Tutorials System Design Tutorial
Distributed File Systems — Complete Guide
Distributed File Systems — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of System Design Tutorial on Toolliyo Academy.
On this page
System Design Tutorial · Lesson 36 of 100
Distributed File Systems
Basics ✓ → Scale → Interview
Scale · 2 — Distributed · ~6 min · Module 4: Caching and Storage
What is this?
Distributed file systems (HDFS-style, clustered FS) present a file namespace across many nodes for analytics and shared datasets.
Why should you care?
ShopNest data science may land huge click logs that object storage or HDFS-like systems hold better than OLTP disks.
See it live — copy this example
Sketch the architecture on paper. These lessons focus on concepts and trade-offs.
Landing: clicks/2026/07/19/*.parquet on distributed FS / lake
Compute: Spark jobs read partitions by date
OLTP remains separateRun Example »
This lesson uses terminal or setup steps. Run commands on your computer — the live editor appears on coding lessons.
What happened?
- These systems optimize throughput for large scans, not single-row checkout latency.
- Keep them off the request path.
Practice next
- Put click logs in a lake/FS, not Postgres.
- Partition files by date.
- Run batch jobs off the landing zone.
- Compact many tiny files into larger parquet.
- Set retention on raw click folders.
Remember
DFS/lakes for big analytic files. Partition for prune. Keep off checkout path.
Clickstream lake
ShopNest ships clicks to a distributed lake nightly.
Outcome: Recommenders train without touching order DB disks.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
