I have a API Gateway that calls a Cloud Function..
I want to create a config file to 3 routes:
..myurl/user
..myurl/client
..myurl/order
My problem is that I would like to use the same Cloud Function wrote in Python to deal with each scenario since it's just a function to get the data and write in BigQuery, in the end my need is just to know if the data is from user, client or order to switch correctly the insert target load.
Today I am using 3 different cloud functions for each path in the API spec.
Related
data source is from SaaS Server's API endpoints, aim to use python to move data into AWS S3 Bucket(Python's Boto3 lib)
API is assigned via authorized Username/password combination and unique api-key.
then every time initially call API need get Token for further info fetch.
have 2 question:
how to manage those secrets above, save to a head file (*.ini, *.json *.yaml) or saved via AWS's Secret-Manager?
the Token is a bit challenging, the basically way is each Endpoint, fetch a new token and do the API call
then that's end of too many pipeline (like if 100 Endpoints info need per downstream business needs) then
need to craft 100 pipeline like an universal template repeating 100 times.
I am new to Python programing world, you all feel free to comment to share any user-case.
Much appreciate !!
I searched and read this show-case
[saving-from-api-to-s3-bucket/74648533]
saving from api to s3 bucket
and
"how-to-write-a-file-or-data-to-an-s3-object-using-boto3"
How to write a file or data to an S3 object using boto3
I found this has been helpful:
#Python-decopule summary: store parameters in .ini or .env files;
#few options of manage(hiding) sensitive info
a. IAM role
b. Store Secrets using **Parameter Store**
c. Store Secrets using **Secrets Manager** - Current method
recommended by AWS
I'm building a flask server in python with Cloud Run, for a chatbot to call.
Sometimes if user wants to do something with the chatbot, the bot need ask the user to login to a 3rd party server before doing the things.
I have two routes:
Route 1 is "/login", it returns a simple iframe which will open a login page in a 3rd party server, generate a "session_id", and save some info I already get to a global variable dict called "runtimes" with the "session_id" as key, so that I can use it later when visitor successfully logged in.
Route 2 is "/callback/<session_id>". After user successfully login to its account, the 3rd party server will call this route with a token in url parameters. Then I will use the "session_id" to read the saved info from "runtimes", and do later things.
It works well in my local machine. But in Google Cloud Run, because it support multiple instances, sometimes it will trigger a new instance when server calls "callback", so it cannot get the "runtime" because they are in different instances.
I know that I can save the runtimes dict to a database to solve this problem, but it looks too overkill...Just not seem right.
Is there any easy way that I can make the "runtimes" be shared between instances?
The solution here is to use a central point of storage: database, memorystore, firestore,... something out of Cloud Run itself.
You can also try the Cloud Run execution runtime v2 that allow you to mount a network disk, such as Cloud Storage or Filestore. You can imagine to store the session data in a file which has the name of the session ID.
Note: On Cloud Run side, something is cooking, but it's not 100% safe, it will be a best effort. A database backup will be required even with that new feature
I am trying to deploy a rest api in cloud run where one endpoint launches an async job. The job is defined inside a function in the code.
It seems one way to do it is to use Cloud Task, but this would mean to make a self-call to another endpoint of the deployed api. Specifically, to create an auxiliary endpoint that contains the job code (e.g. /run-my-function) and another one to set the queue to cloud task that launches the /run-my-function?
Is this the right way to do it or I have misunderstand something? In case it's the right way how to specify the url of the /run-my-function endpoint without explicitly hard-code the cloud run deployed uRL name?
The code for the endpoint that launches the endpoint with the run-my-function code would be:
from google.cloud import tasks_v2
client = tasks_v2.CloudTasksClient()
project = 'myproject'
queue = 'myqueue'
location = 'mylocation'
url = 'https://cloudrunservice-abcdefg-ca.b.run.app/run-my-function'
service_account_email = '12345#cloudbuild.gserviceaccount.com'
parent = client.queue_path(project, location, queue)
task = {
"http_request": {
"http_method": tasks_v2.HttpMethod.POST,
'url': url,
"oidc_token": {"service_account_email": service_account_email},
}
}
response = client.create_task(parent=parent, task=task)
However, this requires to hard-code the service name https://cloudrunservice-abcdefg-ca.b.run.app and to define an auxiliary endpoint /run-my-function that can be called via http
In your code you are able to get the Cloud Run URL without hardcoding it or setting it in an environment variable.
You can have a look to a previous article that I wrote, in the gracefull termison part. I provide a working code in Go, not so difficult to re-implement in Python.
Here the principle:
Get the Region and the project Number from the Metadata server. Keep in mind that Cloud Run has specific metadata like the region
Get the K_SERVICE env var (it's a standard Cloud Run env var)
Perform a call to the Cloud Run Rest API to get the service detail and customize the request with the data got previously
Extract the status.url JSON entry from the response.
Now you have it!
Let me know if you have difficulties to achieve that. I'm not good at Python, but I will be able to write that piece of code!
Can someone help me with executing python function from azure data factory.
I have stored python function in blob and i'm trying to trigger the same.
However i'm not able to do it. Please assist.
Second, Can i parameterize python function call from ADF?
You could get an idea of Azure Function Activity in ADF which allows you to run Azure Functions in a Data Factory pipeline.
And you could duplicate your python function into Python Azure Function.
Also,it want to pass parameters into python function,you could set them into body properties.
The Azure Function Activity supports routing. For example, if your app uses the following routing - https://functionAPP.azurewebsites.net/api/functionName/{value}?code=<secret> - then the functionName is functionName/{value}, which you can parameterize to provide the desired functionName at runtime.
I have an app that is meant to integrate with third-party apps. These apps should be able to trigger a function when data changes.
The way I was envisioning this, I would use a node function to safely prepare data for the third parties, and get the url to call from the app's configuration on firestore. I would call that url from the node function, and wait for it to return, updating results as necessary (actually, triggering a push notification). -- these third-party functions would tend to be python functions, so my demo should be in python.
I have the initial node function and firestore setup so that I am currently triggering a ECONNREFUSED -- because I don't know how to set up the third-party function.
Let's say this is the function I need to trigger:
def hello_world(request):
request_json = request.get_json()
if request_json and 'name' in request_json:
name = request_json['name']
else:
name = 'World'
return 'Hello, {}!\n'.format(name)
Do I need to set up a separate gcloud account to host this function, or can I include it in my firestore functions? If so, how do I deploy this to firestore? Typically with my node functions, I am running firebase deploy and it automagically finds my functions from my index.js file.
If you're asking whether Cloud Functions that are triggered by Cloud Firestore can co-exist in a project with Cloud Functions that are triggered by HTTP(S) requests, then the answer is "yes they can". There is no need to set up a separate (Firebase or Cloud) project for each function type.
However: when you deploy your Cloud Functions through the Firebase CLI with firebase deploy, it will remove any functions that it finds in the project, that are not in the code. If you have functions both in Python and in Node.js, there is never a single codebase that contains both, so a blanket deploy would always delete some of your functions. So in that case, you should use the granular deploy option of the Firebase CLI.