loftwah-study-guide-2025

loftwah.md

Loftwah's Interview Prep Reference

1. Coding (LeetCode)

How to Work

• For each problem solved:
  • Code solution in Ruby or Go
  • Add time and space complexity analysis
  • Write a short "pattern takeaway"
  • Store in leetcode/<category>/<problem>.md

Arrays and Strings

• Two pointers: two-sum, three-sum, container with most water
• Sliding window: longest substring without repeats, minimum window substring
• Prefix sum: subarray sum equals k, maximum subarray
• Interval problems: merge intervals, insert interval

Hashing

• Group anagrams
• Subarray sum problems
• LRU cache implementation

Linked Lists

• Reverse a linked list
• Detect/remove cycle
• Merge two sorted lists
• Merge k sorted lists
• Copy list with random pointer

Stacks and Queues

• Min stack
• Next greater element
• Largest rectangle in histogram
• Daily temperatures
• Valid parentheses

Trees and Graphs

• DFS and BFS traversals
• Binary search tree validation
• Lowest common ancestor
• Level order traversal
• Serialize/deserialize binary tree
• Topological sort (course schedule)
• Shortest path: BFS and Dijkstra
• Union-Find: connected components, Kruskal MST

Dynamic Programming

• Fibonacci variations
• Climbing stairs
• Coin change (min coins and combinations)
• Longest increasing subsequence
• Longest common subsequence
• Palindromic substrings
• Edit distance
• Word break
• Knapsack variations

Sorting and Searching

• Binary search variations
• Search in rotated array
• Median of two sorted arrays
• Kth largest element (Quickselect/Heap)

Advanced / High-Signal

• Implement Trie (prefix tree)
• Word search (backtracking)
• Regular expression matching (DP)
• Sudoku solver

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2. System Design

How to Work

• Each scenario gets a Markdown doc in system-design/
• Include:
  • Assumptions (traffic, scale, SLAs)
  • Architecture diagram (draw.io / Excalidraw / ASCII)
  • Component choices and tradeoffs
  • Risks and mitigations

Core Concepts

• Load balancing: L4 vs L7
• Caching strategies: write-through, write-back, write-around, TTL
• Message queues: SQS, Kafka, RabbitMQ
• Database scaling: sharding, replication, indexing
• Storage design: object store, block storage, distributed file systems
• API design: REST, GraphQL, gRPC
• Authentication and authorisation
• TLS and certificate management
• Secrets management

Practice Scenarios

• URL shortener
• Twitter feed / Facebook news feed
• WhatsApp / Slack real-time messaging
• YouTube / Netflix video streaming with CDN
• Rate limiter (token bucket, leaky bucket)
• Search autocomplete
• Payment system (idempotency, retries, consistency)
• Metrics and monitoring pipeline
• CI/CD pipeline design at scale

Tradeoffs

• CAP theorem: consistency vs availability
• Strong vs eventual consistency
• SQL vs NoSQL
• Push vs pull models
• Batching vs streaming

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3. AWS and DevOps Labs

How to Work

• Each lab in aws-labs/<lab-name>/
• Deliverables:
  • README.md with Objective, Steps, Expected Outcome, Cleanup
  • Terraform or CloudFormation templates
  • Screenshots or CLI outputs proving success
  • Notes on what failed or broke

Compute Labs

• EC2: launch templates + autoscaling groups, serve a web app behind ALB and then NLB
• ECS Fargate: deploy a containerised app behind ALB, scale, observe CloudWatch logs
• ECS EC2 + capacity providers: run same app with EC2 hosts and test scaling
• EKS: deploy app with Deployment, Service, Ingress, HPA
• Lambda: event triggers from S3, API Gateway, DynamoDB streams
• Step Functions: orchestrate Lambda workflow

Networking and Security Labs

• VPC: custom VPC with public/private subnets, NAT, IGW
• Security groups vs NACLs: block/allow traffic and test
• PrivateLink, VPC peering, Transit Gateway connectivity
• IAM: policies, roles, permission boundaries
• KMS: encrypt/decrypt flow
• Secrets Manager vs SSM Parameter Store

Storage and Database Labs

• S3: versioning, lifecycle rules, signed URLs, replication
• RDS: multi-AZ failover, read replicas
• DynamoDB: GSIs, LSIs, streams
• ElastiCache: Redis failover drill

Observability Labs

• CloudWatch: logs, metrics, dashboards, alarms
• CloudTrail: track IAM events
• X-Ray: trace Lambda or API Gateway app

CI/CD and Infra Labs

• CodeBuild + CodePipeline: ECS Fargate deploy pipeline
• GitHub Actions → EKS deploy with kubectl/Helm
• Blue/green and canary deployment demos
• Terraform basics for each of the above

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4. Demo Applications

Deliverables

• Each demo is a directory in aws-labs/demo-apps/
• Includes README.md, Terraform, Dockerfile, and app code

1. Rails or Go API → ECS Fargate

   • Push image to ECR
   • Deploy service behind ALB
   • AutoScaling and CloudWatch alarms
   • CI/CD with CodePipeline

2. Same app → EKS

   • Deployment, Service, Ingress
   • ConfigMaps and Secrets
   • HPA for autoscaling
   • Monitoring with Prometheus/Grafana

3. Extend app with RDS + ElastiCache

   • Connect to Postgres and Redis
   • Failover testing

4. CI/CD for ECS and EKS

   • ECS via CodePipeline
   • EKS via GitHub Actions
   • Blue/green and canary demos

5. Monitoring and Security

   • CloudWatch dashboards
   • SNS alerts
   • KMS encryption for data
   • Secrets Manager rotation

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5. Extras

Linux and Networking

Deliverables: notes and test commands

• netstat, lsof, tcpdump, strace
• Debugging CPU, memory, IO issues

Git

Deliverables: Git repo with branches demonstrating each

• Rebase, cherry-pick, bisect
• Submodules, hooks

Resilience and Operations

Deliverables: markdown writeups of what happened

• Chaos testing: kill pods or instances
• DR strategy document
• Backup and restore workflow test