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@codeflash-ai codeflash-ai bot commented Oct 29, 2025

📄 165% (1.65x) speedup for SimpleHealthServicer.Check in framework/py/flwr/supercore/grpc_health/simple_health_servicer.py

⏱️ Runtime : 1.97 milliseconds 745 microseconds (best of 164 runs)

📝 Explanation and details

The optimization introduces object caching to eliminate repeated object creation overhead. The key change is creating a single cached HealthCheckResponse instance (_SERVING_RESPONSE) at module level, rather than creating a new response object on every method call.

What changed:

  • Added a module-level cached response: _SERVING_RESPONSE = HealthCheckResponse(status=HealthCheckResponse.SERVING)
  • Modified the Check method to return the cached instance instead of creating new objects

Why this is faster:

  • Eliminates object creation overhead: The original code creates a new HealthCheckResponse object for every call (548ns per hit), while the optimized version simply returns a pre-existing object (203ns per hit)
  • Reduces memory allocation: No repeated instantiation means less work for Python's memory allocator
  • Leverages immutability: Since health check responses with SERVING status are identical and immutable, sharing the same instance is safe

Performance gains:

  • 164% speedup (1.97ms → 745μs)
  • Per-call improvement: 548ns → 203ns per method invocation
  • Particularly effective for high-frequency health checks where the same SERVING response is returned repeatedly

This optimization is most beneficial for scenarios with frequent health check requests, as demonstrated by the test cases that make hundreds or thousands of consecutive calls - exactly the use case where gRPC health services typically operate.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 3009 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest  # used for our unit tests
from supercore.grpc_health.simple_health_servicer import SimpleHealthServicer


# Mock objects for grpc and health_pb2
class HealthCheckRequest:
    # Simulate the HealthCheckRequest message
    def __init__(self, service: str = ""):
        self.service = service

class HealthCheckResponse:
    # Simulate the HealthCheckResponse message and status enum
    SERVING = 1
    NOT_SERVING = 2
    UNKNOWN = 0
    SERVICE_UNKNOWN = 3

    def __init__(self, status=None):
        self.status = status

class ServicerContext:
    # Simulate grpc.ServicerContext; not used in our implementation
    pass

# Simulate HealthServicer base class
class HealthServicer:
    pass
from supercore.grpc_health.simple_health_servicer import SimpleHealthServicer

# unit tests

@pytest.fixture
def servicer():
    # Fixture to provide a fresh instance of the servicer for each test
    return SimpleHealthServicer()

@pytest.fixture
def context():
    # Fixture to provide a dummy context object
    return ServicerContext()

# 1. Basic Test Cases

def test_check_returns_serving_for_empty_service(servicer, context):
    """Test that Check returns SERVING for an empty service name."""
    req = HealthCheckRequest(service="")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_nonempty_service(servicer, context):
    """Test that Check returns SERVING for a non-empty service name."""
    req = HealthCheckRequest(service="my_service")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_special_char_service(servicer, context):
    """Test that Check returns SERVING for a service name with special characters."""
    req = HealthCheckRequest(service="service!@#")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

# 2. Edge Test Cases

def test_check_returns_serving_for_long_service_name(servicer, context):
    """Test that Check returns SERVING for a very long service name."""
    long_service_name = "a" * 1000  # 1000 characters
    req = HealthCheckRequest(service=long_service_name)
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_unicode_service_name(servicer, context):
    """Test that Check returns SERVING for a service name with unicode characters."""
    unicode_service_name = "服务"
    req = HealthCheckRequest(service=unicode_service_name)
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_numeric_service_name(servicer, context):
    """Test that Check returns SERVING for a numeric service name."""
    req = HealthCheckRequest(service="123456")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_none_service_name(servicer, context):
    """Test that Check returns SERVING even if service name is None (simulate missing field)."""
    req = HealthCheckRequest(service=None)
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_whitespace_service_name(servicer, context):
    """Test that Check returns SERVING for a service name with only whitespace."""
    req = HealthCheckRequest(service="    ")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_service_name_with_newlines(servicer, context):
    """Test that Check returns SERVING for a service name containing newlines."""
    req = HealthCheckRequest(service="service\nname")
    codeflash_output = servicer.Check(req, context); resp = codeflash_output

# 3. Large Scale Test Cases

def test_check_returns_serving_for_many_requests(servicer, context):
    """Test that Check returns SERVING for a large number of requests with different service names."""
    for i in range(1000):
        req = HealthCheckRequest(service=f"service_{i}")
        codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_returns_serving_for_varied_service_names(servicer, context):
    """Test that Check returns SERVING for a large variety of service names."""
    service_names = [
        "", "a", "A", "service", "SERVICE", "serv1", "serv_2", "serv-3",
        "服务", "сервер", "サービス", "서비스", "serv!@#", " ", "\t", "\n", "a" * 999
    ]
    # Repeat to reach 1000 elements
    service_names = service_names * (1000 // len(service_names))
    for name in service_names:
        req = HealthCheckRequest(service=name)
        codeflash_output = servicer.Check(req, context); resp = codeflash_output

def test_check_performance_under_load(servicer, context):
    """Test performance under load by timing 1000 requests (should not raise or hang)."""
    import time
    start = time.time()
    for i in range(1000):
        req = HealthCheckRequest(service=f"service_{i}")
        codeflash_output = servicer.Check(req, context); resp = codeflash_output
    elapsed = time.time() - start
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from unittest.mock import MagicMock

import grpc
# imports
import pytest
from supercore.grpc_health.simple_health_servicer import SimpleHealthServicer


# Mocks for grpc_health.v1.health_pb2 and grpc_health.v1.health_pb2_grpc
class HealthCheckRequest:
    def __init__(self, service=""):
        self.service = service

class HealthCheckResponse:
    # Simulate the enum for SERVING
    SERVING = 1
    UNKNOWN = 0
    NOT_SERVING = 2
    SERVICE_UNKNOWN = 3

    def __init__(self, status=None):
        self.status = status

class HealthServicer:
    pass
from supercore.grpc_health.simple_health_servicer import SimpleHealthServicer

# ================== UNIT TESTS ==================

@pytest.fixture
def servicer():
    """Fixture for SimpleHealthServicer instance."""
    return SimpleHealthServicer()

@pytest.fixture
def context():
    """Fixture for a mock gRPC ServicerContext."""
    return MagicMock(spec=grpc.ServicerContext)

# 1. Basic Test Cases











def test_check_with_none_context(servicer):
    """Check handles None as context."""
    req = HealthCheckRequest()
    # Should not raise, as context is unused
    codeflash_output = servicer.Check(req, None); resp = codeflash_output

# 3. Large Scale Test Cases







#------------------------------------------------
from supercore.grpc_health.simple_health_servicer import SimpleHealthServicer

To edit these changes git checkout codeflash/optimize-SimpleHealthServicer.Check-mhcbi59g and push.

Codeflash

The optimization introduces **object caching** to eliminate repeated object creation overhead. The key change is creating a single cached `HealthCheckResponse` instance (`_SERVING_RESPONSE`) at module level, rather than creating a new response object on every method call.

**What changed:**
- Added a module-level cached response: `_SERVING_RESPONSE = HealthCheckResponse(status=HealthCheckResponse.SERVING)`
- Modified the `Check` method to return the cached instance instead of creating new objects

**Why this is faster:**
- **Eliminates object creation overhead**: The original code creates a new `HealthCheckResponse` object for every call (548ns per hit), while the optimized version simply returns a pre-existing object (203ns per hit)
- **Reduces memory allocation**: No repeated instantiation means less work for Python's memory allocator
- **Leverages immutability**: Since health check responses with SERVING status are identical and immutable, sharing the same instance is safe

**Performance gains:**
- **164% speedup** (1.97ms → 745μs)
- **Per-call improvement**: 548ns → 203ns per method invocation
- Particularly effective for **high-frequency health checks** where the same SERVING response is returned repeatedly

This optimization is most beneficial for scenarios with frequent health check requests, as demonstrated by the test cases that make hundreds or thousands of consecutive calls - exactly the use case where gRPC health services typically operate.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 29, 2025 18:17
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 29, 2025
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