Grinding - Never back down, never what?

About this repository

Grinding is place that I created to store my material for Backend Engineering Interview Preparation.

1. System Design

Fundamental Concepts

• CAP Theorem
• ACID vs BASE
• Consistency patterns
• Eventual vs Strong consistency
• Load balancing strategies
• DNS and CDN architecture
• Database sharding
• Replication strategies
• Failover and redundancy

Scalability

• Horizontal vs Vertical scaling
• Database indexing strategies
• Caching layers (Client, CDN, Application, Database)
• Message queues and event-driven architectures
• Rate limiting
• Connection pooling
• Backpressure handling
• Data partitioning strategies

High Availability

• Redundancy patterns
• Failover strategies
• Disaster recovery
• Health checks
• Circuit breakers
• Graceful degradation
• Blue-green deployment
• Canary releases
• Feature flags

Common System Design Problems

• Design a CI/CD system
• Design Git hosting service
• Design notification system
• Design rate limiter
• Design distributed cache
• Design job scheduler
• Design monitoring system
• Design authentication system

2. Distributed Systems

Core Concepts

• Distributed consensus (Raft, Paxos)
• Leader election
• Clock synchronization
• Vector clocks
• Quorum
• Gossip protocols
• Distributed transactions
• Split brain problem

Patterns

• Saga pattern
• CQRS
• Event sourcing
• Bulkhead pattern
• Sidecar pattern
• Ambassador pattern
• Circuit breaker pattern
• Retry pattern

Challenges

• Network partitions
• Distributed deadlocks
• Race conditions
• Thundering herd
• Hot spots
• Data consistency
• Service discovery
• Distributed tracing

3. Database Systems

Concepts

• Transaction isolation levels
• Locking mechanisms
• Query optimization
• Execution plans
• Index types
• Materialized views
• Vacuum processing
• Write-ahead logging

PostgreSQL Specific

• MVCC
• Table partitioning
• Inheritance
• Custom types
• Full-text search
• JSON operations
• Window functions
• Common table expressions

Performance

• Query optimization
• Index strategy
• Connection pooling
• Parallel query execution
• Table partitioning
• Vacuum strategies
• Buffer management
• Statistics collection

4. Cloud Native & DevOps

Container Orchestration

• Kubernetes architecture
• Service mesh
• Container security
• Resource management
• Auto-scaling
• Rolling updates
• ConfigMaps and Secrets
• Network policies

CI/CD

• Pipeline design
• Automated testing
• Infrastructure as Code
• Artifact management
• Environment management
• Deployment strategies
• Rollback procedures
• Security scanning

Monitoring & Observability

• Metrics collection
• Log aggregation
• Distributed tracing
• Alert management
• Performance monitoring
• Error tracking
• SLI/SLO/SLA
• Root cause analysis

5. Security

Application Security

• Authentication methods
• Authorization patterns
• OAuth 2.0 and OpenID Connect
• JWT handling
• CSRF protection
• XSS prevention
• SQL injection prevention
• Input validation

Infrastructure Security

• Network security
• Container security
• Secret management
• IAM best practices
• SSL/TLS configuration
• VPC design
• Security groups
• Penetration testing

6. Coding & Algorithms

Data Structures

• Trees (Red-Black, B-Tree)
• Graphs
• Hash tables
• Priority queues
• LRU cache
• Bloom filters
• Skip lists
• Tries

Algorithms

• Graph algorithms
• Dynamic programming
• Greedy algorithms
• Binary search variations
• String matching
• Sorting algorithms
• Tree traversal
• Path finding

Go-Specific Topics

• Goroutines and channels
• Memory model
• Garbage collection
• Interface design
• Error handling
• Context package
• Reflection
• Testing patterns

7. API Design

REST

• Resource modeling
• URL structure
• HTTP methods
• Status codes
• Error handling
• Versioning
• Documentation
• Rate limiting

GraphQL

• Schema design
• Resolvers
• Type system
• Mutations
• Subscriptions
• Batching
• Caching
• N+1 problem

8. Software Architecture

Patterns

• Microservices
• Event-driven
• Layered architecture
• Hexagonal architecture
• Domain-driven design
• Clean architecture
• SOLID principles
• Design patterns

Study Resources

Books

• Designing Data-Intensive Applications
• Clean Architecture
• Cloud Native Patterns
• Database Internals
• Building Microservices
• Go Programming Language
• Site Reliability Engineering
• System Design Interview

Data structure and Algorithm

Prefix sum

Prefix sum helps solve problems that require calculating the sum or range of continuous elements in an array. It is a powerful technique often used in competitive programming and technical interviews to optimize the performance of sum queries.

Sample question:

https://leetcode.com/problems/range-sum-query-immutable/

Golang

Golang is my most beloved programming language, because it clean, simple syntax but have powerful feature like concurrency, blazing fast, good toolbox (go vet, linter, gofmt, go mod, ..ect). Here are some resource that can help you strengthen your Go skill.

• Go 101 books My first Golang book I read, that help me learn core fundamental of Go. Good for starter.
• Concurrency in Go Famous book that help you deeply understanding concurrent concept and practical. High recommend for intermediate and upper Go learner.
• Go Gotchas This blog help you point-out most mistake you can make when coding with very straightforward example and how to address the problem.

My public learning notes

Today I learn This is where I quickly note down things I found that usefull and interest.

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