File size: 2,694 Bytes
4021124 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import json
import time
from contextlib import contextmanager
from typing import Dict, Generator
import pytest
from aws_lambda_powertools import Metrics
from aws_lambda_powertools.metrics import MetricUnit
from aws_lambda_powertools.metrics import metrics as metrics_global
# adjusted for slower machines in CI too
METRICS_VALIDATION_SLA: float = 0.002
METRICS_SERIALIZATION_SLA: float = 0.002
@contextmanager
def timing() -> Generator:
""" "Generator to quickly time operations. It can add 5ms so take that into account in elapsed time
Examples
--------
with timing() as t:
print("something")
elapsed = t()
"""
start = time.perf_counter()
yield lambda: time.perf_counter() - start # gen as lambda to calculate elapsed time
@pytest.fixture(scope="function", autouse=True)
def reset_metric_set():
metrics = Metrics()
metrics.clear_metrics()
metrics_global.is_cold_start = True # ensure each test has cold start
yield
@pytest.fixture
def namespace() -> str:
return "test_namespace"
@pytest.fixture
def metric() -> Dict[str, str]:
return {"name": "single_metric", "unit": MetricUnit.Count, "value": 1}
def add_max_metrics_before_serialization(metrics_instance: Metrics):
metrics_instance.add_dimension(name="test_dimension", value="test")
for i in range(99):
metrics_instance.add_metric(name=f"metric_{i}", unit="Count", value=1)
@pytest.mark.perf
def test_metrics_large_operation_without_json_serialization_sla(namespace):
# GIVEN Metrics is initialized
my_metrics = Metrics(namespace=namespace)
# WHEN we add and serialize 99 metrics
with timing() as t:
add_max_metrics_before_serialization(metrics_instance=my_metrics)
my_metrics.serialize_metric_set()
# THEN completion time should be below our validation SLA
elapsed = t()
if elapsed > METRICS_VALIDATION_SLA:
pytest.fail(f"Metric validation should be below {METRICS_VALIDATION_SLA}s: {elapsed}")
@pytest.mark.perf
def test_metrics_large_operation_and_json_serialization_sla(namespace):
# GIVEN Metrics is initialized with validation disabled
my_metrics = Metrics(namespace=namespace)
# WHEN we add and serialize 99 metrics
with timing() as t:
add_max_metrics_before_serialization(metrics_instance=my_metrics)
metrics = my_metrics.serialize_metric_set()
print(json.dumps(metrics, separators=(",", ":")))
# THEN completion time should be below our serialization SLA
elapsed = t()
if elapsed > METRICS_SERIALIZATION_SLA:
pytest.fail(f"Metric serialization should be below {METRICS_SERIALIZATION_SLA}s: {elapsed}")
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