I want to call AWS transcribe function from an AWS Lambda.
In that lambda handler, I want to start the transcription job but not wait for it to finish in a while loop since it will not be cost-efficient. I don't see any way for the transcription job finish to call another Lambda, or something like that, to store the transcription information in an s3 bucket for example.
Any idea how to solve this?
See Using Amazon EventBridge with Amazon Transcribe.
With Amazon EventBridge, you can respond to state changes in your Amazon Transcribe jobs by initiating events in other AWS services. When a transcription job changes state, EventBridge automatically sends an event to an event stream. You create rules that define the events that you want to monitor in the event stream and the action that EventBridge should take when those events occur. For example, routing the event to another service (or target), which can then take an action. You could, for example, configure a rule to route an event to an AWS Lambda function when a transcription job has completed successfully.
Another alternative is:
when you call StartTranscriptionJob, you supply an S3 bucket name and S3 object key that will receive the transcribed results
you can use the Amazon S3 Event Notifications feature to notify you or to automatically trigger a Lambda function
Related
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 see this question has been asked few times but has not been answered yet. Making another attempt.
What is the basic difference between an S3 event and Cloudwatch events ?
Is one is preferred over the other ?
Appreciate an answer.
Thanks !
S3 Event Notifications are for events that are specific to S3 buckets. S3 Events Notifications can publish events for
New object created
Object removal
Restore object
Reduced Redundancy Storage (RRS) object lost events
Replication events
And it can send notifications to:
SNS topics
SQS queues
Lambda functions
CloudWatch Events, and the associated (preferred, actually) service, Amazon EventBridge, are much broader, and apply to the entire AWS platform. CloudWatch and EventBridge use the same underlying CloudWatch Events API, but EventBridge has more features.
You can use CloudWatch Events/EventBridge to react to any event published by AWS CloudTrail as well as from a very long list of integrated AWS services. These events can also be published on a schedule using a cron-like schedule expression syntax. It can send notifications to more targets as well, including Amazon EC2, Kinesis data streams, ECS tasks, Systems Manager, and much more.
Generally, it's preferable to use EventBridge for anything other than S3. Since EventBridge shares the same underlying API as CloudWatch Events, any change you make to either one will show up in the other. You should use S3 Events for any of the events listed above (see the docs for up an to date list of events).
I have 30 Glue jobs that I want to run in parallel. If one job fails, others must continue. I started with step function, creating state machine that executes runner lambda function which on other hand triggers glue job depending on parameter(name of glue job). For one job there is decent amount of step function logic implemented(retry, error handling etc.)
Is there any way to execute state machine from other state machine? In that way I can have 30 parallel tasks that executes other state machines. If you have any suggestions please feel free to share.
AWS recommends using SNS for a fan out architecture to run parallel jobs from a single S3 event, as you get an overlap error if two lambdas try to use the same S3 event.
You basically send the S3 event to SNS and subscribe your 30 lambdas so they all trigger from the SNS notification (containing details of the S3 event) when it's published.
Create the Topic
Update the Topic Policy to allow Event Notifications from an S3 Bucket
Configure the S3 Bucket to send Event Notifications to the SNS Topic
Create the parallel Lambda functions, one for each job
Modify the Lambda functions to process SNS messages of S3 event notifications instead of the S3 event itself
https://aws.amazon.com/blogs/compute/fanout-s3-event-notifications-to-multiple-endpoints/
There is also another nice example with CloudFormation template https://aws.amazon.com/blogs/compute/messaging-fanout-pattern-for-serverless-architectures-using-amazon-sns/
I am new in AWS lambda area. I am creation a function which will consume Kinesis events. But I want to trigger my lambda function when specific event get push to kinesis (not for all events push to Kinesis). Is there a way that I can configure a filter upfront or my function needs to implement that filter after consuming all events?
One way of doing it is to split out the event you are interested on a separate stream either by:
Use Amazon Kinesis Analytics to copy records to an "event of interest" stream
Trigger another AWS Lambda function to copy records to an "event of interest" stream
Both of these in front of the lambda you currently have, and then connect that lambda to the new stream.
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.