Code Efficiency Best Practices

This guide outlines best practices for writing efficient and performant code with TomatoPy.

Memory Management

Efficient Ingredient Creation

# Good: Create ingredients with minimal memory usage
tomato = Tomato(
    ripeness=0.8,
    variety="San Marzano",
    weight=150
)

# Bad: Creating unnecessary copies
tomato_copy = tomato.copy()  # Unnecessary memory usage

Batch Processing

# Good: Process ingredients in batches
def process_ingredients(ingredients, batch_size=100):
    for i in range(0, len(ingredients), batch_size):
        batch = ingredients[i:i + batch_size]
        process_batch(batch)

# Bad: Processing one at a time
for ingredient in ingredients:
    process_single(ingredient)  # Less efficient

Performance Optimization

Caching Results

from functools import lru_cache

# Good: Cache expensive computations
@lru_cache(maxsize=128)
def analyze_ingredient(ingredient):
    return TasteTester().analyze(ingredient)

# Bad: Recomputing every time
def analyze_ingredient(ingredient):
    return TasteTester().analyze(ingredient)  # No caching

Efficient Data Structures

# Good: Use appropriate data structures
from collections import defaultdict

class Recipe:
    def __init__(self):
        self.ingredients = defaultdict(float)  # Efficient for ingredient tracking

# Bad: Using inefficient structures
class Recipe:
    def __init__(self):
        self.ingredients = []  # Less efficient for lookups

Resource Management

Context Managers

# Good: Use context managers for resource cleanup
with kitchen.temperature_monitor() as monitor:
    result = kitchen.cook(ingredient)
    data = monitor.get_data()

# Bad: Manual resource management
monitor = kitchen.temperature_monitor()
try:
    result = kitchen.cook(ingredient)
    data = monitor.get_data()
finally:
    monitor.cleanup()  # More error-prone

Connection Pooling

# Good: Use connection pooling
from tomatopy import ConnectionPool

pool = ConnectionPool(max_connections=10)
with pool.get_connection() as conn:
    conn.execute_operation()

# Bad: Creating new connections each time
conn = create_connection()  # Less efficient
conn.execute_operation()
conn.close()

Algorithm Optimization

Efficient Search

# Good: Use binary search for sorted data
def find_optimal_temperature(sorted_temps, target):
    left, right = 0, len(sorted_temps) - 1
    while left <= right:
        mid = (left + right) // 2
        if sorted_temps[mid] == target:
            return mid
        elif sorted_temps[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

# Bad: Linear search
def find_optimal_temperature(temps, target):
    for i, temp in enumerate(temps):
        if temp == target:
            return i
    return -1

Parallel Processing

from concurrent.futures import ThreadPoolExecutor

# Good: Process multiple ingredients in parallel
def process_ingredients_parallel(ingredients):
    with ThreadPoolExecutor(max_workers=4) as executor:
        results = list(executor.map(process_ingredient, ingredients))
    return results

# Bad: Sequential processing
def process_ingredients_sequential(ingredients):
    results = []
    for ingredient in ingredients:
        results.append(process_ingredient(ingredient))
    return results

Code Organization

Modular Design

# Good: Modular code structure
class IngredientProcessor:
    def process(self, ingredient):
        self.validate(ingredient)
        self.prepare(ingredient)
        self.analyze(ingredient)

# Bad: Monolithic functions
def process_ingredient(ingredient):
    # All processing in one function
    validate_ingredient(ingredient)
    prepare_ingredient(ingredient)
    analyze_ingredient(ingredient)

Clean Interfaces

# Good: Clean interface design
class Kitchen:
    def cook(self, ingredient, **kwargs):
        self._validate_parameters(kwargs)
        self._prepare_environment()
        return self._execute_cooking(ingredient, kwargs)

# Bad: Complex interface
def cook(ingredient, temperature=None, duration=None, method=None, 
         stirring_frequency=None, humidity=None, pressure=None):
    # Too many parameters
    pass

Error Handling

Efficient Error Recovery

# Good: Graceful error recovery
def process_recipe(recipe):
    try:
        result = kitchen.cook_recipe(recipe)
        return result
    except TemperatureError:
        # Handle temperature error
        return adjust_temperature(recipe)
    except IngredientError:
        # Handle ingredient error
        return substitute_ingredients(recipe)
    finally:
        cleanup_resources()

# Bad: No error recovery
def process_recipe(recipe):
    result = kitchen.cook_recipe(recipe)
    return result  # No error handling

Resource Cleanup

# Good: Proper resource cleanup
class Kitchen:
    def __init__(self):
        self.resources = []

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.cleanup()

# Bad: Manual cleanup
def use_kitchen():
    kitchen = Kitchen()
    try:
        # Use kitchen
        pass
    finally:
        kitchen.cleanup()

Testing and Profiling

Performance Testing

# Good: Performance testing
import time
import cProfile

def profile_operation(func):
    def wrapper(*args, **kwargs):
        profiler = cProfile.Profile()
        result = profiler.runcall(func, *args, **kwargs)
        profiler.print_stats()
        return result
    return wrapper

# Bad: No performance testing
def operation():
    # No performance monitoring
    pass

Memory Profiling

# Good: Memory profiling
from memory_profiler import profile

@profile
def memory_intensive_operation():
    # Operation to profile
    pass

# Bad: No memory profiling
def memory_intensive_operation():
    # No memory monitoring
    pass

Best Practices Summary

1. Memory Management

   • Use efficient data structures

   • Implement batch processing

   • Avoid unnecessary copies

2. Performance Optimization

   • Cache expensive computations

   • Use appropriate algorithms

   • Implement parallel processing

3. Resource Management

   • Use context managers

   • Implement connection pooling

   • Clean up resources properly

4. Code Organization

   • Follow modular design

   • Create clean interfaces

   • Maintain separation of concerns

5. Error Handling

   • Implement graceful recovery

   • Clean up resources properly

   • Log errors appropriately

Next Steps

• Virtual Kitchen Management - Learn kitchen optimization

• API Reference - Explore the full API

• Tutorials - Learn advanced techniques