Friday, June 27, 2014

Continuous Deployment in the Cloud Part1: The Distributed Continuous X process

This is the first part of the Blog Series "Continuous Deployment in the Cloud".

When we started doing Continuous Delivery many of us started building the process around a CI Server. Many of us ran into problems building their pipelines with Jenkins or other CI Tools. There are several reasons to these problems these two blog posts and outline the problems really well.

CI Server is a bad Continuous Delivery/Deployment Orchestrator

Personally Id like to boil down the core of the problem to lack of portability and separation of concerns.

If you model the process in a CI Tool then the process will never ever be portable. Even if you can distribute the process across multiple instances of the CI Tool through different means of generating and publishing process you always need an instance of that tool to run the process. This makes development quite hard since you need a local development instance of that tool.

In most CI Tools the data collected from each job is stored in the CI Tool itself. To make matters even worse its often stored in that instance of the CI Tool. This means that the only way to access the data gathered by the jobs of the pipe is through navigating that instance of the pipe on that instance of the CI Server. This makes it very hard to distribute the Continuous X process over multiple CI Servers, we can use master/slave setups but the problem still persists with multiple masters.

Since the data is often tied to the implementation of the pipe it becomes very hard to visualise historical data. If the current layout of the pipe has changed then we still want to be able to visualise old pipes of our system.

Another problem that arises is historical data and data retention as it is tied to the CI Tool and visualisation is tied to the CI Tool we need to manage the disk space on the CI Tools. We don't want to mix runtime and historical data in CI Tool.

Separating Process Implementation and Process Data Storage

So the first thing we have to deal with in order to distribute our Continuous X process is to move the data out of the process implementation.

In fact the first problem we encounter when distributing a Continuous X process is the Version Number.
What do we use as a version number and where do we get it?

  • Using the CI Server Build Number is extremely bad as you cant even reset your Build Job without encountering problems. 
  • Using a property checked in into your source code repository such as version number in the maven pom or similar is almost worse. You will have a gap in time between the repo fetch, update of version and commit back to repository. If other jobs start in this time frame then they will get the same version number.

So the answer is we get it from a central build data repository. A single database that keeps track of all our deliverables, their versions and their state. By delegating the responsibility of the version number to the Build Data Repository we ensure that the Version Number is created through a atomic update.

The first thing our Process Implementation will do is to get its version number from the Build Data Repository. Then everything we do during the Process Implementation we report it to the Build Data Repository.

So for example we report

  1. Init Pipe - Get Version Number, 
  2. Build Start - Environment, TimeStamp
  3. Unit Test Start - Environment, TimeStamp
  4. Unit Test Done - Environment, TimeStamp, Report
  5. Build Done - Environment, TimeStamp, Report
  6. Deploy to Test Start - Environment, TimeStamp
  7. Deploy to Test Done - Environment, TimeStamp, Report
  8. Test Start - Environment, TimeStamp
  9. Test Done - Environment, TimeStamp, Report
  10. Promote - Environment, TimeStamp, Promoted to PASSED_TEST
  11. Deployment Production Start - Environment, TimeStamp
  12. Deployment Production Done - Environment, TimeStamp, Report
Now we have a Continuous X process implementation that gets the version number from the same place regardless of where its executed and reports all the data into one repository regardless of where its executed. This means that the same process implementation can be executed on any CI Server instance as well as on any Developer machine. This has enabled us to implement a portable and hence distributed Continuous X process.

There are several reasons we might want to run the process locally on our Dev Environment. We need the capability to develop the process implementation and we need to be able to do it without a CI Server.  It gives us a good mindset when making a distributed implementation. If it can run locally then it can run on any build environment. In some cases we maybe don't need a full fledged build environment to run our Continuous X Process. Applications with few developers, few changes and short pipeline execution time don't really need a remote build environment.

Another important reason is that we need to be able to push our code to our customers even if our Continuous X Service is down. Regardless of how high the SLA of the service (internal or cloud based) is we will eventually run into situations where we need to execute it and its down.

Thought its important to note that a pipeline should never allow execution on changes that aren't pushed to the SCM. Every execution has to be traceable to a SCM revision. 

Process Visualization

One of the main problem with CI Tools such as Jenkins is the visualization. Its just not built for the purpose of Continuous Delivery/Deploy and as Ive mentioned above its based on local instances of job pipes. This makes it impossible to visualize a distributed process in a good way and it requires the user to have understanding of the CI Tool. A Continuous Delivery/Deploy process needs to be visualised so that non technical employees can view it.

Good thing we have a Build Data Repository then. Based on the information in the Build Data Repository we can visualize our pipe, its status and result no matter where it was executed. We can also visualise the pipe based on how it looked when it was executed, not how its implemented now.

Logging, Monitoring and Metrics vs the Build Data Repository

What do we store in the Build Data Repository? Do we store everything in there? Like execution logs from the test excutions and metrics?

No. The Build Data Repository is to store the reports from all events builds, deployments, tests, ect but the actual runtime logging should go to where it belongs. Logfiles going into a log repository such as logstash, metrics going into a metrics repository such as graphite.

The Process Visualization could/should aggregate the data from the Build Data, Log and Metric repositories to provide a good view of our process and system.

Provisioning Environments and Executing Tests

Its really important that our Continuous X Process Implementation initiates the full provisioning of the full test/prod environment. By assuring that it does so we ensure that all environmental changes go through our pipe.

What do I define as Environment? Everything LoadBalancers, Network Rules, Middleware nodes, caches, databases, scaling rules, ect, ect.

This concept is a great extension of the Immutable Server pattern. By releasing images instead of artefacts out of our build step (actually build+bake) we enable the creation of Immutable Servers through the entire build pipe.

The process builds a new test environment for each blackbox deployed test execution (basically anything beyond unit test) and then it destroys it again once the test is finished. When we deploy into our next environment call it QA/PreProd what ever then we do it by creating new middleware servers in that environment based on the same image and virtualisation spec as we had in our Test Environment. Once they are upp and running we rotate them into our cluster/load balancer, whatever.

The big difference between our Test, QA and Prod environment deploys is that the first one is a create and destroy scenario while the others are an update scenario as we cannot build new full environments for production. We cannot build new databases, bring up new load balancers, ect for each production deploy. So preferably we separate the handling of Mutable and Immutable infrastructure in one environment so that we can create and destroy our Immutable Servers/Clusters and mutate our databases.

So basically a very low level of detail topology of a Distributed Continuous X implementation looks something like this.

This gives us the fundamental basic understanding of what we need to do inorder to build a scaleable distributed system that handles our Continuous X processes in our company.

In my next post in this series I will go into example of how to build a portable Continuous X Process Implementation that is independent of any CI and Build Tools.

Monday, June 2, 2014

Blog Series: Continuous Deployment in the Cloud

For my next few posts I am going to focus on writing a series of articles how to do Continuous Delivery & Deployment in a cloud environment. Ive always been a bit cautious when it comes to tutorial style blogs, talks and articles. I usually find them to be too shallow and then never reveal the true issues that need to be solved. This often leads to bad, premature and uninformed decisions made by the consumer of the tutorial.

So instead I am going to try provide a much richer series of articles that focus on how to Architect, Test, Deploy and Deliver in a Cloud environment.

In my conference talks I often talk about the key of having a build data repository that is separate from the build engine (CI Tool). More then once I have been asked if I can open source this our tool. Well Im not sure has been my answer. So instead Ive decided to implement a new similar tool and open source it. Im going to over architect it a bit on purpose as its going to be the main example in the article series. The Process Implementation in this series is going to use that tool as its build repo.

This is how the Process Implementation will look like. I will go deeper into this picture as I move forward but a few quick words about it.

The key to a scaleable CD process is for it to be independent of its runtime environment. The CD process drawn here can be executed from a Build Environment and/or a Dev Environment. The dev can push from his own environment right into production or he/she can let the build environment do it from him/her. Regardless of where the process is initiated it will be executed in the same way and it will integrate with the build data repository to which it reports any events that happen on that build and its also from where it gets its version number.

Will I talk about tooling this time? Yes I will. This article will be based on Git as SCM, AWS as Test and Prod Runtime Environments, Travis CI as Build Environment and Gradle as Build Tool but I still have not decided upon test tool most likely it will be RESTAssured.

This series will take some time to write and will mostly be done during the later part of this summer and the fall. If you are interested in this article series and/or the build data repository then please +1 this article to show me that there is interest.