def test_metrics_spillover(capsys, metric, dimension, namespace, a_hundred_metrics):
my_metrics = Metrics()
my_metrics.add_namespace(**namespace)
my_metrics.add_dimension(**dimension)
for _metric in a_hundred_metrics:
my_metrics.add_metric(**_metric)
@my_metrics.log_metrics
def lambda_handler(evt, handler):
my_metrics.add_metric(**metric)
return True
lambda_handler({}, {})
output = capsys.readouterr().out.strip()
spillover_metrics, single_metric = output.split("\n")
spillover_metrics = json.loads(spillover_metrics)
single_metric = json.loads(single_metric)
expected_single_metric = serialize_single_metric(metric=metric, dimension=dimension, namespace=namespace)
expected_spillover_metrics = serialize_metrics(
metrics=a_hundred_metrics, dimensions=[dimension], namespace=namespace
)
remove_timestamp(metrics=[spillover_metrics, expected_spillover_metrics, single_metric, expected_single_metric])
assert single_metric["_aws"] == expected_single_metric["_aws"]
assert spillover_metrics["_aws"] == expected_spillover_metrics["_aws"]
def test_log_metrics_during_exception(capsys, metric, dimension, namespace):
# GIVEN Metrics is initialized
my_metrics = Metrics()
my_metrics.add_metric(**metric)
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
# WHEN log_metrics is used to serialize metrics
# but an error has been raised during handler execution
@my_metrics.log_metrics
def lambda_handler(evt, context):
raise ValueError("Bubble up")
with pytest.raises(ValueError):
lambda_handler({}, {})
output = json.loads(capsys.readouterr().out.strip())
expected = serialize_single_metric(metric=metric,
dimension=dimension,
namespace=namespace)
remove_timestamp(metrics=[output,
expected]) # Timestamp will always be different
# THEN we should log metrics and propagate the exception up
assert expected["_aws"] == output["_aws"]
def test_metrics_spillover(monkeypatch, capsys, metric, dimension, namespace,
a_hundred_metrics):
# GIVEN Metrics is initialized and we have over a hundred metrics to add
my_metrics = Metrics()
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
# WHEN we add more than 100 metrics
for _metric in a_hundred_metrics:
my_metrics.add_metric(**_metric)
# THEN it should serialize and flush all metrics at the 100th
# and clear all metrics and dimensions from memory
output = json.loads(capsys.readouterr().out.strip())
spillover_metrics = output["_aws"]["CloudWatchMetrics"][0]["Metrics"]
assert my_metrics.metric_set == {}
assert len(spillover_metrics) == 100
# GIVEN we add the 101th metric
# WHEN we already had a Metric class instance
# with an existing dimension set from the previous 100th metric batch
my_metrics.add_metric(**metric)
# THEN serializing the 101th metric should
# create a new EMF object with a single metric in it (101th)
# and contain have the same dimension we previously added
serialized_101th_metric = my_metrics.serialize_metric_set()
expected_101th_metric = serialize_single_metric(metric=metric,
dimension=dimension,
namespace=namespace)
remove_timestamp(metrics=[serialized_101th_metric, expected_101th_metric])
assert serialized_101th_metric["_aws"] == expected_101th_metric["_aws"]
def test_log_metrics(capsys, metrics, dimensions, namespace):
# GIVEN Metrics is initialized
my_metrics = Metrics()
my_metrics.add_namespace(**namespace)
for metric in metrics:
my_metrics.add_metric(**metric)
for dimension in dimensions:
my_metrics.add_dimension(**dimension)
# WHEN we utilize log_metrics to serialize
# and flush all metrics at the end of a function execution
@my_metrics.log_metrics
def lambda_handler(evt, ctx):
return True
lambda_handler({}, {})
output = json.loads(capsys.readouterr().out.strip())
expected = serialize_metrics(metrics=metrics,
dimensions=dimensions,
namespace=namespace)
remove_timestamp(metrics=[output,
expected]) # Timestamp will always be different
# THEN we should have no exceptions
# and a valid EMF object should've been flushed correctly
assert expected["_aws"] == output["_aws"]
for dimension in dimensions:
assert dimension["name"] in output
def test_metrics_reuse_metric_set(metric, dimension, namespace):
# GIVEN Metrics is initialized
my_metrics = Metrics()
my_metrics.add_metric(**metric)
# WHEN Metrics is initialized one more time
my_metrics_2 = Metrics()
# THEN Both class instances should have the same metric set
assert my_metrics_2.metric_set == my_metrics.metric_set
def serialize_metrics(metrics: List[Dict], dimensions: List[Dict], namespace: Dict) -> Dict:
""" Helper function to build EMF object from a list of metrics, dimensions """
my_metrics = Metrics()
for dimension in dimensions:
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
for metric in metrics:
my_metrics.add_metric(**metric)
if len(metrics) != 100:
return my_metrics.serialize_metric_set()
def test_multiple_metrics(metrics, dimensions, namespace):
my_metrics = Metrics()
for metric in metrics:
my_metrics.add_metric(**metric)
for dimension in dimensions:
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
output = my_metrics.serialize_metric_set()
expected = serialize_metrics(metrics=metrics, dimensions=dimensions, namespace=namespace)
remove_timestamp(metrics=[output, expected]) # Timestamp will always be different
assert expected["_aws"] == output["_aws"]
def test_log_metrics(capsys, metrics, dimensions, namespace):
my_metrics = Metrics()
my_metrics.add_namespace(**namespace)
for metric in metrics:
my_metrics.add_metric(**metric)
for dimension in dimensions:
my_metrics.add_dimension(**dimension)
@my_metrics.log_metrics
def lambda_handler(evt, handler):
return True
lambda_handler({}, {})
output = json.loads(capsys.readouterr().out.strip())
expected = serialize_metrics(metrics=metrics, dimensions=dimensions, namespace=namespace)
remove_timestamp(metrics=[output, expected]) # Timestamp will always be different
assert expected["_aws"] == output["_aws"]
def test_log_metrics_clear_metrics_after_invocation(metric, dimension,
namespace):
# GIVEN Metrics is initialized
my_metrics = Metrics()
my_metrics.add_metric(**metric)
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
# WHEN log_metrics is used to flush metrics from memory
@my_metrics.log_metrics
def lambda_handler(evt, context):
return True
lambda_handler({}, {})
# THEN metric set should be empty after function has been run
assert my_metrics.metric_set == {}
def test_log_metrics_error_propagation(capsys, metric, dimension, namespace):
# GIVEN Metrics are serialized after handler execution
# WHEN If an error occurs and metrics have been added
# THEN we should log metrics and propagate exception up
my_metrics = Metrics()
my_metrics.add_metric(**metric)
my_metrics.add_dimension(**dimension)
my_metrics.add_namespace(**namespace)
@my_metrics.log_metrics
def lambda_handler(evt, context):
raise ValueError("Bubble up")
with pytest.raises(ValueError):
lambda_handler({}, {})
output = json.loads(capsys.readouterr().out.strip())
expected = serialize_single_metric(metric=metric, dimension=dimension, namespace=namespace)
remove_timestamp(metrics=[output, expected]) # Timestamp will always be different
assert expected["_aws"] == output["_aws"]