Datadog - Docker Usage Patterns

Transcript

1. Docker Usage Patterns No Hype, Just Data Docker Meetup London


   Feb 3, 2016 Benjamin Fernandes Software Engineer — Datadog @LotharSee


2. About Me • MS, CS degree from Ecole Centrale Paris

   • Software Engineer @ Datadog
 • Joined Datadog 3 years ago
 • Worked on Datadog’s integration with Docker and its ecosystem Benjamin Fernandes
 @LotharSee

3. Quick Overview of Datadog Datadog gathers performance data from all

   your application and infrastructure components. • Monitoring for modern applications 1. Dynamic Infrastructure 2. Containers (Docker, ECS, k8s, …) 3. Microservices • Time series storage of metrics and events • Trending, alerting and anomaly detection • We’re hiring! (New York, Paris, remote)

4. Monitor Everything Datadog gathers performance data from all your application

   components.

5. Prologue:
 Docker to ship Open-Source

6. Avoid Dependency Hell

7. What about in production? Docker Adoption Who is running Docker

   today?

8. Adopter: the average number of containers running during the month

   was at least 50% the number of distinct hosts run, or there were at least as many distinct containers as distinct hosts run during the month. Dabbler: used Docker during the month, but did not reach the “adopter” threshold. Abandoner: a currently active company that used Docker in the past, but hasn't used it at all in the last month. Study based on data from 7000 companies. Docker Adoption

9. Are you using Docker? Turns out you aren’t alone! Docker

   Adoption Source: http://dtdg.co/dckr-adopt

10. Fact 1: Docker Adoption Up 5x in 1 Year

11. Docker Adoption Growth We’ve see 5x increase of Docker adoption

    over the last year.

12. Fact 2: Docker now runs on 6% of hosts we

    monitor
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14. Fact 3: Larger Companies Are the Early Adopters!

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16. Fact 4: 2/3 of Companies That Try Docker Adopt It

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18. Fact 5: Users 3x the Number of Containers They Use

    in 5 Months
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20. Fact 6: Most Widely Used Images Are Registry, NGINX, and

    Redis
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22. Fact 7: Docker Hosts Often Run Four Containers at a

    Time

23. Fact 8: VMs Live 4x Longer Than Containers

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25. Bonus fact. Thanks The Onion.

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27. Operational Complexity • Average containers per host: N (N=4, 10/2015)

    • N-times as many “hosts” to manage • Affects • provisioning: prep’ing & building containers • configuration: passing config to containers • orchestration: deciding where/when containers run • monitoring: making sure containers run properly

28. Operational Complexity: Scale 100 instances 400 containers

29. Operational Complexity: Scale 160 metrics per host 640 metrics per

    host Assuming 4 containers per host

30. Operational Complexity: Scale 100 instances 64,000 metrics Assuming 4 containers

    per host

31. Operational Complexity: Velocity

32. So what does that mean for monitoring and management?

33. Monitoring Needs and Pains • Avoid Static config files tracking

    dynamic infrastructure • Avoid a host centric view. Focus on service level. • Use tags, labels, etc on your hosts and metrics to form queries.

34. Query Based Monitoring “Show me rate of HTTP 500 responses

    from nginx” “… in region us-east-1 across all availability zones” “… running my app version 2….” • Pull in labels from your infrastructure whether EC2, Docker or your scheduler. • Ask questions that will ring true regardless of your scale that day. • Know your underlying tech. In this case Docker and how to pull metrics from it.

35. Collecting Docker Metrics

36. Collecting Docker Metrics: stats • Continuous live stream of basic

    CPU, memory, & network • docker stats --no-stream $(docker ps -q)

37. Collecting Docker Metrics: stats api • Stream of JSON, more

    detailed

38. Collecting Docker Metrics: Pseudo Files • If you want to

    do it manually • CPU/Mem metrics • Access via sysfs in /sys/fs/cgroup or /cgroup • By default do not require root access • Network metrics • /proc/$PID/net/dev • Disk IO metrics • /proc/$PID/io
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40. Woof? Woof!