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A301
From Startup to Enterprise, learn how
performance monitoring can help you
scale your application
Michael Dawson, IBM Runtime Technologies
© 2016 IBM Corporation 2
About Michael Dawson
Senior Software Developer @ IBMIBM 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.comTwitter: @mhdawson1
https://www.linkedin.com/in/michael-dawson-6051282
© 2016 IBM Corporation 3
About Toby Corbin
Software Developer @ IBMIBM 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.comTwitter: @TobesCorbin
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
• Lower than expected throughput in the application
© 2016 IBM Corporation 5
Common Scaling Problems
• Lower than expected throughput in the application
• Poor response time
© 2016 IBM Corporation 6
Common Scaling Problems
• 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
• 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
• 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
• 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
• 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
• 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
Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation 15
• “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation
Investigating - “Classic” Deployments
Capture and evaluate
post-mortem
16
• 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
• “Classic” : desktop development, on premise
deployment
© 2016 IBM Corporation
Investigating - “Classic” Deployments
Live
Monitoring
18
• 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
© 2016 IBM Corporation
Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
• Use the same tools for both
20
• “Classic” : desktop development, on premise
deployment
• Use the same tools for both
© 2016 IBM Corporation
Investigating - “Classic” Deployments
21
© 2016 IBM Corporation
Investigating - “Classic” Deployments
• “Classic” : desktop development, on premise
deployment
• Use the same tools for both
22
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
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
• “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
25
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
• “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
26
• 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
© 2016 IBM Corporation
Investigating - “Cloud” Deployments
• “Cloud” : desktop development, on cloud
deployment
• Can we use the same tools for both ?
28
• 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
• 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
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
• Send data to logstash
app
server
app
server
logstash
Log data
Log data
© 2016 IBM Corporation 32
Collecting data in the cloud
• 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
• 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
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
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
Appmetrics-elk demo
• Code walkthrough and demo
• https://github.com/RuntimeTools/appmetrics
• https://github.com/RuntimeTools/appmetrics-elk
37© 2016 IBM Corporation
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
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
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
Michael Dawson
Thank you very much.
IBMRuntime Technologies
michael_dawson@ca.ibm.com
© 2016 IBM Corporation 42
Toby CorbinIBM
Runtime Technologiescorbint@uk.ibm.com
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