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".
Related
What is the easiest way to automatically ingest csv-data from a S3 bucket into a Timestream database ?
I have a s3-bucket which continuasly is generating csv files inside a folder structure. I want to save these files inside a timestream database so i can visualize them inside my grafana instance.
I already tried to do that via a Glue crawler but that wont wont for me. Is there any workaround or tutorial on how to solve this task ?
I do this using a Lambda function, an SNS topic and a queue.
New files in my bucket triggers a notification on an SNS Topic
The notification gets added to an SQS queue.
The lambda function consumes the queue, recovers the bucket and key of the new s3 object, downloads the csv file, does some processing and ingests the data into timestream. The lambda is implemented in Python.
This has been working ok, with the caveat that large files may not ingest fully within the lambda 15 minute limit. Timestream is not super fast. It gets better by using multi-valued records, as well as using the "common attributes' feature of the timestream client in boto3.
(it should be noted that the lambda can be triggered directly by the S3 bucket, if one prefers. Using a queue allows a bit more flexibility, such as being able to manually add files to the queue for reprocessing)
I am new to some AWS services.
Our source team uploads 2 files in a S3 bucket at some interval. They run one pipeline and upload a file in S3. Then they run another process and upload another file in S3. Both their processes run in parallel so files are not uploaded in a specific order.
we need to trigger our Lambda function when both files are uploaded in S3.
We tried to trigger lambda from S3 events and SNS but it trigger lambda twice because of 2 S3 events.
What is the best approach to handle this? Any suggestion would be appreciated.
I am working on a requirement, where i am doing multipart upload of the csv file from on prem server to S3 Bucket.
To achieve this using AWS Lambda I create a presigned url and use this url i am uploading the csv file. Now, once i have the file in AWS S3, i want it to be moved to AWS RDS Oracle DB. Initially i was planning to use AWS Lambda for this.
So once i have the file in S3, it triggers lambda(s3 event) and lambda will push this file to RDS. But with this the issue is with the file Size(600 MB).
I am looking for some other way, where whenever there is a file uploaded to S3, it should trigger any AWS service and that service will push this csv file to RDS. I have gone through AWS DMS/Data Pipeline, but not able to find any way to automate this migration
I need to automate this migration on every s3 upload, that is also cost effective.
Setup S3 Integration and build SPROCS to help automate load. Details found here.
UPDATE:
Looks like you don't even need to create a SPROC. You can just use the RDS procedure as outlined here. You would then just create an event-driven lambda function that is triggered on a given S3 event--e.g. on object PUT(), POST(), COPY, etc..--which passes the S3 metadata requisite to access the event object. Here is a simple Python example of what that Lambda and config might look like. You would then use the metadata passed on the trigger event--as outlined in the Python example--to dynamically create your procedure call then execute that procedure. You can also add the ensuing workflow logic that meets your requirements--i.e. TASK_ID fetch & operational handling, monitoring, etc...--to the same lambda function or separate those concerns by adding additional lambdas. Hope this helps!
I have a Lambda function which is triggered after put/post event of S3 bucket. This works fine if there is only one file uploaded to S3 bucket.
However, at times there could be multiple files uploaded which can take upto 7 minutes to complete the upload process. This triggers my lambda function multiple times which adds overhead of handling this from the code.
Is there any way to either trigger the lambda only once for the complete upload or add delay in the function and avoid multiple execution of Lambda function?
There is no specific interval when the files will be uploaded to S3 hence could not use scheduler.
Delay feature was added for Lambda that has Kinesis or DynamoDB Event Sources recently. But it's not supported for S3 events.
You can send events from S3 to SQS. Then your Lambda will consume SQS events. It consumes them in batch by default.
It seems Multi Part Upload is being used here from the client.
Maybe a duplicate of this? - AWS Lambda and Multipart Upload to/from S3
An alternative might be to have your Lambda function check for existence of all required files before moving on to the action you need to take. The Lambda function would still fire each time, but would exit quickly if not all files have been received yet.
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.