Code Optimizer

Improve code performance, memory usage, and efficiency through systematic optimization.

Core Capabilities

This skill helps optimize code by:

1. Analyzing performance bottlenecks - Identifying slow or inefficient code
2. Suggesting optimizations - Providing concrete improvements with examples
3. Explaining trade-offs - Describing benefits and potential drawbacks
4. Measuring impact - Estimating performance gains
5. Preserving correctness - Ensuring optimizations don't change behavior

Optimization Workflow

Step 1: Identify Optimization Opportunities

Analyze code to find performance bottlenecks.

Look for:

• Nested loops (O(n²) or worse complexity)
• Repeated expensive operations
• Inefficient data structures
• Unnecessary object creation
• Database N+1 queries
• Blocking I/O operations
• Memory leaks or excessive allocation

Quick Analysis Questions:

• What is the time complexity? Can it be reduced?
• Are there repeated calculations that could be cached?
• Is the right data structure being used?
• Are there unnecessary copies or allocations?
• Can operations be batched or parallelized?

Step 2: Categorize the Optimization

Determine the type of optimization needed.

Execution Speed:

• Algorithm optimization (better complexity)
• Loop optimization
• Caching/memoization
• Lazy evaluation
• Parallel processing

Memory Usage:

• Reduce object creation
• Use generators/streams instead of lists
• Clear references to enable garbage collection
• Use appropriate data structures
• Avoid memory leaks

Database Operations:

• Query optimization (indexes, joins)
• Batch operations
• Connection pooling
• Caching
• Reduce round trips

I/O Operations:

• Buffering
• Async/non-blocking I/O
• Batch requests
• Compression
• Caching

Step 3: Propose Optimization with Examples

Provide before/after code with clear explanations.

Optimization Template:

## Optimization: [Brief Description]

### Before (Inefficient)
```[language]
[original code]

Issues:

• Issue 1: [Problem description]
• Issue 2: [Problem description]

Complexity: O([complexity]) Performance: [estimated time/memory]

After (Optimized)

[optimized code]

Improvements:

• Improvement 1: [What changed]
• Improvement 2: [What changed]

Complexity: O([new complexity]) Performance: [estimated time/memory] Gain: [X% faster / Y% less memory]

Why This Works

[Detailed explanation of the optimization]

Trade-offs

Pros:

• [Benefit 1]
• [Benefit 2]

Cons:

• [Drawback 1, if any]
• [Drawback 2, if any]

When to Use

• Use when: [scenario]
• Avoid when: [scenario]

### Step 4: Measure and Validate

Ensure optimization actually improves performance.

**Measurement Techniques:**

**Python:**
```python
import time
import memory_profiler

# Time measurement
start = time.time()
result = function()
elapsed = time.time() - start
print(f"Elapsed: {elapsed:.4f}s")

# Memory measurement
from memory_profiler import profile

@profile
def function():
    # Code to profile
    pass

Java:

// Time measurement
long start = System.nanoTime();
result = function();
long elapsed = System.nanoTime() - start;
System.out.println("Elapsed: " + elapsed / 1_000_000 + "ms");

// Memory measurement
Runtime runtime = Runtime.getRuntime();
long before = runtime.totalMemory() - runtime.freeMemory();
result = function();
long after = runtime.totalMemory() - runtime.freeMemory();
System.out.println("Memory used: " + (after - before) / 1024 + "KB");

Validation Checklist:

• ✓ Correctness: Output matches original
• ✓ Performance: Measurable improvement
• ✓ Memory: Reduced allocation or leaks fixed
• ✓ Maintainability: Code remains readable
• ✓ Edge cases: Handles all inputs correctly

Common Optimizations

Python Optimizations

1. Use List Comprehensions Over Loops

# Before: O(n) with overhead
numbers = []
for i in range(1000):
    if i % 2 == 0:
        numbers.append(i * 2)

# After: O(n) faster execution
numbers = [i * 2 for i in range(1000) if i % 2 == 0]

# Gain: 2-3x faster

2. Use Generators for Large Sequences

# Before: O(n) memory
def get_numbers(n):
    result = []
    for i in range(n):
        result.append(i ** 2)
    return result

numbers = get_numbers(1000000)  # Uses ~8MB memory

# After: O(1) memory
def get_numbers(n):
    for i in range(n):
        yield i ** 2

numbers = get_numbers(1000000)  # Uses minimal memory

# Gain: 99% less memory for large n

3. Use Built-in Functions

# Before: Slower
total = 0
for num in numbers:
    total += num

# After: Faster (C implementation)
total = sum(numbers)

# Gain: 10-20x faster for large lists

4. Avoid Repeated Lookups

# Before: Repeated lookups
for i in range(len(data)):
    process(data[i])

# After: Single lookup
for item in data:
    process(item)

# Or with enumerate
for i, item in enumerate(data):
    process(item)

# Gain: Faster iteration, more Pythonic

5. Use Sets for Membership Testing

# Before: O(n) per lookup
items = [1, 2, 3, 4, 5, ...]  # Large list
if x in items:  # O(n) lookup
    do_something()

# After: O(1) per lookup
items = {1, 2, 3, 4, 5, ...}  # Set
if x in items:  # O(1) lookup
    do_something()

# Gain: 100x faster for large collections

See

references/python_optimizations.md

for comprehensive Python optimization patterns.

Java Optimizations

1. Use StringBuilder for String Concatenation

// Before: O(n²) - creates n strings
String result = "";
for (int i = 0; i < 1000; i++) {
    result += i + ",";  // Creates new string each time
}

// After: O(n) - single buffer
StringBuilder result = new StringBuilder();
for (int i = 0; i < 1000; i++) {
    result.append(i).append(",");
}
String output = result.toString();

// Gain: 100x faster for large loops

2. Use Appropriate Collection Types

// Before: Wrong data structure
List<Integer> numbers = new ArrayList<>();
numbers.contains(42);  // O(n) lookup

// After: Right data structure
Set<Integer> numbers = new HashSet<>();
numbers.contains(42);  // O(1) lookup

// Gain: 1000x faster for large collections

3. Avoid Unnecessary Object Creation

// Before: Creates objects in loop
for (int i = 0; i < 1000; i++) {
    String key = new String("key" + i);  // Unnecessary
    map.put(key, value);
}

// After: Reuse or use literals
for (int i = 0; i < 1000; i++) {
    String key = "key" + i;  // String interning
    map.put(key, value);
}

// Gain: Less GC pressure, faster

4. Use Primitive Collections

// Before: Autoboxing overhead
List<Integer> numbers = new ArrayList<>();
for (int i = 0; i < 1000000; i++) {
    numbers.add(i);  // Boxing int to Integer
}

// After: Primitive arrays or specialized libraries
int[] numbers = new int[1000000];
for (int i = 0; i < 1000000; i++) {
    numbers[i] = i;  // No boxing
}

// Or use TIntArrayList from Trove
TIntArrayList numbers = new TIntArrayList();

// Gain: 50% less memory, faster access

See

references/java_optimizations.md

for comprehensive Java optimization patterns.

Database Optimizations

1. Fix N+1 Query Problem

# Before: N+1 queries
users = User.query.all()  # 1 query
for user in users:
    posts = user.posts.all()  # N queries
    process(posts)

# After: Single query with join
users = User.query.options(
    joinedload(User.posts)
).all()  # 1 query
for user in users:
    posts = user.posts  # Already loaded
    process(posts)

# Gain: 100x faster for large datasets

2. Add Indexes

-- Before: Full table scan O(n)
SELECT * FROM users WHERE email = 'user@example.com';

-- After: Index lookup O(log n)
CREATE INDEX idx_users_email ON users(email);
SELECT * FROM users WHERE email = 'user@example.com';

-- Gain: 1000x faster for large tables

3. Batch Operations

# Before: N round trips
for item in items:
    db.execute("INSERT INTO table VALUES (?)", (item,))
    db.commit()

# After: Single batch
db.executemany("INSERT INTO table VALUES (?)",
               [(item,) for item in items])
db.commit()

# Gain: 10-100x faster

See

references/database_optimizations.md

for comprehensive database optimization patterns.

I/O Optimizations

1. Use Buffered I/O

# Before: Unbuffered (many system calls)
with open('file.txt', 'r') as f:
    for line in f:
        process(line.strip())

# After: Buffered reading
with open('file.txt', 'r', buffering=8192) as f:
    for line in f:
        process(line.strip())

# Gain: 10x faster for small lines

2. Batch API Calls

# Before: N API calls
for user_id in user_ids:
    user = api.get_user(user_id)  # 100 calls
    process(user)

# After: Batch API call
users = api.get_users_batch(user_ids)  # 1 call
for user in users:
    process(user)

# Gain: 100x faster (network latency)

Optimization Process

1. Profile Before Optimizing

Python Profiling:

# Time profiling
python -m cProfile -s cumulative script.py

# Line-by-line profiling
pip install line_profiler
kernprof -l -v script.py

# Memory profiling
pip install memory_profiler
python -m memory_profiler script.py

Java Profiling:

# JVM profiling with VisualVM
jvisualvm

# Or Java Flight Recorder
java -XX:+UnlockCommercialFeatures -XX:+FlightRecorder \
     -XX:StartFlightRecording=duration=60s,filename=recording.jfr \
     MyApp

2. Focus on Hot Paths

Optimize the 20% of code that takes 80% of time.

Find Hot Paths:

• Profile to find slowest functions
• Measure actual execution time
• Focus on code executed frequently
• Ignore code executed rarely

3. Measure Impact

Compare before and after:

import timeit

# Before
before = timeit.timeit(
    'old_function(data)',
    setup='from module import old_function, data',
    number=1000
)

# After
after = timeit.timeit(
    'new_function(data)',
    setup='from module import new_function, data',
    number=1000
)

improvement = (before - after) / before * 100
print(f"Improvement: {improvement:.1f}%")

4. Maintain Readability

Don't sacrifice code clarity for minor gains.

Good Optimization:

# Clear and fast
users = [u for u in all_users if u.is_active]

Bad Optimization:

# Obscure for minimal gain
users = list(filter(lambda u: u.is_active, all_users))

Best Practices

1. Profile first - Don't guess, measure
2. Focus on bottlenecks - Optimize hot paths only
3. Preserve correctness - Test thoroughly after optimizing
4. Document trade-offs - Explain why optimization is worth it
5. Measure improvements - Quantify performance gains
6. Consider maintainability - Don't make code unreadable
7. Use appropriate tools - Profilers, benchmarks, load tests
8. Think about complexity - O(n²) to O(n log n) matters more than micro-optimizations
9. Cache wisely - Balance memory vs. computation
10. Avoid premature optimization - Optimize when proven necessary

Resources

• references/python_optimizations.md

  - Comprehensive Python optimization techniques and patterns

• references/java_optimizations.md

  - Comprehensive Java optimization techniques and patterns

• references/database_optimizations.md

  - Database query and schema optimization strategies

Quick Reference

Optimization Type	Python	Java	Impact
Algorithm complexity	Use better algorithm	Use better algorithm	High
Data structures	set/dict for lookup	HashMap/HashSet	High
String building	join() or f-strings	StringBuilder	High
Generators	yield	Stream API	Medium (memory)
Caching	@lru_cache	ConcurrentHashMap	Medium-High
Batching	Batch DB/API calls	Batch operations	High
Indexing	Use dict/set	Add DB indexes	High
Lazy evaluation	Generators	Streams/Suppliers	Medium