Software Engineer Interview Prep Podcast

Mastering REST API Design & Best Practices

Are you struggling to articulate the exact difference between a basic API and a production-grade, evolvable API during system design interviews? In this deep dive, we break down the 10 pillars of REST API design to help you move beyond simple CRUD operations and start building like a Senior Engineer.

What We Cover in This Episode:

• The Richardson Maturity Model: We explain the progression of RESTful APIs and why reaching Level 3 using Hypermedia (HATEOAS) is the gold standard, allowing clients to discover capabilities dynamically instead of relying on hard-coded URLs.
• URI Rules & HTTP Methods: Learn the strict naming conventions of API design—such as using plural nouns, kebab-case, and completely avoiding verbs in your URLs. We also break down the critical difference between PUT (idempotent full replacement) and PATCH (partial updates).
• Designing for Zero-Downtime: We reveal the definitive rules of backward compatibility and how to safely evolve your API using the Expand-Contract Pattern to migrate fields without ever breaking existing client integrations.
• Standardized Error Contracts: Discover why returning generic error pages is an interview red flag, and how adopting the RFC 7807 Problem Details format provides actionable, machine-readable responses with built-in trace context.
• Performance & Security: We decode advanced caching strategies using ETag and If-None-Match headers to save massive amounts of bandwidth on conditional GET requests. Plus, we contrast rate-limiting algorithms, explaining exactly when to use a Token Bucket for controlled bursting versus a Leaky Bucket for strict throughput guarantees.

Tune in to arm yourself with the precise technical vocabulary, HTTP status codes, and architectural patterns needed to confidently design scalable APIs in your next system design interview!

Episode Description: Mastering Heaps & Priority Queues

Are you struggling to recognize exactly when to use a Priority Queue in your coding interviews? In this deep dive, we break down the Heap data structure from the ground up to help you stop memorizing solutions and start recognizing the core algorithmic patterns.

What We Cover in This Episode:

• The "Flat Tree" Secret: Discover how heaps cleverly flatten complete binary trees into simple arrays using basic math ((i - 1) / 2) to avoid using pointers.
• The O(n) Heapify Magic: We explain the math behind why building a heap from an existing array runs in lightning-fast O(n) time, rather than the expected O(n log n).
• Dangerous Java API Gotchas: We expose the most common traps candidates fall into, such as the deadly integer overflow bug when using (a - b) in custom comparators, and why using a for-each loop on a PriorityQueue will not give you sorted output.
• The 5 Golden Interview Patterns: We decode the 5 recognizable patterns that make up 80% of heap interview questions:

Tune in to master the mental models behind 15 classic algorithm questions and learn to write flawless, bug-free Priority Queue code!

The Sliding Window Algorithm is a powerful technique used to reduce the time complexity of problems involving arrays or strings—specifically those that require finding a sub-segment that meets certain criteria.
Instead of using nested loops O(n^2), the sliding window maintains a dynamic range that "slides" across the data, usually bringing the complexity down to O(n).

Problem:Find the maximum sum of a contiguous subarray of size `k`.

public class SlidingWindow {

public static int findMaxSum(int[] arr, int k) {

int n = arr.length;

if (n < k) return -1;
int windowSum = 0; // 1. Compute sum of the first window for (int i = 0; i < k; i++) {

windowSum += arr[i];

}
int maxSum = windowSum;

// 2. Slide the window from index k to n-1

for (int i = k; i < n; i++) {

// Add the next element, remove the first element of the previous window

windowSum += arr[i] - arr[i - k];

maxSum = Math.max(maxSum, windowSum);

}

return maxSum;

}

}

Video Summary of our audio podcast of [JAVA] Under the hood: Database Connection Pooling in Spring Boot

Video overview of Distributed Rate Limiter

Summary video of [JAVA] Circuit Breaker Deep Dive with Resilience4j

Video Summary of - [DSA] Data Structure and Algorithm (DSA) problem-solving strategies and patterns

1. The Back-of-Envelope Estimation Toolkit

2. Designing a Fintech Payment Processing System

3. The 45-Minute Interview Playbook

1. Storage Strategy & Database Selection

2. Caching Patterns & Disasters

3. Communication & Messaging

4. Apache Kafka Deep Dive

Episode 1 of your 3-part System Design Interview deep-dive podcast series!

This episode focuses on how interviewers at FAANG and Tier-1 financial institutions evaluate you—which is how you think, not just what you know. The hosts will cover:

• The RADIO Framework: The 5-step, 45-minute blueprint for every interview: Requirements, API Design, Data Model, Infrastructure, and Optimise & Operate.
• The #1 Trap for Candidates: Why skipping Non-Functional Requirement (NFR) clarification—like asking about SLAs, active users, and data volume before jumping in—is the main reason senior candidates fail.
• Scalability & The CAP Theorem: A deep dive into Horizontal vs. Vertical scaling, when to use Sharding, and the core trade-offs of the CAP Theorem (Consistency vs. Availability) when network partitions are inevitable.

Episode 2

• The Database Decision Matrix: How to clearly articulate when to use an RDBMS (PostgreSQL) for ACID compliance versus a Document Store (MongoDB) or a Wide-Column Store (Cassandra) for massive write scale.
• Caching Architectures: Explaining the trade-offs between Cache-Aside, Write-Through, and Write-Behind patterns, as well as how to avoid system crashes like Cache Stampedes and Avalanches.
• Kafka Deep Dive: Unpacking how to confidently discuss Kafka offsets, consumer groups, and the critical difference between "At-least-once" delivery and "Exactly-once" financial settlement semantics.

Episode 3

• The Estimation Toolkit: The latency numbers you absolutely must memorize (like L1 cache taking ~1 ns, and a cross-region WAN round-trip taking ~150 ms) and the formulae for calculating daily storage and peak QPS.
• Designing a Fintech Payment System: A walkthrough of designing for extreme correctness (99.999% availability), including the Saga Pattern for distributed transactions, Idempotency Keys to prevent double-charging, and the Outbox Pattern.
• The Minute-by-Minute Playbook: How to perfectly pace your 45-minute interview and the exact trade-off language senior engineers use to close strong.