puppet availability and performance at 100k nodes - puppetconf 2014

puppet @ 100,000+ agents

John Jawed (“JJ”)eBay/PayPal

issues ahead encountered at <1000 agents

but I don’t have 100,000 agents

me

responsible for Puppet/Foreman @ eBay

how I got here:

engineer -> engineer with root access -> system/infrastructure engineer

free time: PuppyConf

puppet @ eBay, quick facts

-> perhaps the largest Puppet deployment-> more definitively the most diverse-> manages core security-> trying to solve the “p100k” problems

#’s• 100K+ agents– Solaris, Linux, and Windows– Production & QA– Cloud (openstack & VMware) + bare metal

• 32 different OS versions, 43 hardware configurations– Over 300 permutations in production

• Countless apps from C/C++ to Hadoop– Some applications over 15+ years old

currently• 3-4 puppet masters per data center• foreman for ENC, statistics, and fact collection• 150+ puppet runs per second• separate git repos per environment, common core

modules– caching git daemon used by ppm’s

nodes growing, sometimes violently

linear growth trendline

setup puppetmasters

setup puppet master, it’s the CA too

sign and run 400 agents concurrently, that’s less than half a percent of all the nodes you need to get through.

not exactly puppet issues

entropy unavailablecrypto is CPU heavy (heavier than you ever have and still believe)passenger children are all busy

OK, let’s setup separate hosts which only function as a CA

multiple dedicated CA’s

much better, distributed the CPU I/O and helped the entropy problem.

the PPM’s can handle actual puppet agent runs because they aren’t tied up signing. Great!

wait, how do the CA’s know about each others certs?

some sort of network file system (NFS sounds okay).

shared storage for CA cluster-> Get a list of pending signing requests (should be small!)# puppet cert list…wait…wait…

optimize CA’s for large # of certs

Traversing a large # of certs is too slow over NFS.

-> Profile-> Implement optimization-> Get patch accepted (PUP-1665, 8x improvement)

<3 puppetlabs team

optimizing foreman- read heavy is fine, DB’s do it well.- read heavy in a write heavy environment is more challenging.

- foreman writes a lot of log, fact, and report data post puppet run.- majority of requests are to get ENC data

- use makara with PG read slaves (https://github.com/taskrabbit/makara) to scale ENC requests

- Needs updates to foreigner (gem)- If ENC requests areslow, puppetmasters fall over.

optimizing foreman

ENC requests load balanced to read slaves

fact/report/host info write requests sent to master

makara knows how to arbitrate the connection (great job TaskRabbit team!)

more optimizationsmake sure RoR cache is set to use dalli (config.cache_store = :dalli_store), see foreman wiki

fact collection optimization (already in upstream), without this reporting facts back to foreman can kill a busy puppetmaster! (if you care: https://github.com/theforeman/puppet-foreman/pull/145)

<3 the foreman team

let’s add more nodes

Adding another 30,000 nodes (that’s 30% coverage).

Agent setup: pretty standard stuff, puppet agent as a service.

results

average puppet run: 29 seconds.

not horrible. but average latency is a lie because that usually represents the mean average (sum of N / N).

the actual puppet run graph looks more like…

curve impossible

No one in operations or infrastructure ever wants a service runtime graph like this.

meanaverage

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 16765 puppet 20 0 341m 76m 3828 S 53.0 0.1 67:14.92 ruby 17197 puppet 20 0 343m 75m 3828 S 40.7 0.1 62:50.01 ruby 17174 puppet 20 0 353m 78m 3996 S 38.7 0.1 70:07.44 ruby 16330 puppet 20 0 338m 74m 3828 S 33.8 0.1 66:08.81 ruby 17231 puppet 20 0 344m 75m 3820 S 29.8 0.1 70:00.47 ruby 17238 puppet 20 0 353m 76m 3996 S 29.8 0.1 69:11.94 ruby 17187 puppet 20 0 343m 76m 3820 S 26.2 0.1 70:48.66 ruby 17156 puppet 20 0 353m 75m 3984 S 25.8 0.1 64:44.62 ruby… system processes

PPM running @ medium load

60 seconds later…idle PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND

17343 puppet 20 0 344m 77m 3828 S 11.6 0.1 74:47.23 ruby 31152 puppet 20 0 203m 9048 2568 S 11.3 0.0 0:03.67 httpd 29435 puppet 20 0 203m 9208 2668 S 10.9 0.0 0:05.46 httpd 16220 puppet 20 0 337m 74m 3828 S 10.3 0.1 70:07.42 ruby 16354 puppet 20 0 339m 75m 3816 S 10.3 0.1 62:11.71 ruby… system processes

120 seconds later…thrashing PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 16765 puppet 20 0 341m 76m 3828 S 94.0 0.1 67:14.92 ruby 17197 puppet 20 0 343m 75m 3828 S 93.7 0.1 62:50.01 ruby 17174 puppet 20 0 353m 78m 3996 S 92.7 0.1 70:07.44 ruby 16330 puppet 20 0 338m 74m 3828 S 90.8 0.1 66:08.81 ruby 17231 puppet 20 0 344m 75m 3820 S 89.8 0.1 70:00.47 ruby 17238 puppet 20 0 353m 76m 3996 S 89.8 0.1 69:11.94 ruby 17187 puppet 20 0 343m 76m 3820 S 88.2 0.1 70:48.66 ruby 17156 puppet 20 0 353m 75m 3984 S 87.8 0.1 64:44.62 ruby17152 puppet 20 0 353m 75m 3984 S 86.3 0.1 64:44.62 ruby17153 puppet 20 0 353m 75m 3984 S 85.3 0.1 64:44.62 ruby17151 puppet 20 0 353m 75m 3984 S 82.9 0.1 64:44.62 ruby… more ruby processes

what we really want

A flat consistent runtime curve, this is important for any production service.Without predictability there is no reliability!

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 16765 puppet 20 0 341m 76m 3828 S 53.0 0.1 67:14.92 ruby 17197 puppet 20 0 343m 75m 3828 S 40.7 0.1 62:50.01 ruby 17174 puppet 20 0 353m 78m 3996 S 38.7 0.1 70:07.44 ruby 16330 puppet 20 0 338m 74m 3828 S 33.8 0.1 66:08.81 ruby 17231 puppet 20 0 344m 75m 3820 S 29.8 0.1 70:00.47 ruby 17238 puppet 20 0 353m 76m 3996 S 29.8 0.1 69:11.94 ruby 17187 puppet 20 0 343m 76m 3820 S 26.2 0.1 70:48.66 ruby 17156 puppet 20 0 353m 75m 3984 S 25.8 0.1 64:44.62 ruby… system processes

consistency @ medium load

hurdle: runinterval

near impossible to get a flat curve because of uneven and chaotic agent run distribution.

runinterval is non-deterministic … even if you manage to sync up service times eventually it’s nebulous.

the puppet agent daemon approach is not going to work.

plan A: puppet via crongenerate run time based some deterministic agent data point (IP, MAC address, hostname, etc.).

IE, if you wanted a puppet run every 30 minutes, your crontab may look like:

08 * * * * puppet agent -t38 * * * * puppet agent -t

plan A yields

Fewer and predictable spikes

Improved.

But does not scale because cronjobs help run times become deterministic but lack even distribution.

eliminate all masters? masterless puppet

kicking the can down the road, somewhere infrastructure still has to serve the files and catalog to agents.

masterless puppet creates a whole host of other issues (file transfer channels, catalog compiler host).

eliminate all masters? masterless puppet

…and the same issues exists in albeit in different forms.

shifts problems to “compile interval” and “manifest/module push interval”.

plan Z: increase your runinterval

Z, the zombie apocalypse plan (do not do this!).

delaying failure till you are no longer responsible for it (hopefully).

alternate setups

SSL termination on load balancer – expensive- LB’s are difficult to deploy, cost more (you still

need fail over otherwise it’s a SPoF!)

caching – cache is meant to make things faster, not required to work. If cache is required to make services functional, solving the wrong problem.

zen moment

maybe the issue isn’t about timing the agent from the host.

maybe the issue is that the agent doesn’t know when there’s enough capacity to reliably and predictably run puppet.

enforcing states is delayed

runinterval/cronjobs/masterless setups still render puppet as a suboptimal solution in a state sensitive environment (customer and financial data).

the problem is not unique to puppet. salt, coreOS, et al. are susceptible.

security trivia

web service REST3DotOh just got compromised and allows a sensitive file managed by puppet to be manipulated.

Q: how/when does puppet set the proper state?

the how; sounds awesome

A: every puppet runs ensures that a file is in its’ intended state and records the previous state if it was not.

the when; sounds far from awesome

A: whenever puppet is scheduled to run next. up to runinterval minutes from the compromise, masterless push, or cronjob execution.

smaller intervals help but…

all the strategies have one common issue:

puppet masters do not scale with smaller intervals, exasperate spikes in the runtime curve.

this needs to change

pvc

“pvc” – open source & lightweight process for a deterministic and evenly distributed puppet service curve…

…and reactive state enforcement puppet runs.

pvca different approach that executes puppet runs based on available capacity and local state changes.

pings from an agent to check if its’ time to run puppet.

file monitoring to force puppet runs when important files change outside of puppet (think /etc/shadow, /etc/sudoers).

pvc

basic concepts:- Frequent pings to determine when to run puppet

- Tied in to backend PPM health/capacity- Frequent fact collection without needing to run puppet- Sensitive files should be subject to monitoring

- on change or updates outside of puppet, immediately run puppet!

- efficiency an important factor.

pvc advantages

-> variable puppet agent run timing- allows the flat and predictable service curve (what we

want).- more frequent puppet runs when capacity is available,

less frequent puppet runs less capacity is available.

pvc advantages

-> improves security (kind of a big deal these days)- puppet runs when state changes rather than waiting to

run.- efficient, uses inotify to monitor files.- if a file being monitored is changed, a puppet run is

forced.

pvc advantages- orchestration between foreman & puppet

- controlled rollout of changes- upload facts between puppet runs into foreman

pvc – backend3 endpoints – all get the ?fqdn=<certname> parameter

GET /host – should pvc run puppet or facter?POST /report – raw puppet run output, files monitored were changedPOST /facts – facter output (puppet facts in JSON)

pvc – /host> curl http://hi.com./host?fqdn=jj.e.com

< PVC_RETURN=0< PVC_RUN=1< PVC_PUPPET_MASTER=puppet.vip.e.com< PVC_FACT_RUN=0< PVC_CHECK_INTERVAL=60< PVC_FILES_MONITORED="/etc/security/access.conf /etc/passwd"

pvc – /facts

allows collecting of facts outside of the normal puppet run, useful for monitoring.

set PVC_FACT_RUN to report facts back to the pvc backend.

pvc – git for auditingpush actual changes between runs into git

- branch per host, parentless branches & commits are cheap.

- easy to audit fact changes (fact blacklist to prevent spam) and changes between puppet runs.

- keeping puppet reports between runs is not helpful.

pvc – incremental rolloutsselect candidate hosts based on your criteria and set an environment variable via the /host endpoint output:

FACTER_UPDATE_FLAG=true

in your manifest, check:

if $::UPDATE_FLAG {…

}

example pvc.confhost_endpoint=http://jj.e.com./hostreport_endpoint=http://jj.e.com./reportfacts_endpoint=http://jj.e.com./factsinfo=1warnings=1

pvc – available on github

$ git clone https://github.com/johnj/pvc

make someone happy, achieve:

wishlist

stuff pvc should probably have:• authentication of some sort• a more general backend, currently tightly integrated

into internal PPM infrastructure health• whatever other users wish it had

misc. lessons learnedyour ENC has to be fast, or your puppetmasters fail without ever doing anything.

upgrade ruby to 2.x for the performance improvements.

serve static module files with a caching http server (nginx).

contact

@johnjawed

https://github.com/johnj

jj@x.com