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APPLYING PRINCIPLES
to SERVERLESSt
a
b
chaos engineering
of
A
E
S
of
Yan Cui
http://theburningmonk.com
@theburningmonk
history of Smallpox
est. 400K deaths per year in 18th Century Europe.
earliest evidence of disease in 3rd Century BC Egyptian Mummy
history of Smallpox
est. 400K deaths per year in 18th Century Europe.
earliest evidence of disease in 3rd Century BC Egyptian Mummy
1798
first vaccine developed
Edward Jenner
1798
first vaccine developed
1980
history of Smallpox
Edward Jenner
WHO certified
global eradication
est. 400K deaths per year in 18th Century Europe.
earliest evidence of disease in 3rd Century BC Egyptian Mummy
Vaccination is the most effective method of
preventing infectious diseases
stimulates the immune system to recognize
and destroy the disease before contracting
the disease for real
Chaos Engineering
controlled experiments to help us learn about
our system’s behaviour and build confidence in
its ability to withstand turbulent conditions
it’s about building confidence,
NOT breaking things
I’m gonna inject
you with a deadly
disease now
http://principlesofchaos.org
STEP 1. define “Steady State”
aka. what does normal, working
condition looks like?
this is not a
steady state
STEP 2.
hypothesize steady state will
continue in both control group
& the experiment group
ie. you should have a reasonable degree of
confidence the system would handle the failure
before you proceed with the experiment
explore unknown unknowns
away from production
treat production with the
care it deserves
the goal is NOT,
to actually hurt production
If you know the system would break,
and you did it anyway…
then it’s NOT a chaos experiment.
It’s called being IRRESPONSIBLE.
STEP 3.
inject realistic failures
e.g. server crash, network error,
HD malfunction, etc.
https://github.com/Netflix/SimianArmy
https://github.com/Netflix/SimianArmy http://oreil.ly/2tZU1Sn
STEP 4.
disprove hypothesis
i.e. look for difference with steady state
if a WEANESS is uncovered,
improve it before the behaviour
manifests in the system at large
Chaos Engineering
controlled experiments to help us learn about
our system’s behaviour and build confidence in
its ability to withstand turbulent conditions
Chaos Engineering
controlled experiments to help us learn about
our system’s behaviour and build confidence in
its ability to withstand turbulent conditions
communication
ensure everyone knows what you’re doing
ensure everyone knows what you’re doing
NO surprises!
communication
Timing
run experiments during office hours
avoid important dates
communication
Timing
contain Blast radius
smallest change that allows
you to detect a signal that
steady state is disrupted
rollback at the first sign of
TROUBLE!
communication
Timing
contain Blast radius
Don’t try to run before you
know how to walk.
by Russ Miles @russmiles
source https://medium.com/russmiles/chaos-engineering-for-the-business-17b723f26361
chaos monkey kills an
EC2 instance
latency monkey induces
artificial delay in APIs
chaos gorilla kills an
AWS Availability Zone
chaos kong kills an
entire AWS region
there is no server…
there is no server…
that you can kill
there are more inherent chaos and
complexity in a Serverless architecture
smaller units of deployment
and A LOT more of them!
more difficult to harden
around boundaries
serverful
serverless
?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
more intermediary services,
and greater variety too
?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
more intermediary services,
and greater variety too
each with its own set of
failure modes
serverful
serverless
more configurations,
more opportunities for misconfiguration
more unknown failure modes in
infrastructure that we don’t control
often there’s little we can do when an
outage occurs in the platform
improperly tuned timeouts
missing error handling
missing fallback when downstream is unavailable
LATENCY INJECTION
STEP 1. define “Steady State”
aka. what does normal, working
condition looks like?
what metrics do you monitor?
9X-percentile latency
error count
yield (% of requests completed)
harvest (completeness of results)
STEP 2.
hypothesize steady state will
continue in both control group
& the experiment group
ie. you should have a reasonable degree of
confidence the system would handle the failure
before you proceed with the experiment
API Gateway
consider the effect of cold-starts
& API Gateway overhead
use short timeout for API calls
the goal of a timeout strategy is to give HTTP
requests the best chance to succeed,
provided that doing so does not cause the
calling function itself to err
fixed timeout are tricky to get right…
fixed timeout are tricky to get right…
too short and you don’t
give requests the best
chance to succeed
fixed timeout are tricky to get right…
too long and you run the
risk of letting the request
timeout the calling function
and it gets worse when you make multiple
API calls in one function…
set the request timeout based on the
amount of invocation time left
log the timeout incident with
as much context as possible
e.g. timeout value, correlation IDs,
request object, …
report custom metrics
be mindful when you sacrifice precision for
availability, user experience is the king
STEP 3.
inject realistic failures
e.g. server crash, network error,
HD malfunction, etc.
where to inject latency?
hypothesis:
function has appropriate timeout on its HTTP
communications and can degrade gracefully
when these requests time out
should also be applied to 3rd parties
services we depend on, e.g. DynamoDB
what’s the blast radius?
http client
public-api-a
http client
public-api-b
internal-api
hypothesis:
all functions have appropriate timeout on
their HTTP communications to this internal
API, and can degrade gracefully when
requests are timed out
large blast radius, risky..
could be effective when used away from
production environment, to weed out
weaknesses quickly
not priming developers to
build more resilient systems
development
development
production
Priming (psychology):
Priming is a technique whereby exposure to one
stimulus influences a response to a subsequent
stimulus, without conscious guidance or intention.
It is a technique in psychology used to train a
person's memory both in positive and negative ways.
make dev environments better resemble the
turbulent conditions you should realistically
expect your system to survive in production
hypothesis:
the client app has appropriate timeout on
their HTTP communication with the server,
and can degrade gracefully when requests
are timed out
STEP 4.
disprove hypothesis
i.e. look for difference with steady state
how to inject latency?
static weaver (e.g. AspectJ, PostSharp),
or dynamic proxies
https://theburningmonk.com/2015/04/design-for-latency-issues/
manually crafted wrapper library
configured in SSM Parameter Store
no injected latency
with injected latency
factory wrapper function
(think bluebird’s promisifyAll function)
ERROR INJECTION
failures are INEVITABLE
the only way to truly know your system’s
resilience against failures is to test it
through controlled experiments
vaccinate your serverless
architecture against failures
Yan Cui
http://theburningmonk.com
@theburningmonk
@theburningmonk
theburningmonk.com
github.com/theburningmonk

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