Velocity 2015 building self healing systems (slide share version)

Building Self-Healing Systems

Todd Minnella and Matt Solnit, SOASTA

Speaker Intro - Todd

● Director of Ops for● Over 25 years in IT● Experience with both

academic andenterprise computing

● Favorite operating system is Tru64● Enjoys solving problems...but loves sleep more!@[email protected]

Speaker Intro - Matt

● VP of Engineering for ● Started programming with Atari BASIC in

elementary school● Ops on the side :-)● First Velocity presentation!

@[email protected]

Who are you? :-)

http://www.cliarthut.com/clip-arts/751/who-are-you-clip-art-751173.jpg

Agenda (1 of 2)

Part One - Theory● Distributed Systems Challenges● Mitigating Failure Impact● Benefits and Risks ● Testing Requirements● Methodology

Agenda (2 of 2)

Part Two - Practice● Description of Demo System● Example #1 - Externally Triggered Full GC● Example #2 - External System Restart● Example #3 - System-initiated Support Case● Tools Demonstrated● Other Ideas for Automation

Part One

Theory

What makes a distributed system?

● Multiple components● Different servers● Different regions (data center or geo)● A component failure != service or app failure● Requires systems thinking

Challenges faced by dist. systems

● Complexity● Uncontrollable elements● Hard to see the whole picture● Impossible for a single person to manage

What can we do about it?

Easy answer:

Add people!But… easy != correct

Better coping strategy

Enable your systems to heal themselves

...which is why we are here!

Benefits of Self-Healing

● Better uptime (at the component level)● Higher service quality● Rapid identification of repeating issues● Improved Ops team morale and productivity

Risk of Self-Healing Systems

● Worse uptime (at the component level)● Lower service quality● Maintenance complexities● Degraded Ops team morale and productivity

So why take the risks?

Implemented well, self-healing systems can make for happier customers!

Failsafe Design

Bibel, G. D. Train Wreck: The Forensics of Rail Disasters. Baltimore: Johns Hopkins UP, 2012. 69-70. Print.

Methodology

Identify the ProblemDesign the SolutionExecute by Hand

Automate the solution Watch and adjust

PSHAW!

Part Two

Practice

Demo Application

Java App Server Farm (n = 2)Amazon Linux EC2 InstanceEC2 Elastic IP addressLoad Balanced via DNS (Dyn Traffic Director)Simple Web Application (HTTP/HTTPS)

Example #1Externally Triggered Full GC

Real-life mPulse example

Started reporting Java statistics to monitoring tool in 2013.When investigating outages, often found an exact correlation with large garbage collections (sound familiar?).Set up an alert to fire when heap usage went above 70%Everybody into the war room!

Real-life mPulse example, cont’d


Engineering looks for a possible memory leak.Eventually someone says, “Just force a GC!”Most of the time, this would fix it. JVM isn’t perfect, if we help it then the system remains stable.Occasionally this didn’t fix it, which would indicate an actual bug.Engineering fixes, deploy, repeat!

“Intermittent gratification”90% of the time, there was no need to gather everyone together.


Engineering says…

Ops, can you fix it?

Identify the Problem

1. Java isn’t garbage-collecting efficiently.2. Tuning the JVM is time-consuming and

dangerous.3. Forcing a collection works, but it requires

waking someone up.

Describe a Solution (1 of 2)

Identify a metric for JVM Heap Use that is indicative of the problem:

Java VM Old % UsedStart monitoring/reporting this metric.Specify a threshold for action:

Old % Used > 65%

Describe a Solution (2 of 2)

When the threshold is reached, take an action:Trigger a full garbage collection

After the action, monitor for success:Old % Used < 65%

Execute by Hand

Trigger the condition that causes the problem (or be patient and let it happen).

Once monitoring indicates high old % used, manually execute the full GC.

Automate the Solution, Manually Trigger

Write a script to check for Java old % used.Run the script via cron or similar mechanism.Report when old % used exceeds threshold.A DevOps human will trigger the full GC.

Script SnippetJAVA_PID=`pgrep -f -u tomcat /usr/lib/jvm/jre/bin/java`

RAW_JSTATS=`jstat -gcutil $JAVA_PID | grep -v "S0"òld_pcnt_used=ècho $RAW_JSTATS | cut -f4 -d" "`

integer_old_pcnt_used=ècho $old_pcnt_used | \ awk '{ printf ("%1.0f", $1) }'`

if [ $integer_old_pcnt_used -gt $oldpcnttrigger ]; thenecho "Would trigger full GC here"

fi

https://github.com/SOASTA/velocity-2015-self-healing-systems

DEMO (part 1)

Automate the Solution, Automate the Trigger

Taking the script shown previously, combine the step that:Reports that old % used > 65%with the step that:Triggers the full GC

DEMO (part 2)

Watch and adjust

Set up the automated script to run in as many test environments as are available/applicable.Review the results (script log, metrics graphs).Does it work?Investigate any issues thoroughly.Potentially, install the script in a dry-run mode in production.

Go Live!

We recommend a gradual deployment.Deploy to a subset of production, then assess.Expand the subset, assess again.When all of production is live, enjoy more sleep!

Example #2Externally Triggered Restart


What is a beacon?{"timestamp":1392256183739,"drop_code":"crumb:missing","http_method":"GET","http_version":"HTTP/1.1","http_referrer":"","headers":{"host":"localhost:8080","accept":"*/*"},"params":{"nt_dns_end":"1392147897985","nt_load_end":"1392147912182","nt_first_paint":"1392147900.964995","mem.used":"131000000","nt_spdy":"0","nt_unload_end":"1392147898577","nt_dns_st":"1392147897985","nt_con_st":"1392147897985","rt.bmr.conEn":"834.00000000006","rt.bmr.resEn":"2320.0000000001637","mem.total":"199000000","nt_nav_st":"1392147897985","nt_domcontloaded_end":"1392147901891","dom.sz":"58549","rt.tstart":"1392147897985","rt.bmr.domSt":"419.0000000000964","nt_con_end":"1392147897985","nt_domint":"1392147901585","nt_red_end":"0","dom.ln":"939","nt_unload_st":"1392147898574","t_done":"14201","nt_load_st":"1392147912129","t_page":"13638","rt.end":"1392147912186","nt_domloading":"1392147898927","nt_res_end":"1392147898571","t_resp":"563","rt.bmr.domEn":"813.0000000001019","rt.tt":"14201","nt_red_cnt":"0","if":"","nt_fet_st":"1392147897985","nt_res_st":"1392147898548","nt_req_st":"1392147897995","nt_nav_type":"0","mob.ct":"0","dom.img":"16","nt_red_st":"0","rt.ss":"1392147897985","config.timedout":"true","rt.bmr.resSt":"2312.0000000001255","rt.si":"3el0j57fms0885mi-n0uk6y","rt.sl":"1","rt.bmr.fetSt":"16.000000000076398","rt.bmr.conSt":"813.0000000001019","nt_domcomp":"1392147912129","dom.script":"27","v":"0.9.1389663787","rt.bmr.reqSt":"834.00000000006","r":"","rt.bstart":"1392147906107","rt.obo":"0","rt.start":"navigation","nt_domcontloaded_st":"1392147901585"}}


Each server processes millions of these per day.Beacons are logged to disk, eventually compressed and uploaded to S3.


Every so often, the background uploader thread stops working.

(we don’t know why yet)

When this happens, we get 10-12 hours before the disk fills up and the server dies.


A simple re-start fixes it.

SO...

While developers are investigating, Ops is getting paged (and woken up) to re-start boxes.

Ops says…

We can do better!

Identify the Problem (Demo App)

● Lack of activity indicates a failed thread● While the issue goes unresolved, data is

delayed (and the disk may fill)

Describe a Solution

● A restart of the application solves the problem

● The application server needs to be removed from service prior to the restart

● The server hosting the application is an AWS instance, and a reboot is fast and effective

Execute by Hand

1. Take the application out-of-service2. Restart the application3. Watch for Self-Check OK4. Put the application back in-service


● Log metrics go to AWS CloudWatch● Lack of activity triggers an Alarm● Alarm triggers a SNS notification● Human being makes the DNS changes and

restart the server.

Developers say…

We can do better!


● EC2 and DynECT both have APIs● DNS changes and reboot can all be

automated● Todd can sleep!


AWS LambdaUpload code to Amazon (Node.js)Attach it to a listener (SNS)No instance required!


Lambda function listens on “logs are not being uploaded” notification.Uses Dyn REST API to disable the DNS record.Uses EC2 API to re-boot the instance.


Lambda function listens on “all OK” notification.Uses Dyn REST API to re-enable the DNS record.

var dynect = require('./dynect_api.js');var AWS = require('aws-sdk');

exports.cloudwatch_alarm_sns_handler = function(event, context) { event.Records.forEach(function(record) { var alarm = JSON.parse(record.Sns.Message);

// Extract the instance status. ALARM means it's down, OK means it's up. var instance_up = alarm.NewStateValue !== "ALARM";

// ...

https://github.com/SOASTA/velocity-2015-self-healing-systems

Node.js code snippet

New workflow

Look, no Todd!

Watch and adjust

● Include Ops team on ALARM and SELFCHECKOK notifications

● Observe effects - use monitoring tools to assess availability

Example #3Application files support ticket


● Customers configure raw beacon uploads to their own S3 buckets.

● Sometimes they breakthings (or AWS accesskey is changed, etc.)

● We log the error, but we don’t monitor it and don’t notify customers.

Identify the Problem

● Another example: yser connecting to a site can’t authenticate successfully

● Assumption is that this is a limited access site

DevOps says…

Now, let’s help our customers succeed!

Describe a Solution

● Notify the Customer Support team● Provide Support with details so that they can

proactively reach out

Execute by Hand

● Examine the logs for the error● Review the situation with Support● Work with Support to handle a case end-to-

end


● Log metrics go to AWS CloudWatch● Presence of error triggers an Alarm● Alarm triggers a SNS notification● Human being can then create a Zendesk

case


● AWS Lambda listens on SNS notification● Collects information from the notification● Files a Zendesk case categorized to go to

the correct team

AWS Lambda Actions

On Failed Login notification● Create a Zendesk case with user details

Watch and adjust

● Ops reviews logs● Ops meets with Support to review case

frequency and outcomes

Testing Requirements

● Start small● Develop (and verify) in stages● Let run in production-like environment● Verify behavior in “dry-run” mode

Tools Demonstrated - AWS

CloudWatch http://aws.amazon.com/cloudwatch/

EC2 http://aws.amazon.com/ec2/

Lambda http://aws.amazon.com/lambda/

Linux http://aws.amazon.com/amazon-linux-ami/

http://aws.amazon.com/cloudwatch/

http://aws.amazon.com/ec2/

http://aws.amazon.com/lambda/

http://aws.amazon.com/amazon-linux-ami/

Tools Demonstrated - Other

Datadog https://www.datadoghq.com/product/

Dyn Traffic Director http://dyn.com/traffic-director/

Monitis http://www.monitis.com/

PagerDuty http://www.pagerduty.com

ZenDesk https://www.zendesk.com

https://www.datadoghq.com/product/

http://dyn.com/traffic-director/

http://www.monitis.com/

http://www.pagerduty.com/

https://www.zendesk.com/

See SOASTA at booth #801

Velocity 2015 building self healing systems (slide share version)

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