Caching

What is caching?

• Caching is the process of storing data in a cache.
• A cache is a temporary storage area.
• Caching is used to speed up data access.
• Caching is used to reduce load on underlying resources.

Real world example

• I have used caching in my project to reduce the load on the database.
• website link :FMC Weekend
• I have used caching in the following way:
  • I have used caching to store the data of the users who have registered for the event.
• Their Profile data is stored in the cache.
• When the user logs in, the data is fetched from the cache.
• If the data is not present in the cache, then the data is fetched from the database and stored in the cache.
• This reduces the load on the database.

Types of caching

• There are two types of caching:
  • In Memory/Local Cache
  • Distributed Cache/Internal Cache

In Memory/Local Cache

• In Memory/Local Cache is a cache that is stored in the memory of the server.
• Example:
  • Memcached

Distributed Cache/Internal Cache

• Distributed Cache/Internal Cache is a cache that is stored in a shared network/memory.
• Example:
  • Redis

When to use caching?

• Caching should be used when:
  • The data is not updated frequently.
  • The data is not sensitive.
  • The data is not large.
  • static content (images, videos,html,css etc.)
  • Example :
    • Read Intensive in Twitter
    • Read Intensive in Facebook
    • Read Intensive in Instagram

Types of caching we are storing in our project

• Application Server Cache
• CDN

Cache Eviction

• Cache eviction is the process of removing an entry from the cache on a definite time or on a condition.

Cache Eviction Techniques

• How we are deleting the data from the cache?
• There are following types of cache eviction techniques:
  • LRU (Least Recently Used) Eviction
  • LFU (Least Frequently Used) Eviction
  • FIFO (First In First Out) Eviction
  • MRU(Most Recently Used) Eviction
  • LFU (Least Frequently Used) Eviction
  • LIFO (Last In First Out) Eviction
  • RR (Random Replacement) Eviction

LRU (Least Recently Used) Eviction

• In this technique, the entry which is least recently used is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

LFU (Least Frequently Used) Eviction

• In this technique, the entry which is least frequently used is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least frequently used is removed from the cache.

FIFO (First In First Out) Eviction

• In this technique, the entry which is first inserted in the cache is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is first inserted in the cache is removed from the cache.

MRU(Most Recently Used) Eviction

• In this technique, the entry which is most recently used is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is most recently used is removed from the cache.

LFU (Least Frequently Used) Eviction

• In this technique, the entry which is least frequently used is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least frequently used is removed from the cache.

LIFO (Last In First Out) Eviction

• In this technique, the entry which is last inserted in the cache is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is last inserted in the cache is removed from the cache.

RR (Random Replacement) Eviction

• In this technique, the entry which is randomly selected is removed from the cache.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is randomly selected is removed from the cache.

Cache Invalidation

• Cache Invalidation is the process of removing an entry from the cache on a definite time or on a condition.

Cache Invalidation Techniques

• How we are deleting the data from the cache?
• There are following types of cache invalidation techniques:
  • Time To Live (TTL)
  • Write Through
  • Write Back

Time To Live (TTL)

• In this technique, the entry is removed from the cache after a definite time.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

Write Through

• In this technique, the entry is removed from the cache after a definite time.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

Write Back

• In this technique, the entry is removed from the cache after a definite time.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

Cache Consistency

• Cache Consistency is the process of maintaining the consistency of the data in the cache.

Cache Consistency Techniques

• How we are maintaining the consistency of the data in the cache?
• There are following types of cache consistency techniques:
  • Write Through
  • Write Back

Write Through

• In this technique, the entry is removed from the cache after a definite time.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

Write Back

• In this technique, the entry is removed from the cache after a definite time.
• Example:
  • If the cache size is 5 and the cache is full.
  • If we want to add a new entry in the cache, then the entry which is least recently used is removed from the cache.

File Based Storage System

What is File Based Storage System?

• File Based Storage System is a system in which the data is stored in the form of files.
• Example:
  • File System

When to use File Based Storage System?

• File Based Storage System should be used when:
  • The data is not updated frequently.
  • The data is not sensitive.
  • The data is not large.
  • Example :
    • Read Intensive in Twitter
    • Read Intensive in Facebook
    • Read Intensive in Instagram

Types of File Based Storage System

• There are two types of File Based Storage System:
  • Local File System
  • Distributed File System

Local File System

• Local File System is a file system that is stored in the memory of the server.
• Example:
  • Ext4

Distributed File System

• Distributed File System is a file system that is stored in a shared network/memory.
• Example:
  • HDFS

Challenges in File Based Storage System

• Data Redundancy
• Data Consistency
• Data Security
• Slow Data Access

Database

RDBMS

• RDBMS stands for Relational Database Management System.
• RDBMS is a database management system that is based on the relational model.
• Stored data in the form of tables.
• Connected data with the help of foreign keys.
• Example:
  • MySQL
  • PostgreSQL
  • Oracle
  • Microsoft SQL Server

Advantages of RDBMS

• Data Redundancy is reduced.
• Data Consistency is maintained.
• Data Security is maintained.
• Data Access is fast.
• Data Integrity is maintained.

Disadvantages of RDBMS

• Rigid Schema
• Scaling is difficult(Horizontal Scaling)
• High Cost
• Not suitable for unstructured data
• Vertical Scaling is possible but it is expensive and limited.

NoSQL

• NoSQL stands for Not Only SQL.
• NoSQL is a database management system that is not based on the relational model.

Types of NoSQL

• There are four types of NoSQL:
  • Key Value Store
    • Generrally used for caching.
    • Example:
      • Redis
      • Memcached
  • Document Store
    • Brimgs both RDMS and NoSQL together.
    • Used for storing unstructured data.
    • Example:
      • MongoDB
      • CouchDB
  • Column Store
    • Used for storing large amount of data.
    • For Data Management during machine learning.
    • Column Store is a type of NoSQL database that stores data in columns rather than rows.
    • Example:
      • Cassandra
      • HBase
  • Graph Database
    • Used for storing data in the form of nodes and edges.
    • Used for storing data in the form of graphs.
    • Google Maps
    • Social Media LinkedIn connection graph
    • Example:
      • Neo4j
      • OrientDB

Examples

• Google Maps - Graph DB
• LinkedIn - Graph DB
• Amazon Cart- Key Valued DB :Redis
• Payment :We won't use NoSQL , We use SQL DB for consistency

Polyglot Persistance

• Multiple Databases are used as per requirement in the system

graph TD;
X("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/psql.png'; width='50' />"<br><br>Postgre SQL)
Y("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/cassandral.png'; width='75' />"<br><br>Cassendra)
Z("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/mong.webp'; width='70' />"<br><br>Mongo DB)
W("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/redis.png'; width='70' />" <br><br>Redis DB)

graph TD;
A[Ecommerce Platform]-->B[Shopping Cart<br> & <br> Session Data]-->C[Key Valued DB]-->W("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/redis.png'; width='70' />"<br><br>Redis DB)
A-->D[Completed Order]-->E[Document DB]-->Z("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/mong.webp'; width='70' />"<br><br>Mongo DB)
A-->F[Inventory <br>&<br>Item Price]-->G[RDBMS<br>Legacy DB]-->X("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/psql.png'; width='50' />"<br><br>PostgreSQL)
A-->H[Customer <br>Social Graph]-->I[Graph DB]-->CC("<img src='https://raw.githubusercontent.com/shubham21155102/Archieve/main/System_Design/neo4j.svg'; width='80' />"<br><br>Neo4j Graph DB)