README.md

May 29, 2026 · View on GitHub

EasyDB

A lightweight desktop data query tool that uses SQL to query local files directly, with a built-in query engine

Total Downloads

Introduction

EasyDB is a lightweight desktop data query tool built with Rust and Tauri, featuring a built-in Apache DataFusion query engine. No need to install a database or any other dependencies — just use SQL to query local files directly.

It treats files as database tables, supporting CSV, TSV, Text, NdJson, Excel, Parquet, MySQL, and PostgreSQL as data sources. It supports complex multi-table JOINs, subqueries, window functions, and other advanced SQL features. It effortlessly handles data files from hundreds of MB to multiple GB with minimal hardware resources.

Core Features

High Performance — Built on Rust and DataFusion, effortlessly handles large files
Low Memory Usage — Runs with minimal hardware resources
Multi-format Support — CSV, TSV, Text, NdJson, Excel, Parquet, MySQL, PostgreSQL
Ready to Use — No file conversion needed, query directly
Cross-platform — Supports macOS and Windows
Full SQL Support — Multi-table JOINs, subqueries, window functions, regex matching, and more
Smart Editor — SQL syntax highlighting, autocomplete (function names + column names), formatting
Drag & Drop SQL Generation — Drag files into the editor to auto-generate query statements
Internationalization — Supports Simplified Chinese and English, auto-detects browser language
Virtual Scrolling & Pagination — Smooth rendering for large datasets with scroll-to-load
Result Export — Export to CSV, TSV, or SQL (INSERT/UPDATE), with MySQL or PostgreSQL dialect options
Query History — Automatically records the last 50 queries with execution status
Modern Interface — Native desktop app built with Tauri v2 + HeroUI

Changelog

See CHANGELOG_EN.md

Features & Roadmap

File Reader Functions

read_csv() — Read CSV files with custom delimiter, header, and schema inference options
read_tsv() — Read TSV files
read_text() — Read text files with custom delimiter
read_json() — Read JSON files (temporarily removed in v2.0, planned for reimplementation)
read_excel() / read_xlsx() — Read Excel files with worksheet selection
read_parquet() — Read Parquet columnar storage files
read_ndjson() — Read NDJSON files
read_mysql() — Read MySQL database tables
read_postgres() — Read PostgreSQL database tables

Scalar Functions

REGEXP_LIKE() — Regular expression matching

Editor & Interaction

Drag & drop file auto-generate SQL (option to insert full SQL or just read_xxx() function)
SQL smart autocomplete (function names, parameter names, result column names)
Execute selected SQL fragment
SQL formatting and clearing

Data Export

Export query results to CSV / TSV / SQL
SQL export supports INSERT and UPDATE statements
SQL export supports MySQL and PostgreSQL dialects
Query history recording

Planned

Excel lazy loading performance optimization
Excel enhanced data type compatibility
Multi-session window support
Directory browsing
S3 remote file support
Direct querying of server files
Data visualization

Technical Architecture

Core Tech Stack

Layer	Technology
Frontend	React 18 + TypeScript + Vite
Backend	Rust + Tauri v2
Query Engine	Apache DataFusion 50.3
UI Framework	HeroUI + Tailwind CSS
Virtual Scroll	@tanstack/react-virtual + @tanstack/react-table
SQL Editor	Ace Editor (react-ace)
SQL Parsing	sqlparser-rs (Rust) + node-sql-parser (JS)
i18n	Lightweight custom i18n, zh-CN / en-US
History Storage	SQLite (rusqlite)

Query Engine Selection

Currently Using: Apache DataFusion

DataFusion is part of the Apache Arrow project, providing complete SQL query capabilities and supporting complex SQL syntax including multi-table JOINs, subqueries, window functions, and other advanced features. Compared to Polars, DataFusion offers more comprehensive SQL compatibility.

Version Evolution: v1.0 previously used the Polars engine, which excelled in stream processing and memory usage but had limitations in complex SQL support. v2.0 switched back to DataFusion for more complete SQL support while maintaining good performance and resource efficiency.

User Guide

Basic Syntax

-- Query CSV files
SELECT *
FROM read_csv('/path/to/file.csv', infer_schema => false)
WHERE "age" > 30
LIMIT 10;

-- Query TSV files
SELECT *
FROM read_tsv('/path/to/file.tsv');

-- Query text files (custom delimiter)
SELECT *
FROM read_text('/path/to/file.txt', delimiter => '\t');

-- Query Excel files (specific worksheet)
SELECT *
FROM read_excel('/path/to/file.xlsx', sheet_name => 'Sheet2')
WHERE "age" > 30;

-- Query NDJSON files
SELECT *
FROM read_ndjson('/path/to/file.json')
WHERE "status" = 'active';

-- Query Parquet files
SELECT *
FROM read_parquet('/path/to/file.parquet');

-- Query MySQL database
SELECT *
FROM read_mysql('users', conn => 'mysql://user:password@localhost:3306/mydb')
WHERE "age" > 30;

-- Query PostgreSQL database
SELECT *
FROM read_postgres('users', host => 'localhost', username => 'postgres', db => 'mydb', pass => 'password')
WHERE "age" > 30;

-- Cross-source join (Excel + MySQL)
SELECT *
FROM read_excel('/path/to/file.xlsx', sheet_name => 'Sheet1') AS t1
INNER JOIN
read_mysql('users', conn => 'mysql://user:password@localhost:3306/mydb') AS t2
ON t1."user_id" = t2."id"
WHERE t1."age" > 30;

-- Regex matching
SELECT *
FROM read_csv('/path/to/file.csv')
WHERE REGEXP_LIKE("Distance", '^([0-9]+)\.([0-9]+)?$');

Supported Data Sources

Format	Function	Description
CSV	`read_csv()`	Custom delimiter, header, schema inference
TSV	`read_tsv()`	Tab-separated files
Text	`read_text()`	General text files with custom delimiter
Excel	`read_excel()` / `read_xlsx()`	`.xlsx` support, optional worksheet
NdJson	`read_ndjson()`	One JSON object per line
Parquet	`read_parquet()`	Columnar storage format
MySQL	`read_mysql()`	Direct MySQL database table connection
PostgreSQL	`read_postgres()`	Direct PostgreSQL database table connection

Function Parameters

read_csv() parameters

Parameter	Type	Default	Description
`infer_schema`	boolean	true	Auto-infer data types (based on first 100 rows)
`has_header`	boolean	true	Whether the file contains a header row
`delimiter`	string	`,`	Field delimiter, supports escape sequences like `\t`, `\n`
`file_extension`	string	`.csv`	File extension

read_excel() parameters

Parameter	Type	Default	Description
`sheet_name`	string	First sheet	Name of the worksheet to read
`infer_schema`	boolean	true	Auto-infer data types

read_mysql() parameters

Parameter	Type	Default	Description
`conn`	string	Required	MySQL connection string, e.g. `mysql://user:password@host:port/database`

read_postgres() parameters

Parameter	Type	Default	Description
`host`	string	Required	PostgreSQL server host
`username`	string	Required	PostgreSQL username
`db`	string	Required	PostgreSQL database name
`pass`	string	Optional	PostgreSQL password
`port`	string	`5432`	PostgreSQL port number
`sslmode`	string	`disable`	SSL mode

read_text() parameters

Parameter	Type	Default	Description
`infer_schema`	boolean	true	Auto-infer data types
`has_header`	boolean	true	Whether the file contains a header row
`delimiter`	string	`\t`	Field delimiter
`file_extension`	string	`.txt`	File extension

Quick Start

System Requirements

macOS: 10.15+ (Catalina or higher)
Windows: Windows 10 or higher
Memory: 4 GB or more recommended
Storage: At least 100 MB available space

Installation

Visit the Releases page and download the installer for your system
macOS: Download the .dmg file, drag to Applications folder
Windows: Download the .exe file, run the installer

FAQ

macOS "Application is damaged and cannot be opened"

This is caused by macOS Gatekeeper blocking unsigned applications. Run the following command in Terminal:

xattr -r -d com.apple.quarantine /Applications/EasyDB.app

If this doesn't work, go to System Preferences > Security & Privacy > General and click "Open Anyway".

SQL Syntax Notes

Field names should be wrapped in double quotes:

SELECT "id", "name" FROM table WHERE "id" = 1;

Backticks also work:

SELECT `id`, `name` FROM table WHERE `id` = 1;

String values in WHERE clauses use single quotes:

SELECT * FROM table WHERE "id" = '1';

EasyDB Server — Server-side version, based on DataFusion
EasyDB App — Desktop client version, based on DataFusion (v2.0+)

Contributing

Contributions are welcome in all forms!

Fork this repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Development Environment

# Clone repository
git clone https://github.com/shencangsheng/easydb_app.git
cd easydb_app

# Install frontend dependencies
yarn

# Start development server
cargo tauri dev

# Build application
cargo tauri build