Terminology.md

System

Sop (std operation procedure)
Rca (root cause analysis)
Modular
Scalable
Scheme agnostic (No SqL) : No Schema
Model
Decouple
Infrastructure
Containerised
Abstract
Monolith
Flexible
Coupling
Cohersion : Related to each other Eg:Car and Wheels
Highly available
Serveless
Test Driven
Event Driven
Domain Driven
Message Bus
Adapter

Code

Strictly Type Code
Dataclass
Unique identifier
Value object pattern
Type parameter
Provision
Disguising the intent and source
class memebers
boilerplate
Time Complexity and Space Complexity
race condition

OOPS

ABstraction : Calling class need not no the inner workings of called class
polymorphism (Bird b = new Pigeon();)
Encapsulation : private , protected
Inheritance :

REST

API based
backend
Endpoint
Interface
Loosely coupled : Modifying one does not impact other
Oltp (fast write, 3nf), olap( fast read, dimension model ie., Star scheme)
webhook : external services to be notified when certain events happen

others

increamental
Edge conditions
Min viable prod
Dependency inversion(dip) high level mod don't depend on low lvl
Orm
Dependency
Infrastructure
Pipeline
Build
Deployment
Delegate
Annotations
Defensive code (inside if / try catch)
promise
compliance Issue
Propose
System Designs
mern stack(mongo, express,react,node)
Npm - node package manager
Node js is a compiler for processing javascript outside browsers.
intermittent
reproducible

Authoization

-https://github.com/j-thepac/Authorization

Trunk Based Development vs Gitflow Based

Trunck Based : User creates a development branch and merges to Main/Master
Git Flow: Has Feature branch, HotFix branch , Version Branches ,

Git Rebase vs Git merge vs Squash Merge vs Cherry Pick [Merge Strategies]

Git merge : Creates 1 new commit containing new changes from Feature Branch
Git Rebase: New commits from Feature Branch on top of Main branch
git squash: Remove unwanted commits before final merge git rebase -i HEAD~5 #Change pick to squash

cherry-pick: To use intermitten commit of 1 branch into another

  	git log branch --oneline #copy the hash
  	git cheery-pick hash1 hash2

Fast Validations [all storages , Env ]

Design Components

𝟬. 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿: A load balancer efficiently distributes incoming network traffic across multiple servers to ensure no single server bears too much load.

𝟭. 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆: An API Gateway acts as a single entry point for all client requests, providing a unified interface to various microservices.

𝟮. 𝗖𝗮𝗰𝗵𝗲: Caching stores recently accessed data for speedy retrieval, reducing the load on databases and improving response times.

𝟯. 𝗖𝗗𝗡: A Content Delivery Network (CDN) stores static content closer to users to minimize latency and speed up content delivery.

𝟰. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Distributed file storage ensures data is redundantly stored across multiple locations for fault tolerance and easy access.

𝟱. 𝗦𝗲𝗮𝗿𝗰𝗵 𝗜𝗻𝗱𝗲𝘅: Using a search index like Elasticsearch allows for scalable and fast search capabilities across large datasets.

𝟲. 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: A notification service handles the delivery of messages or alerts to users across different platforms and devices.

𝟳. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Data processing systems like Hadoop and Spark analyze large volumes of data, enabling scalable and complex computational operations.

𝟴. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝗿: A distributed scheduler orchestrates and manages the execution of distributed tasks across various computing resources.

𝟵. 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗦𝗲𝗿𝘃𝗲𝗿: The metadata server stores information about the structure of data, enabling efficient access and management.

𝟭𝟬. 𝗕𝗹𝗼𝗰𝗸 𝗦𝗲𝗿𝘃𝗲𝗿: Block servers manage the storage blocks in distributed file systems, which helps in data replication and retrieval.

𝟭𝟭. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗟𝗼𝗴𝗴𝗶𝗻𝗴: Distributed logging collects logs from various services in a centralized manner for monitoring and troubleshooting.

𝟭𝟮. 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Replication ensures data availability and durability, while partitioning helps in scaling databases by distributing data across different nodes.

Sharding When data is ingested into dedicated SQL pool, the data is sharded into distributions to optimize the performance . Types:Hash, Round Robin , Replicate
UML Class Diagram
**ERD (Entity RelationShip Diagram ) ** - used in Databases / Warehouses

Algorithms

If asked for all permutations/subsets :

Backtracking
Subset Pattern

def fn( s: str) -> int: #s=123
    r=[""]
    for i in s:
        for j in r:
            r=r+[i+j]
    print(r) 	#['', '1', '2', '21', '3', '31', '32', '321']

If asked to find part of String,Array,LinkedList

2 pointers (sliding Window) Eg: 3. Longest Substring Without Repeating Characters ,167: Two Sum, 15: 3SUM

def fn(s: str) -> int:
    t1,t2,mx=0,1,0
    while t2<len(s)+1:
        temp=s[t1:t2]
        if(len(set(temp)) == len(temp)):  mx=max(len(temp),mx)
        else:t1+=1
        t2+=1
    return mx

If asked to Search in sorted /Linked List

Binary search
- Eg:Bisect Left, Bisect Right implementation
- Eg: 33.Search in Rotated Sorted Array

If given a tree/Grapth (recursion):

DFS (use PreOrder,PosOrder):104. Max dept of Binary Tree
BFS(use Queue): 102. Level order Traversal

If must solve in-place :

Swap corresponding values
Store one or more different values in the same pointer

If asked for maximum/minimum subarray/subset/options :

Dynamic programming (memoization)
Greedy

If asked for top/least K items :

Top K(Heap)

If Dependency Exists (input is DAG):

Topological Pattern: 207 Course Schedule

Greedy Brute Force

j-thepac/Terminology.md