Monitoring In Motion Challenges in monitoring kubernetes, - - PowerPoint PPT Presentation

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Monitoring In Motion Challenges in monitoring kubernetes, - - PowerPoint PPT Presentation

Apr 24, 2023 190 likes •990 views

Monitoring In Motion Challenges in monitoring kubernetes, containers, and dynamic infrastructure. Ilan Rabinovitch ContainerCon Toronto Director, Technical Community Aug 24, 2016 Datadog $ finger ilan@datadog [datadoghq.com] Name:

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Monitoring In Motion

Challenges in monitoring kubernetes, containers, and dynamic infrastructure.

ContainerCon Toronto Aug 24, 2016 Ilan Rabinovitch Director, Technical Community
 Datadog

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$ finger ilan@datadog

[datadoghq.com] Name: Ilan Rabinovitch Role: Director, Technical Community 
 Interests: * Monitoring and Metrics * Large scale web operations

* FL/OSS Community Events

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  • SaaS based infrastructure and app monitoring
  • Open Source Agent
  • Time series data (metrics and events)
  • Processing nearly a trillion data points per day
  • Intelligent Alerting
  • We’re hiring! (www.datadoghq.com/careers/)

Datadog Overview

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Operating Systems, Cloud Providers, Containers, Web Servers, Datastores, Caches, Queues and more...

Monitor Everything

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$ cat ~/.plan

  • 1. Intro: The Importance of Monitoring
  • 2. The Challenge: Monitoring Dynamic Infrastructure
  • 3. Finding the Signal: How do we know what to monitor?
  • 4. Implementation: Applying it to Containerized Workloads
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Our Focus Area

Culture Automation Metrics Sharing

Damon Edwards and John Willis DevOps Day LA

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Culture

“organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations”

  • Melvin E. Conway
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Follow @honest_update on Twitter

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Collecting data is cheap;
 not having it when you need it can be expensive

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Instrument all the things!

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Sharing

Looping Back on Culture Describe the problem as your “enemy” not each other Learn Together

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Sharing

Using and Sharing the same metrics and measurements across teams is key to avoiding misunderstandings.

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Source: http://bit.ly/1SvvbuP

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Source: http://bit.ly/1RQRsXW

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Operational Complexity Increases with..

  • Number of things to measure

  • Velocity of change
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https://www.datadoghq.com/docker-adoption/

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How much we measure? 1 instance

  • 10 metrics from cloud providers

1 operating system (e.g., Linux)

  • 100 metrics

50~ metrics per application

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How much we measure? 1 instance

  • 10 metrics from cloud providers

1 operating system (e.g., Linux)

  • 100 metrics

50~ metrics per application
 N containers

  • 150*N metrics
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Operational Complexity

100

instances

500

containers

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Operational Complexity: Scale

160

metrics per host

800

metrics per host

Assuming 5 containers per host

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Operational Complexity: Scale

100

instances

80,000

metrics

Assuming 5 containers per host

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How much we measure? 1 instance

  • 10 metrics from cloud providers

1 operating system (e.g., Linux)

  • 100 metrics

50~ metrics per application
 N containers

  • 150*N metrics

Metrics Overload!

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Operational Complexity Increases with..

  • Number of things to measure

  • Velocity of change
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Source: Datadog

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Source: http://bit.ly/1qFylWK

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Operational Complexity Increases with..

  • Number of things to measure

  • Velocity of change
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Open Questions

  • Where is my container running?
  • What is the capacity of my cluster?
  • What port is my app running on?
  • What’s the total throughput of my app?
  • What’s its response time per tag? (app, version, region)
  • What’s the distribution of 5xx error per container?
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Source: http://bit.ly/1YxJ7Jy

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More Details at: http://www.datadoghq.com/blog/monitoring-101-alerting/

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Monitoring 101

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Finding Signal - Categorizing Your Metrics

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Examples: NGINX - Metrics

Work Metrics: 


  • Requests Per Second
  • Request Time
  • Error Rates (4xx or 5xx)
  • Success (2xx)

Resource Metrics:


  • Disk I/O
  • Memory
  • CPU
  • Queue Length
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Examples: NGINX - Events

  • Configuration Change
  • Code Deployment
  • Service Started / Stopped
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Examples: Events

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When to let a sleeping engineer lie?

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When to alert?

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Recurse until you find root cause

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What to demand from our monitoring tooling?

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Cryptic Alerts

W H A T ?

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EVERY ALERT MUST BE ACTIONABLE

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Host Centric

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Service Centric

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Static configurations tracking dynamic infrastructure are not a recipe for success.

Static vs Dynamic

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Query Based Monitoring

“What’s the average throughput of application:nginx per version ?” “Alert me when one of my pod from replication controller:foo is not behaving like the others?” “Show me rate of HTTP 500 responses from nginx” “… across all data centers” “… running my app version 2….”

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Getting at the metrics…

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Resource Metrics

Utilization:

  • CPU (user + system)
  • memory
  • i/o
  • network traffic

Saturation

  • throttling
  • swap

Error

  • Network Errors 


(receive vs transmit)

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Container Events

  • Starting / Stopping Containers
  • Scaling Events for Underlying Instances
  • Deploying a new container build
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How do we get at the upper layers?

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Getting at the Metrics

CPU METRICS MEMORY METRICS I/O METRICS NETWORK METRICS pseudo-files Yes Yes Some Yes, in 1.6.1+ stats command Basic Basic No Basic API Yes Yes Some Yes

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Pseudo-files

  • Provide visibility into container metrics via the file system.
  • Generally under: 


/cgroup/<resource>/docker/$CONTAINER_ID/ 


  • r


/sys/fs/cgroup/<resource>/docker/$CONTAINER_ID/


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Pseudo-files: CPU Metrics

$ cat /sys/fs/cgroup/cpuacct/docker/$CONTAINER_ID/cpuacct.stat > user 2451 # time spent running processes since boot > system 966 # time spent executing system calls since boot $ cat /sys/fs/cgroup/cpu/docker/$CONTAINER_ID/cpu.stat > nr_periods 565 # Number of enforcement intervals that have elapsed > nr_throttled 559 # Number of times the group has been throttled > throttled_time 12119585961 # Total time that members of the group were throttled (12.12 seconds)

Pseudo-files: CPU Throttling

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Docker API

  • Detailed streaming metrics as JSON HTTP socket


$ curl -v --unix-socket /var/run/docker.sock http://localhost/containers/ 28d7a95f468e/stats


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STATS Command

# Usage: docker stats CONTAINER [CONTAINER...] $ docker stats $CONTAINER_ID CONTAINER CPU % MEM USAGE/LIMIT MEM % NET I/O BLOCK I/O ecb37227ac84 0.12% 71.53 MiB/490 MiB 14.60% 900.2 MB/275.5 MB 266.8 MB/872.7 MB

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Side Car Containers

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Aren’t we still missing a layer?

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Open Questions

  • What is the capacity of my cluster?
  • What’s the total throughput of my app?
  • What’s its response time per tag? (app, version, region)
  • What’s the distribution of 5xx error per container?
  • Where is my container running? what port?
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Service Discovery

Docker API Orchestrator Monitoring Agent Container

A O A O Containers List & Metadata Additional Metadata (Tags, etc)

Config Backend

Integration Configurations

Host Level Metrics

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Custom Metrics

  • Instrument custom applications

  • You know your key transactions best.

  • Use async protocols like Etys’ STATSD or 


DogstatsD

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Source: http://bit.ly/1NoW6aj

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Resources

Monitoring 101: Alerting 
 https://www.datadoghq.com/blog/monitoring-101-alerting/ Monitoring 101: Collecting the Right Data https://www.datadoghq.com/blog/monitoring-101-collecting-data/ Monitoring 101: Investigating performance issues https://www.datadoghq.com/blog/monitoring-101-investigation/
 The Power of Tagged Metrics https://www.datadoghq.com/blog/the-docker-monitoring-problem/ How to Collect Docker Metrics https://www.datadoghq.com/blog/how-to-collect-docker-metrics/ 8 surprising facts about Docker Adoption https://www.datadoghq.com/docker-adoption/