Files

138 lines
3.6 KiB
Python

from unittest.mock import MagicMock, patch
import pytest
from semantica.graph_store.graph_store import GraphStore
@pytest.fixture
def mock_neo4j_driver():
"""
Creates a mock of of Neo4j Driver
Simulates: Driver -> Session -> Transaction -> Result -> Record
"""
mock_result = MagicMock()
fake_props = {"name": "TestNode", "age": 30}
def get_item(key):
if key == "id":
return 12345
if key == "n":
return fake_props
if key == "count":
return 42
return None
mock_record = MagicMock()
mock_record.__getitem__.side_effect = get_item
mock_record.keys.return_value = ["id", "n"]
mock_record.values.return_value = [12345, fake_props]
# dict conversion - essentially doing it because the db sometimes demands it
mock_record.items.return_value = [("id", 12345), ("n", fake_props)]
# ~~ Result Methods ~~
mock_result = MagicMock()
mock_result.single.return_value = mock_record
mock_result.__iter__.side_effect = lambda: iter([mock_record])
# ~~ Session ~~
mock_session = MagicMock()
mock_session.run.return_value = mock_result
mock_session.__enter__.return_value = mock_session
mock_session.__exit__.return_value = None
# ~~ Driver ~~
mock_driver = MagicMock()
mock_driver.session.return_value = mock_session
mock_driver.verify_connectivity.return_value = True
return mock_driver
@pytest.fixture
def graph_store(mock_neo4j_driver):
"""
Returns a GraphsStore connected to mnock driver.
"""
# ~~ Patch GraphDatbase ~~
with patch("semantica.graph_store.neo4j_store.GraphDatabase") as mockDB:
mockDB.driver.return_value = mock_neo4j_driver
store = GraphStore(
backend="neo4j", uri="bolt://mock:7687", user="mock", password="mock"
)
store.connect()
if hasattr(store, "progress_tracker"):
store.progress_tracker = MagicMock()
return store
# ~~ Benchmarks ~~
def test_node_creation_overhead(benchmark, graph_store):
"""
Benchamrks the full stack overhead for creating a single node.
Path: GraphStore -> NodeManager -> Neo4jStore, Driver
"""
def op():
return graph_store.create_node(
labels=["Person"], properties={"name": "Alexander", "age": 17}
)
result = benchmark(op)
assert result["id"] == 12345
def test_batch_node_creation_overhead(benchmark, graph_store):
"""
Benchmarks the loop overhead in create_nodes (Batch).
Checks if it handles lists efficiently.
"""
nodes = [{"labels": ["Person"], "properties": {"id": i}} for i in range(50)]
def op():
return graph_store.create_nodes(nodes)
result = benchmark(op)
assert len(result) == 50
def test_query_construction_and_parsing(benchmark, graph_store):
"""
Benchmarks every execution overhead.
Measures how fast `QueryEngine` parses result into a Python dict.
"""
query = "MATCH ( n:Person) RETURN n LIMIT 1"
def op():
return graph_store.execute_query(query)
result = benchmark(op)
assert result["success"] is True
assert len(result["records"]) > 0
def test_analytics_shortest_path_overhead(benchmark, graph_store):
"""
Benchmarks the wrapper overhead for graph analytics.
"""
def op():
return graph_store.shortest_path(
start_node_id=1, end_node_id=2, rel_type="KNOWS"
)
try:
benchmark(op)
except Exception:
# v pass as we are only trying to benchmark the function overhead call mainly
pass