According to AWS retry documentation
"
Poll-based (or pull model) event sources that are stream-based: These consist of Kinesis Data Streams or DynamoDB. When a Lambda function invocation fails, AWS Lambda attempts to process the erring batch of records until the time the data expires, which can be up to seven days.
The exception is treated as blocking, and AWS Lambda will not read any new records from the shard until the failed batch of records either expires or is processed successfully. This ensures that AWS Lambda processes the stream events in order.
"
But we need different functionality, We are inserting records in mysql aurora and if anything is fail in lambda then same event processed multiple time from kinesis and we end up to have duplicates records in database. How we can handle this.
thanks
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I am using Lambda function to read data from dyanmoDB streams. Lambda read items from stream and invokes lambda function once for each batch. Lambda invokes lambda function synchronously using event source mapping.
From what i understand from aws docs is, Lambda invokes a lambda function for each batch in the stream. Suppose there are 1000 items in stream instantly and I configures my lambda function to read 100 items in a batch.
So will it invoke 10 lambda function concurrently to process 10 batch of 100 items each?
I am learning AWS. Is my understanding correct? if yes what does synchronously invoked mean?
DynamoDB uses shards* to partition the data inside a table. The data that will be stored in each shard is defined by the HashKey of the table. DynamoDB streams will trigger AWS Lambda for each shard that was updated and aggregate the shard records in a batch. So the number of records in each batch will depend on the number of records updated in each shard. They can be different of course.
Synchronously invoked means that the service that triggered the function will wait until the function ends to finish its own execution. When you trigger asynchronous, you send a request and forget about it. If the downstream function successfully process the stream or not is not in control of the upstream service. DynamoDB invokes Lambda Function synchronously and waits while it works. If it ends successfully, it will mark the stream as processed. If it ends with a failure it will retry a few more times. This is important to allow at least once processing of ddb streams.
*Shards are different partitions of the database. They allow DynamoDB to process parallel queries and updates. Normally they reside in different storages/availability zones.
I'm in the process of writing a Lambda function that processes items from a DynamoDB stream.
I thought part of the point behind Lambda was that if I have a large burst of events, it'll spin up enough instances to get through them concurrently, rather than feeding them sequentially through a single instance. As long as two events have a different key, I am fine with them being processed out of order.
However, I just read this page on Understanding Retry Behavior, which says:
For stream-based event sources (Amazon Kinesis Data Streams and DynamoDB streams), AWS Lambda polls your stream and invokes your Lambda function. Therefore, if a Lambda function fails, AWS Lambda attempts to process the erring batch of records until the time the data expires, which can be up to seven days for Amazon Kinesis Data Streams. The exception is treated as blocking, and AWS Lambda will not read any new records from the stream until the failed batch of records either expires or processed successfully. This ensures that AWS Lambda processes the stream events in order.
Does "AWS Lambda processes the stream events in order" mean Lambda cannot process multiple events concurrently? Is there any way to have it process events from distinct keys concurrently?
With AWS Lambda Supports Parallelization Factor for Kinesis and DynamoDB Event Sources, the order is still guaranteed for each partition key, but not necessarily within each shard when Concurrent batches per shard is set to be greater than 1. Therefore the accepted answer needs to be revised.
Stream records are organized into groups, or shards.
According to Lambda documentation, the concurrency is achieved on shard-level. Within each shard, the stream events are processed in order.
Stream-based event sources : for Lambda functions that process Kinesis
or DynamoDB streams the number of shards is the unit of concurrency.
If your stream has 100 active shards, there will be at most 100 Lambda
function invocations running concurrently. This is because Lambda
processes each shard’s events in sequence.
And according to Limits in DynamoDB,
Do not allow more than two processes to read from the same DynamoDB
Streams shard at the same time. Exceeding this limit can result in
request throttling.
We are contemplating using Amazon web services for our project. Wherein the upstream flow will push the messages into the kinesis and later those messages will be fed into the lambdas, those messages before processing are going to be in order. As per my understanding, the AWS lambdas will scale out horizontally based on the volume of the messages. We have a volume of 400 messages per second, which means AWS lambda will respond to message volume and will instantiate new processes on separate containers to leverage parallelism and in order to achieve parallelism, ordering has to be compromised. So in case of 10 messages, which were ordered, hit the lambda functions and one function takes more time than another, a new function will be provisioned in some container by the AWS to serve the request.
Is the final output going to be in order after all of this processes?
Any help is appreciated.
Thanks.
If you are using Amazon Kinesis, then you can use a Data Transformation to trigger an AWS Lambda function on each incoming record.
This allows the record to be transformed or deleted, before continuing through the Firehose. Thus, records can be processed by Lambda while remaining in the same order. The final data can be delivered to Amazon S3, Amazon Redshift, Amazon Elasticsearch Service or Splunk.
If your application is consuming records from Amazon Kinesis directly (instead of via Firehose), then records will be consumed in order by your application.
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.
Can someone explain what happens to events when a Lambda is subscribed to Kinesis item create events. There is a limit of 100 concurrent requests for an account in AWS, so if 1,000,000 items are added to kinesis how are the events handled, are they queued up for the next available concurrent lambda?
From the FAQ http://aws.amazon.com/lambda/faqs/
"Q: How does AWS Lambda process data from Amazon Kinesis streams and Amazon DynamoDB Streams?
The Amazon Kinesis and DynamoDB Streams records sent to your AWS Lambda function are strictly serialized, per shard. This means that if you put two records in the same shard, Lambda guarantees that your Lambda function will be successfully invoked with the first record before it is invoked with the second record. If the invocation for one record times out, is throttled, or encounters any other error, Lambda will retry until it succeeds (or the record reaches its 24-hour expiration) before moving on to the next record. The ordering of records across different shards is not guaranteed, and processing of each shard happens in parallel."
What this means is if you have 1M items added to Kinesis, but only one shard, the throttle doesn't matter - you will only have one Lambda function instance reading off that shard in serial, based on the batch size you specified. The more shards you have, the more concurrent invocations your function will see. If you have a stream with > 100 shards, the account limit you mention can be easily increased to whatever you need it to be through AWS customer support. More details here. http://docs.aws.amazon.com/lambda/latest/dg/limits.html
hope that helps!