We Keep Coding

getting "errorMessage": "'BadRequestException' object has no attribute 'message'",

call to http()URl & download the file in S3 bucket. its working. then in 2nd part i am calling guardduty & give location of s3 file to create threat intel set. while running code i am getting below error:-
Response
{
"errorMessage": "'BadRequestException' object has no attribute 'message'",
"errorType": "AttributeError",
"requestId": "bec541eb-a315-4f65-9fa9-3f1139e31f86",
"stackTrace": [
" File \"/var/task/lambda_function.py\", line 38, in lambda_handler\n if \"name already exists\" in error.message:\n"
]
}
i want to create threat intel set using the file which is in S3--(downloaded from the URl)
code:-
import boto3
from datetime import datetime
import requests.packages.urllib3 as urllib3
def lambda_handler(event, context):
url='https://rules.emergingthreats.net/blockrules/compromised-ips.txt' # put your url here
bucket = 'awssaflitetifeeds-security' #your s3 bucket
key = 'GDfeeds/compromised-ips.csv' #your desired s3 path or filename
s3=boto3.client('s3')
http=urllib3.PoolManager()
s3.upload_fileobj(http.request('GET', url,preload_content=False), bucket, key)
#------------------------------------------------------------------
# Guard Duty
#------------------------------------------------------------------
location = "https://s3://awssaflitetifeeds-security/GDfeeds/compromised-ips.csv"
timeStamp = datetime.now()
name = "TF-%s"%timeStamp.strftime("%Y%m%d")
guardduty = boto3.client('guardduty')
response = guardduty.list_detectors()
if len(response['DetectorIds']) == 0:
raise Exception('Failed to read GuardDuty info. Please check if the service is activated')
detectorId = response['DetectorIds'][0]
try:
response = guardduty.create_threat_intel_set(
Activate=True,
DetectorId=detectorId,
Format='FIRE_EYE',
Location=location,
Name=name
)
except Exception as error:
if "name already exists" in error.message:
found = False
response = guardduty.list_threat_intel_sets(DetectorId=detectorId)
for setId in response['ThreatIntelSetIds']:
response = guardduty.get_threat_intel_set(DetectorId=detectorId, ThreatIntelSetId=setId)
if (name == response['Name']):
found = True
response = guardduty.update_threat_intel_set(
Activate=True,
DetectorId=detectorId,
Location=location,
Name=name,
ThreatIntelSetId=setId
)
break
if not found:
raise
#-------------------------------------------------------------------
# Update result data
#------------------------------------------------------------------
result = {
'statusCode': '200',
'body': {'message': "You requested: %s day(s) of /view/iocs indicators in CSV"%environ['DAYS_REQUESTED']}
}
except Exception as error:
logging.getLogger().error(str(error))
responseStatus = 'FAILED'
reason = error.message
result = {
'statusCode': '500',
'body': {'message': error.message}
}
finally:
#------------------------------------------------------------------
# Send Result
#------------------------------------------------------------------
if 'ResponseURL' in event:
send_response(event, context, responseStatus, responseData, event['LogicalResourceId'], reason)

The reason you are getting that error message is because the exception being returned from guardduty.create_threat_intel_set does not have the message attribute directly on the exception. I think you want either error.response['Message'] or error.response['Error']['Message'] for this exception case.
A couple of other suggestions:
you should replace the except Exception which is matching the exception showing an already-existing name with something more targeted. I'd recommend looking at what exceptions the guardduty client can throw for the particular operation and catch just the one you care about.
it is likely better to check that error.response['Error']['Code'] is exactly the error you want rather than doing a partial string match.

Possible to use boto3/SDK service resource cross account? [duplicate]

I'm trying to use the AssumeRole in such a way that i'm traversing multiple accounts and retrieving assets for those accounts. I've made it to this point:
import boto3
stsclient = boto3.client('sts')
assumedRoleObject = sts_client.assume_role(
RoleArn="arn:aws:iam::account-of-role-to-assume:role/name-of-role",
RoleSessionName="AssumeRoleSession1")
Great, i have the assumedRoleObject. But now i want to use that to list things like ELBs or something that isn't a built-in low level resource.
How does one go about doing that? If i may ask - please code out a full example, so that everyone can benefit.

Here's a code snippet from the official AWS documentation where an s3 resource is created for listing all s3 buckets. boto3 resources or clients for other services can be built in a similar fashion.
# create an STS client object that represents a live connection to the
# STS service
sts_client = boto3.client('sts')
# Call the assume_role method of the STSConnection object and pass the role
# ARN and a role session name.
assumed_role_object=sts_client.assume_role(
RoleArn="arn:aws:iam::account-of-role-to-assume:role/name-of-role",
RoleSessionName="AssumeRoleSession1"
)
# From the response that contains the assumed role, get the temporary
# credentials that can be used to make subsequent API calls
credentials=assumed_role_object['Credentials']
# Use the temporary credentials that AssumeRole returns to make a
# connection to Amazon S3
s3_resource=boto3.resource(
's3',
aws_access_key_id=credentials['AccessKeyId'],
aws_secret_access_key=credentials['SecretAccessKey'],
aws_session_token=credentials['SessionToken'],
)
# Use the Amazon S3 resource object that is now configured with the
# credentials to access your S3 buckets.
for bucket in s3_resource.buckets.all():
print(bucket.name)

To get a session with an assumed role:
import botocore
import boto3
import datetime
from dateutil.tz import tzlocal
assume_role_cache: dict = {}
def assumed_role_session(role_arn: str, base_session: botocore.session.Session = None):
base_session = base_session or boto3.session.Session()._session
fetcher = botocore.credentials.AssumeRoleCredentialFetcher(
client_creator = base_session.create_client,
source_credentials = base_session.get_credentials(),
role_arn = role_arn,
extra_args = {
# 'RoleSessionName': None # set this if you want something non-default
}
)
creds = botocore.credentials.DeferredRefreshableCredentials(
method = 'assume-role',
refresh_using = fetcher.fetch_credentials,
time_fetcher = lambda: datetime.datetime.now(tzlocal())
)
botocore_session = botocore.session.Session()
botocore_session._credentials = creds
return boto3.Session(botocore_session = botocore_session)
# usage:
session = assumed_role_session('arn:aws:iam::ACCOUNTID:role/ROLE_NAME')
ec2 = session.client('ec2') # ... etc.
The resulting session's credentials will be automatically refreshed when required which is quite nice.
Note: my previous answer was outright wrong but I can't delete it, so I've replaced it with a better and working answer.

You can assume role using STS token, like:
class Boto3STSService(object):
def __init__(self, arn):
sess = Session(aws_access_key_id=ARN_ACCESS_KEY,
aws_secret_access_key=ARN_SECRET_KEY)
sts_connection = sess.client('sts')
assume_role_object = sts_connection.assume_role(
RoleArn=arn, RoleSessionName=ARN_ROLE_SESSION_NAME,
DurationSeconds=3600)
self.credentials = assume_role_object['Credentials']
This will give you temporary access key and secret keys, with session token. With these temporary credentials, you can access any service. For Eg, if you want to access ELB, you can use the below code:
self.tmp_credentials = Boto3STSService(arn).credentials
def get_boto3_session(self):
tmp_access_key = self.tmp_credentials['AccessKeyId']
tmp_secret_key = self.tmp_credentials['SecretAccessKey']
security_token = self.tmp_credentials['SessionToken']
boto3_session = Session(
aws_access_key_id=tmp_access_key,
aws_secret_access_key=tmp_secret_key, aws_session_token=security_token
)
return boto3_session
def get_elb_boto3_connection(self, region):
sess = self.get_boto3_session()
elb_conn = sess.client(service_name='elb', region_name=region)
return elb_conn

with reference to the solution by #jarrad which is not working as of Feb 2021, and as a solution that does not use STS explicitly please see the following
import boto3
import botocore.session
from botocore.credentials import AssumeRoleCredentialFetcher, DeferredRefreshableCredentials
def get_boto3_session(assume_role_arn=None):
session = boto3.Session(aws_access_key_id="abc", aws_secret_access_key="def")
if not assume_role_arn:
return session
fetcher = AssumeRoleCredentialFetcher(
client_creator=_get_client_creator(session),
source_credentials=session.get_credentials(),
role_arn=assume_role_arn,
)
botocore_session = botocore.session.Session()
botocore_session._credentials = DeferredRefreshableCredentials(
method='assume-role',
refresh_using=fetcher.fetch_credentials
)
return boto3.Session(botocore_session=botocore_session)
def _get_client_creator(session):
def client_creator(service_name, **kwargs):
return session.client(service_name, **kwargs)
return client_creator
the function can be called as follows
ec2_client = get_boto3_session(role_arn='my_role_arn').client('ec2', region_name='us-east-1')

If you want a functional implementation, this is what I settled on:
def filter_none_values(kwargs: dict) -> dict:
"""Returns a new dictionary excluding items where value was None"""
return {k: v for k, v in kwargs.items() if v is not None}
def assume_session(
role_session_name: str,
role_arn: str,
duration_seconds: Optional[int] = None,
region_name: Optional[str] = None,
) -> boto3.Session:
"""
Returns a session with the given name and role.
If not specified, duration will be set by AWS, probably at 1 hour.
If not specified, region will be left unset.
Region can be overridden by each client or resource spawned from this session.
"""
assume_role_kwargs = filter_none_values(
{
"RoleSessionName": role_session_name,
"RoleArn": role_arn,
"DurationSeconds": duration_seconds,
}
)
credentials = boto3.client("sts").assume_role(**assume_role_kwargs)["Credentials"]
create_session_kwargs = filter_none_values(
{
"aws_access_key_id": credentials["AccessKeyId"],
"aws_secret_access_key": credentials["SecretAccessKey"],
"aws_session_token": credentials["SessionToken"],
"region_name": region_name,
}
)
return boto3.Session(**create_session_kwargs)
def main() -> None:
session = assume_session(
"MyCustomSessionName",
"arn:aws:iam::XXXXXXXXXXXX:role/TheRoleIWantToAssume",
region_name="us-east-1",
)
client = session.client(service_name="ec2")
print(client.describe_key_pairs())

import json
import boto3
roleARN = 'arn:aws:iam::account-of-role-to-assume:role/name-of-role'
client = boto3.client('sts')
response = client.assume_role(RoleArn=roleARN,
RoleSessionName='RoleSessionName',
DurationSeconds=900)
dynamodb_client = boto3.client('dynamodb', region_name='us-east-1',
aws_access_key_id=response['Credentials']['AccessKeyId'],
aws_secret_access_key=response['Credentials']['SecretAccessKey'],
aws_session_token = response['Credentials']['SessionToken'])
response = dynamodb_client.get_item(
Key={
'key1': {
'S': '1',
},
'key2': {
'S': '2',
},
},
TableName='TestTable')
print(response)

#!/usr/bin/env python3
import boto3
sts_client = boto3.client('sts')
assumed_role = sts_client.assume_role(RoleArn = "arn:aws:iam::123456789012:role/example_role",
RoleSessionName = "AssumeRoleSession1",
DurationSeconds = 1800)
session = boto3.Session(
aws_access_key_id = assumed_role['Credentials']['AccessKeyId'],
aws_secret_access_key = assumed_role['Credentials']['SecretAccessKey'],
aws_session_token = assumed_role['Credentials']['SessionToken'],
region_name = 'us-west-1'
)
# now we make use of the role to retrieve a parameter from SSM
client = session.client('ssm')
response = client.get_parameter(
Name = '/this/is/a/path/parameter',
WithDecryption = True
)
print(response)

Assuming that 1) the ~/.aws/config or ~/.aws/credentials file is populated with each of the roles that you wish to assume and that 2) the default role has AssumeRole defined in its IAM policy for each of those roles, then you can simply (in pseudo-code) do the following and not have to fuss with STS:
import boto3
# get all of the roles from the AWS config/credentials file using a config file parser
profiles = get_profiles()
for profile in profiles:
# this is only used to fetch the available regions
initial_session = boto3.Session(profile_name=profile)
# get the regions
regions = boto3.Session.get_available_regions('ec2')
# cycle through the regions, setting up session, resource and client objects
for region in regions:
boto3_session = boto3.Session(profile_name=profile, region_name=region)
boto3_resource = boto3_session.resource(service_name='s3', region_name=region)
boto3_client = boto3_session.client(service_name='s3', region_name=region)
[ do something interesting with your session/resource/client here ]
Credential Setup (boto3 - Shared Credentials File)
Assume Role Setup (AWS)

After a few days of searching, this is the simplest solution I have found. explained here but does not have a usage example.
import boto3
for profile in boto3.Session().available_profiles:
boto3.DEFAULT_SESSION = boto3.session.Session(profile_name=profile)
s3 = boto3.resource('s3')
for bucket in s3.buckets.all():
print(bucket)
This will switch the default role you will be using. To not make the profile the default, just do not assign it to boto3.DEFAULT_SESSION. but instead, do the following.
testing_profile = boto3.session.Session(profile_name='mainTesting')
s3 = testing_profile.resource('s3')
for bucket in s3.buckets.all():
print(bucket)
Important to note that the .aws credentials need to be set in a specific way.
[default]
aws_access_key_id = default_access_id
aws_secret_access_key = default_access_key
[main]
aws_access_key_id = main_profile_access_id
aws_secret_access_key = main_profile_access_key
[mainTesting]
source_profile = main
role_arn = Testing role arn
mfa_serial = mfa_arn_for_main_role
[mainProduction]
source_profile = main
role_arn = Production role arn
mfa_serial = mfa_arn_for_main_role
I don't know why but the mfa_serial key has to be on the roles for this to work instead of the source account which would make more sense.

Here's the code snippet I used
sts_client = boto3.client('sts')
assumed_role_object = sts_client.assume_role(
RoleArn=<arn of the role to assume>,
RoleSessionName="<role session name>"
)
print(assumed_role_object)
credentials = assumed_role_object['Credentials']
session = Session(
aws_access_key_id=credentials['AccessKeyId'],
aws_secret_access_key=credentials['SecretAccessKey'],
aws_session_token=credentials['SessionToken']
)
self.s3 = session.client('s3')

Kinesis Firehose Lambda Transformation and Dynamic partition

The following data presented is from the faker library. i am trying to learn and implement
dynamic partition in kinesis Firehose
Sample payload Input
{
"name":"Dr. Nancy Mcmillan",
"phone_numbers":"8XXXXX",
"city":"Priscillaport",
"address":"908 Mitchell Views SXXXXXXXX 42564",
"date":"1980-07-11",
"customer_id":"3"
}
Sample Input code
def main():
import boto3
import json
AWS_ACCESS_KEY = "XXXXX"
AWS_SECRET_KEY = "XXX"
AWS_REGION_NAME = "us-east-1"
for i in range(1,13):
faker = Faker()
json_data = {
"name": faker.name(),
"phone_numbers": faker.phone_number(),
"city": faker.city(),
"address": faker.address(),
"date": str(faker.date()),
"customer_id": str(random.randint(1, 5))
}
print(json_data)
hasher = MyHasher(key=json_data)
res = hasher.get()
client = boto3.client(
"kinesis",
aws_access_key_id=AWS_ACCESS_KEY,
aws_secret_access_key=AWS_SECRET_KEY,
region_name=AWS_REGION_NAME,
)
response = client.put_record(
StreamName='XXX',
Data=json.dumps(json_data),
PartitionKey='test',
)
print(response)
Here is lambda code which work fine
try:
import json
import boto3
import base64
from dateutil import parser
except Exception as e:
pass
class MyHasher(object):
def __init__(self, key):
self.key = key
def get(self):
keys = str(self.key).encode("UTF-8")
keys = base64.b64encode(keys)
keys = keys.decode("UTF-8")
return keys
def lambda_handler(event, context):
print("Event")
print(event)
output = []
for record in event["records"]:
dat = base64.b64decode(record["data"])
serialize_payload = json.loads(dat)
print("serialize_payload", serialize_payload)
json_new_line = str(serialize_payload) + "\n"
hasherHelper = MyHasher(key=json_new_line)
hash = hasherHelper.get()
partition_keys = {"customer_id": serialize_payload.get("customer_id")}
_ = {
"recordId": record["recordId"],
"result": "Ok",
"data": hash,
'metadata': {
'partitionKeys':
partition_keys
}
}
print(_)
output.append(_)
print("*****************")
print(output)
return {"records": output}
Sample screenshots show works fine
Here are setting on firehose for dynamic partition
some reason on AWS S3 I see an error folder and all my messages go into that
I have successfully implemented lambda transformation and have made a video which can be found below I am currently stuck on the dynamic partition I have tried reading several posts but that didn't help
https://www.youtube.com/watch?v=6wot9Z93vAY&t=231s
Thank you again and looking forward to hearing from you guys
Refernecs
https://docs.aws.amazon.com/firehose/latest/dev/dynamic-partitioning.html
https://www.youtube.com/watch?v=HcOVAFn-KhM
https://www.youtube.com/watch?v=PoaKgHdJgCE
https://medium.com/#bv_subhash/kinesis-firehose-performs-partitioning-based-on-timestamps-and-creates-files-in-s3-but-they-would-13efd51f6d39
https://www.amazonaws.cn/en/new/2021/s3-analytics-dynamic-partitioning-kinesis-data-firehose/

There are two prefix options for dynamic partitioning. 1) partitionKeyFromQuery 2) partitionKeyFromLambda. If you want firehose to parse record and get partition key then use first option. If you want to provide partition key after performing transformation use second option.
As per your firehose config, you are using lambda to provide partition key (second option) but prefix is provided for first option. To resolve this issue either disable inline parsing and add second option to firehose prefix !{partitionKeyFromLambda:customer_id}/ or remove lambda transformation and keep inline parsing

Create Lambda - DynamoDB Count Function

I am creating a SAM web app, with the backend being an API in front of a Python Lambda function with a DynamoDB table that maintains a count of the number of HTTP calls to the API. The API must also return this number. The yaml code itself loads normally. My problem is writing the Lambda function to iterate and return the count. Here is my code:
def lambda_handler(event, context):
dynamodb = boto3.resource("dynamodb")
ddbTableName = os.environ["databaseName"]
table = dynamodb.Table(ddbTableName)
# Update item in table or add if doesn't exist
ddbResponse = table.update_item(
Key={"id": "VisitorCount"},
UpdateExpression="SET count = count + :value",
ExpressionAttributeValues={":value": Decimal(context)},
ReturnValues="UPDATED_NEW",
)
# Format dynamodb response into variable
responseBody = json.dumps({"VisitorCount": ddbResponse["Attributes"]["count"]})
# Create api response object
apiResponse = {"isBase64Encoded": False, "statusCode": 200, "body": responseBody}
# Return api response object
return apiResponse
I can get VisitorCount to be a string, but not a number. I get this error: [ERROR] TypeError: lambda_handler() missing 1 required positional argument: 'cou    response = request_handler(event, lambda_context)le_event_request
What is going on?
[UPDATE] I found the original error, which was that the function was not properly received by the SAM app. Changing the name fixed this, and it is now being read. Now I have to troubleshoot the actual Python. New Code:
import json
import boto3
import os
dynamodb = boto3.resource("dynamodb")
ddbTableName = os.environ["databaseName"]
table = dynamodb.Table(ddbTableName)
Key = {"VisitorCount": { "N" : "0" }}
def handler(event, context):
# Update item in table or add if doesn't exist
ddbResponse = table.update_item(
UpdateExpression= "set VisitorCount = VisitorCount + :val",
ExpressionAttributeValues={":val": {"N":"1"}},
ReturnValues="UPDATED_NEW",
)
# Format dynamodb response into variable
responseBody = json.dumps({"VisitorCount": ddbResponse["Attributes"]["count"]})
# Create api response object
apiResponse = {"isBase64Encoded": False, "statusCode": 200,"body": responseBody}
# Return api response object
return apiResponse
I am getting a syntax error on Line 13, which is
UpdateExpression= "set VisitorCount = VisitorCount + :val",
But I can't tell where I am going wrong on this. It should update the DynamoDB table to increase the count by 1. Looking at the AWS guide it appears to be the correct syntax.

Not sure what the exact error is but ddbResponse will be like this:
ddbResponse = table.update_item(
Key={
'key1': aaa,
'key2': bbb
},
UpdateExpression= "set VisitorCount = VisitorCount + :val",
ExpressionAttributeValues={":val": Decimal(1)},
ReturnValues="UPDATED_NEW",
)
Specify item to be updated with Key (one item for one Lambda call)
Set Decimal(1) for ExpressionAttributeValues
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStarted.Python.03.html#GettingStarted.Python.03.04

Create Google Cloud Function using API in Python

I'm working on a project with Python(3.6) & Django(1.10) in which I need to create a function at Google cloud using API request.
How can upload code in the form of a zip archive while creating that function?
Here's what I have tried:
From views.py :
def post(self, request, *args, **kwargs):
if request.method == 'POST':
post_data = request.POST.copy()
post_data.update({'user': request.user.pk})
form = forms.SlsForm(post_data, request.FILES)
print('get post request')
if form.is_valid():
func_obj = form
func_obj.user = request.user
func_obj.project = form.cleaned_data['project']
func_obj.fname = form.cleaned_data['fname']
func_obj.fmemory = form.cleaned_data['fmemory']
func_obj.entryPoint = form.cleaned_data['entryPoint']
func_obj.sourceFile = form.cleaned_data['sourceFile']
func_obj.sc_github = form.cleaned_data['sc_github']
func_obj.sc_inline_index = form.cleaned_data['sc_inline_index']
func_obj.sc_inline_package = form.cleaned_data['sc_inline_package']
func_obj.bucket = form.cleaned_data['bucket']
func_obj.save()
service = discovery.build('cloudfunctions', 'v1', http=views.getauth(), cache_discovery=False)
requ = service.projects().locations().functions().generateUploadUrl(parent='projects/' + func_obj.project + '/locations/us-central1', body={})
resp = requ.execute()
print(resp)
try:
auth = views.getauth()
# Prepare Request Body
req_body = {
"CloudFunction": {
"name": func_obj.fname,
"entryPoint": func_obj.entryPoint,
"timeout": '60s',
"availableMemoryMb": func_obj.fmemory,
"sourceArchiveUrl": func_obj.sc_github,
},
"sourceUploadUrl": func_obj.bucket,
}
service = discovery.build('cloudfunctions', 'v1beta2', http=auth, cachce_dicovery=False)
func_req = service.projects().locations().functions().create(location='projects/' + func_obj.project
+ '/locations/-',
body=req_body)
func_res = func_req.execute()
print(func_res)
return HttpResponse('Submitted',)
except:
return HttpResponse(status=500)
return HttpResponse('Sent!')
Updated Code below:
if form.is_valid():
func_obj = form
func_obj.user = request.user
func_obj.project = form.cleaned_data['project']
func_obj.fname = form.cleaned_data['fname']
func_obj.fmemory = form.cleaned_data['fmemory']
func_obj.entryPoint = form.cleaned_data['entryPoint']
func_obj.sourceFile = form.cleaned_data['sourceFile']
func_obj.sc_github = form.cleaned_data['sc_github']
func_obj.sc_inline_index = form.cleaned_data['sc_inline_index']
func_obj.sc_inline_package = form.cleaned_data['sc_inline_package']
func_obj.bucket = form.cleaned_data['bucket']
func_obj.save()
#######################################################################
# FIRST APPROACH FOR FUNCTION CREATION USING STORAGE BUCKET
#######################################################################
file_name = os.path.join(IGui.settings.BASE_DIR, 'media/archives/', func_obj.sourceFile.name)
print(file_name)
service = discovery.build('cloudfunctions', 'v1')
func_api = service.projects().locations().functions()
url_svc_req = func_api.generateUploadUrl(parent='projects/'
+ func_obj.project
+ '/locations/us-central1',
body={})
url_svc_res = url_svc_req.execute()
print(url_svc_res)
upload_url = url_svc_res['uploadUrl']
print(upload_url)
headers = {
'content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600'
}
print(requests.put(upload_url, headers=headers, data=func_obj.sourceFile.name))
auth = views.getauth()
# Prepare Request Body
name = "projects/{}/locations/us-central1/functions/{}".format(func_obj.project, func_obj.fname,)
print(name)
req_body = {
"name": name,
"entryPoint": func_obj.entryPoint,
"timeout": "3.5s",
"availableMemoryMb": func_obj.fmemory,
"sourceUploadUrl": upload_url,
"httpsTrigger": {},
}
service = discovery.build('cloudfunctions', 'v1')
func_api = service.projects().locations().functions()
response = func_api.create(location='projects/' + func_obj.project + '/locations/us-central1',
body=req_body).execute()
pprint.pprint(response)
Now the function has been created successfully, but it fails because the source code doesn't upload to storage bucket, that's maybe something wrong at:
upload_url = url_svc_res['uploadUrl']
print(upload_url)
headers = {
'content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600'
}
print(requests.put(upload_url, headers=headers, data=func_obj.sourceFile.name))

In the request body you have a dictionary "CloudFunction" inside the request. The content of "CloudFunction" should be directly in request.
request_body = {
"name": parent + '/functions/' + name,
"entryPoint": entry_point,
"sourceUploadUrl": upload_url,
"httpsTrigger": {}
}
I recomend using "Try this API" to discover the structure of projects.locations.functions.create .
"sourceArchiveUrl" and "sourceUploadUrl" can't appear together. This is explained in Resorce Cloud Function:
// Union field source_code can be only one of the following:
"sourceArchiveUrl": string,
"sourceRepository": { object(SourceRepository) },
"sourceUploadUrl": string,
// End of list of possible types for union field source_code.
In the rest of the answer I assume that you want to use "sourceUploadUrl". It requires you to pass it a URL returned to you by .generateUploadUrl(...).execute(). See documentation:
sourceUploadUrl -> string
The Google Cloud Storage signed URL used for source uploading,
generated by [google.cloud.functions.v1.GenerateUploadUrl][]
But before passing it you need to upload a zip file to this URL:
curl -X PUT "${URL}" -H 'content-type:application/zip' -H 'x-goog-content-length-range: 0,104857600' -T test.zip
or in python:
headers = {
'content-type':'application/zip',
'x-goog-content-length-range':'0,104857600'
}
print(requests.put(upload_url, headers=headers, data=data))
This is the trickiest part:
the case matters and it should be lowercase. Because the signature is calculated from a hash (here)
you need 'content-type':'application/zip'. I deduced this one logically, because documentation doesn't mention it. (here)
x-goog-content-length-range: min,max is obligatory for all PUT requests for cloud storage and is assumed implicitly in this case. More on it here
104857600, the max in previous entry, is a magical number which I didn't found mentioned anywhere.
where data is a FileLikeObject.
I also assume that you want to use the httpsTrigger. For a cloud function you can only choose one trigger field. Here it's said that trigger is a Union field. For httpsTrigger however that you can just leave it to be an empty dictionary, as its content do not affect the outcome. As of now.
request_body = {
"name": parent + '/functions/' + name,
"entryPoint": entry_point,
"sourceUploadUrl": upload_url,
"httpsTrigger": {}
}
You can safely use 'v1' instead of 'v1beta2' for .create().
Here is a full working example. It would be to complicated if I presented it to you as part of your code, but you can easily integrate it.
import pprint
import zipfile
import requests
from tempfile import TemporaryFile
from googleapiclient import discovery
project_id = 'your_project_id'
region = 'us-central1'
parent = 'projects/{}/locations/{}'.format(project_id, region)
print(parent)
name = 'ExampleFunctionFibonacci'
entry_point = "fibonacci"
service = discovery.build('cloudfunctions', 'v1')
CloudFunctionsAPI = service.projects().locations().functions()
upload_url = CloudFunctionsAPI.generateUploadUrl(parent=parent, body={}).execute()['uploadUrl']
print(upload_url)
payload = """/**
* Responds to any HTTP request that can provide a "message" field in the body.
*
* #param {Object} req Cloud Function request context.
* #param {Object} res Cloud Function response context.
*/
exports.""" + entry_point + """= function """ + entry_point + """ (req, res) {
if (req.body.message === undefined) {
// This is an error case, as "message" is required
res.status(400).send('No message defined!');
} else {
// Everything is ok
console.log(req.body.message);
res.status(200).end();
}
};"""
with TemporaryFile() as data:
with zipfile.ZipFile(data, 'w', zipfile.ZIP_DEFLATED) as archive:
archive.writestr('function.js', payload)
data.seek(0)
headers = {
'content-type':'application/zip',
'x-goog-content-length-range':'0,104857600'
}
print(requests.put(upload_url, headers=headers, data=data))
# Prepare Request Body
# https://cloud.google.com/functions/docs/reference/rest/v1/projects.locations.functions#resource-cloudfunction
request_body = {
"name": parent + '/functions/' + name,
"entryPoint": entry_point,
"sourceUploadUrl": upload_url,
"httpsTrigger": {},
"runtime": 'nodejs8'
}
print('https://{}-{}.cloudfunctions.net/{}'.format(region,project_id,name))
response = CloudFunctionsAPI.create(location=parent, body=request_body).execute()
pprint.pprint(response)
Open and upload a zip file like following:
file_name = os.path.join(IGui.settings.BASE_DIR, 'media/archives/', func_obj.sourceFile.name)
headers = {
'content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600'
}
with open(file_name, 'rb') as data:
print(requests.put(upload_url, headers=headers, data=data))