i have a python api that i have tried on vms, fargate, and lambda.
vms - less errors when capacity is large enough
fargate - second less errors when capacity is large enough, but when autoscaling, i get some 500 errors. looks like it doesn't autoscale quick enough.
lambda - less consistent. when there are a lot of api calls, less errors. but from cold start, it may periodically fail. i do not pre-provision. when i do, i get less errors too.
i read on the below post, cold start for lambda is less than 1 sec? seems like it's more. one caveat is that each lambda function will check for an existing "env" file. if it does not exist, it will download from s3. however this is done only when hitting the api. the lambda function is listening and responding. when you hit api, the lambda function will respond and connect, download the .env file, and process further the api call. fargate also does the same, but less errors again. any thoughts?
i can pre-provision, but it gets kind of expensive. at that point, i might as will go back to VMs with autoscaling groups, but it's less cloud native. the vms provide the fastest response by far and harder to manage.
Can AWS Lambda coldout cause API Gateway timeout(30s)?
i'm using an ALB in front of lambda and fargate. the vms simply use round robin dns.
questions:
am i doing something wrong with fargate or lambda? are they alright for apis or should i just go back to vms?
what or who maintains api connection while lambda is starting up from a cold start? can i have it retry or hold on to the connection longer?
thanks!
am i doing something wrong with fargate or lambda? are they alright for apis or should i just go back to vms?
The one thing that strikes me is downloading env from s3. Wouldn't it be easier and faster to keep your env data in SSM Parameter Store? Or perhaps, passing them as env variables to the lambda function itself.
what or who maintains api connection while lambda is starting up from a cold start? can i have it retry or hold on to the connection longer?
API gateway. Sadly you can't extend 30 s time limit. Its hard limit.
i'm using an ALB in front of lambda and fargate.
It seems to me that you have API gateway->ALB->Lambda function. Why would you need ALB in that? Usually there is no such need.
i can pre-provision, but it gets kind of expensive.
Sadly, this is the only way to minimize cold-starts.
Related
While I have worked with AWS for a bit, I'm stuck on how to correctly approach the following use case.
We want to design an uptime monitor for up to 10K websites.
The monitor should run from multiple AWS regions and ping websites if they are available and measure the response time. With a lambda function, I can ping the site, pass the result to a sqs queue and process it. So far, so good.
However, I want to run this function every minute. I also want to have the ability to add and delete monitors. So if I don't want to monitor website "A" from region "us-west-1" I would like to do that. Or the other way round, add a website to a region.
Ideally, all this would run serverless and deployable to custom regions with cloud formation.
What services should I go with?
I have been thinking about Eventbridge, where I wanted to make custom events for every website in every region and then send the result over SNS to a central processing Lambda. But I'm not sure this is the way to go.
Alternatively, I wanted to build a scheduler lambda that fetches the websites it has to schedule from a DB and then invokes the fetcher lambda. But I was not sure about the delay since I want to have the functions triggered every minute. The architecture should monitor 10K websites and even more if possible.
Feel free to give me any advise you have :)
Kind regards.
In my opinion Lambda is not the correct solution for this problem. Your costs will be very high and it may not scale to what you want to ultimately do.
A c5.9xlarge EC2 costs about USD $1.53/hour and has a 10gbit network. With 36 CPU's a threaded program could take care of a large percentage - maybe all 10k - of your load. It could still be run in multiple regions on demand and push to an SQS queue. That's around $1100/month/region without pre-purchasing EC2 time.
A Lambda, running 10000 times / minute and running 5 seconds every time and taking only 128MB would be around USD $4600/month/region.
Coupled with the management interface you're alluding to the EC2 could handle pretty much everything you're wanting to do. Of course, you'd want to scale and likely have at least two EC2's for failover but with 2 of them you're still less than half the cost of the Lambda. As you scale now to 100,000 web sites it's a matter of adding machines.
There are a ton of other choices but understand that serverless does not mean cost efficient in all use cases.
I have a script running on Lambda, I've set the timeout to maximum 15 mins but it's still giving me time out error, there is not much infomation in the lofs, how I can solve this issue and spot what is taking soo much time? I tested the script locally and it's fairly quick.
Here's the error:
{
"errorMessage": "2020-09-10T18:26:53.180Z xxxxxxxxxxxxxxx Task timed out after 900.10 seconds"
}
If you're exceeding the 15 minutes period there are a few things you should check to identify:
Is the Lambda connecting to resources in a VPC? If so is it attached via VPC config, and do the target resources allow inbound access from the Lambda.
Is the Lambda connecting to a public IP but using VPC configuration? If so it will need a NAT attached to allow outbound access.
Are there any long running processes as part of your Lambda?
Once you've ruled these out consider increasing the available resources of your Lambda, perhaps its hitting a cap and is therefore performing slow. Increasing the memory will also increase the available CPU for you.
Adding comments in the code will log to CloudWatch logs, these can help you identify where in the code the slowness starts. This is done by simply calling the general output/debug function of your language i.e. print() in Python or console.log() in NodeJS.
If the function is still expected to last longer than 15 minutes after this you will need to break it down into smaller functions performing logical segments of the operation
A suggested orchestrator for this would be to use a step function to handle the workflow for each stage. If you need shared storage between each Lambda you can make use of EFS to be attached to all of your Lambdas so that they do not need to upload/download between the operations.
Your comment about it connecting to a SQL DB is likely the key. I assume that DB is in AWS in your VPC. This requires particular setup. Check out
https://docs.aws.amazon.com/lambda/latest/dg/configuration-vpc.html
https://docs.aws.amazon.com/lambda/latest/dg/services-rds-tutorial.html
Another thing you can do is enable debug level logging and then look at the details in CloudWatch after trying to run it. You didn't mention which language your lambda uses, so how to do this could be different for the language you use. Here's how it would be done in python:
LOGGER = logging.getLogger()
LOGGER.setLevel(logging.getLevelName('DEBUG'))
I have a Serverless .net core web api lambda application deployed on AWS.
I have this sitting inside a VPC as I access ElasticSearch service inside that same VPC.
I have two API microservices that connect to the Elasticsearch service.
After a period of non use (4 hours, 6 hours, 18 hours - I'm not sure exactly but seems random), the function becomes unresponsive and I get a 504 gateway timeout error, "Endpoint cannot be found"
I read somewhere that if "idle" for too long, the ENI is released back into the AWS system and that triggering the Lambda again should start it up.
I can't seem to "wake" up the function by calling it as it keeps timing out with the above error (I have also increased the timeouts from default).
Here's the kicker - If I make any changes to the specific lambda function, and save those changes (this includes something as simple as changing the timeout value) - My API calls (BOTH of them, even though different lambdas) start working again like it has "kicked" back in. Obviously the changes do this, but why?
Obviously I don't want timeouts in a production environment regardless of how much, OR how little the lambda or API call is used.
I need a bulletproof solution to this. Surely it's a config issue of some description but I'm just not sure where to look.
I have altered Route tables, public/private subnets, CIDR blocks, created internet gateways, NAT etc. for the VPC. This all works, but these two lambdas, that require VPC access, keeps falling "asleep" somehow.
The is because of Cold Start of Lambda.
There is a new feature which was release in reInvent 2019, where in there is a provisioned concurrency for lambda (don't get confused with reserved concurrency).
Ensure the provisioned concurrency to minimum 1 (or the amount of requests to be served in parallel) to have lambda warm always and serve requests
Ref: https://aws.amazon.com/blogs/aws/new-provisioned-concurrency-for-lambda-functions/
To get more context, Lambda in VPC uses hyperplane ENI and functions in the same account that share the same security group:subnet pairing use the same network interfaces.
If Lambda functions in an account go idle for sometime (typically no usage for 40 mins across all functions using that ENI, as I got this time info from AWS support), the service will reclaim the unused Hyperplane resources and so very infrequently invoked functions may still see longer cold-start times.
Ref: https://aws.amazon.com/blogs/compute/announcing-improved-vpc-networking-for-aws-lambda-functions/
I am currently using AWS EC2 for my workloads.
Now I want to convert the EC2 server to the Serverless Platform i.e(API Gateway and Lambda).
I have also followed different blogs and I am ready to go with the serverless. But, my one concern is on pricing.
How can I predict per month cost for the serverless according to my use of EC2? Will the EC2 Cloudwatch metrics help me to calculate the costing?
How can I make cost comparison?
Firstly, there is no simple answer to your question as a simple lift and shift from a VM to Lambda is not ideal. To make the most of lambda, you need to architect your solution to be serverless. This means making use of the event-driven nature of Lambda.
Now to answer the question simply, you are charged only for the time it takes to serve a request (to the next 100ms). So if your lambda responds to the request in 70ms you pay for 100ms of execution time. If you serve the request in 210ms then you pay for 300ms.
You also pay for the memory allocated to the function on the order of GB per/month.
If you have a good logging or monitoring strategy you could check how long it takes to serve each type of request and how often they occur. If your application is fairly low-scale and is not accessed often (say all requests come within an 8 hour window) then you may end up saving money with lambda as you are only paying AWS for the time spent serving the request.
Also, it may help to read the following article on common pitfalls:
https://medium.com/#emaildelivery/serverless-pitfalls-issues-you-may-encounter-running-a-start-up-on-aws-lambda-f242b404f41c
response from an api from aws api gateway integrated to a lambda
function takes a lot more time than compared to a regular node project
on aws elasticbeanstalk
is there any way to reduce response time for aws api gateway
There's definitely more information needed to answer this question but from what you've said, your problems may be caused by the cold start time of Lambda functions. An Elastic Beanstalk stack will spin up EC2 instances (which are ready once they're spun up and until they're removed). Lambda will create instances of your handler as needed to address incoming traffic. The first time you call a Lambda, it needs to provision an environment for the function for the first time. Depending on the language used, this can take some time. Successive requests should be faster unless you wait a while (in which case the lambda needs to re-initialize).
So here's more information that would be useful in case this answer is not helpful:
How much slower is Lambda than your Elastic Beanstalk stack?
Is it slower only on the first couple requests or does it continue being slow when you keep requesting?
Is it slow every day or only occasionally?