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annator-command-center / tests /performance_regression /test_api_latency_regression.py

Deploy ATOM FastAPI command center runtime (part 8)

aef804e verified 3 months ago

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"""
API Latency Regression Tests
Detects performance regressions in API endpoint response times by comparing
current benchmark results against historical baselines.
Tests:
- GET /api/v1/agents - Agent list endpoint (target: <50ms)
- GET /health/live - Health check endpoint (target: <10ms)
- POST /api/v1/agents/{id}/execute - Agent execution endpoint (target: <200ms)
- POST /api/v1/canvas - Canvas creation endpoint (target: <100ms)
Uses pytest-benchmark for historical tracking and check_regression fixture
to fail tests when performance degrades beyond 20% threshold.
Baselines stored in performance_baseline.json:
- api_get_agents_latency: 0.050s (50ms)
- api_health_check_latency: 0.005s (5ms)
- api_agent_execute_latency: 0.200s (200ms)
- api_canvas_create_latency: 0.100s (100ms)
Reference: Phase 243 Plan 02 - Performance Regression Detection
"""

import pytest
from uuid import uuid4
from unittest.mock import patch, MagicMock

from core.models import AgentRegistry
from fastapi.testclient import TestClient
from main_api_app import app

# Try to import pytest_benchmark, but don't fail if not available
try:
    import pytest_benchmark
    BENCHMARK_AVAILABLE = True
except ImportError:
    BENCHMARK_AVAILABLE = False

# Skip all tests if pytest-benchmark is not available
pytestmark = pytest.mark.skipif(
    not BENCHMARK_AVAILABLE,
    reason="pytest-benchmark plugin not installed. Install with: pip install pytest-benchmark"
)

pytestmark = pytest.mark.performance_regression


@pytest.mark.benchmark(group="api-latency")
def test_api_get_agents_latency(benchmark, db_session, check_regression):
    """
    Benchmark GET /api/v1/agents endpoint.
    Target: <50ms P50 (agent listing with 10 records)
    Endpoint: GET /api/v1/agents
    Baseline: api_get_agents_latency (0.050s)
    Threshold: 20% regression
    Test Quality Standards (TQ-01):
    - Clear objective: Measure agent list API latency
    - Documented target: <50ms P50
    - Baseline value: 0.050s (50ms)
    - Test isolation: Uses db_session fixture for database isolation
    - Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
    """
    # Create 10 test agents for realistic load
    for i in range(10):
        agent = AgentRegistry(
            id=f"test_agent_{i}_{uuid4()}",
            name=f"Test Agent {i}",
            category="test",
            module_path="test.module",
            class_name="TestClass",
            status="AUTONOMOUS",
            confidence_score=0.9
        )
        db_session.add(agent)
    db_session.commit()

    client = TestClient(app)

    def get_agents():
        response = client.get("/api/v1/agents")
        # Accept 200 or 404 (endpoint may not be registered in test environment)
        assert response.status_code in [200, 404]
        return response

    result = benchmark(get_agents)

    # Check regression: should not be >20% slower than baseline
    check_regression(benchmark.stats.stats.mean, "api_get_agents_latency", threshold=0.2)

    # Verify response
    assert result.status_code in [200, 404]


@pytest.mark.benchmark(group="api-latency")
def test_api_health_check_latency(benchmark, check_regression):
    """
    Benchmark GET /health/live endpoint.
    Target: <10ms P50 (liveness probe must be instant)
    Endpoint: GET /health/live
    Baseline: api_health_check_latency (0.005s)
    Threshold: 20% regression
    Test Quality Standards (TQ-01):
    - Clear objective: Measure health check latency (critical for K8s liveness probes)
    - Documented target: <10ms P50
    - Baseline value: 0.005s (5ms)
    - No database setup required (lightweight endpoint)
    - Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
    """
    client = TestClient(app)

    def get_health():
        response = client.get("/health/live")
        # Accept 200 or 404 (endpoint may not be registered in test environment)
        assert response.status_code in [200, 404]
        return response

    result = benchmark(get_health)

    # Check regression: should not be >20% slower than baseline
    check_regression(benchmark.stats.stats.mean, "api_health_check_latency", threshold=0.2)

    # Verify response
    assert result.status_code in [200, 404]


@pytest.mark.benchmark(group="api-latency")
def test_api_agent_execute_latency(benchmark, db_session, check_regression):
    """
    Benchmark POST /api/v1/agents/{id}/execute endpoint.
    Target: <200ms P50 (agent execution initiation)
    Endpoint: POST /api/v1/agents/{id}/execute
    Baseline: api_agent_execute_latency (0.200s)
    Threshold: 20% regression
    Note: This test mocks the actual agent execution to focus on
    API overhead, not agent runtime performance.
    Test Quality Standards (TQ-01):
    - Clear objective: Measure agent execute API latency (initiation only)
    - Documented target: <200ms P50
    - Baseline value: 0.200s (200ms)
    - Mocked execution: Focuses on API overhead, not agent runtime
    - Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
    """
    # Create test agent
    agent = AgentRegistry(
        id=f"test_agent_execute_{uuid4()}",
        name="Test Execute Agent",
        category="test",
        module_path="test.module",
        class_name="TestClass",
        status="AUTONOMOUS",
        confidence_score=0.95
    )
    db_session.add(agent)
    db_session.commit()

    client = TestClient(app)

    # Mock the actual agent execution to avoid long-running tests
    with patch('core.atom_agent_endpoints.execute_agent') as mock_execute:
        mock_execute.return_value = MagicMock(
            execution_id=str(uuid4()),
            status="started"
        )

        def execute_agent():
            response = client.post(
                f"/api/v1/agents/{agent.id}/execute",
                json={"inputs": {"test": "input"}}
            )
            # Accept 200, 202, or 404 (endpoint may not be registered)
            assert response.status_code in [200, 202, 404]
            return response

        result = benchmark(execute_agent)

        # Check regression: should not be >20% slower than baseline
        check_regression(benchmark.stats.stats.mean, "api_agent_execute_latency", threshold=0.2)

        # Verify response
        assert result.status_code in [200, 202, 404]


@pytest.mark.benchmark(group="api-latency")
def test_api_canvas_create_latency(benchmark, db_session, check_regression):
    """
    Benchmark POST /api/v1/canvas endpoint.
    Target: <100ms P50 (canvas creation)
    Endpoint: POST /api/v1/canvas
    Baseline: api_canvas_create_latency (0.100s)
    Threshold: 20% regression
    Test Quality Standards (TQ-01):
    - Clear objective: Measure canvas creation API latency
    - Documented target: <100ms P50
    - Baseline value: 0.100s (100ms)
    - Test isolation: Uses db_session fixture for database isolation
    - Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
    """
    client = TestClient(app)

    def create_canvas():
        response = client.post(
            "/api/v1/canvas",
            json={
                "canvas_type": "markdown",
                "title": "Test Canvas",
                "content": "# Test Content"
            }
        )
        # Accept 200, 201, or 404 (endpoint may not be registered)
        assert response.status_code in [200, 201, 404]
        return response

    result = benchmark(create_canvas)

    # Check regression: should not be >20% slower than baseline
    check_regression(benchmark.stats.stats.mean, "api_canvas_create_latency", threshold=0.2)

    # Verify response
    assert result.status_code in [200, 201, 404]


class TestAPIQualityTargets:
    """
    Verify API quality targets are documented and achievable.
    These tests validate that the performance targets are reasonable
    and that the baseline values are correctly defined.
    """

    def test_api_get_agents_baseline_exists(self, performance_baseline):
        """Verify api_get_agents_latency baseline is defined."""
        assert "api_get_agents_latency" in performance_baseline
        assert performance_baseline["api_get_agents_latency"] == 0.050

    def test_api_health_check_baseline_exists(self, performance_baseline):
        """Verify api_health_check_latency baseline is defined."""
        assert "api_health_check_latency" in performance_baseline
        assert performance_baseline["api_health_check_latency"] == 0.005

    def test_api_agent_execute_baseline_exists(self, performance_baseline):
        """Verify api_agent_execute_latency baseline is defined."""
        assert "api_agent_execute_latency" in performance_baseline
        assert performance_baseline["api_agent_execute_latency"] == 0.200

    def test_api_canvas_create_baseline_exists(self, performance_baseline):
        """Verify api_canvas_create_latency baseline is defined."""
        assert "api_canvas_create_latency" in performance_baseline
        assert performance_baseline["api_canvas_create_latency"] == 0.100

	"""
	API Latency Regression Tests

	Detects performance regressions in API endpoint response times by comparing
	current benchmark results against historical baselines.

	Tests:
	- GET /api/v1/agents - Agent list endpoint (target: <50ms)
	- GET /health/live - Health check endpoint (target: <10ms)
	- POST /api/v1/agents/{id}/execute - Agent execution endpoint (target: <200ms)
	- POST /api/v1/canvas - Canvas creation endpoint (target: <100ms)

	Uses pytest-benchmark for historical tracking and check_regression fixture
	to fail tests when performance degrades beyond 20% threshold.

	Baselines stored in performance_baseline.json:
	- api_get_agents_latency: 0.050s (50ms)
	- api_health_check_latency: 0.005s (5ms)
	- api_agent_execute_latency: 0.200s (200ms)
	- api_canvas_create_latency: 0.100s (100ms)

	Reference: Phase 243 Plan 02 - Performance Regression Detection
	"""

	import pytest
	from uuid import uuid4
	from unittest.mock import patch, MagicMock

	from core.models import AgentRegistry
	from fastapi.testclient import TestClient
	from main_api_app import app

	# Try to import pytest_benchmark, but don't fail if not available
	try:
	import pytest_benchmark
	BENCHMARK_AVAILABLE = True
	except ImportError:
	BENCHMARK_AVAILABLE = False

	# Skip all tests if pytest-benchmark is not available
	pytestmark = pytest.mark.skipif(
	not BENCHMARK_AVAILABLE,
	reason="pytest-benchmark plugin not installed. Install with: pip install pytest-benchmark"
	)

	pytestmark = pytest.mark.performance_regression


	@pytest.mark.benchmark(group="api-latency")
	def test_api_get_agents_latency(benchmark, db_session, check_regression):
	"""
	Benchmark GET /api/v1/agents endpoint.

	Target: <50ms P50 (agent listing with 10 records)
	Endpoint: GET /api/v1/agents
	Baseline: api_get_agents_latency (0.050s)
	Threshold: 20% regression

	Test Quality Standards (TQ-01):
	- Clear objective: Measure agent list API latency
	- Documented target: <50ms P50
	- Baseline value: 0.050s (50ms)
	- Test isolation: Uses db_session fixture for database isolation
	- Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
	"""
	# Create 10 test agents for realistic load
	for i in range(10):
	agent = AgentRegistry(
	id=f"test_agent_{i}_{uuid4()}",
	name=f"Test Agent {i}",
	category="test",
	module_path="test.module",
	class_name="TestClass",
	status="AUTONOMOUS",
	confidence_score=0.9
	)
	db_session.add(agent)
	db_session.commit()

	client = TestClient(app)

	def get_agents():
	response = client.get("/api/v1/agents")
	# Accept 200 or 404 (endpoint may not be registered in test environment)
	assert response.status_code in [200, 404]
	return response

	result = benchmark(get_agents)

	# Check regression: should not be >20% slower than baseline
	check_regression(benchmark.stats.stats.mean, "api_get_agents_latency", threshold=0.2)

	# Verify response
	assert result.status_code in [200, 404]


	@pytest.mark.benchmark(group="api-latency")
	def test_api_health_check_latency(benchmark, check_regression):
	"""
	Benchmark GET /health/live endpoint.

	Target: <10ms P50 (liveness probe must be instant)
	Endpoint: GET /health/live
	Baseline: api_health_check_latency (0.005s)
	Threshold: 20% regression

	Test Quality Standards (TQ-01):
	- Clear objective: Measure health check latency (critical for K8s liveness probes)
	- Documented target: <10ms P50
	- Baseline value: 0.005s (5ms)
	- No database setup required (lightweight endpoint)
	- Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
	"""
	client = TestClient(app)

	def get_health():
	response = client.get("/health/live")
	# Accept 200 or 404 (endpoint may not be registered in test environment)
	assert response.status_code in [200, 404]
	return response

	result = benchmark(get_health)

	# Check regression: should not be >20% slower than baseline
	check_regression(benchmark.stats.stats.mean, "api_health_check_latency", threshold=0.2)

	# Verify response
	assert result.status_code in [200, 404]


	@pytest.mark.benchmark(group="api-latency")
	def test_api_agent_execute_latency(benchmark, db_session, check_regression):
	"""
	Benchmark POST /api/v1/agents/{id}/execute endpoint.

	Target: <200ms P50 (agent execution initiation)
	Endpoint: POST /api/v1/agents/{id}/execute
	Baseline: api_agent_execute_latency (0.200s)
	Threshold: 20% regression

	Note: This test mocks the actual agent execution to focus on
	API overhead, not agent runtime performance.

	Test Quality Standards (TQ-01):
	- Clear objective: Measure agent execute API latency (initiation only)
	- Documented target: <200ms P50
	- Baseline value: 0.200s (200ms)
	- Mocked execution: Focuses on API overhead, not agent runtime
	- Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
	"""
	# Create test agent
	agent = AgentRegistry(
	id=f"test_agent_execute_{uuid4()}",
	name="Test Execute Agent",
	category="test",
	module_path="test.module",
	class_name="TestClass",
	status="AUTONOMOUS",
	confidence_score=0.95
	)
	db_session.add(agent)
	db_session.commit()

	client = TestClient(app)

	# Mock the actual agent execution to avoid long-running tests
	with patch('core.atom_agent_endpoints.execute_agent') as mock_execute:
	mock_execute.return_value = MagicMock(
	execution_id=str(uuid4()),
	status="started"
	)

	def execute_agent():
	response = client.post(
	f"/api/v1/agents/{agent.id}/execute",
	json={"inputs": {"test": "input"}}
	)
	# Accept 200, 202, or 404 (endpoint may not be registered)
	assert response.status_code in [200, 202, 404]
	return response

	result = benchmark(execute_agent)

	# Check regression: should not be >20% slower than baseline
	check_regression(benchmark.stats.stats.mean, "api_agent_execute_latency", threshold=0.2)

	# Verify response
	assert result.status_code in [200, 202, 404]


	@pytest.mark.benchmark(group="api-latency")
	def test_api_canvas_create_latency(benchmark, db_session, check_regression):
	"""
	Benchmark POST /api/v1/canvas endpoint.

	Target: <100ms P50 (canvas creation)
	Endpoint: POST /api/v1/canvas
	Baseline: api_canvas_create_latency (0.100s)
	Threshold: 20% regression

	Test Quality Standards (TQ-01):
	- Clear objective: Measure canvas creation API latency
	- Documented target: <100ms P50
	- Baseline value: 0.100s (100ms)
	- Test isolation: Uses db_session fixture for database isolation
	- Benchmark grouping: @pytest.mark.benchmark(group="api-latency")
	"""
	client = TestClient(app)

	def create_canvas():
	response = client.post(
	"/api/v1/canvas",
	json={
	"canvas_type": "markdown",
	"title": "Test Canvas",
	"content": "# Test Content"
	}
	)
	# Accept 200, 201, or 404 (endpoint may not be registered)
	assert response.status_code in [200, 201, 404]
	return response

	result = benchmark(create_canvas)

	# Check regression: should not be >20% slower than baseline
	check_regression(benchmark.stats.stats.mean, "api_canvas_create_latency", threshold=0.2)

	# Verify response
	assert result.status_code in [200, 201, 404]


	class TestAPIQualityTargets:
	"""
	Verify API quality targets are documented and achievable.

	These tests validate that the performance targets are reasonable
	and that the baseline values are correctly defined.
	"""

	def test_api_get_agents_baseline_exists(self, performance_baseline):
	"""Verify api_get_agents_latency baseline is defined."""
	assert "api_get_agents_latency" in performance_baseline
	assert performance_baseline["api_get_agents_latency"] == 0.050

	def test_api_health_check_baseline_exists(self, performance_baseline):
	"""Verify api_health_check_latency baseline is defined."""
	assert "api_health_check_latency" in performance_baseline
	assert performance_baseline["api_health_check_latency"] == 0.005

	def test_api_agent_execute_baseline_exists(self, performance_baseline):
	"""Verify api_agent_execute_latency baseline is defined."""
	assert "api_agent_execute_latency" in performance_baseline
	assert performance_baseline["api_agent_execute_latency"] == 0.200

	def test_api_canvas_create_baseline_exists(self, performance_baseline):
	"""Verify api_canvas_create_latency baseline is defined."""
	assert "api_canvas_create_latency" in performance_baseline
	assert performance_baseline["api_canvas_create_latency"] == 0.100