From the Blogosphere
Top @Docker Metrics | @DevOpsSummit #DevOps #APM #ML #Monitoring
Each container runs a single process, has its own environment, utilizes virtual networks or has many methods of managing storage
By: Sematext Blog
Dec. 31, 2016 09:30 PM
Container Monitoring: Top Docker Metrics to Watch
Monitoring of Docker environments is challenging. Why? Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and applications they run. These servers and applications running on them are typically very static, with very long uptimes. Docker deployments are different: a set of containers may run many applications, all sharing the resources of one or more underlying hosts. It's not uncommon for Docker servers to run thousands of short-term containers (e.g., for batch jobs) while a set of permanent services runs in parallel. Traditional monitoring tools not used to such dynamic environments are not suited for such deployments. On the other hand, some modern monitoring solutions (e.g. SPM from Sematext) were built with such dynamic systems in mind and even have out of the box reporting for docker monitoring. Moreover, container resource sharing calls for stricter enforcement of resource usage limits, an additional issue you must watch carefully. To make appropriate adjustments for resource quotas you need good visibility into any limits containers have reached or errors they have caused. We recommend using alerts according to defined limits; this way you can adjust limits or resource usage even before errors start happening.
Docker Containers != VMs or Servers. Forget your grandpa's old monitoring. Use monitoring designed for Docker.
Watch Resources of Your Docker Hosts
When the resource usage is optimized, a high CPU utilization might actually be expected and even desired, and alerts might make sense only for when CPU utilisation drops (service outages) or increases for a longer period over some max limit (e.g. 85%).
An overutilized Docker host is a sign of trouble.
No, Linux didn't eat your RAM.
Host Disk Space
Good kids clean up their rooms.
Because disk space is very critical it makes sense to define alerts for disk space utilization to serve as early warnings and provide enough time to clean up disks or add additional volumes. For example, SPM automatically sets alert rules for disk space usage for you, so you don't have to remember to do it.
A good practice is to run tasks to clean up the disk by removing unused containers and images frequently.
Total Number of Running Containers
When cluster managers like Docker Swarm, Mesos, Kubernetes, CoreOS/Fleet automatically schedule containers to run on different hosts using different scheduling policies, the number of containers running on each host can help one verify the activated scheduling policies. A stacked bar chart displaying the number of containers on each host and the total number of containers provides a quick visualization of how the cluster manager distributed the containers across the available hosts.
Container counts per Docker host over time
Use anomaly detection, not threshold-based alerts
This metric can have different "patterns" depending on the use case. For example, batch jobs running in containers vs. long running services commonly result in different container count patterns. A batch job typically starts a container on demand, or starts it periodically, and the container with that job terminates after a relatively short time. In such a scenario one might see a big variation in the number of containers running resulting in a "spiky" container count metric. On the other hand, long running services such as web servers or databases typically run until they get re-deployed during software updates. Although scaling mechanisms might increase or decrease the number of containers depending on load, traffic, and other factors, the container count metric will typically be relatively steady because in such cases containers are often added and removed more gradually. Because of that, there is no general pattern we could use for a default Docker alert rule on the number of running containers.
Nevertheless, alerts based on anomaly detection, which detect sudden changes in the number of the containers in total (or for specific hosts) in a short time window, can be very handy for most of the use cases. The simple threshold-based alerts make sense only when the maximum or minimum number of running containers is known, and in dynamic environments that scale up and down based on external factors, this is often not the case.
Use modern Docker monitoring solutions
Container CPU - Throttled CPU Time
Container CPU usage and throttled CPU time
The following screenshot shows containers with 5% CPU quota using the command "docker run -cpu-quota=5000 nginx", we see clearly how the throttled CPU grows until it reaches around 5%, enforced by the Docker engine.
Container CPU usage and throttled CPU time with CPU quota of 5%
Container Memory - Fail Counters
Docker Memory Fail Counters tell you when containers need more memory.
A spike in memory fail counters is a critical event and putting alerts on the memory fail counter is very helpful to detect wrong settings for the memory limits or to discover containers that try to consume more memory than expected.
Container Memory Usage
Container memory usage
Don't like to see your container swapping?
Container swap, memory pages, and swap rate
Container Disk I/O
Container I/O throughput
To limit a Docker container from eating all your disk IO use
Container Network Metrics
Moreover, not only errors or dropped packets are important to watch out for. Today, most of the applications are deeply dependent on network communication. Throughput of virtual networks could be a bottleneck especially for containers like load balancers. In addition, the network traffic might be a good indicator how much applications are used by clients and sometimes you might see high spikes, which could indicate denial of service attacks, load tests, or a failure in client apps. So watch the network traffic - it is a useful metric in many cases.
Network traffic and transmission rates
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