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How to build observability into Serverless
Yan Cui @theburningmonk
Abraham Wald
Abraham Wald
Abraham Wald
Abraham Wald
Wald noted that the study only
considered the aircraft that had survived
their missions—the bombers that had
been shot down were not present for the
damage assessment.
The holes in the returning aircraft, then,
represented areas where a bomber could
take damage and still return home safely.
Abraham Wald
Wald noted that the study only
considered the aircraft that had survived
their missions—the bombers that had
been shot down were not present for the
damage assessment.
The holes in the returning aircraft, then,
represented areas where a bomber could
take damage and still return home safely.
survivor bias in monitoring
survivor bias in monitoring
Only focus on failure modes that we were able to successfully
identify through investigation and postmortem in the past.
The bullet holes that shot us down and we couldn’t identify stay
invisible, and will continue to shoot us down.
What do I mean by “observability”?
Monitoring
watching out for
known failure modes
in the system,
e.g. network I/O, CPU,
memory usage, …
Observability
being able to debug
the system, and gain
insights into the
system’s behaviour
In control theory, observability is a measure of how well
internal states of a system can be inferred from
knowledge of its external outputs.
https://en.wikipedia.org/wiki/Observability
Known Success
Known SuccessKnown Errors
Known SuccessKnown Errors
easy to monitor!
Known SuccessKnown Errors
Known Unknowns
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
invisible bullet
holes
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
only alert on
this
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
alert on the
absence of this!
Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
what went wrong?
These are the four pillars of the Observability Engineering
team’s charter:
• Monitoring
• Alerting/Visualization
• Distributed systems tracing infrastructure
• Log aggregation/analytics
“
” http://bit.ly/2DnjyuW- Observability Engineering at Twitter
microservices death stars circa 2015
mm… I wonder what’s
going on here…
microservices death stars circa 2015
I got this!
microservices death stars circa 2015
About me
▪ Principal Engineer at DAZN
▪ AWS Serverless Hero
▪ Author of Production-Ready Serverless* by Manning
▪ Blogger**
▪ Speaker
* https://bit.ly/production-ready-serverless
** https://theburningmonk.com
https://www.ft.com/content/07d375ee-6ee5-11e8-92d3-6c13e5c92914
https://www.theguardian.com/media/2018/may/14/streaming-service-dazn-netflix-sport-us-boxing-eddie-hearn
About DAZN
▪ Available in 7 countries - Austria, Switzerland, Germany, Japan, Canada,
Italy and USA
▪ Available on 30+ platforms
About DAZN
▪~1,000,000 concurrent viewers at peak
follow @dazneng for
updates about the
engineering team
We’re hiring! Visit
engineering.dazn.com
to learn more.
WE’RE HIRING!
new
challenges
NO ACCESS
to underlying OS
NOWHERE
to install agents/daemons
•nowhere to install agents/daemons
new challenges
user request
user request
user request
user request
user request
user request
user request
critical paths:
minimise user-facing latency
handler
handler
handler
handler
handler
handler
handler
user request
user request
user request
user request
user request
user request
user request
critical paths:
minimise user-facing latency
StatsD
handler
handler
handler
handler
handler
handler
handler
rsyslog
background processing:
batched, asynchronous, low
overhead
user request
user request
user request
user request
user request
user request
user request
critical paths:
minimise user-facing latency
StatsD
handler
handler
handler
handler
handler
handler
handler
rsyslog
background processing:
batched, asynchronous, low
overhead
NO background processing
except what platform provides
•no background processing
•nowhere to install agents/daemons
new challenges
EC2
concurrency used to be
handled by your code
EC2
Lambda
Lambda
Lambda
Lambda
Lambda
now, it’s handled by the
AWS Lambda platform
EC2
logs & metrics used to be
batched here
EC2
Lambda
Lambda
Lambda
Lambda
Lambda
now, they are batched in each
concurrent execution, at best…
HIGHER concurrency to log
aggregation/telemetry system
•higher concurrency to telemetry system
•nowhere to install agents/daemons
•no background processing
new challenges
Lambda
cold start
Lambda
data is batched between
invocations
Lambda
idle
data is batched between
invocations
Lambda
idle
garbage collectiondata is batched between
invocations
Lambda
idle
garbage collectiondata is batched between
invocations
HIGH chance of data loss
•high chance of data loss (if batching)
•nowhere to install agents/daemons
•no background processing
•higher concurrency to telemetry system
new challenges
Lambda
my code
send metrics
my code
send metrics
my code
send metrics
internet internet
press button something happens
http://bit.ly/2Dpidje
?
functions are often chained together
via asynchronous invocations
?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
tracing ASYNCHRONOUS
invocations through so many
different event sources is difficult
•asynchronous invocations
•nowhere to install agents/daemons
•no background processing
•higher concurrency to telemetry system
•high chance of data loss (if batching)
new challenges
These are the four pillars of the Observability Engineering
team’s charter:
• Monitoring
• Alerting/Visualization
• Distributed systems tracing infrastructure
• Log aggregation/analytics
“
” http://bit.ly/2DnjyuW- Observability Engineering at Twitter
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 Request Id
your log message
one log group per
function
one log stream for each
concurrent invocation
logs are not easily searchable in
CloudWatch Logs
me
CloudWatch Logs
CloudWatch Logs is an async event source for Lambda
Concurrent Executions
Time
regional max
concurrency
functions that are
delivering business value
Concurrent Executions
Time
regional max
concurrency
functions that are
delivering business value
ship logs
either set concurrency limit on the log shipping function
(and potentially lose logs due to throttling)
or…
1 shard = 1 concurrent execution
i.e. control the no. of concurrent
executions with no. of shards
…
CloudWatch Logs
CloudWatch Logs
use structured logging with JSON
https://stackify.com/what-is-structured-logging-and-why-developers-need-it/ https://blog.treasuredata.com/blog/2012/04/26/log-everything-as-json/
https://www.loggly.com/blog/8-handy-tips-consider-logging-json/
traditional loggers are too heavy for Lambda
CloudWatch Logs
$0.50 per GB ingested
$0.03 per GB archived per month
CloudWatch Logs
$0.50 per GB ingested
$0.03 per GB archived per month
1M invocation of a 128MB function =
$0.000000208 * 1M + $0.20 =
$0.408
DON’T leave debug logging ON in production
have to redeploy ALL the
functions along the call path to
collect all relevant debug logs
https://github.com/middyjs/middy
EC2
Lambda
Lambda
Lambda
Lambda
Lambda
Concurrency is handled by
the AWS Lambda platform
sampling decision has to be
followed by an entire call chain
Initial Request ID
User ID
Session ID
User-Agent
Order ID
…
every function needs to do the right thing and
propagate information such as correlation IDs
along to APIs, streams, queues, etc.
invest in tools to make it easy to do the “right thing”
nonintrusive
nonintrusive
extensible
nonintrusive
extensible
consistent
nonintrusive
extensible
consistent
works for streams
EC2
Lambda
Lambda
Lambda
Lambda
Lambda
Concurrency is handled by
the AWS Lambda platform
store correlation IDs in global variable
use middleware to auto-capture incoming correlation IDs
extract correlation IDs from
invocation event, and store them in
the correlation-ids module
reset
logger to always include captured correlation IDs
HTTP and AWS SDK clients to auto-forward correlation IDs on
context.awsRequestId
get-index
context.awsRequestId x-correlation-id
get-index
{
“headers”: {
“x-correlation-id”: “…”
},
…
}
get-index
{
“body”: null,
“resource”: “/restaurants”,
“headers”: {
“x-correlation-id”: “…”
},
…
}
get-index get-restaurants
get-restaurants
global.CONTEXT
global.CONTEXT
x-correlation-id = …
x-correlation-xxx = …
get-index
headers[“User-Agent”]
headers[“Debug-Log-Enabled”]
headers[“User-Agent”]
headers[“Debug-Log-Enabled”]
headers[“x-correlation-id”]
capture
forward
function
event
log.info(…)
nonintrusive
extensible
consistent
works for streams
MONITORING
•no background processing
•nowhere to install agents/daemons
new challenges
my code
send metrics
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
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
m
etrics
CloudWatch
trade-off
delay
cost
concurrency
trade-off
delay
cost
concurrency
no latency overhead
API Gateway
send custom metrics
asynchronously
SNS KinesisS3API Gateway
…
send custom metrics
asynchronously
send custom metrics as
part of function invocation
TRACING
X-Ray
don’t span over async
invocations
good for identifying dependencies of a function,
but not good enough for tracing the entire call
chain as user request/data flows through the
system via async event sources.
don’t span over non-AWS services
write structured logs
instrument your code
make it easy to do the right thing
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/prod-ready-serverless
get 40% off with:
ytcui
@theburningmonk
theburningmonk.com
github.com/theburningmonk
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/prod-ready-serverless
get 40% off with:
ytcui

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