This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Every analyst has stared at a folder of Parquet or CSV files and thought: "I just want to SQL this." File SQL does exactly that — inside VS Code. Drop in a file path, an S3 URI, or a whole folder. Each subfolder becomes a table. Query it with DuckDB. Done. Why it stands out: Local files and S3 — one extension, one workflow, Right-click any file in Explorer → instant SQL table, Multi-tab query editor with autocomplete, Runs entirely on your machine
Hey I'm Arunkumar
I built File SQL because I kept doing the same annoying thing at work: someone would send me a Parquet file or an S3 path, and I'd spin up a Jupyter notebook just to peek inside it. Six lines of pandas boilerplate later I'd forgotten what I was even looking for.
So — right-click a file in VS Code, get a SQL table. That was the whole idea.
It works on CSV, JSON, Parquet, TSV, and a few others. If you point it at a folder (local or S3), each subfolder becomes its own table, which turned out to be really handy for partitioned datasets. Under the hood it's DuckDB, so queries on a few million rows feel instant on a laptop.
A few things I care about:
- Local files and S3 both work the same way. Most tools pick one.
- Nothing leaves your machine. No accounts, no telemetry, no cloud backend.
- Free and MIT- licensed. I use it every day myself.
I'd love to hear:
- Any feature that would make it a daily-driver for you?
- Bugs — please break it and tell me how 🙏
Does the query results pane let you export back out to Parquet or CSV directly, or do you have to pipe through another tool for that round trip?
Does the right-click → table thing handle nested Parquet partitions automatically, or do you have to flatten the folder structure first?
How does this handle really large parquet files on S3 without downloading the whole thing first?
Right-clicking a Parquet file in the explorer and getting an instant SQL table feels like the obvious thing that should have existed years ago. Really clean workflow for quick local analysis.
Love this, being able to right-click a Parquet file and query it right in VS Code saves so much context switching. One thing that would make it even better: add a way to persist and share connection configs and saved queries per project, so my whole team can run the same SQL against the same folder structure without each person setting it up from scratch.
About File SQL on Product Hunt
“Query local & S3 files with SQL — inside VS Code.”
File SQL was submitted on Product Hunt and earned 7 upvotes and 11 comments, placing #96 on the daily leaderboard. Every analyst has stared at a folder of Parquet or CSV files and thought: "I just want to SQL this." File SQL does exactly that — inside VS Code. Drop in a file path, an S3 URI, or a whole folder. Each subfolder becomes a table. Query it with DuckDB. Done. Why it stands out: Local files and S3 — one extension, one workflow, Right-click any file in Explorer → instant SQL table, Multi-tab query editor with autocomplete, Runs entirely on your machine
File SQL was featured in Analytics (172.8k followers), GitHub (41.3k followers), Data & Analytics (5.7k followers) and Database (2.2k followers) on Product Hunt. Together, these topics include over 45.3k products, making this a competitive space to launch in.
Who hunted File SQL?
File SQL was hunted by Arunkumar Bhat. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Arunkumar Bhat