While testing in demo and stage is good (indeed, essential), testing in production is all too often overlooked. Deploying to production and hoping for the best is a gamble, not a strategy.
In this talk, we discuss
1) Better production deployment and testing strategies including dark pool testing, canary releases and feature switching.
2) After deployment, your work is still not done. We'll talk about Observability, including monitoring, tracing and metrics.
3) Finally, even with the best deployment strategies and monitoring techniques, your software WILL fail in production. It's a question of when, not if. So why not simulate those failures first? We'll finish with game days and chaos engineering.
This talk should be of interest to all developers, QA and Ops folks who are responsible for getting working software in front of users.
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We need Observability in our systems
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Everything is sometimes broken
Something is always broken
If nothing seems broken...
…your monitoring is broken
It’s impossible to predict the myriad states of partial failures we’ll see
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Testing in Production
Chaos
Engineering
Carefully planned experiments
designed to reveal weaknesses in our
systems
aka
Resilience Engineering
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Game Days
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An exercise where we place systems
under stress to
learn and improve resilience
(And even just getting the team together to discuss resilience can be worthwhile)
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Chaos Engineering – a step by step guide
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Hypothesis
(Steady state)
Minimize
Blast Radius
Run
Analyze
Increase
Repeat,
Automate
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Reading material
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Chaos Engineering (free eBook)
https://www.oreilly.com/webops-perf/free/chaos-engineering.csp
Distributed Systems Observability (free eBook)
https://distributed-systems-observability-ebook.humio.com/
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Reading material
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shaunabram.com
Principles of Chaos Engineering
principlesofchaos.org
How to run a Game Day
https://www.gremlin.com/community/tutorials/how-to-run-a-gameday/
Testing in production:
https://medium.com/@copyconstruct/testing-in-production-the-safe-way-18ca102d0ef1
Monitoring in the time of Cloud Native
https://medium.com/@copyconstruct/monitoring-in-the-time-of-cloud-native-c87c7a5bfa3e
Deploy != Release
https://blog.turbinelabs.io/deploy-not-equal-release-part-one-4724bc1e726b
Joke… or Good?
Non-prod = pale imitation, like mocks, or “it works on my machine”
Prod is different; 4th trimester…
“Testing in production”
You may have seen this meme before: The DosEquis guys saying
“I don’t always test, but when I do, I test in production”
“Testing in production” has been kind of a joke -> what you’re really saying is you don’t test anywhere.
And instead you’re just winging it: deploying to production and <CROSS FINGERS> hoping it all works.
But then I began to look at it differently.
The DosEquis guy usually says “I don’t always drink beer, but when I do, I drink DosEquis”
Meaning DosEquis is the best beer to drink.
So, the implication here is not that testing in production is a joke, but that Production is actually the BEST place to test.
And I’m increasingly believing that to be the case. Or, at least, that production is an environment we shouldn’t be ignoring for testing.
After all, prod is the only place your software has an impact on your customers is production.
But there has been this status quo of production being sacrosanct. Instead of testing there, it is common to keep a non-prod env, such as staging, as identical to production as possible, and test there.
Such environments are usually a pale imitation of production however.
Testing in staging is kind of like testing with mocks, an imitation, but not the real thing.
Saying “works in staging” is only one step better than “works on my machine”.
Production is a different beast!
I’ve heard of software being released to production as being like a baby’s 4th trimester.
When software leave’s it artificial environments and slams into the real world
But what makes the real world of production so special?
Serious question: In what ways in Production different from other environments?
Hardware & Cluster size, Data
Configuration, Traffic, Monitoring
Some things we can only test in production
As our architecture becomes more compilated (particularly with Microservices), we need to consider all options to allow us to test and deliver working software to our customer. Including testing in production.
So should we skip testing in non-prod first? No!
Testing in production is by no means a substitute for pre-production testing
I’ve given talks on
unit testing, integration testing
Mocks
About code coverage and Continuous Integration
I believe very firmly in all those things.
Testing in Production is an addition to all those.
Most production testing is really validation only – although there is at least one exception (A/B testing)
Respect production
Beware of unwanted side effects
Stateless services are good candidates
Think SAFE methods e.g. GET, HEAD
Consider testing using expected failures of others e.g. PUT that results in 400 error (still tells you something)
Or at least be able to tell the difference between test data and “real” prod data
Today we’re going to cover some of the different ways we can test in production
We’ll start with Observability, the foundation for any testing in production. Observability = Knowing what the heck your app is doing anyway. Going beyond just logs and alerting
Around deployment & release times
Chaos engineering. Perhaps the most advanced form of production testing, but I would argue its actually not that advanced. I talk about what is is, some basic rules for doing and how we’ve been starting to use it where I work.
Observability is
The ability…
Being able to answer questions that you have never thought of before
You can think of it as the next step beyond just monitoring and alerting
Systems have become more distributed, and in the case of containerization, more ephemeral. It is increasingly difficult to know what our software is doing
And Observability means bringing better visibility into systems
To have better visibility, we need to acknowledge that…
Everything is sometimes broken
Something is always broker
-> No complex system is ever fully healthy
If nothing broken…
Distributed systems are unpredictable. In particular, it’s impossible to predict all the ways a system might fail
Failure needs to be embraced at every phase (from design to implementation, testing, deployment, and operation)
Ease of debugging is of high importance
Logging:
Structured logging: plain text -> splunk friendly -> json
Eventlog can be a great source of logs for debugging too
Consider sampling rather than aggregation
Metrics: Time series metrics, like tracking system stats such as CPU and mem usage, stats like # logins
Tracing: Distributed traceability using Correlation ID lib; Zipkin etc
Alerting: Useful for about proactively learning about, typically, predictable issues
Tools: e.g. Splunk, NR, OverOps; Wavefront
EPX, TDA (Thread Dump Analyzer) UX
OverOps / HoneyComb
Stacktraces and exception trackers?
OK, so that was Observability
The ability to answer questions about our applications behavior in production. Questions we may have never even though of before.
And with Observability in place, what types of production testing can we do…
Let’s move onto…
Testing at Release time
Let’s start by defining some terms:
Deployment vs release
When talking with engineers, I usually use the term Chaos Engineering, because it sounds cool! When talking with management, I tend to use the term resilience engineering, since it sounds less scary. The terms are synonymous. In the past, terms such as Disaster Recovery and Contingency Planning have been used to describe somewhat similar processes.
Whatever term you use, it basically refers to
->
Conducting carefully planned experiments designed to reveal weaknesses in our systems.
In other words, CE is the practice of confirming that your applications work as you expect them to in production.
Despite the name, Chaos Engineering is not about introducing Chaos into your system! Instead it is about identifying any chaos already there, so that you can remediate.
For example, if you GIVE A GOOD CANONICAL EXAMPLE OF A CHAOS ENGINEERING EXPERIMENT HERE
if you believe your application will failover to something if x happens
or can handle x requests per second before failing.
What are Game Days?
If Chaos Engineering is the theory, Game days are the practice; the execution
Game days are where you start with Chaos engineering
->
Game days are “An exercise where we place systems under stress to learn and improve resilience”
Systems can be technology, people, process
They are like fire drills – an opportunity to practice a potentially dangerous scenario in a safer environment
To start with, what are we trying to test! Pick a hypothesis.
Typically in Chaos Engineering experiments, the hypothesis is that if I do X (take out a server, kill a region), everything should be OK
But we need to be specific about how to measure things are OK
If out hypothesis is “if we fail primary DB, everything should be ok,”
Then we need to define what OK is!
And a big part of defining OK is to define “Steady State”
Steady state is essentially what the key metrics are for you to monitor as part of your test. It could be things like:
Loan application remain constant
Or response times remain in an acceptable range
If you don’t define steady state, how do you know your test is working on not? How do you know if you are breaking things?
With a hypothesis in mind, and a way to test, but first think abut blast radius
2. Minimize the blast radius
The blast radius refers to to how much damage can be done by the experiment
If you take out a server, and everything is in fact NOT OK, how bad might it be
Try to ensure that you limit he possible damage
For example, if your hypothesis is that
When Foo service is running in a pool of 2 servers
And one of those servers dies, CPU and memory utilization should increase on the remain servers, but response time remain unaffected
That is a fine thing to test
But if you have 10 services depending on that service (even in non prod), and your wrong that response times will be unaffected, you may have caused 10 other services to have problems
So a way to limit the blast radius in that test would be to test using a pool of Foo Service that only one other service relies on. Hopefully a service that you also control and that is closely monitored as part of the test.
Another way to minimize possible damage is to make sure that you have the equivalent of a big red Stop Test button!
If you metrics aren’t looking good, have the ability to abort the test immediately.
Remember: our goal here is to build confidence in the resilience of the system and we so that with Controlled, contained experiments, that grow in scope incrementally.
3. Run the experiment
Figure out the best way to test your hypothesis
If you plan to take out a server, how do you do it?
ssh in and kill -9? Orderly shutdown? Have Ops do it for you? Do you simulate failure by using bogus IP addresses, or simply removing a server from a VIP pool?
And again, stop if metrics or alert dictate
4. Analyze the results
Were your expectations correct?
Did your system handle things correctly
Did you spot issue with you alerts, metrics that should be improved before any future tests
5. Increase scope
The idea is to start small
1 service, in non-prod, and gradually expand to prod.
And the goal should be prod. Prod is where’s it’s at!
That brings us to the end of the presentation
We have talked about Testing in production
No longer a joke, instead increasingly viewed as a best practice. It is not a replacement for the essential and high value non-prod testing we do, but instead an addition.
Observability: Testing in production, and indeed in all envs, requires being able to understand what our applications do. Conventional logs, monitoring and alerting are all good, but Observability is about more than that. It’s about the ability to answer complex questions about our apps at run time. Questions we may not have even thought of before like: why is my app slow. Is it me or a downstream service? Where is all my memory being used. We can use metrics, tracing, any tools at our disposal so that we can see what’s going on when things go wrong. Or better still, to proactively spot problems in advance.
And with Observability in place, we can actually start to test in production!
We ran through different types of
Testing at Release
We can do, including
after deployment (Config, smoke, load, shadow)
At release time: Canary and internal release
After release: Feature flags and A/B testing
Finally, even when everything is up and running in prod, customers are using it, and all looks good, there is still more testing we can do
Chaos Engineering
Not introducing chaos, but exposing the already present chaos!
Carefully planned experiments designed to reveal weaknesses in our systems
