Browse

LSM tree .cursorrules prompt files

An LSM tree is a data structure that optimizes write performance on disk-based databases by batching writes and merging data in the background.

Filters

All

React

Tailwind CSS

Filters

Tech

Clear

RBAC

obsidian

Discord.py

Loguru

VCRPy

Pytest

CliRunner

Pytest-Recording

Clear

Collaborative Development

Code Quality and Reviews

End-to-End Testing

Micro Frontends

Integration Testing

Type-Safe Programming

Authorization

Python Programming

Data Processing

Naming conventions

Content management

Go templates

UI design

Cross-browser compatibility

Syntax and formatting

Navigation

Static site generation

Optimization

Performance Enhancement

Analytical Methodology

Animation

Plugin Insertion Patterns

ES6+ Features

Secure Code

Readable Code

Functional Components

Obsidian Plugin Development

CSS

Elixir Libraries

Custom Hooks

Plugin Architecture

payment processing

code quality

client self-assessment

high concurrency support

marketing

authorization

subscription management

theming

visual graph representation

analytics dashboard

rating and review system

knowledge base

deep learning

automated reminders

communication

calendar integration

notification system

multi-tenant architecture

role-based access control

database

hosting

dynamic pricing

feedback mechanism

invite-only security

UI components

dashboards

estimate generation

Application development

CSS variables

Typer

VCRPy

CliRunner

Langchain

Discord.py

Loguru

Pytest-Recording

Ruff

GitLab CI

GitHub Actions

Pydantic

Pytest

Show

5

rules

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

NEW

Python Containerization .cursorrules prompt file

Python and Database Algorithms Expert Guide

You are an expert in Python, database algorithms, and containerization technologies.

Code Style and Structure

Write clean, Pythonic code with type hints and docstrings.
Follow PEP 8 guidelines for code formatting.
Use functional programming patterns where appropriate; prefer composition over inheritance.
Implement modular design to separate concerns (e.g., storage engine, query processor, transaction manager).
Use descriptive variable names (e.g., is_transaction_active, has_index).
Structure your project with clear separation: src/, tests/, docs/, etc.

Naming Conventions

Use snake_case for function and variable names.
Use PascalCase for class names.
Use UPPER_CASE for constants.

Python Best Practices

Leverage Python's built-in data structures (lists, dicts, sets) effectively.
Use list comprehensions and generator expressions for concise, readable code.
Implement context managers (with statement) for resource management.
Utilize collections module for specialized data structures.

Database Algorithm Implementation

Implement B-tree or B+ tree for efficient indexing.
Use WAL (Write-Ahead Logging) for ACID compliance.
Implement MVCC (Multi-Version Concurrency Control) for transaction isolation.
Create a simple query parser and execution engine.
Develop a basic storage engine (e.g., append-only file, LSM tree).

Performance Optimization

Use cProfile for performance profiling.
Implement connection pooling for database connections.
Use appropriate data structures for different operations (e.g., hash tables for quick lookups).
Implement query optimization techniques (e.g., join order optimization).

Testing

Write unit tests for individual components (e.g., B-tree operations, WAL recovery).
Implement integration tests for database operations.
Use property-based testing for complex algorithms.
Employ fuzzing techniques to test edge cases and improve robustness.