Moltres Documentation
Moltres is the missing DataFrame layer for SQL in Python. It provides a PySpark-style DataFrame API that compiles to SQL and executes directly in your database with full SQL CRUD support and optional pandas/polars result formats.
Use these docs to:
Understand Moltres concepts and architecture
Follow step‑by‑step guides and recipes
Explore framework and tooling integrations
Look up the full, generated API reference
Note
New to Moltres? Start with Getting Started with Moltres, then explore the “Guides & How‑To” and “Integrations” sections below. The API reference is designed for day‑to‑day lookups once you are familiar with the basics.
Getting started & migration
Getting started & migration
Guides & how-to
Guides & how-to
- Common Patterns and Use Cases
- Performance Optimization Guide
- Error Handling and Debugging Guide
- Advanced Topics
- Best Practices Guide
- Using the Pandas-Style Interface in Moltres
- Overview
- Getting Started
- Filtering with Query
- String Accessor
- Data Inspection
- GroupBy Operations
- Merging DataFrames
- Sorting
- Dropping Duplicates
- Assigning New Columns
- Renaming Columns
- Dropping Columns
- Chaining Operations
- Boolean Indexing
- Collecting Results
- Error Handling
- Limitations and Differences from Pandas
- Best Practices
- Performance Considerations
- Examples
- Data Reshaping
- Sampling and Limiting
- Concatenation
- Advanced Filtering
- SQL Expressions and CTEs
- Next Steps
- Using the Polars-Style Interface in Moltres
- Overview
- Getting Started
- Filtering
- Selecting Columns
- Adding and Modifying Columns
- GroupBy Operations
- Joins
- Sorting
- Data Manipulation
- Limiting and Sampling
- Data Inspection
- Collecting Results
- Chaining Operations
- Comparison with Polars LazyFrame
- Key Differences
- Reading Files (Polars-Style)
- Writing Files (Polars-Style)
- String Operations (
.strnamespace) - DateTime Operations (
.dtnamespace) - Window Functions
- Conditional Expressions (
when().then().otherwise()) - Additional Operations
- Set Operations
- SQL Expression Selection
- Common Table Expressions (CTEs)
- Additional Utility Methods
- Best Practices
- See Also
Framework & tooling integrations
Concepts, operations, and internals
Concepts & operations
- Why Moltres?
- Moltres Design Notes
- Performance Tuning Guide
- Runtime & Platform Support
- Security Guide
- Testing Guide
- Debugging Guide
- Deployment Guide
- Troubleshooting Guide
- Common Patterns and Examples
- Showcase: Memory-Efficient Operations
- Showcase: CRUD + DataFrame Workflow
- Async Examples
- Basic Query Patterns
- Aggregations
- Joins
- Complex Queries
- Data Mutations
- File Operations
- Streaming for Large Datasets
- Table Management
- Working with Results
- Error Handling
- Performance Tips
- Common Patterns
- Use Cases by Audience
- Frequently Asked Questions (FAQ)
Comparisons
Internal and archive docs
Internal & archive
- Business Case: Moltres - The Missing DataFrame Layer for SQL in Python
- Project Charter: Moltres Development Acceleration
- Preliminary Project Scope Statement: Moltres Development Acceleration
- Moltres Package Plan
- 🐦🔥 Moltres: The Missing DataFrame Layer for SQL in Python
- Moltres Integration Features Plan
- Priority Implementation Roadmap
- Migration Guide
- PySpark to Moltres Migration Guide
- PySpark Feature Comparison
- PySpark Interface Audit for Moltres
- PySpark to Moltres Migration: Working Around Inconsistencies
- Performance Service Level Agreements (SLAs)
- Runtime Support Matrix
- Test Harness Reference
- Operational Runbooks
- Create new table
- Use ALTER TABLE (database-specific)
- Use streaming mode
- Plan: Improve pytest-green-light for Parallel Test Execution
- Release Process