Skip to main content

Logging for Express - Logging Requests through Morgan Middleware

Logging requests for your Express App can be accomplished using Morgan. It is a simple to use package.

HOW TO INSTALL?

Install Morgan using Node Package Manager (NPM)
npm install --save morgan

HOW TO INCLUDE?

Include Morgan middleware in your application in the following way in the main app.js/server.js
const morgan = require('morgan');

HOW TO USE?

Finally use it as a middleware after creating express instance.
app.use(morgan('combined'));
On running your application, you will see logs being printed on server side console. One of the example of logs is as follows:
::1 - - [28/Jun/2019:06:07:27 +0000] "GET / HTTP/1.1" 304 - "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36"

SAVE LOGS TO FILE

Import fs module into your express app
const fs = require('fs');
Set a constant that will write incoming logs into a file named access.log
const accessLogStream = fs.createWriteStream(
path.join(__dirname, 'access.log'),
{ flags: 'a' }
);
accessLogStream is the constant
fs.createWriteStream writes to the file.
The first argument defines where to write, i.e. the file path.
Second argument { flags: 'a' } defines that the incoming logs to be appended.
Finally feed this file to morgan.
app.use(morgan('combined', { stream: accessLogStream }));

That's it folks, you are set!

Comments

Post a Comment

Popular posts from this blog

Difference between .exec() and .execPopulate() in Mongoose?

Here I answer what is the difference between .exec() and .execPopulate() in Mongoose? .exec() is used with a query while .execPopulate() is used with a document Syntax for .exec() is as follows: Model.query() . populate ( 'field' ) . exec () // returns promise . then ( function ( document ) { console . log ( document ); }); Syntax for .execPopulate() is as follows: fetchedDocument . populate ( 'field' ) . execPopulate () // returns promise . then ( function ( document ) { console . log ( document ); }); When working with individual document use .execPopulate(), for model query use .exec(). Both returns a promise. One can do without .exec() or .execPopulate() but then has to pass a callback in populate.
1 comment
Read more

Python - List - Append, Count, Extend, Index, Insert, Pop, Remove, Reverse, Sort

🐍 Advance List List is widely used and it's functionalities are heavily useful. Append Adds one element at the end of the list. Syntax list1.append(value) Input l1 = [1, 2, 3] l1.append(4) l1 Output [1, 2, 3, 4] append can be used to add any datatype in a list. It can even add list inside list. Caution: Append does not return anything. It just appends the list. Count .count(value) counts the number of occurrences of an element in the list. Syntax list1.count(value) Input l1 = [1, 2, 3, 4, 3] l1.count(3) Output 2 It returns 0 if the value is not found in the list. Extend .count(value) counts the number of occurrences of an element in the list. Syntax list1.extend(list) Input l1 = [1, 2, 3] l1.extend([4, 5]) Output [1, 2, 3, 4, 5] If we use append, entire list will be added to the first list like one element. Extend, i nstead of considering a list as one element, it joins the two lists one after other. Append works in the following way. Input l1 = [1, 2, 3] l1.append([4, 5]) Output...
Post a Comment
Read more

Machine Learning — Supervised, Unsupervised, and Reinforcement — Explanation with Example

🤖 Let's take an example of machine learning and see how it can be performed in three different ways — Supervised, Unsupervised, and Reinforcement. We want a program to be able to identify apple in pictures Supervised Learning You will create or use a model that takes a set of pictures of apple and it analyses the commonality in those pictures. Now when you show a new picture to the program, it will identify whether it has an apple or not. It can also provide details on how confident is the program about it. Unsupervised Learning In this method, you create or use a model that goes through some images and tries to group them as per the commonalities it observes such as color, shape, size, partern, etc. And now you can go through the groups and inform the program what to call them. So, you can inform the program about the group that is apple mostly. Next time you show a picture, it can tell if an apple is there or not. Reinforcement Learning Here the model you create or...
Post a Comment
Read more