EasyDB

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

English | 中文

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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, JSON, 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, JSON, 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 with standard JSON arrays and NDJSON (auto-detected)
• read_ndjson() — Read NDJSON files (one JSON object per line)
• read_excel() / read_xlsx() — Read Excel files with worksheet selection
• read_parquet() — Read Parquet columnar storage 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 JSON files (standard JSON array or NDJSON, auto-detected)
SELECT *
FROM read_json('/path/to/file.json')
WHERE "status" = 'active';

-- Query NDJSON files
SELECT *
FROM read_ndjson('/path/to/file.ndjson')
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
JSON	read_json()	Standard JSON arrays and NDJSON, auto-detected
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_json() parameters

Parameter	Type	Default	Description
file_extension	string	Path extension	File extension override (e.g. NDJSON content stored in a .json file)

read_json() auto-detects the format from file content: leading [ is parsed as a standard JSON array, leading { as NDJSON. Works with both .json and .ndjson files.

read_ndjson() parameters

Parameter	Type	Default	Description
file_extension	string	.ndjson	File extension

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

1. Visit the Releases page and download the installer for your system
2. macOS: Download the .dmg file, drag to Applications folder
3. 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';

Non-standard File Extensions

For files without CSV, XLSX, JSON, or Parquet extensions, EasyDB automatically uses the read_text() function.

Project Background

From Server to App

EasyDB Server is primarily deployed on Linux servers as a web service for efficient querying of large-scale text files. Although Docker deployment is available, the local experience on macOS and Windows is not as convenient.

EasyDB App is specifically optimized for macOS and Windows to improve the local user experience.

Project Naming

• 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!

1. Fork this repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. 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

Prerequisites

• Rust 1.89+
• Node.js 18+
• Yarn

License

MIT License © Cangsheng Shen

Author

Cangsheng Shen

• GitHub: @shencangsheng
• Email: shencangsheng@126.com

Acknowledgments

Thanks to the following open source projects:

• Apache DataFusion — High-performance SQL query engine
• datafusion-table-providers — DataFusion extension
• Tauri — Modern desktop application framework
• React — User interface library
• HeroUI — UI component library
• calamine — Excel file parsing
• Ace Editor — Code editor

Contributors

Contact

• Bug Reports: GitHub Issues
• Discussions: GitHub Discussions
• Email: shencangsheng@126.com

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Made with ❤️ by Cangsheng Shen