Aws perf load testing

AWS Performance & Load Testing Skill

Generate benchmarks and load tests by reading handler code and API specs.

Performance Tests (pytest-benchmark + tracemalloc)

Phase 1: Build Realistic Events

Read the handler code to understand:

• What event shape does it expect? (API GW proxy, S3, SQS, direct)
• What are the hot paths? (most common code branches)
• What external calls does it make? (need to mock for benchmarks)

Phase 2: Generate Benchmark Tests

See references/benchmark-patterns.md for full patterns.

"""Performance benchmarks for {handler_name}."""
import json
import pytest
from unittest.mock import patch, MagicMock

pytestmark = pytest.mark.performance


@pytest.fixture
def mock_context():
    ctx = MagicMock()
    ctx.function_name = "test-function"
    ctx.memory_limit_in_mb = 256
    ctx.get_remaining_time_in_millis.return_value = 30000
    return ctx


class TestHandlerLatency:
    """Benchmark handler execution time."""

    def test_get_request_latency(self, mock_context, benchmark):
        # Mock external dependencies so we measure handler logic only
        with patch("handler.main.boto3") as mock_boto:
            mock_boto.client.return_value.get_object.return_value = {
                "Body": MagicMock(read=lambda: b'{"data": "value"}')
            }

            from handler.main import lambda_handler
            event = {
                "httpMethod": "GET",
                "pathParameters": {"id": "bench-123"},
                "headers": {"x-api-key": "test"},
            }

            result = benchmark(lambda_handler, event, mock_context)
            assert result["statusCode"] == 200

    def test_post_request_latency(self, mock_context, benchmark):
        with patch("handler.main.boto3") as mock_boto:
            mock_boto.client.return_value.put_item.return_value = {}

            from handler.main import lambda_handler
            event = {
                "httpMethod": "POST",
                "body": json.dumps({"name": "Benchmark"}),
                "headers": {"x-api-key": "test"},
            }

            result = benchmark(lambda_handler, event, mock_context)
            assert result["statusCode"] in (200, 201)


class TestMemoryUsage:
    """Profile handler memory consumption."""

    def test_memory_within_limit(self, mock_context):
        import tracemalloc
        tracemalloc.start()

        with patch("handler.main.boto3"):
            from handler.main import lambda_handler
            event = {"httpMethod": "GET", "pathParameters": {"id": "mem-test"}}
            lambda_handler(event, mock_context)

        current, peak = tracemalloc.get_traced_memory()
        tracemalloc.stop()

        peak_mb = peak / 1024 / 1024
        limit_mb = mock_context.memory_limit_in_mb
        assert peak_mb < limit_mb * 0.8, (
            f"Peak {peak_mb:.1f}MB is >80% of {limit_mb}MB limit"
        )

Performance Thresholds

Suggested starting points — adapt to your service's SLAs and requirements:

Metric	Lambda	Batch	API GW E2E
p95 latency	< 500ms	N/A	< 3s
p99 latency	< 1s	N/A	< 5s
Error rate	< 0.1%	0%	< 1%
Memory peak	< 80% of limit	< 2GB	N/A
Cold start	< 3s	N/A	N/A

Load Tests (Locust)

Phase 1: Build Endpoint Map

Read the API spec (OpenAPI/Swagger) or discover endpoints from handler routes:

Method	Path	Weight	Category
GET	/resource/{id}	5	read
POST	/resource	1	write
POST	/resource/search	3	read

Phase 2: Generate Locust Users

See references/locust-patterns.md for full patterns.

"""Load test for {service_name} API."""
from locust import HttpUser, task, between, tag

class ApiUser(HttpUser):
    wait_time = between(1, 3)

    def on_start(self):
        self.client.headers.update({
            "Content-Type": "application/json",
            # Add auth headers from spec
        })

    @tag("read")
    @task(5)  # weight = 5 (most common)
    def get_resource(self):
        self.client.get(
            "/resource/LOAD-TEST-id",
            name="/resource/{id}",
        )

    @tag("write")
    @task(1)  # weight = 1 (least common)
    def create_resource(self):
        self.client.post(
            "/resource",
            json={"name": "LOAD-TEST-item"},
            name="/resource",
        )

Load Test Commands

# Quick smoke (10 users, 1 minute)
locust -f tests/load/locustfile.py --host=$API_BASE_URL \
  --users=10 --spawn-rate=2 --run-time=1m --headless --csv=tests/reports/smoke

# Standard load (50 users, 5 minutes)
locust -f tests/load/locustfile.py --host=$API_BASE_URL \
  --users=50 --spawn-rate=5 --run-time=5m --headless \
  --csv=tests/reports/load --html=tests/reports/load.html

# Stress (ramp to 200 users, 10 minutes)
locust -f tests/load/locustfile.py --host=$API_BASE_URL \
  --users=200 --spawn-rate=10 --run-time=10m --headless --csv=tests/reports/stress

# Soak (steady 30 users, 1 hour)
locust -f tests/load/locustfile.py --host=$API_BASE_URL \
  --users=30 --spawn-rate=30 --run-time=1h --headless --csv=tests/reports/soak

Load Test Output

Locust generates:

• *_stats.csv — per-endpoint avg/min/max/p50/p95/p99
• *_failures.csv — failed request details
• *_stats_history.csv — time-series data for graphing
• *.html — interactive dashboard

Safety Constraints

• Never run load tests against production without explicit confirmation
• All test data uses LOAD-TEST- prefix
• Load tests must have --run-time set (no unbounded runs)
• Monitor CloudWatch during load tests for throttling