Hundreds of microservices, millions of AWS Lambda invocations, and dozens of global regions—the way we design, build, and operate cloud infrastructure and applications is increasingly distributed and composed of ephemeral components. From experience, we know a key to success with these systems is the ability to understand them using data. While there is considerable knowledge around how to use metrics and logs to analyze and troubleshoot traditional applications and infrastructure, emerging technology like serverless functions and orchestrated containers require a new observability approach. This is especially true when trying to understand the relationship between new services, like an IoT or mobile backend, and legacy systems. Session sponsored by New Relic

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Improving Microservice and
Serverless Observability with
Monitoring Data
C L A Y S M I T H , N E W R E L I C
@ S M I T H C L A Y
AWS re:INVENT
N o v e m b e r 2 9 , 2 0 1 7

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What’s observability?
A measure of how well we can
understand a system from the
work it does
“I know how long all the methods in
this service take to execute”

3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What’s instrumentation?
“This method took 25 ms to execute”
Instrumentation: measuring events in software using code
(a type of white-box monitoring)

4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Agenda
1. System architectures of past, present, and future
2. Collecting the right data to understand modern architectures
3. Observability requirements for modern architectures
4. Case study: AWS Lambda observability
5. Q&A with New Relic customer

5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How did you monitor apps in 1967?
Attribution: Bundesarchiv, B 145 Bild-F038812-0014 / Schaack, Lothar / CC-BY-SA 3.0
1. People in lab coats looking
at blinking lights
2. Autotest (IBM System/360)
• Status printouts at
different points during
program execution
• Main storage printout in
the event of failure (!)
• Automatic patch card
inclusion (?)
Source: IBM System/360 Programmer’s Basic Operating
System Programmer’s Guide (September 1967)

6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Good news: We don’t have to wear
lab coats anymore
Attribution: Flickr / Heisenberg Media/8408215473 / CC-BY-SA 3.0
1. People in jeans and hoodies
looking at screens
2. Various types of machine data
from different sources
• Infrastructure
• Backend apps and services
• …Mobile, browser,
IoT, Edge, and so on

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Software architecture continues to change

8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
It’s globally distributed in multiple regions

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
And compute is getting physically closer with
edge computing and IoT

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The architecture is also extremely dynamic
Docker container lifespan in minutes (1-100), New Relic April 2017

11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
More New Relic customers
run complex, distributed systems
New Relic service map of reference Telco architecture

12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Good data can help with the technical shift to
new systems
• Improved debugging and troubleshooting
• Designs validated with data
• Reduced defects; more issues caught
proactively
• Improved feature velocity
Technical

13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Good data can help with the cultural shift to
new systems
• Builds transparency across teams
• Shared understanding of complex components
• Decisions not (entirely) driven or explained by
gut feelings or guessing
• Freedom to experiment
• Blameless culture
• Context not control
Cultural

14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Instrumentation
increases
observability

15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How do we make
microservices and serverless
functions observable?
But...

16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
#1: Observable systems should emit events:
Metrics, logs, and traces
16
“The database won’t start after the update”
“Our application is 35% slower than last week
after this configuration change”
“What are the dependencies for this service?”
Logs
Metrics
Traces
New Relic provides
*via Partner
Integrations

17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
#2: All components (not just critical services!)
should be instrumented
BrowserMobile
Server (Virtual)
Hardware and
Managed Services
Host Operating
System and
Containers
Application
Amazon EC2 Instance

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
#3: Instrumentation should not be opt-in,
manual, or hard to do
On-Premises
Web
Server
On Premises
Relational Data
Synthetic
customers
Customers
Public Cloud
Micro Services
API
Browser
Apps
Mobile
NoSQL
Data Store

19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Lambda case study

20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Which monitoring batteries are included?
Amazon CloudWatch metrics
Amazon CloudWatch logs

21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Lambda: Key metrics
1. Invocations
2. Errors
3. Dead letter error
4. Duration
5. Throttles
6. Iterator age (stream-based invocations only)
http://docs.aws.amazon.com/lambda/latest/dg/monitoring-functions-metrics.html

22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What else provides AWS Lambda observability?
AWS X-Ray
Request tracing for many AWS-managed services

23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS X-Ray trace: Example
A “cold start” trace initiated from in AWS X-Ray. Annotations in red.

24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Warm start in an X-Ray trace
Note the function executes almost immediately after the service
receives the request

25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Traces in aggregate show interesting trends

26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Serverless architecture for aggregating traces

27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What does the data show in insights?

28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A-ha moment: It was underprovisioned
with memory!
Memory: 768 mb
Memory: 1152 mb

29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Lessons learned
• Instrument for observability: “What are the internal
lambda service latencies for my function?”
• Find the right balance of metrics, logs, and
traces for a given system: “Over 24 hours, what’s
the distribution of function duration for my function?”
• Use analytics to diagnose: “Are cold starts
significant; what other factors are at play?”

30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Q&A with Marcus Irven, Scripps Network
Serverless architectures in production

31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank you!
CLAY SMITH, NEW RELIC
TWITTER: @SMITHCLAY