Alephgrid

An open-source spreadsheet for data that doesn't fit in a spreadsheet.

Hundreds of millions of rows, real spreadsheet formulas, SQL when you want it.

Runs on your laptop or your own server. Your data never leaves it.

Open a CSV or Parquet file with hundreds of millions of rows, then scroll, sort, filter, edit and add calculated columns in your browser. Next to those big tables, build ordinary sheets with formatting, charts and Excel formulas such as XLOOKUP and SUMIFS, and point those formulas straight at the big tables. Everything runs on a DuckDB file on your machine. There are no seats, tiers or paywalls: export, sign-in, teams and every other feature are free.

alephgrid flights.parquet          # opens http://127.0.0.1:7878 with the file loaded

alephgrid --demo-rows 100000000 # or try it on 100M generated rows

Status: pre-release. The first release (v0.1.0) is being prepared. Build from source or

with Docker today; published binaries and images come with the release. See

what is shipped and what is not.

Why Alephgrid

• Big tables stay fast. Opening a workbook reads nothing into memory. The grid asks only for the rows on screen, so scrolling to row 900,000,000 is a range read. Sorts build a permutation, not a copy; calculated columns are computed only for the rows you look at.
• A real spreadsheet next to them. Sheets use Excel syntax and an Excel-compatible engine (IronCalc), with formatting, merged cells, frozen panes, charts, colour rules, comments and XLSX import and export.
• Spreadsheet formulas over big tables. =XLOOKUP(A2, customers[id], customers[region]) and =SUMIFS(orders[amount], orders[region], A2) in a sheet cell run as one query over the whole table, not one lookup per row. See table formulas.
• No SQL required, SQL available. Filters, pivots, joins and charts work without writing SQL; the SQL workspace stays closed until you open it, and a server can turn it off.
• Yours to run. One binary with the UI embedded, data in a plain directory, Apache-2.0. Teams get spaces, roles, single sign-on, audit, quotas and backups in the same binary.
• Agents through MCP. Give an AI agent read access to a workbook's tables, sheets and saved queries over the Model Context Protocol, with a receipt for every result.

Features

The full inventory, including what is partial or not planned, is in docs/SPREADSHEET_FEATURES.md.

Quick start

Install a release

From v0.1.0 (being prepared; see releasing):

npx alephgrid flights.parquet                         # npm, or: pnpm dlx / yarn dlx / bunx alephgrid

docker run -p 7878:7878 -v alephgrid-data:/data ghcr.io/alephgrid/alephgrid

or download alephgrid-VERSION-TARGET.tar.gz for Linux (x86_64, arm64) or macOS (Apple silicon, Intel) from GitHub Releases and check it against SHA256SUMS. On Windows, use Docker or WSL.

From source

Requires Rust 1.88+ and Node 20+.

git clone https://github.com/alephgrid/alephgrid && cd alephgrid

(cd web && npm install && npm run build) # build the UI (embedded into the binary) cargo build --release ./target/release/alephgrid --demo-rows 10000000

The first build compiles DuckDB from source, which takes several minutes.

Docker (for a team)

docker compose up -d

docker compose logs alephgrid | grep "setup code" # the one-time code that creates the admin open http://localhost:7878/setup

With --auth accounts, people sign in with a password or single sign-on (OpenID Connect), and spaces grant them roles; see docs/TEAMS.md. Without it (the default on your own machine), there is no sign-in and the server only listens on 127.0.0.1. Running it for others, backups, upgrades and restores are covered in docs/OPERATIONS.md.

Options

alephgrid --help lists every option.

Formulas

In sheets, formulas use Excel syntax: =SUM(B2:B9), =XLOOKUP(...), =Budget!B4*2. A sheet formula can read a whole table by name:

=SUMIFS(orders[amount], orders[region], $A2, orders[month], $B$1)

=XLOOKUP(A2, customers[id], customers[region], "none")

Fill or drag it like any formula; each copy reads its own row. The formula catalog lists supported functions with tested examples, and known gaps are documented with reproducible inputs.

In tables, calculated columns refer to columns by name, and the leading = is optional:

=[fare_usd] / [distance_mi]

=CASE WHEN [dep_delay_min] > 15 THEN 'late' ELSE 'on time' END =[origin] || '→' || [dest]

Typical flow: summarise 100M rows with Analyze or SQL, choose *Open in sheet*, then model on top of the result.

How it is built

• DuckDB underneath. Every workbook is one DuckDB file: columnar, compressed and parallel across cores. Imports use DuckDB's own CSV, Parquet and JSON readers.
• IronCalc for sheets, stored in the same workbook file.
• Rust server (axum, HTTP and WebSocket) with the UI embedded; TypeScript client with a canvas grid, no framework. Wire types are generated from Rust, and a test fails when they drift.

See docs/ARCHITECTURE.md.

Measured (20M-row table, 9 columns, debug build, 8-core / 8 GB laptop)