Completing the Microservices Puzzle: Kubernetes, Prometheus and FreshTracks.io

Completing the Microservices Puzzle:
Kubernetes, Prometheus and FreshTracks.io
Bob Cotton
ACT38T
CA ACCELERATOR ZONE
Co-Founder
FreshTracks.io
Co-Founder
FreshTracks.io
Marc Chipouras

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The content provided in this CA World 2017 presentation is intended for informational purposes only and does not form any type
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For Informational Purposes Only
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Abstract
DevOps encourages teams to move from monoliths to
microservices. Cloud native architectures demand new
technologies to implement and run those microservices
smoothly. The open source community has solutions to
many of the problems microservices generate. Docker,
Kubernetes and Prometheus are small subset of this
constantly changing ecosystem.
This session will discuss how to monitor your cloud native
applications. This session will dive into the strengths and
weaknesses of the existing open source tools and
highlight how FreshTracks.io is uniquely positioned to
add value within your monitoring suite. After attending
this session, you will be able to speak knowledgeably
about how to weave these technologies together to
enhance microservice observability and implement new
monitoring strategies for your own DevOps teams.
Bob
Cotton
FreshTracks.io
Founder
Marc
Chipouras
FreshTracks.io
Founder

Behind the Scenes
With over two decades of combined data wrangling at CA Agile Central,
Marc Chipouras and Bob Cotton developed a deep understanding of the power of
information to transform decision making. Infrastructure and application metrics
combined with detailed user-level trace information empowered everyone to make better
decisions. System and customer behavior influenced how and what to build next.
With this experience Marc and Bob found themselves increasingly frustrated with the
current state of both commercial and open-source application and infrastructure
monitoring tools. Specifically how these tools were not designed to handle the
ephemeral nature of cloud infrastructure and orchestrated container environments.
The technology landscape is changing in new and exciting ways. As is often the case,
new technologies lead to new problems. New problems demand new solutions.

Agenda
WHY MICROSERVICES?
WHY CONTAINERS?
FRESHTRACKS.IO - THE NEW SOLUTION
WHY ORCHESTRATION?
THE NEW MONITORING PROBLEM
OPEN SOURCE MONITORING SOLUTIONS
1
2
3
4
5
6

New Problems Demand New Solutions
Containers enable
Developer productivity
and simplicity.
Kubernetes
standardizes a fractured
DevOps practice.
Dynamic
Application
Monitoring
FreshTracks.io provides
insights to ensure systems
are stable and available.
Deployment and
Lifecycle
Management
Microservice
Architectures

Why Microservices?

Benefits of Microservices
▪ Developer Productivity
▪ Separation of Concerns
▪ Independently Deployable
▪ Independently Scalable

Microservice Complexity
▪ More Moving Pieces
▪ Logging
▪ Debugging
▪ Balancing Load
▪ Multiple/Rolling Deployment
▪ Monitoring

Why Containers?

Application Dependencies on the Machine
Kernel Kernel
Shared Libraries
Application Dependencies
Application

Application Dependencies in the Container
Kernel
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application

Same Container Runs Everywhere
Kernel
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Dev Test Prod
Kernel
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Kernel
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application
Shared Libraries
Application
Dependencies
Application

Container Benefits
▪ Dependencies managed at
development time
▪ Build once, run everywhere
▪ Lighter than VMs
– Startup in 10ths of seconds
In the end, deploy and test faster

Why Orchestration?

Automated Deployment Is
Custom Code
▪ DevOps says to “automate all the
things." However, we usually
re-create all things.
▪ All varies by technology
(Java, Ruby, Python etc)
– Polyglot shop? Good luck!

Lifecycle Management Is
Custom Code
▪ Manage the number of instances
▪ Rolling deployment
▪ Graceful rollbacks
▪ Auto scaling
▪ Hybrid cloud abstractions

Orchestration Is a Solution

Container Orchestration Is a Solution
▪ Standardized solution for:
– Packaging
▪ Containers
– Deployments
▪ Declarative manifests
– Lifecycle Management
▪ Declarative manifests
▪ Rolling deployments
– Infrastructure Interfacing
▪ Cloud storage
▪ GPUs

Container orchestration use is doubling annually.*
53% of teams using container orchestration
are using Kubernetes.**
Container Orchestration Is Growing
* DataDog
**Cloud Foundry

The New Monitoring Problem

Quick Definition of “Monitoring”
▪ Aggregated measures
▪ Collected periodically from hosts and applications
▪ Drawn on a chart
▪ Time on the x axis
▪ Monitoring is the source
for most alerting

Monitoring - “The Old Way”
▪ Host based
– A known collection of machines with names
▪ Simple metric names
– Prone to abuse by embedding meaning in the name
region.az.hostname.appname.metricname = 10

Monitoring Challenged by New Technologies
▪ The move from the data center to the cloud

Monitoring Challenged by New Technologies

Monitoring Evolved
▪ Metrics 2.0
– label/value pairs on metrics
metric = “load1”
value = 5
host = “fecaed1.aws.com”
ip = “192.168.1.1”
role = “appserver”
...

Monitoring Evolved - Labeled Metrics on Hosts
value = 5
ip = “192.168.1.1”
value = 3
host = “e9dghf.aws.com”
ip = “192.168.1.2”
value = 2.5
host = “ha7fsd.aws.com”
ip = “192.168.1.3”
value = 5
ip = “192.168.1.1”
value = 3
host = “e9dghf.aws.com”
ip = “192.168.1.2”
value = 2.5
ip = “192.168.1.3”
value = 2.5
host = “deh2ia.aws.com”
ip = “192.168.1.4”
value = 2.5
host = “fhd1ea.aws.com”
ip = “192.168.1.5”
value = 2.5
ip = “192.168.1.3”
value = 2.5
host = “deh2ia.aws.com”
ip = “192.168.1.4”
value = 2.5
host = “fhd1ea.aws.com”
ip = “192.168.1.5”

Monitoring Evolved - Labeled Metrics
value = 5
ip = “192.168.1.1”
metric = “http_response_time”
value = 200
ip = “192.168.1.1”
container = “business_app:latest”
value = 5
ip = “192.168.1.1”
value = 5
ip = “192.168.1.1”
value = 5
ip = “192.168.1.1”
value = 5
ip = “192.168.1.1”
value = 5
ip = “192.168.1.1”
value = 5
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”
value = 200
ip = “192.168.1.1”

Open Source Solutions to
New Monitoring Problems

Prometheus
▪ Based on monitoring patterns at Google
▪ High-performance timeseries database
▪ Alert definition and generation
▪ Open sourced by SoundCloud
▪ Second project admitted to the
Cloud Native Computing Foundation after Kubernetes
▪ Adoption surge is tracking Kubernetes

63% of teams using Kubernetes are
monitoring with Prometheus
Prometheus Adoption Is Surging
TheNewStack.io

Prometheus' Strengths
▪ Designed to support orchestration and containers
▪ Best open source Monitoring 2.0 solution
▪ Active development and support community
▪ Highly performant
▪ Alert manager included
▪ Can be federated

Prometheus Is Not a Complete Solution
▪ No adaptive thresholds
▪ No anomaly detection
▪ No advanced analytics for alerting
▪ Does not connect the relationships between
application and infrastructure metrics
▪ Manual dashboard creation

FreshTracks.io
The New Monitoring Solution
FreshTracks.io adds machine learning to Prometheus
FreshTracks.io provides deep Kubernetes analytics

FreshTracks.io
Extends Prometheus

FreshTracks Adds Machine Learning to Prometheus
▪ Learn adaptive thresholds for your changing metrics
▪ Identify anomalies across your system
▪ Important and actionable alerts ensure engineers
spend more time where it matters

Aggregate Metrics Across Containers
▪ All metrics are anomalous some of the time
▪ Aberrations are important or interesting
▪ Individual anomalies may not impact customers
By aggregating metrics across containers
FreshTracks.io alerts only when services and
customers are impacted

FreshTracks Designed
for Kubernetes

Correlate Infrastructure
With Applications
Kubernetes models the internals of a cluster
– Cluster : Deployment : Pod : Container : Service
FreshTracks Correlates metrics gathered from
– Kubernetes infrastructure
– Kubernetes orchestration layer
– Application metrics

System Events at the
Heart of FreshTracks Analytics
▪ Workload deployment events
▪ Kubernetes orchestration events
▪ Kubernetes topology
▪ Feature toggle and application events
FreshTracks analyzes anomalies
in context of events, ensuring alerts
are important and actionable.

Putting It All Together
▪ FreshTracks adds machine learning to Prometheus
– Adaptive thresholds and anomaly detection
– Metrics correlated across ephemeral containers
▪ FreshTracks provides deep Kubernetes analytics
– Metrics analyzed in context of Kubernetes topology
– System events native to anomaly detection
FreshTracks' actionable alerts on the events that matter most.

New Problems Demand New Solutions
Deployment and
Lifecycle
Management
Microservice
Architectures
Containers enable
Developer Productivity
and Simplicity.
Kubernetes standardizes a
fractured DevOps practice.
FreshTracks provides
insights to ensure systems
are stable and available.
Dynamic
Application
Monitoring

New Solutions to
New Problems

FreshTracks.io
Join the Beta

CA Accelerator
For more information on CA Accelerator,
please visit: ca.com/accelerator

Completing the Microservices Puzzle: Kubernetes, Prometheus and FreshTracks.io

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