Om's Brain

2. Dropbox

6 min read

0️⃣ HLD

dropbox.excalidraw

1️⃣ Upload Flow

Initial Approach (Naive)

Client ──> File Service ──> S3
  • Problem: File uploaded twice (Client → File Service and File Service → S3).
  • Waste of: Bandwidth, CPU, and Network resources.

Better Approach

Instead of transferring the file through the server, we use presigned URLs.

  1. Step 1: Client sends metadata only (Client → File Service).
  2. Step 2: File Service requests a Presigned URL (File Service → S3).
  3. Step 3: S3 returns the Presigned URL, which the service sends back to the client.
  4. Step 4: Client uploads the file directly to S3 (Client → S3).

Benefits

  • ✅ No double upload
  • ✅ Less server load
  • ✅ Faster uploads

2️⃣ Why Chunking Is Needed

  • Problem: Uploading a large file (e.g., 50 GB upload at 100 Mbps takes ~72 minutes). If the internet disconnects, the client has to start from the beginning ❌.
  • Solution (Chunking): Split the file into small, manageable pieces (e.g., 5 MB chunks).
    • Example: A 50 GB File becomes Chunk 1, Chunk 2, Chunk 3 ... Chunk N.
    • The client uploads chunks independently.

3️⃣ File Metadata With Chunks

{
  "fileId": "string",
  "chunks": [
    {
      "chunkId": "string",
      "status": "string",
      "s3Link": "string"
    }
  ]
}

4️⃣ Resumable Uploads

If the upload stops, the client can resume by comparing already uploaded chunks against local chunks:

  • Already Uploaded: Chunk 1, Chunk 2, Chunk 3
  • Missing: Chunk 4, Chunk 5

The upload resumes from Chunk 4 instead of starting from the beginning.

5️⃣ Fingerprinting

  • Problem: How do we uniquely identify chunks?
  • Solution: Hash(chunk bytes). For example, SHA-256(chunk) produces a unique fingerprint used as the chunkId.
  • Benefits:
    • Detect duplicate chunks (de-duplication)
    • Resume uploads easily
    • Verify chunk/file integrity

6️⃣ Trust But Verify Pattern

  • Problem: The client claims: “Chunk uploaded successfully.” Can we trust the client? No.
  • Verification Flow:
    1. Client notifies the Metadata Service that the chunk is uploaded.
    2. Metadata Service queries S3 to check: Does the chunk really exist?
    3. If yes, update the chunk status in the database.

7️⃣ Alternative: S3 Notifications

Instead of the client telling the server, we use event notifications:

Chunk Uploaded to S3 ──> S3 Notification Event ──> Metadata Service
  • Advantages: More reliable and removes client dependency.
  • Tradeoff: Increases architectural complexity.

8️⃣ Download Flow

  1. Client queries the Metadata DB to get the S3 Link for the file chunks.
  2. Client downloads chunks directly from S3.
  • Why Direct Download? Avoids routing bytes through our server (S3 → File Service → Client), which saves an extra network hop and server bandwidth.

9️⃣ Optimizing Upload Speed

A. Parallel Chunk Uploads

Instead of uploading chunks sequentially (Chunk 1 → Chunk 2 → Chunk 3), upload multiple chunks in parallel.

  • Benefits: Better bandwidth utilization and faster uploads.

B. Compression

Compress files on the client-side before uploading.

  • Good Candidates (Highly compressible): Text files, DOCX, CSV.
  • Poor Candidates (Already compressed): JPEG, PNG, MP4.
  • Note: Store the compressionAlgorithm inside the metadata so the client knows how to decompress it.

C. CDN (Optional)

Use Client → CDN → S3 upload/download paths.

  • Use Case: Mostly useful for shared public files where many users download the same file.
  • Limitation: Not very useful when users mostly access private, unique files. They will be anyways getting uploaded to nearby S3 (mumbai for pune users)

🔟 Client Architecture

+-------------------+
| Client App        |
+-------------------+
| Local Folder      |
| Local DB          |
+-------------------+
  • Local Folder: The designated directory (e.g., “Dropbox Folder”) storing the actual files.
  • Local DB: A lightweight client-side DB (e.g., SQLite) storing metadata (fileId, hash, timestamps, chunk info).
    • Used For: Sync decisions, duplicate detection, and reconciliation.

1️⃣1️⃣ Detecting Local Changes

The OS provides file watcher APIs to notify the app when files change:

  • Windows: FileSystemWatcher
  • MacOS: FSEvents

Change Detection Flow:

Local File Changed ──> File Watcher ──> Trigger Upload

1️⃣2️⃣ Sync Design (Remote → Local)

Polling

The client periodically queries GET /changes to retrieve a list of changed files and downloads the updates.

  • Why Polling?
    • Simple, reliable, and easy to scale.
  • Why not WebSockets?
    • WebSockets require persistent connections, adding overhead like connection management and stateful servers. For a file-sync service like Dropbox, standard HTTP polling is typically sufficient and easier to scale.

1️⃣3️⃣ Adaptive Polling

Adjust polling frequency dynamically based on user activity:

  • High Activity: APP Opened - Poll more frequently. 10 sec
  • Idle State: Poll less frequently.
  • Benefits: Lowers server load and provides a faster sync experience when the user is actively working.

Also we can create a Refresh Button on Local App

1️⃣4️⃣ Delta Sync - When File Edited

  • Problem: Without delta sync, changing a tiny portion (e.g., 1 KB) of a large file (e.g., 50 GB) would require re-downloading the entire file ❌.
  • Solution: Sync only the modified chunks.
    • Example: For a file with Chunk 1, Chunk 2, Chunk 3 (changed), and Chunk 4, the client only downloads/uploads Chunk 3.
  • Benefits: Faster syncing and minimal bandwidth usage.

1️⃣5️⃣ Consistency Options

Option 1: Poll Metadata DB (Client → Metadata DB)

Query changes since a specific timestamp.

  • Pros: Simple and easy to implement.
  • Cons: High read load on the database.

Option 2: Event Bus + Cursor (e.g., Kafka)

File Changed Service ──> Kafka ──> Client (via syncCursor)
  • Pros: Full audit trail, version history, ability to replay events, and rollback support.
  • Cons: High complexity and requires more infrastructure.
  • Verdict: Start with polling the DB as the simpler solution, and present the Kafka/Event Bus model as the advanced optimization tradeoff.

1️⃣6️⃣ Reconciliation

  • Problem: Despite real-time sync, divergence can occur (Local State != Remote State).
  • Solution: Run a periodic reconciliation task (e.g., daily or weekly) to compare Local DB vs Remote Metadata.
  • Process: Compare file fingerprints, chunk IDs, and metadata, then resolve mismatches.
  • Purpose: Guarantees high data integrity over the long term.

1️⃣7️⃣ Interview Deep Dives

  • Mid-Level (L4 / E4): Focus on Chunking and basic Syncing mechanisms.
  • Senior (L5): Discuss Chunking, Delta Sync, Presigned URLs, and Reconciliation.
  • Staff (L6+): Additionally cover Event-driven sync, Kafka cursor model, Multi-region architecture, Conflict resolution, and Storage optimization.

1️⃣8️⃣ Core Features Enabled:

  • ✅ Presigned URLs
  • ✅ Chunking & Fingerprinting
  • ✅ Resumable Uploads & Delta Sync
  • ✅ Adaptive Polling & Reconciliation
  • ✅ High Availability

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