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
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
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
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
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
DNS Resolution Process

The DNS resolution process involves a series of recursive and iterative queries, utilizing a distributed and hierarchical infrastructure of DNS servers, resolvers, and caching mechanisms.

1. Recursive and Iterative DNS queries

DNS resolution is the process of converting a domain name into its corresponding IP address. There are two types of DNS queries involved in this process: recursive and iterative queries.

  • Recursive query: In a recursive query, the DNS resolver asks for the complete answer to a query from the DNS server. If the server has the answer, it responds with the required information. If not, the server takes responsibility for contacting other DNS servers to find the answer and then returns it to the resolver. Recursive queries put more responsibility on the DNS server to find the requested information.

  • Iterative query: In an iterative query, the DNS resolver asks the DNS server for the best answer it has at the moment. If the server doesn't have the complete answer, it responds with a referral to another server that might have more information. The resolver then contacts that server with a new iterative query, repeating the process until it finds the complete answer. In iterative queries, the resolver takes on more responsibility for finding the requested information.

2. DNS caching and TTL (Time To Live)

To speed up the DNS resolution process, resolvers and servers cache the results of previous queries. When a resolver receives a query, it first checks its cache to see if the answer is already available. If it finds the cached information, it returns the answer without contacting other servers, saving time and reducing network traffic.

Each DNS record has an associated Time To Live (TTL) value, which specifies how long the record should be stored in the cache. TTL is measured in seconds, and once the TTL expires, the cached information is removed to ensure that outdated information is not used.

3. Negative caching

Negative caching is the process of caching the non-existence of a DNS record. When a resolver receives a query for a non-existent domain or record, it caches this information as a negative response, preventing repeated queries for the same non-existent resource. This reduces the load on DNS servers and improves overall performance.

In short, the DNS resolution process involves converting a domain name into its corresponding IP address using recursive and iterative queries. Resolvers and servers cache the results of previous queries to speed up the resolution process, with TTL values determining how long the records are stored. Negative caching helps improve performance by caching the non-existence of DNS records.

Introduction to DNS
DNS Load Balancing and High Availability
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On this page

  1. Recursive and Iterative DNS queries
  1. DNS caching and TTL (Time To Live)
  1. Negative caching