Leveraging In-Memory Key
Value Stores for Large Scale
Operations with Redis and
CFEngine
Mike Svoboda
Staff Systems and Automation Engineer
www.linkedin.com/in/mikesvoboda
msvoboda@linkedin.com
https://github.com/linkedin/sysops-api
My Background with
LinkedIn / CFEngine
 Hired at LinkedIn into System Operations in 2010
 When I started, our server count was 300 machines
 Implemented CFEngine automation in 2010
 Since then, we have grown 100 times that size
 Created our Redis API in 2012 to provide visibility
What is Redis?
 Redis is an in-memory key value store, similar to
Memcached with additional features
 Offers on disk persistence (snapshots to disk) - You
can use this as a real database instead of just a
volatile cache
 Offers simple data structures out of the box and
commands to work with them natively
 dictionaries, lists, sets, sorted sets, etc.
 Highly scalable data store - A single Redis server
can satisfy hundreds of thousands of requests per
second
 Supports transactions - Group commands together
so they are executed as a single transaction.
What is CFEngine?
CFEngine:
 Is an IT infrastructure automation framework that helps
manage infrastructure throughout its lifecycle
 Builds, deploys, and manages systems
 Provides auditing
 Maintains infrastructure by enforcing intended system state
for compliance
 Runs on the smallest embedded devices, servers, desktops,
mainframes, and big iron. CFEngine easily supports tens of
thousands of hosts. Provides horizontal scalability.
How CFEngine works
CFEngine reduces
operational costs
 Using CFEngine automation is
more effective than hiring
additional headcount
 Stop fighting fires every day
 Allow operations to focus on
tomorrow’s problems
 Stay ahead of the curve
 Keeping the lights on is
automated
 Respond to outages rapidly
Why LinkedIn chose CFEngine
 Very mature codebase
 Not dependent on underlying virtual machines like
Ruby, Python, Perl, etc.
 Flexible architecture
 Easily scale upwards to support thousands of
machines
 Just as simple to support smaller environments
 Zero reported security vulnerabilities
 Lightweight footprint
What CFEngine has done for
LinkedIn
Since implementing CFEngine:
 Operations has become extremely agile
 Quickly respond and resolve outages
 System administration workload has reduced, even with
100x the amount of servers
 Have built new datacenter in minutes with little effort
 Real time visibility after creating our Redis infrastructure,
driven by CFEngine execution
 Can answer any question imaginable about all of our servers in
seconds
 Know every action that happens on our machines
How LinkedIn uses CFEngine
Functions we have automated:
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Hardware failure detection
Account administration
Privilege escalation
Software deployment
O/S configuration management
Process / service management
Software deployment
System monitoring
You never need to log into a machine to manage it
Two problems still existed for Linkedin that
automation didn’t address
 The company wanted to be able to answer any question
imaginable about production.
 We didn’t want to break production by pushing new
automation changes.
To solve both problems, we needed visibility.
Problem #1: The company wants
questions answered. STAT!
 Management / Engineers want to have questions answered
immediately and ask several times a day interrupting your
work.
LinkedIn was hunting for data
What LinkedIn sysadmins were doing
• Questions about Infrastructure were answered by sysadmins
SSHing to machines to hunt for data.
• As our scale increased, we used a remote execution tool to
parallelize some variant of SSH / DSH
 Thousands of network connections
were made to remote machines
from a single host to fetch data.
 Did I get results from everything?
 Parse results after collection
Forcing command execution on
remote machines doesn’t scale
 Machines were missed, data wasn’t collected
 Firewalls mangled packets
 SSHD offline or didn’t spawn on the remote host
 Depended on system accounts being valid
 Network connections failed to the remote machine
 Data collection shouldn’t be complicated
 Unsure if we were able to collect all of the necessary
data.
Problem #2: We didn’t want to break production
by pushing new automation changes.
 Ops was hesitant of using automation because they
didn’t know where things would break
 When automation was expanded, we didn’t know where
systems need alternative behavior to work correctly (or
where they have been modified by developers with root
access)
 Ops had to be agile. We have to work fast. The
business needs us to modify production multiple times a
day, but we had to make changes without breaking it
Automation changes were
happening in the blind
 Sysadmins were under pressure from
 large ticket queues
 numerous change requests
 business needs to scale
 Automation changes were being performed without fully
understanding the impact before that change was
executed
 We realized that this could lead to mistakes, disasters,
outages, and pink slips. To keep this from happening, I
built our Redis API to provide visibility.
To provide visibility, we had to
scale data collection
 We had to build a reliable system that was extremely fast,
which could give us results of remote command execution
from tens of thousands of systems in seconds
 Querying this data could not put load on production
systems
 The cache needed to be publically available to the
company via an API so they could answer their own
questions
 We needed to quickly add new data into the cache before
pushing automation changes to view production impact.
We built a cache and populated it with
data to answer arbitrary questions
 Instead of executing commands remotely, we have CFEngine
populate the cache with commonly queried data
 CFEngine executes expensive commands like lshw or
dmidecode once and make the output available for everybody
to use
 Data collection becomes a scheduled event that happens once
a day - This data collection becomes a cost of doing business
 With the same data being gathered on all machines, it
becomes trivial to compare two or more pieces of hardware
Architecture of the Cache
Step 1: Rely on CFEngine
execution to drive data
insertion
Step 2: Shard your data
Step 3: Use software load
balancing!
Step 1: CFEngine drives data insertion
Leverage automation to change what you insert
or remove from the cache
The cache is a simple dictionary,
sharded over multiple Redis servers.
Step 2: Extract Sharded Data
 Determine scope. How much data do I need to answer
my question?
 For each CFEngine policy server running Redis, search
Redis for matching keys in the dictionary
 For each key we find from a search, perform the
relevant data extraction



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Contents
Md5sum
os.stat()
wordcount
Step 3: Use Software
Load Balancing!
 Have clients populate multiple Redis servers on
insertion - Pick a Redis server at random on
extraction (Load balancing)
 If we don’t get a response from our first choice,
pick another Redis server at random (failover)
 Find randomized CFEngine policy servers with Redis
from each level in the scope
 If the CFEngine policy server responds, push it
into a list of machines we need to query for data
 If the CFEngine policy server doesn’t respond,
pick another one at random (fail over)
Local Scope
Example: Local cache extraction
$ time extract_sysops_cache.py \
--search /etc/passwd \
--contents | grep msvoboda | wc -l
487
real
0m1.813s
user 0m1.484s
sys
0m0.087s
Site (datacenter) Scope
Example: Site cache extraction
$ time extract_sysops_cache.py \
--site lva1 \
--search /etc/passwd \
--contents | grep msvoboda | wc -l
8687
real
0m19.169s
user 0m30.286s
sys
0m1.271s
Global Scope
Example: Global cache
extraction
$ time extract_sysops_cache.py \
--scope global \
--search /etc/passwd \
--contents | grep msvoboda | wc -l
27344
real
0m44.827s
user
1m39.532s
sys
0m4.288s
Make it fast!
Become Multithreaded
Make it faster!
Build a Redis pipeline
Cache extraction with a pipeline
Extracting the Cache for Fun
and Profit
[msvoboda@esv4-infra01 ~]$ extract_sysops_cache.py \
--scope local \
--search mps*cm.conf \
--md5sum \
--prefix-hostnames
esv4-2360-mps01.corp.linkedin.com#/etc/cm.conf
esv4-2360-mps02.corp.linkedin.com#/etc/cm.conf
esv4-2360-mps03.corp.linkedin.com#/etc/cm.conf
esv4-2360-mps04.corp.linkedin.com#/etc/cm.conf
12721673715de3ee6b9dec487529355e
56b03a16c69e5b246a565dbcda44ba28
11e20e28ec60ac6c71cbb71b0a6c9b35
55402eda02e7f5c17dc7535455adc097
Make it fastest!
Compression is significant!
 Less network overhead on cache insertion
 Less network overhead on cache extraction
 More stuff we can put into the Cache
 With less network I/O = faster results delivered
 Less CPU usage on extraction
Seconds for cache insertion
CPU cycles for cache insertion
Data size in megabytes of the cache
for an entire datacenter
Time for cross country complete
datacenter cache extraction
Drink from the firehose
With Redis API, you can now be confident in
pushing automation changes
 You know what systems will be affected before a change
 You aren’t hit with surprises in production
 You have added visibility
 You don’t have to log into machines to modify or update
Summary
Before implementation After implementation of
of CFEngine & Redis API
CFEngine & Redis API
at LinkedIn
at LinkedIn
Headcount
6 people supporting a
few hundred machines
6 people supporting tens of
thousands of machines
Time spent
Hours to build a single
machine
Build complete datacenters
in minutes
Productivity
Hours spent collecting
data before change,
change itself causing
outages
Can focus on building
infrastructure, team
became proactive to fix
future problems, not
reactive / firefighting
Ease of scaling
server deployment
Incredibly difficult
to respond to change,
low visibility into
production
Superior administration,
rapid response to changing
needs, complete system
visibility
Open Source
Questions?
msvoboda@linkedin.com
www.linkedin.com/in/mikesvoboda
You can download the code from this
presentation here:
https://github.com/linkedin/sysops-api