1 points | by hilti 5 hours ago
1 comments
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}}}
- user.profile.email - user.profile.age
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
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:
Automatically becomes filterable columns: 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