When you say serverless to someone, the first conclusion they jump to is that you are running your code without any servers.
This can be quite a valid conclusion if you are using a public cloud service like AWS, but when it comes to running in your own environment, you can’t avoid having to run on a server of some sort.
This blog post is an extract from Kubernetes for Serverless Applications by Russ McKendrick.
Before we discuss what we mean by serverless and Functions as a Service, we should discuss how we got here. As people who work with me will no doubt tell you, I like to use the pets versus cattle analogy a lot as this is quite an easy way to explain the differences in modern cloud infrastructures versus a more traditional approach.
The pets, cattle, chickens insects, and snowflakes analogy
I first came across the pets versus cattle analogy back in 2012 from a slide deck published by Randy Bias. The slide deck was used during a talk Randy Bias gave at the cloudscaling conference on architectures for open and scalable clouds. Towards the end of the talk, he introduced the concept of pets versus cattle, which Randy attributes to Bill Baker who at the time was an engineer at Microsoft.
The slide deck primarily talks about scaling out and not up; let’s go into this in a little more detail and discuss some of the additions that have been made since the presentation was first given five years ago.
Pets: the bare metal servers and virtual machines
Pets are typically what we, as system administrators, spend our time looking after. They are traditional bare metal servers or virtual machines:
- We name each server as you would a pet. For example, app-server01.domain.com and database-server01.domain.com.
- When our pets are ill, you will take them to the vets. This is much like you, as a system administrator, would reboot a server, check logs, and replace the faulty components of a server to ensure that it is running healthily.
- You pay close attention to your pets for years, much like a server. You monitor for issues, patch them, back them up, and ensure they are fully documented.
There is nothing much wrong with running pets. However, you will find that the majority of your time is spent caring for them—this may be alright if you have a few dozen servers, but it does start to become unmanageable if you have a few hundred servers.
Cattle: the sort of instances you run on public clouds
Cattle are more representative of the instance types you should be running in public clouds such as Amazon Web Services ( AWS) or Microsoft Azure, where you have auto scaling enabled.
- You have so many cattle in your herd you don’t name them; instead they are given numbers and tagged so you can track them. In your instance cluster, you can also have too many to name so, like cattle, you give them numbers and tag them. For example, an instance could be called ip123067099123.domain.com and tagged as app-server.
- When a member of your herd gets sick, you shoot it, and if your herd requires it you replace it. In much the same way, if an instance in your cluster starts to have issues it is automatically terminated and replaced with a replica.
- You do not expect the cattle in your herd to live as long as a pet typically would, likewise you do not expect your instances to have an uptime measured in years.
- Your herd lives in a field and you watch it from afar, much like you don’t monitor individual instances within your cluster; instead, you monitor the overall health of your cluster. If your cluster requires additional resources, you launch more instances and when you no longer require a resource, the instances are automatically terminated, returning you to your desired state.
Chickens: an analogy for containers
In 2015, Bernard Golden added to the pets versus cattle analogy by introducing chickens to the mix in a blog post titled Cloud Computing: Pets, Cattle and Chickens? Bernard suggested that chickens were a good term for describing containers alongside pets and cattle:
- Chickens are more efficient than cattle; you can fit a lot more of them into the same space your herd would use. In the same way, you can fit a lot more containers into your cluster as you can launch multiple containers per instance.
- Each chicken requires fewer resources than a member of your herd when it comes to feeding. Likewise, containers are less resource-intensive than instances, they take seconds to launch, and can be configured to consume less CPU and RAM.
- Chickens have a much lower life expectancy than members of your herd. While cluster instances can have an uptime of a few hours to a few days, it is more than possible that a container will have a lifespan of minutes.
Insects: An analogy for serverless
Keeping in line with the animal theme, Eric Johnson wrote a blog post for RackSpace which introduced insects. This term was introduced to describe serverless and Functions as a Service.
Insects have a much lower life expectancy than chickens; in fact, some insects only have a lifespan of a few hours. This fits in with serverless and Functions as a Service as these have a lifespan of seconds.
Around the time Randy Bias gave his talk which mentioned pets versus cattle, Martin Fowler wrote a blog post titled SnowflakeServer. The post described every system administrator’s worst nightmare:
- Every snowflake is unique and impossible to reproduce. Just like that one server in the office that was built and not documented by that one guy who left several years ago.
- Snowflakes are delicate. Again, just like that one server—you dread it when you have to log in to it to diagnose a problem and you would never dream of rebooting it as it may never come back up.
Bringing the pets, cattle, chickens, insects and snowflakes analogy together…
When I explain the analogy to people, I usually sum up by saying something like this:
Organizations who have pets are slowly moving their infrastructure to be more like cattle. Those who are already running their infrastructure as cattle are moving towards chickens to get the most out of their resources. Those running chickens are going to be looking at how much work is involved in moving their application to run as insects by completely decoupling their application into individually executable components.
But the most important take away is this:
No one wants to or should be running snowflakes.
Serverless and insects
As already mentioned, using the word serverless gives the impression that servers will not be needed. Serverless is a term used to describe an execution model.
When executing this model you, as the end user, do not need to worry about which server your code is executed on as all of the decisions on placement, server management, and capacity are abstracted away from you—it does not mean that you literally do not need any servers.
Now there are some public cloud offerings which abstract so much of the management of servers away from the end user that it is possible to write an application which does not rely on any user-deployed services and that the cloud provider will manage the compute resources needed to execute your code.
Typically these services, which we will look at in the next section, are billed for the resources used to execute your code in per second increments.
So how does that explanation fits in with the insect analogy?
Let’s say I have a website that allows users to upload photos. As soon as the photos are uploaded they are cropped, creating several different sizes which will be used to display as thumbnails and mobile-optimized versions on the site.
In the pets and cattle world, this would be handled by a server which is powered on 24/7 waiting for users to upload images. Now this server probably is not just performing this one function; however, there is a risk that if several users all decide to upload a dozen photos each, then this will cause load issues on the server where this function is being executed.
We could take the chickens approach, which has several containers running across several hosts to distribute the load. However, these containers would more than likely be running 24/7 as well; they will be watching for uploads to process. This approach could allow us to horizontally scale the number of containers out to deal with an influx of requests.
Using the insects approach, we would not have any services running at all. Instead, the function should be triggered by the upload process. Once triggered, the function will run, save the processed images, and then terminate. As the developer, you should not have to care how the service was called or where the service was executed, so long as you have your processed images at the end of it.