The document discusses challenges with observability in serverless applications and proposes solutions. Some key challenges include not having access to the underlying OS, nowhere to install agents/daemons, no background processing, higher concurrency to telemetry systems, and high chance of data loss if batching logs and metrics. The document covers solutions for logging, monitoring, and distributed tracing in serverless environments using services like CloudWatch Logs, API Gateway, and X-Ray.

1. How to build observability into Serverless
Yan Cui @theburningmonk

2. Agenda
▪ What do I mean by observability?
▪ New challenges with serverless
▪ Logging
▪ Monitoring
▪ Tracing

3. After the talk
▪ Slides will be available on slideshare
▪ Find links to slides and video at https://theburningmonk.com/
oreillyvelocity2018

5. Abraham Wald

6. Abraham Wald

7. Abraham Wald

8. 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.

9. 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.

10. survivor bias in monitoring

11. 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.

12. What do I mean by “observability”?

13. Monitoring
watching out for
known failure modes
in the system,
e.g. network I/O, CPU,
memory usage, …

14. Observability
being able to debug
the system, and gain
insights into the
system’s behaviour

15. 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

16. Known Success

17. Known SuccessKnown Errors

18. Known SuccessKnown Errors
easy to monitor!

19. Known SuccessKnown Errors
Known Unknowns

20. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns

21. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
invisible bullet
holes

22. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns

23. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
only alert on
this

24. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
alert on the
absence of this!

25. Known SuccessKnown Errors
Known UnknownsUnknown Unknowns
what went wrong?

26. 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

27. microservices death stars circa 2015

28. mm… I wonder what’s
going on here…
microservices death stars circa 2015

33. I got this!
microservices death stars circa 2015

36. 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

39. https://www.ft.com/content/07d375ee-6ee5-11e8-92d3-6c13e5c92914

40. https://www.theguardian.com/media/2018/may/14/streaming-service-dazn-netflix-sport-us-boxing-eddie-hearn

41. About DAZN
▪ Available in 7 countries - Austria, Switzerland, Germany, Japan, Canada,
Italy and USA
▪ Available on 30+ platforms

42. About DAZN
▪~1,000,000 concurrent viewers at peak

43. follow @dazneng for
updates about the
engineering team
We’re hiring! Visit
engineering.dazn.com
to learn more.
WE’RE HIRING!

44. new
challenges

45. NO ACCESS
to underlying OS

46. NOWHERE
to install agents/daemons

47. •nowhere to install agents/daemons
new challenges

48. 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

49. 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

50. 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

51. •no background processing
•nowhere to install agents/daemons
new challenges

52. EC2
concurrency used to be
handled by your code

53. EC2
Lambda
Lambda
Lambda
Lambda
Lambda
now, it’s handled by the
AWS Lambda platform

54. EC2
logs & metrics used to be
batched here

55. EC2
Lambda
Lambda
Lambda
Lambda
Lambda
now, they are batched in each
concurrent execution, at best…

58. HIGHER concurrency to log
aggregation/telemetry system

59. •higher concurrency to telemetry system
•nowhere to install agents/daemons
•no background processing
new challenges

60. Lambda
cold start

61. Lambda
data is batched between
invocations

62. Lambda
idle
data is batched between
invocations

63. Lambda
idle
garbage collectiondata is batched between
invocations

64. Lambda
idle
garbage collectiondata is batched between
invocations
HIGH chance of data loss

65. •high chance of data loss (if batching)
•nowhere to install agents/daemons
•no background processing
•higher concurrency to telemetry system
new challenges

66. Lambda

67. my code
send metrics

68. my code
send metrics

69. my code
send metrics
internet internet
press button something happens

71. http://bit.ly/2Dpidje

72. ?
functions are often chained together
via asynchronous invocations

73. ?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES

74. ?
SNS
Kinesis
CloudWatch
Events
CloudWatch
LogsIoT
DynamoDB
S3 SES
tracing ASYNCHRONOUS
invocations through so many
different event sources is difficult

75. •asynchronous invocations
•nowhere to install agents/daemons
•no background processing
•higher concurrency to telemetry system
•high chance of data loss (if batching)
new challenges

76. 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

77. LOGGING

79. 2016-07-12T12:24:37.571Z 994f18f9-482b-11e6-8668-53e4eab441ae
GOT is off air, what do I do now?

80. 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

82. one log group per
function
one log stream for each
concurrent invocation

83. logs are not easily searchable in
CloudWatch Logs
me

84. CloudWatch Logs

86. CloudWatch Logs is an async event source for Lambda

87. Concurrent Executions
Time
regional max
concurrency
functions that are
delivering business value

88. Concurrent Executions
Time
regional max
concurrency
functions that are
delivering business value
ship logs

89. either set concurrency limit on the log shipping function
(and potentially lose logs due to throttling)
or…

91. 1 shard = 1 concurrent execution
i.e. control the no. of concurrent
executions with no. of shards

92. …

93. CloudWatch Logs

99. CloudWatch Logs

100. use structured logging with JSON

101. https://stackify.com/what-is-structured-logging-and-why-developers-need-it/ https://blog.treasuredata.com/blog/2012/04/26/log-everything-as-json/

102. https://www.loggly.com/blog/8-handy-tips-consider-logging-json/

104. traditional loggers are too heavy for Lambda

108. CloudWatch Logs
$0.50 per GB ingested
$0.03 per GB archived per month

109. 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

110. DON’T leave debug logging ON in production

112. have to redeploy ALL the
functions along the call path to
collect all relevant debug logs

115. https://github.com/middyjs/middy

117. EC2
Lambda
Lambda
Lambda
Lambda
Lambda
Concurrency is handled by
the AWS Lambda platform

123. sampling decision has to be
followed by an entire call chain

124. Initial Request ID
User ID
Session ID
User-Agent
Order ID
…

125. nonintrusive

126. nonintrusive
extensible

127. nonintrusive
extensible
consistent

128. nonintrusive
extensible
consistent
works for streams

129. EC2
Lambda
Lambda
Lambda
Lambda
Lambda
Concurrency is handled by
the AWS Lambda platform

130. store correlation IDs in global variable

133. use middleware to auto-capture incoming correlation IDs

134. extract correlation IDs from
invocation event, and store them in
the correlation-ids module
reset

136. logger to always include captured correlation IDs

137. HTTP and AWS SDK clients to auto-forward correlation IDs on

138. context.awsRequestId
get-index

139. context.awsRequestId x-correlation-id
get-index

140. {
“headers”: {
“x-correlation-id”: “…”
},
…
}
get-index

141. {
“body”: null,
“resource”: “/restaurants”,
“headers”: {
“x-correlation-id”: “…”
},
…
}
get-index get-restaurants

142. 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(…)

145. nonintrusive
extensible
consistent
works for streams

146. MONITORING

148. •no background processing
•nowhere to install agents/daemons
new challenges

149. my code
send metrics
internet internet
press button something happens

150. 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

151. Amazon found every 100ms of latency cost them 1% in sales.
http://bit.ly/2EXPfbA

152. 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

153. CloudWatch Logs AWS Lambda
ELK stack
logs
m
etrics
CloudWatch

155. trade-off
delay
cost
concurrency

156. trade-off
delay
cost
concurrency
no latency overhead

157. API Gateway
send custom metrics
asynchronously

158. SNS KinesisS3API Gateway
…
send custom metrics
asynchronously
send custom metrics as
part of function invocation

161. TRACING

162. X-Ray

166. 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.

167. don’t span over non-AWS services

171. write structured logs

172. instrument your code

173. make it easy to do the right thing

174. 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

175. @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