Autopilot Pattern Applications 开发模式

转自: http://autopilotpattern.io/,一种不错的应用开发模式

The autopilot pattern automates in code the repetitive and boring operational tasks of an application, including startup, shutdown, scaling, and recovery from anticipated failure conditions for reliability, ease of use, and improved productivity.

Why do we need to do this?

We need to make the applications not yet developed and those we already have easier to build, test, deploy, and scale. Consider the applications we see in science fiction and in our devops dreams:

Apps that can be deployed and scaled with a single click.
Apps and workflows that can be tested and automated in CI/CD.
Apps and workflows that work the same on our laptops as in the cloud (public and private cloud).
Apps and workflows that aren't married to any specific infrastructure or scheduler.

Applications like this have been possible for years, but they're still rare. However, modern tools and infrastructure, as well as repeatable patterns are emerging to make these applications easy to build and available to all, regardless of scale.

By starting with a pattern that automates application operations where we develop: on our laptops or other location of the developer's choice, we can gain the ability to scale in production and automate testing and deployment in a way that accelerates development and eliminates the pain we often experience in turning our application dreams into a production reality.

Consider the benefits of the autopilot pattern:

Easy deploys and scaling.
We’ve automated the boring stuff, use time you save with that however you want.
By automating those operational tasks in code, we’ve made the app self-documenting.
And by keeping that automation code in the same repo with the application, we’ve increased visibility and the opportunity for participation to all.
Most importantly, however, these apps work the same on our laptops as in the cloud.

Who is this for?

The autopilot pattern is for both developers and operators. It's designed to be easy enough to use that we can use it early in development but robust enough to work at the largest scale we can imagine for our apps.

The autopilot pattern is for solo developers, startups, and large enterprises. It's for operators that want to bring sanity to their lives and for developers who want to make their apps easy to use. It's for microservices applications with dozens of components, it's for tiered applications, it even works with applications that run in a single container. It's for people building apps for distribution and re-use, and for in-house teams working on apps that will never leave the organization.

The autopilot pattern is ideal for:

Developers who want to work on their laptops, on a plane, without needing internet access.
Operators who want to deploy in public or private clouds.
Site reliability engineers who want automated and repeatable workflows from development to deploy.
Startups and enterprises that want *aaS convenience without *aaS lock-in.
Applications running in a single container.
Tiered, traditional applications.
Microservices applications with any number of components.

Most importantly, it's designed to live and grow with our apps at all stages of development and operations.

How do we do it?

The autopilot pattern automates the lifecycle of each component of the application. Whether we architect our application as a single container, in tiers, or as microservices, each container that makes up the application has its own lifecycle, and its own set of actions that are necessary during that lifecycle. Each of these application components are often applications in themselves, like a database server, in-memory cache, or the reverse proxy that fronts our application, in addition to the Node.js, Python, Ruby, or other code that makes the set of components a complete application.

Most autopilot pattern implementations embrace the Docker ideal of single-purpose, or single-service containers, but this is not strictly required. The autopilot pattern does require us (both developers and operators) to think about how we operate our applications at critical points in the lifecycle of each component. The following questions may help uncover the details we need to consider:

What resources does the container have to connect to?
- How does the Node.js code connect to its database, for example? Or, how does Nginx connect to its back ends?
How is the container configured?
- Does the application have a configuration file? Do we start it with arguments? Does it use environment variables?
Does anything have to be done before the application in the container starts?
- This can include injecting the resources identified in #1 into the configuration file from #2 before we start the application. Or it could require finding the application's peers, such as when starting an HA raft of Consul or cluster of database instances.
How is the application in the container started?
- This is commonly what we use for the docker run command or CMD argument in the Dockerfile.
How will other containers discover this container?
- The application should self-register in a service catalog like Consul so that it can be discovered. This is how our Python or Node.js (or other) application can discover its database, and how Nginx or HAproxy can discover our app server.
How do we know if the application in the container is healthy?
- Is this a Dockerized database? Let's run a query to check if it's working correctly. Each app probably has one or more unique tests we can do to see if it's working.
What are the key metrics that indicate the container's load?
- Just as our health checks are often unique to each application, so are our performance metrics. Is our Nginx instance overloaded? Check connection stats in stub_status. Is MySQL running at capacity? Look for queries in a wait state.
If the resources the container connects to change, how do we update the application in the container with those new resources?
- What happens if the resources from #1 change? Do we simply rewrite the config file? Do we need to signal the app? How do we tell Nginx about new upstreams, or our Python app about a change in the database hosts?
Does the container create any data on disk that needs to be shared by other instances or backed up in any way?
- Some databases and a few applications require special handling of data on disk. User uploads in WordPress are one example.
Does anything have to be done before or after shutting down the container?
- Scaling down a database like Couchbase may require removing the instance from the cluster before stopping it so that data can be safely rebalanced to other nodes.

The start of each container marks the start of that container's lifecycle. Just as the documentation for many applications must tell us how to configure and start the application, we can write code that automates those activities as we start the container for that application. The same goes for other events throughout the application's lifecycle as well.

Some applications are emerging with at least some of this logic built in. Traefik is a proxy server with automatic discovery of its back ends using Consul or other service catalogs (however, Traefik does not self-register in those service catalogs so that it can be used by other applications, like one that automatically updates the DNS and CDN with the proxy instances as the origin). ContainerPilot, a helper written in Golang that lives inside the container can help with this. Using a small configuration file, ContainerPilot can trigger events inside the container that we can use to automate operations on these events, including preStart (formerly onStart), health, onChange, preStop, and postStop.

Events inside the container that drive the autopilot pattern.

There appears to be great commonality about the events in each application's lifecycle. Not every application requires specific attention on each of the events identified above, but the operations of most (if not all) applications can be automated based on those events. Those operational details, however, are likely unique for each application.

See the autopilot pattern example for detailed instructions on how to apply the autopilot pattern, and list of implementations below for practical implementations.

How does this differ from previous approaches to automation?

Automation is not new, but the approach to automation in the autopilot pattern has some significant differences when compared to previous approaches to automation based on configuration management tools.

Configuration management tools are very helpful in managing the configuration of a compute node, but tying container configuration to those CM tools creates dependencies that require infrastructure preparation prior to app deploys and make it difficult to re-use application containers across different infrastructure and in different contexts. This barrier to re-use within a single organization is even more remarkable between organizations. The autopilot pattern is designed to be infrastructure independent for more reusability and easy deploys.

Very few of us who are lucky enough to work in environments with full configuration management in production use the same CM strategy when developing on our laptops. Instead, the application is configured differently and often behaves differently. Those differences exacerbate the "works on my machine" problem, but also slow our progress in more completely automating application operations. The autopilot pattern is designed to work on our laptops and in the cloud, keeping both the operational details and behaviors the same across all environments.

Setting up configuration management solutions is complex. We all dream of doing it, but it’s a step too far for so many of us to complete. The autopilot pattern, meanwhile, leverages what we’re already using, just adds operational logic to it.

Configuration management solutions often depend on repositories of cookbooks and configuration details separate from the application, making it hard for developers to know how the application is configured in production, and difficult for operators to recognize when changes need to be made to the configuration as the application is changed. The autopilot pattern keeps the operational code and configuration templates in the same repo with the application itself, increasing visibility and participation for all. In fact, because the application is operated and configured the same way everywhere, from development to CI to QA, and finally deploy, we're testing it thoroughly and experiencing the behavior and any changes at every step.

How does this differ from scheduler-backed container automation?

Some approaches to application automation depend on deep integration with the scheduler. This approach to automation moves the automation logic out of the application, which violates one of the key values of the autopilot pattern: keep the automation for each application component in the same repo with that component's code. The separation of application logic from its lifecycle automation unnecessarily separates operations from development and, because developers often don't run complex schedulers on their laptops, exacerbates the "works on my machine" problem.

What infrastructure do we need?

A new class of infrastructure is emerging that supports these modern apps. This infrastructure puts containers first and provides convenient, straightforward networking:

Focused on containers.
Provisioning APIs that combine infrastructure provisioning arguments with application execution arguments.
The ability to deploy containers across multiple compute nodes using that provisioning API.
Some form of service discovery built-in.
Network virtualization that gives each container its own VNIC, eliminates port conflicts, and simplifies communication between containers across different compute nodes.

This infrastructure makes it possible to deploy autopilot pattern applications without preparation. The hosts do not need to be prepared with special configuration files or additional software to run the applications. These features are critical to making the one-click deploys and scaling possible, and it's necessary to preserve compatibility of the application and workflow from development to production.

Solutions that provide this autopilot pattern infrastructure include: