Technical Overview

What is Musoq?

Musoq is a SQL-like query engine that enables developers to query diverse data sources without requiring a traditional database. It transforms SQL queries into executable C# code that can process files, APIs, databases, and other data sources through a flexible plugin architecture.

Core Architecture Principles

1. SQL-First Design

• Familiar SQL syntax for data querying
• Extensions for non-relational data scenarios
• Type-safe query processing

2. Plugin-Based Extensibility

• Modular data source plugins
• Custom function libraries
• Clean separation of concerns

3. Dynamic Compilation

• Runtime C# code generation
• JIT compilation for performance
• Type safety throughout the pipeline

4. Unified Data Access

• Single query language for multiple data sources
• Consistent API across different data types
• Seamless data source composition

Key Components at a Glance

┌─────────────────────────────────────────────────────────────┐
│                     MUSOQ ARCHITECTURE                     │
├─────────────────────────────────────────────────────────────┤
│  Parser     │  Converter  │  Evaluator  │  Schema System  │
│  --------   │  ---------  │  ---------  │  -------------  │
│  • Lexer    │  • AST      │  • Compiler │  • Data Sources │
│  • AST      │    Transform│  • Runtime  │  • Type System  │
│  • Syntax   │  • Code Gen │  • Execute  │  • Plugins      │
│    Analysis │  • Optimize │             │                 │
└─────────────────────────────────────────────────────────────┘

Parser Module

• Input: SQL query string
• Output: Abstract Syntax Tree (AST)
• Purpose: Validate syntax and create structured representation

Schema System

• Input: Data source specifications
• Output: Type information and data access methods
• Purpose: Provide unified interface to diverse data sources

Converter Module

• Input: AST + Schema information
• Output: Optimized AST + Generated C# code
• Purpose: Transform query logic into executable code

Evaluator Module

• Input: Generated C# code
• Output: Query results
• Purpose: Compile and execute queries with runtime support

Data Flow Example

Let’s trace a simple query through the system:

Input Query

SELECT Name, Size 
FROM #os.files('/Documents') 
WHERE Extension = '.pdf'
ORDER BY Size DESC

1. Parsing Phase

// Tokens: SELECT, Name, Comma, Size, FROM, Hash, os, Dot, files, ...
// AST: SelectNode { Fields: [Name, Size], From: FunctionNode { Schema: "os", Method: "files" }, ... }

2. Schema Resolution

// Resolve #os.files -> OSSchema.GetFilesRowSource
// Infer types: Name (string), Size (long), Extension (string)

3. Code Generation

// Generated C# (simplified):
public Table Execute() {
    var source = new OSFilesRowSource("/Documents");
    return source.Rows
        .Where(f => f["Extension"].ToString() == ".pdf")
        .Select(f => new { Name = f["Name"], Size = f["Size"] })
        .OrderByDescending(f => f.Size)
        .ToTable();
}

4. Compilation & Execution

// Compile to assembly, create instance, execute
var results = compiledQuery.Run();

Plugin Development Overview

Creating a Data Source Plugin

// 1. Schema Definition
public class MyDataSchema : SchemaBase
{
    public override string Name => "mydata";
    
    public override RowSource GetRowSource(string name, RuntimeContext context, params object[] parameters)
    {
        return new MyDataRowSource(parameters);
    }
}

// 2. Data Source Implementation
public class MyDataRowSource : RowSource
{
    public override IEnumerable<IObjectResolver> Rows
    {
        get
        {
            foreach (var item in GetMyData())
                yield return new EntityResolver<MyDataItem>(item, MyDataMapping.Instance);
        }
    }
}

// 3. Usage in Queries
// SELECT * FROM #mydata.source('parameter')

Performance Characteristics

Compilation Strategy

• First Execution: Parse → Transform → Compile → Execute
• Subsequent Executions: Cache hit → Execute directly
• Memory Usage: Minimal object allocation during execution

Optimization Techniques

• Predicate Pushdown: WHERE clauses pushed to data sources
• Lazy Evaluation: Data processed on-demand
• Type Inference: Compile-time type checking

Scalability Considerations

• Data Size: Optimized for small to medium datasets
• Query Complexity: Handles complex SQL constructs efficiently
• Plugin Ecosystem: Modular design supports growing data source library

Use Cases and Benefits

Development Scenarios

• Code Analysis: Query source code structure and metrics
• Log Processing: Analyze application logs with SQL
• File System Operations: Directory traversal and file analysis
• Data Transformation: Convert between different data formats

Operational Benefits

• No Database Required: Direct data source querying
• Familiar Syntax: SQL knowledge immediately applicable
• Rapid Prototyping: Quick data exploration and analysis
• Integration Friendly: Easy to embed in applications

Integration Patterns

Embedded Usage

var engine = new MusoqEngine();
var results = engine.Execute("SELECT COUNT(*) FROM #os.files('/logs')");

CLI Usage

musoq "SELECT Name FROM #git.commits('/repo') WHERE AuthorEmail LIKE '%@company.com'"

Custom Plugin Integration

engine.RegisterSchema<MyCustomSchema>();
var results = engine.Execute("SELECT * FROM #mycustom.data()");

Next Steps

For comprehensive technical details, implementation guides, and advanced usage patterns, see the complete Architecture Documentation.

Key topics covered in the full documentation:

• Detailed component architecture
• Plugin development guide
• Performance optimization strategies
• Error handling patterns
• Testing approaches
• Extension points and customization options