Media Crawler Architecture: Queue, Storage and Scaling

What you will achieve

By the end of this course you will be able to build and operate a media crawler that you can keep running for months, not just a weekend script. Concretely, you will be able to:

• Read a Facebook, Weibo or Bilibili feed response and turn each media item into a clean, normalized record.
• Download images and videos with a streaming pipeline that handles chunked transfer, resume, retries and timeouts.
• Select video resolutions from manifests, then fetch and assemble fragments into a playable file.
• Store media with content hashing and integrity checks so the same asset is never saved twice.
• Run collection as a queue-based worker service with backpressure and a rate budget, so it stays stable under load.
• Sync incrementally using canonical URLs, cursors and dedupe, so repeated runs only fetch what is new.
• Observe, recover and scale the service when workers fail or traffic grows.

The through-line is a single tool: configurable, durable on disk, and stable enough to trust with long-running jobs.

Who this course is for

This is an intermediate course. You should already be comfortable writing code in Python or JavaScript. You do not need any prior crawling experience, and there are no other prerequisites.

The course is a good fit if you have written scripts before and now want to know what separates a script from a service: storage layout, retries, deduplication, queues and recovery. It assumes you are willing to read code and run it, and that you care about the engineering around the download as much as the download itself.

One deliberate scope decision: this course does not spend time on anti-bot evasion. The time goes into media download engineering and application architecture, roughly half the course each. The goal is a crawler that behaves predictably and is maintainable, built on responsible, rate-limited access to public content you have the right to collect.

Course roadmap

Module 1: Foundations and Capstone Skeleton You start with how the three platforms actually serve media, then lay down the capstone blueprint: configuration, storage layout and the pipeline modules. The module closes with your first real step, normalizing media records from feed responses, and a checkpoint quiz on platform anatomy and the project skeleton.

Module 2: Media Download Engineering This is the download half of the course. You build streaming image downloads with chunks, resume, retries and timeouts, then move to video: manifests, fragments and resolution selection. The capstone step here adds storage, content hashing and integrity checks, followed by a checkpoint on download engineering.

Module 3: Architecture, Scheduling and Scaling The second half turns the pipeline into a service. You design queue-based workers with a task model, backpressure and a rate budget, then make runs idempotent with incremental sync, canonical URLs, cursors and dedupe. The final capstone step covers observability, recovery and scaling out, with a closing checkpoint on architecture and scaling.

How the course works

The course has 12 lessons and takes about 275 minutes in total, so plan for roughly four to five hours of focused work. Each module mixes three lesson types:

• Theory lessons explain how the platforms and the engineering patterns work, always with the capstone in view.
• Capstone steps are hands-on: you write the code that becomes part of your tool.
• Checkpoint quizzes at the end of each module confirm you can apply the concepts before moving on.

You can work through the course linearly, and for a first pass that is the recommended path, because each module builds on the artifacts of the previous one. If you already know a topic, the checkpoint quizzes are the fastest way to decide whether to skip a lesson. Skipping is safe as long as you can pass the checkpoint and your project code still runs.

The course project

The project is called the Media Crawler Capstone: one continuous tool you build across all three modules, not a series of disconnected exercises.

By the end you will have a configurable crawler that collects images and videos from Facebook, Weibo and Bilibili, stores them without duplicates, and runs as a scalable worker-based service. The thread runs through specific lessons:

• 1.2 defines the blueprint: config, storage layout and pipeline modules.
• 1.3 normalizes media records from feed responses, giving the pipeline its input shape.
• 2.3 adds storage, content hashing and integrity checks, so files on disk are verifiable and deduplicated.
• 3.3 adds observability, recovery and scaling out, turning the pipeline into a service you can operate.

Lessons 2.1, 2.2, 3.1 and 3.2 supply the download and architecture techniques those capstone steps depend on. Each checkpoint asks you to demonstrate the project as it stands at that point, so you always know your tool still works.

If you want a crawler you can leave running and trust, start with lesson 1.1.