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Leveraging In-Memory Key Value Stores for Large Scale Operations with Redis and CFEngine Mike Svoboda Staff Systems and Automation Engineer

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Presentation on theme: "Leveraging In-Memory Key Value Stores for Large Scale Operations with Redis and CFEngine Mike Svoboda Staff Systems and Automation Engineer"— Presentation transcript:

1 Leveraging In-Memory Key Value Stores for Large Scale Operations with Redis and CFEngine Mike Svoboda Staff Systems and Automation Engineer

2 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

3 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.

4 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.

5 How CFEngine works

6 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

7 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

8 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

9 How LinkedIn uses CFEngine Functions we have automated: 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

10 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.

11 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.

12 LinkedIn was hunting for data

13 What LinkedIn sysadmins were doing 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 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

14 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.

15 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

16 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.

17 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.

18 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

19 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!

20 Step 1: CFEngine drives data insertion Leverage automation to change what you insert or remove from the cache

21 The cache is a simple dictionary, sharded over multiple Redis servers.

22 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  Contents  Md5sum  os.stat()  wordcount

23 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)

24 Local Scope

25 Example: Local cache extraction $ time \ --search /etc/passwd \ --contents | grep msvoboda | wc -l 487 real0m1.813s user0m1.484s sys0m0.087s

26 Site (datacenter) Scope

27 Example: Site cache extraction $ time \ --site lva1 \ --search /etc/passwd \ --contents | grep msvoboda | wc -l 8687 real0m19.169s user0m30.286s sys0m1.271s

28 Global Scope

29 Example: Global cache extraction $ time \ --scope global \ --search /etc/passwd \ --contents | grep msvoboda | wc -l 27344 real0m44.827s user1m39.532s sys0m4.288s

30 Make it fast! Become Multithreaded

31 Make it faster! Build a Redis pipeline

32 Cache extraction with a pipeline

33 Extracting the Cache for Fun and Profit [msvoboda@esv4-infra01 ~]$ \ --scope local \ --search mps*cm.conf \ --md5sum \ --prefix-hostnames 12721673715de3ee6b9dec487529355e 56b03a16c69e5b246a565dbcda44ba28 11e20e28ec60ac6c71cbb71b0a6c9b35 55402eda02e7f5c17dc7535455adc097

34 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

35 Seconds for cache insertion

36 CPU cycles for cache insertion

37 Data size in megabytes of the cache for an entire datacenter

38 Time for cross country complete datacenter cache extraction

39 Drink from the firehose

40 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

41 Summary Before implementation of CFEngine & Redis API at LinkedIn After implementation of CFEngine & Redis API at LinkedIn Headcount6 people supporting a few hundred machines 6 people supporting tens of thousands of machines Time spentHours to build a single machine Build complete datacenters in minutes ProductivityHours 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

42 Open Source Questions? You can download the code from this presentation here:

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