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Top @Docker Metrics | @DevOpsSummit #DevOps #APM #AI #CD #Monitoring

Each container runs a single process, has its own environment, utilizes virtual networks or has many methods of managing storage

Container Monitoring: Top Docker Metrics to Watch
By Stefan Thies

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.

SPM Docker Metrics Overview

Note: All images in this post are from Sematext's SPM Performance Monitoring tool and its Docker monitoring integration.

Watch Resources of Your Docker Hosts

Host CPU
Understanding the CPU utilization of hosts and containers helps one optimize the resource usage of Docker hosts. The container CPU usage can be throttled in order to avoid a single busy container slowing down other containers by taking away all available CPU resources. Throttling the CPU time is a good way to ensure a minimum of processing power for essential services - it's like the good old nice levels in Unix/Linux.

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.
An underutilized host is a sign you are wasting money.


Host Memory
The total memory used in each Docker host is important to know for the current operations and for capacity planning. Dynamic cluster managers like Docker Swarm use the total memory available on the host and the requested memory for containers to decide on which host a new container should ideally be launched. Deployments might fail if a cluster manager is unable to find a host with sufficient resources for the container. That's why it is important to know the host memory usage and the memory limits of containers. Adjusting the capacity of new cluster nodes according to the footprint of Docker applications could help optimize the resource usage.


No, Linux didn't eat your RAM.
But when buffered || cached memory goes to 0 it's time to expand the cluster


Host Disk Space
Docker images and containers consume additional disk space. For example, an application image might include a Linux operating system and might have a size of 150-700 MB depending on the size of the base image and installed tools in the container. Persistent Docker volumes consume disk space on the host as well. In our experience watching the disk space and using cleanup tools is essential for continuous operations of Docker hosts.


Good kids clean up their rooms.
Good Docker ops clean up their disks by removing unused containers & images.


DIsk Space on Docker Nodes
Disk space usage on Docker hosts

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
The current and historical number of containers is an interesting metric for many reasons. For example, it is very handy during deployments and updates to check that everything is running like before.

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.

Docker Container Count

Container counts per Docker host over time


Use anomaly detection, not threshold-based alerts
to catch sudden container migrations that mean trouble.


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.

Container Metrics
Container metrics are basically the same metrics available for every Linux process, but include limits set via cgroups by Docker, such as limits for CPU or memory usage. Please note that sophisticated monitoring solutions like SPM for Docker are able to aggregate Container Metrics on different levels like Docker Hosts/Cluster Nodes, Image Name or ID and Container Name or ID. Having the ability to do that makes it easy to track resources usage by hosts, application types (image names) or specific containers. In the following examples we might use aggregations on various levels.


Use modern Docker monitoring solutions
to slice & dice by host, node, image or container.
You'll need that.


Container CPU - Throttled CPU Time
One of the most basic bits of information is information about how much CPU is being consumed by all containers, images, or by specific containers. A great advantage of using Docker is the capability to limit CPU utilisation by containers. Of course, you can't tune and optimize something if you don't measure it, so monitoring such limits is the prerequisite. Observing the total time that a container's CPU usage was throttled provides the information one needs to adjust the setting for  CPU shares in Docker. Please note that CPU time is throttled only when the host CPU usage is maxed out. As long as the host has spare CPU cycles available for Docker it will not throttle containers' CPU usage. Therefore, the throttled CPU is typically zero and a spike of this metric is a typically a good indication of one or more containers needing more CPU power than the host can provide.

Docker Container CPU usage

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.

CPU-throttled-time

Container CPU usage and throttled CPU time with CPU quota of 5%

Container Memory - Fail Counters
It is a good practice to set memory limits for containers. Doing that helps avoid a memory-hungry container taking all available memory and starving all other containers on the same server. Runtime constraints on resources can be defined in the Docker run command. For example, "-m 300M" sets the memory limit for the container to 300 MB. Docker exposes a metric called container memory fail counters. This counter is increased each time memory allocation fails - that is, each time the pre-set memory limit is hit. Thus, spikes in this metric indicate one or more containers needing more memory than was allocated. If the process in the container terminates because of this error, we might also see out of memory events from Docker.


Docker Memory Fail Counters tell you when containers need more memory.
Alerts are your friends.


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.

image03

Container Memory Usage
Different applications have different memory footprints. Knowing the memory footprint of the application containers is important for having a stable environment. Container memory limits ensure that applications perform well, without using too much memory, which could affect other containers on the same host. The best practice is to tune memory setting in a few iterations:

  1. Monitor memory usage of the application container
  2. Set memory limits according to the observations
  3. Continue monitoring of memory, memory fail counters, and Out-Of-Memory events. If OOM events happen, the container memory limits may need to be increased, or debugging is required to find the reason for the high memory consumptions.

Docker Container Memory Usage

Container memory usage

Container Swap
Like the memory of any other process, a container's memory could be swapped to disk. For applications like Elasticsearch or Solr one often finds instructions to deactivate swap on the Linux host - but if you run such applications on Docker it might be sufficient just to set "-memory-swap=-1" in the Docker run command!


Don't like to see your container swapping?
Use -memory-swap=-1 in the Docker run command and be done with it!


Docker Container Swap

Container swap, memory pages, and swap rate

Container Disk I/O
In Docker multiple applications use the same resources concurrently. Thus, watching the disk I/O helps one define limits for specific applications and give higher throughput to critical applications like data stores or web servers, while throttling disk I/O for batch operations. For example, the command docker run -it -device-write-bps /dev/sda:1mb mybatchjob would limit the container disk writes to a maximum of 1 MB/s.

Docker Container I/O

Container I/O throughput


To limit a Docker container from eating all your disk IO use
e.g. -device-write-bps /dev/sda:1mb


Container Network Metrics
Networking for containers can be very challenging. By default all containers share a network, or containers might be linked together to share a separated network on the same host. However, when it comes to networking between containers running on different hosts an overlay network is required, or containers could share the host network. Having many options for network configurations means there are many possible causes of network errors.

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.

Docker Container Network I/o

Network traffic and transmission rates

Summary
There you have it - the top Docker metrics to watch. Staying focused on these top metrics and corresponding analysis will help you stay on the road while driving towards successful Docker deployments on many platforms such as Docker Swarm, Docker Cloud, Docker Datacenter or any other platform supporting Docker containers. If you'd like to learn even more about Docker Monitoring and Logging stay tuned on sematext.com/blog or follow @sematext.

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@DevOpsSummit will expand the DevOps community, enable a wide sharing of knowledge, and educate delegates and technology providers alike. Recent research has shown that DevOps dramatically reduces development time, the amount of enterprise IT professionals put out fires, and support time generally. Time spent on infrastructure development is significantly increased, and DevOps practitioners report more software releases and higher quality. Sponsors of @DevOpsSummit will benefit from unmatched branding, profile building and lead generation opportunities through:

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Sponsorship Opportunities

Sponsors of Cloud Expo @ThingsExpo will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35 minute technical session
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  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage
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For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez (@GonzalezCarmen) today by email at events (at) sys-con.com, or by phone 201 802-3021.

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Track 1. FinTech
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Sematext is a globally distributed organization that builds innovative Cloud and On Premises solutions for performance monitoring, alerting and anomaly detection (SPM), log management and analytics (Logsene), and search analytics (SSA). We also provide Search and Big Data consulting services and offer 24/7 production support for Solr and Elasticsearch.

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"When we talk about cloud without compromise what we're talking about is that when people think about 'I need the flexibility of the cloud' - it's the ability to create applications and run them in a cloud environment that's far more flexible,” explained Matthew Finnie, CTO of Interoute, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Wooed by the promise of faster innovation, lower TCO, and greater agility, businesses of every shape and size have embraced the cloud at every layer of the IT stack – from apps to file sharing to infrastructure. The typical organization currently uses more than a dozen sanctioned cloud apps and will shift more than half of all workloads to the cloud by 2018. Such cloud investments have delivered measurable benefits. But they’ve also resulted in some unintended side-effects: complexity and risk. End users now struggle to navigate multiple environments with varying degrees of performance. Companies are unclear on the security of their data and network access. And IT squads are overwhelmed trying to monitor and manage it all.
It is ironic, but perhaps not unexpected, that many organizations who want the benefits of using an Agile approach to deliver software use a waterfall approach to adopting Agile practices: they form plans, they set milestones, and they measure progress by how many teams they have engaged. Old habits die hard, but like most waterfall software projects, most waterfall-style Agile adoption efforts fail to produce the results desired. The problem is that to get the results they want, they have to change their culture and cultures are very hard to change. To paraphrase Peter Drucker, "culture eats Agile for breakfast." Successful approaches are opportunistic and leverage the power of self-organization to achieve lasting change.
In 2014, Amazon announced a new form of compute called Lambda. We didn't know it at the time, but this represented a fundamental shift in what we expect from cloud computing. Now, all of the major cloud computing vendors want to take part in this disruptive technology. In his session at 20th Cloud Expo, Doug Vanderweide, an instructor at Linux Academy, discussed why major players like AWS, Microsoft Azure, IBM Bluemix, and Google Cloud Platform are all trying to sidestep VMs and containers with heavy investments in serverless computing, when most of the industry has its eyes on Docker and containers.
@DevOpsSummit at Cloud Expo taking place Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center, Santa Clara, CA, is co-located with the 21st International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long development cycles that produce software that is obsolete at launch. DevOps may be disruptive, but it is essential.
SYS-CON Events announced today that CA Technologies has been named "Platinum Sponsor" of SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. CA Technologies helps customers succeed in a future where every business - from apparel to energy - is being rewritten by software. From planning to development to management to security, CA creates software that fuels transformation for companies in the application economy. With CA software at the center of their IT strategy, organizations can leverage the technology that changes the way we live - from the data center to the mobile device. CA's software and solutions help customers thrive in the new application economy by delivering the means to deploy, monitor and secure their applications and infrastructure.