ANTHONY BUSHONG: Mapping the planet,
tracking space debris, delivering breaking news.
Use cases like these and so many more
have been built atop Cloud Run, our platform
for serverless containers in Google Cloud.
So stick around to learn how we're moving serverless forward
with Cloud Run.
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At its inception, Cloud Run was ideal for web services.
You issue HTTP requests, the service
does something and returns a response.
For workloads like these, Cloud Run
was great in that it automatically
scaled the web service to meet traffic demands of any size.
And it still is, but industries of all kinds wanted that same
serverless experience-- write code,
let the platform scale and do the rest--
for all workloads, not just web serving.
Examples of these include regularly
scheduled jobs and scripts, and these jobs and scripts
could range from orchestrating internal workflows
to doing lightweight data transformations.
They might even run on a schedule,
or they might run when an event happens,
and they exit when they're done.
Now technically, this was possible with services
in Cloud Run.
Your web service could respond to an HTTP request,
for example, by running the work typically done by the job.
But what if the job runs longer than a typical timeout
for web services?
You'd need to do things like slice the job into smaller
pieces, and workarounds like these
require code changes, making it harder
to adopt a serverless platform.
That's why we're building atop this model
and moving serverless forward in Cloud Run.
With a couple of new features, we now
can support workloads like data transformations, database
migrations, even workloads that need
network file system access.
Let's dive in.
Two features in Cloud Run expand our serverless support
for more workload types.
The first is Cloud Run Jobs.
This allows you to bring containers with long run
to completion jobs.
No need to hack around HTTP requests and rewrite code.
The second is the second generation of our Cloud Run
execution environment.
This environment provides faster CPU and network performance
to intensive workloads that need it and lets
you mount network file systems.
It even brings full compatibility
with all Linux features.
Let's dig a bit deeper into each.
With Cloud Run Jobs, imagine you have old legacy scripts that
do batch processing.
With Cloud Run Jobs, you don't need workarounds
like wrapping it with a web server.
Instead, as long as you can containerize them,
you can make them serverless.
With Cloud Run Jobs, these scripts
can spin up automatically on a schedule,
run tasks in parallel, and spin down upon completion.
This serverless experience gives you
more time to focus on the business
with the simplicity of provisioning
via a single gcloud command.
Now let's dig into the second generation execution
environment.
Right off the bat, this environment
provides faster CPU and network performance
for CPU intensive workloads or workloads
sensitive to packet loss.
But in addition to this, it also supports all kernel level
Linux features-- syscalls, cgroups, namespaces,
you name it.
This increases the number of migration targets of legacy
software that used to not be able to run
in Cloud Run due to some missing Linux support.
Another important point-- mounting file systems,
whether or not they are in Google Cloud,
is possible in the second generation environment.
This means that workloads can use NFS, SMB, CIFS, or FUSE
to access data they might need to process,
even if it's outside of Google Cloud.
With this support, workloads won't need a rewrite
at the storage layer.
Taking advantage of all the benefits
of the second generation execution environment
is baked into a simple argument you provide
when deploying to Cloud Run.
With these two features, we're enabling more kinds
of workloads to do amazing things in Cloud Run,
moving serverless forward.
To try out Cloud Run Jobs or the new Cloud Run second generation
environment, check out the documentation
in the description below.
And if you want to be notified of what the Cloud Run
team comes up with next, be sure to like
and subscribe to the Google Cloud Tech
channel for future updates.
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