performance_optimization.md•8.69 kB
# Performance Optimization Framework
This template provides a structured approach for identifying and addressing performance issues in software systems.
## Performance Assessment Process
### 1. Establish Baseline
1. **Define Metrics**: Identify key performance indicators (KPIs)
2. **Measurement Setup**: Configure monitoring and profiling tools
3. **Load Testing**: Design realistic test scenarios
4. **Benchmark**: Capture baseline metrics
5. **Document**: Record environment details and configurations
### 2. Identify Bottlenecks
1. **Data Collection**: Gather metrics under various conditions
2. **Analysis**: Determine performance limiting factors
3. **Prioritization**: Rank issues by impact and effort to fix
4. **Root Cause**: Dig deeper to find underlying causes
5. **Documentation**: Record findings with evidence
### 3. Optimize
1. **Plan**: Develop optimization strategy
2. **Implement**: Apply targeted improvements
3. **Measure**: Verify impact against baseline
4. **Iterate**: Continue until performance goals met
5. **Document**: Record changes and their effects
## Performance Metrics and Benchmarks
### System-Level Metrics
- **CPU Usage**: Overall utilization, per-process, per-thread
- **Memory Usage**: Total, per-process, memory leaks, GC behavior
- **Disk I/O**: Read/write operations, latency, throughput
- **Network**: Throughput, latency, packet loss, connection count
### Application-Level Metrics
- **Response Time**: Average, percentiles (p50, p90, p95, p99)
- **Throughput**: Requests or transactions per second
- **Error Rate**: Percentage of failed operations
- **Concurrency**: Number of simultaneous users/connections
- **Resource Utilization**: How efficiently resources are used
### Database Metrics
- **Query Execution Time**: Duration of queries
- **Index Usage**: Hit ratio, scan vs. seek operations
- **Connection Pool**: Utilization, wait time
- **Lock Contention**: Wait time, deadlocks
- **Cache Performance**: Hit ratio, eviction rate
## Profiling Techniques
### CPU Profiling
1. **Sampling Profiler**: Periodically sample call stack
2. **Instrumentation Profiler**: Add timing code to functions
3. **Flame Graphs**: Visualize call stack and CPU time
4. **Hot Spot Analysis**: Identify CPU-intensive operations
### Memory Profiling
1. **Heap Snapshots**: Capture memory usage at a point in time
2. **Allocation Profiling**: Track object creation and destruction
3. **Leak Detection**: Identify unreleased resources
4. **Garbage Collection Analysis**: Understand GC behavior
### I/O Profiling
1. **Disk I/O Monitoring**: Track read/write operations
2. **Network Monitoring**: Analyze packet flow and delays
3. **Database Query Analysis**: Examine query plans and execution
4. **Resource Contention**: Identify lock and wait conditions
## Common Optimization Patterns
### Algorithm Optimization
1. **Complexity Reduction**: Improve algorithmic efficiency (O(n²) → O(n log n))
2. **Memoization**: Cache expensive function results
3. **Lazy Evaluation**: Compute values only when needed
4. **Parallel Processing**: Utilize multiple cores effectively
5. **Batch Processing**: Combine multiple operations
### Memory Optimization
1. **Object Pooling**: Reuse objects instead of creating new ones
2. **Memory-Efficient Data Structures**: Choose appropriate structures
3. **Reduce Allocations**: Minimize temporary object creation
4. **Appropriate Data Types**: Use size-appropriate types
5. **Compression**: Reduce memory footprint with compression
### I/O Optimization
1. **Caching**: Store frequently accessed data in memory
2. **Buffering**: Combine multiple I/O operations
3. **Asynchronous I/O**: Non-blocking operations
4. **Connection Pooling**: Reuse database connections
5. **Data Locality**: Organize data to improve access patterns
## Database Query Optimization
### Query Analysis
1. **Explain Plans**: Analyze how queries are executed
2. **Slow Query Logs**: Identify problematic queries
3. **Query Profiling**: Measure query performance
4. **Index Analysis**: Evaluate index usage and effectiveness
### Optimization Techniques
1. **Indexing**: Create appropriate indexes
2. **Query Rewriting**: Restructure queries for efficiency
3. **Denormalization**: Strategic duplication for performance
4. **Partitioning**: Divide large tables
5. **Connection Management**: Optimize connection pooling
## Frontend Performance Improvements
### Loading Performance
1. **Bundle Optimization**: Minimize and split JavaScript bundles
2. **Resource Minification**: Minify CSS and JavaScript
3. **Image Optimization**: Compress and properly size images
4. **Lazy Loading**: Defer non-critical resource loading
5. **Caching Strategy**: Implement effective browser caching
### Rendering Performance
1. **Critical Rendering Path**: Optimize resources loading order
2. **Virtual DOM Efficiency**: Minimize unnecessary renders
3. **Code Splitting**: Load code only when needed
4. **Web Workers**: Offload CPU-intensive tasks
5. **Animation Optimization**: Use GPU-accelerated properties
## Load Testing Methodologies
### Test Types
1. **Load Test**: Performance under expected load
2. **Stress Test**: Performance under extreme conditions
3. **Endurance Test**: Performance over extended periods
4. **Spike Test**: Performance under sudden load increases
5. **Scalability Test**: Performance as system scales
### Testing Process
1. **Scenario Definition**: Define realistic user journeys
2. **Test Environment**: Set up representative environment
3. **Execution**: Run tests with appropriate tool
4. **Monitoring**: Capture detailed metrics during test
5. **Analysis**: Identify bottlenecks and limitations
## Language-Specific Optimization
### Python
```python
# Before optimization
def calculate_sum(numbers):
result = 0
for num in numbers:
result += num
return result
# After optimization (using built-in sum)
def calculate_sum(numbers):
return sum(numbers)
# Before optimization
def process_data(data):
result = []
for item in data:
if item > 0:
result.append(item * 2)
return result
# After optimization (using list comprehension)
def process_data(data):
return [item * 2 for item in data if item > 0]
```
### JavaScript
```javascript
// Before optimization
function findUser(users, id) {
for (let i = 0; i < users.length; i++) {
if (users[i].id === id) {
return users[i];
}
}
return null;
}
// After optimization
function findUser(users, id) {
return users.find(user => user.id === id) || null;
}
// Before optimization (creating many objects)
function processItems(items) {
const results = [];
for (let i = 0; i < items.length; i++) {
const newItem = { ...items[i], processed: true };
results.push(newItem);
}
return results;
}
// After optimization (reusing objects where possible)
function processItems(items) {
return items.map(item => ({ ...item, processed: true }));
}
```
### SQL
```sql
-- Before optimization
SELECT *
FROM orders
WHERE customer_id = 123;
-- After optimization
SELECT order_id, order_date, total
FROM orders
WHERE customer_id = 123;
-- Before optimization
SELECT o.order_id, c.name
FROM orders o, customers c
WHERE o.customer_id = c.customer_id
AND o.order_date > '2023-01-01';
-- After optimization
SELECT o.order_id, c.name
FROM orders o
INNER JOIN customers c ON o.customer_id = c.customer_id
WHERE o.order_date > '2023-01-01';
```
## Performance Optimization Checklist
### General
- [ ] Established performance baselines
- [ ] Identified critical performance metrics
- [ ] Profiled application under realistic load
- [ ] Applied appropriate optimization patterns
- [ ] Verified improvements against baseline
- [ ] Documented optimizations and their impact
### Backend
- [ ] Optimized database queries
- [ ] Implemented appropriate caching
- [ ] Improved algorithm efficiency
- [ ] Optimized I/O operations
- [ ] Enhanced memory usage
- [ ] Implemented connection pooling
- [ ] Applied asynchronous processing where appropriate
### Frontend
- [ ] Minimized bundle size
- [ ] Optimized images and other assets
- [ ] Implemented lazy loading
- [ ] Improved rendering performance
- [ ] Applied proper caching strategies
- [ ] Reduced network requests
### Database
- [ ] Created appropriate indexes
- [ ] Optimized query structure
- [ ] Applied denormalization where beneficial
- [ ] Implemented database-level caching
- [ ] Optimized transaction management
### Infrastructure
- [ ] Scaled resources appropriately
- [ ] Optimized server configurations
- [ ] Implemented load balancing
- [ ] Applied CDN for static assets
- [ ] Configured appropriate timeouts