From 3f270a98f135fb3864cbbbd64f87344b3ab32a12 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Thu, 30 Oct 2025 07:00:25 +0000
Subject: [PATCH 1/3] Initial plan


From 08c56323257d5ccb5f76f9fa8926769af5d84761 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Thu, 30 Oct 2025 07:07:52 +0000
Subject: [PATCH 2/3] Add comprehensive performance optimization guide and
 examples

Co-authored-by: topchen2025 <227556749+topchen2025@users.noreply.github.com>
---
 PERFORMANCE_OPTIMIZATION_GUIDE.md             | 235 ++++++++++++++++++
 README.md                                     |  70 ++++++
 examples/README.md                            |  55 ++++
 .../improved_example.cpython-312.pyc          | Bin 0 -> 7629 bytes
 .../inefficient_example.cpython-312.pyc       | Bin 0 -> 6488 bytes
 examples/benchmark.py                         | 111 +++++++++
 examples/improved_example.py                  | 222 +++++++++++++++++
 examples/inefficient_example.py               | 164 ++++++++++++
 8 files changed, 857 insertions(+)
 create mode 100644 PERFORMANCE_OPTIMIZATION_GUIDE.md
 create mode 100644 README.md
 create mode 100644 examples/README.md
 create mode 100644 examples/__pycache__/improved_example.cpython-312.pyc
 create mode 100644 examples/__pycache__/inefficient_example.cpython-312.pyc
 create mode 100644 examples/benchmark.py
 create mode 100644 examples/improved_example.py
 create mode 100644 examples/inefficient_example.py

diff --git a/PERFORMANCE_OPTIMIZATION_GUIDE.md b/PERFORMANCE_OPTIMIZATION_GUIDE.md
new file mode 100644
index 0000000..064d95d
--- /dev/null
+++ b/PERFORMANCE_OPTIMIZATION_GUIDE.md
@@ -0,0 +1,235 @@
+# Performance Optimization Guide
+
+## Overview
+This guide provides best practices and examples for identifying and improving slow or inefficient code. While this repository currently contains documentation, this guide demonstrates common performance issues and their solutions.
+
+## Common Performance Issues and Solutions
+
+### 1. Algorithm Complexity
+**Issue**: Using algorithms with poor time complexity for large datasets.
+
+**Bad Practice** - O(n²) nested loops:
+```python
+# Inefficient: O(n²) time complexity
+def find_duplicates_slow(items):
+    duplicates = []
+    for i in range(len(items)):
+        for j in range(i + 1, len(items)):
+            if items[i] == items[j] and items[i] not in duplicates:
+                duplicates.append(items[i])
+    return duplicates
+```
+
+**Good Practice** - O(n) using sets:
+```python
+# Efficient: O(n) time complexity
+def find_duplicates_fast(items):
+    seen = set()
+    duplicates = set()
+    for item in items:
+        if item in seen:
+            duplicates.add(item)
+        else:
+            seen.add(item)
+    return list(duplicates)
+```
+
+### 2. Unnecessary Database Queries (N+1 Problem)
+**Issue**: Making multiple database queries in a loop instead of batch processing.
+
+**Bad Practice**:
+```python
+# Inefficient: N+1 queries
+def get_user_posts_slow(user_ids):
+    results = []
+    for user_id in user_ids:
+        user = db.query("SELECT * FROM users WHERE id = ?", user_id)
+        posts = db.query("SELECT * FROM posts WHERE user_id = ?", user_id)
+        results.append({'user': user, 'posts': posts})
+    return results
+```
+
+**Good Practice**:
+```python
+# Efficient: Batch queries
+def get_user_posts_fast(user_ids):
+    users = db.query("SELECT * FROM users WHERE id IN (?)", user_ids)
+    posts = db.query("SELECT * FROM posts WHERE user_id IN (?)", user_ids)
+    
+    # Organize results
+    users_dict = {u['id']: u for u in users}
+    posts_dict = {}
+    for post in posts:
+        posts_dict.setdefault(post['user_id'], []).append(post)
+    
+    return [{'user': users_dict[uid], 'posts': posts_dict.get(uid, [])} 
+            for uid in user_ids]
+```
+
+### 3. String Concatenation in Loops
+**Issue**: Building strings through repeated concatenation creates many intermediate objects.
+
+**Bad Practice**:
+```python
+# Inefficient: Creates many intermediate strings
+def build_html_slow(items):
+    html = ""
+    for item in items:
+        html += f"<li>{item}</li>\n"
+    return f"<ul>\n{html}</ul>"
+```
+
+**Good Practice**:
+```python
+# Efficient: Using join() or list accumulation
+def build_html_fast(items):
+    parts = ["<ul>"]
+    parts.extend(f"<li>{item}</li>" for item in items)
+    parts.append("</ul>")
+    return "\n".join(parts)
+```
+
+### 4. Not Using Caching
+**Issue**: Recalculating expensive operations repeatedly.
+
+**Bad Practice**:
+```python
+# Inefficient: Recalculates Fibonacci every time
+def fibonacci_slow(n):
+    if n <= 1:
+        return n
+    return fibonacci_slow(n - 1) + fibonacci_slow(n - 2)
+```
+
+**Good Practice**:
+```python
+# Efficient: Using memoization
+from functools import lru_cache
+
+@lru_cache(maxsize=None)
+def fibonacci_fast(n):
+    if n <= 1:
+        return n
+    return fibonacci_fast(n - 1) + fibonacci_fast(n - 2)
+```
+
+### 5. Loading Entire Files into Memory
+**Issue**: Reading large files all at once can cause memory issues.
+
+**Bad Practice**:
+```python
+# Inefficient: Loads entire file into memory
+def process_large_file_slow(filename):
+    with open(filename, 'r') as f:
+        data = f.read()
+        lines = data.split('\n')
+        return [line.upper() for line in lines if line.strip()]
+```
+
+**Good Practice**:
+```python
+# Efficient: Process line by line
+def process_large_file_fast(filename):
+    result = []
+    with open(filename, 'r') as f:
+        for line in f:
+            stripped = line.strip()
+            if stripped:
+                result.append(stripped.upper())
+    return result
+```
+
+### 6. Not Using Appropriate Data Structures
+**Issue**: Using lists when sets or dictionaries would be more efficient.
+
+**Bad Practice**:
+```python
+# Inefficient: O(n) lookup time with list
+def find_common_elements_slow(list1, list2):
+    common = []
+    for item in list1:
+        if item in list2 and item not in common:
+            common.append(item)
+    return common
+```
+
+**Good Practice**:
+```python
+# Efficient: O(1) lookup time with sets
+def find_common_elements_fast(list1, list2):
+    return list(set(list1) & set(list2))
+```
+
+### 7. Inefficient Regular Expressions
+**Issue**: Compiling regex patterns repeatedly in loops.
+
+**Bad Practice**:
+```python
+# Inefficient: Compiles regex on every iteration
+import re
+
+def extract_emails_slow(texts):
+    emails = []
+    for text in texts:
+        matches = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
+        emails.extend(matches)
+    return emails
+```
+
+**Good Practice**:
+```python
+# Efficient: Compile regex once
+import re
+
+EMAIL_PATTERN = re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b')
+
+def extract_emails_fast(texts):
+    emails = []
+    for text in texts:
+        matches = EMAIL_PATTERN.findall(text)
+        emails.extend(matches)
+    return emails
+```
+
+### 8. Premature Optimization
+**Important Note**: Always profile your code before optimizing. Focus on:
+1. **Correctness first**: Make it work correctly
+2. **Profile**: Measure where the bottlenecks actually are
+3. **Optimize**: Focus on the actual bottlenecks
+4. **Measure again**: Verify improvements
+
+## Performance Testing Tools
+
+### Python
+- `cProfile`: Built-in profiler
+- `timeit`: Measure execution time
+- `memory_profiler`: Track memory usage
+- `py-spy`: Sampling profiler
+
+### JavaScript
+- Chrome DevTools Performance tab
+- `console.time()` and `console.timeEnd()`
+- `performance.now()`
+
+### Java
+- JProfiler
+- VisualVM
+- Java Mission Control
+
+## Best Practices Checklist
+
+- [ ] Use appropriate data structures for the problem
+- [ ] Minimize database queries (use batch operations)
+- [ ] Cache expensive computations
+- [ ] Avoid nested loops where possible
+- [ ] Use generators for large datasets
+- [ ] Profile before optimizing
+- [ ] Consider time vs space tradeoffs
+- [ ] Use connection pooling for databases
+- [ ] Implement pagination for large result sets
+- [ ] Use indexes on database columns used in WHERE/JOIN clauses
+- [ ] Close resources properly (files, connections, etc.)
+
+## Conclusion
+
+Performance optimization should be data-driven. Always measure before and after optimizations to ensure they provide real benefits. Remember: premature optimization is the root of all evil, but knowing these patterns helps you write better code from the start.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..1506304
--- /dev/null
+++ b/README.md
@@ -0,0 +1,70 @@
+# IRIS Study Repository
+
+This repository contains documentation and code examples for studying InterSystems IRIS and software performance optimization.
+
+## Contents
+
+### Documentation
+- **GCOS.pdf**: Global Caché Object Server documentation
+- **RCOS.pdf**: Relational Caché Object Server documentation
+- **InterSystems 常用术语.pdf**: Common terminology for InterSystems (Chinese)
+
+### Performance Optimization Resources
+
+#### Performance Optimization Guide
+See [PERFORMANCE_OPTIMIZATION_GUIDE.md](PERFORMANCE_OPTIMIZATION_GUIDE.md) for comprehensive information on:
+- Common performance issues and their solutions
+- Algorithm complexity considerations
+- Database query optimization
+- Caching strategies
+- String operations best practices
+- Appropriate data structure selection
+- Performance testing tools
+
+#### Code Examples
+The [examples/](examples/) directory contains practical demonstrations:
+- **inefficient_example.py**: Intentionally slow code showing common anti-patterns
+- **improved_example.py**: Optimized versions with best practices
+- **benchmark.py**: Performance comparison script showing actual improvements
+
+## Running the Examples
+
+To see the performance improvements in action:
+
+```bash
+# Run benchmark comparison
+python examples/benchmark.py
+
+# Run individual examples
+python examples/inefficient_example.py
+python examples/improved_example.py
+```
+
+## Key Learnings
+
+The examples demonstrate typical performance improvements:
+- **Find Duplicates**: ~500x faster (O(n²) → O(n))
+- **Fibonacci Calculation**: ~900x faster (with memoization)
+- **List Membership Testing**: ~70x faster (list → set)
+- **Scalability**: Fibonacci(100) computed instantly vs impossible with naive approach
+
+## Performance Best Practices
+
+1. ✅ Profile before optimizing
+2. ✅ Use appropriate data structures
+3. ✅ Minimize algorithm complexity
+4. ✅ Cache expensive computations
+5. ✅ Batch database operations
+6. ✅ Process large files incrementally
+7. ✅ Compile regex patterns once
+8. ✅ Use generators for large datasets
+
+## Contributing
+
+This is a study repository. Feel free to add more examples or improve existing documentation.
+
+## Resources
+
+- [InterSystems Documentation](https://docs.intersystems.com/)
+- [Python Performance Tips](https://wiki.python.org/moin/PythonSpeed/PerformanceTips)
+- [Algorithm Complexity Reference](https://www.bigocheatsheet.com/)
diff --git a/examples/README.md b/examples/README.md
new file mode 100644
index 0000000..c382dff
--- /dev/null
+++ b/examples/README.md
@@ -0,0 +1,55 @@
+# Performance Optimization Examples
+
+This directory contains practical examples demonstrating common performance issues and their solutions.
+
+## Files
+
+- **inefficient_example.py**: Contains intentionally slow and inefficient code to demonstrate common performance anti-patterns
+- **improved_example.py**: Contains optimized versions of the same functions with best practices
+- **benchmark.py**: Compares the performance of both versions and shows the improvements
+
+## Running the Examples
+
+### Run Inefficient Examples
+```bash
+python examples/inefficient_example.py
+```
+
+### Run Improved Examples
+```bash
+python examples/improved_example.py
+```
+
+### Run Performance Benchmark
+```bash
+python examples/benchmark.py
+```
+
+## Key Performance Issues Demonstrated
+
+1. **Algorithm Complexity**: O(n²) vs O(n) implementations
+2. **Memoization**: Caching expensive recursive calculations
+3. **String Building**: String concatenation vs join()
+4. **Data Structures**: List vs Set for membership testing
+5. **Regex Compilation**: Repeated compilation vs compiled patterns
+6. **Batch Processing**: N+1 queries vs batch operations
+7. **Caching**: Redundant loading vs cached configuration
+
+## Expected Performance Improvements
+
+When running the benchmark, you should see:
+- Find Duplicates: ~100-1000x faster
+- Fibonacci(25): ~1000-10000x faster
+- String Building: ~10-50x faster
+- List Membership: ~50-100x faster
+
+## Learning Objectives
+
+After reviewing these examples, you should understand:
+1. How to identify performance bottlenecks
+2. When to use different data structures
+3. The importance of algorithm complexity
+4. How to apply memoization and caching
+5. Best practices for string operations
+6. How to avoid N+1 query problems
+7. The value of benchmarking before and after optimization
diff --git a/examples/__pycache__/improved_example.cpython-312.pyc b/examples/__pycache__/improved_example.cpython-312.pyc
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diff --git a/examples/benchmark.py b/examples/benchmark.py
new file mode 100644
index 0000000..eee115b
--- /dev/null
+++ b/examples/benchmark.py
@@ -0,0 +1,111 @@
+"""
+Performance Benchmark Comparison
+Compares the performance of inefficient vs improved code examples.
+"""
+
+import time
+import sys
+from inefficient_example import (
+    find_duplicates_slow,
+    fibonacci_slow,
+    build_string_slow,
+    find_in_list_slow
+)
+from improved_example import (
+    find_duplicates_fast,
+    fibonacci_fast,
+    build_string_fast,
+    find_in_list_fast
+)
+
+
+def benchmark_function(func, *args, iterations=1):
+    """Run a function multiple times and return average execution time."""
+    total_time = 0
+    for _ in range(iterations):
+        start = time.time()
+        result = func(*args)
+        total_time += time.time() - start
+    return total_time / iterations, result
+
+
+def format_speedup(slow_time, fast_time):
+    """Calculate and format the speedup factor."""
+    if fast_time == 0:
+        return "∞x faster"
+    speedup = slow_time / fast_time
+    return f"{speedup:.2f}x faster"
+
+
+def print_benchmark_result(name, slow_time, fast_time):
+    """Print formatted benchmark results."""
+    speedup = format_speedup(slow_time, fast_time)
+    print(f"{name:30} | Slow: {slow_time:8.6f}s | Fast: {fast_time:8.6f}s | {speedup}")
+
+
+def main():
+    print("=" * 80)
+    print("Performance Benchmark: Inefficient vs Improved Code")
+    print("=" * 80)
+    print()
+    
+    # Benchmark 1: Find Duplicates
+    print("1. Find Duplicates (1500 items, 500 duplicates)")
+    test_data = list(range(1000)) + list(range(500))
+    slow_time, _ = benchmark_function(find_duplicates_slow, test_data)
+    fast_time, _ = benchmark_function(find_duplicates_fast, test_data)
+    print_benchmark_result("Find Duplicates", slow_time, fast_time)
+    print()
+    
+    # Benchmark 2: Fibonacci
+    print("2. Fibonacci Calculation")
+    fib_n = 25
+    print(f"   Computing Fibonacci({fib_n})...")
+    slow_time, slow_result = benchmark_function(fibonacci_slow, fib_n)
+    fast_time, fast_result = benchmark_function(fibonacci_fast, fib_n)
+    print_benchmark_result("Fibonacci", slow_time, fast_time)
+    print(f"   Results match: {slow_result == fast_result}")
+    print()
+    
+    # Benchmark 3: String Building
+    print("3. String Building (5000 items)")
+    items = range(5000)
+    slow_time, slow_result = benchmark_function(build_string_slow, items)
+    fast_time, fast_result = benchmark_function(build_string_fast, items)
+    print_benchmark_result("String Building", slow_time, fast_time)
+    print(f"   Results match: {slow_result == fast_result}")
+    print()
+    
+    # Benchmark 4: List Membership Testing
+    print("4. List Membership Testing (10000 items, searching 1000)")
+    items = list(range(10000))
+    search_values = list(range(0, 10000, 10))
+    slow_time, slow_result = benchmark_function(find_in_list_slow, items, search_values)
+    fast_time, fast_result = benchmark_function(find_in_list_fast, items, search_values)
+    print_benchmark_result("List Membership", slow_time, fast_time)
+    print(f"   Results match: {set(slow_result) == set(fast_result)}")
+    print()
+    
+    # Demonstrate scalability with larger Fibonacci
+    print("5. Scalability Test: Large Fibonacci")
+    print(f"   Computing Fibonacci(100) - Only possible with optimized version!")
+    try:
+        start = time.time()
+        result = fibonacci_fast(100)
+        elapsed = time.time() - start
+        print(f"   Fibonacci(100) = {result}")
+        print(f"   Time taken: {elapsed:.6f}s")
+        print(f"   (Slow version would take years to complete!)")
+    except Exception as e:
+        print(f"   Error: {e}")
+    print()
+    
+    print("=" * 80)
+    print("Summary:")
+    print("The optimized versions demonstrate significant performance improvements")
+    print("by using appropriate algorithms and data structures.")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/improved_example.py b/examples/improved_example.py
new file mode 100644
index 0000000..62ba788
--- /dev/null
+++ b/examples/improved_example.py
@@ -0,0 +1,222 @@
+"""
+Improved Code Examples
+This file contains optimized versions of the inefficient code from inefficient_example.py
+Each function demonstrates performance best practices.
+"""
+
+import time
+import re
+from functools import lru_cache
+
+
+def find_duplicates_fast(items):
+    """
+    Improvement: O(n) time complexity using sets
+    Performance: Dramatically faster for large lists
+    """
+    seen = set()
+    duplicates = set()
+    for item in items:
+        if item in seen:
+            duplicates.add(item)
+        else:
+            seen.add(item)
+    return list(duplicates)
+
+
+@lru_cache(maxsize=None)
+def fibonacci_fast(n):
+    """
+    Improvement: O(n) time complexity with memoization
+    Performance: Can handle much larger values of n
+    """
+    if n <= 1:
+        return n
+    return fibonacci_fast(n - 1) + fibonacci_fast(n - 2)
+
+
+def build_string_fast(items):
+    """
+    Improvement: Using join() for O(n) complexity
+    Performance: Much faster as no intermediate strings created
+    """
+    return ",".join(str(item) for item in items)
+
+
+def find_in_list_fast(items, search_values):
+    """
+    Improvement: Converting to set for O(1) lookup time
+    Performance: Efficient even for large datasets
+    """
+    items_set = set(items)
+    return [value for value in search_values if value in items_set]
+
+
+def process_file_fast(filename):
+    """
+    Improvement: Processing file line-by-line
+    Performance: Memory efficient for files of any size
+    """
+    result = []
+    with open(filename, 'r') as f:
+        for line in f:
+            stripped = line.strip()
+            if stripped:
+                result.append(stripped.upper())
+    return result
+
+
+# Compile regex pattern once at module level
+EMAIL_PATTERN = re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b')
+
+
+def extract_emails_fast(texts):
+    """
+    Improvement: Compile regex pattern once
+    Performance: Avoids repeated compilation overhead
+    """
+    emails = []
+    for text in texts:
+        matches = EMAIL_PATTERN.findall(text)
+        emails.extend(matches)
+    return emails
+
+
+def calculate_statistics_fast(numbers):
+    """
+    Improvement: Single pass through data where possible
+    Performance: Reduces iterations and improves cache efficiency
+    """
+    if not numbers:
+        return {'mean': 0, 'median': 0, 'variance': 0, 'count': 0}
+    
+    # Single pass for mean
+    total = 0
+    count = 0
+    for x in numbers:
+        total += x
+        count += 1
+    
+    mean = total / count
+    
+    # Single pass for variance
+    variance_sum = 0
+    for x in numbers:
+        variance_sum += (x - mean) ** 2
+    variance = variance_sum / count
+    
+    # For median, we still need to sort, but we can optimize
+    sorted_numbers = sorted(numbers)
+    mid = count // 2
+    if count % 2 == 0:
+        median = (sorted_numbers[mid - 1] + sorted_numbers[mid]) / 2
+    else:
+        median = sorted_numbers[mid]
+    
+    return {
+        'mean': mean,
+        'median': median,
+        'variance': variance,
+        'count': count
+    }
+
+
+def get_user_data_fast(user_ids, db_connection):
+    """
+    Improvement: Batch query instead of N+1 queries
+    Performance: Single database round-trip instead of multiple
+    """
+    # Simulated batch query (would be actual DB call in real code)
+    # In real SQL: SELECT * FROM users WHERE id IN (user_ids)
+    results = [f"User-{user_id}" for user_id in user_ids]
+    return results
+
+
+def check_all_conditions_fast(value):
+    """
+    Improvement: Order conditions by cost and use short-circuit evaluation
+    Performance: Exits early if cheap conditions fail
+    """
+    # Check cheap conditions first
+    if not (value > 0 and value < 100):
+        return False
+    
+    # Only run expensive checks if cheap ones pass
+    expensive_check_1 = time.sleep(0.001) or True
+    if not expensive_check_1:
+        return False
+    
+    expensive_check_2 = time.sleep(0.001) or True
+    if not expensive_check_2:
+        return False
+    
+    expensive_check_3 = time.sleep(0.001) or True
+    return expensive_check_3
+
+
+# Cache configuration at module level
+_CONFIG_CACHE = None
+
+
+def load_configuration_fast():
+    """
+    Improvement: Cache configuration instead of reloading
+    Performance: Avoids redundant operations
+    """
+    global _CONFIG_CACHE
+    if _CONFIG_CACHE is None:
+        _CONFIG_CACHE = {
+            'database_url': 'localhost:5432',
+            'timeout': 30,
+            'max_connections': 10
+        }
+    return _CONFIG_CACHE
+
+
+def batch_process_items(items, batch_size=100):
+    """
+    Best Practice: Process large datasets in batches
+    Performance: Reduces memory usage and allows progress tracking
+    """
+    for i in range(0, len(items), batch_size):
+        batch = items[i:i + batch_size]
+        # Process batch
+        yield [item * 2 for item in batch]
+
+
+def lazy_evaluation_example(n):
+    """
+    Best Practice: Use generators for lazy evaluation
+    Performance: Memory efficient, only computes values as needed
+    """
+    for i in range(n):
+        if i % 2 == 0:
+            yield i * i
+
+
+if __name__ == "__main__":
+    # Demonstrate improved performance
+    print("Running optimized examples...")
+    
+    # Example 1: Find duplicates
+    test_data = list(range(1000)) + list(range(500))
+    start = time.time()
+    duplicates = find_duplicates_fast(test_data)
+    print(f"Find duplicates (fast): {time.time() - start:.4f}s")
+    
+    # Example 2: Fibonacci
+    start = time.time()
+    result = fibonacci_fast(20)
+    print(f"Fibonacci 20 (fast): {time.time() - start:.4f}s")
+    
+    # Example 3: String building
+    start = time.time()
+    result = build_string_fast(range(1000))
+    print(f"Build string (fast): {time.time() - start:.4f}s")
+    
+    # Example 4: Demonstrate even larger Fibonacci is now feasible
+    start = time.time()
+    result = fibonacci_fast(100)
+    print(f"Fibonacci 100 (fast): {time.time() - start:.4f}s - Result: {result}")
+    
+    print("\nCompare these times with inefficient_example.py!")
diff --git a/examples/inefficient_example.py b/examples/inefficient_example.py
new file mode 100644
index 0000000..7c02afb
--- /dev/null
+++ b/examples/inefficient_example.py
@@ -0,0 +1,164 @@
+"""
+Inefficient Code Examples
+This file contains intentionally inefficient code to demonstrate common performance issues.
+See improved_example.py for optimized versions.
+"""
+
+import time
+import re
+
+
+def find_duplicates_slow(items):
+    """
+    Issue: O(n²) time complexity with nested loops
+    Performance: Very slow for large lists
+    """
+    duplicates = []
+    for i in range(len(items)):
+        for j in range(i + 1, len(items)):
+            if items[i] == items[j] and items[i] not in duplicates:
+                duplicates.append(items[i])
+    return duplicates
+
+
+def fibonacci_slow(n):
+    """
+    Issue: Exponential time complexity O(2^n) due to repeated calculations
+    Performance: Unusable for n > 35
+    """
+    if n <= 1:
+        return n
+    return fibonacci_slow(n - 1) + fibonacci_slow(n - 2)
+
+
+def build_string_slow(items):
+    """
+    Issue: String concatenation in loop creates many intermediate objects
+    Performance: O(n²) due to string immutability
+    """
+    result = ""
+    for item in items:
+        result += str(item) + ","
+    return result[:-1] if result else ""
+
+
+def find_in_list_slow(items, search_values):
+    """
+    Issue: Using list for membership testing (O(n) per lookup)
+    Performance: Inefficient for large datasets
+    """
+    found = []
+    for value in search_values:
+        if value in items:
+            found.append(value)
+    return found
+
+
+def process_file_slow(filename):
+    """
+    Issue: Loading entire file into memory at once
+    Performance: Can cause memory issues with large files
+    """
+    with open(filename, 'r') as f:
+        content = f.read()
+        lines = content.split('\n')
+        return [line.strip().upper() for line in lines if line.strip()]
+
+
+def extract_emails_slow(texts):
+    """
+    Issue: Compiling regex pattern on every iteration
+    Performance: Unnecessary overhead from repeated compilation
+    """
+    emails = []
+    for text in texts:
+        matches = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
+        emails.extend(matches)
+    return emails
+
+
+def calculate_statistics_slow(numbers):
+    """
+    Issue: Multiple passes through data for different calculations
+    Performance: O(n) per calculation instead of single pass
+    """
+    total = sum(numbers)
+    count = len(numbers)
+    mean = total / count if count > 0 else 0
+    
+    # Separate pass for variance
+    variance = sum((x - mean) ** 2 for x in numbers) / count if count > 0 else 0
+    
+    # Separate sorting for median
+    sorted_numbers = sorted(numbers)
+    median = sorted_numbers[count // 2] if count > 0 else 0
+    
+    return {
+        'mean': mean,
+        'median': median,
+        'variance': variance,
+        'count': count
+    }
+
+
+def get_user_data_slow(user_ids, db_connection):
+    """
+    Issue: N+1 query problem - making separate query for each user
+    Performance: High database load, slow response time
+    """
+    results = []
+    for user_id in user_ids:
+        # Simulated database query (would be actual DB call in real code)
+        user = f"User-{user_id}"
+        results.append(user)
+    return results
+
+
+def check_all_conditions_slow(value):
+    """
+    Issue: Not using short-circuit evaluation effectively
+    Performance: Evaluates all conditions even when early ones fail
+    """
+    expensive_check_1 = time.sleep(0.001) or True  # Simulated expensive operation
+    expensive_check_2 = time.sleep(0.001) or True  # Simulated expensive operation
+    expensive_check_3 = time.sleep(0.001) or True  # Simulated expensive operation
+    
+    if expensive_check_1 and expensive_check_2 and expensive_check_3:
+        return value > 0 and value < 100
+    return False
+
+
+def load_configuration_slow():
+    """
+    Issue: Loading configuration on every call
+    Performance: Redundant I/O operations
+    """
+    config = {
+        'database_url': 'localhost:5432',
+        'timeout': 30,
+        'max_connections': 10
+    }
+    return config
+
+
+if __name__ == "__main__":
+    # Demonstrate slow performance
+    print("Running inefficient examples...")
+    
+    # Example 1: Find duplicates
+    test_data = list(range(1000)) + list(range(500))
+    start = time.time()
+    duplicates = find_duplicates_slow(test_data)
+    print(f"Find duplicates (slow): {time.time() - start:.4f}s")
+    
+    # Example 2: Fibonacci (don't test with large numbers!)
+    start = time.time()
+    result = fibonacci_slow(20)
+    print(f"Fibonacci 20 (slow): {time.time() - start:.4f}s")
+    
+    # Example 3: String building
+    start = time.time()
+    result = build_string_slow(range(1000))
+    print(f"Build string (slow): {time.time() - start:.4f}s")
+    
+    print("\nSee improved_example.py for optimized versions!")

From 3b254eddbb4c54acdc6d0bf29fa5bc5a01723ba2 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Thu, 30 Oct 2025 07:08:22 +0000
Subject: [PATCH 3/3] Add .gitignore and remove Python cache files

Co-authored-by: topchen2025 <227556749+topchen2025@users.noreply.github.com>
---
 .gitignore                                    |  37 ++++++++++++++++++
 .../improved_example.cpython-312.pyc          | Bin 7629 -> 0 bytes
 .../inefficient_example.cpython-312.pyc       | Bin 6488 -> 0 bytes
 3 files changed, 37 insertions(+)
 create mode 100644 .gitignore
 delete mode 100644 examples/__pycache__/improved_example.cpython-312.pyc
 delete mode 100644 examples/__pycache__/inefficient_example.cpython-312.pyc

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c6f9226
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,37 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+ENV/
+env/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
diff --git a/examples/__pycache__/improved_example.cpython-312.pyc b/examples/__pycache__/improved_example.cpython-312.pyc
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