A Break Down of HTTP Status Codes

Introduction

HTTP which stands for **Hypertext Transfer Protocol **helps to indicate if a response is successful or not. Status codes are guided by section 10 of RFC 2616. Most of the time, the users do not see the codes returned from the browser, but they are returned every time the browser interacts with the server.

Status codes can help provide a better user experience for the audience using an application, in terms of displaying the right error message and success message.

There are five standard groups Status codes are divided into and they can be identified by the first digit of the code.

These status codes are up to 80 in number, but we will be discussing the ones that are most relevant and used by developers when creating an application.

Informational Response (100–199)

100 (Continue)

At this level, the client should continue with the request. It is an indication that everything is going smooth so far and the request has not yet been rejected by the server.

If the request has however been completed, then the response should be ignored.

101 (Switching Protocol)

At this stage, the **HTTP **response code indicates that the server is switching protocol as requested by a client. The server then makes an upgrade in the response header to indicate that the protocol has been switched.

103 (Early Hints)

This response status code is mainly used to return some response headers before the server returns an HTTP message.

Successful Response (200–299)

200 (OK)

The **HTTP 200 **status code indicates that the response has succeeded. That is, the communication between the client and the server has run smoothly without any bug.

The response gotten will depend on the request method used. This is summarized below:

  • **_GET — _**The resource data has been fetched.
  • HEAD— The headers are in the message body but without the resource data.
  • _POST _— The response will contain the result of the data transmitted to the body.
  • _OPTIONS _— A representation of the response option.
  • _TRACE _— The message body contains the request as received by the server.

201 (Created)

This represents the fact that the request has not only succeeded, but a resource has also been created. This status code usually comes with a POST request, since it describes the resource that is being sent to the server.

202 (Accepted)

This means a client has requested to create something on the server, then the request made is accepted, but is not yet completed because it is still undergoing some processing.

The made request may or may not be acted upon, as it may be disallowed during the processing.

203 (Non-Authoritative Information)

This indicates a successful request, but the entity-header has been modified from that of the origin server. It allows the proxy to send a warning to recipients when a change has been applied.

The use of this status code is not required and is only needed when the response would be anything other than 200 (OK).

204 (No Content)

This means the sent request was received, but there’s no additional data to send in the response payload. The PUT method is mostly used for a 204 response and it is cacheable by default.

205 (Reset Content)

Here the request is processed successfully, however, the response tells the user to reset the document view, so the request returns to the original state which it was received from the server.

#web dev #http #analysis #tutoiral #data analysis

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A Break Down of HTTP Status Codes
Tyrique  Littel

Tyrique Littel

1604008800

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

Outline

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:

Scanning

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:

Python

1

import io

2

import tokenize

3

4

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

5

6

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

7

    print(token)

Python

1

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

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

Samanta  Moore

Samanta Moore

1621137960

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

Houston  Sipes

Houston Sipes

1604088000

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

  • Readability
  • Maintainability
  • Code Quality
  • Premature Optimization

Solutions

  1. Refactor the code
  2. Use better names

Examples

  • Optimized loops

Exceptions

  • Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

Tyrique  Littel

Tyrique Littel

1601046000

Spring Boot — Mapping HTTP Response Status Codes to Custom Error Pages

In the video below, we take a closer look at Spring Boot mapping HTTP response status codes to custom error pages. Let’s get started!

#java #spring boot #http #http response status codes

Fannie  Zemlak

Fannie Zemlak

1604048400

Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

  • Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

  • Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

  • Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company