Hey HN! Author here.

I built this after struggling with Excel/Pandas for large JSONL log files from IoT devices. The data had deeply nested objects and millions of rows.

The key challenge was speed + usability. I wanted to just double-click a file and see my data instantly, with the ability to filter nested fields.

How it works:

1. Multi-threaded parsing with simdjson's on-demand API

2. Automatic flattening of nested objects into dot notation

3. Virtual scrolling (only render visible rows)

4. Lazy extraction (parse nested JSON on display, not on load)

Example - this JSON:

  {"user": {"profile": {"email": "test@example.com", "age": 30}}}

Automatically becomes filterable columns:

  - user.profile.email

  - user.profile.age

No configuration needed. Just works.

Tech stack: C++ with simdjson for parsing, ImGui for UI, HelloImGui for cross-platform support. Native binary, not Electron (~15MB app).

Performance: 5GB file with 10M records loads in ~2 seconds on M2 Mac. Tested up to 50M rows without issues.

Available for Mac (Silicon/Intel) and Windows. Linux coming soon.

Launch discount: 50% off with code HN50 for next 48 hours.

- Mac: https://iotdatasystems.gumroad.com/l/jsonlviewerpro_mac

- Windows: https://iotdatasystems.gumroad.com/l/jsonlviewerpro_win

Happy to answer questions about the implementation or design decisions!

Use cases I've heard so far:

- DevOps analyzing structured logs

- Data scientists previewing datasets

- Backend developers debugging API responses

- IoT engineers reviewing device telemetry