I'm new to Azure. I need http triggered function to perform some simple actions on my blob storage. This will be the part of pipeline in datafactory but first I need to figure it out how to edit blobs via functions. I'm stucked now because I have no idea which API/methods I could use. Appreciate for your help. Below is some part of my code.
def main(req):
logging.info('Python HTTP trigger function processed a request.')
name = req.params.get('name')
if not name:
try:
req_body = req.get_json()
except ValueError:
pass
else:
name = req_body.get('name')
if name:
requested_file = requests.get("web address")
### Should I connect to blob here?
with open("file.txt", "wb") as file:
file.write(requested_file.content)
return func.HttpResponse(f"Hello, {name}. This HTTP triggered function executed successfully.")
else:
return func.HttpResponse(
"This HTTP triggered function executed successfully. Pass a name in the query string or in the request body for a personalized response.",
status_code=200
)
You can use either the Python SDK
Or use bindings and triggers for Azure Functions
How you would do it is use the bindings to pull in your blob and then use the bindings to write out your blob. This is actually a pretty good example of it:
Similarly with the SDK, you want to make sure that you are calling in and writing out. Make sure all your keys are correct and your containers are too!
Related
I would like to know if there exist a method to check if a blob(task) is successfully saved within azure and if there was a way to get returned something if save was successful.
The current code I have uploads to azure using blobserviceclient, and what I am trying to do is deleting tasks that was uploaded in azure and not deleting the ones that were was not saved in archive.
what I have tried to implement is this, however by doing so it checks if the file exist within azure instead of checking and getting returned something if upload was successful within azure.
async def if_uploaded(works):
container_name = "works"
try:
block_blob_service = BlockBlobService(account_name=accountName, account_key=accountKey,
socket_timeout=10000)
isExist = block_blob_service.exists(container_name, filename)
if isExist:
return True
else:
return False
except Exception as e:
print(e)
The code for upload is
async def upload(works):
I would like to know if there exist a method to check if a blob(task)
is successfully saved within azure and if there was a way to get
returned something if save was successful.
If you do not get any error while uploading that would mean the blob got uploaded successfully. You don't really need to do anything else to check if the upload operation was successful or not.
To check if the blob upload was successful, just wrap your upload code in try/except block. Something like:
try:
await upload_file(blob_service_client, container_name, file_name, content)
# blob upload successful...do something here...
except:
# blob upload failed...do something here...
print("An exception occurred")
I have a situation where I am trying to create two Cloud Functions namely CF1 & CF2 and I have one Cloud Scheduler. Both cloud functions are having authenticated invocation enabled. My flow is Cloud Scheduler will trigger CF1. On completion of CF1, the CF1 will trigger CF2 as a http call. I have referred Cannot invoke Google Cloud Function from GCP Scheduler to access authenticated CF1 from Cloud Scheduler and able to access CF1. But I am getting problem when accessing CF2 from CF1. The CF1 does not trigger CF2 and also not giving any error message. Do we need to follow any other technique when accessing authenticated Cloud Function from another authenticated Cloud Function.
CF1 code:
import json
import logging
from requests_futures.sessions import FuturesSession
def main(request):
# To read parameter values from request (url arguments or Json body).
raw_request_data = request.data
string_request_data = raw_request_data.decode("utf-8")
request_json: dict = json.loads(string_request_data)
request_args = request.args
if request_json and 'cf2_endpoint' in request_json:
cf2_endpoint = request_json['cf2_endpoint']
elif request_args and 'cf2_endpoint' in request_args:
cf2_endpoint = request_args['cf2_endpoint']
else:
cf2_endpoint = 'Invalid endpoint for CF2'
logger = logging.getLogger('test')
try:
session = FuturesSession()
session.get("{}".format(cf2_endpoint))
logger.info("First cloud function executed successfully.")
except RuntimeError:
logger.error("Exception occurred {}".format(RuntimeError))
CF2 code:
import logging
def main(request):
logger = logging.getLogger('test')
logger.info("second cloud function executed successfully.")
Current output logs:
First cloud function executed successfully.
Expected output logs:
First cloud function executed successfully.
second cloud function executed successfully.
Note: Same flow is working if I use unauthenticated access to the both cloud functions.
Two things are happening here:
You're not using request-futures entirely correctly. Since the request is made asynchronously, you need to block on the result before the function implicitly returns, otherwise it might return before your HTTP request completes (although it probably is in this example):
session = FuturesSession()
future = session.get("{}".format(cf2_endpoint))
resp = future.result() # Block on the request completing
The request you're making to the second function is not actually an authenticated request. Outbound requests from a Cloud Function are not authenticated by default. If you looked at what the actual response is above, you would see:
>>> resp.status_code
403
>>> resp.content
b'\n<html><head>\n<meta http-equiv="content-type" content="text/html;charset=utf-8">\n<title>403 Forbidden</title>\n</head>\n<body text=#000000 bgcolor=#ffffff>\n<h1>Error: Forbidden</h1>\n<h2>Your client does not have permission to get URL <code>/function_two</code> from this server.</h2>\n<h2></h2>\n</body></html>\n'
You could jump through a lot of hoops to properly authenticate this request, as detailed in the docs: https://cloud.google.com/functions/docs/securing/authenticating#function-to-function
However, a better alternative would be to make your second function a "background" function and invoke it via a PubSub message published from the first function instead:
from google.cloud import pubsub
publisher = pubsub.PublisherClient()
topic_name = 'projects/{project_id}/topics/{topic}'.format(
project_id=<your project id>,
topic='MY_TOPIC_NAME', # Set this to something appropriate.
)
def function_one(request):
message = b'My first message!'
publisher.publish(topic_name, message)
def function_two(event, context):
message = event['data'].decode('utf-8')
print(message)
As long as your functions have the permissions to publish PubSub messages, this avoids the need to add authorization to the HTTP requests, and also ensures at-least-once delivery.
Google Cloud Function provide REST API interface what include call method that can be used in another Cloud Function HTTP invokation.
Although the documentation mention using Google-provided client libraries there is still non one for Cloud Function on Python.
And instead you need to use general Google API Client Libraries. [This is the python one].3
Probably, the main difficulties while using this approach is an understanding of authentification process.
Generally you need provide two things to build a client service:
credentials ans scopes.
The simpliest way to get credentials is relay on Application Default Credentials (ADC) library. The rigth documentation about that are:
https://cloud.google.com/docs/authentication/production
https://github.com/googleapis/google-api-python-client/blob/master/docs/auth.md
The place where to get scopes is the each REST API function documentation page.
Like, OAuth scope: https://www.googleapis.com/auth/cloud-platform
The complete code example of calling 'hello-world' clound fucntion is below.
Before run:
Create default Cloud Function on GCP in your project.
Keep and notice the default service account to use
Keep the default body.
Notice the project_id, function name, location where you deploy function.
If you will call function outside Cloud Function environment (locally for instance) setup the environment variable GOOGLE_APPLICATION_CREDENTIALS according the doc mentioned above
If you will call actualy from another Cloud Function you don't need to configure credentials at all.
from googleapiclient.discovery import build
from googleapiclient.discovery_cache.base import Cache
import google.auth
import pprint as pp
def get_cloud_function_api_service():
class MemoryCache(Cache):
_CACHE = {}
def get(self, url):
return MemoryCache._CACHE.get(url)
def set(self, url, content):
MemoryCache._CACHE[url] = content
scopes = ['https://www.googleapis.com/auth/cloud-platform']
# If the environment variable GOOGLE_APPLICATION_CREDENTIALS is set,
# ADC uses the service account file that the variable points to.
#
# If the environment variable GOOGLE_APPLICATION_CREDENTIALS isn't set,
# ADC uses the default service account that Compute Engine, Google Kubernetes Engine, App Engine, Cloud Run,
# and Cloud Functions provide
#
# see more on https://cloud.google.com/docs/authentication/production
credentials, project_id = google.auth.default(scopes)
service = build('cloudfunctions', 'v1', credentials=credentials, cache=MemoryCache())
return service
google_api_service = get_cloud_function_api_service()
name = 'projects/{project_id}/locations/us-central1/functions/function-1'
body = {
'data': '{ "message": "It is awesome, you are develop on Stack Overflow language!"}' # json passed as a string
}
result_call = google_api_service.projects().locations().functions().call(name=name, body=body).execute()
pp.pprint(result_call)
# expected out out is:
# {'executionId': '3h4c8cb1kwe2', 'result': 'It is awesome, you are develop on Stack Overflow language!'}
I am trying to send variables from my flutter app through a http trigger to a python script on google cloud functions.
I can successfully trigger the function but my function does not receive the variable.
Here is my relevant app code:
callCloudFunction() async {
final HttpsCallable callable = CloudFunctions.instance.getHttpsCallable(
functionName: 'testPath',
);
dynamic resp = await callable.call(<String, dynamic>{
'uid': uid,
}).catchError((onError) {
//Handle your error here if the function failed
});
}
Here is my relevant python code from cloud functions:
def main(request):
name = 'empty'
request_args = request.args
request_json = request.get_json(silent=True)
if request_json and 'uid' in request_json:
name = request_json['uid']
elif request_args and 'uid' in request_args:
name = request_args['uid']
print(name)
No issues trigger the function. It has other functionality that I did not show here so I can confirm the trigger works. Its just passing that variable "uid" that is not working.
You're using the Firebase SDK for Cloud Functions to invoke a callable function, but your python function is just a regular HTTP function. This isn't going to work without implementing the protocol that the SDK uses to communicate with the function. Backend support for callables is only provided for nodejs when deploying with the Firebase CLI.
It will probably be easier if you just make a normal HTTP request from the client instead of writing all the code required for the protocol.
I needed to use the request.form.get('uid') to get data from my post request. Hope this helps someone!
Am beginner to Amazon web services.
I have a below lambda python function
import sys
import logging
import pymysql
import json
rds_host=".amazonaws.com"
name="name"
password="123"
db_name="db"
port = 3306
def save_events(event):
result = []
conn = pymysql.connect(rds_host, user=name, passwd=password, db=db_name,
connect_timeout=30)
with conn.cursor(pymysql.cursors.DictCursor) as cur:
cur.execute("select * from bodyPart")
result = cur.fetchall()
cur.close()
print ("Data from RDS...")
print (result)
cur.close()
bodyparts = json.dumps(result)
bodyParts=(bodyparts.replace("\"", "'"))
def lambda_handler(event, context):
save_events(event)
return bodyParts
using an above function am sending json to the client using API gateway, now suppose user selects an item from the list and send it back, in form of json where will i get http request and how should i process that request
I just made an additional information for #Harsh Manvar.
The easiest way I think is you can use
api-gateway-proxy-integration-lambda
Currently API Gateway support AWS lambda very good, you can pass request body (json) by using event.body to your lambda function.
I used it everyday in my hobby project (a Slack command bot, it is harder because you need to map from application/x-www-form-urlencoded to json through mapping template)
And for you I think it is simple because you using only json as request and response. The key is you should to select Integratiton type to Lambda function
You can take some quick tutorials in Medium.com for more detail, I only link the docs from Amazon.
#mohith: Hi man, I just made a simple approach for you, you can see it here.
The first you need to create an API (see the docs above) then link it to your Lambda function, because you only use json, so you need to check the named Use Lambda Proxy integration like this:
Then you need to deploy it!
Then in your function, you can handle your code, in my case, I return all the event that is passed to my function like this:
Finally you can post to your endpoint, I used postman in my case:
I hope you get my idea, when you successfully deployed your API then you can do anything with it in your front end.
I suggest you research more about CloudWatch, when you work with API Gateway, Lambda, ... it is Swiss army knife, you can not live without it, it is very easy for tracing and debug your code.
Please do not hesitate to ask me anything.
you can use aws service called API-gateway it will give you endpoint for http api requests.
this api gateway make connection with your lambda and you can pass http request to lambda.
here sharing info about creating rest api on lambda you can check it out : https://docs.aws.amazon.com/apigateway/latest/developerguide/how-to-create-api.html
aws also provide example for lambda GET, POST lambda example.you just have to edit code it will automatically make api-gateway.as reference you can check it.
From Lambda Console > create function > choose AWS serverless repository > in search bar type "get" and search > api-lambda-dynamodb > it will take value from user and process in lambda.
here sharing link you can direct check examples : https://console.aws.amazon.com/lambda/home?region=us-east-1#/create?tab=serverlessApps
I am trying to build an application using Google Cloud Platform AutoML using Python. My overall code flow looks like this:
User Interacts--> data sent to PubSub--> callback invokes my AutoML--> Result
The snippet that calls pubsub looks like this:
blob=blob+bytes(doc_type,'utf-8')
publisher.publish(topic,blob)
future=subscriber.subscribe(subscription,callback=callback)
#flash("The object is "+future,'info')
try:
future.result()
except Exception as ex:
subscriber.close()
In PubSub callback:
def callback(message):
new_message=message.data
display_name,score=predict_value(new_message,"modelID","projectid",'us-central1')
message.ack()
And my predict_value gets the model_id, project id and compute region and performs the prediction.
When I directly call predict_value without using PubSub it is working fine. If I do like this, I am getting the below error:
google.api_core.exceptions.PermissionDenied: 403 Permission 'automl.models.predict' denied on resource 'projects/projectID/locations/us-central1/models/' (or it may not exist).
Please help me to resolve the issue
Thank you so much for all your responses. I have just fixed the issue using the below snippet example
def receive_messages_synchronously(project, subscription_name):
"""Pulling messages synchronously."""
# [START pubsub_subscriber_sync_pull]
# project = "Your Google Cloud Project ID"
# subscription_name = "Your Pubsub subscription name"
subscriber = pubsub_v1.SubscriberClient()
subscription_path = subscriber.subscription_path(
project, subscription_name)
# Builds a pull request with a specific number of messages to return.
# `return_immediately` is set to False so that the system waits (for a
# bounded amount of time) until at lease one message is available.
response = subscriber.pull(
subscription_path,
max_messages=3,
return_immediately=False)
ack_ids = []
for received_message in response.received_messages:
print("Received: {}".format(received_message.message.data))
ack_ids.append(received_message.ack_id)
# Acknowledges the received messages so they will not be sent again.
subscriber.acknowledge(subscription_path, ack_ids)
# [END pubsub_subscriber_sync_pull]
The reason being the subscription that is created uses the pull request. I guess the callback method concept used is mainly for "push" which may be the reason because I didnt give the endpoint and token to publish the message. Hope what I am guessing is correct. Let me know your views as well.
This is likely due to one of two factors:
invalid credentials being used when sending the request to the AutoML API - it is very likely that pubsub executes in other context and can't get the default credentials
invalid model resource name (make sure it is correct) - it should be something like: "projects/12423534/locations/us-central1/models/23432423"