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in production
an experience reportan experience report
what you should know before you go to production
ServerlessServerless
Yan Cui
http://theburningmonk.com
@theburningmonk
Domas Lasauskas
apr, 2016
hey guys, vote on this post
and I’ll announce a winner at
10PM tonight
10PM
traffic
10PM
traffic
70-100x
low utilisation to leave room for spikes
EC2 scaling is slow, so scale earlier
lots of $$$ for unused resources
up to 30 mins for deployment
deployment required downtime
- Dan North
“lead time to someone saying
thank you is the only reputation
metric that matters.”
“what would good
look like for us?”
be small
be fast
have zero downtime
have no lock-step
DEPLOYMENTS SHOULD...
FEATURES SHOULD...
be deployable independently
be loosely-coupled
WE WANT TO...
minimise cost for unused resources
minimise ops effort
reduce tech mess
deliver visible improvements faster
nov, 2016
170 Lambda functions in prod
1.2 GB deployment packages in prod
95% cost saving vs EC2
15x no. of prod releases per month
time
is a good fit
1st function in prod!
time
is a good fit
?
time
is a good fit
1st function in prod!
ALERTING
CI / CD
TESTING
LOGGING
MONITORING
Practices ToolsPrinciples
what is good? how to make it good? with what?
Principles outlast Tools
170 functions
? ?
time
is a good fit
1st function in prod!
SECURITY
DISTRIBUTED TRACING
CONFIG MANAGEMENT
evolving the PLATFORM
rebuilt search
Legacy Monolith Amazon Kinesis Amazon Lambda
Amazon CloudSearch
Legacy Monolith Amazon Kinesis Amazon Lambda
Amazon CloudSearchAmazon API Gateway Amazon Lambda
new analytics pipeline
Legacy Monolith Amazon Kinesis Amazon Lambda
Google BigQuery
Legacy Monolith Amazon Kinesis Amazon Lambda
Google BigQuery
1 developer, 2 days
design production
(his 1st serverless project)
Legacy Monolith Amazon Kinesis Amazon Lambda
Google BigQuery
“nothing ever got done
this fast at Skype!”
- Chris Twamley
- Dan North
“lead time to someone saying
thank you is the only reputation
metric that matters.”
Rebuilt
with Lambda
Rebuilt
with Lambda
BigQuery
BigQuery
grapheneDB
BigQuery
grapheneDB
BigQuery
grapheneDB
BigQuery
nov, 2016
getting PRODUCTION READY
choose a tried-and-tested
deployment framework,
don’t invent your own
http://serverless.com
https://github.com/awslabs/serverless-application-model
http://apex.run
https://apex.github.io/up
https://github.com/claudiajs/claudia
https://github.com/Miserlou/Zappa
http://gosparta.io/
TESTING
amzn.to/29Lxuzu
Level of Testing
1.Unit
do our objects do the right thing?
are they easy to work with?
Level of Testing
1.Unit
2.Integration
does our code work against code we
can’t change?
handler
handler
test by invoking
the handler
Level of Testing
1.Unit
2.Integration
3.Acceptance
does the whole system work?
Level of Testing
unit
integration
acceptance
feedback
confidence
Don’t Mock Types You Can’t Change
Don’t Mock Types You Can’t Change
Services
Paul Johnston
The serverless approach to
testing is different and may
actually be easier.
http://bit.ly/2t5viwK
LambdaAPI Gateway DynamoDB
LambdaAPI Gateway DynamoDB
Unit Tests
LambdaAPI Gateway DynamoDB
Unit Tests
Mock/Stub
is our request correct?
is the request mapping
set up correctly?is the API resources
configured correctly?
are we assuming the
correct schema?
LambdaAPI Gateway DynamoDB
is Lambda proxy
configured correctly?
is IAM policy set
up correctly?
is the table created?
what unit tests will not tell you…
most Lambda functions are simple
have single purpose, the risk of
shipping broken software has largely
shifted to how they integrate with
external services
observation
optimize towards shipping working
software, even if it means slowing
down your feedback loop…
learning the wrong thing faster does not
help us deliver working software faster
Legacy Monolith Amazon Kinesis Amazon Lambda
Amazon CloudSearchAmazon API Gateway Amazon Lambda
Legacy Monolith Amazon Kinesis Amazon Lambda
Amazon CloudSearchAmazon API Gateway Amazon Lambda
Test Input
Legacy Monolith Amazon Kinesis Amazon Lambda
Amazon CloudSearchAmazon API Gateway Amazon Lambda
Test Input
Validate
CI + CD PIPELINE
Yan
the earlier you consider CI/CD
the more time you save in
the long run
“…We prefer to have the end-to-end
tests exercise both the system and the
process by which it’s built and
deployed…
This sounds like a lot of effort (it is), but
has to be done anyway repeatedly
during the software’s lifetime…”
Testing End-to-End
Yan
deployment scripts that only
live on the CI box is a disaster
waiting to happen…
Jenkins build config deploys and tests
unit + integration tests
deploy
acceptance tests
if [ "$1" = "deploy" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE 'node_modules/.bin/sls' deploy -s $STAGE -r $REGION
elif [ "$1" = "int-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run int-$STAGE
elif [ "$1" = "acceptance-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run acceptance-$STAGE
else
usage
exit 1
fi
if [ "$1" = "deploy" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE 'node_modules/.bin/sls' deploy -s $STAGE -r $REGION
elif [ "$1" = "int-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run int-$STAGE
elif [ "$1" = "acceptance-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run acceptance-$STAGE
else
usage
exit 1
fi
install Serverless framework
as dev dependency
if [ "$1" = "deploy" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE 'node_modules/.bin/sls' deploy -s $STAGE -r $REGION
elif [ "$1" = "int-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run int-$STAGE
elif [ "$1" = "acceptance-test" ] && [ $# -eq 4 ]; then
STAGE=$2
REGION=$3
PROFILE=$4
npm install
AWS_PROFILE=$PROFILE npm run acceptance-$STAGE
else
usage
exit 1
fi
install Serverless framework
as dev dependency
mitigate version conflicts
build.sh allows repeatable builds on both local & CI
Auto Auto Manual
LOGGING
2016-07-12T12:24:37.571Z 994f18f9-482b-11e6-8668-53e4eab441ae
GOT is off air, what do I do now?
2016-07-12T12:24:37.571Z 994f18f9-482b-11e6-8668-53e4eab441ae
GOT is off air, what do I do now?
UTC Timestamp API Gateway Request Id
your log message
Yan
Logs are not easily searchable
in CloudWatch Logs.
CloudWatch Logs
CloudWatch Logs AWS Lambda ELK stack
…
CloudWatch Events
DISTRIBUTED TRACING
a user
my followers didn’t receive
my new post!
where could the
problem be?
correlation IDs*
* eg. request-id, user-id, yubl-id, etc.
wrap HTTP client & AWS SDK clients
to forward captured correlation IDs
kinesis client
http client
sns client
use X-Ray for performance tracing
Amazon X-Ray
Amazon X-Ray
X-Ray traces do not span over API
Gateway, or async event sources
MONITORING + ALERTING
no place to install agents/daemons
• invocation Count
• error Count
• latency
• throttling
• granular to the minute
• support custom metrics
• same metrics as CW
• better dashboard
• support custom metrics
https://www.datadoghq.com/blog/monitoring-lambda-functions-datadog/
my code
my code
my code
internet internet
press button something happens
those extra 10-20ms for
sending custom metrics
would compound when
you have microservices
and multiple APIs are
called within one slice
of user event
Amazon found every 100ms of latency
cost them 1% in sales.
http://bit.ly/2EXPfbA
no more background processing,
other than what the platform provides
console.log(“hydrating yubls from db…”);
console.log(“fetching user info from user-api”);
console.log(“MONITORING|1489795335|27.4|latency|user-api-latency”);
console.log(“MONITORING|1489795335|8|count|yubls-served”);
timestamp metric value
metric type
metric namemetrics
logs
CloudWatch Logs AWS Lambda
ELK stack
logs
metrics
CloudWatch
don’t forget to setup dashboards
& CW alarms
CONFIG MANAGEMENT
design for easy & quick
propagation of config changes
me
Environment variables make it
hard to share configurations
across functions.
me
Environment variables make it
hard to implement fine-grained
access to sensitive info.
config service
goes here
SSM
Parameter
Store
sensitive data should be encrypted
in-flight, and at-rest
enforce role-based access to sensitive
configuration values
SSM Parameter Store
HTTPS
role-based access
encrypted in-flight
SSM Parameter Store
encrypt
role-based access
SSM Parameter Store
encrypted at-rest
HTTPS
role-based access
SSM Parameter Store
encrypted in-flight
invest into a robust client library
fetch & cache at cold-start
invalidate at interval & weak signals
“DevOps is a set of practices intended to
reduce the time between committing a
change to a system and the change being
placed into normal production, while
ensuring high quality.”
https://en.wikipedia.org/wiki/DevOps#Definitions_and_History
NoOps
Serverless ops is different…
remember the principles
rethink the approach
API Gateway and Kinesis
Authentication & authorisation (IAM, Cognito)
Testing
Running & Debugging functions locally
Log aggregation
Monitoring & Alerting
X-Ray
Correlation IDs
CI/CD
Performance and Cost optimisation
Error Handling
Configuration management
VPC
Security
Leading practices (API Gateway, Kinesis, Lambda)
Canary deployments
http://bit.ly/production-ready-serverless
get 40% off
with: ytcui
@theburningmonk
theburningmonk.com
github.com/theburningmonk

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