Performance Decorator¶

Optimizes system performance focusing on specific bottlenecks.

Category: Devops And Infrastructure

Parameters¶

Bottleneck Options¶

• cpu: Focus on CPU optimization strategies including algorithm efficiency, parallel processing, and computational load balancing.
• memory: Focus on memory usage optimization including allocation patterns, garbage collection, caching strategies, and memory leaks.
• io: Focus on I/O performance including disk access patterns, buffering strategies, and asynchronous operations.
• network: Focus on network performance including latency reduction, bandwidth optimization, and connection management.
• database: Focus on database performance including query optimization, indexing strategies, connection pooling, and data access patterns.
• algorithm: Focus on algorithmic efficiency including time complexity, space complexity, and algorithm selection.

Approach Options¶

• identify: Identify the specific performance bottlenecks through profiling and analysis without implementing solutions.
• measure: Measure and quantify performance metrics to establish baselines and identify improvement opportunities.
• optimize: Implement specific optimization techniques to address known performance issues.
• comprehensive: Perform a comprehensive performance analysis including identification, measurement, and optimization recommendations.

Constraints Options¶

• cost: Optimize within cost constraints, prioritizing solutions with minimal additional resource requirements.
• time: Optimize with time constraints in mind, focusing on solutions that can be implemented quickly.
• complexity: Optimize while maintaining code simplicity and maintainability.
• compatibility: Optimize while ensuring compatibility with existing systems and interfaces.
• none: Optimize for maximum performance without specific constraints.

Examples¶

Database performance optimization with compatibility constraints¶

+++Performance(bottleneck=database, approach=comprehensive, constraints=compatibility)
Optimize the performance of our product search functionality which is currently taking 5+ seconds to return results.

A comprehensive analysis of database performance issues affecting the product search functionality, with optimization recommendations that maintain compatibility with existing systems.

CPU performance identification¶

+++Performance(bottleneck=cpu, approach=identify)
Our image processing service is using 100% CPU during peak loads.

An analysis identifying specific CPU bottlenecks in the image processing service without implementation details.

Algorithm optimization with time constraints¶

+++Performance(bottleneck=algorithm, approach=optimize, constraints=time)
Our sorting algorithm needs to be faster for the upcoming release.

Specific algorithm optimization recommendations that can be implemented quickly before the upcoming release.

Model-Specific Implementations¶

gpt-4-turbo¶

Instruction: Analyze and improve system performance by identifying and resolving bottlenecks.

Notes: Simplified instruction for models with more limited context windows.

Implementation Guidance¶

Database query optimization¶

Original Prompt:

Optimize the performance of our product search functionality which is currently taking 5+ seconds to return results.

Transformed Prompt:

Analyze and optimize system performance with a focus on efficiency and scalability. Focus on database performance including query optimization, indexing strategies, connection pooling, and data access patterns. Perform a comprehensive performance analysis including identification, measurement, and optimization recommendations. Optimize while ensuring compatibility with existing systems and interfaces.

Optimize the performance of our product search functionality which is currently taking 5+ seconds to return results.

Notes: This example focuses on database optimization with a comprehensive approach while maintaining compatibility with existing systems.

Transformation Details¶

Base Instruction: Analyze and optimize system performance with a focus on efficiency and scalability.

Placement: prepend

Composition Behavior: accumulate

Parameter Effects:

• bottleneck:
• When set to cpu: Focus on CPU optimization strategies including algorithm efficiency, parallel processing, and computational load balancing.
• When set to memory: Focus on memory usage optimization including allocation patterns, garbage collection, caching strategies, and memory leaks.
• When set to io: Focus on I/O performance including disk access patterns, buffering strategies, and asynchronous operations.
• When set to network: Focus on network performance including latency reduction, bandwidth optimization, and connection management.
• When set to database: Focus on database performance including query optimization, indexing strategies, connection pooling, and data access patterns.

• When set to algorithm: Focus on algorithmic efficiency including time complexity, space complexity, and algorithm selection.

• approach:

• When set to identify: Identify the specific performance bottlenecks through profiling and analysis without implementing solutions.
• When set to measure: Measure and quantify performance metrics to establish baselines and identify improvement opportunities.
• When set to optimize: Implement specific optimization techniques to address known performance issues.

• When set to comprehensive: Perform a comprehensive performance analysis including identification, measurement, and optimization recommendations.

• constraints:

• When set to cost: Optimize within cost constraints, prioritizing solutions with minimal additional resource requirements.
• When set to time: Optimize with time constraints in mind, focusing on solutions that can be implemented quickly.
• When set to complexity: Optimize while maintaining code simplicity and maintainability.
• When set to compatibility: Optimize while ensuring compatibility with existing systems and interfaces.
• When set to none: Optimize for maximum performance without specific constraints.

Compatibility¶

• Requires: None
• Conflicts: None
• Compatible Models: gpt-4-turbo, gpt-4o, claude-3-7-sonnet-latest, llama-3.2
• Standard Version: 1.0.0 - 2.0.0

Related Decorators¶

• CodeReview: Enhances Performance Performance decorator can enhance code review by focusing specifically on performance aspects of the code.
• Simplify: Conflicts with Performance Performance optimizations sometimes increase code complexity, which may conflict with simplification goals.