I'm working on a project for a client who does message pre and post-processing at very high volumes. I'm trying to figure out a reliable configuration between two or three API servers that will push outgoing email messages to any of two or more instances of Postfix. This is for outbound only and it's preferred not to have a single point of failure.
I am a bit lost within the AWS ecosystem and all I know is we cannot use SES and the client is set up for high volume smtp with Amazon, so throttling is not an issue.
I've looked into ELB, HAProxy, and a few other things but the whole thing has now gotten muddy and I'm not sure if I'm just overthinking it now.
Any quick thoughts would be appreciated.
Thanks
I am creating a panic button app that requires clients to remain connected for several hours in case there is a panic alert triggered. AWS WebSockets disconnect after 2 hours, which defeats the purpose of my app.
I talked to AWS and they confirmed that there is no way to extend that. I need to find an alternative. What is the best way to keep clients connected for a long time? I have computers that need to remain connected sometimes for up to 8 or 10 hours and they can't disconnect because if they do and there is a panic alert triggered, they would not get it.
I wanted to go serverless, but I do not know if it is possible given the limitation of websockets disconnecting after 2 hrs. I did some research and people mentioned that I should use ELB and EC2. Would I be able to deploy a Socket.io app this way? If not socket.io, are there any other options? How expensive can it get to keep clients connected to an EC2 instance with socket.io installed on it?
I have an EC2 with the HTTP server; I want to stop and start it automatically when it was not responding on HTTP port after 2 minutes.
What is the best way to implement on AWS without using the scale group and elastic load balancer(ELB).
As I mentioned before, I don't need to create the new instance, just stop and start.
First instead of stopping and starting instance considering restarting service with help of monit or another monitoring service because restarting instance will take time and not a good idea.
But if you are worried about instance going down scenarios, you can configure auto healing(https://aws.amazon.com/blogs/aws/new-auto-recovery-for-amazon-ec2/).
Another custom way of doing would be, inside instance do a simple hello check using curl and store the response log and schedule it in a cron, Sync the log to cloudwatch and in cloudwatch you can plot metric using logs, and configure alarm if the metric count goes below a threshold for 2 mins, you can write lambda function to restart the instance, and associate the lambda to the alarm.(https://aws.amazon.com/premiumsupport/knowledge-center/start-stop-lambda-cloudwatch/). Since you have mentioned you are having one instance this approach will work, if you have more than one instance you need to handle namespace, But again restarting instance is a not a good idea.
I am using Route53 healthcheck which, when triggered, sends notification to SNS topic that triggers Lambda function that reboots the server.
I'm looking to set up Auto-Scaling for a service running on AWS ECS. From the ECS Auto-Scaling docs it suggests to use SurgeQueueLength to determine whether to trigger an autoscale event. We use an Application Load Balancer which does not have this metric, looking through the table of metrics nothing seems equivalent. Am I missing something or is this just a missing feature in ALBs at present?
Disclaimer: I don't have experience with Application Load Balancers. I'm just deriving these facts from AWS docs. For a more hands on read you might read the ALB section of this medium post
You are correct, in the CloudWatch metrics for Application Load Balancers there is no SurgeQueueLength. This is also confirmed in this thread by an AWS employee, however, these metrics could be used as a CloudWatch metric to trigger auto scaling:
TargetConnectionErrorCount: IMO this is corresponding best to the SurgeQueueLength as it indicates that the Loadbalancer tried to open a connection to a backend node and failed
HTTPCode_ELB_5XX_Count: depending on the backend nodes this might be an indicator that they refuse new connections because e.g. their max connection count is reached
RejectedConnectionCount: this is what the AWS employee suggested in the treadh linked above. Buuut.. the doc says "number of connections that were rejected because the load balancer had reached its maximum number of connections" this seems more like a limit on aws side which you cannot really influence (i.e. it is not described in the limits on ALBs)
RequestCountPerTarget: that's the average number of connections a backend node gets per minute. When you track that over a period of time you might be able to evaluate a "healthy threshold"
TargetResponseTime: number of seconds a backend node needs to answer a request. Another candidate for evaluating as "healthy threshold" (i.e. "what's the maximum response time you want the end user to experience?")
Overall it seems that there is no "clear correct answer" to your question and the answer is a "it depends on your situation".
The question which suggests itself is: "why are there no queue metrics such as SurgeQueueLength". This is nowhere answered in the docs. I guess this is either because ALBs are designed differently than ELBs or it is a metric which is just not exposed yet.
ALBs are designed differently and don't have SurgeQueueLength or SpillOver metrics. Source: AWS Staff.
I have a web app which runs behind Amazon AWS Elastic Load Balancer with 3 instances attached. The app has a /refresh endpoint to reload reference data. It need to be run whenever new data is available, which happens several times a week.
What I have been doing is assigning public address to all instances, and do refresh independently (using ec2-url/refresh). I agree with Michael's answer on a different topic, EC2 instances behind ELB shouldn't allow direct public access. Now my problem is how can I make elb-url/refresh call reaching all instances behind the load balancer?
And it would be nice if I can collect HTTP responses from multiple instances. But I don't mind doing the refresh blindly for now.
one of the way I'd solve this problem is by
writing the data to an AWS s3 bucket
triggering a AWS Lambda function automatically from the s3 write
using AWS SDK to to identify the instances attached to the ELB from the Lambda function e.g. using boto3 from python or AWS Java SDK
call /refresh on individual instances from Lambda
ensuring when a new instance is created (due to autoscaling or deployment), it fetches the data from the s3 bucket during startup
ensuring that the private subnets the instances are in allows traffic from the subnets attached to the Lambda
ensuring that the security groups attached to the instances allow traffic from the security group attached to the Lambda
the key wins of this solution are
the process is fully automated from the instant the data is written to s3,
avoids data inconsistency due to autoscaling/deployment,
simple to maintain (you don't have to hardcode instance ip addresses anywhere),
you don't have to expose instances outside the VPC
highly available (AWS ensures the Lambda is invoked on s3 write, you don't worry about running a script in an instance and ensuring the instance is up and running)
hope this is useful.
While this may not be possible given the constraints of your application & circumstances, its worth noting that best practice application architecture for instances running behind an AWS ELB (particularly if they are part of an AutoScalingGroup) is ensure that the instances are not stateful.
The idea is to make it so that you can scale out by adding new instances, or scale-in by removing instances, without compromising data integrity or performance.
One option would be to change the application to store the results of the reference data reload into an off-instance data store, such as a cache or database (e.g. Elasticache or RDS), instead of in-memory.
If the application was able to do that, then you would only need to hit the refresh endpoint on a single server - it would reload the reference data, do whatever analysis and manipulation is required to store it efficiently in a fit-for-purpose way for the application, store it to the data store, and then all instances would have access to the refreshed data via the shared data store.
While there is a latency increase adding a round-trip to a data store, it is often well worth it for the consistency of the application - under your current model, if one server lags behind the others in refreshing the reference data, if the ELB is not using sticky sessions, requests via the ELB will return inconsistent data depending on which server they are allocated to.
You can't make these requests through the load balancer, So you will have to open up the security group of the instances to allow incoming traffic from source other than the ELB. That doesn't mean you need to open it to all direct traffic though. You could simply whitelist an IP address in the security group to allow requests from your specific computer.
If you don't want to add public IP addresses to these servers then you will need to run something like a curl command on an EC2 instance inside the VPC. In that case you would only need to open the security group to allow traffic from some server (or group of servers) that exist in the VPC.
I solved it differently, without opening up new traffic in security groups or resorting to external resources like S3. It's flexible in that it will dynamically notify instances added through ECS or ASG.
The ELB's Target Group offers a feature of periodic health check to ensure instances behind it are live. This is a URL that your server responds on. The endpoint can include a timestamp parameter of the most recent configuration. Every server in the TG will receive the health check ping within the configured Interval threshold. If the parameter to the ping changes it signals a refresh.
A URL may look like:
/is-alive?last-configuration=2019-08-27T23%3A50%3A23Z
Above I passed a UTC timestamp of 2019-08-27T23:50:23Z
A service receiving the request will check if the in-memory state is at least as recent as the timestamp parameter. If not, it will refresh its state and update the timestamp. The next health-check will result in a no-op since your state was refreshed.
Implementation notes
If refreshing the state can take more time than the interval window or the TG health timeout, you need to offload it to another thread to prevent concurrent updates or outright service disruption as the health-checks need to return promptly. Otherwise the node will be considered off-line.
If you are using traffic port for this purpose, make sure the URL is secured by making it impossible to guess. Anything publicly exposed can be subject to a DoS attack.
As you are using S3 you can automate your task by using the ObjectCreated notification for S3.
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html
https://docs.aws.amazon.com/cli/latest/reference/s3api/put-bucket-notification.html
You can install AWS CLI and write a simple Bash script that will monitor that ObjectCreated notification. Start a Cron job that will look for the S3 notification for creation of new object.
Setup a condition in that script file to curl "http: //127.0.0.1/refresh" when the script file detects new object created in S3 it will curl the 127.0.0.1/refresh and done you don't have to do that manually each time.
I personally like the answer by #redoc, but wanted to give another alternative for anyone that is interested, which is a combination of his and the accepted answer. Using SEE object creation events, you can trigger a lambda, but instead of discovering the instances and calling them, which requires the lambda to be in the vpc, you could have the lambda use SSM (aka Systems Manager) to execute commands via a powershell or bash document on EC2 instances that are targeted via tags. The document would then call 127.0.0.1/reload like the accepted answer has. The benefit of this is that your lambda doesn't have to be in the vpc, and your EC2s don't need inbound rules to allow the traffic from lambda. The downside is that it requires the instances to have the SSM agent installed, which sounds like more work than it really is. There's AWS AMIs already optimized with SSM agent stuff, but installing it yourself in the user data is very simple. Another potential downside, depending on your use case, is that it uses an exponential ramp up for simultaneous executions, which means if you're targeting 20 instances, it runs one 1, then 2 at once, then 4 at once, then 8, until they are all done, or it reaches what you set for the max. This is because of the error recovery stuff it has built in. It doesn't want to destroy all your stuff if something is wrong, like slowly putting your weight on some ice.
You could make the call multiple times in rapid succession to call all the instances behind the Load Balancer. This would work because the AWS Load Balancers use round-robin without sticky sessions by default, meaning that each call handled by the Load Balancer is dispatched to the next EC2 Instance in the list of available instances. So if you're making rapid calls, you're likely to hit all the instances.
Another option is that if your EC2 instances are fairly stable, you can create a Target Group for each EC2 Instance, and then create a listener rule on your Load Balancer to target those single instance groups based on some criteria, such as a query argument, URL or header.