Video upload and processing pipeline in HLD - Architecture Diagram

System Overview - Video upload and processing pipeline

This system allows users to upload videos which are then processed asynchronously to generate different formats and thumbnails. The key requirements are to handle large video files efficiently, process videos without blocking user requests, and deliver processed videos quickly.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Upload Service ---> Message Queue ---> Processing Service ---> Storage Service
                                      |                                   |
                                      v                                   v
                                  Notification Service                 CDN Cache

Components

User

client

Uploads videos and requests processed video playback

Load Balancer

load_balancer

Distributes incoming upload and playback requests evenly to API Gateway instances

API Gateway

api_gateway

Handles client requests, routes upload and playback requests to appropriate services

Upload Service

service

Receives video uploads, stores raw videos temporarily, and enqueues processing tasks

Message Queue

queue

Decouples upload from processing by holding video processing tasks asynchronously

Processing Service

service

Consumes tasks from queue, processes videos into various formats and thumbnails

Storage Service

storage

Stores processed videos and thumbnails for delivery

Notification Service

service

Sends notifications to users when processing is complete

CDN Cache

cache

Caches processed videos close to users for fast playback

Request Flow - 14 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Upload Service

Upload Service → Message Queue

Message Queue → Processing Service

Processing Service → Storage Service

Processing Service → Notification Service

User → Load Balancer

Load Balancer → API Gateway

API Gateway → CDN Cache

CDN Cache → User

CDN Cache → Storage Service

Storage Service → CDN Cache

CDN Cache → User

Failure Scenario

Component Fails:Message Queue

Impact:Video processing tasks cannot be queued, causing processing delays and possible upload failures

Mitigation:Use a replicated queue system with failover; buffer uploads temporarily in Upload Service until queue recovers

Architecture Quiz - 3 Questions

Which component ensures video processing happens asynchronously after upload?

A Message Queue

B Load Balancer

C CDN Cache

D API Gateway

Design Principle

This design uses asynchronous processing with a message queue to handle heavy video processing without blocking user uploads. Caching with a CDN improves playback speed and reduces load on storage. Load balancing and API gateway provide scalability and routing control.

Practice

1. Which component in a video upload and processing pipeline is primarily responsible for converting raw uploaded videos into multiple formats suitable for playback?

easy

A. Content delivery network (CDN)

B. Video processing service

C. Metadata database

D. Upload service

Solution

1. Step 1: Identify the role of each component

   The upload service handles receiving videos, the metadata database stores info, and CDN delivers content. The processing service converts videos.

2. Step 2: Match the function to the question

   Converting raw videos into multiple formats is done by the video processing service to ensure compatibility.

3. Final Answer:

   Video processing service -> Option B

4. Quick Check:

   Conversion = Video processing service [OK]

Hint: Processing means converting video formats [OK]

Common Mistakes:

• Confusing upload service with processing
• Thinking CDN does video conversion
• Assuming metadata database handles video files

2. Which of the following is the correct sequence of steps in a typical video upload and processing pipeline?

easy

A. Storage -> Upload service -> Video processing -> Metadata update

B. Video processing -> Upload service -> Metadata update -> Storage

C. Metadata update -> Upload service -> Storage -> Video processing

D. Upload service -> Video processing -> Storage -> Metadata update

Solution

1. Step 1: Understand the logical flow

   Users first upload videos, then videos are processed, stored, and metadata is updated last.

2. Step 2: Match the sequence to options

   Upload service -> Video processing -> Storage -> Metadata update correctly shows upload first, then processing, storage, and metadata update.

3. Final Answer:

   Upload service -> Video processing -> Storage -> Metadata update -> Option D

4. Quick Check:

   Upload first, then process, store, update metadata [OK]

Hint: Upload happens before processing and storage [OK]

Common Mistakes:

• Starting with processing before upload
• Updating metadata before storage
• Mixing storage and upload order

3. Consider a video upload pipeline where the upload service places video metadata into a queue for processing. If the processing service crashes and stops consuming messages, what will happen to the queue and user experience?

medium

A. Upload service will reject new uploads immediately

B. Queue will empty quickly; users get instant processing

C. Queue will fill up, causing delays; users see slow processing

D. Metadata database will automatically process videos

Solution

1. Step 1: Understand queue behavior when consumer stops

   If the processing service crashes, it stops consuming messages, so the queue fills up with unprocessed metadata.

2. Step 2: Impact on user experience

   Since processing is delayed, users experience slow video availability or processing delays.

3. Final Answer:

   Queue will fill up, causing delays; users see slow processing -> Option C

4. Quick Check:

   Processing down -> queue fills -> delays [OK]

Hint: No consumer means queue backs up [OK]

Common Mistakes:

• Assuming queue empties without consumer
• Thinking upload service rejects uploads immediately
• Believing metadata DB processes videos automatically

4. In a video processing pipeline, a developer notices that some videos fail to process and the system does not retry them. Which change will fix this issue?

medium

A. Implement a retry mechanism in the processing service for failed jobs

B. Remove the queue to speed up processing

C. Store videos only after processing completes

D. Disable metadata updates to avoid conflicts

Solution

1. Step 1: Identify cause of failure handling

   Failures without retries mean the system lacks a retry mechanism for failed processing jobs.

2. Step 2: Choose fix to handle failures

   Adding retries ensures failed jobs are re-attempted, improving reliability.

3. Final Answer:

   Implement a retry mechanism in the processing service for failed jobs -> Option A

4. Quick Check:

   Retries fix failed processing [OK]

Hint: Retries fix failed processing jobs [OK]

Common Mistakes:

• Removing queue breaks asynchronous design
• Storing before processing causes errors
• Disabling metadata updates unrelated to retries

5. You need to design a scalable video upload and processing pipeline that supports millions of daily uploads with minimal user wait time. Which architectural choice best supports this goal?

hard

A. Use asynchronous upload service with message queues and distributed processing workers

B. Process videos synchronously during upload to ensure immediate availability

C. Store all videos on a single server to simplify management

D. Update metadata only after manual verification to ensure accuracy

Solution

1. Step 1: Analyze scalability and user wait time needs

   Millions of uploads require asynchronous handling and distributed processing to avoid bottlenecks and reduce wait time.

2. Step 2: Evaluate architectural options

   Asynchronous upload with queues and distributed workers allows parallel processing and smooth scaling.

3. Final Answer:

   Use asynchronous upload service with message queues and distributed processing workers -> Option A

4. Quick Check:

   Asynchronous + distributed = scalable and fast [OK]

Hint: Async + queues + distributed workers scale best [OK]

Common Mistakes:

• Synchronous processing causes delays
• Single server storage limits scalability
• Manual metadata delays pipeline