I have a scheduled error handling lambda, I would like to use Serverless technology here as opposed to a spring boot service or something.
The lambda will read from an s3 bucket and process accordingly. The problem is at times the s3 bucket may have high volume of data to be processed. long running operations aren't suited to lambdas.
One solution I can think of is have the lambda read and process one item from the bucket and on success trigger another instance of the same lambda unless the bucket is empty/fully-processed. The thing i don't like is that this is synchronous and quite slow. I also need to be conscious of running too many lambdas at the same time as we are hitting a REST endpoint as part of the error flow and don't want to overload it with too many requests.
I am thinking it would be nice to have maybe 3 instances of the lambdas running at the same time until the bucket is empty but not really sure, I am wondering if anyone has any nice patterns that could be used here or suggestions on best practices?
Thanks
Create a S3 bucket for processing your files.
Enable a trigger S3 -> Lambda, on every new file in the bucket lambda will be invoked to process the file, every file is processed separately. https://docs.aws.amazon.com/AmazonS3/latest/user-guide/enable-event-notifications.html
Once the file is processed you could either delete or move file to other place.
About concurrency please have a look at provisioned concurrency https://docs.aws.amazon.com/lambda/latest/dg/configuration-concurrency.html
Update:
As you still plan to use a scheduler lambda and S3
Lambda reads/lists only the filenames and puts messages into SQS to process the file.
A new Lambda to consume SQS messages and process the file.
Note: I would recommend using SQS initially if the files/messages are not so big, it has built it recovery mechanics, DLQ , delays, visibility etc which you could benefit more than the simple S3 storage, second way is just create message with file reference and still use SQS.
I'd separate the lambda that is called by the scheduler from the lambda that is doing the actual processing. When the scheduler calls the first lambda, it can look at the contents of the bucket, then spawn the worker lambdas to process the objects. This way you have control over how many objects you want per worker.
Given your requirements, I would recommend:
Configure an Amazon S3 Event so that a message is pushed to an Amazon SQS queue when the objects are created in the S3 bucket
Schedule an AWS Lambda function at regular intervals that will:
Check that the external service is working
Invoke a Lambda function to process one message from the queue, and keep looping
The hard part would be throttling the second Lambda function so that it doesn't try to send all request at once (which might impact that external service).
You could probably do this by using a Step Function to trigger Lambda and then, if it was successful, trigger another Lambda function. This could even be done in parallel, such as allowing up to three parallel Lambda executions. The benefit of using Step Functions is that there is no cost for "waiting" for each Lambda to finish executing.
So, the Step Function flow would be something like:
Invoke a "check external service" Lambda function
If it fails, then quit the flow
Invoke the "processing" Lambda function
Get one message
Process the message
If successful, remove the message from the queue
Return success/fail
If it was successful, keep looping until the queue is empty
Related
My architecture allows files to be put in s3 for which Lambda function runs concurrently. However, the files being put in S3 are somehow overwriting because of some other process in a gap of milliseconds. Those multiple put events for the same file are causing the lambda to trigger multiple times for the same event.
Is there a threshold I can set on s3 events (something that doesn't trigger the lambda multiple times for the same file event.)
Or what kind of s3 event only occurs when a file is created and not updated?
There is already a code in place which checks if the trigger file is present. if not, it creates the trigger file. But that is also of no use since the other process is very fast to put files is s3.
Something like this below -
try:
s3_client.head_object(Bucket=trigger_bucket, Key=trigger_file)
except ClientError as _:
create_trigger_file(
s3_client, trigger_bucket, trigger_file
)
You could configure Amazon S3 to send events to an Amazon SQS FIFO (first-in-first-out) queue. The queue could then trigger the Lambda function.
The benefit of using a FIFO queue is that each message has a Message Group ID. A FIFO queue will only provide one message to the AWS Lambda function per Message Group ID. It will not send another message with the same Message Group ID until the earlier one has been fully processed. If you set the Message Group Id to be the Key of the S3 object, then it would effectively have a separate queue for each object created in S3.
This method would allow Lambda functions to run in parallel for different objects, but for each particular Key there would only be a maximum of one Lambda function executing.
It appears your problem is that multiple invocations of the AWS Lambda function are attempting to access the same files at the same time.
To avoid this, you could modify the settings on the Lambda function to Manage Lambda reserved concurrency - AWS Lambda by setting the reserved concurrency to 1. This will only allow a single invocation of the Lambda function to run at any time.
I guess the problem is that your architecture needs to write to the same file. This is not scalable. From the documentation:
Amazon S3 does not support object locking for concurrent writers. If two PUT requests are simultaneously made to the same key, the request with the latest timestamp wins. If this is an issue, you must build an object-locking mechanism into your application.
So, think about your architecture. Why do you have a process that wants to process multiple times to the same file at the same time? The lambda's that create these S3 files, do they need to write to the same file? If I understand your use case correctly, every lambda could create an unique file. For example, based on the name of the PDF you want to create or with some timestamp added to it. That ensures you don't have write collisions. You could create lifecycle rules on the S3 bucket to delete the files after a day or so, such that you don't increase your storage costs too much. Or have a lambda delete the file when it is finished with it.
I have a service that uses a JSON file on an S3 bucket for its configuration.
I would like to be able to modify this file, but I'm going to run into a concurrency issue as multiple administrators will be able to write in this file at the same time.
I'm going to use an SNS Topic to trigger a lambda that will write the config changes.
For the moment, I'm going to check the queue every minute and then handle the messages, so that I am sure that I don't have multiple instances of lambda running at the same time and writing in the same file.
Is there any way to have an SNS topic to trigger a lambda function for each message, and then wait for this message to be handled and then move on to the next one?
Cheers,
Julien
You can achieve this by setting the max concurrent executions of your Lambda function to 1. See the documentation for more details about managing concurrency for Lambdas.
i have an aws lambda function to do some statistics on over 1k of stock tickers after market close. i have an option like below.
setup a cron job in ec2 instance and trigger a cron job to submit 1k http request asyn (e.g. http://xxxxx.lambdafunction.xxxx?ticker= to trigger the aws lambda function (or submit 1k request to SNS and let lambda to pickup.
i think it should run fine, but much appreciate if there is any serverless/PaaS approach to trigger task
On top of my head, Here are a couple of ways to achieve what you need:
Option 1: [Cost-Effective]
Post all the ticks to AWS FIFO SQS queue.
Define triggers on this queue to invoke lambda function.
Result: Since you are posting all the events in FIFO queue that maintains the order, all the events will be polled sequentially. More-over SQS to lambda trigger will help you scale automatically based on the number of message in the queue.
Option 2: [Costly and can easily scale for real-time processing]
Same as above, but instead of posting to FIFO queue, post to Kinesis Stream.
Enable Kinesis stream to trigger lambda function.
Result: Kinesis will ensure the order of event arriving in the stream and lambda function invocation will be invoked based on the number of shards in the stream. This implementation scales significantly. If you have any future use-case for real-time processing of tickers, this could be a great solution.
Option 3: [Cost Effective, alternate to Option:1]
Collect all ticker events(1k or whatever) and put it into a file.
Upload this file to AWS S3 bucket.
Enable S3 event notification to trigger proxy lambda function.
This proxy lambda function reads the s3 file and based on the total number of events in the file, it will spawn n parallel actor lambda function.
Actor lambda function will process each event.
Result: Easy to implement, cost-effective and provides easy scaling based on your custom algorithm to distribute the load in the proxy lambda function.
Option 4: [All-serverless]
Write a lambda function that gets the list of tickers from some web-server.
Define an AWS cloud watch rule for generating events based on cron/frequency.
Add a trigger to this cloudwatch rule to invoke proxy lambda function.
Proxy lambda function will use any combination of above options[1, 2 or 3] to trigger the actor lambda function for processing the records.
Result: Everything can be configured via AWS console and easy to use. Alternatively, you can also write your AWS cloud formation template to generate all the required resources in a single go.
Having said that, now I will leave this up to you to choose the right solution based on your business/cost requirements.
You can use lambda fanout option.
You can follow these steps to process 1k or more using serverless aproach.
1.Store all the stock tickers in a S3 file.
2.Create a master lambda which will read the s3 file and split the stocks in groups of 10.
3. Create a child lambda which will make the async call to external http service and fetch the details.
4. In the master lambda Loop through these groups and invoke 100 child lambdas passing in each group and return the results to the
Master lambda
5. Collect all the information returned from the child lambdas and continue with your processing here.
Now you can trigger this master lambda at the end of markets everyday using CloudWatch time based rule scheduler.
This is a complete serverless approach.
I have a Lambda function that’s triggered by a PUT to an S3 bucket.
I want to limit this Lambda function so that it’s only running one instance at a time – I don’t want two instances running concurrently.
I’ve had a look through the Lambda configuration and docs, but I can’t see anything obvious. I can about writing my own locking system, but it would be nice if this was already a solved problem.
How can I limit the number of concurrent invocations of a Lambda?
AWS Lambda now supports concurrency limits on individual functions:
https://aws.amazon.com/about-aws/whats-new/2017/11/set-concurrency-limits-on-individual-aws-lambda-functions/
I would suggest you to use Kinesis Streams (or alternatively DynamoDB + DynamoDB Streams, which essentially have the same behavior).
You can see Kinesis Streams as as queue. The good part is that you can use a Kinesis Stream as a Trigger to you Lambda function. So anything that gets inserted into this queue will automatically be passed over to your function, in order. So you will be able to process those S3 events one by one, one Lambda execution after the other (one instance at a time).
In order to do that, you'll need to create a Lambda function with the simple purpose of getting S3 Events and putting them into a Kinesis Stream. Then you'll configure that Kinesis Stream as your Lambda Trigger.
When you configure the Kinesis Stream as your Lambda Trigger I suggest you to use the following configuration:
Batch size: 1
This means that your Lambda will be called with only one event from Kinesis. You can select a higher number and you'll get a list of events of that size (for example, if you want to process the last 10 events in one Lambda execution instead of 10 consecutive Lambda executions).
Starting position: Trim horizon
This means it'll behave as a queue (FIFO)
A bit more info on AWS May Webinar Series - Streaming Data Processing with Amazon Kinesis and AWS Lambda.
I hope this helps anyone with a similar problem.
P.S. Bear in mind that Kinesis Streams have their own pricing. Using DynamoDB + DynamoDB Streams might be cheaper (or even free due to the non-expiring Free Tier of DynamoDB).
No, this is one of the things I'd really like to see Lambda support, but currently it does not. One of the problems is that if there were a lot of S3 PUT operations happening AWS would have to queue up all the Lambda invocations somehow, and there is currently no support for that.
If you built a locking mechanism into your Lambda function, what would you do with the requests you don't process due to a lock? Would you just throw those S3 notifications away?
The solution most people recommend is to have S3 send the notifications to an SQS queue, and then have your Lambda function scheduled to run periodically, like once a minute, and check if there is an item in the queue that needs to be processed.
Alternatively, have S3 send the notifications to SQS and just have a t2.nano EC2 instance with a single-threaded service polling the queue.
I know this is an old thread, but I ran across it trying to figure out how to make sure my time sequenced SQS messages were processed in order coming out of a FIFO queue and not getting processed simultaneously/out-of-order via multiple Lambda threads running.
Per the documentation:
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 your
function returns an error, the function attempts all retries 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.
Link: https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html
So essentially, as long as you use a FIFO queue and submit your messages that need to stay in sequence with the same MessageGroupID, SQS/Lambda automatically handles the sequencing without any additional settings necessary.
Have the S3 "Put events" cause a message to be placed on the queue (instead of involving a lambda function). The message should contain a reference to the S3 object. Then SCHEDULE a lambda to "SHORT POLL the entire queue".
PS: S3 events can not trigger a Kinesis Stream... only SQS, SMS, Lambda (see http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#supported-notification-destinations). Kinesis Stream are expensive and used for real-time event handling.
I have a very simple task to perform on many files as they are uploaded to an S3 bucket. The task can easily be performed by a simple Lambda function, and the resulting files written back to S3.
Connecting this Lambda function to an event triggered by S3 events is trivial, but I have some added complications:
The files must be processed (if the Lambda function fails then the operation needs to be attempted again, until it succeeds, given a reasonable amount of retries)
I need to know when all files have finished being processed
It seems to me that a putting a message queue in the system (SQS) seems to be a sensible option. That way, any failed messages will be retried after the Visibility timeout. Also I will be able to query the queue length to see if there are still operations in flight.
I can connect S3 events to an SQS queue. The problem is that I cannot directly trigger Lambda invocations from an SQS queue.
File --> S3 --> SQS --??--> Lambda ----> S3
\
`-> If successful delete message from SQS
I could use Kinesis to take messages from the queue and trigger lambda functions, but this seems a bit overkill? Likewise, I could have dedicated instances polling the queue and operating on it, but for such a simple function, I don't really want to have to run a cluster of instances.
Is there a good design pattern for this?
For S3 to Lambda directly, I think you're right that you need to worry about the lambda function not correctly working / crashing and the processing for that particular S3 object not taking place.
That being said, I would suggest doing S3 to Lambda directly coupled with a mechanism that allows you to detect and re-request processing of items that failed to process (i.e. a nanny). You could periodically schedule a job through Lambda (http://docs.aws.amazon.com/lambda/latest/dg/with-scheduled-events.html) that looks for old items (older than a certain threshold) that were not processed. Most processing is going to be done on the direct (happy) path.
One way this could be done would be using SQS with delayed delivery and with lambda scheduled function.
So you would have
File --> S3 --> Lambda --> S3 (happy path)
\-> SQS (delayed)
\-> Lambda(scheduled job) -> S3 (unhappy path)
In the scheduled job you could check if processing happened and just ack the message. The scheduled job can trigger the same Lambda job used for the normal processing.
Also, don't forget to configure a dead letter queue for the SQS queue to catch things that fail processing over multiple retries.
Keep in mind that you could potentially bypass SQS completely if you have a way of figuring out what was processed and what was not process by inspecting the S3 bucket.
Also keep in mind that all of the above assumes that it okay if you process something that was already process (i.e. edge cases involving timeouts on lamda function, delay on the queue and sqs delivery)