def handle(self, *args, **options):
for module_name in options["module"].split(","):
importlib.import_module(module_name)
descriptors = get_lambda_descriptors()
if not descriptors:
self.stdout.write("No descriptors found. Exiting.")
return
all_lambdas = LAMBDA.list_functions()
response = IAM.get_role(
RoleName=settings.LAMBDA_DEFAULT_EXECUTION_ROLE_NAME)
execution_role_arn = response["Role"]["Arn"]
self.stdout.write("Execution Role Arn: %r" % (execution_role_arn))
artifact_path = options["artifact"]
self.stdout.write("Using code at path: %r" % (artifact_path))
with open(artifact_path, "rb") as artifact:
code_payload_bytes = artifact.read()
self.stdout.write("Code Payload Bytes: %s" % (len(code_payload_bytes)))
for descriptor in descriptors:
self.stdout.write("About to deploy: %s" % (descriptor["name"]))
existing_lambda = None
for func in all_lambdas["Functions"]:
if func["FunctionName"] == descriptor["name"]:
existing_lambda = func
if existing_lambda:
self.stdout.write("Lambda exists - will update.")
LAMBDA.update_function_configuration(
FunctionName=descriptor["name"],
Role=execution_role_arn,
Handler=descriptor["handler"],
Timeout=descriptor["cpu_seconds"],
MemorySize=descriptor["memory"],
)
LAMBDA.update_function_code(
FunctionName=descriptor["name"],
ZipFile=code_payload_bytes,
)
else:
self.stdout.write("New Lambda - will create.")
LAMBDA.create_function(
FunctionName=descriptor["name"],
Runtime="python2.7",
Role=execution_role_arn,
Handler=descriptor["handler"],
Code={"ZipFile": code_payload_bytes},
Timeout=descriptor["cpu_seconds"],
MemorySize=descriptor["memory"],
)
if descriptor["stream_name"]:
# This lambda would like to be registered as a listener on a kinesis stream
stream_name = descriptor["stream_name"]
batch_size = descriptor["stream_batch_size"]
self.stdout.write(
"Applying event source mapping for stream: %s" %
(stream_name))
target_event_source = get_kinesis_stream_arn_from_name(
stream_name)
event_source_list = LAMBDA.list_event_source_mappings(
FunctionName=descriptor["name"])
if event_source_list["EventSourceMappings"]:
# We need to update the existing mappings
# First we need to remove any stale event source mappings.
# Then we need to look for an existing match and update it.
# Finally if we didn't update an existing match, we need to create a new mapping.
# So long as we don't DELETE an in-use mapping
# it will not loose its place in the stream.
update_existing_mapping_success = False
for mapping in event_source_list["EventSourceMappings"]:
mapping_uuid = mapping["UUID"]
mapping_event_source = mapping["EventSourceArn"]
mapping_batch_size = mapping["BatchSize"]
if mapping_event_source != target_event_source:
# Delete this event source, it's stale.
self.stdout.write("Deleting stale mapping: %r" %
(mapping_event_source))
LAMBDA.delete_event_source_mapping(
UUID=mapping_uuid)
else:
update_existing_mapping_success = True
if mapping_batch_size != batch_size:
# The batch size is the only thing that might have changed
self.stdout.write(
"Updating existing stream batch size from %s to %s"
% (mapping_batch_size, batch_size))
LAMBDA.update_event_source_mapping(
UUID=mapping_uuid, BatchSize=batch_size)
else:
# Nothing has changed.
self.stdout.write("No changes required.")
if not update_existing_mapping_success:
# We didn't find an existing mapping to update, so we still must create one
self.stdout.write(
"Creating new mapping for event source: %s" %
target_event_source)
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON")
else:
# No mappings currently exist, so we need to create a new mapping
self.stdout.write(
"Creating new mapping for event source: %s" %
target_event_source)
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON")
if options["wait"]:
self.wait_for_complete_deployment(timeout=30)
def handle(self, *args, **options):
for module_name in options["module"].split(","):
importlib.import_module(module_name)
descriptors = get_lambda_descriptors()
if not descriptors:
self.stdout.write("No descriptors found. Exiting.")
return
all_lambdas = LAMBDA.list_functions()
response = IAM.get_role(RoleName=settings.LAMBDA_DEFAULT_EXECUTION_ROLE_NAME)
execution_role_arn = response["Role"]["Arn"]
self.stdout.write("Execution Role Arn: %r" % (execution_role_arn))
artifact_obj = options["artifact"]
artifact_bucket = settings.AWS_LAMBDA_ARTIFACTS_BUCKET
self.stdout.write(
"Using code at S3 path: %r/%r" % (artifact_bucket, artifact_obj)
)
for descriptor in descriptors:
self.stdout.write("About to deploy: %s" % (descriptor["name"]))
existing_lambda = None
for func in all_lambdas["Functions"]:
if func["FunctionName"] == descriptor["name"]:
existing_lambda = func
if existing_lambda:
self.stdout.write("Lambda exists - will update.")
LAMBDA.update_function_configuration(
FunctionName=descriptor["name"],
Role=execution_role_arn,
Handler=descriptor["handler"],
Timeout=descriptor["cpu_seconds"],
MemorySize=descriptor["memory"],
)
LAMBDA.update_function_code(
FunctionName=descriptor["name"],
S3Bucket=artifact_bucket,
S3Key=artifact_obj,
)
else:
self.stdout.write("New Lambda - will create.")
LAMBDA.create_function(
FunctionName=descriptor["name"],
Runtime="python2.7",
Role=execution_role_arn,
Handler=descriptor["handler"],
Code={
"S3Bucket": artifact_bucket,
"S3Key": artifact_obj,
},
Timeout=descriptor["cpu_seconds"],
MemorySize=descriptor["memory"],
)
if descriptor["stream_name"]:
# This lambda would like to be registered as a listener on a kinesis stream
stream_name = descriptor["stream_name"]
batch_size = descriptor["stream_batch_size"]
self.stdout.write("Applying event source mapping for stream: %s" % (stream_name))
target_event_source = get_kinesis_stream_arn_from_name(stream_name)
event_source_list = LAMBDA.list_event_source_mappings(
FunctionName=descriptor["name"]
)
if event_source_list["EventSourceMappings"]:
# We need to update the existing mappings
# First we need to remove any stale event source mappings.
# Then we need to look for an existing match and update it.
# Finally if we didn't update an existing match, we need to create a new mapping.
# So long as we don't DELETE an in-use mapping
# it will not loose its place in the stream.
update_existing_mapping_success = False
for mapping in event_source_list["EventSourceMappings"]:
mapping_uuid = mapping["UUID"]
mapping_event_source = mapping["EventSourceArn"]
mapping_batch_size = mapping["BatchSize"]
if mapping_event_source != target_event_source:
# Delete this event source, it's stale.
self.stdout.write("Deleting stale mapping: %r" % (mapping_event_source))
LAMBDA.delete_event_source_mapping(UUID=mapping_uuid)
else:
update_existing_mapping_success = True
if mapping_batch_size != batch_size:
# The batch size is the only thing that might have changed
self.stdout.write("Updating existing stream batch size from %s to %s" % (
mapping_batch_size,
batch_size
))
LAMBDA.update_event_source_mapping(
UUID=mapping_uuid,
BatchSize=batch_size
)
else:
# Nothing has changed.
self.stdout.write("No changes required.")
if not update_existing_mapping_success:
# We didn't find an existing mapping to update, so we still must create one
self.stdout.write("Creating new mapping for event source: %s" % target_event_source)
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON"
)
else:
# No mappings currently exist, so we need to create a new mapping
self.stdout.write("Creating new mapping for event source: %s" % target_event_source)
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON"
)
if options["wait"]:
self.wait_for_complete_deployment(timeout=30)
def handle(self, *args, **options):
for module_name in options["module"].split(","):
importlib.import_module(module_name)
descriptors = get_lambda_descriptors()
if not descriptors:
self.output("No descriptors found. Exiting.")
return
all_lambdas = LAMBDA.list_functions()
iam_default_role_response = IAM.get_role(
RoleName=settings.LAMBDA_DEFAULT_EXECUTION_ROLE_NAME
)
execution_role_arn = iam_default_role_response["Role"]["Arn"]
iam_private_role_response = IAM.get_role(
RoleName=settings.LAMBDA_PRIVATE_EXECUTION_ROLE_NAME
)
private_execution_role_arn = iam_private_role_response["Role"]["Arn"]
self.output("Execution Role Arn: %r" % (execution_role_arn))
self.output("Private Execution Role Arn: %r" % (private_execution_role_arn))
artifact_obj = options["artifact"]
artifact_bucket = options["artifact_bucket"]
self.output("Using code at S3 path: %r/%r" % (artifact_bucket, artifact_obj))
for descriptor in descriptors:
self.output("About to deploy: %r" % (descriptor["name"]))
existing_lambda = None
for func in all_lambdas["Functions"]:
if func["FunctionName"] == descriptor["name"]:
existing_lambda = func
lambda_args = {
"Runtime": descriptor["runtime"],
"FunctionName": descriptor["name"],
"Handler": descriptor["handler"],
"Timeout": descriptor["cpu_seconds"],
"MemorySize": descriptor["memory"],
}
if descriptor["requires_vpc_access"]:
lambda_args["Role"] = private_execution_role_arn
lambda_args["VpcConfig"] = {
"SubnetIds": [settings.AWS_PROD_PRIVATE_SUBNET],
"SecurityGroupIds": [settings.AWS_PROD_SECURITY_GROUP_ID],
}
else:
lambda_args["Role"] = execution_role_arn
if existing_lambda:
self.output("Updating lambda with arguments %r" % (lambda_args))
LAMBDA.update_function_configuration(**lambda_args)
LAMBDA.update_function_code(
FunctionName=descriptor["name"],
S3Bucket=artifact_bucket,
S3Key=artifact_obj,
)
else:
lambda_args["Code"] = {
"S3Bucket": artifact_bucket,
"S3Key": artifact_obj,
}
self.output("Creating lambda with arguments %r" % (lambda_args))
LAMBDA.create_function(**lambda_args)
if descriptor["stream_name"]:
# This lambda would like to be registered as a listener on a kinesis stream
stream_name = descriptor["stream_name"]
batch_size = descriptor["stream_batch_size"]
self.output("Applying event source mapping for stream: %s" % (stream_name))
target_event_source = get_kinesis_stream_arn_from_name(stream_name)
event_source_list = LAMBDA.list_event_source_mappings(
FunctionName=descriptor["name"]
)
if event_source_list["EventSourceMappings"]:
# We need to update the existing mappings
# First we need to remove any stale event source mappings.
# Then we need to look for an existing match and update it.
# Finally if we didn't update an existing match, we need to create a new mapping.
# So long as we don't DELETE an in-use mapping
# it will not loose its place in the stream.
update_existing_mapping_success = False
for mapping in event_source_list["EventSourceMappings"]:
mapping_uuid = mapping["UUID"]
mapping_event_source = mapping["EventSourceArn"]
mapping_batch_size = mapping["BatchSize"]
if mapping_event_source != target_event_source:
# Delete this event source, it's stale.
self.output("Deleting stale mapping: %r" % (mapping_event_source))
LAMBDA.delete_event_source_mapping(UUID=mapping_uuid)
else:
update_existing_mapping_success = True
if mapping_batch_size != batch_size:
# The batch size is the only thing that might have changed
self.output("Updating existing stream batch size from %s to %s" % (
mapping_batch_size,
batch_size
))
LAMBDA.update_event_source_mapping(
UUID=mapping_uuid,
BatchSize=batch_size
)
else:
# Nothing has changed.
self.output("No changes required.")
if not update_existing_mapping_success:
# We didn't find an existing mapping to update, so we still must create one
self.output("Creating new mapping for event source: %s" % (target_event_source))
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON"
)
else:
# No mappings currently exist, so we need to create a new mapping
self.output("Creating new mapping for event source: %s" % (target_event_source))
LAMBDA.create_event_source_mapping(
EventSourceArn=target_event_source,
FunctionName=descriptor["name"],
BatchSize=batch_size,
StartingPosition="TRIM_HORIZON"
)
if options["wait"]:
self.wait_for_complete_deployment(timeout=30)
