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This article shows you how to carry out the principle analysis of jupyter, the content is concise and easy to understand, it will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

Keyword: tool layer devops

It's hard to characterize something like jupyter, which started as an enhanced python repl environment, then became an CS architecture and supports multiple languages, S is the language kernel,C is notebook,console,qtconsole, and things like that can be deployed separately.

IPython 3.x is the last monolithic version of IPython, including laptop servers, qtconsole, and so on. Starting with IPython 4.0, language-independent parts of the project: notebook format, message protocol, qtconsole, notebook web applications, and so on, have been transferred to a new project called Jupyter. IPython itself focuses on interactive Python, part of which is providing a Python kernel for Jupyter.

For a quick taste of the new, download the py distribution of Anaconda under windows. The first to use ipy4 is the Anaconda2 2.4.0 version of the Anaconda2 series. Of course, we focus on the differences between jupyter system and traditional CS programs:

First of all, it is not an application, but focuses on the language system. To say that it is an application, it is only a "sharp tool for programming education", "a multilingual online IDE", and an application in language systems (so, also a CS application).

Second, it at least has the following characteristics, first of all, the surface, those intuitive things:

Jupyter is a distributed IDE

1. With the language as the back end, the client accepts the execution result of the server and outputs the execution result directly. In terms of cell on the page. 2the two ends of CS compose a distributed DEMO SHOW system.

In short, it is IPython, one of its great advantages is that it can combine the code writing process, the run result display together, and persist in a notebook, and the completion process is supported by jupyter.

Let's talk about something a little deeper:

Jupyter may be a distributed devops computing environment with its own development and release.

It enhances the language IDE, which is a distributed interactive development environment (made into CS and WEB, mostly associated with WEB, the application architecture is already distributed).

It changes the way people collaborate on development. When people release ipynb, they can share source files and execute results without having to download them to their machines and run them once in a local language system. If this result can directly form applications (code block blocks divided into cell can form software like language source files and intra-language modules), it will be enough to bring a strong innovation to the programming world. To give full play to the characteristics of straight men, to put it simply, don't blame me for two points:

First, it changes the way software collaborates so that ugc,ugc=user generated content, where c is coding or codes, enables W component development to go online and store the results directly.

PS: what does that mean?

If github is a place where people submit static source repositories, developers are involved in development in the form of offline. So if there is a jupyter hub, then it is the place where running software is combined to form larger software. This sentence implies the word component, which is a big part of all modern languages. In fact, to put it simply, demo is a component, which can be put into a multiplex of working dropin, and the things that can directly aggregate building blocks from running program components are "components". If these components can be directly integrated online, the running results are also hosted. Then it can immediately produce a "dynamic github". If your app is small enough, one ipynb is enough. In this way, users can program and build APP directly on-line. Because the development language system and the running environment are online, the results only need to be presented online. Users only need to reuse ipynb contribution codes as c in ugc. This is of great use to community applications or game applications that require users to contribute code to complete logic, and it enables manufacturers to connect directly to third-party extenders. It can enrich an application ecology very quickly.

Second, its debuggability minimizes the non-threshold of W-person component development. Because it's a DEMO effect instant show system.

Taken together, it only distributes IDE, and its architecture and product positioning can also be used as "dynamic github" and so on, which is good to understand.

Download address is attached (the name of the software engitor means engitor= "engine tool editor" because the language system supported by jupyter should be applied directly by toolkit. It is a content generation tool and demonstration system for generating programs in editing mode. The software has integrated support for msyscuione/langsys/qtcling and unzipped it to D disk msyscuione after download)

The above content is how to analyze the principle of jupyter. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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