New serverless pattern - lambda-esm-kinesis-filters-terraform#1625
Merged
mavi888 merged 4 commits intoaws-samples:mainfrom Sep 4, 2023
Conversation
Closed
Contributor
|
Thanks for submitting your pattern. You can share your pattern using this link :) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Issue #, if available:
Description of changes:
This pattern demonstrates the ability to filter Amazon Kinesis events so that only a subset of all events is sent to an AWS Lambda function for processing. Demo stack will create a single Amazon Kinesis Data Stream (kinesis_stream_lambda_esm) and two AWS Lambda functions (esm_lambda_with_filter and esm_lambda_with_no_filter), one with specific filter criteria and one without any filtering criterias, that are subscribed to that stream using different filter configurations.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.