I'm looking for help with an architectural design decision I'm making with a product.
We've got multiple producers (initiated by API Gateway calls into Lambda) that put messages on a SQS queue (the request queue). There can be multiple simultaneous calls, so there would be multiple Lambda instances running in parallel.
Then we have consumers (lets say twenty EC2 instances) who long-poll on the SQS for the message to process them. They take about 30-45 seconds to process a message each.
I would then ideally like to send the response back to the producer that issued the request - and this is the part I'm struggling with with SQS. I would in theory have a separate response queue that the initial Lambda producers would then be consuming, but there doesn't seem to be a way to cherry pick the specific correlated response. That is, each Lambda function might pick up another function's response. I'm looking for something similar to this design pattern: http://soapatterns.org/design_patterns/asynchronous_queuing
The only option that I can see is to create a new SQS Response queue for each Lambda API call, passing in its ARN in the message for the consumers to put the response on, but I can't imagine that's very efficient - especially when there's potentially hundreds of messages a minute? Am I missing something obvious?
I suppose the only other alternative would be setting up a bigger message broker (e.g. RabbitMQ/ApacheMQ) environment, but I'd like to avoid that if possible.
Thanks!
Create a (Temporary) Response Queue For Every Request
To late for the party, but i was thinking that i might find some help in what i want to achieve, #MattHouser #Zaheer Ally , or give an idea to someone working on a related issue.
I am facing a similar challenge. I have an API that upon request by a client, needs to communicate to multiple external APIs and collect (delayed) results.
Since my PHP API is synchronous, it can only perform these requests sequentially. So, i was thinking to use a request queue, where the producer (API) would send messages. Then, multiple workers would consume these messages, each of them performing one of these external API calls.
To get the results back, the producer would have created a temporary response queue, the name-identifier of which would be embedded in the message sent to workers. Hence, each worker would 'publish' his results on this temporary queue.
In the meantime, the producer would keep polling the temporary queue until he received the expected number of messages. Finally, he would delete the queue and send the collected results back to the client.
Yes, you could use RabbitMQ for a more "rpc" queue pattern.
But if you want to stay within AWS, try using something other than SQS for the response.
Instead, you could use S3 for the response. When your producer puts the item into SQS, include in the message an S3 destination for the response. When your consumer completes the tasks, put the response in the desired S3 location.
Then you can check S3 for the response.
Update
You may be able to accomplish an RPC-like message queue using Redis.
https://github.com/ServiceStack/ServiceStack/wiki/Messaging-and-redis
Then, you can use AWS ElastiCache for your Redis cluster. This would completely replace the use of SQS.
Another option would be to use Redis' pub/sub mechanism to asynchronously notify your lambda that the backend work is done. You can use AWS's Elasticache for Redis for an all-AWS-managed solution. Your lambda function would generate a UUID for each request, use that as the channel name to subscribe to, pass it along in the SQS message, and then the backend workers would publish a notification to that channel when the work is done.
I was facing this same problem so I tried it out, and it does work. Whether it's worth the effort over just polling S3 is another question. You have to configure the lambda functions to run inside your VPC, so they can access your Redis. I was going to have to do this anyway since I'd want the workers, in my case also lambda functions, to be able to access my Elasticsearch and RDS. But there are some considerations: most importantly, you need to use a private subnet with a NAT Gateway (or your own NAT Instance), so it can get out to the Internet and AWS managed services (including SQS).
One other thing I just stumbled across is that requests through API Gateway currently cannot take longer than 29 seconds, and this cannot be increased by AWS. You mentioned your jobs take 30 or more seconds, so this could be a showstopper for you using API Gateway and Lambda in this way anyway.
AWS now provides a Java client that supports temporary queues. This is useful for request/response patterns. I can't see a non-Java version.
Related
I am learning Apache Kafka as Queue.
I can understand queue is needed when I run web server not to drop burst traffic.
Queue can help not to drop data for rush hours.
Unless using Queue, the only thing I can do is to put more server as much as rush hour traffic.
Is it right?
If it is right,
Assume that, I use aws api gateway + lambda for web server.
aws lambda can be auto scale. So my lambda web server never drop burst traffic. It means Queue such as Kafka is not needed in this case ?
Surely if I need any pub/sub architecture, Kafka is needed.
Is it right what I think?
API Gateway is typically used for cases where you care about the result of the API call and want to do something with the response. In this case, you need to wait for the Lambda function to finish and return the result so it can be passed back to the client. You don't need a queue because Lambda will scale out and add processes for each request. The limit would be the 10,000 requests per second of API Gateway, or the capacity of any downstream systems like a database.
Kafka is designed for real-time data streaming cases; things where you want to process data immediately, such as transcribing video. It is different than pub/sub. Consumers request data from Kafka. If the process requires merging data from multiple input sources on an on-going basis, then Kafka is a good fit. To say this another way, if the size of the input has no upper bound, stream processing is a good choice. A similar service that is available on AWS is Amazon Kinesis.
Pub/sub (such as Amazon SNS, which can easily trigger Lambda functions) is better for use cases where the size of the input, or the size of a useful batch, can be easily defined, but where data should still be processed near real-time. In a pub/sub system, events are published to subscribers rather than subscribers requesting them.
Another option is a queue like Amazon SQS, which can be useful if there is a bottleneck somewhere else in the system, such as database write capacity, or a Lambda concurrency limit. In this architecture, consumers request items from the queue when they are ready to process them, so it is better for use-cases where results are not immediately required.
I have a PHP web application that is running on an ec2 server. The app is integrated with another service which involves subscribing to a number of webhooks.
The number of requests the server is receiving from these webhooks has become unmanageable, and I'm looking for a more efficient way to deal with data coming from these webhooks.
My initial thought was to use API gateway and put these requests into an SQS queue and read from this queue in batches.
However, I would like these batches to be read by the ec2 instance because the code used to process the webhooks is code reused throughout my application.
Is this possible or am I forced to use a lambda function with SQS? Is there a better way?
The approach you suggested (API Gateway + SQS) will work just fine. There is no need to use AWS Lambda. You'll want to use the AWS SDK for PHP when writing the application code that receives messages from your SQS queue.
I've used this pattern before and it's a great solution.
. . . am I forced to use a lamda function with SQS?
SQS plus Lambda is basically free. At this time, you get 1M (million) lambda calls and 1M (million) SQS requests per month. Remember that those SQS Requests may contain up to 10 messages and that's a potential 10M messages, all inside the free tier. Your EC2 instance is likely always on. Your lambda function is not. Even if you only use Lambda to push the SQS data to a data store like RDBMS for your EC2 to periodically poll, the operation would be bullet-proof and very inexpensive. With the introduction of SQS you could transition the common EC2 code to Lambda function(s). These now have a run time of 15 minutes.
To cite my sources:
SQS pricing for reference: https://aws.amazon.com/sqs/pricing/
Lambda pricing for reference: https://aws.amazon.com/lambda/pricing/
I have an external data source as an ActiveMQ topic. I can only connect and consume messages. They come pretty rarely, about 1 message per 10-30 seconds.
I want to collect all the messages and put them into the database.
Also I'd like to have an active web page that can receive the new messages over WebSockets and draw a chart.
I have a prototype built with Python/Flask/MongoDB/SocketIO, BUT...
I would like to use Amazon AWS cloud infrastructure to avoid processing the data on servers.
I believe that AWS Lambda can accept the messages and store them into the database (DynamoDB?) and also send a notification (maybe using SQS) being transformed into WebSocket message. (Not everything is clear there yet, maybe simple ajax polling will be enough).
Here is a question: how it would be possible to consume the messages from external ActiveMQ topic and process it with AWS Lambda?
I was looking on Kinesis, but it looks it only supports the data being pushed to it, but not polling for the data by some protocol...
You can use Lambda as a cron-like facility and poll on a schedule. See Lambda Scheduled Events.
Here is the simplified scheme I am trying to make work:
http requests --> (Gateway API + lambda A) --> SQS --> (lambda B
?????) --> DynamoDB
So it should work as shown: data coming from many http requests (up to 500 per second, for example)
is placed into SQS queue by my lambda function A. Then the other function, B, processes the queue:
reads up to 10 items (on some periodical basis) and writes them to DynamoDB with BatchWriteItem.
The problem is that I can't figure out how to trigger the second lambda function. It should be called frequently, multiple times per second (or at least once per second), because I need all the data from the queue to get into DynamoDB ASAP (that's why calling lambda function B via scheduled events as described here is not a option)
Why don't I want to write directly into DynamoDB, without SQS?
That would be great for me to avoid using SQS at all. The problem that I am trying to address with SQS is DynamoDB throttling. Not even throttling itself but the way it is handled while writing data to DynamoDB with AWS SDK: when writing records one by one and getting them throttled, AWS SDK silently retries writing, resulting in increasing of the request processing time from the http client's point of view.
So I would like to temporarily store data in the queue, send response "200 OK" back to client, and then get queue processed by separate function,
writing multiple records with one DynamoDB's BatchWriteItem call (which
returns Unprocessed items instead of automatic retry in case of throttling). I would even prefer to lose some records instead of increasing the lag between a record being received and stored in DynamoDB
UPD: If anyone is interested, I have found how to make aws-sdk skip automatic retries in case of throttling: there is a special parameter maxRetries. Anyway, going to use Kinesis as suggested below
[This doesn't directly answer your explicit question, so in my experience it will be downvoted :) However, I will answer the fundamental problem you are trying to solve.]
The way we take a flood of incoming requests and feed them to AWS Lambda functions for writing in a paced manner to DynamoDB is to replace SQS in the proposed architecture with Amazon Kinesis streams.
Kinesis streams can drive AWS Lambda functions.
Kinesis streams guarantee ordering of the delivered messages for any given key (nice for ordered database operations).
Kinesis streams let you specify how many AWS Lambda functions can be run in parallel (one per partition), which can be coordinated with your DynamoDB write capacity.
Kinesis streams can pass multiple available messages in one AWS Lambda function invocation, allowing for further optimization.
Note: It's really the AWS Lambda service that reads from Amazon Kinesis streams then invokes the function, and not Kinesis streams directly invoking AWS Lambda; but sometimes it's easier to visualize as Kinesis driving it. The result to the user is nearly the same.
You can't do this directly integrating SQS and Lambda, unfortunately. But don't fret too much yet. There is a solution! You need to add another amazon service into the mix and all your problems will be solved.
http requests --> (Gateway API + lambda A) --> SQS + SNS --> lambda B --> DynamoDB
You can trigger an SNS notification to the second lambda service to kick it off. Once it is started, it can drain the queue and write all the results into DynamoDB. To better understand possible event sources for Lambda check out these docs.
As of June 28, 2018, you can now use SQS to trigger AWS Lambda functions natively. A workarounds is no longer needed!
https://aws.amazon.com/blogs/aws/aws-lambda-adds-amazon-simple-queue-service-to-supported-event-sources/
And in Nov 2019, support for FIFO queues was added:
https://aws.amazon.com/blogs/compute/new-for-aws-lambda-sqs-fifo-as-an-event-source/
Another solution would be to just add the item to SQS, call the targeted Lambda function with Event so it is asynchronous.
The asynchronous Lambda can then get from SQS as many item as you want and process them.
I would also add a scheduled call to the asynchronous Lambda to handle any items in the queue that was in error.
[UPDATE] You can now setup Lambda trigger on new message on queue
Maybe a more cost-efficient solution would be to keep everything in the SQS (as it is), then run a scheduled event that invokes a multi-threaded Lambda function that processes items from the queue?
This way, your queue worker can match your limits exactly. If the queue is empty, function can finish prematurely or start polling in single thread.
Kinesis sounds a like an over-kill for this case – you don't need the original order, for instance. Plus running multiple Lambdas simultaneously is surely more expensive than running just one multi-threaded Lambda.
Your Lambda will be all about I/O, making external calls to AWS services, so one function may fit very well.
Here's how I collect messages from an SQS queue:
package au.com.redbarn.aws.lambda2lambda_via_sqs;
import java.util.List;
import com.amazonaws.services.lambda.runtime.Context;
import com.amazonaws.services.lambda.runtime.RequestHandler;
import com.amazonaws.services.lambda.runtime.events.SQSEvent;
import com.amazonaws.services.lambda.runtime.events.SQSEvent.SQSMessage;
import lombok.extern.log4j.Log4j2;
#Log4j2
public class SQSConsumerLambda implements RequestHandler<SQSEvent, String> {
#Override
public String handleRequest(SQSEvent input, Context context) {
log.info("message received");
List<SQSMessage> records = input.getRecords();
for (SQSMessage record : records) {
log.info(record.getBody());
}
return "Ok";
}
}
Add your DynamoDB code to handleRequest() and Lambda B is done.
Here's my solution to this problem:
HTTP request --> DynamoDb --> Stream --> Lambda Function
In this solution, you have to set up a stream for the table. The stream is handled with a Lambda function that you'll write and that's it. No need to use SQS or anything else.
Of course, this is a simplified design and it works only for simple problems. For more complicated scenarios, use Kinesis (as mentioned in the other answers).
Here's a link to AWS documentation on the topic.
I believe AWS had now come up with a way where SQS can trigger a lambda function. So I guess we can use SQS for smoothening burst loads of data to dynamo incase you don't care about the order of messages. Check their blog on this new update: https://aws.amazon.com/blogs/aws/aws-lambda-adds-amazon-simple-queue-service-to-supported-event-sources/
I am using firebase to notify web browsers (javascript clients) about changes on specific topics. I am very happy with it. However I would really like to (only) use aws web services.
Unfortunately I am not able to determine whether it is possible to build such a service on aws. I am not talking about having EC2 instances running some firebase / fanout.io alternatives. I am talking about utilizing services such as lambda, dynamodb streams, SNS & SQS.
Are there any socket notification services available or is it possible to achieve something similar by using the provided services?
I looked into this very recently with the same idea, but eventually I came down on just using fanout. AWS does not provide server-push HTTP notification services out of the box.
Lambda functions are billed per 100 ms, so any long-polling against lambda will end up billing for the entirety of the time the client is connected.
SNS does not provide long polling to browsers; the available clients are geared towards mobile, email, HTTP/S, and other Amazon products like Lambda and SQS.
SQS would require a dedicated queue per client as it does not support broadcast.
Now, if the lambda pricing doesn't bother you, you could possibly do this:
Write a lambda function that is called via the API service that opens up a connection to SQS and waits for a message. The key is to start the lambda call from HTTP, but within the function wait on the queue (using Boto, for example, if you are writing this in Python). This code would need to create a queue dedicated to servicing one particular client, uniquely keyed by something like a GUID that is passed in by the client.
Link to the lambda function using the Amazon API service.
Call the lambda function via the API from the browser and wait for it to either receive a message on the dedicated SQS queue or timeout, probably using long-polling both in the API connection and the SQS connection. Fully draining the queue (or at least taking as many messages in a batch up to some limit) would be advisable here as well in order to reduce the number of calls to the API.
Publish your event to the dedicated SQS queue associated with the client. This will require the publisher to know the client's unique key.
Return the event read from SQS as the result of the lambda call.
Some problems with this approach:
Lambda pricing - not terribly expensive, but something like fanout is basically free
You would need a dedicated SQS queue per client; cleanup might become a problem
SQS bills on number of calls, which includes checking for a message. Long-polling SQS will alleviate some of this
You would need to write the JavaScript client to call the lambda API endpoint repeatedly in a long-polling fashion
Lambda is currently limited as to the number of concurrently running functions it supports (100 right now but you can contact support to bump that up)
Some benefits with this approach:
SQS queues are persistent, so unless a message is processed successfully it will go back on the queue after the visibility timeout
You can set up CloudWatch to monitor all of the API, Lambda, and SQS events
Other Notes
You could call the SQS APIs directly from the browser by using Lambda to issue temporary security credentials via STS. Receiving a message in JavaScript is documented here: http://docs.aws.amazon.com/AWSJavaScriptSDK/guide/browser-examples.html#Receiving_a_message I do not, however, know off the top of my head if you would run into cross-domain issues.
Your only other option, if it must be all AWS, is to use load-balanced EC2 instances running something like fanout as you mentioned.
Using fanout is very little work: it's both extremely affordable and already built and tested.