JH H

JH H

he/him

Aspiring PhD student interested in distributed systems and data-intensive computing. I enjoy building reliable, scalable systems and understanding how storage, replication, and consistency mechanisms shape real-world performance. Always learning, experimenting, and writing about systems.

Database Learning Path

5 minute read

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This learning path is for students who want to go beyond “I know SQL” and build a system-level understanding of databases — enough to:

  • design schemas and write correct, efficient queries
  • understand how relational engines and storage engines work
  • use NoSQL systems such as MongoDB and Redis appropriately
  • connect database concepts with distributed systems and modern data platforms

The roadmap is divided into three levels:

  • Beginner
  • Intermediate
  • Advanced

1. What exactly is a “database”?

Before diving into resources, it’s helpful to clarify some overloaded terms.

SQL vs. MySQL

  • SQL is a language (Structured Query Language).
  • MySQL is a relational database management system (DBMS) that uses SQL.

MySQL vs. “database”

MySQL is one specific relational database system.
Other relational engines include PostgreSQL, Oracle, SQL Server, etc.

SQL vs. SQL databases

  • SQL is the language.
  • A SQL database is any relational engine that uses SQL (e.g., MySQL, PostgreSQL, SQLite).

SQL databases vs. NoSQL databases

  • SQL databases: relational, schema-based, strong consistency by default.
  • NoSQL databases: document, key–value, column-family, graph models; more flexible or scalable.

NoSQL is not a “replacement” for SQL — it is a complementary model.
Choose based on workload and consistency requirements.

MySQL vs. PostgreSQL

Both are relational databases:

  • MySQL: widely used, strong for read-heavy workloads.
  • PostgreSQL: object-relational, highly extensible, standards-compliant.

Either is fine for learning.

2. What we want to learn

A complete database learning path spans multiple systems:

  • SQL language
  • Relational engines (MySQL, PostgreSQL)
  • Document stores (MongoDB)
  • In-memory data structures (Redis)
  • Distributed systems foundations

Prerequisites

  • Basic programming experience (Python, Java, Go, C++, etc.)
  • Command-line familiarity
  • Understanding of basic data structures

🔰 Beginner Level

Goal:
Learn SQL basics, relational databases, NoSQL overview, and get hands-on experience.

1. Learn SQL basics

Resources

Recommended practice after completing basics:
Install MySQL locally and practice queries.

2. Introductory SQL books

Choose one:

(A) SQL Essentials / MySQL Essentials

Example download (one edition):
https://drive.google.com/file/d/1QJv4JF4DWJdO7V_ZxWLzhis4yDuDxvno/view

Topics covered:

  • SELECT queries
  • JOINs and aggregations
  • schema design
  • inserts/updates/deletes
  • user and permission basics

Workflow: read → type every query → run it.

(B) SQL Basics (Beginner’s Tutorial)

Example link:
https://drive.google.com/file/d/12I-7TYkq8rGxjyeCOV_NX2r0sR3LxGht/view

Uses PostgreSQL; highly visual, very beginner-friendly.

3. MongoDB: Learn with MongoDB University

This is MongoDB’s official training platform.
Take MongoDB for Developers (7-week pace):

You will learn:

  • installation & basic CRUD
  • data modeling
  • building a blog application
  • assignments + quizzes each week

Optional deeper reading:

MongoDB: The Definitive Guide
https://drive.google.com/file/d/1CgQL2LjAOvAM8g6aVoY4_d5pWx9A5XL3/view

4. General database theory

Read selected chapters (not cover-to-cover) from:

Database System Concepts
https://drive.google.com/file/d/11rIdRiVYrFChEiqmYCuVpN6A4wgTErMw/view

Focus on:

  • relational model
  • storage & indexing
  • transactions
  • DB architecture

5. Redis basics

Pick one:

Optional video course:
Redis 6 from Beginner to Advanced
https://www.bilibili.com/video/BV1Rv41177Af/

6. Distributed systems perspective

Distributed Systems for Fun and Profit
https://book.mixu.net/distsys/

A short and accessible overview of distributed thinking — essential context for distributed databases.

7. Optional: HBase

HBase: The Definitive Guide
https://drive.google.com/file/d/1pHaX4c0iPsL24OKhg46ZcSjz8ccwlJIm/view

⚙️ Intermediate Level

Goal:
Understand SQL engine internals, storage engines, transactions, NoSQL internals, and official docs.

1. CMU 15-445 / 15-645: Database Systems

Covers:

  • storage and buffer pools
  • B+-trees
  • query optimization
  • MVCC
  • logging and recovery

Assignments are based on BusTub, a real DBMS implementation.

2. MySQL official documentation

How to read:

  • browse the doc structure first
  • look up specific topics deeply
  • read entire chapters when exploring a new feature

3. InnoDB internals

MySQL Technical Insider: The InnoDB Storage Engine
https://drive.google.com/file/d/1kSQsS9-_QujtdpMVVV2Lnvy0Olv63bXa/view

Recommended chapters: 2, 4, 5.

4. MongoDB official documentation

Best source for modeling, aggregation, indexes, deployments.

5. Redis internals

Redis Design and Implementation
https://drive.google.com/file/d/1PyPTLFGywqbHEmKvHQrV0rjJD4THPjcJ/view

Paired with source code reading → excellent for understanding systems internals.

6. Redis official documentation and references

7. Distributed systems (again)

MIT 6.824 Distributed Systems

Implement Raft, MapReduce, replicated key-value stores.

8. PostgreSQL official documentation

Deep, well-structured, and great for understanding relational engine design.

🚀 Advanced Level

Goal:
Performance tuning, indexing, internals, distributed storage, and research papers.

1. Indexing and performance

Database Index Design and Optimization
https://drive.google.com/file/d/10SrTxPtZVrI4f93m7AmAXcutWJvYGZKe/view

Learn:

  • how DB engines choose access paths
  • estimating query costs
  • designing and tuning indexes with first principles

2. High-performance MySQL

High Performance MySQL
https://drive.google.com/file/d/1nwhSrOXYufqbzIMC99cHQNjL7IZRIWhq/view

Covers:

  • indexing
  • schema design
  • replication
  • sharding
  • hardware & server configuration
  • performance tuning

3. Advanced Redis books

4. CMU 15-721 (Advanced Database Systems)

Graduate-level seminar focusing on:

  • modern DBMS research
  • column stores
  • log-structured storage
  • query optimizers
  • HTAP
  • distributed transactions

5. Google’s foundational data systems papers

  • Google File System (GFS)
  • MapReduce
  • Bigtable

Example links:
https://drive.google.com/file/d/1Gm6ICrMofFzRUd9B4GBvVLY3tZH-z9xX/view
https://drive.google.com/file/d/1wzaVK5PStnudeWA_PTvE5uL-VEw9OaWz/view
https://drive.google.com/file/d/1nhbid-TlM73bCKQ4G3DwNRxTInAlo8fR/view

6. Paper reading

Readings in Databases
https://github.com/rxin/db-readings

Pick a theme → read 2–3 papers → write notes.

🧭 Putting it all together

One recommended progression:

  1. Beginner
    • Learn SQL via tutorials + one book
    • Install MySQL/PostgreSQL and practice
    • Learn MongoDB & Redis basics
  2. Intermediate
    • Take CMU 15-445
    • Read selected chapters of Database System Concepts
    • Dive into MySQL/MongoDB/Redis documentation
    • Take MIT 6.824 to connect DBs with distributed systems
  3. Advanced
    • Read about indexing and optimization
    • Study engine internals (InnoDB, Postgres, Redis)
    • Take CMU 15-721
    • Start reading DB research papers

Most importantly: alternate theory and hands-on practice.
Databases become intuitive only when you build, debug, measure, and reason about real systems.

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