Performance Reference¶
Comprehensive guide to optimizing MCP Standards Server performance.
Performance Overview¶
The MCP Standards Server is designed for high performance with: - Sub-100ms response times for standard operations - Horizontal scalability - Efficient resource utilization - Intelligent caching strategies
Performance Metrics¶
Key Metrics¶
| Metric | Target | Description |
|---|---|---|
| Response Time (p50) | <50ms | Median response time |
| Response Time (p95) | <100ms | 95th percentile |
| Response Time (p99) | <200ms | 99th percentile |
| Throughput | >1000 req/s | Requests per second |
| CPU Usage | <70% | Average CPU utilization |
| Memory Usage | <512MB | Process memory |
| Cache Hit Rate | >90% | L1+L2 cache hits |
Monitoring¶
from src.core.performance.metrics import PerformanceMonitor
monitor = PerformanceMonitor()
# Track operation
with monitor.track("validation"):
result = validate_code(code)
# Get metrics
metrics = monitor.get_metrics()
print(f"Average time: {metrics.avg_time}ms")
print(f"Operations/sec: {metrics.throughput}")
Optimization Strategies¶
1. Caching Optimization¶
# Aggressive caching configuration
cache:
l1:
max_size: 50000
ttl: 600
preload: true
l2:
pipeline_size: 100
compression: true
warming:
parallel: true
batch_size: 1000
2. Database Optimization¶
# Use connection pooling
from sqlalchemy import create_engine
engine = create_engine(
"postgresql://localhost/mcp",
pool_size=20,
max_overflow=40,
pool_pre_ping=True,
pool_recycle=3600
)
# Batch operations
def bulk_insert_standards(standards):
with engine.begin() as conn:
conn.execute(
insert(StandardsTable),
standards
)
3. Async Operations¶
import asyncio
from concurrent.futures import ThreadPoolExecutor
class AsyncValidator:
def __init__(self):
self.executor = ThreadPoolExecutor(max_workers=10)
async def validate_files(self, files):
"""Validate files concurrently."""
tasks = [
self.validate_file(file)
for file in files
]
return await asyncio.gather(*tasks)
async def validate_file(self, file):
"""Validate single file asynchronously."""
loop = asyncio.get_event_loop()
return await loop.run_in_executor(
self.executor,
self._validate_sync,
file
)
Code Optimization¶
1. Efficient Pattern Matching¶
# Bad - Multiple regex compilations
def check_patterns(code):
if re.search(r'TODO', code):
violations.append('todo')
if re.search(r'FIXME', code):
violations.append('fixme')
# Good - Compile once, reuse
class PatternChecker:
def __init__(self):
self.patterns = {
'todo': re.compile(r'TODO'),
'fixme': re.compile(r'FIXME')
}
def check_patterns(self, code):
return [
name for name, pattern in self.patterns.items()
if pattern.search(code)
]
2. Memory-Efficient Processing¶
# Bad - Load entire file
def process_large_file(path):
content = open(path).read()
return process(content)
# Good - Stream processing
def process_large_file(path):
results = []
with open(path) as f:
for chunk in iter(lambda: f.read(4096), ''):
results.extend(process_chunk(chunk))
return results
3. Lazy Loading¶
class StandardsRepository:
def __init__(self):
self._standards = None
self._index = None
@property
def standards(self):
"""Lazy load standards."""
if self._standards is None:
self._standards = self._load_standards()
return self._standards
@property
def search_index(self):
"""Lazy load search index."""
if self._index is None:
self._index = self._build_index()
return self._index
Profiling¶
CPU Profiling¶
import cProfile
import pstats
def profile_operation():
profiler = cProfile.Profile()
profiler.enable()
# Operation to profile
validate_directory("src/")
profiler.disable()
# Analysis
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
stats.print_stats(20) # Top 20 functions
Memory Profiling¶
from memory_profiler import profile
@profile
def memory_intensive_operation():
# Track memory usage line by line
large_data = load_standards() # +50MB
processed = process_data(large_data) # +30MB
return compress(processed) # -60MB
Line Profiling¶
from line_profiler import LineProfiler
def profile_critical_path():
lp = LineProfiler()
lp.add_function(critical_function)
# Run with profiling
lp.enable()
result = critical_function()
lp.disable()
# Show results
lp.print_stats()
Benchmarking¶
Micro-benchmarks¶
import timeit
# Compare implementations
def benchmark_implementations():
implementations = {
'regex': lambda: regex_validate(code),
'ast': lambda: ast_validate(code),
'hybrid': lambda: hybrid_validate(code)
}
for name, func in implementations.items():
time = timeit.timeit(func, number=1000)
print(f"{name}: {time:.4f}s")
Load Testing¶
# locustfile.py for load testing
from locust import HttpUser, task, between
class MCPUser(HttpUser):
wait_time = between(1, 3)
@task(3)
def validate_code(self):
self.client.post("/api/validate", json={
"code": "def test(): pass",
"standard": "python-best-practices"
})
@task(1)
def get_standards(self):
self.client.get("/api/standards")
Configuration Tuning¶
High-Performance Configuration¶
performance:
# Worker configuration
workers: ${CPU_COUNT}
threads_per_worker: 4
# Request handling
request_timeout: 30
keepalive_timeout: 5
# Resource limits
max_request_size: 10485760 # 10MB
max_memory_per_request: 104857600 # 100MB
# Optimization flags
enable_jit: true
enable_async: true
enable_caching: true
# Garbage collection
gc:
threshold0: 700
threshold1: 10
threshold2: 10
Database Performance¶
database:
# Connection pool
pool_size: 20
max_overflow: 40
pool_timeout: 30
pool_recycle: 3600
# Query optimization
echo: false
statement_timeout: 5000
# Indexes
auto_create_indexes: true
index_cache_size: 100000
Scaling Strategies¶
Horizontal Scaling¶
# docker-compose.yml
version: '3.8'
services:
mcp-server:
image: mcp-standards-server
deploy:
replicas: 4
resources:
limits:
cpus: '2'
memory: 1G
environment:
- REDIS_URL=redis://redis:6379
- DATABASE_URL=postgresql://db:5432/mcp
nginx:
image: nginx
ports:
- "80:80"
volumes:
- ./nginx.conf:/etc/nginx/nginx.conf
depends_on:
- mcp-server
Vertical Scaling¶
# Optimize for multi-core
import multiprocessing
def parallel_validation(files):
cpu_count = multiprocessing.cpu_count()
with multiprocessing.Pool(cpu_count) as pool:
results = pool.map(validate_file, files)
return results
Performance Best Practices¶
1. Minimize I/O¶
# Bad - Multiple file reads
for standard_id in standard_ids:
with open(f"standards/{standard_id}.yaml") as f:
standards.append(yaml.load(f))
# Good - Batch read
all_standards = load_all_standards() # Single I/O
standards = [all_standards[sid] for sid in standard_ids]
2. Use Appropriate Data Structures¶
# Bad - O(n) lookup
violations = []
for rule in rules:
if rule.id in violations:
continue
# Good - O(1) lookup
violations = set()
for rule in rules:
if rule.id in violations:
continue
3. Avoid Premature Optimization¶
# Profile first
with profile_context():
result = operation()
# Then optimize hot paths only
if is_hot_path:
result = optimized_operation()
else:
result = simple_operation()
Troubleshooting Performance¶
Common Issues¶
- Slow Response Times
- Check cache hit rates
- Profile database queries
-
Review async operation usage
-
High Memory Usage
- Implement pagination
- Use generators for large datasets
-
Check for memory leaks
-
CPU Bottlenecks
- Parallelize CPU-intensive tasks
- Optimize regex patterns
- Consider caching computed results
Performance Debugging¶
import logging
import time
logging.basicConfig(level=logging.DEBUG)
class PerformanceDebugger:
def __init__(self):
self.timings = {}
def track(self, operation):
start = time.time()
yield
duration = time.time() - start
self.timings[operation] = duration
if duration > 0.1: # Log slow operations
logging.warning(
f"Slow operation: {operation} took {duration:.3f}s"
)