I have setup an SQS queue where S3 paths are being pushed whenever there is a file upload.
I have also set up a Lambda with an SQS trigger and a batch size of 1.
In my scenario, I have to process n files at a time. Lets say (n = 10).
Say, there are 100 messages in the queue. In my current implementation I'm doing the following steps:
Whenever there is a message in the input queue, Lambda will be triggered
First I check the active number of concurrent executions I have. If am already running 10 executions, the code will simply return without doing anything. If it is less than 10, it reads one message from the queue and calls for processing.
Once the processing is done, the message will be manually deleted from the queue.
With the above mentioned approach, I'm able to process n files at a time. However, Say 100 files lands into S3 at the same time.
It leads to 100 lambda calls. Since we have a condition check in Lambda, the first 10 messages go for processing and the remaining 90 messages go to the in-flight mode.
Now, when some of my processing is done (say 3/10 got over), still the main queue is empty since the messages are still in-flight.
As per my understanding, if processing a file takes x minutes, the visibility timeout of the messages in the queue should be lesser than x (<x) . So that the message would once be available in the queue.
But it also leads to another problem. Say the batch took some more time to complete, message would come back to queue. Lambda would be triggered and once again it goes to the flight mode.
Is there any way, I can control the number of triggers made in lambda. For example: only first 10 messages should be processed however remaining 90 messages should remain visible in the queue. Or is there any other way I can make this design simple ?
I don't want to wait until 10 messages. Even if there are only 5 messages, it should trigger those files. And I don't want to call the Lambda in timely fashion (ex: calling it every 5 minutes).
There is a setting in Lambda called Reserved Concurrency, I'm going to quote from the docs (emphasis mine):
Reserved concurrency – Reserved concurrency creates a pool of requests that can only be used by its function, and also prevents its function from using unreserved concurrency.
[...]
To ensure that a function can always reach a certain level of concurrency, configure the function with reserved concurrency. When a function has reserved concurrency, no other function can use that concurrency. Reserved concurrency also limits the maximum concurrency for the function, and applies to the function as a whole, including versions and aliases.
For a deeper dive, check out this article from the documentation.
You can use this to limit how many Lambdas can be triggered in parallel - if no Lambda execution contexts are available, SQS invocations will wait.
This is only necessary if you want to limit how many files can be processed in parallel. If there is no actual need to limit this, it won't cost you more to let Lambda scale out for you.
You don't have to limit your concurrent Lambda execution. AWS already handling that for you. Here are the list of maximum concurrent per region from this document: https://docs.aws.amazon.com/lambda/latest/dg/invocation-scaling.html
Burst concurrency quotas
3000 – US West (Oregon), US East (N. Virginia), Europe (Ireland)
1000 – Asia Pacific (Tokyo), Europe (Frankfurt), US East (Ohio)
500 – Other Regions
In this document: https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html
Scaling and processing
For standard queues, Lambda uses long polling to poll a queue until it
becomes active. When messages are available, Lambda reads up to 5
batches and sends them to your function. If messages are still
available, Lambda increases the number of processes that are reading
batches by up to 60 more instances per minute. The maximum number of
batches that can be processed simultaneously by an event source
mapping is 1000.
For FIFO queues, Lambda sends messages to your function in the order
that it receives them. When you send a message to a FIFO queue, you
specify a message group ID. Amazon SQS ensures that messages in the
same group are delivered to Lambda in order. Lambda sorts the messages
into groups and sends only one batch at a time for a group. If the
function returns an error, all retries are attempted on the affected
messages before Lambda receives additional messages from the same
group.
Your function can scale in concurrency to the number of active message
groups. For more information, see SQS FIFO as an event source on the
AWS Compute Blog.
You can see that Lambda is handling the scaling up automatically. No need to artificially limit the number of Lambda running to 10.
The idea of Lambda is you want to run as many tasks as possible so that you can achieve parallel execution in the shortest time.
Related
My requirement is like this.
Read from a SQS every 2 hours, take all the messages available and then process it.
Processing includes creating a file with details from SQS messages and sending it to an sftp server.
I implemented a AWS Lambda to achieve point 1. I have a Lambda which has an sqs trigger. I have set batch size as 50 and then batch window as 2 hours. My assumption was that Lambda will get triggered every 2 hours and 50 messages will be delivered to the lambda function in one go and I will create a file for every 50 records.
But I observed that my lambda function is triggered with varied number of messages(sometimes 50 sometimes 20, sometimes 5 etc) even though I have configured batch size as 50.
After reading some documentation I got to know(I am not sure) that there are 5 long polling connections which lambda spawns to read from SQS and this is causing this behaviour of lambda function being triggered with varied number of messages.
My question is
Is my assumption on 5 parallel connections being established correct? If yes, is there a way I can control it? I want this to happen in a single thread / connection
If 1 is not possible, what other alternative do I have here. I do not want to have one file created for every few records. I want one file to be generated every two hours with all the messages in sqs.
A "SQS Trigger" for Lambda is implemented with the so-called Event Source Mapping integration, which polls, batches and deletes messages from the queue on your behalf. It's designed for continuous polling, although you can disable it. You can set a maximum batch size of up to 10,000 records a function receives (BatchSize) and a maximum of 300s long polling time (MaximumBatchingWindowInSeconds). That doesn't meet your once-every-two-hours requirement.
Two alternatives:
Remove the Event Source Mapping. Instead, trigger the Lambda every two hours on a schedule with an EventBridge rule. Your Lambda is responsible for the SQS ReceiveMessage and DeleteMessageBatch operations. This approach ensures your Lambda will be invoked only once per cron event.
Keep the Event Source Mapping. Process messages as they arrive, accumulating the partial results in S3. Once every two hours, run a second, EventBridge-triggered Lambda, which bundles the partial results from S3 and sends them to the SFTP server. You don't control the number of Lambda invocations.
Note on scaling:
<Edit (mid-Jan 2023): AWS Lambda now supports SQS Maximum Concurrency>
AWS Lambda now supports setting Maximum Concurrency to the Amazon SQS event source, a more direct and less fiddly way to control concurrency than with reserved concurrency. The Maximum Concurrency setting limits the number of concurrent instances of the function that an Amazon SQS event source can invoke. The valid range is 2-1000 concurrent instances.
The create and update Event Source Mapping APIs now have a ScalingConfig option for SQS:
aws lambda update-event-source-mapping \
--uuid "a1b2c3d4-5678-90ab-cdef-11111EXAMPLE" \
--scaling-config '{"MaximumConcurrency":2}' # valid range is 2-1000
</Edit>
With the SQS Event Source Mapping integration you can tweak the batch settings, but ultimately the Lambda service is in charge of Lambda scaling. As the AWS Blog Understanding how AWS Lambda scales with Amazon SQS standard queues says:
Lambda consumes messages in batches, starting at five concurrent batches with five functions at a time. If there are more messages in the queue, Lambda adds up to 60 functions per minute, up to 1,000 functions, to consume those messages.
You could theoretically restrict the number of concurrent Lambda executions with reserved concurrency, but you would risk dropped messages due to throttling errors.
You could try to set the ReservedConcurrency of the function to 1. That may help. See the docs for reference.
A simple solution would be to create a CloudWatch Event Trigger (similar to a Cronjob) that triggers your Lambda function every two hours. In the Lambda function, you call ReceiveMessage on the Queue until you get all messages, process them and afterward delete them from the Queue. The drawback is that there may be too many messages to process within 15 minutes so that's something you'd have to manage.
I've set a Lambda trigger with a SQS Queue. Lambda's reserved concurrency is set to 1000. However, there are millions of messages waiting in the queue need to be processed and it only invokes around 50 Lambdas at the same time. Ideally, I want SQS to trigger 1000 (or close to 1000) Lambda functions concurrently. Do I miss any configuration in SQS or Lambda? Thank you for any suggestion.
As stated in AWS Lambda developer guide:
...Lambda increases the number of processes that are reading batches by up to 60 more instances per minute. The maximum number of batches that an event source mapping can process simultaneously is 1,000.
So the behavior that you encountered (only invokes around 50 Lambdas at the same time) is actually expected.
If you are not using already, I would suggest doing batch processing in your lambda (so you can process 10 messages per invocation). If that is still not enough, you can potentially create more queues and lambdas to divide your load (considering that order is not relevant in your case), or move away from it and start polling the queue directly with EC2/ECS (which can increase your costs considerably however).
I need to process the elements of an SQS Queue sequentially using an AWS Lambda. I need to process the elements sequentially as I don't want to impact the DB when processing multiple elements in parallel. Note that this process is not time-sensitive.
I have noticed that AWS Lambda reads up to 5 batches when messages are available, then invokes 5 Lambdas in parallel, which I want to avoid. Lambda may increase that number to up to 1000 batches. https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html
Any help will be appreciated, thanks!
You just need to specify the --batch-size parameter to 1.
Other things to look at to achieve the desired outcome.
Make sure you're using FIFO SQS queue
Fetch 1 message at the time using batch-size
Configure a Dead Letter Queue (DLQ) for the SQS itself, so the messages that can't be processed after a several attempts are sent to DLQ SQS
Check the doco for details on each parameter supported by event source mapping
https://docs.aws.amazon.com/cli/latest/reference/lambda/create-event-source-mapping.html
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html
It appears that your requirement is to:
Process a single message at any time
No parallel processing
Order is not important
Time is not important
This can be achieved by setting a concurrency limit on the AWS function, which limits the number of parallel executions.
From Set Concurrency Limits on Individual AWS Lambda Functions:
This feature allows you to throttle a given function if it reaches a maximum number of concurrent executions allowed, which you can choose to set. This is useful when you want to limit traffic rates to downstream resources called by Lambda (e.g. databases).
Since order is not important, you can use a Standard Queue (instead of a FIFO queue).
I'm struggling to establish a queue in an AWS environment where the tasks are consumed by one Lambda / worker.
AWS Lambda automatically scales however I don't want that. The trouble is the function makes several complex changes to a database and there can be race conditions. Unfortunately this is out of my control.
Therefore it is easier to ensure there is one worker instead of solving the complex SQL issues. So what I want is whenever there is a messages in the queue, a single worker receives the messages and completes the tasks sequentially. Order does not matter.
Set the concurrency limit on the Lambda function to 1.
As you've noticed that 'built-in' SQS starts with a minimum of five workers and scales up.
I have two suggestions for you, however:
If you only have one shard, then kinesis (with a batch-size of one item), will ensure sequential, ordered, execution. This is because Kinesis is parallel by shard (and one shard can take 1000 records/second, so it's probably fine to only have one!) and the built-in lambda trigger takes a customisable batch size (which can be 1) and waits for it to complete before taking the next batch.
If you need to use SQS, then the "old" way of integrating (prior to the SQS trigger) will give you a "most likely one" and sequential execution. This is when you actually trigger your lambda on a Scheduled CloudWatch Event, which allows you to have a single lambda checking the queue every X (configured by you). The challenge here is if X is shorter than the amount of time it takes to process a message, then a second lambda will run in parallel (there are patterns such as having X = the timeout of your lambda, and just having your lambda run for 5 minutes going through the queue one message at a time).
I have 20K message in SQS queue. I also have a lambda will process the SQS messages, and put data into ElasticSearch server.
I have configured SQS as the lambda's trigger, and limited the Lambda's SQS batch size to be 10. I also limited the only one instance of the lambda can be run at a giving time.
However, sometime I see over 10K in-flight messages from the AWS console. Should it be max at 10 in-flight messages?
Because of this, the lambdas will only able to process 9K of the SQS message properly.
Below is a screen capture to show that I have limited the lambda to have only 1 instance running at a giving time.
I've been doing some testings and contacting AWS tech support at the same time.
What I do believe at the moment is that:
Amazon Simple Queue Service supports an initial burst of 5 concurrent function invocations and increases concurrency by 60 concurrent invocations per minute. Doc
1/ The thing that does that polling, is a separate entity. It is most likely to be a lambda function that will long-poll the SQS and then, invoke our lambda functions.
2/ That polling Lambda does not take into account any of our Receiver-Lambda at all. It does not care whether the function is running at max capacity or not, or how many max concurrency is available for the Receiver-Lambda
3/ Due to that combination. The behavior is not what we expected from the Lambda-SQS integration. And worse, If you have suddenly, millions of message burst in your queue. The Receiver-Lambda concurrency can never catch up with the amount of messages that the polling Lambda is sending, result in loss of work
The test:
Create one Lambda function that takes 30 seconds to return true;
Set that function's concurrency to 50;
Push 300 messages into the queue ( Visibility timeout : 10 Minutes, batch message count: 1, no re-drive )
The result:
Amount of messages available just increase gradually
At first, there are few enough messages to be processed by Receiver-Lambda
After half a minute, there are more messages available than what Receiver-Lambda can handle
These message would be discarded to dead queue. Due to polling Lambda unable to invoke Receiver-Lambda
I will update this answer as soon as I got the confirmation from AWS support
Support answer. As of Q1 2019, TL;DR version
1/ The assumption was correct, there was a "Poller"
2/ That Poller do not take into consideration of reserved concurrency
as part of its algorithm
3/ That poller have hard limit of 1000
Q2-2019 :
The above information need to be updated. Support said that the poller correctly consider reserved concurrency but it should be at least 5. The SQS-Lambda integration is still being updated and this answer will not. So please consult AWS if you get into some weird issues