Sheldon  Grant

Sheldon Grant

1614221465

7 Simple MongoDB/Mongoose Tips Make Your Code Faster

Learn How To Power Up Your MongoDB Or Mongoose With Few Tricks.

Image for post

Photo by  Benjamin Sow on  Unsplash

Last year, somewhere around Christmas, I started my journey as a NodeJS developer with MongoDB. I know in starting. I made many rookie mistakes and spend plenty of hours in StackOverflow and GitHub to find an appropriate solution. After several exhausting experiences. I find small but very useful tricks in MongoDB or mongoose.

1. lean()

When you execute any query in mongoose before the result, mongoose performs hydrate() a model function, which is used to create a new document from existing raw data, pre-saved in the DB. The returned document Is an instance of Mongoose Document class which is much heavy because they have a lot of internal state for change tracking. **lean()**create a shortcut from thehydrate()function and make queries faster and less memory-intensive but the return documents are plain old JavaScript objects (POJOs) not mongoose documents.

// Module that estimates the size of an object in memory
const sizeof = require('object-sizeof');

const normalDoc = await MyModel.findOne();
// To enable the `lean` option for a query, use the `lean()` function.
const leanDoc = await MyModel.findOne().lean();

sizeof(normalDoc); // >= 1000
sizeof(leanDoc); // 86, 10x smaller!

#nodejs #javascript #mongodb #mongoose

What is GEEK

Buddha Community

7 Simple MongoDB/Mongoose Tips Make Your Code Faster
Sheldon  Grant

Sheldon Grant

1614221465

7 Simple MongoDB/Mongoose Tips Make Your Code Faster

Learn How To Power Up Your MongoDB Or Mongoose With Few Tricks.

Image for post

Photo by  Benjamin Sow on  Unsplash

Last year, somewhere around Christmas, I started my journey as a NodeJS developer with MongoDB. I know in starting. I made many rookie mistakes and spend plenty of hours in StackOverflow and GitHub to find an appropriate solution. After several exhausting experiences. I find small but very useful tricks in MongoDB or mongoose.

1. lean()

When you execute any query in mongoose before the result, mongoose performs hydrate() a model function, which is used to create a new document from existing raw data, pre-saved in the DB. The returned document Is an instance of Mongoose Document class which is much heavy because they have a lot of internal state for change tracking. **lean()**create a shortcut from thehydrate()function and make queries faster and less memory-intensive but the return documents are plain old JavaScript objects (POJOs) not mongoose documents.

// Module that estimates the size of an object in memory
const sizeof = require('object-sizeof');

const normalDoc = await MyModel.findOne();
// To enable the `lean` option for a query, use the `lean()` function.
const leanDoc = await MyModel.findOne().lean();

sizeof(normalDoc); // >= 1000
sizeof(leanDoc); // 86, 10x smaller!

#nodejs #javascript #mongodb #mongoose

Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

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

Query of MongoDB | MongoDB Command | MongoDB | Asp.Net Core Mvc

https://youtu.be/FwUobnB5pv8

#mongodb tutorial #mongodb tutorial for beginners #mongodb database #mongodb with c# #mongodb with asp.net core #mongodb

Madaline  Mertz

Madaline Mertz

1623768000

Make Your Python Code Faster

I’m sure you heard a lot of people complaining that Python is so slow. I see people compare Python to C in the context of performance only, but they don’t compare in the context of fast development.

It is a dynamically-typed language meaning its variable types are not predefined, although, this is a double-edged sword as being dynamically-typed is what makes Python such an elegant language. So Python is a slower language to run, but faster to type.

Let’s look at some minor tips that could have a major impact on your overall code performance in the long run.

#optimization #slow #tips #coding #python #make your python code faster