def _preprocess_records(self, processor: Dict,
records: List[Record]) -> List[Dict]:
"""Preprocess the list of records by calling the given processor (e.g., Lamnda function)."""
proc_type = processor.get("Type")
parameters = processor.get("Parameters", [])
parameters = {
p["ParameterName"]: p["ParameterValue"]
for p in parameters
}
if proc_type == "Lambda":
lambda_arn = parameters.get("LambdaArn")
# TODO: add support for other parameters, e.g., NumberOfRetries, BufferSizeInMBs, BufferIntervalInSeconds, ...
client = aws_stack.connect_to_service("lambda")
records = keys_to_lower(records)
# Convert the record data to string (for json serialization)
for record in records:
if "data" in record:
record["data"] = to_str(record["data"])
if "Data" in record:
record["Data"] = to_str(record["Data"])
event = {"records": records}
event = to_bytes(json.dumps(event))
response = client.invoke(FunctionName=lambda_arn, Payload=event)
result = response.get("Payload").read()
result = json.loads(to_str(result))
records = result.get("records", []) if result else []
else:
LOG.warning("Unsupported Firehose processor type '%s'", proc_type)
return records
def get_params(resource_props, stack_name, resources, resource_id):
stage_name = resource_props.get("StageName", "default")
resources[resource_id]["Properties"]["StageName"] = stage_name
result = keys_to_lower(resource_props)
param_names = [
"restApiId",
"deploymentId",
"description",
"cacheClusterEnabled",
"cacheClusterSize",
"variables",
"documentationVersion",
"canarySettings",
"tracingEnabled",
"tags",
]
result = select_attributes(result, param_names)
result["tags"] = {t["key"]: t["value"] for t in result.get("tags", [])}
result["stageName"] = stage_name
return result
def get_params(params, **kwargs):
result = keys_to_lower(params)
param_names = [
"restApiId",
"stageName",
"deploymentId",
"description",
"cacheClusterEnabled",
"cacheClusterSize",
"variables",
"documentationVersion",
"canarySettings",
"tracingEnabled",
"tags",
]
result = select_attributes(result, param_names)
result["tags"] = {
t["key"]: t["value"]
for t in result.get("tags", [])
}
return result
def lambda_keys_to_lower(key=None):
return lambda params, **kwargs: common.keys_to_lower(params.get(key) if key else params)