Grokking System Design Fundamentals
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System Design Fundamentals
Introduction to System Design
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Caching
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
CAP & PACELC Theorems
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Databases (SQL vs. NoSQL)
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
What are Indexes?
Types of Indexes
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Difference Between Long-Polling, WebSockets, and Server-Sent Events
What is Quorum?
What is Heartbeat?
What is Checksum?
Uses of Checksum
What is Leader and Follower Pattern?
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Quiz
Data Replication vs. Data Mirroring

Data replication and data mirroring are both methods used in managing and safeguarding data, particularly in the context of databases and storage systems. While they share similarities in creating copies of data, they serve different purposes and have distinct operational characteristics.

Data Replication

Definition

  • Data Replication involves copying data from one location to another. The replication can be synchronous or asynchronous.

Characteristics

  • Asynchronous/Synchronous: Data replication can be done in real-time (synchronous) or with some delay (asynchronous).
  • Multiple Copies: Often creates multiple copies of data, which can be stored across different servers or locations.
  • Purpose: Enhances data availability and accessibility, used for load balancing, and enables data analysis without impacting the primary data source.
  • Use Cases: In distributed databases, backup systems, and data warehouses.

Example

  • A company might replicate its database across multiple data centers to ensure that if one data center goes down, the others can still serve the data.

Data Mirroring

Definition

  • Data Mirroring refers to the process of creating an exact replica of a database or storage system, usually in real-time.

Characteristics

  • Synchronous: Mirroring is typically synchronous, meaning the data in the primary and mirror locations are always in sync.
  • Redundancy for High Availability: Primarily used for redundancy and high availability.
  • Mirror Copy: Usually involves a one-to-one relationship between the original and the mirror. If data changes in the original location, it is immediately written to the mirror.
  • Use Cases: In critical applications requiring high availability and data integrity, such as financial transaction systems.

Example

  • A financial services firm may use data mirroring to ensure that all transactional data is instantly copied to a secondary server, which can take over with no data loss in case the primary server fails.

Key Differences

  1. Real-Time Synchronization:

    • Replication: Can be either synchronous or asynchronous.
    • Mirroring: Typically synchronous.
  2. Purpose and Use:

    • Replication: Used for load balancing, data localization, and reporting.
    • Mirroring: Primarily for disaster recovery and high availability.
  3. Number of Copies:

    • Replication: Can create multiple copies of data in different locations.
    • Mirroring: Usually involves a single mirror copy.
  4. Performance Impact:

    • Replication: Can be designed to minimize performance impact.
    • Mirroring: Since it’s synchronous, it might have a more significant impact on performance.
  5. Flexibility:

    • Replication: More flexible in terms of configuration and use cases.
    • Mirroring: More rigid, focused on creating a real-time exact copy for redundancy.

Choosing between data replication and data mirroring depends on the specific requirements of the system in terms of availability, performance, and the nature of the data being managed. In many systems, both techniques are used in conjunction to achieve both scalability and high availability.

In-Memory Database vs. On-Disk Database
Database Federation
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Data Replication

Definition

Characteristics

Example

Data Mirroring

Definition

Characteristics

Example

Key Differences