Madyson  Reilly

Madyson Reilly


Generate Code from JSON Schema Files


Generate code from JSON schema files.

Table of contents


This is a command line tool to take a json-schema file and generate code automatically.

For instance this json-schema definition:

  "$schema": "",
  "title": "Test",
  "type": "object",
  "properties": {
    "id": { "type": "integer" }

will generate this Python code:

class Test(object):
    def __init__(self, data=None):
        data = data or {} = data.get("id")

or this JavaScript+Flow code:

export class Test {
  id: ?number;

  constructor(data: Object = {}) { =;

Currently this tool generates code for Python and JavaScript with Flow annotations but it can be extended to generate code for any language.

The code generation is divided in two stages:

  1. generate the AST for the target language from the json-schema file
  2. convert the AST into the target language

This allows the tool to be language agnostic, that is it just needs to generate the AST in JSON format for the target language and then a language specific tool will convert this AST into proper code.

Currently supported languages

List of currently supported languages:

  • Python 2.7+
  • JavaScript ES7+ with Flow annotations
  • pure Flow annotations


  • Python 3.x
  • Node v8.4+


Until this pull request in setuptools is fixed, the only way to install json-schema-codegen is to clone the repo:

git clone


usage: [-h] [--prefix PREFIX] [--language LANGUAGE]
                       [--output OUTPUT]

positional arguments:
  schema                Definition of the PRD as JSON schema

optional arguments:
  -h, --help            show this help message and exit
  --prefix PREFIX, -p PREFIX
                        Optional prefix for generated classes
  --language LANGUAGE, -l LANGUAGE
                        Output language. Default is python
  --output OUTPUT, -o OUTPUT
                        Output filename for the generated code

Code generation

Python 2

The generation of Python’s code is integrated into the tool so it needs just a single invocation:

json_codegen --language python --output <output_py_file> <json-schema>

Python 3

The egenerator of pure Python 3 compatible code:

json_codegen --language python3 --output <output_py_file> <json-schema>

Python 3+Marshmallow

The generation of Python 3’s code with Marshmallow support is integrated into the tool so it needs just a single invocation:

json_codegen --language python3+marshmallow --output <output_py_file> <json-schema>

JavaScript+Flow and Flow

Generating JavaScript+Flow and Flow code involves two steps, generating the AST:

json_codegen --language [javascript+flow|flow] --output <output_ast_json> <json-schema>

and generating the code from the AST:

bin/ast_to_js <output_ast_json> <output_js_file>

Download Details:

Author: expobrain
Download Link: Download The Source Code
Official Website:
License: MIT

#json #developer #python

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Generate Code from JSON Schema Files
Brain  Crist

Brain Crist


SCHEMAS in SQL Server -MS SQL Server – Zero to Hero Query Master


This is part 3 of “MS SQL Server- Zero to Hero” and in this article, we will be discussing about the SCHEMAS in SQL SERVER. Before getting into this article, please consider to visit previous articles in this series from below,

A glimpse of previous articles
Part 1

In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.

Part 2
  • We learned to create a database and maintain it using SQL statements.
  • Best practice methods were also mentioned.

#sql server #benefits of schemas #create schema in sql #database schemas #how to create schema in sql server #schemas #schemas in sql server #sql server schemas #what is schema in sql server

Brandon  Adams

Brandon Adams


What is JSON? | JSON Objects and JSON Arrays | Working with JSONs Tutorial

In this video, we work with JSONs, which are a common data format for most web services (i.e. APIs). Thank you for watching and happy coding!

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Quick Code Tutorials:

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#jsons #json arrays #json objects #what is json #jsons tutorial #blondiebytes

Tyrique  Littel

Tyrique Littel


Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer


We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:


The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:



import io


import tokenize



code = b"color = input('Enter your favourite color: ')"



for token in tokenize.tokenize(io.BytesIO(code).readline):





TokenInfo(type=62 (ENCODING),  string='utf-8')


TokenInfo(type=1  (NAME),      string='color')


TokenInfo(type=54 (OP),        string='=')


TokenInfo(type=1  (NAME),      string='input')


TokenInfo(type=54 (OP),        string='(')


TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")


TokenInfo(type=54 (OP),        string=')')


TokenInfo(type=4  (NEWLINE),   string='')


TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Dicanio Rol

Dicanio Rol


JSON Schema,, JSON-LD: What’s the Difference?

Recently, I have seen several questions like “what’s the difference between JSON-LD and JSON Schema” or “can I use JSON Schema and”. I come from a linked data background (which is close to the world of but have recently started using JSON Schema a lot and I have to admit that there is no trivial answer to these questions. There is the obvious similarity in the standard names like “Schema” and “JSON”. If you compare the page for  Person to this  example on the JSON Schema page, you have to admit that they kind of look alike. Combine this with the fact that touts JSON-LD, which — by design — very much looks like regular JSON completes the confusion. So there definitely are enough reasons to write this article.

JSON Schema

JSON Schema is to JSON what XML Schema is to XML. It allows you to specify the structure of a JSON document. You can state that the field “email” must follow a certain regular expression or that an address has “street_name”, “number”, and “street_type” fields.  Michael Droettboom’s book  “Understanding JSON Schema” illustrates validation quite nicely with red & green examples.

The main use case for JSON Schema seems to be in JSON APIs where it plays two major roles:

  1. Clients and servers can validate request and response objects in a generic way. This makes development a lot easier, since the implementation can “outsource” these checks to a standard component. Once a message passed the validation, you can safely assume that the document adheres to the rules.
  2. As with any API, documentation is key when developers write code that uses it. JSON Schema is increasingly used to describe the structure of requests and responses by embedding it in an overall API description. Swagger is probably the most prominent example of this paradigm. Consider the pet-store example. Scroll all the way down on the page and you see this JSON Schema definition of “Pet”, which is a basic element in requests and responses of this API (you can find the actual JSON Schema embedded in the raw Swagger file — note that currently there are still some differences between the OpenAPI specification and JSON Schema which will be resolved with OpenAPI 3.1).

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As with all things related to code, reuse is a good idea. JSON Schema has the ability to import schemas using the $ref keyword. There are also efforts to share schemas.  JSON Schema Store is one example. Its main use case is to support syntax highlighting for editors, for instance when editing a swagger file. At the time of writing, it contains over 250 schemas including — drum-roll please / you certainly guessed it — These describe things like  Action and  Place. So the idea could be to centrally define JSON Schema building blocks that can be re-used in different APIs, making it easier to consume them, maybe even to the point where intelligent software can interact with APIs automatically. But before we get carried away, let’s have a look at

#schema #swagger #json-ld #json-schema

Samanta  Moore

Samanta Moore


Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review