When I try to make a call from my front-end (NextJS) I get the following error Error: Body must be a string. Received: undefined.
I can successfully make a call from my AppSync console, but not from my front-end. I have pushed the following schema.graphql file with amplify push:
type Mutation {
...
cancelCard(input: CancelCardInput!): AWSJSON
#function(name: "myp-service-prod-cancel-stepfunction-lambda")
}
I'm seeing logs in CloudWatch when I trigger the Lambda from the Lambda and AppSync consoles, but something is blocking the calls from my javascript:
import { cancelCard } from "../src/graphql/mutations";
const lambdaResponse = await API.graphql(
graphqlOperation(cancelCard, {
input: { id: cardId },
})
)
The problem was that I had performed amplify codegen configure and updated from javascript to typescript. As a result, Amplify had updated my src/ folder to have .ts files in, but not removed the .js files. This meant that where I had:
src/graphql/
mutations.ts
mutations.js
My app was ignoring the mutations.ts file.
Deleting the js files solved the issue.
I was reading this Reddit thread where a user mentioned that 540s is the limit of Firebase Functions and that moving to Cloud Run was recommended.
As others have said 540s is the maximum timeout and if you want to increase it without changing much else about your code, consider moving to Cloud Run. - #samtstern on Reddit
After looking at the Node.JS QuickStart documentation
and other content on YouTube and Google, I did not find a good guide explaining how to move your Firebase Function to Cloud Run.
One of the issues that were not addressed by what I read, for example: what do I replace the firebase-functions package with to define the function? Etc...
So, how may I move my Firebase Function over to Cloud Run to not run into the 540s max timeout limitation ?
const functions = require('firebase-functions');
const runtimeOpts = {timeoutSeconds: 540,memory: '2GB'}
exports.hourlyData = functions.runWith(runtimeOpts).pubsub.schedule('every 1 hours')
Preface: The following steps have been generalized for a wider audience than just the OP's problem (covers HTTP Event, Scheduled and Pub/Sub Functions) and have been adapted from the documentation linked in the question: Deploying Node.JS Images on Cloud Run.
Step 0: Code/Architecture Review
More often than not, exceeding the 9-minute timeout of a Cloud Function is a result of a bug in your code - make sure to evaluate this before switching to Cloud Run as this will just make the problem worse. The most common of these is sequential instead of parallelized asynchronous processing (normally caused by using await in a for/while loop).
If your code is doing meaningful work that is taking a long time, consider sharding it out to "subfunctions" that can all work on the input data in parallel. Instead of processing data for every user in your database, you can use a single function to trigger multiple instances of a function that each that take care of different user ID ranges such as a-l\uf8ff, m-z\uf8ff, A-L\uf8ff, M-Z\uf8ff and 0-9\uf8ff.
Lastly, Cloud Run and Cloud Functions are quite similar, they are designed to take a request, process it and then return a response. Cloud Functions have a limit of up to 9 minutes and Cloud Runs have a limit of up to 60 minutes. Once that response has been completed (because the server ended the response, the client lost connection or the client aborted the request), the instance is severely throttled or terminated. While you can use WebSockets and gRPC for a persistent communication between server and client when using Cloud Run, they are still subject to this limitation. See the Cloud Run: General development tips documentation for more information.
Like other serverless solutions, your client and server need to be able to handle connecting to different instances. Your code shouldn't make use of local state (like a local store for session data). See the Setting request timeout documentation for more information.
Step 1: Install Google Cloud SDK
I'll refer you to the Installing Google Cloud SDK documentation for this step.
Once installed, call gcloud auth login and login with the account used for the target Firebase project.
Step 2: Get your Firebase Project settings
Open up your project settings in the Firebase Console and take note of your Project ID and your Default GCP resource location.
Firebase Functions and Cloud Run instances should be co-located with your GCP resources where possible. In Firebase Functions, this is achieved by changing the region in code and deploying using the CLI. For Cloud Run, you specify these parameters on the command line as flags (or use the Google Cloud Console). For the below instructions and for simplicity, I will be using us-central1 as my Default GCP resources location is nam5 (us-central).
If using the Firebase Realtime Database in your project, visit your RTDB settings in the Firebase Console and take note of your Database URL. This is usually of the form https://PROJECT_ID.firebaseio.com/.
If using Firebase Storage in your project, visit your Cloud Storage settings in the Firebase Console and take note of your Bucket URI. From this URI, we need to take note of the host (ignore the gs:// part) which is usually of the form PROJECT_ID.appspot.com.
Here's a table that you can copy to help keep track:
Project ID:
PROJECT_ID
Database URL:
https://PROJECT_ID.firebaseio.com
Storage Bucket:
PROJECT_ID.appspot.com
Default GCP Resource Location:
Chosen Cloud Run Region:
Step 3: Create Directories
In your Firebase Project directory or a directory of your choosing, create a new cloudrun folder.
Unlike Firebase Cloud Functions, where you can define multiple functions in a single module of code, each Cloud Run image uses its own module of code. For this reason, each Cloud Run image should be stored in its own directory.
As we are going to define a Cloud Run instance called helloworld, we'll create a directory called helloworld inside cloudrun.
mkdir cloudrun
mkdir cloudrun/helloworld
cd cloudrun/helloworld
Step 4: Create package.json
For correct deployment of the Cloud Run image, we need to provide a package.json that is used to install dependencies in the deployed container.
The format of the package.json file resembles:
{
"name": "SERVICE_NAME",
"description": "",
"version": "1.0.0",
"private": true,
"main": "index.js",
"scripts": {
"start": "node index.js"
"image": "gcloud builds submit --tag gcr.io/PROJECT_ID/SERVICE_NAME --project PROJECT_ID",
"deploy:public": "gcloud run deploy SERVICE_NAME --image gcr.io/PROJECT_ID/SERVICE_NAME --allow-unauthenticated --region REGION_ID --project PROJECT_ID",
"deploy:private": "gcloud run deploy SERVICE_NAME --image gcr.io/PROJECT_ID/SERVICE_NAME --no-allow-unauthenticated --region REGION_ID --project PROJECT_ID",
"describe": "gcloud run services describe SERVICE_NAME --region REGION_ID --project PROJECT_ID --platform managed",
"find": "gcloud run services describe SERVICE_NAME --region REGION_ID --project PROJECT_ID --platform managed --format='value(status.url)'"
},
"engines": {
"node": ">= 12.0.0"
},
"author": "You",
"license": "Apache-2.0",
"dependencies": {
"express": "^4.17.1",
"body-parser": "^1.19.0",
/* ... */
},
"devDependencies": {
/* ... */
}
}
In the above file, SERVICE_NAME, REGION_ID and PROJECT_ID are to be swapped out as appropriate with the details from step 2. We also install express and body-parser to handle the incoming request.
There are also a handful of module scripts to help with deployment.
Script Name
Description
image
Submits the image to Cloud Build to be added to the Container Registry for other commands.
deploy:public
Deploys the image from the above command to be used by Cloud Run (while allowing any requester to invoke it) and returns its service URL (which is partly randomized).
deploy:private
Deploys the image from the above command to be used by Cloud Run (while requiring that the requester that invokes it is an authorized user/service account) and returns its service URL (which is partly randomized).
describe
Gets the statistics & configuration of the deployed Cloud Run.
find
Extracts only the service URL from the response of npm run describe
Note: Here, "Authorized User" refers to a Google Account associated with the project, not an ordinary Firebase User. To allow a Firebase User to invoke your Cloud Run, you must deploy it using deploy:public and handle token validation in your Cloud Run's code, rejecting requests appropriately.
As an example of this file filled in, you get this:
{
"name": "helloworld",
"description": "Simple hello world sample in Node with Firebase",
"version": "1.0.0",
"private": true,
"main": "index.js",
"scripts": {
"start": "node index.js"
"image": "gcloud builds submit --tag gcr.io/com-example-cloudrun/helloworld --project com-example-cloudrun",
"deploy:public": "gcloud run deploy helloworld --image gcr.io/com-example-cloudrun/helloworld --allow-unauthenticated --region us-central1 --project com-example-cloudrun",
"deploy:public": "gcloud run deploy helloworld --image gcr.io/com-example-cloudrun/helloworld --no-allow-unauthenticated --region us-central1 --project com-example-cloudrun",
"describe": "gcloud run services describe helloworld --region us-central1 --project com-example-cloudrun --platform managed",
"find": "gcloud run services describe helloworld --region us-central1 --project com-example-cloudrun --platform managed --format='value(status.url)'"
},
"engines": {
"node": ">= 12.0.0"
},
"author": "You",
"license": "Apache-2.0",
"dependencies": {
/* ... */
},
"devDependencies": {
/* ... */
}
}
Step 5: Create your container files
To tell Cloud Build what container to use for your Cloud Run image, you must create a Dockerfile for your image. To prevent sending the wrong files to the server, you should also specify a .dockerignore file.
In this file, we use the Firebase Project settings from Step 2 to recreate the process.env.FIREBASE_CONFIG environment variable. This variable is used by the Firebase Admin SDK and contains the following information as a JSON string:
{
databaseURL: "https://PROJECT_ID.firebaseio.com",
storageBucket: "PROJECT_ID.appspot.com",
projectId: "PROJECT_ID"
}
Here is cloudrun/helloworld/Dockerfile:
# Use the official lightweight Node.js 14 image.
# https://hub.docker.com/_/node
FROM node:14-slim
# Create and change to the app directory.
WORKDIR /usr/src/app
# Copy application dependency manifests to the container image.
# A wildcard is used to ensure copying both package.json AND package-lock.json (when available).
# Copying this first prevents re-running npm install on every code change.
COPY package*.json ./
# Install production dependencies.
# If you add a package-lock.json, speed your build by switching to 'npm ci'.
# RUN npm ci --only=production
RUN npm install --only=production
# Copy local code to the container image.
COPY . ./
# Define default configuration for Admin SDK
# databaseURL is usually "https://PROJECT_ID.firebaseio.com", but may be different.
# TODO: Update me
ENV FIREBASE_CONFIG={"databaseURL":"https://PROJECT_ID.firebaseio.com","storageBucket":"PROJECT_ID.appspot.com","projectId":"PROJECT_ID"}
# Run the web service on container startup.
CMD [ "node", "index.js" ]
Here is cloudrun/helloworld/.dockerignore:
Dockerfile
.dockerignore
node_modules
npm-debug.log
Step 6: Create & deploy your entry point
When a new Cloud Run instance is launched, it will normally specify the port it wants your code to listen on using the PORT environment variable.
Variant: Migrating a HTTP Event Function
When you use a HTTP Event function from the firebase-functions package, it internally handles body-parsing on your behalf. The Functions Framework uses the body-parser package for this and defines the parsers here.
To handle user authorization, you could use this validateFirebaseIdToken() middleware to check the ID token given with the request.
For a HTTP-based Cloud Run, configuring CORS will be required to invoke it from a browser. This can be done by installing the cors package and configuring it appropriately. In the below sample, cors will reflect the origin sent to it.
const express = require('express');
const cors = require('cors')({origin: true});
const app = express();
app.use(cors);
// To replicate a Cloud Function's body parsing, refer to
// https://github.com/GoogleCloudPlatform/functions-framework-nodejs/blob/d894b490dda7c5fd4690cac884fd9e41a08b6668/src/server.ts#L47-L95
// app.use(/* body parsers */);
app.enable('trust proxy'); // To respect X-Forwarded-For header. (Cloud Run is behind a load balancer proxy)
app.disable('x-powered-by'); // Disables the 'x-powered-by' header added by express (best practice)
// Start of your handlers
app.get('/', (req, res) => {
const name = process.env.NAME || 'World';
res.send(`Hello ${name}!`);
});
// End of your handlers
const port = process.env.PORT || 8080;
app.listen(port, () => {
console.log(`helloworld: listening on port ${port}`);
});
In the $FIREBASE_PROJECT_DIR/cloudrun/helloworld directory, execute the following commands to deploy your image:
npm run image // builds container & stores to container repository
npm run deploy:public // deploys container image to Cloud Run
Variant: Invoke using Cloud Scheduler
When invoking a Cloud Run using the Cloud Scheduler, you can choose which method is used to invoke it (GET, POST (the default), PUT, HEAD, DELETE). To replicate a Cloud Function's data and context parameters, it is best to use POST as these will then be passed in the body of the request. Like Firebase Functions, these requests from Cloud Scheduler may be retried so make sure to handle idempotency appropriately.
Note: Even though the body of a Cloud Scheduler invocation request is JSON-formatted, the request is served with Content-Type: text/plain, which we need to handle.
This code has been adapted from the Functions Framework source (Google LLC, Apache 2.0)
const express = require('express');
const { json } = require('body-parser');
async function handler(data, context) {
/* your logic here */
const name = process.env.NAME || 'World';
console.log(`Hello ${name}!`);
}
const app = express();
// Cloud Scheduler requests contain JSON using
"Content-Type: text/plain"
app.use(json({ type: '*/*' }));
app.enable('trust proxy'); // To respect X-Forwarded-For header. (Cloud Run is behind a load balancer proxy)
app.disable('x-powered-by'); // Disables the 'x-powered-by' header added by express (best practice)
app.post('/*', (req, res) => {
const event = req.body;
let data = event.data;
let context = event.context;
if (context === undefined) {
// Support legacy events and CloudEvents in structured content mode, with
// context properties represented as event top-level properties.
// Context is everything but data.
context = event;
// Clear the property before removing field so the data object
// is not deleted.
context.data = undefined;
delete context.data;
}
Promise.resolve()
.then(() => handler(data, context))
.then(
() => {
// finished without error
// the return value of `handler` is ignored because
// this isn't a callable function
res.sendStatus(204); // No content
},
(err) => {
// handler threw error
console.error(err.stack);
res.set('X-Google-Status', 'error');
// Send back the error's message (as calls to this endpoint
// are authenticated project users/service accounts)
res.send(err.message);
}
)
});
const port = process.env.PORT || 8080;
app.listen(port, () => {
console.log(`helloworld: listening on port ${port}`);
});
Note: The Functions Framework handles errors by sending back a HTTP 200 OK response with a X-Google-Status: error header. This effectively means "failed successfully". As an outsider, I'm not sure why this is done but I can assume it's so that the invoker knows to not bother retrying the function - it'll just get the same result.
In the $FIREBASE_PROJECT_DIR/cloudrun/helloworld directory, execute the following commands to deploy your image:
npm run image // builds container & stores to container repository
npm run deploy:private // deploys container image to Cloud Run
Note: In the following setup commands (only need to run these once), PROJECT_ID, SERVICE_NAME, SERVICE_URL and IAM_ACCOUNT will need to be substituted as appropriate.
Next we need to create a service account that Cloud Scheduler can use to invoke the Cloud Run. You can call it whatever you want such as scheduled-run-invoker. The email of this service account will be referred to as IAM_ACCOUNT in the next step. This Google Cloud Tech YouTube video (starts at the right spot, about 15s) will quickly show what you need to do. Once you've created the account, you can create the Cloud Scheduler job following the next 30 or so seconds of the video or use the following command:
gcloud scheduler jobs create http scheduled-run-SERVICE_NAME /
--schedule="every 1 hours" /
--uri SERVICE_URL /
--attempt-deadline 60m /
--http-method post /
--message-body='{"optional-custom-data":"here","if-you":"want"}' /
--oidc-service-account-email IAM_ACCOUNT
--project PROJECT_ID
Your Cloud Run should now be scheduled.
Variant: Invoke using Pub/Sub
To my understanding, the deploy process is the same as for a scheduled run (deploy:private) but I'm unsure about the specifics. However, here is the Cloud Run source for a Pub/Sub parser:
This code has been adapted from the Functions Framework source (Google LLC, Apache 2.0)
const express = require('express');
const { json } = require('body-parser');
const PUBSUB_EVENT_TYPE = 'google.pubsub.topic.publish';
const PUBSUB_MESSAGE_TYPE =
'type.googleapis.com/google.pubsub.v1.PubsubMessage';
const PUBSUB_SERVICE = 'pubsub.googleapis.com';
/**
* Extract the Pub/Sub topic name from the HTTP request path.
* #param path the URL path of the http request
* #returns the Pub/Sub topic name if the path matches the expected format,
* null otherwise
*/
const extractPubSubTopic = (path: string): string | null => {
const parsedTopic = path.match(/projects\/[^/?]+\/topics\/[^/?]+/);
if (parsedTopic) {
return parsedTopic[0];
}
console.warn('Failed to extract the topic name from the URL path.');
console.warn(
"Configure your subscription's push endpoint to use the following path: ",
'projects/PROJECT_NAME/topics/TOPIC_NAME'
);
return null;
};
async function handler(message, context) {
/* your logic here */
const name = message.json.name || message.json || 'World';
console.log(`Hello ${name}!`);
}
const app = express();
// Cloud Scheduler requests contain JSON using
"Content-Type: text/plain"
app.use(json({ type: '*/*' }));
app.enable('trust proxy'); // To respect X-Forwarded-For header. (Cloud Run is behind a load balancer proxy)
app.disable('x-powered-by'); // Disables the 'x-powered-by' header added by express (best practice)
app.post('/*', (req, res) => {
const body = req.body;
if (!body) {
res.status(400).send('no Pub/Sub message received');
return;
}
if (typeof body !== "object" || body.message === undefined) {
res.status(400).send('invalid Pub/Sub message format');
return;
}
const context = {
eventId: body.message.messageId,
timestamp: body.message.publishTime || new Date().toISOString(),
eventType: PUBSUB_EVENT_TYPE,
resource: {
service: PUBSUB_SERVICE,
type: PUBSUB_MESSAGE_TYPE,
name: extractPubSubTopic(req.path),
},
};
// for storing parsed form of body.message.data
let _jsonData = undefined;
const data = {
'#type': PUBSUB_MESSAGE_TYPE,
data: body.message.data,
attributes: body.message.attributes || {},
get json() {
if (_jsonData === undefined) {
const decodedString = Buffer.from(base64encoded, 'base64')
.toString('utf8');
try {
_jsonData = JSON.parse(decodedString);
} catch (parseError) {
// fallback to raw string
_jsonData = decodedString;
}
}
return _jsonData;
}
};
Promise.resolve()
.then(() => handler(data, context))
.then(
() => {
// finished without error
// the return value of `handler` is ignored because
// this isn't a callable function
res.sendStatus(204); // No content
},
(err) => {
// handler threw error
console.error(err.stack);
res.set('X-Google-Status', 'error');
// Send back the error's message (as calls to this endpoint
// are authenticated project users/service accounts)
res.send(err.message);
}
)
});
const port = process.env.PORT || 8080;
app.listen(port, () => {
console.log(`helloworld: listening on port ${port}`);
});
I want to import a helper function into a Node.js Lambda function in Serverless (AWS). I've tried using Layer functions, as well as a wrapper module for Serverless - but nothing has worked so far.
This is the function I want to use with other Lambda functions - helperFunc.js:
class HelperClass { ... } //a helper class that I want to use in other functions
module.exports = HelperClass
This is one of my Lambda functions. It also serves an API Gateway endpoint - users.js:
"use strict";
const helperClass = require('./helperFunc') //I don't know how to do this
module.exports.get = (event, context, callback) => {
const params = { ... } //DynamoDB Params
// a bunch of code that uses the helper class wrapper
callback(null, {
headers: {
"Access-Control-Allow-Origin" : "*", // Required for CORS support to work
"Access-Control-Allow-Credentials" : true // Required for cookies, authorization headers with HTTPS
}, body: JSON.stringify(res), statusCode: 200})
}
};
This is what my serverless.yml currently looks like:
...
getUsers:
handler: src/users.get
events:
- http:
path: users
method: get
authorizer: authFunc
And this what my project directory looks like:
./
serverless.yml
./src
helperFunc.js
users.js
./auth
UPDATE 1: I was finally able to achieve this functionality using Lambda Layers. However, it still feels as if it's not the best way to do this mainly due to the complex directory set-up.
Here's the updated project directory:
./
serverless.yml
./layers/helperFunc/nodejs/node_modules/helperFunc
index.js
./src
users.js
./auth
And here is the updated serverless.yml file:
layers:
helperFunc:
path: layers/helperFunc
...
functions:
getUsers:
handler: src/users.get
layers:
- {Ref: HelperFuncLambdaLayer} # referencing the layer in cf
events:
- http:
path: users
method: get
authorizer: authFunc
The solution is to use serverless-webpack-plugin.
We use environment variables within our angular app to read settings etc but is there a way to generate assets/files on build?
Basically we'd like to create an 'auth/settings.js' file in the assets folder containing client id's and apiUrl's unique to each environment. These will be used in the index.html (so outside of the angular app bootstrap )
e.g. the values in the environment.ts exported into a js / json file output to the assets folder so they can be read in index.html
export const environment = {
production: false,
title: 'default',
clientId: 'xxxx-xxxx-xxxx-xxxx-xxxx',
clientUrl: 'https://localhost:4200/app',
apiUrl: 'https://localhost/api'
};
I have read that you can use mulitapps:
https://github.com/angular/angular-cli/wiki/stories-multiple-apps
This may work but looks like a lot of copy and pasting and we'll have quite a few versions of the build - I'm not sure if you can declare the common settings once and just extend the extra app settings (inheritance)?
Thanks
What we are doing in our case is actually having an config.json and config.[env-name].json files in app/config folder that configured in project assets. The config.json file is getting fetched before angular bootstrap using browser Fetch API
On our build server we are just replacing the content of config.json withconfig.staging.json or config.prod.json based on environment build. Also we have AppSettings class that gets created on bootstrap. Here is how it is looks like:
fetch(configUrl, { method: 'get' })
.then((response) => {
response.json()
.then((data: any) => {
if (environment.production) {
enableProdMode();
};
platformBrowserDynamic([{ provide: AppSettings, useValue: new AppSettings(data.config) }]).bootstrapModule(AppModule);
});
});
UPDATE:
If you need to stick some values based on your env in to index.html you might need to consider doing that on your build server. You can rather string replace the values or you can have index.[env-name].thml files so you just overwrite the index.html based on environment build.
Also check out this issues
- https://github.com/angular/angular-cli/issues/7506
- https://github.com/angular/angular-cli/issues/3855
Node.js Alexa Task Issue
I'm currently coding a Node.js Alexa Task via AWS Lambda, and I have been trying to code a function that receives information from the OpenWeather API and parses it into a variable called weather. The relevant code is as follows:
var request = require('request');
var weather = "";
function isBadWeather(location) {
var endpoint = "http://api.openweathermap.org/data/2.5/weather?q=" + location + "&APPID=205283d9c9211b776d3580d5de5d6338";
var body = "";
request(endpoint, function (error, response, body) {
if (!error && response.statusCode == 200) {
body = JSON.parse(body);
weather = body.weather[0].id;
}
});
}
function testWeather()
{
setTimeout(function() {
if (weather >= 200 && weather < 800)
weather = true;
else
weather = false;
console.log(weather);
generateResponse(buildSpeechletResponse(weather, true), {});
}, 500);
}
I ran this snippet countless times in Cloud9 and other IDEs, and it seems to be working flawlessly. However, when I zip it into a package and upload it to AWS Lambda, I get the following error:
{
"errorMessage": "Cannot find module '/var/task/index'",
"errorType": "Error",
"stackTrace": [
"Function.Module._load (module.js:276:25)",
"Module.require (module.js:353:17)",
"require (internal/module.js:12:17)"
]
}
I installed module-js, request, and many other Node modules that should make this code run, but nothing seems to fix this issue. Here is my directory, just in case:
- planyr.zip
- index.js
- node_modules
- package.json
Does anyone know what the issue could be?
Fixed it! My issue was that I tried to zip the file using my Mac's built-in compression function in Finder.
If you're a Mac user, like me, you should run the following script in terminal when you are in the root directory of your project (folder containing your index.js, node_modules, etc. files).
zip -r ../yourfilename.zip *
For Windows:
Compress-Archive -LiteralPath node_modules, index.js -DestinationPath yourfilename.zip
Update to the accepted answer: When this error occurs, it means your zip file is not in the valid form which AWS requires.
If you double click on zip you will find your folder inside that your code file,but lambda wants that when you double click on zip it shoud show direct code files.
To achieve this:
open terminal
cd your-lambda-folder
zip -r index.zip *
Then, upload index.zip to AWS Lambda.
Check that file name and handler name are same:
That means that zip file has bundle.js file that exports handler function:
exports.handler = (event, context, callback) => {//...}
In my case it was because I had the handler file in inner src directory.
I had to change the 'Handler' property within Lambda from:
index.handler
to
src/index.handler
This is probably a permissions issue with files inside your deployment zip.
Try chmod 777 your files before packaging them in a zip file.
In my case the archive contained a folder "src" with index.js file, so I had to put to the handler: "src/index.handler"
In my case I had to replace
exports.handler = function eventHandler (event, context) {
with
exports.handler = function (event, context, callback) {
I got this error when I was using lambci/lambda:nodejs8.10 in windows.
I'd tried all of the solution listed above but none of which could help me deal with my issue(even though the error stack look the same as the question).
Here is my simple solution:
using --entrypoint flag to run a container to find out if the file is mounted into the container. It turns out I may got the share drive issue with my Docker Desktop.
I switched my docker daemon that day before, but everything works fine except this problem.
Anyway, remount my drive to Docker Desktop, you can both use the docker command or just open the Docker Desktop setting to apply.
In my case this was caused by Node running out of memory. I fixed that by adding --memory-size 1500 to my aws lambda create-function ... command.