Performance Profiling Guide¶

This guide covers tools and techniques for profiling MCP Gateway performance under load. Use these methods to identify bottlenecks, optimize queries, and diagnose production issues.

Quick Reference¶

Tool	Purpose	When to Use
Locust	Load testing	Simulate concurrent users
PostgreSQL EXPLAIN	Query analysis	Find slow/inefficient queries
pg_stat_activity	Connection monitoring	Debug idle transactions
pg_stat_user_tables	Table scan stats	Find full table scans
py-spy	Python CPU profiling	Find CPU hotspots
memray	Python memory profiling	Find memory leaks and allocation hotspots
docker stats	Resource monitoring	Track CPU/memory usage
Redis CLI	Cache analysis	Check hit rates

Load Testing with Locust¶

Starting a Load Test¶

# Start Locust web UI
make load-test-ui

# Open browser to http://localhost:8089
# Configure users (e.g., 3000) and spawn rate (e.g., 100/s)

Monitoring Locust Stats via API¶

# Get current stats as JSON
curl -s http://localhost:8089/stats/requests | python3 -c "
import sys, json
data = json.load(sys.stdin)

print('=== TOP SLOWEST ENDPOINTS ===')
stats = sorted(data.get('stats', []), key=lambda x: x.get('avg_response_time', 0), reverse=True)[:10]
print(f\"{'Endpoint':<45} {'Reqs':>8} {'Avg':>8} {'P95':>8} {'P99':>8}\")
print('-' * 85)
for s in stats:
    name = s.get('name', '')[:43]
    p95 = s.get('response_time_percentile_0.95', 0)
    p99 = s.get('response_time_percentile_0.99', 0)
    print(f\"{name:<45} {s.get('num_requests', 0):>8} {s.get('avg_response_time', 0):>8.0f} {p95:>8.0f} {p99:>8.0f}\")

print()
print(f\"RPS: {data.get('total_rps', 0):.1f}, Users: {data.get('user_count', 0)}, Failures: {data.get('total_fail_count', 0)}\")
"

Checking for Errors¶

curl -s http://localhost:8089/stats/requests | python3 -c "
import sys, json
data = json.load(sys.stdin)
print('=== ERRORS ===')
for e in data.get('errors', []):
    print(f\"  {e.get('name')}: {e.get('occurrences')} - {e.get('error')[:80]}\")
"

PostgreSQL Profiling¶

EXPLAIN ANALYZE¶

Use EXPLAIN ANALYZE to understand query execution plans and find slow queries:

docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT COUNT(*), AVG(response_time)
FROM tool_metrics
WHERE timestamp >= NOW() - INTERVAL '7 days';
"

Key metrics to watch:

Metric	Good	Bad
`Seq Scan`	On small tables (<1000 rows)	On large tables
`Index Scan`	On filtered queries	Missing when expected
`Rows Removed by Filter: 0`	Filter matches few rows	Filter matches all rows
`Shared Buffers Hit`	High ratio	Low ratio (disk I/O)

Example: Detecting Non-Selective Filters

Parallel Seq Scan on tool_metrics
  Filter: (timestamp >= (now() - '7 days'::interval))
  Rows Removed by Filter: 0  <-- ALL rows match = index not useful

This indicates the filter matches 100% of rows, so PostgreSQL chooses a sequential scan over an index scan.

Table Scan Statistics¶

Monitor which tables are being scanned excessively:

docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
SELECT
    relname as table_name,
    pg_size_pretty(pg_total_relation_size(relid)) as total_size,
    n_live_tup as live_rows,
    seq_scan,
    seq_tup_read,
    idx_scan,
    CASE WHEN seq_scan > 0 THEN seq_tup_read / seq_scan ELSE 0 END as avg_rows_per_seq_scan
FROM pg_stat_user_tables
ORDER BY seq_tup_read DESC
LIMIT 15;
"

Warning signs:

seq_tup_read in billions = excessive full table scans
avg_rows_per_seq_scan equals live_rows = scanning entire table each time
High seq_scan count with large tables = missing index or non-selective filter

Connection State Analysis¶

Check for idle-in-transaction connections (a sign of long-running requests or connection leaks):

docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
SELECT
    state,
    COUNT(*) as count,
    MAX(EXTRACT(EPOCH FROM (NOW() - state_change)))::int as max_age_seconds
FROM pg_stat_activity
WHERE datname = 'mcp'
GROUP BY state
ORDER BY count DESC;
"

Healthy state:

state               | count | max_age_seconds
--------------------+-------+-----------------
idle                |    70 |             200
active              |     5 |               0
idle in transaction |     3 |               1

Unhealthy state (connection exhaustion risk):

state               | count | max_age_seconds
--------------------+-------+-----------------
idle in transaction |    60 |             120  <-- Problem!
idle                |    38 |             500
active              |     2 |               0

Finding Stuck Queries¶

docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
SELECT
    pid,
    state,
    EXTRACT(EPOCH FROM (NOW() - state_change))::numeric(8,2) as idle_seconds,
    LEFT(query, 100) as query_snippet
FROM pg_stat_activity
WHERE datname = 'mcp' AND state = 'idle in transaction'
ORDER BY state_change
LIMIT 15;
"

Reset Statistics¶

To get fresh statistics for a specific test:

docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
SELECT pg_stat_reset();
"

Python Profiling with py-spy¶

py-spy is a sampling profiler for Python that can attach to running processes without code changes.

Installing py-spy¶

pip install py-spy

# Or on the host (to profile container processes)
sudo pip install py-spy

Profiling a Running Container¶

# Find the Python process ID
docker exec mcp-context-forge-gateway-1 ps aux | grep python

# Run py-spy from host (requires root)
sudo py-spy top --pid $(docker inspect --format '{{.State.Pid}}' mcp-context-forge-gateway-1)

# Generate a flamegraph
sudo py-spy record -o profile.svg --pid $(docker inspect --format '{{.State.Pid}}' mcp-context-forge-gateway-1) --duration 30

Profiling Locally¶

# Profile the development server
py-spy top -- python -m mcpgateway

# Generate flamegraph
py-spy record -o flamegraph.svg -- python -m mcpgateway

Interpreting Flamegraphs¶

Wide bars = functions consuming the most CPU time
Deep stacks = many nested function calls
Look for: Template rendering, JSON serialization, database queries

Memory Profiling with memray¶

memray is a memory profiler for Python that tracks allocations in Python code, native extension modules, and the Python interpreter itself. It's ideal for finding memory leaks, high-water marks, and allocation hotspots.

Installing memray¶

pip install memray

# Or in a container
docker exec mcp-context-forge-gateway-1 pip install memray

Profiling Locally¶

# Run your application with memray tracking
memray run -o output.bin python -m mcpgateway

# Or run a specific script
memray run -o output.bin python script.py

Attaching to a Running Process¶

memray can attach to an already-running Python process to capture memory allocations:

# Find the Python process ID inside the container
docker exec mcp-context-forge-gateway-1 ps aux | grep python

# Attach memray to a running process (requires ptrace permissions)
# Option 1: Run memray inside the container
docker exec -it mcp-context-forge-gateway-1 memray attach <PID> -o /tmp/profile.bin

# Option 2: If using privileged container or with SYS_PTRACE capability
docker exec mcp-context-forge-gateway-1 memray attach --aggregate <PID> -o /tmp/profile.bin

# After capturing, copy the profile out
docker cp mcp-context-forge-gateway-1:/tmp/profile.bin ./profile.bin

Note: memray attach requires ptrace permissions. You may need to run the container with --cap-add=SYS_PTRACE or in privileged mode for profiling.

Generating Reports¶

memray provides multiple output formats:

# Interactive flamegraph (opens in browser)
memray flamegraph output.bin -o flamegraph.html

# Table view (terminal-friendly)
memray table output.bin

# Tree view (call hierarchy)
memray tree output.bin

# Summary statistics
memray stats output.bin

# Identify memory leaks
memray summary output.bin

Live Mode (Real-time Monitoring)¶

For development, use live mode to see allocations in real-time:

# Run with live TUI
memray run --live python -m mcpgateway

# Attach to running process with live mode
memray attach --live <PID>

Container Profiling Workflow¶

Complete workflow for profiling a gateway container:

# 1. Install memray in the container (if not already installed)
docker exec mcp-context-forge-gateway-1 pip install memray

# 2. Find worker PIDs
docker exec mcp-context-forge-gateway-1 ps aux | grep "mcpgateway work" | head -5

# 3. Attach to one worker (e.g., PID 123) for 60 seconds
docker exec mcp-context-forge-gateway-1 timeout 60 memray attach 123 -o /tmp/worker_profile.bin || true

# 4. Copy profile to host
docker cp mcp-context-forge-gateway-1:/tmp/worker_profile.bin ./worker_profile.bin

# 5. Generate reports
memray flamegraph worker_profile.bin -o memory_flamegraph.html
memray stats worker_profile.bin
memray table worker_profile.bin | head -50

Container Profiling Limitations:

memray attach requires gdb or lldb, which may not be available in minimal containers
Python version must match between memray and the target process (e.g., memray compiled for Python 3.13 won't work with Python 3.12 containers)
Requires ptrace permissions (--cap-add=SYS_PTRACE or privileged mode)
For production containers without pip, consider:
Building a debug image with memray pre-installed
Using memray run locally to reproduce the issue
Using py-spy for CPU profiling (works cross-version and is more portable)

Interpreting memray Output¶

Flamegraph: - Width = amount of memory allocated by that call stack - Color: Red = Python code, Green = C extensions, Blue = Python internals - Click on frames to zoom in

Table view columns: - Total memory = all memory allocated by this function and its callees - Own memory = memory allocated directly by this function - Allocations = number of allocation calls

Common patterns to look for: - Large allocations in template rendering (Jinja2) - JSON serialization of large datasets - ORM model instantiation (SQLAlchemy) - Response buffering in ASGI middleware - Caches growing unbounded

Example high-memory patterns:

# Pattern: Large list comprehensions in API responses
mcpgateway/main.py:handle_rpc    Total: 500MB  Own: 450MB  Allocations: 10000

# Pattern: Template rendering accumulating data
jinja2/environment.py:render    Total: 200MB  Own: 50MB   Allocations: 5000

py-spy vs memray¶

Aspect	py-spy	memray
Focus	CPU time	Memory allocation
Overhead	Very low (~1%)	Medium (10-30%)
Attach support	Yes	Yes
Native code	No	Yes
Use when	High CPU usage	OOM errors, memory leaks

Use py-spy when CPU is the bottleneck. Use memray when memory usage is high or you're seeing OOM kills.

Container Resource Monitoring¶

Real-time Stats¶

# Watch all containers
docker stats

# Filter to specific containers
docker stats --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}" \
  mcp-context-forge-gateway-1 \
  mcp-context-forge-postgres-1 \
  mcp-context-forge-redis-1

Snapshot Stats¶

docker stats --no-stream --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}" \
  | grep -E "gateway|postgres|redis|nginx"

Healthy resource usage:

Container	CPU	Memory
gateway (each)	<400%	<4GB
postgres	<150%	<1GB
redis	<20%	<100MB

Redis Cache Analysis¶

Check Hit Rate¶

docker exec mcp-context-forge-redis-1 redis-cli info stats | grep -E "keyspace|ops_per_sec|hits|misses"

Calculate hit rate:

docker exec mcp-context-forge-redis-1 redis-cli info stats | python3 -c "
import sys
stats = {}
for line in sys.stdin:
    if ':' in line:
        k, v = line.strip().split(':')
        stats[k] = int(v) if v.isdigit() else v

hits = stats.get('keyspace_hits', 0)
misses = stats.get('keyspace_misses', 0)
total = hits + misses
hit_rate = (hits / total * 100) if total > 0 else 0
print(f'Hits: {hits}, Misses: {misses}, Hit Rate: {hit_rate:.1f}%')
"

Good hit rate: >90% for cached data

Check Key Counts¶

docker exec mcp-context-forge-redis-1 redis-cli dbsize

# List keys by pattern
docker exec mcp-context-forge-redis-1 redis-cli keys "mcpgw:*" | head -20

Tool lookup cache keys (invoke hot path):

docker exec mcp-context-forge-redis-1 redis-cli keys "mcpgw:tool_lookup:*" | head -20

Gateway Log Analysis¶

Check for Errors¶

docker logs mcp-context-forge-gateway-1 2>&1 | grep -iE "error|exception|timeout|warning" | tail -30

Count Error Types¶

docker logs mcp-context-forge-gateway-1 2>&1 | grep -i "error" | \
  sed 's/.*\(Error[^:]*\).*/\1/' | sort | uniq -c | sort -rn | head -10

Check for Idle Transaction Timeouts¶

docker logs mcp-context-forge-gateway-1 2>&1 | grep -c "idle transaction timeout"

Complete Profiling Session Example¶

Here's a workflow for diagnosing performance issues under load:

# 1. Reset PostgreSQL statistics
docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "SELECT pg_stat_reset();"

# 2. Start load test
make load-test-ui
# Configure 3000 users in browser, start test

# 3. Take samples every 30 seconds
for i in {1..5}; do
  echo "=== SAMPLE $i ==="

  # Locust stats
  curl -s http://localhost:8089/stats/requests | python3 -c "
import sys, json
d = json.load(sys.stdin)
admin = next((s for s in d.get('stats', []) if s.get('name') == '/admin/'), {})
print(f\"RPS: {d.get('total_rps', 0):.0f}, /admin/ avg: {admin.get('avg_response_time', 0):.0f}ms\")
"

  # Connection states
  docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
    SELECT state, COUNT(*) FROM pg_stat_activity WHERE datname='mcp' GROUP BY state;
  "

  # Container CPU
  docker stats --no-stream --format "{{.Name}}: {{.CPUPerc}}" | grep gateway

  sleep 30
done

# 4. Final analysis
docker exec mcp-context-forge-postgres-1 psql -U postgres -d mcp -c "
SELECT relname, seq_scan, seq_tup_read, idx_scan
FROM pg_stat_user_tables
ORDER BY seq_tup_read DESC LIMIT 10;
"

Common Performance Issues¶

Issue: High Sequential Scan Count¶

Symptom: seq_tup_read in billions

Causes: - Missing index - Non-selective filter (e.g., 7-day filter matches all recent data) - Short cache TTL causing repeated queries

Solutions: - Add covering index - Increase cache TTL - Add materialized view for aggregations

Issue: Many Idle-in-Transaction Connections¶

Symptom: 50+ connections in idle in transaction state

Causes: - N+1 query patterns - Long-running requests holding transactions - Missing connection pool limits

Solutions: - Use batch queries instead of loops - Set idle_in_transaction_session_timeout - Optimize slow queries

Issue: Health Check Endpoints Holding PgBouncer Connections¶

Symptom: SELECT 1 queries stuck in idle in transaction state for minutes

SELECT left(query, 50), count(*), avg(EXTRACT(EPOCH FROM (NOW() - state_change)))::int as avg_age
FROM pg_stat_activity
WHERE state = 'idle in transaction' AND datname = 'mcp'
GROUP BY left(query, 50);

        query         | count | avg_age
----------------------+-------+---------
 SELECT 1             |    45 |     139

Causes: - PgBouncer in transaction mode holds backend connections until COMMIT/ROLLBACK - Health endpoints using Depends(get_db) rely on dependency cleanup, which may not execute on timeout/cancellation - async def endpoints calling blocking SQLAlchemy code on event loop thread - Cross-thread session usage when mixing asyncio.to_thread with Depends(get_db)

Solutions:

Use dedicated sessions instead of Depends(get_db) - Health endpoints should create and manage their own sessions to avoid double-commit and cross-thread issues:

@app.get("/health")
def healthcheck():  # Sync function - FastAPI runs in threadpool
    """Health check with dedicated session."""
    db = SessionLocal()
    try:
        db.execute(text("SELECT 1"))
        db.commit()  # Explicitly release PgBouncer connection
        return {"status": "healthy"}
    except Exception as e:
        try:
            db.rollback()
        except Exception:
            try:
                db.invalidate()  # Remove broken connection from pool
            except Exception:
                pass
        return {"status": "unhealthy", "error": str(e)}
    finally:
        db.close()

Use sync functions for simple blocking operations - FastAPI automatically runs def (sync) route handlers in a threadpool:

# BAD: async def with blocking calls stalls event loop
@app.get("/health")
async def healthcheck():
    db.execute(text("SELECT 1"))  # Blocks event loop!

# GOOD: sync def runs in threadpool automatically
@app.get("/health")
def healthcheck():
    db.execute(text("SELECT 1"))  # Runs in threadpool

For async endpoints, create sessions inside asyncio.to_thread - All DB operations must happen in the same thread:

@app.get("/ready")
async def readiness_check():
    def _check_db() -> str | None:
        # Session created IN the worker thread
        db = SessionLocal()
        try:
            db.execute(text("SELECT 1"))
            db.commit()
            return None
        except Exception as e:
            try:
                db.rollback()
            except Exception:
                try:
                    db.invalidate()
                except Exception:
                    pass
            return str(e)
        finally:
            db.close()

    error = await asyncio.to_thread(_check_db)
    if error:
        return {"status": "not ready", "error": error}
    return {"status": "ready"}

Mirror get_db cleanup pattern - Use rollback → invalidate → close:

except Exception as e:
    try:
        db.rollback()
    except Exception:
        try:
            db.invalidate()  # Remove broken connection from pool
        except Exception:
            pass  # nosec B110 - Best effort cleanup

Why not use Depends(get_db)?

get_db commits after yield, causing double-commit if endpoint commits
With asyncio.to_thread, the session is created in one thread but used in another
Health endpoints should test actual DB connectivity, not be mockable via dependency_overrides

Issue: High Gateway CPU¶

Symptom: Gateway at 600%+ CPU

Causes: - Template rendering overhead - JSON serialization of large responses - Pydantic validation overhead

Solutions: - Enable response caching - Paginate large result sets - Use orjson for serialization (enabled by default)

Performance Profiling Guide¶

Quick Reference¶

Load Testing with Locust¶

Starting a Load Test¶

Monitoring Locust Stats via API¶

Checking for Errors¶

PostgreSQL Profiling¶

EXPLAIN ANALYZE¶

Table Scan Statistics¶

Connection State Analysis¶

Finding Stuck Queries¶

Reset Statistics¶

Python Profiling with py-spy¶

Installing py-spy¶

Profiling a Running Container¶

Profiling Locally¶

Interpreting Flamegraphs¶

Memory Profiling with memray¶

Installing memray¶

Profiling Locally¶

Attaching to a Running Process¶

Generating Reports¶

Live Mode (Real-time Monitoring)¶

Container Profiling Workflow¶

Interpreting memray Output¶

py-spy vs memray¶

Container Resource Monitoring¶

Real-time Stats¶

Snapshot Stats¶

Redis Cache Analysis¶

Check Hit Rate¶

Check Key Counts¶

Gateway Log Analysis¶

Check for Errors¶

Count Error Types¶

Check for Idle Transaction Timeouts¶

Complete Profiling Session Example¶

Common Performance Issues¶

Issue: High Sequential Scan Count¶

Issue: Many Idle-in-Transaction Connections¶

Issue: Health Check Endpoints Holding PgBouncer Connections¶

Issue: High Gateway CPU¶

See Also¶