I have an event-driven data pipeline on AWS which processes millions of files. each file in my s3 bucket triggers a lambda. the lambda processes the data in the file and dumps the processed data to an s3 bucket, which in turn triggers another lambda etc.
Downstream of my pipeline I have a lambda which creates an Athena database and table. This lambda is triggered as soon as an object is dumped under the appropriate key of my s3 bucket. It's enough to call this lambda that creates my Athena database and table only once.
how can I avoid letting my labda being triggered multiple times?
This is your existing flow:
S3 trigger Lambda once new file arrives (event driven)
"Lambda to process the file" and then deliver to another S3
The other S3 also triggers another lambda
Your step 3 is not even driven, you are enforcing an event.
I suggest you the following flow:
S3 trigger Lambda once new file arrives (event driven)
"Lambda to process the file" and then deliver to another S3
Only two steps, the lambda that process the file should use Athena SDK and check if the desired table already exists, and only if not, then you call the Lambda that creates the Athena table. The delivery S3 should not trigger the lambda for Athena.
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TechStack: salesforce data ->Aws Appflow->s3 ->databricks job
Hello! I have an appflow flow that is grabbing salesforce data and uploading it to s3 in a folder with multiple parquet files. I have an lambda that is listening to the prefix where this folder is being dropped. This lambda then triggers a databricks job which is an ingestion process I have created.
My main issue is that when these files are being uploaded to s3 it is triggering my lambda 1 time per file that is uploaded, and was curious as to how I can have the lambda run just once.
Amazon AppFlow publishes a Flow notification - Amazon AppFlow when a Flow is complete:
Amazon AppFlow is integrated with Amazon CloudWatch Events to publish events related to the status of a flow. The following flow events are published to your default event bus.
AppFlow End Flow Run Report: This event is published when a flow run is complete.
You could trigger the Lambda function when this Event is published. That way, it is only triggered when the Flow is complete.
I hope I've understood your issue correctly but it sounds like your Lambda is working correctly if you have it setup to run every time a file is dropped into the S3 bucket as the S3 trigger will call the Lambda upon every upload.
If you want to reduce the amount of time your Lambda runs is setup an Event Bridge trigger to check the bucket for new files you could run this off an Event Bridge CRON to ping the Lambda on a defined schedule. You could then send all the files to your data bricks block in bulk rather than individually.
I have gone through couple of stackoverflow questions regarding hourly backups from DDB to S3 where the best solution turned out to be to enable DDB Stream, subscribe lambda function and push to S3.
I am trying to understand if directly pushing from Lambda to S3 is fine or from Lambda to Kinesis Firehose and then to S3. Can someone share what is the advantage if we introduce Firehose in between. We anyways trigger lambda only after specific batch window that implies we are already buffering there.
Thanks in advance.
Firehose gives you the possibility to convert and compress your data. In addition you can directly attach a Glue Metadata table, so you can query your data with Athena.
You can write a Lambda function that reads a DynamoDB table, gets a result set, encodes the data to some format (ie, JSON), then place that JSON into an Amazon S3 bucket. You can use scheduled events to fire off the Lambda function on a regular schedule.
Here in AWS tutorial that shows you how to use scheduled events to invoke a Lambda function:
Creating scheduled events to invoke Lambda functions
This AWS tutorial also shows you how to read data from an Amazon DynamoDB table from a Lambda function.
I have a use case, I have a list of pdf files stored in S3 Bucket, I have listed them and push them to SQS for Text Extraction, Created one Lambda for processing those files by providing bucket information and TextExtraxtion Information of AWS.
The issue is, Lambda is getting Timeout, as SQS trigger multiple lambda instance of all files and all of them waiting for Text Extract Service.
Lambda to trigger one by one, for all SQS message(FileName) so that Timeout does not occur, As we do have a limit for accessing AWS TextExtract
Processing 100+ files is a time consuming task, I would suggest taking no more than 10 files per Lambda execution.
Use SQS with Lambda as an event source.
https://dzone.com/articles/amazon-sqs-as-an-event-source-to-aws-lambda-a-deep
My current workflow is as follows:
User drops file into s3 bucket -> s3 bucket triggers event to lambda -> lambda processes the file in s3 bucket. It also invokes other lambdas.
I want to handle the scenario where multiple users will drop files in the s3 bucket simultaneously. I want to process the files such that the file put first gets processed first. To handle this, I want the lambda to process each file in a gap of 15 minutes (for example).
So, I want to use SQS to queue the input file drop events. S3 can trigger an event to SQS. A cloudwatch event can trigger a lambda in every 15 minutes, and the lambda can poll the SQS queue for the first s3 file drop event, and process it.
The problem with SQS is that Standard SQS queues do not adhere to order, and FIFO SQS queues are not compatible with S3 (Ref: Error setting up notifications from S3 bucket to FIFO SQS queue due to required ".fifo" suffix)
What approach should I use to solve this problem?
Thanks,
Swagatika
You could have Amazon S3 trigger an AWS Lambda function, which then pushes the file info into a FIFO Amazon SQS queue.
There is a new capability where SQS can trigger Lambda, but you'd have to experiment to see how/whether that works with FIFO queues. If it works well, that could eliminate the '15 minutes' thing.
Using Lambda to move files from an S3 to our Redshift.
The data is placed in the S3 using an UNLOAD command directly from the data provider's Redshift. It comes in 10 different parts that, due to running in parallel, sometimes complete at different times.
I want the Lambda trigger to wait until all the data is completely uploaded before firing the trigger to import the data to my Redshift.
There is an event option in Lambda called "Complete Multipart Upload." Does the UNLOAD function count as a multipart upload within Lambda? Or would the simple "POST" event not fire until all the parts are completely uploaded by the provider?
There is no explicit documentation confirming that Redshift's UNLOAD command counts as a Multipart upload, or any confirming that the trigger will not fire until the data provider's entire upload is complete.
For Amazon S3, a multi-part upload is a single file, uploaded to S3 in multiple parts. When all parts have been uploaded, the client calls CompleteMultipartUpload. Only after the client calls CompleteMultipartUpload will the file appear in S3.
And only after the file is complete will the Lambda function be triggered. You will not get a Lambda trigger for each part.
If your UNLOAD operation is generating multiple objects/files in S3, then it is NOT an S3 "multi-part upload".