API Driven Metal = OpenCrowbar + Chef Provisioning

The OpenCrowbar community created a Chef-Provisioning driver that allows you to quickly build hardware clusters using Chef cookbooks.

2012-08-05_14-13-18_310When we started using Chef in 2011, there was a distinct gap around bootstrapping systems.  The platform did a great job of automation and even connecting services together (via the Search anti-pattern, see below) but lacked a way to build the initial clusters automatically.

The current answer to this problem from Chef is refreshingly simply: a cookbook API extension called Chef Provisioning.  This approach uses the regular Chef DSL in recipes to create request and bind a cluster into Chef.  Basically, the code simply builds an array of nodes using an API that creates the nodes if they are missing from the array in the code.  Specifically, when a node is missing from the array, Chef calls out to create the node in an external system.

For clouds, that means using the API to request a server and then inject credentials for Chef management.  It’s trickier for physical gear because you cannot just make a server in the configuration you need it in.  Physical systems must first be discovered and profiled to ready state: the system must know how many NICs and disk drives are available to correctly configure the hardware prior to laying down the Operating System.

Consequently, Chef Provisioning automation is more about reallocation of existing discovered physical assets to Chef.  That’s exactly the approach the OpenCrowbar team took for our Chef Provisioning driver.

OpenCrowbar interacts with Chef Provisioning by pulling nodes from the System deployment into a Chef Provisioning deployment.  That action then allows the API client to request specific configurations like Operating System or network that need to be setup for Chef to execute.  Once these requests are made, Crowbar will simply run its normal annealing processes to ready state and then injects the Chef credentials.  Chef waits until the work queue is empty and then takes over management of the asset.  When Chef is finished, Crowbar can be instructed to reconfigure the node back to a base state.

Does that sound simple?  It is simple because the Crowbar APIs match the Chef needs very cleanly.

It’s worth noting that this integration is a great test of the OpenCrowbar API design.  Over the last two years, we’ve evolved the API to make it more final result focused.  Late binding is a critical concept for the project and the APIs reflect that objective.  For Chef Provisioning, we allow the integration to focus on simple requests like “give me a node then put this O/S on the node and go.”  Crowbar has the logic needed to figure out how to accomplish those objectives without much additional instruction.

Bonus Side Note: Why Search can become an anti-pattern?  

Search is an incredibly powerful feature in Chef that allows cross-role and cross-node integration; unfortunately, it’s also very difficult to maintain as complexity and contributor counts grow.  The reason is that search creates “forward dependencies” in the scripts that require operators creating data to be aware of downstream, hidden consumers.  High Availability (HA) is a clear example.  If I add a new “cluster database” role to the system then it is very likely to return multiple results for database searches.  That’s excellent until I learn that my scripts have coded search to assume that we only return one result for database lookups.  It’s very hard to find these errors since the searches are decoupled and downstream of the database cookbook.  Ultimately, the community had to advise against embedded search for shared cookbooks

Starting RackN – Delivering open ops by pulling an OpenCrowbar Bunny out of our hat

When Dell pulled out from OpenCrowbar last April, I made a commitment to our community to find a way to keep it going.  Since my exit from Dell early in October 2014, that commitment has taken the form of RackN.

Rack N BlackToday, we’re ready to help people run and expand OpenCrowbar (days away from v2.1!). We’re also seeking investment to make the project more “enterprise-ready” and build integrations that extend ready state.

RackN focuses on maintenance and support of OpenCrowbar for ready state physical provisioning.  We will build the community around Crowbar as an open operations core and extend it with a larger set of hardware support and extensions.  We are building partnerships to build application integration (using Chef, Puppet, Salt, etc) and platform workloads (like OpenStack, Hadoop, Ceph, CloudFoundry and Mesos) above ready state.

I’ve talked with hundreds of people about the state of physical data center operations at scale. Frankly, it’s a scary state of affairs: complexity is increasing for physical infrastructure and we’re blurring the lines by adding commodity networking with local agents into the mix.

Making this jumble of stuff work together is not sexy cloud work – I describe it as internet plumbing to non-technical friends.  It’s unforgiving, complex and full of sharp edge conditions; however, people are excited to hear about our hardware abstraction mission because it solves a real pain for operators.

I hope you’ll stay tuned, or even play along, as we continue the Open Ops journey.

Unicorn captured! Unpacking multi-node OpenStack Juno from ready state.

OpenCrowbar Packstack install demonstrates that abstracting hardware to ready state smooths install process.  It’s a working balance: Crowbar gets the hardware, O/S & networking right while Packstack takes care of OpenStack.

LAYERSThe Crowbar team produced the first open OpenStack installer back in 2011 and it’s been frustrating to watch the community fragment around building a consistent operational model.  This is not an OpenStack specific problem, but I think it’s exaggerated in a crowded ecosystem.

When I step back from that experience, I see an industry wide pattern of struggle to create scale deployments patterns that can be reused.  Trying to make hardware uniform is unicorn hunting, so we need to create software abstractions.  That’s exactly why IaaS is powerful and the critical realization behind the OpenCrowbar approach to physical ready state.

So what has our team created?  It’s not another OpenStack installer – we just made the existing one easier to use.

We build up a ready state infrastructure that makes it fast and repeatable to use Packstack, one of the leading open OpenStack installers.  OpenCrowbar can do the same for the OpenStack Chef cookbooks or Salt Formula.   It can even use Saltstack, Chef and Puppet together (which we do for the Packstack work)!  Plus we can do it on multiple vendors hardware and with different operating systems.   Plus we build the correct networks!

For now, the integration is available as a private beta (inquiries welcome!) because our team is not in the OpenStack support business – we are in the “get scale systems to ready state and integrate” business.  We are very excited to work with people who want to take this type of functionality to the next level and build truly repeatable, robust and upgradable application deployments.

OpenCrowbar bootstrap positions SSH Keys for hand-offs

I was reading a ComputerWorld article about how Google and Amazon achieve scale.  The theme: you must do better than linear cost scale and the only way to achieve that is to automate and commoditize hardware.  I find interesting parallels in the Crowbar physical devops effort.

KeysAs the OpenCrowbar team continues to explore the concepts around “ready state,” I discover more and more small ops nuisances that need to be included in the build up before installing software.  These small items quickly add up at scale breaking the rule above.

I’ve already posted about the performance benefit of building a Squid Proxy fabric as part of the underlying ops environment.  As we work on Chef Metal, SaltStack and Packstack integrations (private beta), we’ve rediscovered the importance of management/population of SSH public keys.

In cloud infrastructure, key injection is taken for granted; however, it’s not an automatic behavior in the physical ops.  Since OpenCrowbar handles keys by default but other tools (like Cobbler or Razor) expect that you will use kickstart to inject your SSH keys when you install the Operating System..

Including keys in kickstart (which I’m using generically instead of preseed, auto-yast, jumpstart, etc) hand generated scripts is a potentially dangerous security practice since it makes it difficult to propagate and manage your keys.  It also means that every time a new operating system update is released that you may have to update and retest your kickstarts.  OpenCrowbar has the same challenge but our approach allows everyone can share in the work because our bootstrapping files are scripted and generic.

OpenCrowbar takes care of these ready state configurations in our integrations with these DevOps platforms.  Our experience has been that little items like SSH keys and proxy configurations can make a disproportionate advantage in running scale ops or during iterative development.

Need a physical ops baseline? Crowbar continues to uniquely fill gap

Robots Everywhere!I’ve been watching to see if other open “bare metal” projects would morph to match the system-level capabilities that we proved in Crowbar v1 and honed in the re-architecture of OpenCrowbar.  The answer appears to be that Crowbar simply takes a broader approach to solving the physical ops repeatably problem.

Crowbar Architect Victor Lowther says “What makes Crowbar a better tool than Cobbler, Razor, or Foreman is that Crowbar has an orchestration engine that can be used to safely and repeatably deploy complex workloads across large numbers of machines. This is different from (and better than, IMO) just being able to hand responsibility off to Chef/Puppet/Salt, because we can manage the entire lifecycle of a machine where Cobbler, Razor and Chef cannot, we can describe how we want workloads configured at a more abstract level than Foreman can, and we do it all using the same API and UI.”

Since we started with a vision of an integrated system to address the “apply-rinse-repeat” cycle; it’s no surprise that Crowbar remains the only open platform that’s managed to crack the complete physical deployment life-cycle.

The Crowbar team realized that it’s not just about automation setting values: physical ops requires orchestration to make sure the values are set in the correct sequence on the appropriate control surface including DNS, DHCP, PXE, Monitoring, et cetera.  Unlike architectures for aaS platforms, the heterogeneous nature of the physical control planes requires a different approach.

We’ve seen that making more and more complex kickstart scripts or golden images is not a sustainable solution.  There is simply too much hardware variation and dependency thrash for operators to collaborate with those tools.  Instead, we’ve found that decomposing the provisioning operations into functional layers with orchestration is much more multi-site repeatable.

Accepting that physical ops (discovered infrastructure) is fundamentally different from cloud ops (created infrastructure) has been critical to architecting platforms that were resilient enough for the heterogeneous infrastructure of data centers.

If we want to start cleaning up physical ops, we need to stop looking at operating system provisioning in isolation and start looking at the full server bring up as just a part of a broader system operation that includes networking, management and operational integration.

OpenCrowbar 2.B to deliver multiple hardware vendor support and advanced integrations

I’ve stayed quiet on the subject of Crowbar for a few months, but that does not mean that Crowbar has been.  Activity has been picking up, after Dell pulled resources off, to complete hardware configuration.

[Disclosure: As of 10/3/2014, I am no longer a Dell employee]

With the re-addition of hardware configuration, OpenCrowbar delivers the essential requirements for Ready State and we’ve piloted integration that shows how to drive Crowbar via the API.

From BuildersKnowledge

There has been substantial burn-down on the Broom release theme of hardware workload deliverable which mainly focus on the IPMI/BMC, RAID and BIOS functions working in the framework.  It has required us to add additional out-of-band abstractions (“hammers”) and node abstractions (“quirks”).

We’ve also had a chance to work ahead on the Camshaft release theme of tools integration components like:

  •         SaltStack Integration – Crowbar sets up a Salt master and minions on discovered metal (pull request)
  •         Chef Metal Integration – a Chef Metal driver talks to the Crowbar API to claim discovered servers from an allocation pool (Judd’s repo).
  •         Puppet Integration – Crowbar is able to use the stand-alone mode to execute Puppet manifests on the nodes (as a replacement for Foreman) (puppet sa client).
  •         Chef Integration – not new, but worth including in the list so it’s not overlooked! (chef-client install)
  • We also added some essential operational configurations including Squid proxy setup and auto configuration and preparing a Consul foundation for future integration with HashiCorp tools

These initial integration are key to being able to bring in OpenStack via Packstack, Chef Cookbooks, or Salt formulas.  Since Crowbar is agnostic about OS, Hardware and Configuration Management tools (Chef, Puppet, Salt), I am seeing interest from several fronts in parallel.  There seems to be substantial interest in RDO + Centos 7 using Packstack or Chef.  Happily, OpenCrowbar.Broom is ready to sweep in those workloads.

There is significant need for Crowbar to deliver ready state under these deployers.  For example, preparing the os, disk, monitoring, cache, networking and SDN infrastructure (OVS, Contrails) are outside the scripts but essential to a sustainable deployment.

These ready state configurations are places where Crowbar creates repeatable cross-platform base that spans the operational choices.

Apply, Rinse, Repeat! How do I get that DevOps conditioner out of my hair?

I’ve been trying to explain the pain Tao of physical ops in a way that’s accessible to people without scale ops experience.   It comes down to a yin-yang of two elements: exploding complexity and iterative learning.

Science = Explosions!Exploding complexity is pretty easy to grasp when we stack up the number of control elements inside a single server (OS RAID, 2 SSD cache levels, 20 disk JBOD, and UEFI oh dear), the networks that server is connected to, the multi-layer applications installed on the servers, and the change rate of those applications.  Multiply that times 100s of servers and we’ve got a problem of unbounded scope even before I throw in SDN overlays.

But that’s not the real challenge!  The bigger problem is that it’s impossible to design for all those parameters in advance.

When my team started doing scale installs 5 years ago, we assumed we could ship a preconfigured system.  After a year of trying, we accepted the reality that it’s impossible to plan out a scale deployment; instead, we had to embrace a change tolerant approach that I’ve started calling “Apply, Rinse, Repeat.”

Using Crowbar to embrace the in-field nature of design, we discovered a recurring pattern of installs: we always performed at least three full cycle installs to get to ready state during every deployment.

  1. The first cycle was completely generic to provide a working baseline and validate the physical environment.
  2. The second cycle attempted to integrate to the operational environment and helped identify gaps and needed changes.
  3. The third cycle could usually interconnect with the environment and generally exposed new requirements in the external environment
  4. The subsequent cycles represented additional tuning, patches or redesigns that could only be realized after load was applied to the system in situ.

Every time we tried to shortcut the Apply-Rinse-Repeat cycle, it actually made the total installation longer!  Ultimately, we accepted that the only defense was to focus on reducing A-R-R cycle time so that we could spend more time learning before the next cycle started.