The Backend Engineering Show with Hussein Nasser

The Internals of MongoDB

Feb 19, 2024

Explore the evolution of MongoDB's internal architecture, from document storage to index representation. Learn about page sizes in MongoDB, MySQL, and PostgreSQL. Delve into the importance of efficient disk I/O and backend engineering fundamentals. Discover the transition to document-oriented storage and cluster B-tree indexing in MongoDB, comparing it with other DBMS.

44:57

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Quick takeaways

Database systems share similar storage fundamentals - MongoDB, MySQL, and PostgreSQL use fixed-size pages for data storage.

MongoDB's evolution to cluster collections enhances storage efficiency by focusing on reducing IO operations and improving indexing mechanisms.

Deep dives

Evolution of MongoDB Internal Architecture

MongoDB's evolution from a document-based database to including cluster collections in version 5.3 is a significant change in its internal architecture. The shift towards schema-less storage and the development of cluster collections showcase MongoDB's continuous progress in refining its storage methods and enhancing its capabilities.

Introduction

3min

Exploring Database Internals and APIs in Backend Engineering

3min

Evolution of Databases Towards Document-Oriented Storage

14min

Exploring MongoDB's Memory Map Version One Storage Engine

4min

Evolution of Locking Models in Databases

10min

MongoDB Internal Evolution and Cluster B-Tree Indexing

11min

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I’m a big believer that database systems share similar core fundamentals at their storage layer and understanding them allows one to compare different DBMS objectively. For example, How documents are stored in MongoDB is no different from how MySQL or PostgreSQL store rows.

Everything goes to pages of fixed size and those pages are flushed to disk.

Each database define page size differently based on their workload, for example MongoDB default page size is 32KB, MySQL InnoDB is 16KB and PostgreSQL is 8KB.

The trick is to fetch what you need from disk efficiently with as fewer I/Os as possible, the rest is API.

In this video I discuss the evolution of MongoDB internal architecture on how documents are stored and retrieved focusing on the index storage representation. I assume the reader is well versed with fundamentals of database engineering such as indexes, B+Trees, data files, WAL etc, you may pick up my database course to learn the skills.

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The Backend Engineering Show with Hussein Nasser

The Internals of MongoDB

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Evolution of MongoDB Internal Architecture

Difference Between SQL and NoSQL

Challenges and Innovations in Storage Engines

Optimizing IO Operations and Indexing Strategies

Remember Everything You Learn from Podcasts