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- Welcome.

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We are gonna start this
lesson by talking about,

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one of the most important

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and well integrated services
in the entire AWS ecosystem,

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AWS Lambda.

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So, what exactly is this service?

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It's deployed at a region scope.

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So, it's going to fit the
profile of many of the services

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that operate as sort of a
software as a service offering.

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Well, Lambda is designed to
deploy serverless functions

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on behalf of the customer

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and these can be deployed
using different run times.

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There are a list of supported run times

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that are managed by AWS
with specific versions

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of each of those run times.

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But there's also an option,

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where you can deploy your own run time,

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using a specific framework.

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And so let's talk about
these a little bit more

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when we dig into the actual configuration

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of a Lambda function.

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Lambda functions also support
what we call triggers,

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which determine the conditions
under which the function

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is invoked.

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And these triggers can range
through so many different areas

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of the AWS ecosystem
from EventBridge rules

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to S3 bucket actions, to SNS topics,

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to any number of other types of triggers.

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So, some of the different
features of Lambda functions.

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First one I've mentioned
triggers the ability

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to invoke a function,

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based on an event,
action or time schedule.

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We have concurrency,

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which means if you have multiple triggers,

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happening at the same time,

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they can invoke multiple parallel copies

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of the same function,

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which gives it a great ability
to scale according to load.

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But you also wanna make
sure that you are optimizing

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for cost and potentially
avoiding any unexpected load

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on backend resources,

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so you can configure
the number of functions

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that can be invoked
concurrently using this feature.

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The next feature, scaling,

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I mentioned already with the concurrency.

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So, if you have multiple actions
invoking the same trigger,

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you can choose to have multiple copies

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of your function running at the same time.

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And this scaling is
going to be very quick,

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especially when compared to
EC2 or container-based scaling.

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And finally if you want to
deploy your Lambda function,

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so that it has a footprint
in one of your VPC subnets,

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it supports that.

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There are some limitations
with Lambda functions,

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including timeouts that have
a maximum of 15 minutes.

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So, it means you can't just execute,

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a Lambda function forever.

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It does need to have a specific end point.

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The different run times

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that are supported are for the most part,

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all the popular languages
that are used today,

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Node.js, Python, Ruby, Java, Go, .NET

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and the ability to
create a custom runtime.

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So, if you wanted to execute Fortran code

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in a Lambda function,

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there's a way to make that happen.

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Now, in terms of provisioning
a Lambda function,

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there are a number of parameters

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that you need to take into account.

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Some of these are required,

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others are optional.

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Triggers are not actually required.

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You can invoke a Lambda function
just by using the AWS CLI.

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The function code required,

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so the actual zip file

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or pasting code into the
console and so forth.

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Permissions are required.

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And if you don't provide them,

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the Lambda function will create them,

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using a default template.

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This is just an IAM role that
can be unique per function.

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And some of the default
permissions include the ability

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to push metrics into CloudWatch

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and logs into CloudWatch Logs.

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Tags are optional.

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The compute resources
while there's a default,

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you get to choose the amount of memory

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that is allocated to the function

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and that in turn determines the CPU

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that it can take advantage of.

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Network placement is optional.

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There are a number of debugging
and error handling options,

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including the ability to use
X-Ray tracing of your code.

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And that afore mentioned
concurrency option

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is also something that
you want to consider

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that even though it's optional,

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you may want to have a hard
coded value for every function,

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so that it can't be invoked
more often than what you expect.