Introduction to Juice

Juice is a SQL mapper framework based on Golang, aiming to provide a straightforward and effort-saving SQL mapper to allow developers to focus more on business logic development.

If you are a Golang developer or searching for an easy-to-use SQL mapper framework, Juice might be your best choice.

Project Homepage

https://github.com/go-juicedev/juice

Documentation Links

• English: https://juice-doc.readthedocs.io/projects/juice-doc-en/en/latest/

• 简体中文: https://juice-doc.readthedocs.io/en/latest/

Features

• 🚀 Lightweight, High Performance, No Third-party Dependencies

  Depends only on the Go standard library, small binary size, fast startup.

• 🔧 Dynamic SQL Support for Flexible Complex Query Construction

  Full support for dynamic tags like if, where, set, foreach, choose, etc.

• 🗄️ Multi-datasource Support with Master-Slave Switching

  Easily implement read-write separation and multi-datasource management.

• 🎯 Generic Result Set Mapping, Type Safe

  Fully utilizing Go generics, providing compile-time type checking.

• 🔗 Middleware Mechanism, Extensible Architecture

  Built-in debug and timeout middleware, supports custom extensions.

• 📝 Custom Expressions and Functions

  Support for custom functions and expressions in dynamic SQL.

• 🛠️ Code Generation Tools

  Optional code generation tools to improve development efficiency.

• 🔒 Transaction Management

  Concise transaction API, supporting nesting and propagation.

• 🔍 SQL Debugging and Performance Monitoring

  Built-in SQL logging and execution time statistics.

• 📊 Multiple Database Support

  MySQL, PostgreSQL, SQLite, Oracle, and other database/sql compatible databases.

• 🔧 Connection Pool Management

  Flexible connection pool configuration to optimize resource usage.

Why Choose Juice

Design Philosophy

Juice follows these core design philosophies:

• Simplicity Over Complexity: API design is concise and intuitive, with a gentle learning curve.

• Explicit Over Implicit: SQL statements are clearly visible, ensuring predictable behavior.

• Performance First: Optimizations like zero dependencies, object pooling, and pre-allocation.

• Type Safety: Fully taking advantage of Go generics for compile-time type checks.

• Extensibility: Middleware mechanism supports flexible extensions.

Comparison with Other Frameworks

Feature	Juice	GORM	sqlx	ent	Note
Zero Dependency	✅	❌	❌	❌	Depends only on Go stdlib
Dynamic SQL	✅	Partial	❌	❌	Complete MyBatis-style dynamic SQL
Generics Support	✅	❌	❌	✅	Go Generics
SQL Visibility	✅	❌	✅	❌	Centralized SQL management
Learning Curve	Low	Medium	Low	High	MyBatis-like design
Performance	🚀 Excellent	Good	🚀 Excellent	Good	Close to native database/sql
Code Gen	✅	✅	❌	✅	Optional tools
Middleware	✅	✅	❌	✅	Flexible interceptor mechanism

Applicable Scenarios

Juice is particularly suitable for:

✅ Complex Query Scenarios

• Complex SQL queries required

• Dynamic query condition construction

• Precise control over SQL execution

✅ High Performance Requirements

• Performance-critical applications

• High concurrency scenarios

• Scenarios requiring near-native performance

✅ Team Collaboration

• Clear division between DBA and developers

• Centralized SQL management and view

• SQL version control

✅ Microservices Architecture

• Lightweight, suitable for containerized deployment

• Zero dependencies, reducing supply chain risks

• Fast startup, low resource footprint

❌ Less Suitable Scenarios

• Simple CRUD-heavy applications (GORM might be better)

• Scenarios requiring automatic migrations (ent might be better)

• Teams unfamiliar with SQL

Performance Features

Juice focuses heavily on performance optimization:

Zero Dependency Design

• Only standard library dependencies

• Reduced dependency chain, lower risk

• Smaller binary size

Object Pool Optimization

• Uses sync.Pool to reuse strings.Builder

• Reduces memory allocation and GC pressure

• Improves performance in high concurrency

Pre-allocation Strategy

• Slice capacity pre-allocation

• String builder capacity estimation

• Reduces memory reallocation

Smart Caching

• Prepared statement caching

• Reflection result caching

• Expression compilation caching

Batch Optimization

• Smart batch processing

• Prepared statement reuse

• Reduces database round-trips

Installation

Requirements

• Go 1.25+ for the current main branch. Older Juice releases may support older Go versions; check the go.mod file for the version you use.

go get -u github.com/go-juicedev/juice

Quick Start

1. Write SQL mapper configuration file

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <environments default="prod">
        <environment id="prod">
            <dataSource>root:qwe123@tcp(localhost:3306)/database</dataSource>
            <driver>mysql</driver>
        </environment>
    </environments>

    <mappers>
        <mapper namespace="main">
            <select id="HelloWorld">
                select "hello world" as message
            </select>
        </mapper>
    </mappers>
</configuration>

Attention

Note: The format of dataSource is the same as used in the sql.Open() function.

2. Write the code

package main

import (
    "context"
    "fmt"
    "github.com/go-juicedev/juice"

    _ "github.com/go-sql-driver/mysql"
)

func HelloWorld() {}

func main() {
    cfg, err := juice.NewXMLConfiguration("config.xml")
    if err != nil {
        fmt.Println(err)
        return
    }

    engine, err := juice.Default(cfg)
    if err != nil {
        fmt.Println(err)
        return
    }
    defer engine.Close()

    message, err := juice.NewGenericManager[string](engine).Object(HelloWorld).QueryContext(context.Background(), nil)
    if err != nil {
        fmt.Println(err)
        return
    }
    fmt.Println(message)
}

Attention

Note: Although juice does not rely on third-party libraries, it does require a database driver. For example, if you want to use MySQL, you need to install github.com/go-sql-driver/mysql first.

If you encounter errors, it might be because your database is not running, or there is a mistake in your database configuration. Check your database configuration is correct.

3. Run the code

go run main.go

4. Output

[juice] 2022/11/05 19:56:49 [main.HelloWorld]  select "hello world" as message  []  5.3138ms
hello world

If you see the above output result, congratulations, you have successfully run Juice.

More Examples

User Repository Example

Configuration (user_mapper.xml)

<?xml version="1.0" encoding="UTF-8"?>
<mapper namespace="repository.UserRepository">
    <!-- Query user by ID -->
    <select id="GetUserByID">
        SELECT id, name, email, age, created_at
        FROM users
        WHERE id = #{id}
    </select>

    <!-- Dynamic condition search -->
    <select id="SearchUsers">
        SELECT id, name, email, age
        FROM users
        <where>
            <if test='name != ""'>
                AND name LIKE concat('%', #{name}, '%')
            </if>
            <if test='minAge > 0'>
                AND age >= #{minAge}
            </if>
            <if test='maxAge > 0'>
                AND age <= #{maxAge}
            </if>
        </where>
        ORDER BY created_at DESC
    </select>

    <!-- Batch Insert -->
    <insert id="BatchInsertUsers" batchSize="100">
        INSERT INTO users (name, email, age)
        VALUES
        <foreach collection="users" item="user" separator=",">
            (#{user.name}, #{user.email}, #{user.age})
        </foreach>
    </insert>

    <!-- Dynamic Update -->
    <update id="UpdateUser">
        UPDATE users
        <set>
            <if test='name != ""'>
                name = #{name},
            </if>
            <if test='email != ""'>
                email = #{email},
            </if>
            <if test='age > 0'>
                age = #{age},
            </if>
        </set>
        WHERE id = #{id}
    </update>
</mapper>

Go Code

package repository

import (
    "context"
    "github.com/go-juicedev/juice"
)

type User struct {
    ID        int64  `column:"id"`
    Name      string `column:"name"`
    Email     string `column:"email"`
    Age       int    `column:"age"`
    CreatedAt string `column:"created_at"`
}

type UserRepository interface {
    GetUserByID(ctx context.Context, id int64) (*User, error)
    SearchUsers(ctx context.Context, name string, minAge, maxAge int) ([]*User, error)
    BatchInsertUsers(ctx context.Context, users []*User) error
    UpdateUser(ctx context.Context, id int64, name, email string, age int) error
}

type userRepositoryImpl struct {
    engine juice.Manager
}

func NewUserRepository(engine juice.Manager) UserRepository {
    return &userRepositoryImpl{engine: engine}
}

func (r *userRepositoryImpl) GetUserByID(ctx context.Context, id int64) (*User, error) {
    params := juice.H{"id": id}
    return juice.NewGenericManager[*User](r.engine).
        Object(r.GetUserByID).
        QueryContext(ctx, params)
}

func (r *userRepositoryImpl) SearchUsers(ctx context.Context, name string, minAge, maxAge int) ([]*User, error) {
    params := juice.H{
        "name":   name,
        "minAge": minAge,
        "maxAge": maxAge,
    }
    return juice.NewGenericManager[[]*User](r.engine).
        Object(r.SearchUsers).
        QueryContext(ctx, params)
}

func (r *userRepositoryImpl) BatchInsertUsers(ctx context.Context, users []*User) error {
    params := juice.H{"users": users}
    _, err := r.engine.Object(r.BatchInsertUsers).ExecContext(ctx, params)
    return err
}

func (r *userRepositoryImpl) UpdateUser(ctx context.Context, id int64, name, email string, age int) error {
    params := juice.H{
        "id":    id,
        "name":  name,
        "email": email,
        "age":   age,
    }
    _, err := r.engine.Object(r.UpdateUser).ExecContext(ctx, params)
    return err
}

FAQ

Q: What is the difference between Juice and MyBatis?

A: Juice borrows design concepts from MyBatis but is optimized for Go: - Fully utilizes Go generics for type-safe APIs - Zero dependencies, lightweight - More idiomatic to Go

Q: Why use XML instead of annotations?

A: Go does not support annotations. XML configuration offers: - Centralized SQL management for easier review and version control - Independent SQL optimization by DBAs - Support for complex dynamic SQL - IDE plugin support (syntax highlighting, auto-completion)

Q: How is Juice’s performance?

A: Juice’s performance is close to native database/sql: - Zero dependencies, no extra overhead - Object pooling to reduce GC pressure - Pre-allocation strategies to reduce memory allocation

Q: How to debug SQL statements?

A: Juice provides multiple ways to debug: - Use DebugMiddleware to print SQL and parameters - Enable debug mode in configuration - Use IDEA plugin to view SQL - Check generated SQL logs

Q: Which databases are supported?

A: Juice supports all database/sql compatible databases: - MySQL / MariaDB - PostgreSQL - SQLite - Oracle - SQL Server - Others supported by database/sql

Community & Support

Get Help

• GitHub Issues: https://github.com/go-juicedev/juice/issues

• Documentation: https://juice-doc.readthedocs.io/

• Examples: https://github.com/go-juicedev/juice/tree/main/examples

Contribute

Pull Requests and Issues are welcome!

IDEA Plugin

Install the Juice plugin for a better development experience:

• Syntax highlighting

• Auto-completion

• SQL navigation

• Error checking

Plugin URL: https://plugins.jetbrains.com/plugin/26401-juice

Detailed Documentation

Core Features

• Configuration Details
  • Configuration File Format
  • Basic Structure
  • environments
  • Datasource Switching
  • Configuration Value Providers
  • Connection Pool Configuration
  • Global Settings
  • Connection Pool Tuning Guide
• SQL Mappers
  • mappers Tag
  • mapper Tag
  • SQL Statement Tags
  • Parameter Handling
  • Advanced Features
  • Best Practices
• Security Guide
  • Security Baseline
  • Parameter Binding Rules (Required)
  • Safe Patterns for Dynamic SQL
  • Logging and Auditing
  • Principle of Least Privilege for Database Access
  • Transactions and Security
  • Security Checklist Before Release
• Result Mapping
  • Native sql.Rows Support
  • Generic Result Mapping
  • Custom Result Mapping
  • Auto-Increment Primary Keys
  • Batch Insert Optimization
• Transaction Management
  • Overview
  • Transaction Interfaces
  • Manual Transactions
  • Functional Transactions
  • Nested Call Semantics
  • Isolation Level and Read-Only Options
  • Further Reading
• Transaction Semantics Reference
  • Overview
  • Semantics Matrix
  • Behavior Details
  • How to Choose in Practice
• Multi-Datasource and Transaction Interaction
  • Goal
  • Key Conclusions
  • Interaction Matrix
  • Recommended Patterns
  • Anti-Pattern (Not Recommended)
  • Troubleshooting Tips
• Dynamic SQL
  • Conditional Logic with if
  • Dynamic WHERE with where
  • Dynamic SET with set
  • Iterating Collections with foreach
  • Custom Wrapping with trim
  • Conditional Branching with choose
  • Reusing SQL Fragments with sql and include
• Real-World Query Patterns
  • Multi-Condition Search
  • Complex Statistical Queries
  • Dynamic Batch Operations
• Performance Optimization Tips
  • Avoid Common Performance Pitfalls
  • Index Optimization Suggestions
  • Query Optimization
  • Batch Operation Optimization
• Debugging and Monitoring
  • SQL Debugging Tips
  • Performance Analysis
• Best Practices Summary
  • Dynamic SQL Design Principles
  • Checklist
• Expression System
  • Overview
  • Usage Restrictions
  • Basic Operators
  • Built-In Functions
  • Custom Functions
  • Function Calls
  • Methods on Custom Types
  • Attribute Access
  • Map Operations
  • Array Operations
• Raw SQL Execution
  • Overview
  • Basic Usage
  • Result Set Mapping
  • Transaction Support
  • Notes
  • sql.DB Execution
• Middleware
  • What Middleware Is
  • Custom Middleware

Advanced Features

• End-to-End Best Practices
  • When to Use This Template
  • Recommended Directory Layout
  • Configuration Recommendations
  • Bootstrap and Dependency Injection
  • Recommended Request Flow
  • Read-Write Splitting in Practice
  • Error Handling Conventions
  • Observability Recommendations
  • Pre-Launch Checklist
• Code Generation
  • Installing juicecli
  • Quick Start
  • Using the Generated Code
  • Command Breakdown
  • Interface Constraints
  • Notes
  • go generate
• Cache Mechanism
  • Enable Caching
  • Cache Invalidation
  • Custom Caching
  • Secondary Cache
• Hot Update
• Extensions
  • Embedding Configuration Files into the Executable
  • Read-Write Splitting
  • Tracing
  • XML Document Constraints
• GoLand Plugin
  • Introduction
  • Preview
  • Installation
  • Main Features
  • Usage Examples
  • Shortcuts
  • Common Issues
  • Feedback and Support

Others

• 吃包辣条