Choria Network Protocols – Transport

The old MCollective protocols are now ancient and was quite Ruby slanted – full of Symbols and used YAML and quite language specific – in Choria I’d like to support other Programming Languages, REST gateways and so forth, so a rethink was needed.

I’ll look at the basic transport protocol used by the Choria NATS connector, usually it’s quite unusual to speak of Network Protocols when dealing with messages on a broker but really for MCollective it is exactly that – a Network Protocol.

The messages need enough information for strong AAA, they need to have an agreed on security structure and within them live things like RPC requests. So a formal specification is needed which is exactly what a Protocol is.

While creating Choria the entire protocol stack has been redesigned on every level except the core MCollective messages – Choria maintains a small compatibility layer to make things work. To really achieve my goal I’d need to downgrade MCollective to pure JSON data at which point multi language interop should be possible and easy.

Networks are Onions


Network protocols tend to come in layers, one protocol within another within another. The nearer you go to the transport the more generic it gets. This is true for HTTP within TCP within IP within Ethernet and likewise it’s true for MCollective.

Just like for TCP/IP and HTTP+FTP one MCollective network can carry many protocols like the usual RPC others can exist, a typical MCollective install uses 2 protocols at this inner most layer.

( middleware protocol
  ( transport packet that travels over the middleware
      ( security plugin internal representation
        ( mcollective core representation that becomes M::Message
          ( MCollective Core Message )
          ( RPC Request, RPC Reply )
          ( Other Protocols, .... )
        )
      )
    )
  )
)

Here you can see when you do mco rpc puppet status you’ll be creating a RPC Request wrapped in a MCollective Message, wrapped in a structure the Security Plugin dictates, wrapped in a structure the Connector Plugin dictates and from there to your middleware like NATS.

Today I’ll look at the Transport Packet since that is where Network Federation lives which I spoke about yesterday.

Transport Layer


The Transport Layer packets are unauthenticated and unsigned, for MCollective security happens in the packet carried within the transport so this is fine. It’s not inconceivable that a Federation might only want to route signed messages and it’s quite easy to add later if needed.

Of course the NATS daemons will only accept TLS connections from certificates signed by the CA so these network packets are encrypted and access to the transport medium is restricted, but the JSON data you’ll see below is sent as is.

In all the messages shown below you’ll see a seen-by header, this is a feature of the NATS Connector Plugin that records the connected NATS broker, we’ll soon expose this information to MCollective API clients so we can make a traceroute tool for Federations. This header is optional and off by default though.

I’ll show messages in Ruby format here but it’s all JSON on the wire.

Message Targets


First it’s worth knowing where things are sent on the NATS clusters. The targets used by the NATS connector is pretty simple stuff, there will no doubt be scope for improvement once I look to support NATS Streaming but for now this is adequate.

  • Broadcast Request for agent puppet in the mycorp sub collective – mycorp.broadcast.agent.puppet
  • Directed Request to a node for any agent in the mycorp sub collective – mycorp.node.node1.example.net
  • Reply to a node identity dev1.example.net with pid 9999 and a message sequence of 10mycorp.reply.node1.example.net.9999.10

As the Federation Brokers are independent of Sub Collectives they are not prefixed with any collective specific token:

  • Requests from a Federation Client to a Federation Broker Cluster called productionchoria.federation.production.federation queue group production_federation
  • Replies from the Collective to a Federation Broker Cluster called productionchoria.federation.production.collective queue group production_collective
  • production cluster Federation Broker Instances publishes statistics – choria.federation.production.stats

These names are designed so that in smaller setups or in development you could use a single NATS cluster with Federation Brokers between standalone collectives. Not really a recommended thing but it helps in development.

Unfederated Messages


Your basic Unfederated Message is pretty simple:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev1.example.net",
    "seen-by" => ["dev1.example.net", "nats1.example.net"],
    "reply-to" => "mcollective.reply.dev1.example.net.999999.0",
  }
}
  • it’s is a discovery request within the sub collective mcollective and would be published to mcollective.broadcast.agent.discovery.
  • it is sent from a machine identifying as dev1.example.net
  • we know it’s traveled via a NATS broker called nats1.example.net.
  • responses to this message needs to travel via NATS using the target mcollective.reply.dev1.example.net.999999.0.

The data is completely unstructured as far as this message is concerned it just needs to be some text, so base64 encoded is common. All the transport care for is getting this data to its destination with metadata attached, it does not care what’s in the data.

The reply to this message is almost identical but even simpler:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev2.example.net",
    "seen-by" => ["dev1.example.net", "nats1.example.net", "dev2.example.net", "nats2.example.net"],
  }
}

This reply will travel via mcollective.reply.dev1.example.net.999999.0, we know that the node dev2.devco.net is connected to nats2.example.net.

We can create a full traceroute like output with this which would show dev1.example.net -> nats1.example.net -> nats2.example.net -> dev2.example.net

Federated Messages


Federation is possible because MCollective will just store whatever Headers are in the message and put them back on the way out in any new replies. Given this we can embed all the federation metadata and this metadata travels along with each individual message – so the Federation Brokers can be entirely stateless, all the needed state lives with the messages.

With Federation Brokers being clusters this means your message request might flow over a cluster member a but the reply can come via b – and if it’s a stream of replies they will be load balanced by the members. The Federation Broker Instances do not need something like Consul or shared store since all the data needed is in the messages.

Lets look at the same Request as earlier if the client was configured to belong to a Federation with a network called production as one of its members. It’s identical to before except the federation structure was added:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev1.example.net",
    "seen-by" => ["dev1.example.net", "nats1.fed.example.net"],
    "reply-to" => "mcollective.reply.dev1.example.net.999999.0",
    "federation" => {
       "req" => "68b329da9893e34099c7d8ad5cb9c940",
       "target" => ["mcollective.broadcast.agent.discovery"]
    }
  }
}
  • it’s is a discovery request within the sub collective mcollective and would be published via a Federation Broker Cluster called production via NATS choria.federation.production.federation.
  • it is sent from a machine identifying as dev1.example.net
  • it’s traveled via a NATS broker called nats1.fed.example.net.
  • responses to this message needs to travel via NATS using the target mcollective.reply.dev1.example.net.999999.0.
  • it’s federated and the client wants the Federation Broker to deliver it to it’s connected Member Collective on mcollective.broadcast.agent.discovery

The Federation Broker receives this and creates a new message that it publishes on it’s Member Collective:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev1.example.net",
    "seen-by" => [
      "dev1.example.net",
      "nats1.fed.example.net", 
      "nats2.fed.example.net", 
      "fedbroker_production_a",
      "nats1.prod.example.net"
    ],
    "reply-to" => "choria.federation.production.collective",
    "federation" => {
       "req" => "68b329da9893e34099c7d8ad5cb9c940",
       "reply-to" => "mcollective.reply.dev1.example.net.999999.0"
    }
  }
}

This is the same message as above, the Federation Broker recorded itself and it’s connected NATS server and produced a message, but in this message it intercepts the replies and tell the nodes to send them to choria.federation.production.collective and it records the original reply destination in the federation header.

A node that replies produce a reply, again this is very similar to the earlier reply except the federation header is coming back exactly as it was sent:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev2.example.net",
    "seen-by" => [
      "dev1.example.net",
      "nats1.fed.example.net", 
      "nats2.fed.example.net", 
      "fedbroker_production_a", 
      "nats1.prod.example.net",
      "dev2.example.net",
      "nats2.prod.example.net"
    ],
    "federation" => {
       "req" => "68b329da9893e34099c7d8ad5cb9c940",
       "reply-to" => "mcollective.reply.dev1.example.net.999999.0"
    }
  }
}

We know this node was connected to nats1.prod.example.net and you can see the Federation Broker would know how to publish this to the client – the reply-to is exactly what the Client initially requested, so it creates:

{
  "data" => "... any text ...",
  "headers" => {
    "mc_sender" => "dev2.example.net",
    "seen-by" => [
      "dev1.example.net",
      "nats1.fed.example.net", 
      "nats2.fed.example.net", 
      "fedbroker_production_a", 
      "nats1.prod.example.net",
      "dev2.example.net",
      "nats2.prod.example.net",
      "nats3.prod.example.net",
      "fedbroker_production_b",
      "nats3.fed.example.net"
    ],
  }
}

Which gets published to mcollective.reply.dev1.example.net.999999.0.

Route Records


You noticed above there’s a seen-by header, this is something entirely new and never before done in MCollective – and entirely optional and off by default. I anticipate you’d want to run with this off most of the time once your setup is done, it’s a debugging aid.

As NATS is a full mesh your message probably only goes one hop within the Mesh. So if you record the connected server you publish into and the connected server your message entered it’s destination from you have a full route recorded.

The Federation Broker logs and MCollective Client and Server logs all include the message ID so you can do a full trace in message packets and logs.

There’s a PR against MCollective to expose this header to the client code so I will add something like mco federation trace some.node.example.net which would send a round trip to that node and tell you exactly how the packet travelled. This should help a lot in debugging your setups as they will now become quite complex.

The structure here is kind of meh and I will probably improve on it once the PR in MCollective lands and I can see what is the minimum needed to do a full trace.

By default I’ll probably record the identities of the MCollective bits when Federated and not at all when not Federated. But if you enable the setting to record the full route it will produce a record of MCollective bits and the NATS nodes involved.

In the end though from the Federation example we can infer a network like this:

Federation NATS Cluster

  • Federation Broker production_a -> nats2.fed.example.net
  • Federation Broker production_b -> nats3.fed.example.net
  • Client dev1.example.net -> nats1.fed.example.net

Production NATS Cluster:

  • Federation Broker production_a -> nats1.prod.example.net
  • Federation Broker production_b -> nats3.prod.example.net
  • Server dev2.example.net -> nats2.prod.example.net

We don’t know the details of all the individual NATS nodes that makes up the entire NATS mesh but this is good enough.

Of course this sample is the pathological case where nothing is connected to the same NATS instances anywhere. In my tests with a setup like this the overhead added across 10 000 round trips against 3 nodes – so 30 000 replies through 2 x Federation Brokers – was only 2 seconds, I couldn’t reliably measure a per message overhead as it was just too small.

The NATS gem do expose the details of the full mesh though since NATS will announce it’s cluster members to clients, I might do something with that not sure. Either way, auto generated network maps should be totally possible.

Conclusion


So this is how Network Federation works in Choria. It’s particularly nice that I was able to do this without needing any state on the cluster thanks to past self making good design decisions in MCollective.

Once the seen-by thing is figured out I’ll publish JSON Schemas for these messages and declare protocol versions.

I can probably make future posts about the other message formats but they’re a bit nasty as MCollective itself is not yet JSON safe, the plan is it would become JSON safe one day and the whole thing will become a lot more elegant. If someone pings me for this I’ll post it otherwise I’ll probably stop here.

Choria Network Federation

Running large or distributed MCollective networks have always been a pain. As much as Middleware is an enabler it starts actively working against you as you grow and as latency increases, this is felt especially when you have geographically distributed networks.

Federation has been discussed often in the past but nothing ever happened, NATS ended up forcing my hand because it only supports a full mesh mode. Something that would not be suitable for a globe spanning network.

Overview


I spent the last week or two building in Federation first into the Choria network protocol and later added a Federation Broker. Federation can be used to connect entirely separate collectives together into one from the perspective of a client.

Here we can see a distributed Federation of Collectives. Effectively London, Tokyo and New York are entirely standalone collectives. They are smaller, they have their own middleware infrastructure, they even function just like a normal collective and can have clients communicating with those isolated collectives like always.

Here I have 5 node NATS meshes in every region. We then add a Federation Broker cluster, I’d suggest 1 instance on every NATS box, and these provide bridging services to a central Federation network.

Clients who connect to the central Federation network and that are configured correctly will interact with all the isolated collectives as if they are one. All current MCollective features keep working and Sub Collectives can span the entire Federation.

Impact


The advantages in large networks are obvious, instead of one giant 100 000 node middleware you now need to built 10 x 10 000 node networks, something that is a lot easier to do – especially with NATS, it’s more or less trivial.

Not so obvious is how this scales wrt MCollective. MCollective has a mode called Direct Addressed where the client would need to create 1 message for every node targeted in the request. Generally very large requests are discouraged so it works ok.

These requests being made on the client ends up having to travel individually all across the globe and this is where it starts to hurt.

With Federation though since the Federation Brokers are in reality Choria Network Protocol aware the client will divide the task of producing these per client messages into groups of 200 and pass the request to the Federation Broker Cluster. The cluster will then, in a load shared fashion, do the work for the client. Since the Federation Broker tends to be near the individual Collective this yields a massive reduction in work and traffic. The Federation Broker Instances are entirely state free so you can run as many as you like and they will share the workload more or less evenly across them.

In my tests against large collectives this speeds up the request significantly and greatly reduce the client load.

In the simple broadcast case there is no speed up, but when doing 10 000 requests in a loop the overhead of Federation was about 2 seconds over the 10 000 requests – so hardly noticeable.

Future Direction


The Choria protocol supports Federation in a way that is not tied to its specific Federation Broker implementation. The basic POC Federation Broker was around 200 lines so not really a great challenge to write. I imagine in time we might see a few options here:

  • You can use different CAs in various places in your Federated network. The Federation Broker using Choria Security super user certificates can provide user id mapping and rewriting between the Collectives
  • If you want to build a SaaS management services ontop of Choria a Federated network makes a really safe way to reach into managed networks without exposing the collectives to each other in any way. A client in one member Collective cannot use the Federation Brokers to access another Collective.
  • Custom RBAC and Auditing schemes can be built at the Federation Broker layer where the requests can be introspected and only ones matching policy are passed to the managed Collective
  • Federation is tailor made to provide Protocol translation. Different protocol Collectives can be bridged together. An older MCollective SSL based collective can be reached from a Choria collective via a Federation Broker providing translation capabilities. Ditto a Websocket interface to Collectives can be a Federation Broker listening on Websocket while speaking NATS on the other end.

The security implications are huge, isolated collectives with isolated CAs and unique user auditing, authorization and authentication needs bridged together via a custom RBAC layer that is horizontally scalable is quite a big deal – and why I never did this, I needed to do this in a way where the Federation would not be a SPOF.

Protocol translation is equally massive, as I move towards looking at ways to fork MCollective, given the lack of cooperation from Puppet Inc, this gives me a very solid way forward to not throw away peoples investment in older MCollective while wishing to find a way to move forward.

Availability


This will be released in version 0.0.25 of the Choria module which should be sometime this week. I’ve published pre-release docs already. Expect it to be deployable with very little effort via Puppet, given a good DNS setup it needs almost no configuration at all.

I’ll make a follow up post that explores the network protocol that made this possible to build with zero stored state in the Federation Broker Instances – a major achievement in my book.

Choria Update

Recently at Config Management Camp I’ve had many discussions about Orchestration, Playbooks and Choria, I thought it’s time for another update on it’s status.

I am nearing version 1.0.0, there are a few things to deal with but it’s getting close. Foremost I wanted to get the project it’s own space on all the various locations like GitHub, Forge, etc.

Inevitably this means getting a logo, it’s been a bit of a slog but after working through loads of feedback on Twitter and offers for assistance from various companies I decided to go to a private designer called Isaac Durazo and the outcome can be seen below:


 

The process of getting the logo was quite interesting and I am really pleased with the outcome, I’ll blog about that separately.

Other than the logo the project now has it’s own GitHub organisation at https://github.com/choria-io and I have moved all the forge modules to it’s own space as well https://forge.puppet.com/choria.

There are various other places the logo show up like in the Slack notifications and so forth.

On the project front there’s a few improvements:

  • There is now a registration plugin that records a bunch of internal stats on disk, the aim is for them to be read by Collectd and Sensu
  • A new Auditing plugin that emits JSON structured data
  • Several new Data Stores for Playbooks – files, environment.
  • Bug fixes on Windows
  • All the modules, plugins etc have moved to the Choria Forge and GitHub
  • Quite extensive documentation site updates including branding with the logo and logo colors.

There is now very few things left to do to get 1.0.0 out but I guess another release or two will be done before then.

So from now to update to coming versions you need to use the choria/mcollective_choria module which will pull in all it’s dependencies from the Choria project rather than my own Forge.

Still no progress on moving the actual MCollective project forward but I’ve discussed a way to deal with forking the various projects in a way that seems to work for what I want to achieve. In reality I’ll only have time to do that in a couple of months so hopefully something positive will happen in the mean time.

Head over to Choria.io to take a look.

Choria Playbooks – Data Sources

About a month ago I blogged about Choria Playbooks – a way to write series of actions like MCollective, Shell, Slack, Web Hooks and others – contained within a YAML script with inputs, node sets and more.

Since then I added quite a few tweaks, features and docs, it’s well worth a visit to choria.io to check it out.

Today I want to blog about a major new integration I did into them and a major step towards version 1 for Choria.

Overview


In the context of a playbook or even a script calling out to other system there’s many reasons to have a Data Source. In the context of a playbook designed to manage distributed systems the Data Source needed has some special needs. Needs that tools like Consul and etcd fulfil specifically.

So today I released version 0.0.20 of Choria that includes a Memory and a Consul Data Source, below I will show how these integrate into the Playbooks.

I think using a distributed data store is important in this context rather than expecting to pass variables from the Playbook around like on the CLI since the business of dealing with the consistency, locking and so forth are handled and I can’t know all the systems you wish to interact with, but if those can speak to Consul you can prepare an execution environment for them.

For those who don’t agree there is a memory Data Store that exists within the memory of the Playbook. Your playbook should remain the same apart from declaring the Data Source.

Using Consul


Defining a Data Source


Like with Node Sets you can have multiple Data Sources and they are identified by name:

data_stores:
  pb_data:
    type: consul
    timeout: 360
    ttl: 20

This creates a Consul Data Source called pb_data, you need to have a local Consul Agent already set up. I’ll cover the timeout and ttl a bit later.

Playbook Locks


You can create locks in Consul and by their nature they are distributed across the Consul network. This means you can ensure a playbook can only be executed once per Consul DC or by giving a custom lock name any group of related playbooks or even other systems that can make Consul locks.

---
locks:
  - pb_data
  - pb_data/custom_lock

This will create 2 locks in the pb_data Data Store – one called custom_lock and another called choria/locks/playbook/pb_name where pb_name is the name from the metadata.

It will try to acquire a lock for up to timeout seconds – 360 here, if it can’t the playbook run fails. The associated session has a TTL of 20 seconds and Choria will renew the sessions around 5 seconds before the TTL expires.

The TTL will ensure that should the playbook die, crash, machine die or whatever, the lock will release after 20 seconds.

Binding Variables


Playbooks already have a way to bind CLI arguments to variables called Inputs. Data Sources extend inputs with extra capabilities.

We now have two types of Input. A static input is one where you give the data on the CLI and the data stays static for the life of the playbook. A dynamic input is one bound against a Data Source and the value of it is fetched every time you reference the variable.

inputs:
  cluster:
    description: "Cluster to deploy"
    type: "String"
    required: true
    data: "pb_data/choria/kv/cluster"
    default: "alpha"

Here we have a input called cluster bound to the choria/kv/cluster key in Consul. This starts life as a static input and if you give this value on the CLI it will never use the Data Source.

If however you do not specify a CLI value it becomes dynamic and will consult Consul. If there’s no such key in Consul the default is used, but the input remains dynamic and will continue to consult Consul on every access.

You can force an input to be dynamic which will mean it will not show up on the CLI and will only speak to a data source using the dynamic: true property on the Input.

Writing and Deleting Data


Of course if you can read data you should be able to write and delete it, I’ve added tasks to let you do this:

locks:
  - pb_data
 
inputs:
  cluster:
    description: "Cluster to deploy"
    type: "String"
    required: true
    data: "pb_data/choria/kv/cluster"
    default: "alpha"
    validation: ":shellsafe"
 
hooks:
  pre_book:
    - data:
        action: "delete"
        key: "pb_data/choria/kv/cluster"
 
tasks:
  - shell:
      description: Deploy to cluster {{{ inputs.cluster }}}
      command: /path/to/script --cluster {{{ inputs.cluster }}}
 
  - data:
      action: "write"
      value: "bravo"
      key: "pb_data/choria/kv/cluster"
 
  - shell:
      description: Deploy to cluster {{{ inputs.cluster }}}
      command: /path/to/script --cluster {{{ inputs.cluster }}}

Here I have a pre_book task list that ensures there is no stale data, the lock ensures no other Playbook will mess around with the data while we run.

I then run a shell command that uses the cluster input, with nothing there it uses the default and so deploys cluster alpha, it then writes a new value and deploys cluster brova.

This is a bit verbose I hope to add the ability to have arbitrarily named tasks lists that you can branch to, then you can have 1 deploy task list and use the main task list to set up variables for it and call it repeatedly.

Conclusion


That’s quite a mouthful, the possibilities of this is quite amazing. On one hand we have a really versatile data store in the Playbooks but more significantly we have expanded the integration possibilities by quite a bit, you can now have other systems manage the environment your playbooks run in.

I will soon add task level locks and of course Node Set integration.

For now only Consul and Memory is supported, I can add others if there is demand.

Choria Playbooks

Today I am very pleased to release something I’ve been thinking about for years and actively working on since August.

After many POCs and thrown away attempts at this over the years I am finally releasing a Playbook system that lets you run work flows on your MCollective network – it can integrate with a near endless set of remote services in addition to your MCollective to create a multi service playbook system.

This is a early release with only a few integrations but I think it’s already useful and I’m looking for feedback and integrations to build this into something really powerful for the Puppet eco system.

The full docs can be found on the Choria Website, but below you can get some details.

Overview


Today playbooks are basic YAML files. They do not have a pseudo programming language in them though I am not against the idea. Eventually I envision a Service to execute playbooks on your behalf, but today you just run them in your shell. I do not anticipate YAML to be the end format of playbooks but it’s good enough for today.

Playbooks have a basic flow that is more or less like this:

  1. Discover named Node Sets
  2. Validate the named Node Sets meet expectations such as reachability and versions of software available on them
  3. Run a pre_book task list that lets you do prep work
  4. Run the main tasks task list where you do your work, around every task certain hook lists can be run
  5. Run either the on_success or on_fail task list for notification of Slacks etc
  6. Run the post_book task list for cleanups etc

Today a task can be a MCollective request, a shell script or a Slack notification. I imagine this list will grow huge, I am thinking you will want to ping webhooks, or interact with Razor to provision machines and wait for them to finish building, run Terraform or make EC2 API requests. This list of potential integrations is endless and you can use any task in any of the above task lists.

A Node Set is simply a named set of nodes, in MCollective that would be certnames of nodes but the playbook system itself is not limited to that. Today Node Sets can be resolved from MCollective Discovery, PQL Queries (PuppetDB), YAML files with groups of nodes in them or a shell command. Again the list of integrations that make sense here is huge. I imagine querying PE or Foreman for node groups, querying etcd or Consul for service members. Talking to random REST services that return node lists or DB queries. Imagine using Terraform outputs as Node Set sources or EC2 API queries.

In cases where you wish to manage nodes via MCollective but you are using a cached discovery source you can ask node sets to be tested for reachability over MCollective. And node sets that need certain MCollective agents can express this desire as SemVer version ranges and the valid network state will be asserted before any playbook is run.

Example


I’ll show an example here of what I think you will be able to achieve using these Playbooks.

Here we have a web stack and we want to do Blue/Green deploys against it, sub clusters have a fact cluster. The deploy process for a cluster is:

  • Gather input from the user such as cluster to deploy and revision of the app to deploy
  • Discover the Haproxy node using Node Set discovery from PQL queries
  • Discover the Web Servers in a particular cluster using Node Set discovery from PQL queries
  • Verify the Haproxy nodes and Web Servers are reachable and running the versions of agents we need
  • Upgrade the specific web tier using:
    1. Tell the ops room on slack we are about to upgrade the cluster
    2. Disable puppet on the webservers
    3. Wait for any running puppet runs to stop
    4. Disable the nodes on a particular haproxy backend
    5. Upgrade the apps on the servers using appmgr#upgrade to the input revision
    6. Do up to 10 NRPE checks post upgrade with 30 seconds between checks to ensure the load average is GREEN, you’d use a better check here something app specific
    7. Enable the nodes in haproxy once NRPE checks pass
    8. Fetch and display the status of the deployed app – like what version is there now
    9. Enable Puppet

Should the task list all FAIL we run these tasks:

  1. Call a webhook on AWS Lambda
  2. Tell the ops room on slack
  3. Run a whole other playbook called deploy_failure_handler with the same parameters

Should the task list PASS we run these tasks:

  1. Call a webhook on AWS Lambda
  2. Tell the ops room on slack

This example and sample playbooks etc can be found on the Choria Site.

Status


Above is the eventual goal. Today the major missing piece here that I think MCollective needs to be extended with the ability for Agent plugins to deliver a Macro plugin. A macro might be something like Puppet.wait_till_idle(:timeout => 600), this would be something you call after disabling the nodes and you want to be sure Puppet is making no more changes, you can see the workflow above needs this.

There is no such Macros today, I will add a stop gap solution as a task that waits for a certain condition but adding Macros to MCollective is high on my todo list.

Other than that it works, there is no web service yet so you run them from the CLI and the integrations listed above is all that exist, they are quite easy to write so hoping some early adopters will either give me ideas or send PRs!

This is available today if you upgrade to version 0.0.12 of the ripienaar-mcollective_choria module.

Again see the Choria Website for much more details on this feature.

An update on my Choria project

Some time ago I mentioned that I am working on improving the MCollective Deployment story.

I started a project called Choria that aimed to massively improve the deployment UX and yield a secure and stable MCollective setup for those using Puppet 4.

The aim is to make installation quick and secure, towards that it seems a common end to end install from scratch by someone new to project using a clustered NATS setup can take less than a hour, this is a huge improvement.

Further I’ve had really good user feedback, especially around NATS. One user reports 2000 nodes on a single NATS server consuming 300MB RAM and it being very performant, much more so than the previous setup.

It’s been a few months, this is whats changed:

  • The module now supports every OS AIO Puppet supports, including Windows.
  • Documentation is available on choria.io, installation should take about a hour max.
  • The PQL language can now be used to do completely custom infrastructure discovery against PuppetDB.
  • Many bugs have been fixed, many things have been streamlined and made more easy to get going with better defaults.
  • Event Machine is not needed anymore.
  • A number of POC projects have been done to flesh out next steps, things like a very capable playbook system and a revisit to the generic RPC client, these are on GitHub issues.

Meanwhile I am still trying to get to a point where I can take over maintenance of MCollective again, at first Puppet Inc was very open to the idea but I am afraid it’s been 7 months and it’s getting nowhere, calls for cooperation are just being ignored. Unfortunately I think we’re getting pretty close to a fork being the only productive next step.

For now though, I’d say the Choria plugin set is production ready and stable any one using Puppet 4 AIO should consider using these – it’s about the only working way to get MCollective on FOSS Puppet now due to the state of the other installation options.

Puppet Query Language

For a few releases now PuppetDB had a new query language called Puppet Query Language or PQL for short. It’s quite interesting, I thought a quick post might make a few more people aware of it.

Overview


To use it you need a recent PuppetDB and as this is quite a new feature you really want the latest PuppetDB. There is nothing to enable when you install it the feature is already active. The feature is marked as experimental so some things will change as it moves to production.

PQL Queries look more or less like this:

nodes { certname ~ 'devco' }

This is your basic query it will return a bunch of nodes, something like:

[
  {
    "deactivated": null,
    "latest_report_hash": null,
    "facts_environment": "production",
    "cached_catalog_status": null,
    "report_environment": null,
    "latest_report_corrective_change": null,
    "catalog_environment": "production",
    "facts_timestamp": "2016-11-01T06:42:15.135Z",
    "latest_report_noop": null,
    "expired": null,
    "latest_report_noop_pending": null,
    "report_timestamp": null,
    "certname": "devco.net",
    "catalog_timestamp": "2016-11-01T06:42:16.971Z",
    "latest_report_status": null
  }
]

There are a bunch of in-built relationships between say a node and it’s facts and inventory, so queries can get quite complex:

inventory[certname] { 
  facts.osfamily = "RedHat" and
  facts.dc = "linodeldn" and
  resources { 
    type = "Package" and
    title = "java" and
    parameters.ensure = "1.7.0" 
  } 
}

This finds all the RedHat machines in a particular DC with Java 1.7.0 on them. Be aware this will also find machines that are deactivated.

I won’t go into huge details of the queries, the docs are pretty good – examples, overview.

So this is quite interesting, this finally gives us a reasonably usable DB to do queries that mcollective discovery used to be used for – but of course its not a live view nor does it have any clue what the machines are up to but as a cached data source for discovery this is interesting.

Using


CLI


You can of course query this stuff on the CLI and I suggest you familiarise yourself with JQ.

First you’ll have to set up your account:

{
  "puppetdb": {
    "server_urls": "https://puppet:8081",
    "cacert": "/home/rip/.puppetlabs/etc/puppet/ssl/certs/ca.pem",
    "cert": "/home/rip/.puppetlabs/etc/puppet/ssl/certs/rip.mcollective.pem",
    "key": "/home/rip/.puppetlabs/etc/puppet/ssl/private_keys/rip.mcollective.pem"
  }
}

This is in ~/.puppetlabs/client-tools/puppetdb.conf which is a bit senseless to me since there clearly is a standard place for config files, but alas.

Once you have this and you installed the puppet-client-tools package you can do queries like:

$ puppet query "nodes { certname ~ 'devco.net' }"

Puppet Code


Your master will have the puppetdb-termini package on it and this brings with it Puppet functions to query PuppetDB so you do not need to use a 3rd party module anymore:

$nodes = puppetdb_query("nodes { certname ~ 'devco' }")

Puppet Job


At the recent PuppetConf Puppet announced that their enterprise tool puppet job supports using this as discovery, if I remember right it’s something like:

$ puppet job run -q 'nodes { certname ~ 'devco' }'

MCollective


At PuppetConf I integrated this into MCollective and my Choria tool, both these are still due a release (MCO-776, choria #61):

Run Puppet on all the nodes matched by the query:

$ puppet query "nodes { certname ~ 'devco.net' }"|mco rpc puppet runonce

The above is a bit limited in that the apps in question have to specifically support this kind of STDIN discovery – the rpc app does.

I then also added support to the Choria CLI:

$ mco puppet runonce -I "pql:nodes[certname] { certname ~ 'devco.net' }"

These queries are a bit special in that they must return just the certname as here, I’ll document this up. The reason for this is that they are actually part of a much larger query done in the Choria discovery system (that uses PQL internally and is a good intro on how to query this API from code).

Here’s an example of a complex query – as used by Choria internally – that limits nodes to ones in a particular collective, our PQL query who have mcollective installed and running. You can see you can nest and combine queries into quite complex ones:

nodes[certname, deactivated] { 
  # finds nodes in the chosen inventory via a fact
  (certname in inventory[certname] { 
    facts.mcollective.server.collectives.match("d+") = "mcollective" 
  }) and 
 
  # does the supplied PQL query
  (certname in nodes[certname] {
    certname ~ 'devco.net'
  }) and
 
  # limited to machines with mcollective installed
  (resources {
    type = "Class" and title = "Mcollective"
  }) and 
 
  # who also have the service started
  (resources {
    type = "Class" and title = "Mcollective::Service"
  }) 
}

Conclusion


This is really handy and I hope more people will become familiar with it. I don’t think this quite rolls off the fingers easily – but neither does SQL or any other similar system so par for the course. What is amazing is that we can get nearer to having a common language across CLI, Code, Web UIs and 3rd party tools for describing queries of our estate so this is a major win.

Puppet 4 Sensitive Data Types

You often need to handle sensitive data in manifests when using Puppet. Private keys, passwords, etc. There has not been a native way to deal with these and so a cottage industry of community tools have spring up.

To deal with data at rest various Hiera backends like the popular hiera-eyaml exist, to deal with data on nodes a rather interesting solution called binford2k-node_encrypt exist. There are many more but less is more, these are good and widely used.

The problem is data leaks all over the show in Puppet – diffs, logs, reports, catalogs, PuppetDB – it’s not uncommon for this trusted data to show up all over the place. And dealing with this problem is a huge scope issue that will require adjustments to every component – Puppet, Hiera / Lookup, PuppetDB, etc.

But you have to start somewhere and Puppet is the right place, lets look at the first step.

Sensitive[T]


Puppet 4.6.0 introduce – and 4.6.1 fixed – a new data type that decorates other data telling the system it’s sensitive. And this data cannot by accident become logged or leaked since the type will only return a string indicating it’s redacted.

It’s important to note this is step one of a many step process towords having a unified blessed way of dealing with Sensitive data all over. But lets take a quick look at them. The official specification for this feature lives here.

In the most basic case we can see how to make sensitive data, how it looks when logged or leaked by accident:

$secret = Sensitive("My Socrates Note")
notice($secret)

This prints out the following:

Notice: Scope(Class[main]): Sensitive [value redacted]

To unwrap this and gain access to the real original data:

$secret = Sensitive(hiera("secret"))
 
$unwrapped = $secret.unwrap |$sensitive| { $sensitive }
notice("Unwrapped: ${unwrapped}")
 
$secret.unwrap |$sensitive| { notice("Lambda: ${sensitive}") }

Here you can see how to assign it unwrapped to a new variable or just use it in a block. Important to note you should never print these values like this and ideally you’d only ever use them inside a lambda if you have to use them in .pp code. Puppet has no concept of private variables so this $unwrapped variable could be accessed from outside of your classes. A lambda scope is temporary and private.

The output of above is:

Notice: Scope(Class[main]): Unwrapped: Too Many Secrets
Notice: Scope(Class[main]): Lambda: Too Many Secrets

So these are the basic operations, you can now of course pass the data around classes.

class mysql (
  Sensitive[String] $root_pass
) {
  # somehow set the password
}
 
class{"mysql":
  root_pass => Sensitive(hiera("mysql_root"))
}

Note here you can see the class specifically wants a String that is sensitive and not lets say a Number using the Sensitive[String] markup. And if you attempted to pass Sensitive(1) into it you’d get a type missmatch error.

Conclusion


So this appears to be quite handy, you can see down the line that lookup() might have a eyaml like system and emit Sensitive data directly and perhaps some providers and types will support this. But as I said it’s early days so I doubt this is actually useful yet.

I mentioned how other systems like PuppetDB and so forth also need updates before this is useful and indeed today PuppetDB is oblivious to these types and stores the real values:

$ puppet query 'resources[parameters] { type = "Class" and title = "Test" }'
...
  {
    "parameters": {
      "string": "My Socrates Note"
    }
  },
...

So this really does not yet serve any purpose but as a step one it’s an interesting look at what will come.

Interacting with the Puppet CA from Ruby

I recently ran into a known bug with the puppet certificate generate command that made it useless to me for creating user certificates.

So I had to do the CSR dance from Ruby myself to work around it, it’s quite simple actually but as with all things in OpenSSL it’s weird and wonderful.

Since the Puppet Agent is written in Ruby and it can do this it means there’s a HTTP API somewhere, these are documented reasonably well – see /puppet-ca/v1/certificate_request/ and /puppet-ca/v1/certificate/. Not covered is how to make the CSRs and such.

First I have a little helper to make the HTTP client:

def ca_path; "/home/rip/.puppetlabs/etc/puppet/ssl/certs/ca.pem";end
def cert_path; "/home/rip/.puppetlabs/etc/puppet/ssl/certs/rip.pem";end
def key_path; "/home/rip/.puppetlabs/etc/puppet/ssl/private_keys/rip.pem";end
def csr_path; "/home/rip/.puppetlabs/etc/puppet/ssl/certificate_requests/rip.pem";end
def has_cert?; File.exist?(cert_path);end
def has_ca?; File.exist?(ca_path);end
def already_requested?;!has_cert? && File.exist?(key_path);end
 
def http
  http = Net::HTTP.new(@ca, 8140)
  http.use_ssl = true
 
  if has_ca?
    http.ca_file = ca_path
    http.verify_mode = OpenSSL::SSL::VERIFY_PEER
  else
    http.verify_mode = OpenSSL::SSL::VERIFY_NONE
  end
 
  http
end

This is a HTTPS client that uses full verification of the remote host if we have a CA. There’s a small chicken and egg where you have to ask the CA for it’s own certificate where it’s a unverified connection. If this is a problem you need to arrange to put the CA on the machine in a safe manner.

Lets fetch the CA:

def fetch_ca
  return true if has_ca?
 
  req = Net::HTTP::Get.new("/puppet-ca/v1/certificate/ca", "Content-Type" => "text/plain")
  resp, _ = http.request(req)
 
  if resp.code == "200"
    File.open(ca_path, "w", Ob0644) {|f| f.write(resp.body)}
    puts("Saved CA certificate to %s" % ca_path)
  else
    abort("Failed to fetch CA from %s: %s: %s" % [@ca, resp.code, resp.message])
  end
 
  has_ca?
end

At this point we have the CA and saved it, future requests will be verified against this CA. If you put the CA there using some other means this will do nothing.

Now we need to start making our CSR, first we have to make a private key, this is a 4096 bit key saved in pem format:

def write_key
  key = OpenSSL::PKey::RSA.new(4096)
  File.open(key_path, "w", Ob0640) {|f| f.write(key.to_pem)}
  key
end

And the CSR needs to be made using this key, Puppet CSRs are quite simple with few fields filled in, can’t see why you couldn’t fill in more fields and of course it now supports extensions, I didn’t add any of those here, just a OU:

def write_csr(key)
  csr = OpenSSL::X509::Request.new
  csr.version = 0
  csr.public_key = key.public_key
  csr.subject = OpenSSL::X509::Name.new(
    [
      ["CN", @certname, OpenSSL::ASN1::UTF8STRING],
      ["OU", "my org", OpenSSL::ASN1::UTF8STRING]
    ]
  )
  csr.sign(key, OpenSSL::Digest::SHA1.new)
 
  File.open(csr_path, "w", Ob0644) {|f| f.write(csr.to_pem)}
 
  csr.to_pem
end

Let’s combine these to make the key and CSR and send the request to the Puppet CA, this request is verified using the CA:

def request_cert
  req = Net::HTTP::Put.new("/puppet-ca/v1/certificate_request/%s?environment=production" % @certname, "Content-Type" => "text/plain")
  req.body = write_csr(write_key)
  resp, _ = http.request(req)
 
  if resp.code == "200"
    puts("Requested certificate %s from %s" % [@certname, @ca])
  else
    abort("Failed to request certificate from %s: %s: %s: %s" % [@ca, resp.code, resp.message, resp.body])
  end
end

You’ll now have to sign the cert on your Puppet CA as normal, or use autosign, nothing new here.

And finally you can attempt to fetch the cert, this method is designed to return false if the cert is not yet ready on the master – ie. not signed yet.

def attempt_fetch_cert
  return true if has_cert?
 
  req = Net::HTTP::Get.new("/puppet-ca/v1/certificate/%s" % @certname, "Content-Type" => "text/plain")
  resp, _ = http.request(req)
 
  if resp.code == "200"
    File.open(cert_path, "w", Ob0644) {|f| f.write(resp.body)}
    puts("Saved certificate to %s" % cert_path)
  end
 
  has_cert?
end

Pulling this all together you have some code to make keys, CSR etc, cache the CA and request a cert is signed, it will then do a wait for cert like Puppet does till things are signed.

def main
  abort("Already have a certificate '%s', cannot continue" % @certname) if has_cert?
 
  make_ssl_dirs
  fetch_ca
 
  if already_requested?
    puts("Certificate %s has already been requested, attempting to retrieve it" % @certname)
  else
    puts("Requesting certificate for '%s'" % @certname)
    request_cert
  end
 
  puts("Waiting up to 120 seconds for it to be signed")
  puts
 
  12.times do |time|
    print "Attempting to download certificate %s: %d / 12r" % [@certname, time]
 
    break if attempt_fetch_cert
 
    sleep 10
  end
 
  abort("Could not fetch the certificate after 120 seconds") unless has_cert?
 
  puts("Certificate %s has been stored in %s" % [@certname, ssl_dir])
end