all the rays inside it and calculate a picture of the room. Simply, yeah. That's what we're fighting over.
📝 [Google AR and VR: Experimenting with Light Fields][33]
[![][34]][35]
Saying optics, I with the [guys from Stanford][36] mean not only lenses but everything in between the object and sensor. Even the aperture and shutter. Sorry, photography snobs. I feel your pain.
#### Multi-camera
<https://i.vas3k.ru/851.jpg>
In 2014, the HTC One (M8) was released and became the first smartphone with two cameras and amusing computational photography [features][37] such as replacing the background with rain or sparkles.
The race has begun. Everybody started putting two, three, five lenses into their smartphones, trying to argue whether telephoto or wide-angle lens is better. Eventually, we got the [Light L16][38] camera. 16-lensed, as you can guess.
<https://i.vas3k.ru/859.jpg>Light L16
L16 was no longer a smartphone, but rather a new kind of pocket camera. It promised to reach the quality of top DSLRs with a high-aperture lens and full-frame sensor while yet fitting into your pocket. The power of computational photography algorithms was the main selling point.
<https://i.vas3k.ru/854.jpg>Telephoto-periscope, P30 Pro
It had 16 lenses: 5 x 28mm wide-angle and 5 x 70mm and 6 x 150mm telephoto. Each telephoto was periscope-style, meaning that the light did not flow directly through the lens to the sensor, but was reflected by a mirror inside the body. This configuration made it possible to fit a sufficiently long telephoto into a flat body, rather than stick out a "pipe" from it. Huawei recently did the same thing in the P30 Pro.
Each L16 photo was shot simultaneously on 10 or more lenses, and then the camera combined them to get a 52-megapixel image. According to the creators' idea, simultaneous shooting with several lenses made it possible to catch the same amount of light as with the large digital camera lens, artfully bypassing all the laws of optics.
Talking of software features, the first version had a depth of field and focus control in post-production. Minimal set. Having photos from different perspectives made it possible to compute the depth of the image and apply a decent software blur. Everything seemed nice on paper, so before the release, everybody even had hope for a bright computing future.
<https://i.vas3k.ru/88y.jpg>
In March 2018, Light L16 penetrated the market and… [miserably failed][39]. Yes, technologically it was in the future. However, at a price of $2000 it had no optical stabilization, so that the photos were always blurred (no wonder with 70-150 mm lenses), the autofocus was tediously slow, the algorithms of combining several pictures gave strange sharpness fluctuations, and there was no use for the camera in the dark, as it had no algorithms such as Google's HDR+ or Night Sight. Modern $500 point-and-shoot cameras with RAW support were able to do it from the start, so sales were discontinued after the first batch.
However, Light did not shut down at this point (hehe, pun). It raised the cash and continues to work on the new version with redoubled force. For instance, their technologies used in the recent [Nokia 9][40], which is a terrible dream of trypophobe. The idea is encouraging, so we are waiting for further innovations.
🎥 [Light L16 Review: Optical Insanity][41]
#### [
Coded Aperture
Deplur + Depth Map](#scroll220)
We're entering the area of telescopes, X-rays, and other fog of war. We won't go deep, but it's safer to fasten your seatbelts. The story of the coded aperture began where it was physically impossible to focus the rays: for gamma and X-ray radiation. Ask your physics teacher; they will explain why.
The essence of the coded aperture is to replace the standard petal diaphragm with a pattern. The position of the holes should ensure that the overall shape is maximally varied depending on the defocus — the more diverse, the better. Astronomers invented the whole range of [such patterns][42] for their telescopes. I'll cite the very classical one here.
<https://i.vas3k.ru/88z.jpg>
How does this work?
When we focus on the object, everything beyond our depth of field is blurred. Physically, blur is when a lens projects one ray onto several pixels of the sensor due to defocus. So a street lamp turns into a bokeh pancake.
Mathematicians use the term convolution and deconvolution to refer to these operations. Let's remember these words cause they sound cool!
<https://i.vas3k.ru/890.jpg>
Technically, we can turn any convolution back if we know the kernel. That's what mathematicians say. In reality, we have a limited sensor range and non-ideal lens, so all of our bokeh is far from the mathematical ideal and cannot be fully restored.
📝 [High-quality Motion Deblurring from a Single Image][43]
We can still try if we know the kernel of the convolution. Not gonna keep you waiting — the kernel is actually the shape of the aperture. In other words, the aperture makes a mathematical convolution using pure optics.
The problem is that the standard round aperture remains round at any level of blurring. Our kernel is always about the same; it's stable, but not very useful. In case of encoded aperture, rays with different defocus degrees will be encoded with different kernels. Readers with IQ > 150 have already guessed what will happen next.
The only issue remains is to understand which kernel is encoded in each area of the image. You can try it on manually, by testing different kernels and looking where the convolution turns out to be more accurate, but this is not our way. A long time ago, people invented the Fourier transform for this. Don't want to abuse you with calculus, so I'll add a link to my favorite explanation for those who are interested.
🎥 [But what is the Fourier Transform? A visual introduction][44]
All you need to know is that the Fourier transform allows you to find out which waves are dominant in the pile of overlapped ones. In the case of music, the Fourier will show the frequency of the notes in the complex chord. In the case of photography, it is the main pattern of overlapping light rays, which is the kernel of the convolution.
Since the form of the coded aperture is always different depending on the distance to the object — we can calculate that distance mathematically using only one simple image shot with a regular sensor!
Using the inverse convolution on the kernel, we can restore the blurred areas of the image. Bring back all the scattered pixels.
<https://i.vas3k.ru/872.jpg>The convolution kernel is at the top right
That's how most deblur tools work. It works even with an average round aperture, yet the result is less accurate.
The downside of the coded aperture is the noise and light loss, which we can't ignore. Lidars and fairly accurate ToF-cameras have wholly negated all the ideas of using a coded aperture in consumer gadgets. If you've seen it somewhere, write in comments.
📝 [Image and Depth from a Conventional Camera with a Coded Aperture][45]
📝 [Coded Aperture. Computational Photography WS 07/08][46]
🎥 [Coded aperture projection (SIGGRAPH 2008 Talks)][47]
#### Phase Coding (Wavefront Coding)
According to the latest news, the light is half the wave. Coding the aperture, we control the transparency of the lens, means we control the wave amplitude. Besides the amplitude, there is a phase, which can also be coded.
And yes. It can be done with an additional lens, which reverses the phase of light passing through it. Like on the Pink Floyd cover.
<https://i.vas3k.ru/892.jpg>
Then everything works like any other optical encoding. Different areas of the image encoded in different ways, and we can algorithmically recognize and fix them somehow. To shift the focus, for example.
What is good about phase coding is that we don't lose brightness. All photons reach the sensor, unlike in the coded aperture, where they bump into impenetrable parts of it (after all in the other half of the standards said that light is a particle).
The bad part is that we will always lose sharpness, as even the utterly focused objects will be smoothly blurred in the sensor, and we will have to call Fourier to gather them together for us. I'll attach the link with more detailed description and examples of photos below.
📝 [Computational Optics by Jongmin Baek, 2012][36]
#### [Flutter Shutter Fighting the motion blur](#scroll240)
The last thing we can code throughout the path of light to the sensor is the shutter. Instead of usual "open — wait — close" cycle, we will move the shutter several times per shot to result with the desired shutter speed. Sort of as in a multi-exposure, where one shot is exposed several times.
Let's imagine we decided to take pictures of a fast-moving car at night to see its license plate afterward. We don't have a flash, we can't use slow shutter speed, either we'll blur everything. It is necessary to lower the shutter speed, but so we get to a completely black image, and won't recognize the car. What to do?
It also is possible to take this shot in flutter shutter movements, so that the car smear not evenly, but like a "ladder" with a known interval. Thus, we encode the blur with a random sequence of open-close of the shutter, and we can try to decode it with the same inverse convolution. Appears it works much better than trying to get back pixels, evenly blurred with long shutter speed.
<https://i.vas3k.ru/893.jpg>
There are several algorithms for that. For the hardcore details, I'll again include links to some smart Indian guys' work.
📝 [Coded exposure photography: motion deblurring using fluttered shutter][48]
🎥 [Flutter Shutter Coded Filter][49]
Soon we'll go so goddamn crazy, so we'd want to control the lighting after the photo was taken too. To change the cloudy weather to a sunny one, or to change the lights on a model's face after shooting. Now it seems a bit wild, but let's talk again in ten years.
We've already invented a dumb device to control the light — a flash. They have come a long way: from the large lamp boxes that helped avoid the technical limitations of early cameras, to the modern LED flashes that spoil our pictures, so we mainly use them as a flashlight.
[![][50]][51]
#### Programmable Flash
It's been a long time since all smartphones switched to Dual LED flashes — a combination of orange and blue LEDs with brightness being adjusted to the color temperature of the shot. In the iPhone, for example, it's called True Tone and controlled by a small piece of code with a hacky formula. Even developers are not allowed to control it.
📝 [Demystifying iPhone’s Amber Flashlight][52]
<https://i.vas3k.ru/87k.jpg>
Then we started to think about the problem of all flashes — the overexposed faces and foreground. Everyone did it in their own way. iPhone got [Slow Sync Flash][53], which made camera artificially increase shutter speed in the dark. Google Pixel and other Android smartphones start using their depth sensors to combine images with and without flash, quickly made one by one. The foreground was taken from the photo without the flash when the background remained illuminated.
<https://i.vas3k.ru/86r.jpg>
The further use of a programmable multi-flash is vague. The only interesting application was found in computer vision, where it was used once in assembly scheme (like for Ikea book shelves) to detect the borders of objects more accurately. See the article below.
📝 [Non-photorealistic Camera:
Depth Edge Detection and Stylized Rendering using Multi-Flash Imaging][54]
#### Lightstage
Light is fast. It's always made light coding an easy thing to do. We can change the lighting a hundred times per shot and still not get close to its speed. That's how Lighstage was created back in 2005.
<https://i.vas3k.ru/86d.jpg>
🎥 [Lighstage demo video][55]
The essence of the method is to highlight the object from all possible angles in each shot of a real 24 fps movie. To get this done, we use 150+ lamps and a high-speed camera that captures hundreds of shots with different lighting conditions per shot.
A similar approach is now used when shooting mixed CGI graphics in movies. It allows you to fully control the lighting of the object in the post-production, placing it in scenes with absolutely random lighting. We just grab the shots illuminated from the required angle, tint them a little, done.
<https://i.vas3k.ru/86s.jpg>
<https://i.vas3k.ru/86e.jpg>
Unfortunately, it's hard to do it on mobile devices, but probably someone will like the idea. I've seen the app from guys who shot a 3D face model, illuminating it with the phone flashlight from different sides.
#### Lidar and Time-of-Flight Camera
Lidar is a device that determines the distance to the object. Thanks to a recent hype of self-driving cars, now we can find a cheap lidar on any dumpster. You've probably seen these rotating thingys at their roof? These are lidars.
We still can't fit a laser lidar into a smartphone, but we can go with its younger brother — [time-of-light camera][56]. The idea is ridiculously simple — a special separate camera with an LED-flash above it. The camera measures how quickly the light reaches the objects and creates a depth map of the image.
<https://i.vas3k.ru/868.jpg>
The accuracy of modern ToF cameras is about a centimeter. The latest Samsung and Huawei top models use them to create a bokeh map and for better autofocus in the dark. The latter, by the way, is quite good. I wish everybody had one.
Knowing the exact depth of field will be useful in the coming era of augmented reality. It will be much more accurate and effortless to shoot at the surfaces with lidar to make the first mapping in 3D than analyzing camera images.
#### Projector Illumination
To finally get serious about the computational lighting, we have to switch from regular LED flashes to projectors — devices that can project a 2D picture on a surface. Even a simple monochrome grid will be a good start for smartphones.
The first benefit of the projector is that it can illuminate only the part of the image that needs to be illuminated. No more burnt faces in the foreground. Objects can be recognized and ignored, just like laser headlights of modern car don't blind the oncoming drivers but illuminate pedestrians. Even with the minimum resolution of the projector, such as 100x100 dots, the possibilities are exciting.
<https://i.vas3k.ru/86i.jpg>Today, you can't surprise a kid with a car with a controllable light
The second and more realistic use of the projector is to project an invisible grid on a scene to detect its depth map. With a grid like this, you can safely throw away all your neural networks and lidars. All the distances to the objects in the image now can be calculated with the simplest computer vision algorithms. It was done in Microsoft Kinect times (rest in peace), and it was great.
Of course, it's worth to remember here the Dot Projector for Face ID on iPhone X and above. That's our first small step towards projector technology, but quite a noticeable one.
<https://i.vas3k.ru/86j.jpg>Dot Projector in iPhone X
<https://j.gifs.com/wVrzx8.gif>
It's time to reflex a bit. Observing what major technology companies are doing, it becomes clear that our next 10 years will be tightly tied to augmented reality. Today AR still looks like a toy to play [with 3D wifey][57], to [try on sneakers][58], to see [how the makeup looks][59], or to train [the U.S. Army][60]. Tomorrow we won't even notice we're using it every day. Dense flows of cash in this area are already felt from the Google and Nvidia offices.
For photography, AR means the ability to control the 3D scene. Scan the area, like smartphones with [Tango][61] do, add new objects, like in [HoloLenz][62], all such things. Don't worry about the poor graphics of modern AR-apps. As soon as game dev companies invade the area with their battle royales, everything becomes much better than PS4.
<https://i.vas3k.ru/87f.jpg> <https://i.vas3k.ru/87h.jpg>
By [Defected Pixel][63]
Remember that epic [fake Moon Mode][64] presented by Huawei? If you missed it: when Huawei camera detects you're going to take a photo of moon, it puts a pre-prepared high-resolution moon picture on top of your photo. Because it looks cooler, indeed! True Chinese cyberpunk.
<https://i.vas3k.ru/869.jpg>Life goal: be able to bend the truth like Huawei
When all the jokes were joked in twitter, I thought about that situation — Huawei gave people exactly what they promised. The moon was real, and the camera lets you shoot it THIS awesome. No deception. Tomorrow, if you give people the opportunity to replace the sky on their photos with beautiful sunsets, half the planet will be amazed.
> In the future, machines will be "finishing up" and re-painting our photos for us
Pixel, Galaxy and other Android-phones have some stupid AR-mode today. Some let you add cartoon characters to take photos with them, others spread emojis all over the room, or put a mask on your face just like in a Snapchat.
These are just our first naive steps. Today, Google camera has Google Lens, that finds information about any object you point your camera at. Samsung does the same with Bixby. For now, these tricks are only made to humiliate the iPhone users, but it's easy to imagine the next time you're taking a pic with the Eiffel Tower, your phone says: you know, your selfie is shit. I'll put a nice sharp picture of the tower in the background, fix your hair, and remove a pimple above your lip. If you plan to post it to Instagram, VSCO L4 filter will work the best for it. You're welcome, leather bastard.
After a while, the camera will start to replace the grass with greener one, your friends with better ones, and boobs with bigger ones. Or something like that. A brave new world.
<https://i.vas3k.ru/894.jpg>
In the beginning it's gonna look ridiculous. Probably even terrible. The photo-aesthetes will be enraged, the fighters for natural beauty will launch a company to ban neural networks usage, but the mass audience will be delighted.
Because photography always was just a way to express and share emotions. Every time there is a tool to express more vividly and effectively, everyone starts using it — emoji, filters, stickers, masks, audio messages. Some will already find the list disgusting, but it can be easily continued.
Photos of the "objective reality" will seem as boring as your great-grandmother's pictures on the chair. They won't die but become something like paper books or vinyl records — a passion of enthusiasts, who see a special deep meaning in it. "Who cares of setting up the lighting and composition when my phone can do the same". That's our future. Sorry.
The mass audience doesn't give a shit about objectivity. It needs algorithms to make their faces younger, and vacations cooler than their coworker or neighbor. The augmented reality will re-draw the reality for them, even with a higher level of detail than it really is. It may sound funny, but we'll start to improve the graphics in the real world.
And yes, as it always does, it all starts with teenagers with their "strange, stupid hobbies for idiots". That's what happens all the time. When you stop understanding something — this IS the future.
It is hard to legibly compare the differences in modern smartphone cameras, as due to the huge competition in the market, everyone implements the new features almost simultaneously. There's no way to be objective in the world where Google announces a new Night Mode, then Samsung and Xiaomi just copy it in a new firmware after a month. So I'm not gonna even try to be objective here.
In the pictures below, I briefly described the main features that I found interesting (in the context of this article) — ignoring the most obvious things like Dual LED flashes, automatic white balance, or panorama mode. In the next section, you can share your insights about your favorite smartphone. Crowdsourcing!
#### A place to brag about your smartphone
For this comparison, I only took four phones that I tested myself. Of course, there are thousands more in the world. If you have or had an interesting phone, please tell us a few words about its camera and your experience below in comments.
Throughout history, each human technology becomes more advanced as soon as it stops copying living organisms. Today, it is hard to imagine a car with joints and muscles instead of wheels. Planes with fixed wings fly 800+ km/h — birds don't even try. There are no analogs to the computer processor in nature at all.
The most exciting part of the list is what's not in it. Camera sensors. We still haven't figured out anything better than imitating the eye structure. The same crystalline lens and a set of RGGB-cones as retina has.
Computational photography has added a "brain" to this process. A processor that handles visual information not only by reading pixels through the optic nerve but also by complementing the picture based on its experience. Yes, it opens up a lot of possibilities for us today, but there is a hunch we're still trying to wave with hand-made wings instead of inventing a plane. One that will leave behind all these shutters, apertures, and Bayer filters.
The beauty of the situation is that we can't even imagine today what it's going to be.
Most of us will even die without knowing.
And it's wonderful.
--------------------------------------------------------------------------------
via: https://vas3k.com/blog/computational_photography/
作者：[vas3k][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://vas3k.com/
[b]: https://github.com/lujun9972
[1]: https://www.dropbox.com/sh/pqw8x5vepavffq5/AADEbPpQr71JUr31A6g96zHxa?dl=0
[2]: https://vas3k.com/donate/
[3]: https://vas3k.ru/blog/computational_photography/
[4]: https://en.wikipedia.org/wiki/Computational_photography
[5]: https://medium.com/hd-pro/computational-photography-will-revolutionize-digital-imaging-a25d34f37b11
[6]: https://achael.github.io/_pages/imaging/
[7]: http://alumni.media.mit.edu/~jaewonk/Publications/Comp_LectureNote_JaewonKim.pdf
[8]: https://graphics.stanford.edu/talks/compphot-publictalk-may08.pdf
[9]: https://github.com/danielgindi/Instagram-Filters/blob/master/InstaFilters/Resources_for_IF_Filters/xproMap.png
[10]: https://en.wikipedia.org/wiki/OpenCL
[11]: https://una.im/CSSgram/
[12]: http://blog.dehancer.com/category/examples/
[13]: https://developer.apple.com/library/archive/documentation/GraphicsImaging/Conceptual/CoreImaging/ci_autoadjustment/ci_autoadjustmentSAVE.html
[14]: http://vas3k.com/blog/machine_learning/
[15]: https://www.pixelmator.com/pro/machine-learning/
[16]: https://paperswithcode.com/task/image-enhancement
[17]: http://people.ee.ethz.ch/~ihnatova/#dataset
[18]: https://en.wikipedia.org/wiki/Epsilon_photography
[19]: https://en.wikipedia.org/wiki/Shutter_lag
[20]: https://www.youtube.com/watch?v=FmB1LztzEVM
[21]: https://www.cambridgeincolour.com/tutorials/cameras-vs-human-eye.htm
[22]: https://en.wikipedia.org/wiki/F-number
[23]: https://www.youtube.com/watch?v=iLtWyLVjDg0&t=0
[24]: https://en.wikipedia.org/wiki/Capybara
[25]: https://ai.googleblog.com/2014/10/hdr-low-light-and-high-dynamic-range.html
[26]: https://www.blog.google/products/pixel/see-light-night-sight/
[27]: https://en.wikipedia.org/wiki/Optical_flow
[28]: https://ai.googleblog.com/2018/11/night-sight-seeing-in-dark-on-pixel.html
[29]: https://ai.googleblog.com/2018/02/introducing-hdr-burst-photography.html
[30]: https://recombu.com/mobile/article/focus-shifting-explained_m20454-html
[31]: https://www.imaging-resource.com/news/2015/09/15/sony-mirrorless-cameras-will-soon-focus-as-fast-as-dslrs-if-this-patent-bec
[32]: https://www.youtube.com/watch?v=MytCfECfqWc
[33]: https://www.blog.google/products/google-ar-vr/experimenting-light-fields/
[34]: https://i.vas3k.ru/full/871.gif
[35]: https://i.vas3k.ru/full/full/871.gif
[36]: http://graphics.stanford.edu/courses/cs478/lectures/02292012_computational_optics.pdf
[37]: https://www.computerworld.com/article/2476104/in-pictures--here-s-what-the-htc-one-s-dual-cameras-can-do.html
[38]: https://light.co/camera
[39]: https://petapixel.com/2017/12/08/review-light-l16-brilliant-braindead/
[40]: https://www.nokia.com/phones/en_int/nokia-9-pureview/
[41]: https://www.youtube.com/watch?v=W3pBp12r-m0
[42]: http://ipl.uv.es/?q=es/content/page/ibis-coded-mask
[43]: http://jiaya.me/papers/deblur_siggraph08.pdf
[44]: https://www.youtube.com/watch?v=spUNpyF58BY
[45]: https://graphics.stanford.edu/courses/cs448a-08-spring/levin-coded-aperture-sig07.pdf
[46]: https://www.eecs.tu-berlin.de/fileadmin/fg144/Courses/07WS/compPhoto/Coded_Aperture.pdf
[47]: https://www.youtube.com/watch?v=4kh71S446FM
[48]: http://www.cs.cmu.edu/~ILIM/projects/IM/aagrawal/sig06/CodedExposureLowres.pdf
[49]: https://www.youtube.com/watch?v=gGvvqj-lF5o
[50]: https://i.vas3k.ru/full/87b.gif
[51]: https://i.vas3k.ru/full/full/87b.gif
[52]: https://medium.com/@thatchaponunprasert/demystifying-iphones-amber-flashlight-519352db10bd
[53]: https://www.reddit.com/r/iphone/comments/71myyp/a_feature_from_the_new_new_iphone_a_few_talk_about_is/
[54]: https://www.eecis.udel.edu/~jye/lab_research/SIG04/SIG_YU_RASKAR.pdf
[55]: https://www.youtube.com/watch?v=wT2uFlP0MlU
[56]: https://en.m.wikipedia.org/wiki/Time-of-flight_camera
[57]: https://youtu.be/p9oDlvOV3qs?t=161
[58]: https://www.youtube.com/watch?v=UmJriqzDUTo
[59]: https://www.youtube.com/watch?v=dpSP6ZM5XGo
[60]: https://www.youtube.com/watch?time_continue=87&v=x8p19j8C6VI
[61]: https://en.wikipedia.org/wiki/Tango_%28platform%29
[62]: https://youtu.be/e-n90xrVXh8?t=314
[63]: https://vk.com/pxirl
[64]: https://www.androidauthority.com/huawei-p30-pro-moon-mode-controversy-978486/

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sources/tech/20190705 Copy and paste at the Linux command line with xclip.md
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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Copy and paste at the Linux command line with xclip)
[#]: via: (https://opensource.com/article/19/7/xclip)
[#]: author: (Scott Nesbitt https://opensource.com/users/scottnesbitt)
Copy and paste at the Linux command line with xclip
======
Learn how to get started with the Linux xclip utility.
![Green paperclips][1]
How do you usually copy all or part of a text file when working on the Linux desktop? Chances are you open the file in a text editor, select all or just the text you want to copy, and paste it somewhere else.
That works. But you can do the job a bit more efficiently at the command line using the [xclip][2] utility. xclip provides a conduit between commands you run in a terminal window and the clipboard in a Linux graphical desktop environment.
### Installing xclip
xclip isn't standard kit with many Linux distributions. To see if it's installed on your computer, open a terminal window and type **which xclip**. If that command returns output like _/usr/bin/xclip_, then you're ready to go. Otherwise, you need to install xclip.
To do that, use your distribution's package manager. Or, if you're adventurous, [grab the source code][2] from GitHub and compile it yourself.
### Doing the basics
Let's say you want to copy the contents of a file to the clipboard. There are two ways to do that with xclip. Type either:
```
`xclip file_name`
```
or
```
`xclip -sel clip file_name`
```
What's the difference between the two commands (aside from the second one being longer)? The first command works if you use the middle button on the mouse to paste text. However, not everyone does. Many people are conditioned to use a right-click menu or to press Ctrl+V to paste text. If you're one of those people (I am!), using the **-sel clip** option ensures you can paste what you want to paste.
### Using xclip with other applications
Copying the contents of a file directly to the clipboard is a neat parlor trick. Chances are, you won't be doing that very often. There are other ways you can use xclip, and those involve pairing it with another command-line application.
That pairing is done with a _pipe_ (|). The pipe redirects the output of one command line application to another. Doing that opens several possibilities. Let's take a look at three of them.
Say you're a system administrator and you need to copy the last 30 lines of a log file into a bug report. Opening the file in a text editor, scrolling down to the end, and copying and pasting is a bit of work. Why not use xclip and the [tail][3] utility to quickly and easily do the deed? Run this command to copy those last 30 lines:
```
`tail -n 30 logfile.log | xclip -sel clip`
```
Quite a bit of my writing goes into some content management system (CMS) or another for publishing on the web. However, I never use a CMS's WYSIWYG editor to write—I write offline in [plain text][4] formatted with [Markdown][5]. That said, many of those editors have an HTML mode. By using this command, I can convert a Markdown-formatted file to HTML using [Pandoc][6] and copy it to the clipboard in one fell swoop:
```
`pandoc -t html file.md | xclip -sel clip`
```
From there, I paste away.
Two of my websites are hosted using [GitLab Pages][7]. I generate the HTTPS certificates for those sites using a tool called [Certbot][8], and I need to copy the certificate for each site to GitLab whenever I renew it. Combining the [cat][9] command and xclip is faster and more efficient than using an editor. For example:
```
`cat /etc/letsencrypt/live/website/fullchain.pem | xclip -sel clip`
```
Is that all you can do with xclip? Definitely not. I'm sure you can find more uses to fit your needs.
### Final thoughts
Not everyone will use xclip. That's fine. It is, however, one of those little utilities that really comes in handy when you need it. And, as I've discovered on a few occasions, you don't know when you'll need it. When that time comes, you'll be glad xclip is there.
--------------------------------------------------------------------------------
via: https://opensource.com/article/19/7/xclip
作者：[Scott Nesbitt][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/scottnesbitt
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/life_paperclips.png?itok=j48op49T (Green paperclips)
[2]: https://github.com/astrand/xclip
[3]: https://en.wikipedia.org/wiki/Tail_(Unix)
[4]: https://plaintextproject.online
[5]: https://gumroad.com/l/learnmarkdown
[6]: https://pandoc.org
[7]: https://docs.gitlab.com/ee/user/project/pages/
[8]: https://certbot.eff.org/
[9]: https://en.wikipedia.org/wiki/Cat_(Unix)

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (10 ways to get started with Linux)
[#]: via: (https://opensource.com/article/19/7/ways-get-started-linux)
[#]: author: (Seth Kenlon https://opensource.com/users/seth/users/don-watkins)
10 ways to get started with Linux
======
Ready to dive in and learn Linux? Try these 10 ways to get started.
![Penguins gathered together in the Artic][1]
The article _[What is a Linux user?][2]_ by Anderson Silva made it clear that these days people are as likely to use Linux (in some way) as they are to use Windows, as long as your definition of "using Linux" is sufficiently broad. Still, if you don't have enough Linux in your life, now is a great time to try Linux in a way you've never tried before.
Here are 10 ways to get started with Linux. Try one or try them all.
### 1\. Join a free shell
![Free shell screenshot][3]
There are a lot of people running Linux with more Linux servers than they know what to do with (keep in mind that a "Linux server" can be anything from the latest supercomputer to a discarded 12-year-old laptop). To put excess computers to good use, many administrators open their spare boxes up for free shell accounts.
If you want to log time in a Linux terminal to learn commands, shell scripting, Python, and the basics of web development, a free shell account is an easy, no-cost way to get started. Here's a short list to try:
* [Freeshell.de][4] is a public access Linux system that's been online since 2002. You get SSH access (to experiment in a Linux shell), IPv6, and OpenSSL, and you can request a MySQL database.
* [Blinkenshell][5] provides a Linux shell to learn Unix, use IRC, host simple websites, and share files. It's been online since 2006.
* [SDF Public Access Unix System][6] was established in 1987 to offer free NetBSD accounts. NetBSD isn't Linux, of course, but it's open source Unix, so it offers a similar experience. It also has several homebrewed applications, so it straddles the line between old-school BBS and plain-old free shell.
Free shell accounts are subject to a lot of abuse, so the more you demonstrate trustworthiness and willingness to participate in the goings-on of the collective, the better your experience. You can often gain access (through a special request or a small donation to demonstrate goodwill) to database engines, compilers, and advanced programming languages. You can also ask for additional software or libraries to be installed, subject to administrator approval.
#### How to use it
Public access shell accounts are a great way to try out a real Linux system. The fact that you don't get root access means you get to learn local software management without having to mow your own lawn or fix leaky faucets. You can do just enough real-life activities to make them viable for getting real work done, although they're not reliable enough to be mission critical.
### 2\. Try Linux on Windows with WSL 2
Believe it or not, Microsoft started shipping Linux with Windows as of June 2019, meaning you can run Linux applications from Windows as the second iteration of the [Windows Subsystem for Linux][7] (WSL 2). While it's primarily aimed at developers, Windows users will find WSL 2 to be a Linux environment from the comfort of a familiar desktop without any virtualization taking up extra resources. This is Linux running as a process on your Windows machine. At this time, it's still a new initiative and a work in progress, so it's subject to change. If you try to push it too far too soon, you're likely to encounter a bug or two, but if you're just looking to get started with Linux, learn some commands, and get a feel for getting serious work done in a text-based environment, WSL 2 may be exactly what you need.
#### How to use it
WSL doesn't yet have a clear pathway or purpose, but it provides a Linux environment on your Windows machine. You get root access and can run Linux distributions and applications, so it's an easy and seamless way to learn. However, even though WSL _is Linux_, it's not exactly a typical Linux experience. It's Linux provided by Windows, and that's not what you're likely to encounter in the real world. WSL is a development and educational tool, but if it's available to you, then you should use it.
### 3\. Carry Linux on a bootable thumb drive
![Porteus Linux][8]
Carry Linux, installed to a USB thumb drive, everywhere you go, and boot any computer you encounter from that thumb drive. You get a personalized Linux desktop, and you don't have to worry about the data on the host computer you've booted from. The computer doesn't touch your Linux OS, and your Linux OS doesn't affect the computer. It's ideal for public computers at hotel business centers, libraries, schools, or just to give yourself an excuse to boot into Linux from time to time.
Unlike many other quick hacks to get to a Linux shell, this method offers you a full and robust Linux system, complete with a desktop, access to whatever software you need, and persistent data storage.
The system never changes. Any data you want to save is written into a compressed filesystem, which is then applied as an overlay to the system when you boot. This flexibility allows you to choose whether to boot in persistent mode, saving all data back to the thumb drive, or in an ephemeral mode, so everything you do disappears once you power down. In other words, you can use this as a secure kiosk on an untrusted computer or as your portable OS on computers you trust.
There are many [thumb drive distributions][9] you can try, some with minimal desktop environments for low-powered computers and others with a full desktop. I'm partial to [Porteus][10] Linux. I've carried it on my keychain every day for the past eight years, using it as my primary computing platform during business travel as well as a utility disk if computer problems strike at work or home. It's a reliable and stable operating system that's fun and easy to use.
On Mac or Windows, download the [Fedora Media Writer][11] to create a bootable thumb drive of whatever portable distribution you download.
#### How to use it
Booting a "live Linux" from a USB thumb drive provides a complete Linux distribution. While data storage is done a little differently from a system you install to a hard drive, everything else is as you'd expect from a Linux desktop. There's little you can't do on a portable Linux OS, so install one on your keychain to unlock the full potential of every computer you encounter.
### 4\. Take an online tour
![Linux tour screenshot][12]
Somebody over at Ubuntu thought up the brilliant idea of hosting an Ubuntu GNOME desktop in the browser. To try it out for yourself, open a web browser and navigate to [tour.ubuntu.com][13]. You can select which activity you want demonstrated, or you can skip individual lessons and click the Show Yourself Around button.
Even if you're new to the Linux desktop, you might find showing yourself around is more familiar than you might expect. From the online tour, you can look around, see what applications are available, and view what a typical default Linux desktop is like. You can't adjust settings or launch another tour in Firefox (it was the first thing I tried, naturally), and while you can go through the motions of installing applications, you can't launch them. But if you've never used a Linux desktop before and you want to see what all the fuss is about, this is the whirlwind tour.
#### How to use it
An online tour is truly just a tour. If you've never seen a Linux desktop in action, this is an opportunity to get a glimpse of what it's like. Not intended for serious work, this is an attractive display to entice passers-by.
### 5\. Run Linux in the browser with JavaScript
![JSLinux][14]
Not so long ago, virtualization used to be computationally expensive, limited to users with premium hardware. Now virtualization has been optimized to the point that it can be performed by a JavaScript engine, thanks to Fabrice Bellard, the creator of the excellent and open source [QEMU][15] machine emulator and virtualizer.
Bellard also started the JSLinux project, which allows you to run Linux and other operating systems in a browser, in his spare time for fun. It's still an experimental project, but it's a technical marvel. Open a web browser to the [JSLinux][16] page, and you can boot a text-based Linux shell or a minimal graphical Linux environment. You can upload and download files to your JSLinux host or (theoretically) send your files to a network backup location, because JSLinux has access to the internet through a VPN socket (although at capped speeds, dependent upon the VPN service).
#### How to use it
You won't be doing serious work on JSLinux anytime soon, and the environment is arguably too unusual to learn broad lessons about how Linux normally works. If, however, you're bored of running Linux on a plain old PC and would like to try Linux on a truly distinctive platform, JSLinux is in a class all its own.
### 6\. Read about it
Not every Linux experience happens on the computer. Maybe you're the sort of person who likes to keep your distance, observe, and do your research before jumping into something new, or maybe you're just not clear yet on what "Linux" encompasses, or maybe you love full immersion. There's a wealth of information to read about how Linux works, what it's like to run Linux, and what's happening in the Linux world.
The more you get familiar with the world of open source, the easier it is to understand the common lingo and to separate urban myth from actual experience. We publish [book lists][17] from time to time, but one of my favorites is [_The Charm of Linux_][18] by Hazel Russman. It's a tour through Linux from many different angles, written by an independent author out of excitement over discovering Linux.
#### How to use it
Nothing beats kicking back with a good book. This is the least traditional method of experiencing Linux, but for people who love the printed word, it's both comforting and effective.
### 7\. Get a Raspberry Pi
![Raspberry Pi 4][19]
If you're using a [Raspberry Pi][20], you're running Linux. It's that easy to get started with Linux and low-powered computing. The great thing about the Pi, aside from it costing well under $100, is that its [website][21] is designed for education. You can learn all about what the Pi does, and while you're at it, all about what Linux can do for you.
#### How to use it
The Pi is, by design, a low-powered computer. That means you can't do as much multitasking as you might be used to, but that's a convenient way to keep yourself from getting overwhelmed. The Raspberry Pi is a great way to learn Linux and all of the possibilities that come with it, and it's a fun way to discover the power of eco-friendly, small-form-factor, simplified computing. And be sure to stay tuned to Opensource.com—especially during Pi Week every March—for [tips][22] and [tricks][23] and [fun][24] [activities][25].
### 8\. Climb aboard the container craze
If you work near the back end of the mythical [cloud][26], then you've heard about the container craze. While you can run Docker and Kubernetes on Windows, Azure, Mac, and Linux, you may not know that the containers themselves are Linux. Cloud computing apps and infrastructure are literally minimal Linux systems that run partly virtualized and partly on bare metal. If you launch a container, you are launching a miniature, hyper-specific Linux distribution.
Containers are [different][27] than virtual machines or physical servers. They're not intended to be used as a general-purpose operating system. However, if you are developing in a container, you might want to pause and have a look around. You'll get a glimpse of how a Linux system is structured, where important files are kept, and which commands are the most common. You can even [try a container online][28], and you can read all about how they work in my article about going [behind the scenes with Linux containers][29].
#### How to use it
Containers are, by design, specific to a single task, but they're Linux, so they're extremely flexible. You can use them as they're intended, or you can build a container out into a mostly complete system for your Linux experiments. It's not a desktop Linux experience, but it's a full Linux experience.
### 9\. Install Linux as a VM
Virtualization is the easy way to try an operating system, and [VirtualBox][30] is a great open source way to virtualize. VirtualBox runs on Windows and Mac, so you can install Linux as a virtual machine (VM) and use it almost as if it were just another application. If you're not accustomed to installing an operating system, VirtualBox is also a very safe way to try Linux without accidentally installing it over your usual OS.
#### How to use it
Running Linux as a VM is convenient and easy, either as a trial run or an alternative to dual-booting or rebooting when you need a Linux environment. It's full-featured and, because it uses virtual hardware, the host operating system drives your peripherals. The only disadvantage to running Linux as a virtual machine is primarily psychological. If you intend to use Linux as your main OS, but end up defaulting to the host OS for all but the most Linux-specific tasks, then the VM has failed you. Otherwise, a VM is a triumph of modern technology, and using Linux in VirtualBox provides you with all the best features Linux has to offer.
### 10\. Install it
![Fedora Silverblue][31]
When in doubt, there's always the traditional route. If you want to give Linux the attention it deserves, you can download Linux, burn the installer to a thumb drive (or a DVD, if you prefer optical media), and install it on your computer. Linux is open source, so it can be distributed by anyone who wants to take the time to bundle Linux—and all the bits and pieces that make it usable—into what is commonly called a _distribution_ (or "distro") for short. Ask any Linux user, and you're bound to get a different answer for which distribution is "best" (mostly because the term "best" is often left undefined). Most people admit that you should use the Linux distribution that works for you, meaning that you should test a few popular distros and settle on the one that makes your computer behave as you expect it to behave. This is a pragmatic and functional approach. For example, should a distribution fail to recognize your webcam and you want your webcam to work, then use a distribution that recognizes your webcam.
If you've never installed an operating system before, you'll find most Linux distributions include a friendly and easy installer. Just download a distribution (they are delivered as ISO files), and download the [Fedora Media Writer][11] to create a bootable installation thumb drive.
#### How to use it
Installing Linux and using it as an operating system is a step toward becoming familiar and familial with it. There's no wrong way to use it. You might discover must-have features you never knew you needed, you might learn more about computers than you ever imagined you could, and you may shift in your worldview. Or you might use a Linux desktop because it was easy to download and install, or because you want to cut out the middleman of some corporate overlord, or because it helps you get your work done.
Whatever your reason, just give Linux a try with any (or all) of these options.
--------------------------------------------------------------------------------
via: https://opensource.com/article/19/7/ways-get-started-linux
作者：[Seth Kenlon][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/seth/users/don-watkins
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/OSDC_Penguin_Image_520x292_12324207_0714_mm_v1a.png?itok=p7cWyQv9 (Penguins gathered together in the Artic)
[2]: https://opensource.com/article/19/6/what-linux-user
[3]: https://opensource.com/sites/default/files/uploads/freeshell.png (Free shell screenshot)
[4]: https://freeshell.de
[5]: https://blinkenshell.org/wiki/Start
[6]: https://sdf.org/
[7]: https://devblogs.microsoft.com/commandline/wsl-2-is-now-available-in-windows-insiders/
[8]: https://opensource.com/sites/default/files/uploads/porteus.jpg (Porteus Linux)
[9]: https://opensource.com/article/19/6/tiny-linux-distros-you-have-try
[10]: http://porteus.org
[11]: https://getfedora.org/en/workstation/download/
[12]: https://opensource.com/sites/default/files/uploads/linux_tour.jpg (Linux tour screenshot)
[13]: http://tour.ubuntu.com/en/#
[14]: https://opensource.com/sites/default/files/uploads/jslinux.jpg (JSLinux)
[15]: https://www.qemu.org
[16]: https://bellard.org/jslinux/
[17]: https://opensource.com/article/19/1/tech-books-new-skils
[18]: http://www.lulu.com/shop/hazel-russman/the-charm-of-linux/paperback/product-21229401.html
[19]: https://opensource.com/sites/default/files/uploads/raspberry-pi-4-case.jpg (Raspberry Pi 4)
[20]: https://opensource.com/resources/raspberry-pi
[21]: https://www.raspberrypi.org/
[22]: https://opensource.com/article/19/3/raspberry-pi-projects
[23]: https://opensource.com/article/19/3/piflash
[24]: https://opensource.com/article/19/3/gamepad-raspberry-pi
[25]: https://opensource.com/life/16/3/make-music-raspberry-pi-milkytracker
[26]: https://opensource.com/resources/cloud
[27]: https://opensource.com/article/19/6/how-ssh-running-container
[28]: https://linuxcontainers.org/lxd/try-it/
[29]: https://opensource.com/article/18/11/behind-scenes-linux-containers
[30]: https://virtualbox.org
[31]: https://opensource.com/sites/default/files/uploads/fedora-silverblue.png (Fedora Silverblue)

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Command line quick tips: Permissions)
[#]: via: (https://fedoramagazine.org/command-line-quick-tips-permissions/)
[#]: author: (Paul W. Frields https://fedoramagazine.org/author/pfrields/)
Command line quick tips: Permissions
======
![][1]
Fedora, like all Linux based systems, comes with a powerful set of security features. One of the basic features is _permissions_ on files and folders. These permissions allow files and folders to be secured from unauthorized access. This article explains a bit about these permissions, and shows you how to share access to a folder using them.
### Permission basics
Fedora is by nature a multi-user operating system. It also has _groups_, which users can be members of. But imagine for a moment a multi-user system with no concept of permissions. Different logged in users could read each other’s content at will. This isn’t very good for privacy or security, as you can imagine.
Any file or folder on Fedora has three sets of permissions assigned. The first set is for the _user_ who owns the file or folder. The second is for the _group_ that owns it. The third set is for everyone else who’s not the user who owns the file, or in the group that owns the file. Sometimes this is called the _world_.
### What permissions mean
Each set of permissions comes in three flavors — _read_, _write_, and _execute_. Each of these has an initial that stands for the permission, thus _r_, _w_, and _x_.
#### File permissions
For _files_, here’s what these permissions mean:
* Read (r): the file content can be read
* Write (w): the file content can be changed
* Execute (x): the file can be executed — this is used primarily for programs or scripts that are meant to be run directly
*
You can see the three sets of these permissions when you do a long listing of any file. Try this with the _/etc/services_ file on your system:
```
$ ls -l /etc/services
-rw-r--r--. 1 root root 692241 Apr 9 03:47 /etc/services
```
Notice the groups of permissions at the left side of the listing. These are provided in three sets, as mentioned above — for the user who owns the file, for the group that owns the file, and for everyone else. The user owner is _root_ and the group owner is the _root_ group. The user owner has read and write access to the file. Anyone in the group _root_ can only read the file. And finally, anyone else can also only read the file. (The dash at the far left shows this is a regular file.)
By the way, you’ll commonly find this set of permissions on many (but not all) system configuration files. They are only meant to be changed by the system administrator, not regular users. Often regular users need to read the content as well.
#### Folder (directory) permissions
For folders, the permissions have slightly different meaning:
* Read (r): the folder contents can be read (such as the _ls_ command)
* Write (w): the folder contents can be changed (files can be created or erased in this folder)
* Execute (x): the folder can be searched, although its contents cannot be read. (This may sound strange, but the explanation requires more complex details of file systems outside the scope of this article. So just roll with it for now.)
Take a look at the _/etc/grub.d_ folder for example:
```
$ ls -ld /etc/grub.d
drwx------. 2 root root 4096 May 23 16:28 /etc/grub.d
```
Note the _d_ at the far left. It shows this is a directory, or folder. The permissions show the user owner (_root_) can read, change, and _cd_ into this folder. However, no one else can do so — whether they’re a member of the _root_ group or not. Notice you can’t _cd_ into the folder, either:
```
$ cd /etc/grub.d
bash: cd: /etc/grub.d: Permission denied
```
Notice how your own home directory is setup:
```
$ ls -ld $HOME
drwx------. 221 paul paul 28672 Jul 3 14:03 /home/paul
```
Now, notice how no one, other than you as the owner, can access anything in this folder. This is intentional! You wouldn’t want others to be able to read your private content on a shared system.
### Making a shared folder
You can exploit this permissions capability to easily make a folder to share within a group. Imagine you have a group called _finance_ with several members who need to share documents. Because these are user documents, it’s a good idea to store them within the _/home_ folder hierarchy.
To get started, [use][2] _[sudo][2]_ to make a folder for sharing, and set it to be owned by the _finance_ group:
```
$ sudo mkdir -p /home/shared/finance
$ sudo chgrp finance /home/shared/finance
```
By default the new folder has these permissions. Notice how it can be read or searched by anyone, even if they can’t create or erase files in it:
```
drwxr-xr-x. 2 root root 4096 Jul 6 15:35 finance
```
That doesn’t seem like a good idea for financial data. Next, use the _chmod_ command to change the mode (permissions) of the shared folder. Note the use of _g_ to change the owning group’s permissions, and _o_ to change other users’ permissions. Similarly, _u_ would change the user owner’s permissions:
```
$ sudo chmod g+w,o-rx /home/shared/finance
```
The resulting permissions look better. Now, anyone in the _finance_ group (or the user owner _root_) have total access to the folder and its contents:
```
drwxrwx---. 2 root finance 4096 Jul 6 15:35 finance
```
If any other user tries to access the shared folder, they won’t be able to do so. Great! Now our finance group can put documents in a shared place.
### Other notes
There are additional ways to manipulate these permissions. For example, you may want any files in this folder to be set as owned by the group _finance_. This requires additional settings not covered in this article, but stay tuned to the Magazine for more on that topic soon.
--------------------------------------------------------------------------------
via: https://fedoramagazine.org/command-line-quick-tips-permissions/
作者：[Paul W. Frields][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://fedoramagazine.org/author/pfrields/
[b]: https://github.com/lujun9972
[1]: https://fedoramagazine.org/wp-content/uploads/2018/10/commandlinequicktips-816x345.jpg
[2]: https://fedoramagazine.org/howto-use-sudo/

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[#]: collector: (lujun9972)
[#]: translator: (robsean)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Debian 10 (Buster) Installation Steps with Screenshots)
[#]: via: (https://www.linuxtechi.com/debian-10-buster-installation-guide/)
[#]: author: (Pradeep Kumar https://www.linuxtechi.com/author/pradeep/)
Debian 10 (Buster) Installation Steps with Screenshots
======
Debian Project has released its latest and stable operating system as **Debian 10**, code name for Debian 10 is “**Buster**“, this release will get 5 years of support. Debian 10 is available for both 32-bit and 64-bit systems. This release comes with lot of new features, some of them are listed below:
* Introduction of new Debian 10 theme known as “**FuturePrototype**”
* Updated Desktop GNOME 3.30, Cinnamon 3.8, KDE Plasma 5.14, MATE 1.20 &amp; Xfce 4.12
* LTS kernel 4.19.0-4
* New Python 3 (3.7.2), Perl 5.28 &amp; PHP 7.3
* Iptables are replaced by nftables
* Updated LibreOffice 6.1 &amp; GIMP 2.10.8
* Updated OpenJDK 11, MariaDB 10.3 and Apache 2.4.38
* Updated Chromium 73.0 &amp; Firefox 60.7
* Improved support for UEFI (Unified Extensible Firmware Interface)
In this article we will demonstrate how to install Debian 10 “Buster” workstation on your Laptop &amp; Desktop.
**Recommended System Requirements for Debian 10**
* 2 GB RAM
* 2 GHz Dual Core Processor
* 10 GB Free Hard disk space
* Bootable Installation Media (USB/ DVD)
* Internet connectivity (Optional)
Let’s jump into the installation steps of Debian 10
### Step:1) Download Debian 10 ISO file
Download the Debian 10 ISO file from its official portal,
<https://www.debian.org/releases/buster/debian-installer/>
Once the ISO file is downloaded, burn it either into USB or DVD and make it bootable.
### Step:2) Boot Your System with Installation Bootable Media (USB / DVD)
Reboot your system in which you will be installing Debian 10 and go to bios settings and set the boot medium as USB or DVD.  Once the system is booted with installation bootable media then we will get the following screen.
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Choose-Graphical-Install-Debian-10.jpg>
Choose the First option “**Graphical Install**”
### Step:3) Choose Your preferred Language, Location and Keyboard Layout
In this step you will be asked to choose your preferred language
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Choose-Language-Debian10-Installation.jpg>
Click on Continue
Select your preferred location, based on location, time zone will be automatically set for your system.
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Select-Location-Debain10-Installation.jpg>
Now Choose your keyboard layout that suits to your installation,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Keyboard-Layout-Debian10-Installation.jpg>
Click on Continue to proceed further,
### Step:4) Set Host name and Domain Name for Debian 10 System
Set the hostname that suits to your environment and then click on Continue, in my case I am specifying the hostname as “**debian10-buster**”
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Hostname-During-Debian10-Installation.jpg>
Specify the domain name that suits to environment and installation and then click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Domain-Name-During-Debian10-Installation.jpg>
### Step:5) Specify the root user’s password
Specify the root password in the screen below and then click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Root-Password-Debian10-Installation.jpg>
### Step:6) Create Local User and its password
In this step you will be prompted to specify local user details like full name, user name and its password,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Local-User-FullName-Debian10.jpg>
Click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/UserName-LocalUser-Debian10-Installation.jpg>
Click on Continue and specify the password in the next window
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Password-Localuser-Debian10.jpg>
### Step:7) Choose Hard Disk Partitioning Scheme for Debian 10
In this step, select partitioning scheme for Debian 10, in my case I have 40 GB hard disk available for OS installation. Partitioning scheme is of two types:
* Guided Partitioning (Installer will automatically create required partitions)
* Manual Partitioning (As name suggests using this we can create manual partitions scheme)
In this tutorial we will use guided partitions with LVM on my 42 GB hard drive,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Choose-Guided-Partitioning-Scheme-Debain10-Installation.jpg>
Click on Continue to proceed further,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Hard-Disk-Debian10-Installation.jpg>
As we can see I have around 42 GB hard disk space, so choose Continue
In the next screen, you will be asked to choose partitions, if are a new Linux user then choose the first option and in case you want a separate home partition then choose second option and else third option, which will create a separate partition for /home, /var and /tmp.
In my case I am going to create separate partition for /home, /var and /tmp by choosing the third option.
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Guided-Separate-Partitions-Debian10-Installation.jpg>
In the next screen select “**yes**” to write changes to disk and Configure LVM and then click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Write-Changes-Disk-Debian10-Installation.jpg>
In the next screen, partition table will be displayed, cross verify the partitions size, file system type and mount point.
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Debian10-Partition-Table.jpg>
Click on Continue to proceed further,
In the next screen, choose “yes” to write changes to disk,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Choose-Yes-Write-Changes-Disk-Debian10-Installation.jpg>
Click on Continue to proceed with installation,
### Step:7) Debian 10 Installation Started
In this step, Installation of Debian 10 has been started and is in progress,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Debian10-Installation-Progress.jpg>
During the installation, installer will prompt you to scan CD/DVD for configuring package manager, Choose No and then click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Scan-DVD-Debian10-Installation.jpg>
In the next screen choose “yes” if you want to configure Package Manager based on Network but for this to work, make sure your system is connected to Internet, else Choose No
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Network-Mirror-Debian10-Package-Manager.jpg>
Click on continue to configure package manager based on your location, In next couple of screens you will be prompted to choose location and Debian package repository URL, then you will get below screen
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Apt-Config-Debian10-Installation.jpg>
Choose “No” to skip package survey step and then click on Continue
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Pkg-Survey-Debian10-Installation.jpg>
In the next window, you will be prompted to choose Desktop environment and other packages, in my case I am selecting **Gnome Desktop**, **SSH Server** and **Standard System utilities**
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Software-Selection-Debian10-Installation.jpg>
Click on Continue to proceed with Installation,
Choose the option “yes” to install Grub Boot Loader
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Install-Grub-Bootloader-Debian10-Installation.jpg>
Click on Continue to proceed further, then in the next window choose the disk (/dev/sda) on which bootloader will be installed
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Install-grub-on-dev-sda-debian10.jpg>
Click on Continue to proceed with installation, Once the installation is completed then installer will prompt us to reboot the system,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Installation-Completed-Debain10.jpg>
Click on Continue to reboot your system and don’t forget to change boot medium from Bios settings so that system boots up with hard disk on which we have installed Debian 10 OS.
### Step:8) Boot your newly installed system with Debian 10
Once we reboot the system after the successful installation we will get below bootloader screen
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Bootloader-Screen-Debian10.jpg>
Choose the first option “**Debian GNU/Linux**” and hit enter,
Once the system boots up, use the same local user and its password that we have created during the installation,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Local-User-Debian10-Login.jpg>
Following will be Desktop screen after successful login,
<https://www.linuxtechi.com/wp-content/uploads/2019/07/Desktop-Screen-Debian10-Buster.jpg>
This confirms that Debian 10 has been installed successfully, that’s all from this article, explore this exciting Linux Distribution and have fun 😊
--------------------------------------------------------------------------------
via: https://www.linuxtechi.com/debian-10-buster-installation-guide/
作者：[Pradeep Kumar][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.linuxtechi.com/author/pradeep/
[b]: https://github.com/lujun9972

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: translator: (geekpi)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (The case for making the transition from sysadmin to DevOps engineer)
[#]: via: (https://opensource.com/article/19/7/devops-vs-sysadmin)
[#]: author: (Taz Brown https://opensource.com/users/heronthecli)
The case for making the transition from sysadmin to DevOps engineer
======
There's a learning curve, but there's no time like the present to get
started.
![Different color butterflies][1]
The year is 2019, and [DevOps][2] is the hot topic. The day of the system administrator (sysadmin) has gone the way of mainframes if you will—but really, has it? The landscape has shifted as it so often does in technology. There is now this thing called DevOps, which can’t exist without Ops.
I considered myself on the Ops side of the aisle prior to the evolution of DevOps as we know today. As a system administrator or engineer, it feels like you are stuck in a time warp, with a small tinge of fear because what you knew and must learn varies greatly, and is now much more time-sensitive than you might have anticipated.
![Sysadmin/DevOps workstation][3]
Why is this situation a problem? Well, it is not so much a problem as it is a bit of a barrier, at first. Web-scale products are built on Linux and other open source software, and the market for skilled professionals to maintain them is drying up. The demand has surpassed the available pool of talent. As a system administrator, you can no longer continue to operate at your current skill level. You need automation skills to manage large server/node environments, as well as to understand how everything works so you know what’s going wrong, and when and how to heal said environments.
The path you must follow to get to DevOps is paved with many twists and turns as you learn the new technologies and tools needed to support the ever-changing environments the new DevOps way. So what is it like, or how can one transition from the system administrator mentality and world to the way of DevOps? Not surprisingly, this process begins with your thinking. It is not easy to change the way you have been doing things the last 10 or 20 years, but it’s mandatory.
To start, embrace the idea that DevOps is not a position, but a set of practices. These practices lead to cohesion, breaking down silos, mitigating mistakes and bugs, more frequent and timely software life cycles, better communication between Dev and Ops, and constant testing and retesting for not only the code but the whole [continuous integration and delivery (CI/CD)][4] process.
Along with changing your mindset comes acquiring the necessary skills in order to sustain and support your infrastructure, and ensure its reliability and availability to continuously integrate and deliver applications, services, and software.
One area that you, as an Ops person, may be lacking is programming or coding skills. The way that sysadmins script as part of automating server patching, managing user accounts and files, and troubleshooting and documenting problems, is considered somewhat antiquated. Though scripting is still used on a smaller scale today, DevOps is about large-scale implementation, testing, building, and deploying.
When you address automation, you have to address your probable weaknesses, which can be intimidating with the evolution of DevOps and [infrastructure automation][5] requiring that you be programmatic when you are not a developer.
What’s the solution? You have to learn at least one programming language—such as Python—in order to stay relevant and competitive. It can be hard as an Ops professional, though, to shake the feeling that programming is for developers, and while you don’t have to acquire expert programming knowledge, it’s advantageous to know how to script, whether it be in Python, Bash, or even [Powershell][6].
Learning some programming so you aren’t in the weeds when working with the developers on the DevOps team, or with a client as a consultant, will demand your time and attention. Whether it’s 30 minutes or an hour a day, learning this skill has to become a priority.
While there are common tasks among sysadmins and DevOps, there are some vital differences. Some have argued that a _sysadmin_ is more focused on configuring, maintaining, and keeping servers computer systems up and that while a _DevOps_-principled engineer can do everything a _sysadmin_ does, a _sysadmin_ cannot do everything a _DevOps_-principled engineer does.
Does this opinion hold water?
### System administration: One is the loneliest number
While this article discusses the differences and similarities between system administration and DevOps, my belief is that there are really no major differences between them. System administrators have always performed the functions that DevOps have; they just didn’t call it DevOps back then. I think it’s important not to differentiate things for the sake of differentiating when it’s not entirely called for at all. You have to remember that DevOps is not a job title or position as a system administrator is, but a descriptor.
I have to mention this because it would do DevOps and system administration a disservice not to point this out: System administration, in its traditional sense, involves possessing a certain set of skills and a focus on differing infrastructures. Not a one glove fits all proposition, but there are many common functional tasks carried out by sysadmins.
Some of the traditional sysadmin tasks include being a handyman or woman of sorts, with no specialization. You might be the only sysadmin in your organization, so you are a Jack or Jill of all trades. You do everything from maintaining the printers and copy machines to performing network-related tasks like configuring and managing routers and switches, as well as setting firewall policies and rules.
You are also responsible for upgrading hardware, checking and analyzing logs, security auditing, patching servers, troubleshooting, performing root cause analysis, and automating—usually through PowerShell scripting, Python, or Bash scripting. One example of [scripting][7] is with user and group account management. Creating users and setting permissions can be a tedious task since users come and go almost every day. Scripting means freeing up more time for larger scale infrastructure projects like switch and server refreshes, and other revenue-generating projects, though IT is often perceived to be a cost center.
The goal of a sysadmin is not to waste time, and to save money any and every way possible. There are also sysadmins that work as part of a larger team with, say, Linux admins, Windows admins, database admins, storage admins, and so on. You might be on a traditional follow-the-sun schedule, or maybe a traditional 9 to 5 schedule, or maybe you work in a 24-hour datacenter.
Sysadmins have had to evolve their state of mind over the years and think more strategically, considering the business in unison with their day-to-day responsibilities. The teams and departments they work with are challenged by lack of resources while trying to constantly stay aligned with the day-to-day business parameters.
### DevOps: Development and operations are one
[DevOps][8] is considered to be a philosophy in which IT, operations (Ops), and development is done. This way of looking at things is arguably the biggest game-changer to IT. Under the umbrella of DevOps are a team of software developers on one side of the aisle, and a team of operations folks on the other. Huddled in the same area are likely a product management team, a QA team, and a UX design team. These teams combine strengths to streamline and stabilize operations for rolling out new apps, and update the code to support and improve the whole business.
At the core of DevOps is the software lifecycle development process. As operations is responsible for supporting developers, developers are tasked with knowing more than just the APIs that execute on the systems and their operating systems. They must also understand what’s under the hood and running their software—the hardware and operating system(s)—so they can better handle bug issues, solve problems, and communicate with operations.
Sysadmins have the ability to transition to a DevOps team, as long as they are willing to learn current and emerging technologies, and are open to innovative ideas and solutions. These sysadmins don’t have to be full-blown programmers if they come from a traditional operations background, but learning a programming language like Ruby, Python, or Go will help to galvanize their place on the DevOps team. While sysadmins have traditionally been more alone in their day-to-day work and often thought of as loners, this is the complete opposite experience needed for an agile team applying DevOps principles.
The topic of automation has become more and more important. Both sysadmins and DevOps are interested in scaling quickly, reducing errors, and finding and resolving existing errors fast. So automation is a similarity among these two fields. Sysadmins are responsible for cloud services like AWS, Azure, and Google Cloud Platform. They have to understand CI/CD pipelines and how to carry them out by using [Jenkins][9], for instance.
Also, sysadmins need to use configuration and orchestration tools like [Ansible][10], which is used to deploy ten or twenty servers in parallel. The premise is [Infrastructure as Code][11]. Everything is software, and software is everything. Essentially, some rethinking is necessary if the sysadmin of the future is to remain relevant. SysAdmins come from the Ops side and have to be able to effectively work with developers and vice versa. Two heads are definitely better than one.
One last important piece of the puzzle is [Git][12]. Git has not traditionally been a part of a sysadmin's day-to-day responsibilities. This version control management system is used heavily by software engineering teams, DevOps, development teams, agile teams, and more. If you are working within the software lifecycle development process, you will work with it.
Git is vast. You might never learn every Git command, but you will come to understand that this tool is the nucleus of collaboration, communication, and software production. Having a fundamental knowledge of Git will be important if you are on a DevOps team.
If you are a sysadmin, you will need to level up your Git knowledge, understand the psychology of version control, and learn those common commands like **git status**, **git commit -m**, **git add**, **git pull**, **git push**, **git rebase**, **git branch**, **git diff**, and beyond. There are plenty of online training courses and books on the subject so that you can go from a novice to a pro with some dedicated focus. There are also [Git cheat sheets][13] out there as well so you don’t have to remember every command, but the more you use Git the better for you.
### Conclusion
Ultimately, it's up to you whether you want to remain a sysadmin or transition to DevOps. As you can see, there's a learning curve, but there's no time like the present to get started. Pick a programming language to learn, and while you're learning it, take advantage of the chance to [learn Git][14], a CI/CD tool like [Jenkins][15], and a Configuration and IT Automation tool like [Ansible][16]. Whatever you decide, make sure to always be learning and labbing.
--------------------------------------------------------------------------------
via: https://opensource.com/article/19/7/devops-vs-sysadmin
作者：[Taz Brown][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/heronthecli
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/bug-insect-butterfly-diversity-inclusion.png?itok=msS3ceW4 (Different color butterflies)
[2]: https://opensource.com/resources/devops
[3]: https://opensource.com/sites/default/files/uploads/sysadmindevopsworkstation_600px.png (Sysadmin/DevOps workstation)
[4]: https://en.wikipedia.org/wiki/CI/CD
[5]: https://www.ibm.com/developerworks/library/a-devops2/index.html
[6]: https://docs.microsoft.com/en-us/powershell/scripting/overview?view=powershell-6
[7]: https://www.geeksforgeeks.org/introduction-to-scripting-languages/
[8]: https://devops.com/
[9]: https://jenkins.io/
[10]: https://en.wikipedia.org/wiki/Ansible_(software)
[11]: https://en.wikipedia.org/wiki/Infrastructure_as_code
[12]: https://git-scm.com/
[13]: https://github.github.com/training-kit/downloads/github-git-cheat-sheet.pdf
[14]: https://opensource.com/life/16/7/stumbling-git
[15]: https://opensource.com/article/18/11/getting-started-jenkins-x
[16]: https://opensource.com/article/19/2/quickstart-guide-ansible

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Clear is better than clever)
[#]: via: (https://dave.cheney.net/2019/07/09/clear-is-better-than-clever)
[#]: author: (Dave Cheney https://dave.cheney.net/author/davecheney)
Clear is better than clever
======
_This article is based on my_ [_GopherCon Singapore 2019_][1] _presentation. In the presentation I referenced material from my post [on declaring variables][2] and my [GolangUK 2017 presentation on SOLID design][3]. For brevity those parts of the talk have been elided from this article. If you prefer, you can [watch the recording of the talk][4]._
* * *
Readability is often cited as one of Go’s core tenets, I disagree. In this article I’ll discuss the differences between clarity and readability, show you what I mean by clarity and how it applies to Go code, and argue that Go programmers should strive for clarity–not just readability–in their programs.
### Why would I read your code?
Before I pick apart the difference between clarity and readability, perhaps the question to ask is, “why would I read your code?” To be clear, when I say _I_, I don’t mean me, I mean you. And when I say _your code_ I also mean you, but in the third person. So really what I’m asking is, “why would _you read_ another person’s code?”
I think Russ Cox, paraphrasing Titus Winters, put it best:
> Software engineering is what happens to programming when you add time and other programmers.
*–*Russ Cox, GopherCon Singapore 2018
The answer to the question, “why would I read your code” is, because we have to work together. Maybe we don’t work in the same office, or live in the same city, maybe we don’t even work at the same company, but we do collaborate on a piece of software, or more likely consume it as a dependency.
This is the essence of Russ and Titus’ quote; software engineering is the collaboration of software engineers over time. I have to read your code, and you read mine, so that I can understand it, so that you can maintain it, and in short, so that any programmer can change it.
Russ is making the distinction between software programming and software engineering. The former is a program you write for yourself, the latter is a program—​a project, a service, a product—​that many people will contribute to over time. Engineers will come and go, teams will grow and shrink, requirements will change, features will be added and bugs fixed. This is the nature of software engineering.
### We don’t read code, we decode it
> It was sometime after that presentation that I finally realized the obvious: Code is not literature. We don’t read code, we _decode_ it.
_–_[Peter Seibel][5]
The author Peter Seibel suggests that programs are not read, but are instead decoded. In hindsight this is obvious, after all we call it source code, not source literature. The source code of a program is an intermediary form, somewhere between our concept — what’s inside our heads — and the computer’s executable notation.
In my experience, the most common complaint when faced with a foreign codebase written by someone, or some team, is the code is unreadable. Perhaps you agree with me?
But readability as a concept is subjective. Readability is nit picking about line length and variable names. Readability is holy wars about brace position. Readability is the hand to hand combat of style guides and code review guidelines that regulate the use of whitespace.
### Clarity ≠ Readability
Clarity, on the other hand, is the property of the code on the page. Clear code is independent of the low level details of function names and indentation because clear code is concerned with what the code is doing, not just how it is written down.
When you or I say that a foreign codebase is unreadable, what I think what we really mean is, _I don’t understand it_. For the remainder of this article I want to try to explore the difference between clear code and code that is simply readable, because the goal is not how quickly you can read a piece of code, but how quickly you can grasp its meaning.
### Keep to the left
Go programs are traditionally written in a style that favours guard clauses and preconditions. This encourages the success path to proceed down the page, rather than indented inside a conditional block. Mat Ryer calls this [line of sight coding][6], because, the active part of your function is not at risk of sliding out of sight beyond the right hand margin of your screen.
By keeping conditional blocks short, and for the exceptional condition, we avoid nested blocks and potentially complex value shadowing. The successful flow of control continues down the page. At every point in the sequence of instructions, if you’ve arrived at that point, you are confident that a growing set of preconditions holds true.
```
func ReadConfig(path string) (*Config, error) {
      f, err := os.Open(path)
if err != nil {
return nil, err
}
defer f.Close()
       // ...
}
```
The canonical example of this is the classic error check idiom; `if err != nil` then return it to the caller, else continue with the function. We can generalise this pattern a little and in pseudocode we have:
```
if some condition {
// true: cleanup
return
}
// false: continue
```
If _some condition_ is true, then return to the caller, else continue onwards towards the end of the function. 
This form holds true for all preconditions, error checks, map lookups, length checks, and so forth. The exact form of the precondition’s check changes, but the pattern is always the same; the cleanup code is inside the block, terminating with a return, the success condition lies outside the block, and is only reachable if the precondition is false.
Even if you are unsure what the preceding and succeeding code does, how the precondition is formed, and how the cleanup code works, it is clear to the reader that this is a guard clause.
### Structured programming
Here we have a `comp` function that takes two `int`s and returns an `int`;
```
func comp(a, b int) int {
if a < b {
return -1
}
if a > b {
              return 1
       }
       return 0
}
```
The `comp` function is written in a similar form to guard clauses from earlier. If `a` is less than `b`, the return -1 path is taken. If `a` is greater than `b`, the return 1 path is taken. Else, `a` and `b` are by induction equal, so the final return 0 path is taken.
```
func comp(a, b int) int {
if condition A {
body A
}
       if condition B {
                body B
       }
       return 0
}
```
The problem with `comp` as written is, unlike the guard clause, someone maintaining this function has to read all of it. To understand when 0 is returned, we have to read the conditions _and the body_ of each clause. This is reasonable when you’re dealing with functions which fit on a slide, but in the real world complicated functions–​the ones we’re paid for our expertise to maintain–are rarely slide sized, and their conditions and bodies are rarely simple.
Let’s address the problem of making it clear under which condition 0 will be returned:
```
func comp(a, b int) int {
if a < b {
return -1
} else if a > b {
               return 1
       } else {
               return 0
       }
}
```
Now, although this code is not what anyone would argue is readable–​long chains of `if else if` statements are broadly discouraged in Go–​it is clearer to the reader that zero is only returned if none of the conditions are met.
How do we know this? The Go spec declares that each function that returns a value must end in a terminating statement. This means that the body of all conditions must return a value. Thus, this does not compile:
```
func comp(a, b int) int {
        if a > b {
                a = b // does not compile
        } else if a < b {
              return 1
      } else {
              return 0
       }
}
```
Now it is clear to the reader that this code isn’t actually a series of conditions, instead this is an example of selection. Only one path can be taken regardless of the operation of the condition blocks. Based on the inputs one of -1, 0, or 1 will always be returned. 
```
func comp(a, b int) int {
      if a < b {
              return -1
      } else if a > b {
              return 1
      } else {
              return 0
      }
}
```
However this code is hard to read as each of the conditions is written differently, the first is a simple `if a < b`, the second is the unusual `else if a > b`, and the last conditional is actually unconditional.
But it turns out there is a statement which we can use to make our intention much clearer to the reader; `switch`.
```
func comp(a, b int) int {
        switch {
        case a < b:
                return -1
        case a > b:
                return 1
        default:
                return 0
        }
}
```
Now it is clear to the reader that this is a selection. Each of the selection conditions are documented in their own case statement, rather than varying `else` or `else if` clauses.
By moving the default condition inside the switch, the reader only has to consider the cases that match their condition, as none of the cases can fall out of the switch block because if the default clause.1
> Structured programming submerges _structure_ and emphasises _behaviour_
*–Richard Bircher, *[_The limits of software_][7]
I found this quote recently and I think it is apt. My arguments for clarity are in truth arguments to emphasise the behaviour of the code, rather than be side tracked by minutiae of the structure itself. Said another way, what is the code is trying to do, _not how it is trying to do it_.
### Guiding principles
I opened this article with a discussion of readability vs clarity and hinted that there were other principles of well written Go code. It seems fitting to close on a discussion of those other principles.
Last year [Bryan Cantrill gave a wonderful presentation on operating system principles][8], wherein he highlighted that different operating systems focus on different principles. It is not that they ignore the principles that differ between their competitors, just that when the chips are down, they prioritise a core set. So what is that core set of principles for Go?
#### Clarity
If you were going to say readability, hopefully I’ve provided you with an alternative.
> Programs must be written for people to read, and only incidentally for machines to execute.
_–Hal Abelson and Gerald Sussman_. _Structure and Interpretation of Computer Programs_
Code is read many more times than it is written. A single piece of code will, over its lifetime, be read hundreds, maybe thousands of times. It will be read those hundreds or thousands of times because it must be understood. Clarity is important because all software, not just Go programs, is written by people to be read by other people. The fact that software is also consumed by machines is secondary.
> The most important skill for a programmer is the ability to effectively communicate ideas.
_–Gastón Jorquera_
Legal documents are double spaced to aide the reader, but to the layperson that does nothing to help them comprehend what they just read. Readability is a property of how easy it was to read the words on the screen. On the other hand, clarity is the answer to the question “did you understand what you just read?”.
If you’re writing a program for yourself, maybe it only has to run once, or you’re the only person who’ll ever see it, then do what ever works for you. But if this is a piece of software that more than one person will contribute to, or that will be used by people over a long enough time that requirements, features, or the environment it runs in may change, then your goal must be for your program to be maintainable.
The first step towards writing maintainable code is making sure intent of the code is clear.
#### Simplicity
The next principle is obviously simplicity. Some might argue the most important principle for any programming language, perhaps the most important principle full stop.
Why should we strive for simplicity? Why is important that Go programs be simple?
> The ability to simplify means to eliminate the unnecessary so that the necessary may speak
_–Hans Hofmann_
We’ve all been in a situation where you say “I can’t understand this code”. We’ve all worked on programs we were scared to make a change because we were worried it’ll break another part of the program; a part you don’t understand and don’t know how to fix. 
This is complexity. Complexity turns reliable software in unreliable software. Complexity is what kills software projects. Clarity and simplicity are interlocking forces that lead to maintainable software.
#### Productivity
The last Go principle I want to highlight is productivity. Developer productivity boils down to this; how much time do you spend doing useful work verses waiting for your tools or hopelessly lost in a foreign code-base. Go programmers should feel that they can get a lot done with Go.
> “I started another compilation, turned my chair around to face Robert, and started asking pointed questions. Before the compilation was done, we’d roped Ken in and had decided to do something.”
_–Rob Pike, [Less is Exponentially more][9]_ [][9]
The joke goes that Go was designed while waiting for a C++ program to compile. Fast compilation is a key feature of Go and a key recruiting tool to attract new developers. While compilation speed remains a constant battleground, it is fair to say that compilations which take minutes in other languages, take seconds in Go. This helps Go developers feel as productive as their counterparts working in dynamic languages without the maintenance issues inherent in those languages.
> Design is the art of arranging code to work _today_, and be changeable  _forever._
_–Sandi Metz_
More fundamental to the question of developer productivity, Go programmers realise that code is written to be read and so place the act of reading code above the act of writing it. Go goes so far as to enforce, via tooling and custom, that all code be formatted in a specific style. This removes the friction of learning a project specific dialect and helps spot mistakes because they just look incorrect.
Go programmers don’t spend days debugging inscrutable compile errors. They don’t waste days with complicated build scripts or deploying code to production. And most importantly they don’t spend their time trying to understand what their coworker wrote.
> Complexity is anything that makes software hard to understand or to modify.
_–John Ousterhout_, [A Philosophy of Software Design][10]
Something I know about each of you reading this post is you will eventually leave your current employer. Maybe you’ll be moving on to a new role, or perhaps a promotion, perhaps you’ll move cities, or follow your partner overseas. Whatever the reason, we must all consider the succession of the maintainership of the programs we create.
If we strive to write programs that are clear, programs that are simple, and to focus on the productivity of those working with us that will set all Go programmers in good stead.
Because if we don’t, then as we move from job to job we’ll leave behind programs which cannot be maintained. Programs which cannot be changed. Programs which are too hard to onboard new developers, and programs which feel like career digression for those that work on them.
If software cannot be maintained, then it will be rewritten; and that could be the last time your company invests in Go.
1. The `fallthrough` keyword complicates this analysis, hence the general disapproval of `fallthrough` in switch statements.
#### Related posts:
1. [Accidental method value][11]
2. [Unhelpful abstractions][12]
3. [What is the zero value, and why is it useful?][13]
4. [Let’s talk about logging][14]
--------------------------------------------------------------------------------
via: https://dave.cheney.net/2019/07/09/clear-is-better-than-clever
作者：[Dave Cheney][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://dave.cheney.net/author/davecheney
[b]: https://github.com/lujun9972
[1]: https://2019.gophercon.sg/
[2]: https://dave.cheney.net/2014/05/24/on-declaring-variables
[3]: https://dave.cheney.net/2016/08/20/solid-go-design
[4]: https://www.youtube.com/watch?v=NwEuRO_w8HE
[5]: http://www.gigamonkeys.com/code-reading/
[6]: https://medium.com/@matryer/line-of-sight-in-code-186dd7cdea88
[7]: https://www.amazon.com/Limits-Software-People-Projects-Perspectives/dp/0201433230
[8]: https://www.slideshare.net/bcantrill/platform-values-rust-and-the-implications-for-system-software
[9]: https://commandcenter.blogspot.com/2012/06/less-is-exponentially-more.html
[10]: https://www.amazon.com/Philosophy-Software-Design-John-Ousterhout/dp/1732102201/ref=sr_1_3?ie=UTF8&qid=1524677319&sr=8-3&keywords=john+ousterhout
[11]: https://dave.cheney.net/2014/05/19/accidental-method-value (Accidental method value)
[12]: https://dave.cheney.net/2016/02/06/unhelpful-abstractions (Unhelpful abstractions)
[13]: https://dave.cheney.net/2013/01/19/what-is-the-zero-value-and-why-is-it-useful (What is the zero value, and why is it useful?)
[14]: https://dave.cheney.net/2015/11/05/lets-talk-about-logging (Let’s talk about logging)

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[#]: collector: (lujun9972)
[#]: translator: (geekpi)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Copy and paste at the Linux command line with xclip)
[#]: via: (https://opensource.com/article/19/7/xclip)
[#]: author: (Scott Nesbitt https://opensource.com/users/scottnesbitt)
使用 xclip 在 Linux 命令行中复制粘贴
======
了解如何在 Linux 中使用 xclip。
![Green paperclips][1]
在使用 Linux 桌面工作时，你通常如何复制全部或部分文本？你可能会在文本编辑器中打开文件，选择全部或仅选择要复制的文本，然后将其粘贴到其他位置。
这样没问题。但是你可以使用 [xclip][2] 在命令行中更有效地完成工作。xclip 提供了在终端窗口中运行的命令与 Linux 图形桌面环境中的剪贴板之间的管道。
### 安装 xclip
xclip 并不是许多 Linux 发行版的标准套件。要查看它是否已安装在你的计算机上，请打开终端窗口并输入 **which xclip**。如果该命令返回像 _/usr/bin/xclip_ 这样的输出，那么你可以开始使用了。否则，你需要安装 xclip。
为此，请使用你的发行版的包管理器。如果你喜欢冒险，你可以[从 GitHub 获取源代码][2]并自己编译。
### 基础使用
假设你要将文件的内容复制到剪贴板。在 xclip 中可以使用两种方法。输入：
```
`xclip file_name`
```
或者
```
`xclip -sel clip file_name`
```
两个命令之间有什么区别（除了第二个命令更长）？第一个命令在你如果使用鼠标中键粘贴的情况下有效。但是，不是每个人都这样做。许多人习惯使用右键单击菜单或按 Ctrl+V 粘贴文本。如果你时其中之一（我就是！），使用 **-sel clip** 选项可确保你可以粘贴要粘贴的内容。
### 将 xclip 与其他应用一起使用
将文件内容直接复制到剪贴板是个巧妙的技巧。很可能你不会经常这样做。还有其他方法可以使用 xclip，其中包括将其与另一个命令行程序结合。
结合是用_管道_（|）完成的。管道将一个命令行程序的输出重定向到另一个命令行程序。这样我们就会有更多的可能性，我们来看看其中的三个。
假设你是系统管理员，你需要将日志文件的最后 30 行复制到 bug 报告中。在文本编辑器中打开文件，向下滚动到最后，复制和粘贴有一点工作量。为什么不使用 xclip 和 [tail][3] 来快速轻松地完成？运行此命令以复制最后 30 行：
```
`tail -n 30 logfile.log | xclip -sel clip`
```
我的写作有相当一部分用于内容管理系统 （CMS） 或者在其他网络中发布。但是，我从不使用 CMS 的 WYSIWYG 编辑器来编写 - 我离线采用 [Markdown][5] 格式编写[纯文本][4]。也就是说，许多编辑器都有 HTML 模式。通过使用此命令，我可以使用 [Pandoc][6] 将 Markdown 格式的文件转换为 HTML 并将其一次性复制到剪贴板：
```
`pandoc -t html file.md | xclip -sel clip`
```
在其他地方，我粘贴完成。
我的两个网站使用 [GitLab Pages][7 ]托管。我使用名为 [Certbot][8] 的工具为这些站点生成 HTTPS 证书，每当我更新它时，我需要将每个站点的证书复制到 GitLab。结合 [cat][9] 命令和 xclip 比使用编辑器更快，更有效。例如：
```
`cat /etc/letsencrypt/live/website/fullchain.pem | xclip -sel clip`
```
这就是全部可以用 xclip 做的事么？当然不是。我相信你可以找到更多用途来满足你的需求。
### 最后总结
不是每个人都会使用 xclip。没关系。然而，它是一个在你需要它时非常方便的一个小工具。而且，正如我几次发现的那样，你不知道什么时候需要它。等到时候，你会很高兴 xclip 在那里。
--------------------------------------------------------------------------------
via: https://opensource.com/article/19/7/xclip
作者：[Scott Nesbitt][a]
选题：[lujun9972][b]
译者：[geekpi](https://github.com/geekpi)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/scottnesbitt
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/life_paperclips.png?itok=j48op49T (Green paperclips)
[2]: https://github.com/astrand/xclip
[3]: https://en.wikipedia.org/wiki/Tail_(Unix)
[4]: https://plaintextproject.online
[5]: https://gumroad.com/l/learnmarkdown
[6]: https://pandoc.org
[7]: https://docs.gitlab.com/ee/user/project/pages/
[8]: https://certbot.eff.org/
[9]: https://en.wikipedia.org/wiki/Cat_(Unix)