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Database Internals for System Design & HLD Interviews

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A Deep-Dive Technical Series for Modern Backend Engineers, Architects & CTOs

Subtitle: Understand how databases really work — WAL, B+ Trees, LSM Trees, SSTables, MVCC, compaction, buffer pools, replication and distributed storage. Build the mental models needed to excel in high-level design interviews and design systems that scale.

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⭐ Series Overview

Modern backend systems — from messaging platforms to fintech ledgers and real-time analytics — are built on powerful storage engines. But most engineers only learn how to use databases, not how they work internally.

This series bridges that gap.

You will learn the internal mechanics of PostgreSQL, MySQL, Cassandra, DynamoDB, RocksDB, Bigtable and Kafka, while building intuition that helps you:

  • Design more scalable systems
  • Pass deep HLD/System Design interviews
  • Understand performance bottlenecks
  • Make stronger architectural decisions

Each article is practical, diagram-rich, interview-focused, and written with the precision expected from a CTO-level engineer.

📚 Articles in the Series

0. Introduction

Why Modern HLD Interviews Now Require Database Internals How Big Tech and Indian product companies evolved system design interviews to include deep internals like LSM trees, WAL, MVCC and compaction.

1. Write-Ahead Logs & Durable Write Paths

Learn the foundation of all storage engines: WAL, fsync, redo logs, crash recovery workflows, and how PostgreSQL, Cassandra and Kafka implement durability.

2. B+ Trees: The OLTP Workhorse

Deep-dive into B+ Trees, node layouts, page splits, fill factor, hot pages, secondary indexes, clustered indexes, and why they dominate relational DBs.

3. LSM Trees: The Write-Optimized Revolution

Understand the log-structured design powering Bigtable, Cassandra, LevelDB and RocksDB. Learn memtables, immutable SSTables, bloom filters, compaction strategies and trade-offs.

4. SSTables: Immutable Storage for Scale

Explore SSTable internals — data blocks, index blocks, bloom filters, compression, checksums — and how major databases persist structured data to disk.

5. Compaction: The Hidden Cost Behind LSM Engines

Write amplification, compaction debt, levelled vs tiered compaction, tombstones, backpressure management and real performance tuning techniques.

6. MVCC: High-Concurrency Reads & Writes Without Locks

Snapshot isolation, version chains, undo/redo logs, vacuuming, and how PostgreSQL and InnoDB avoid reader-writer conflicts.

7. Buffer Pools, Caches & Page Replacement

OS page cache vs DB buffer pool, dirty page flushing, write-back semantics, LRU/CLOCK optimization, read-ahead and query performance implications.

8. Query Execution: What Really Happens During SELECT

Planner, optimizer, executor, join strategies, cost modeling, vectorized execution and columnar optimizations.

9. Parquet, ORC & Columnar Storage Internals

Columnar layout, predicate pushdown, RLE/dictionary encodings, bit packing, compression workflows and why modern analytics systems rely on columnar formats.

10. Distributed Storage: Replication, Consistency & Quorums

Leader-follower replication, write-ahead replication, quorums, hinted handoff, anti-entropy, consistency levels and CAP trade-offs.

11. Architecture of Bigtable, Cassandra & DynamoDB

Case studies mapping theory to practice: SSTables + LSM + replication + gossip + consistent hashing + adaptive capacity.

12. Build Your Own Storage Engine (Capstone)

Step-by-step design of a minimal RocksDB-style engine: WAL, memtable, SSTable flushing, bloom filters, compaction, caching and tuning.

🎯 Who This Series Is For

  • Senior Backend Engineers preparing for HLD / System Design Interviews
  • Principal Engineers & Architects
  • Database engineers
  • CTOs & technical founders
  • Anyone wanting to truly understand how scalable systems store data

🔥 Why This Series Matters

Most articles online focus on how to use a database. This series focuses on how databases work internally, which is what modern engineering interviews — especially in India — now expect.

You won’t just memorize features; you’ll develop the mental models behind:

  • Write paths
  • Read paths
  • Index structures
  • Concurrency control
  • Durability semantics
  • Distributed consistency
  • Storage trade-offs

These concepts are timeless. They will serve you for the next 20 years.

📬 Stay Updated

This series is updated regularly with new diagrams, case studies, interview questions and code examples.