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Monitoring Microservices &
Containers: A Challenge
Adrian Cockcroft @adrianco
Technology Fellow - Battery Ventures
May 2015
Monitoring
!
Update of my monitoring rules from Monitorama 2014
Rule #1: Spend more time working on code
that analyzes the meaning of metrics, than
code that collects, moves, stores and
displays metrics.
Rule #2: Metric to display latency needs to
be less than human attention span (~10s)
Rule #3: Validate that your measurement
system has enough accuracy and precision.
Collect histograms of response time.
Rule #4: Monitoring systems need to be
more available and scalable than the
systems being monitored.
Rule #5: Optimize for distributed,
ephemeral, cloud native, containerized
microservices.
Rule #6: Fit metrics to models to understand
relationships. (New rule)
Container
Instance
e.g. Machine
failure affects
all instances
and containers
inside itZone/DC
Region
Microservice
Model Infrastructure as a
Containment Hierarchy
Machine
Many tools use a naming scheme to imply this model, but
most can’t reason about the relationships
Request
Model Applications and Networks
as a Dataflow Graph
APM Tools often model these as business transactions
Microservice Zone/DC
Region
Developer Developer
Model Deployment Ownership
and Support
Developer Developer
Developer Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Developer Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Monitoring
Tools
DeveloperDeveloper Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Monitoring
Tools
DeveloperDeveloper Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate
DeveloperDeveloper Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Manager Manager
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate
DeveloperDeveloper Developer
Model Deployment Ownership
and Support
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Manager Manager
VP
Engineering
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate
Infrastructure, flow and ownership models
are orthogonal and need to be linked to
make sense of the metrics
Monitoring Rules by @adrianco
1. Spend more time on analysis than data collection and display
2. Reduce key business metric latency to less than 10s
3. Validate your measurement system, use histograms
4. Be more available and scalable than the services being monitored
5. Optimize for distributed, ephemeral cloud native applications
6. Fit metrics to models to understand relationships
Microservices
Microservices
@ideavist
A Microservice Definition
!
Loosely coupled service oriented
architecture with bounded contexts
A Microservice Definition
!
Loosely coupled service oriented
architecture with bounded contexts
If every service has to be
updated at the same time
it’s not loosely coupled
A Microservice Definition
!
Loosely coupled service oriented
architecture with bounded contexts
If every service has to be
updated at the same time
it’s not loosely coupled
If you have to know too much about surrounding
services you don’t have a bounded context. See the
Domain Driven Design book by Eric Evans.
Complexity
Monolithic apps have unlimited invisible
internal dependencies
!
Vastly more complex than explicit visible
microservice dependencies
Speed
Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours
Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours
AWS Lambda Events
• Respond in milliseconds
• Live for seconds
Speeding Up Deployments
Measuring CPU usage once a minute makes no sense for containers…
Coping with rate of change is a big challenge for monitoring tools.
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours
AWS Lambda Events
• Respond in milliseconds
• Live for seconds
Scale
A Possible Hierarchy
Continents
Regions
Zones
Services
Versions
Containers
Instances
How Many?
3 to 5
2-4 per Continent
1-5 per Region
100’s per Zone
Many per Service
1000’s per Version
10,000’s
It’s much more challenging
than just a large number of
machines
Flow
Some tools can show
the request flow
across a few services
But interesting
architectures have a
lot of microservices!
Flow visualization is
a challenge.
See http://www.slideshare.net/LappleApple/gilt-from-monolith-ruby-app-to-micro-service-scala-service-architecture
Failures
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?
By design, everything works
with 2 of 3 zones running.
This is not an outage, inform
but don’t touch anything!
Halt deployments perhaps?
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?
By design, everything works
with 2 of 3 zones running.
This is not an outage, inform
but don’t touch anything!
Halt deployments perhaps?
Challenge: understand and
communicate common
microservice failure patterns.
Testing
Testing monitoring tools at scale
gets expensive quickly…
Simulation
Simulated Microservices
Model and visualize microservices
Simulate interesting architectures
Generate large scale configurations
Eventually stress test real tools
!
See github.com/adrianco/spigo
Simulate Protocol Interactions in Go
Visualize with D3
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash
Data
Access
Layer
Priam Cassandra
Datastore
Three
Availability
Zones
netflixoss.go architecture
!!!!!!!!!asgard.Create(cname, asgard.PriamCassandraPkg, regions, priamCassandracount, "eureka", cname)
asgard.Create(tname, asgard.StaashPkg, regions, staashcount, cname)
asgard.Create(jname, asgard.KaryonPkg, regions, javacount, tname)
asgard.Create(nname, asgard.KaryonPkg, regions, nodecount, jname)
asgard.Create(zuname, asgard.ZuulPkg, regions, zuulcount, nname)
asgard.Create(elbname, asgard.ElbPkg, regions, 0, zuname)
asgard.Run(asgard.Create(dns, asgard.DenominatorPkg, 0, 0, elbname), jname) // victimize a javaweb
Tooling
New tier
name
Tier
package
Region
count: 1
Node
count
List of tier
dependencies
Run and log results to json
$ spigo -a netflixoss -d 10 -j
2015/05/21 00:05:32 netflixoss: scaling to 100%
2015/05/21 00:05:32 netflixoss.edda: starting
2015/05/21 00:05:32 netflixoss.us-east-1.zoneA.eureka.eureka.eureka0: starting
2015/05/21 00:05:32 netflixoss.us-east-1.zoneB.eureka.eureka.eureka1: starting
2015/05/21 00:05:32 netflixoss.us-east-1.zoneC.eureka.eureka.eureka2: starting
2015/05/21 00:05:32 netflixoss.*.*.www.denominator.www0 activity rate 10ms
2015/05/21 00:05:37 chaosmonkey delete: netflixoss.us-east-1.zoneC.javaweb.karyon.javaweb14
2015/05/21 00:05:42 asgard: Shutdown
2015/05/21 00:05:42 netflixoss.us-east-1.zoneB.eureka.eureka.eureka1: closing
2015/05/21 00:05:42 netflixoss.us-east-1.zoneA.eureka.eureka.eureka0: closing
2015/05/21 00:05:42 netflixoss.us-east-1.zoneC.eureka.eureka.eureka2: closing
2015/05/21 00:05:42 spigo: complete
2015/05/21 00:05:42 netflixoss.edda: closing
10 sec
run time
edda.go logs
config to json
eureka.go
service
registry per
zone
Chaos
monkey
victim!
Simianviz from json logs
http://simianviz.divshot.io/netflixoss/1
ELB splits
traffic over
zones in
single region
microservices
Cassandra
Cluster
Six regions
Big thanks to @kurtiskemple
Why Build Spigo?
Generate test microservice configurations at scale
Stress monitoring tools and simulated game day training
!
Eventually (i.e. not implemented yet)
Dynamically vary configuration: autoscale, code push
Chaos gorilla for zone, region failures and partitions
Websocket connection between spigo and simianviz display
!
My challenge to you:
Build your architecture in Spigo.
Stress monitoring tools with it.
Help fix monitoring for microservices!
!
@mgroeniger
Questions?
Disclosure: some of the companies mentioned may be Battery Ventures Portfolio Companies
See www.battery.com for a list of portfolio investments
● Microservices Challenges
● Speed and Scale
● Flow and Failures
● Testing and Simulation
!
● Battery Ventures http://www.battery.com
● Adrian’s Tweets @adrianco and Blog http://perfcap.blogspot.com
● Slideshare http://slideshare.com/adriancockcroft
● Github http://github.com/adrianco/spigo
What does @adrianco do?
@adrianco
Technology Due
Diligence on Deals
Presentations at
Conferences
Presentations at
Companies
Technical Advice
for Portfolio
Companies
Program
Committee for
Conferences
Networking with
Interesting PeopleTinkering with
Technologies
Maintain Deep
Relationship with
Cloud Vendors
| Battery Ventures
Portfolio Companies for Enterprise IT
Security
Visit http://www.battery.com/our-companies/ for a full list of all portfolio companies in which all Battery Funds have invested.
Palo Alto Networks
Enterprise IT
Operations &
Management
Big DataCompute
Networking
Storage

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Gluecon Monitoring Microservices and Containers: A Challenge

  • 1. Monitoring Microservices & Containers: A Challenge Adrian Cockcroft @adrianco Technology Fellow - Battery Ventures May 2015
  • 2. Monitoring ! Update of my monitoring rules from Monitorama 2014
  • 3. Rule #1: Spend more time working on code that analyzes the meaning of metrics, than code that collects, moves, stores and displays metrics.
  • 4. Rule #2: Metric to display latency needs to be less than human attention span (~10s)
  • 5. Rule #3: Validate that your measurement system has enough accuracy and precision. Collect histograms of response time.
  • 6. Rule #4: Monitoring systems need to be more available and scalable than the systems being monitored.
  • 7. Rule #5: Optimize for distributed, ephemeral, cloud native, containerized microservices.
  • 8. Rule #6: Fit metrics to models to understand relationships. (New rule)
  • 9.
  • 10. Container Instance e.g. Machine failure affects all instances and containers inside itZone/DC Region Microservice Model Infrastructure as a Containment Hierarchy Machine Many tools use a naming scheme to imply this model, but most can’t reason about the relationships
  • 11.
  • 12. Request Model Applications and Networks as a Dataflow Graph APM Tools often model these as business transactions Microservice Zone/DC Region
  • 13. Developer Developer Model Deployment Ownership and Support Developer Developer
  • 14. Developer Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer
  • 15. Developer Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer Monitoring Tools
  • 16. DeveloperDeveloper Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer Monitoring Tools
  • 17. DeveloperDeveloper Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer Site Reliability Monitoring Tools Availability Metrics 99.95% customer success rate
  • 18. DeveloperDeveloper Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer Manager Manager Site Reliability Monitoring Tools Availability Metrics 99.95% customer success rate
  • 19. DeveloperDeveloper Developer Model Deployment Ownership and Support Micro service Micro service Micro service Micro service Micro service Micro service Micro service Developer Developer Manager Manager VP Engineering Site Reliability Monitoring Tools Availability Metrics 99.95% customer success rate
  • 20. Infrastructure, flow and ownership models are orthogonal and need to be linked to make sense of the metrics
  • 21. Monitoring Rules by @adrianco 1. Spend more time on analysis than data collection and display 2. Reduce key business metric latency to less than 10s 3. Validate your measurement system, use histograms 4. Be more available and scalable than the services being monitored 5. Optimize for distributed, ephemeral cloud native applications 6. Fit metrics to models to understand relationships
  • 24. A Microservice Definition ! Loosely coupled service oriented architecture with bounded contexts
  • 25. A Microservice Definition ! Loosely coupled service oriented architecture with bounded contexts If every service has to be updated at the same time it’s not loosely coupled
  • 26. A Microservice Definition ! Loosely coupled service oriented architecture with bounded contexts If every service has to be updated at the same time it’s not loosely coupled If you have to know too much about surrounding services you don’t have a bounded context. See the Domain Driven Design book by Eric Evans.
  • 28. Monolithic apps have unlimited invisible internal dependencies ! Vastly more complex than explicit visible microservice dependencies
  • 29. Speed
  • 30. Speeding Up Deployments Datacenter Snowflakes • Deploy in months • Live for years
  • 31. Speeding Up Deployments Datacenter Snowflakes • Deploy in months • Live for years Virtualized and Cloud • Deploy in minutes • Live for weeks
  • 32. Speeding Up Deployments Datacenter Snowflakes • Deploy in months • Live for years Virtualized and Cloud • Deploy in minutes • Live for weeks Container Deployments • Deploy in seconds • Live for minutes/hours
  • 33. Speeding Up Deployments Datacenter Snowflakes • Deploy in months • Live for years Virtualized and Cloud • Deploy in minutes • Live for weeks Container Deployments • Deploy in seconds • Live for minutes/hours AWS Lambda Events • Respond in milliseconds • Live for seconds
  • 34. Speeding Up Deployments Measuring CPU usage once a minute makes no sense for containers… Coping with rate of change is a big challenge for monitoring tools. Datacenter Snowflakes • Deploy in months • Live for years Virtualized and Cloud • Deploy in minutes • Live for weeks Container Deployments • Deploy in seconds • Live for minutes/hours AWS Lambda Events • Respond in milliseconds • Live for seconds
  • 35. Scale
  • 36. A Possible Hierarchy Continents Regions Zones Services Versions Containers Instances How Many? 3 to 5 2-4 per Continent 1-5 per Region 100’s per Zone Many per Service 1000’s per Version 10,000’s It’s much more challenging than just a large number of machines
  • 37. Flow
  • 38. Some tools can show the request flow across a few services
  • 39. But interesting architectures have a lot of microservices! Flow visualization is a challenge. See http://www.slideshare.net/LappleApple/gilt-from-monolith-ruby-app-to-micro-service-scala-service-architecture
  • 41. ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Simple NetflixOSS style microservices architecture on three AWS Availability Zones
  • 42. ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Simple NetflixOSS style microservices architecture on three AWS Availability Zones
  • 43. ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Simple NetflixOSS style microservices architecture on three AWS Availability Zones Zone partition/failure What should you do? What should monitors show?
  • 44. ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Simple NetflixOSS style microservices architecture on three AWS Availability Zones Zone partition/failure What should you do? What should monitors show? By design, everything works with 2 of 3 zones running. This is not an outage, inform but don’t touch anything! Halt deployments perhaps?
  • 45. ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Simple NetflixOSS style microservices architecture on three AWS Availability Zones Zone partition/failure What should you do? What should monitors show? By design, everything works with 2 of 3 zones running. This is not an outage, inform but don’t touch anything! Halt deployments perhaps? Challenge: understand and communicate common microservice failure patterns.
  • 47. Testing monitoring tools at scale gets expensive quickly…
  • 49. Simulated Microservices Model and visualize microservices Simulate interesting architectures Generate large scale configurations Eventually stress test real tools ! See github.com/adrianco/spigo Simulate Protocol Interactions in Go Visualize with D3 ELB Load Balancer Zuul API Proxy Karyon Business Logic Staash Data Access Layer Priam Cassandra Datastore Three Availability Zones
  • 50. netflixoss.go architecture !!!!!!!!!asgard.Create(cname, asgard.PriamCassandraPkg, regions, priamCassandracount, "eureka", cname) asgard.Create(tname, asgard.StaashPkg, regions, staashcount, cname) asgard.Create(jname, asgard.KaryonPkg, regions, javacount, tname) asgard.Create(nname, asgard.KaryonPkg, regions, nodecount, jname) asgard.Create(zuname, asgard.ZuulPkg, regions, zuulcount, nname) asgard.Create(elbname, asgard.ElbPkg, regions, 0, zuname) asgard.Run(asgard.Create(dns, asgard.DenominatorPkg, 0, 0, elbname), jname) // victimize a javaweb Tooling New tier name Tier package Region count: 1 Node count List of tier dependencies
  • 51. Run and log results to json $ spigo -a netflixoss -d 10 -j 2015/05/21 00:05:32 netflixoss: scaling to 100% 2015/05/21 00:05:32 netflixoss.edda: starting 2015/05/21 00:05:32 netflixoss.us-east-1.zoneA.eureka.eureka.eureka0: starting 2015/05/21 00:05:32 netflixoss.us-east-1.zoneB.eureka.eureka.eureka1: starting 2015/05/21 00:05:32 netflixoss.us-east-1.zoneC.eureka.eureka.eureka2: starting 2015/05/21 00:05:32 netflixoss.*.*.www.denominator.www0 activity rate 10ms 2015/05/21 00:05:37 chaosmonkey delete: netflixoss.us-east-1.zoneC.javaweb.karyon.javaweb14 2015/05/21 00:05:42 asgard: Shutdown 2015/05/21 00:05:42 netflixoss.us-east-1.zoneB.eureka.eureka.eureka1: closing 2015/05/21 00:05:42 netflixoss.us-east-1.zoneA.eureka.eureka.eureka0: closing 2015/05/21 00:05:42 netflixoss.us-east-1.zoneC.eureka.eureka.eureka2: closing 2015/05/21 00:05:42 spigo: complete 2015/05/21 00:05:42 netflixoss.edda: closing 10 sec run time edda.go logs config to json eureka.go service registry per zone Chaos monkey victim!
  • 52. Simianviz from json logs http://simianviz.divshot.io/netflixoss/1 ELB splits traffic over zones in single region microservices Cassandra Cluster Six regions Big thanks to @kurtiskemple
  • 53. Why Build Spigo? Generate test microservice configurations at scale Stress monitoring tools and simulated game day training ! Eventually (i.e. not implemented yet) Dynamically vary configuration: autoscale, code push Chaos gorilla for zone, region failures and partitions Websocket connection between spigo and simianviz display !
  • 54. My challenge to you: Build your architecture in Spigo. Stress monitoring tools with it. Help fix monitoring for microservices! ! @mgroeniger
  • 55. Questions? Disclosure: some of the companies mentioned may be Battery Ventures Portfolio Companies See www.battery.com for a list of portfolio investments ● Microservices Challenges ● Speed and Scale ● Flow and Failures ● Testing and Simulation ! ● Battery Ventures http://www.battery.com ● Adrian’s Tweets @adrianco and Blog http://perfcap.blogspot.com ● Slideshare http://slideshare.com/adriancockcroft ● Github http://github.com/adrianco/spigo
  • 56. What does @adrianco do? @adrianco Technology Due Diligence on Deals Presentations at Conferences Presentations at Companies Technical Advice for Portfolio Companies Program Committee for Conferences Networking with Interesting PeopleTinkering with Technologies Maintain Deep Relationship with Cloud Vendors
  • 57. | Battery Ventures Portfolio Companies for Enterprise IT Security Visit http://www.battery.com/our-companies/ for a full list of all portfolio companies in which all Battery Funds have invested. Palo Alto Networks Enterprise IT Operations & Management Big DataCompute Networking Storage