Scaling your application efficiently is is key to achieving a good rate of return and performance monitoring is an important tool to ensure you scale as expected. Performance monitoring of single Node.js applications is relatively straight forward with a variety of technigues and tooling options available to a developer. In this presentation, we will follow the journey of how to apply these techniques when scaling up to a clustered Node.js deployment in the cloud. We will show how to use freely available monitoring tooling and open source solutions like appmetrics, Elasticsearch and Kibana to provide real-time monitoring and performance tracking for Enterprise solutions. Come and learn how to keep on top on how your application is performing and find out about problems before they occur.

1. A301
From Startup to Enterprise, learn how
performance monitoring can help you
scale your application
Michael Dawson, IBM Runtime Technologies

2. © 2016 IBM Corporation 2
About Michael Dawson
Senior Software Developer @ IBM
IBM Runtime Technologies Node.js Technical Lead
Node.js collaborator and CTC member
Active in LTS, build, benchmarking , api
and post-mortem working groups
Contact me:
michael_dawson@ca.ibm.com
Twitter: @mhdawson1
https://www.linkedin.com/in/michael-dawson-6051282

3. © 2016 IBM Corporation 3
About Toby Corbin
Software Developer @ IBM
IBM Runtime Technologies monitoring lead
15 years at IBM
Last 8 working on monitoring solutions for Java and more
recently Node.js
Contact me:
corbint@uk.ibm.com
Twitter: @TobesCorbin

4. Agenda
• Common scaling problems
• Common problems – what can I do ?
• Applying performance monitoring
• “Classic” deployment environment
• Demo
• ”Cloud” deployment environment
• Collecting data in the cloud
• Demo
• Summary
© 2016 IBM Corporation 4

5. • Lower than expected throughput in the application
© 2016 IBM Corporation 5
Common Scaling Problems

6. • Lower than expected throughput in the application
• Poor response time
© 2016 IBM Corporation 6
Common Scaling Problems

7. • Lower than expected throughput in the application
• Poor response time
• Higher than expected CPU usage for the level of
throughput/load
© 2016 IBM Corporation 7
Common Scaling Problems

8. • Lower than expected throughput in the application
• Poor response time
• Higher than expected CPU usage for the level of
throughput/load
• Extremely slow progression looking similar to a
hang or becoming unresponsive
© 2016 IBM Corporation 8
Common Scaling Problems

9. • Essentially single threaded
© 2016 IBM Corporation 9
Common Scaling Problems – Node.js Context

10. © 2016 IBM Corporation 10
Common Scaling Problems – Node.js Context

11. • Identify limiting factor
• CPU
• I/O
• Memory
• Synchronization
• Performance monitoring is the tool for the job
© 2016 IBM Corporation
Common Scaling Problems – What can I do ?
11

12. • CPU
• Faster CPU
• More CPUs (+ more Node.js instances)
• Reduce CPU Usage
• Extract CPU Intensive work
© 2016 IBM Corporation
Common Scaling Problems – What can I do ?
12

13. • I/O
• Identify which I/O is bottleneck
• Optimize I/O provider (database, file system etc.)
• Optimize requests profile
© 2016 IBM Corporation
Common Scaling Problems – What can I do ?
13

14. • Memory
• Heap - garbage collector
• Tune heap size
• Optimize allocation profile
• Native memory
• Increase system memory
• Optimize allocation profile
• Node.js
• Default heap ~ 1.5 G
• --max-old-space-size=XXX
© 2016 IBM Corporation
Common Scaling Problems – What can I do ?
14

15. Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation 15

16. • “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation
Investigating - “Classic” Deployments
Capture and evaluate
post-mortem
16

17. • Capture and evaluate post-mortem
• Turn on application logging and send to a file
• Capture verbosegc output
• Generate a heap dump to see where memory is being
used – https://github.com/bnoordhuis/node-heapdump
• nodereport - https://github.com/nodejs/nodereport
© 2016 IBM Corporation
--trace_gc
--trace_gc_nvp
--trace_gc_verbose
Investigating - “Classic” Deployments
17

18. • “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation
Investigating - “Classic” Deployments
Live
Monitoring
18

19. • Understand your applications behavior as it
occurs
• Connect a remote debugger
• Use live performance monitoring tools
© 2016 IBM Corporation
Investigating - “Classic” Deployments
© 2016 IBM Corporation

20. © 2016 IBM Corporation
Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
• Use the same tools for both
20

21. • “Classic” : desktop development, on premise
deployment
• Use the same tools for both
© 2016 IBM Corporation
Investigating - “Classic” Deployments
21

22. © 2016 IBM Corporation
Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
• Use the same tools for both
22

23. Live Performance Monitoring
Provides insight into your applications behavior
At runtime, you connect a GUI to your application to
visualize the data
© 2016 IBM Corporation 23

24. Live monitoring demo
• Demo of Health Center, development monitoring solution
• Free from Eclipse marketplace
• http://marketplace.eclipse.org/content/ibm-monitoring-and-diagnostic-tools-health-center
• http://www.ibm.com/developerworks/java/jdk/tools/healthcenter/
© 2016 IBM Corporation 24

25. • “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
25

26. © 2016 IBM Corporation
Investigating - “Cloud” Deployments
• “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
26

27. • No persistent local file system
• What happens if the container drops and
restarts ?
• Where do you now send your log data ?
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
27

28. © 2016 IBM Corporation
Investigating - “Cloud” Deployments
• “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
28

29. • Application could be anywhere in the cloud
• Can’t just attach a remote debugger anymore
• Blocked ports
• Multiple instances
• Proxies
• We need a new approach for collecting data in the
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
29

30. • Elastic Search - https://www.elastic.co/products/elasticsearch
• Logstash - https://www.elastic.co/products/logstash
• Kibana - https://www.elastic.co/products/kibana
© 2016 IBM Corporation
Collecting data in the cloud – ELK (open source)
30

31. Collecting data in the cloud
• Data now has to be sent somewhere and stored
app
server
app
server
Log data
Log data
© 2016 IBM Corporation 31

32. • Send data to logstash
app
server
app
server
logstash
Log data
Log data
© 2016 IBM Corporation 32
Collecting data in the cloud

33. • Elasticsearch can aggregate over this data
app
server
app
server
logstash Elasticsearch
Log data
Log data
© 2016 IBM Corporation 33
Collecting data in the cloud

34. • Kibana can be used to visualize
app
server
app
server
logstash Elasticsearch
Log data
Log data
© 2016 IBM Corporation 34
Collecting data in the cloud

35. What about performance monitoring data ?
• ELK + appmetrics
• Build your own
• appmetrics API – capture
• push to elastic search
• appmetrics-elk
• – prebuilt integration
© 2016 IBM Corporation
3
5

36. Collecting data in the cloud
• Elasticsearch can correlate log and metrics data
app
server
app
server
logstash Elasticsearch
Log data
Log data
Monitored data
Monitored data
© 2016 IBM Corporation 36

37. Appmetrics-elk demo
• Code walkthrough and demo
• https://github.com/RuntimeTools/appmetrics
• https://github.com/RuntimeTools/appmetrics-elk
37© 2016 IBM Corporation

38. Appmetrics-elk demo
• Same data, different visualization
© 2016 IBM Corporation 38

39. Managed monitoring solutions
• Transactions and utilizations vs code
• Products like IBM Application Performance
Management (APM) provide contextual monitoring
software.
• E.g. your transactions are going slow
• Generally focus on high level dash boarding, not
low level detail of the VM
© 2016 IBM Corporation 39

40. Summary
• Common scaling problems
• Common problems – what can I do ?
• Applying performance monitoring
• “Classic” deployment environment
• Demo
• ”Cloud” deployment environment
• Collecting data in the cloud
• Demo
© 2016 IBM Corporation 40

41. Summary
• Performance monitoring is key tool for scaling
your applications
• “Classic” and “Cloud” environment may require
different approaches
• The same data can be used in both cases
© 2016 IBM Corporation 41

42. Michael Dawson
Thank you very much.
IBM
Runtime Technologies
michael_dawson@ca.ibm.com
© 2016 IBM Corporation 42
Toby Corbin
IBM
Runtime Technologies
corbint@uk.ibm.com

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