How can I achieve an 'app-wide' global variable that is shared across Cloud Function instances and function invocations? I want to create a truly 'global' object that is initialized only once per the lifetime of all my functions.
Context:
My app's entire backend is Firestore + Firebase Cloud Functions. That is, I use a mix of background (Firestore) triggers and HTTP functions to implement backend logic. Additionally, I rely on a 3rd-party location service to continually listen to location updates from sensors. I want just a single instance of the client on which to subscribe to these updates.
The problem is that Firebase/Google Cloud Functions are stateless, meaning that function instances don't share memory/objects/state. If I call functionA, functionB, functionC, there's going to be at least 3 instances of locationService clients created, each listening separately to the 3rd party service so we end up with duplicate invocations of the location API callback.
Sample code:
// index.js
const functions = require("firebase-functions");
exports.locationService = require('./location_service');
this.locationService.initClient();
// define callable/HTTP functions & Firestore triggers
...
and
// location_service.js
var tracker = require("third-party-tracker-js");
const self = (module.exports = {
initClient: function () {
tracker.initialize('apiKey')
.then((client)=>{
client.setCallback(async function(payload) {
console.log("received location update: ", payload)
// process the payload ...
// with multiple function instances running at once, we receive as many callbacks for each location update
})
client.subscribeProject()
.then((subscription)=>{
subscription.subscribe()
.then((subscribeMsg)=>{
console.log("subscribed to project with message: ", subscribeMsg); // success
});
// subscription.unsubscribe(); // ??? at what point should we unsubscribe?
})
.catch((err)=>{
throw(err)
})
})
.catch((err)=>{
throw(err)
})
},
});
I realize what I'm trying to do is roughly equivalent to implementing a daemon in a single-process environment, and it appears that serverless environments like Firebase/Google Cloud Functions aren't designed to support this need because each instance runs as its own process. But I'd love to hear any contrary ideas and possible workarounds.
Another idea...
Inspired by this related SO post and the official GCF docs on stateless functions, I thought about using Firestore to persist a tracker value that allows us to conditionally initialize the API client. Roughly like this:
// read value from db; only initialize the client if there's no valid subscription
let locSubscriberActive = await getSubscribeStatusFromDb();
if (!locSubscriberActive) {
this.locationService.initClient();
}
// in `location_service.js`, do setSubscribeStatusToDb(); // set flag to true when we call subscribe(). reset when we get terminated
The problem faced: at what point do I unset/reset that value? Intuitively, I would do so the moment the function instance that initialized the client gets recycled/killed. However, it appears that it is not possible to know when a Firebase Cloud Function instance is terminated? I searched everywhere but couldn't find docs on how to detect such an event...
What you're trying to do is not at all supported in Cloud Functions. It's important to realize that there may be any number of server instances allocated for each deployed function. That's how Cloud Functions scales up and down to match the load on the function in a cost-effective way. These instances might be terminated at any time for any reason. You have no indication when an instance terminates.
Also, instances are not capable of performing any computation when they are idle. CPU resources are clamped down after a function terminates, and are spun up again when the next function is invoked on that instance. You can't have any "daemon" code running when a function is not actively being invoked. I don't know what your locationService does, but it is certainly doing nothing at all after a function terminates, regardless of how it terminated.
For any sort of long-running or daemon-like code, Cloud Functions is not a suitable product. You should instead consider also using another product that lets you run code 24/7 without disruptions. App Engine and Compute Engine are viable alternatives, and you will have to think carefully about if and how you want their server instances to scale with load.
When using the SNS client found on AWS-SDK:
const sns = new AWS.SNS({});
Should I reuse this object across calls to save a handshake with the server?
This kind of object is usually stateless and benefits from pooling/cache; However the docs aren't really clear about that.
I believe you should initiate the class outside of your lambda.
AWS will reuse the instance when possible.
E.g.
const AWS = require('aws-sdk')
const sns = new AWS.SNS()
module.exports.handler = async input => {
// use sns class here
return input
}
EDIT:
Link to the official documentation that explains how the lambda execution context works: https://docs.aws.amazon.com/lambda/latest/dg/running-lambda-code.html
Any declarations in your Lambda function code (outside the handler
code, see Programming Model) remains initialized, providing additional
optimization when the function is invoked again. For example, if your
Lambda function establishes a database connection, instead of
reestablishing the connection, the original connection is used in
subsequent invocations. We suggest adding logic in your code to check
if a connection exists before creating one.
I'm trying to take advantage of db connection reuse in Lambda, by keeping the code outside of the handler.
For example - something like:
import dbconnection from './connection'
const handler(event, context, callback){
//use dbconnection
}
The issue is I don't decide what database to connect to until I do a lookup to see where they should be connecting. In my specific case I have 'customer=foo' in a query param then I can look to see that foo should connect to database1.
So what I need to do is something like this :
const dbconnection = require('./connection)('database1')
The way it is now I need to do this in every handler method which is expensive.
Is there some way I can pull the query parameter, look up my database and set it / switch it globally within the Lambda execution context?
I've tried this:
import dbconnection from './connection'
const handler(event, context, callback){
const client = dbconnection.setDatabase('database1')
}
....
./connection.js
setDatabase(database) {
if(this.currentDatabase !== database) {
// connect to different database
this.currentDatabase = database;
}
}
Everything works locally with sls offline but doesn't work through the AWS Lambda execution context. Thoughts?
You can either hardcode (or provide it via environment variable) it or not. If you can, then pull it out of then handler and it will not be executed each time. If you can't, as you have mentioned, then what you are trying to do is to make lambda stateful. Lambda was designed to be stateless and AWS intentionally doesn't expose specific informations about the underlying containers so that you don't start doing something like what you are trying to do now - introducing state to it.
I'm trying to query a DynamoDB table from a Lambda function (for an Alexa skill), but when I send the intent that calls require('aws-sdk'), the skill seems to hang and timeout. The Alexa test page just says "There was a problem with the requested skill's response", and I don't see any errors in the CloudWatch logs. I have the skill set up to catch any errors and return them as a spoken response, so I'm sure it's not an uncaught exception. I've also tried wrapping the require in a try/catch block, but that didn't work either.
This is the module that gets loaded with require if the test database intent request is received:
const AWS = require('aws-sdk');
module.exports = () => {
return 'Success!';
};
If I comment out require('aws-sdk'), the function works properly and Alexa responds with "Success".
Why does my skill break when all I'm doing is requiring the aws-sdk module?
I'm very new to AWS and this is my first experience trying to access a DynamoDB table in a Lambda function.
The Lambda function is uploaded as a zip that contains my source code, package.json (that includes aws-sdk as a dependency), and the node_modules folder.
After hours of debugging, I've found that changing import * as AWS from 'aws-sdk'; to import {DynamoDB} from 'aws-sdk'; (or {CloudFront} or whatever you actually use) made the timeout issue disappear. Mind you, the time to actually connect to DynamoDB was never an issue for me, it was always the import line where the timeout happened.
This can be fixed by either increasing the timeout or the memory allotted to the lambda function.
This is probably because the SDK is too big to be imported by the default timeout value of 3 seconds and the default memory value of 128 MB.
This is why it will probably work if you try importing smaller components like only DynamoDB.
Lambda, when using NodeJS, uses a callback continuation model. Your module should export a function that takes three parameters: event, context, and callback.
Event provides input parameters.
The other two are used for returning control from your handler function, depending on the NodeJS version you are using.
Try adding the three parameters that I mentioned and the, from within your exported handler function, call:
module.export = function(event, context, callback) {
callback(‘success’);
}
Bear in mind, I wrote this on mobile off the top of my mind, so you may need to make small afjustments to the code but the idea is the same. Don’t return directly from the function, but call the callback to supply the response as a continuation. And note that in earlier versions of NodeJS, prior to version 4, you would have to use the context to set success or failure, rather than calling the callback.
For more details, see the Lambda with NodeJS tech docs on AWS.
The other thing to keep in mind is that for Alexa specifically, the response should be in the correct format. That is a JSON response that contains all the necessary elements as explained in the Alexa Skills Kit tech docs.
The Alexa ASK sdk that you’ve included generates those responses but I thought I should point you to the actual docs in case you were going to try building the response by hand to understand how it works.
I'm looking to create a RESTful API using AWS Lambda/API Gateway connected to a MongoDB database. I've read that connections to MongoDB are relatively expensive so it's best practice to retain a connection for reuse once its been established rather than making new connections for every new query.
This is pretty straight forward for normal applications as you can establish a connection during start up and reuse it during the applications lifetime. But, since Lambda is designed to be stateless retaining this connection seems to be less straight forward.
Therefore, I'm wondering what would be the best way to approach this database connection issue? Am I forced to make new connections every time a Lambda function is invoked or is there a way to pool/cache these connections for more efficient queries?
Thanks.
AWS Lambda functions should be defined as stateless functions, so they can't hold state like a connection pool.
This issue was also raised in this AWS forum post. On Oct 5, 2015 AWS engineer Sean posted that you should not open and close connection on each request, by creating a pool on code initialization, outside of handler block. But two days later the same engineer posted that you should not do this.
The problem is that you don't have control over Lambda's runtime environment. We do know that these environments (or containers) are reused, as describes the blog post by Tim Wagner. But the lack of control can drive you to drain all your resources, like reaching a connection limit in your database. But it's up to you.
Instead of connecting to MongoDB from your lambda function you can use RESTHeart to access the database through HTTP. The connection pool to MongoDB is maintained by RESTHeart instead. Remember that in regards to performance you'll be opening a new HTTP connection to RESTHeart on each request, and not using a HTTP connection pool, like you could do in a tradicional application.
You should assume lambdas to be stateless but the reality is that most of the time the vm is simply frozen and does maintain some state. It would be inefficient for Amazon to spin up a new process for every request so they often re-use the same process and you can take advantage of this to avoid thrashing connections.
To avoid connecting for every request (in cases where the lambda process is re-used):
Write the handler assuming the process is re-used such that you connect to the database and have the lamba re-use the connection pool (the db promise returned from MongoClient.connect).
In order for the lambda not to hang waiting for you to close the db connection, db.close(), after servicing a request tell it not wait for an empty event loop.
Example:
var db = MongoClient.connect(MongoURI);
module.exports.targetingSpec = (event, context, callback) => {
context.callbackWaitsForEmptyEventLoop = false;
db.then((db) => {
// use db
});
};
From the documentation about context.callbackWaitsForEmptyEventLoop:
callbackWaitsForEmptyEventLoop
The default value is true. This property is useful only to modify the default behavior of the callback. By default, the callback will wait until the Node.js runtime event loop is empty before freezing the process and returning the results to the caller. You can set this property to false to request AWS Lambda to freeze the process soon after the callback is called, even if there are events in the event loop. AWS Lambda will freeze the process, any state data and the events in the Node.js event loop (any remaining events in the event loop processed when the Lambda function is called next and if AWS Lambda chooses to use the frozen process). For more information about callback, see Using the Callback Parameter.
Restheart is a REST-based server that runs alongside MongoDB. It maps most CRUD operations in Mongo to GET, POST, etc., requests with extensible support when you need to write a custom handler (e.g., specialized geoNear, geoSearch query)
I ran some tests executing Java Lambda functions connecting to MongoDB Atlas.
As already stated by other posters Amazon does reuse the Instances, however these may get recycled and the exact behaviour cannot be determined. So one could end up with stale connections. I'm collecting data every 5 minutes and pushing it to the Lambda function every 5 minutes.
The Lambda basically does:
Build up or reuse connection
Query one record
Write or update one record
close the connection or leave it open
The actual amount of data is quite low. Depending on time of the day it varies from 1 - 5 kB. I only used 128 MB.
The Lambdas ran in N.Virgina as this is the location where the free tier is tied to.
When opening and closing the connection each time most calls take between 4500 - 9000 ms. When reusing the connection most calls are between 300 - 900 ms. Checking the Atlas console the connection count stays stable. For this case reusing the connection is worth it. Building up a connection and even disconnecting from a replica-set is rather expensive using the Java driver.
For a large scale deployment one should run more comprehensive tests.
Yes, there is a way to cache/retain connection to MongoDB and its name is pool connection. and you can use it with lambda functions as well like this:
for more information you can follow these links:
Using Mongoose With AWS Lambda
Optimizing AWS Lambda(a bit out date)
const mongoose = require('mongoose');
let conn = null;
const uri = 'YOUR CONNECTION STRING HERE';
exports.handler = async function(event, context) {
// Make sure to add this so you can re-use `conn` between function calls.
context.callbackWaitsForEmptyEventLoop = false;
const models = [{name: 'User', schema: new mongoose.Schema({ name: String })}]
conn = await createConnection(conn, models)
//e.g.
const doc = await conn.model('User').findOne({})
console.log('doc: ', doc);
};
const createConnection = async (conn,models) => {
// Because `conn` is in the global scope, Lambda may retain it between
// function calls thanks to `callbackWaitsForEmptyEventLoop`.
// This means your Lambda function doesn't have to go through the
// potentially expensive process of connecting to MongoDB every time.
if (conn == null || (conn && [0, 3].some(conn.readyState))) {
conn = await mongoose.createConnection(uri, {
// Buffering means mongoose will queue up operations if it gets
// disconnected from MongoDB and send them when it reconnects.
// With serverless, better to fail fast if not connected.
bufferCommands: false, // Disable mongoose buffering
bufferMaxEntries: 0, // and MongoDB driver buffering
useNewUrlParser: true,
useUnifiedTopology: true,
useCreateIndex: true
})
for (const model of models) {
const { name, schema } = model
conn.model(name, schema)
}
}
return conn
}
Unfortunately you may have to create your own RESTful API to answer MongoDB requests until AWS comes up with one. So far they only have what you need for their own Dynamo DB.
The short answer is yes, you need to create a new connection AND close it before the lambda finishes.
The long answer is actually during my tests you can pass down your DB connections in your handler like so(mysql example as that's what I've got to hand), you can't rely on this having a connection so check my example below, it may be that once your Lambda's haven't been executed for ages it does lose the state from the handler(cold start), I need to do more tests to find out, but I have noticed if a Lambda is getting a lot of traffic using the below example it doesn't create a new connection.
// MySQL.database.js
import * as mysql from 'mysql'
export default mysql.createConnection({
host: 'mysql db instance address',
user: 'MYSQL_USER',
password: 'PASSWORD',
database: 'SOMEDB',
})
Then in your handler import it and pass it down to the lambda that's being executed.
// handler.js
import MySQL from './MySQL.database.js'
const funcHandler = (func) => {
return (event, context, callback) => {
func(event, context, callback, MySQL)
}
}
const handler = {
someHandler: funcHandler(someHandler),
}
export default handler
Now in your Lambda you do...
export default (event, context, callback, MySQL) => {
context.callbackWaitsForEmptyEventLoop = false
// Check if their is a MySQL connection if not, then open one.
// Do ya thing, query away etc etc
callback(null, responder.success())
}
The responder example can he found here. sorry it's ES5 because that's where the question was asked.
Hope this helps!
Official Best Practice for Connecting from AWS Lambda
You should define the client to the MongoDB server outside the AWS
Lambda handler function. Don't define a new MongoClient object each
time you invoke your function. Doing so causes the driver to create a
new database connection with each function call. This can be expensive
and can result in your application exceeding database connection
limits.
As an alternative, do the following:
Create the MongoClient object once.
Store the object so your function can reuse the MongoClient across function invocations.
Step 1
Isolate the call to the MongoClient.connect() function into its own module so that the connections can be reused across functions. Let's create a file mongo-client.js for that:
mongo-client.js:
const { MongoClient } = require('mongodb');
// Export a module-scoped MongoClient promise. By doing this in a separate
// module, the client can be shared across functions.
const client = new MongoClient(process.env.MONGODB_URI);
module.exports = client.connect();
Step 2
Import the new module and use it in function handlers to connect to database.
some-file.js:
const clientPromise = require('./mongodb-client');
// Handler
module.exports.handler = async function(event, context) {
// Get the MongoClient by calling await on the connection promise. Because
// this is a promise, it will only resolve once.
const client = await clientPromise;
// Use the connection to return the name of the connected database for example.
return client.db().databaseName;
}
Resources
For more info, check this Docs.
We tested an AWS Lambda that connected every minute to our self managed MongoDB.
The connections were unstable and the Lambda failed
We resolved the issue by wrapping the MongoDB with Nginx reverse proxy stream module:
How to setup MongoDB behind Nginx Reverse Proxy
stream {
server {
listen <your incoming Mongo TCP port>;
proxy_connect_timeout 1s;
proxy_timeout 3s;
proxy_pass stream_mongo_backend;
}
upstream stream_mongo_backend {
server <localhost:your local Mongo TCP port>;
}
}
In addition to saving the connection for reuse, increase the memory allocation for the lambda function. AWS allocates CPU proportionally to the memory allocation and when changing from 128MB to 1.5Gb the connection time dropped from 4s to 0.5s when connecting to mongodb atlas.
Read more here: https://aws.amazon.com/lambda/faqs/
I was facing the same issue few times ago but I have resolved with by putting my mongo on same account of EC2.
I have created a mongo DB on the same AWS EC2 account where my lambda function reside.
Now I can access my mongo from the lambda function with the private IP.