notes/session-10.txt at main · daws-88s/notes · GitHub

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Root servers
	13 root servers across the world managed by different organisations and goverments to control TLD's and part of ICANN. DNS resolutions requests from ISP comes to root servers
TLD
	Top level domains, basically who sells the domains. It has the complete details of the domains booked, avaialalbe, buyer information, etc.
Registars
	Who sells the domains, basically mediators between TLD and end users. Who can manage the domain too
NS
	Who is currently managing the domain, we can buy domain with one registar and we can transfer the management to another registar by updating NS records.

A record is mapping the IP address to domain

db.daws88s.online ->

systemctl status
netstat -lntp
ps -ef | grep node
curl http://localhost:8080/health
telnet IP port

frontend.daws88s.online

HTTP status codes
===================

http://daws88s.online/api/transaction

http://daws88s.online/api/transaction
POST

GET
200 OK

100 -> informational
Continuing or switching protocols from HTTPS to HTTP

2XX -> Success
	200 -> OK
	201 -> Created
	204 -> Deleted

3XX -> redirectional
	301 -> URL redirection perm
	302 -> temp redirection
	304 -> Cache

4XX -> Client side error
	404 -> Not found
	400 -> Bad request, invalid JSON
	401 -> Unauthorised, no authentication
	403 -> Forbidden, no permission to this

https://emacet.ap.gov.in/api/results/2026/1234567

https://emacet.ap.gov.in/api/results/2026/ -> GOV admins

{
	"amountt": 200,
	"description": ""
}

5XX -> Server side error

	500 -> Internal server error
	503 -> Service unavailable

Linkedin Blog
===============
Images
crispy text

Types of DNS records
=====================
NS 		-> Nameservers, who is managing the domain
SOA 	-> Start of authority
A 		-> IPv4
AAAA 	-> IPv6
MX 		-> Mail records
TXT 	-> Verification
CNAME	-> Canonical name, another domain

give free for public
paid for business

SQL
======
MySQL
Oracle
Postgress
MSSQL

Schema -> Tables -> Rows and Columns

SQL is not good for huge amount of data

NoSQL
======
MongoDB

data is in documents

{
	"name": "mouse",
	"vendor": "amazon"
}

{
	"name": "mouse",
	"vendor": "amazon",
	"faq": ""
}

proper data structure -> SQL
un proper data structure, huge data -> NoSQL

Redis
======
Caching Database, key value pair data

Backend -> Database
	1. Connecting establish -> Open
	2. Request and response
	3. Connection close

Backend -> Cache(If not exist) -> Connects to DB -> Keep it inside Cache

TTL -> get response

Queue
======
http -> wait for 30 sec, if server is not responding then error

synchronus

joindevops -> ramesh
asynchronus

order place -> delivery details