Tips for monitoring Rancher Server

Last week I encountered an interesting bug in Rancher that managed to cause some major problems across my Rancher infrastructure.  Basically, the bug was causing of the Rancher agent clients to continuously bounce between disconnected/reconnected/finished and reconnecting states, which only manifested itself either after a 12 hour period or by deactivating/activating agents (for example adding a new host to an environment).  The only way to temporarily fix the issue was to restart the rancher-server container.

With some help, we were eventually able to resolve the issue.  I picked up a few nice lessons along the way and also became intimately acquainted with some of the inner workings of Rancher.  Through this experience I learned some tips on how to effectively monitor the Rancher server environment that I would otherwise not have been exposed to, which I would like to share with others today.

All said and done, I view this experience as a positive one.  Hitting the bug has not only helped mitigate this specific issue for other users in the future but also taught me a lot about the inner workings of Rancher.  If you’re interested in the full story you can read about all of the details about the incident, including steps to reliably reproduce and how the issue was ultimately resolved here.  It was a bug specific to Rancher v1.5.1-3, so upgrading to 1.5.4 should fix this issue if you come across it.

Before diving into the specifics for this post, I just want to give a shout out to the Rancher community, including @cjellik, @ibuildthecloud, @ecliptok and @shakefu.  The Rancher developers, team and community members were extremely friendly and helpful in addressing and fixing the issue.  Between all the late night messages in the Rancher slack, many many logs, countless hours debugging and troubleshooting I just wanted to say thank you to everyone for the help.  The small things go a long way, and it just shows how great the growing Rancher community is.

Effective monitoring

I use Sysdig as the main source of container and infrastructure monitoring.  To accomplish the metric collection, I run the Sysdig agent as a systemd service when a server starts up so when a server dies and goes away or a new one is added, Sysdig is automatically started up and begins dumping that metric data into the Sysdig Cloud for consumption through the web interface.

I have used this data to create custom dashboards which gives me a good overview about what is happening in the Rancher server environment (and others) at any given time.

sysdig dashboard

The other important thing I discovered through this process, was the role that the Rancher database plays.  For the Rancher HA setup, I am using an externally hosted RDS instance for the Rancher database and was able to fine found some interesting correlations as part of troubleshooting thanks to the metrics in Sysdig.  For example, if the database gets stressed it can cause other unintended side effects, so I set up some additional monitors and alerts for the database.

Luckily Sysdig makes the collection of these additional AWS metrics seamless.  Basically, Sysdig offers an AWS integration which pull in CloudWatch metrics and allows you to add them to dashboards and alert on them from Sysdig, which has been very nice so far.

Below are some useful metrics in helping diagnose and troubleshoot various Rancher server issues.

  • Memory usage % (server)
  • CPU % (server)
  • Heap used over time (server)
  • Number of network connections (server)
  • Network bytes by application (server)
  • Freeable memory over time (RDS)
  • Network traffic over time (RDS)

As you can see, there are quite a few things you can measure with metrics alone.  Often though, this isn’t enough to get the entire picture of what is happening in an environment.


It is also important to have access to (useful) logs in the infrastructure in order to gain insight into WHY metrics are showing up the way they do and also to help correlate log messages and errors to what exactly is going on in an environment when problems occur.  Docker has had the ability for a while now to use log drivers to customize logging, which has been helpful to us.  In the beginning, I would just SSH into the server and tail the logs with the “docker logs” command but we quickly found that to be cumbersome to do manually.

One alternative to tailing the logs manually is to configure the Docker daemon to automatically send logs to a centralized log collection system.  I use Logstash in my infrastructure with the “gelf” log driver as part of the bootstrap command that runs to start the Rancher server container, but there are other logging systems if Logstash isn’t the right fit.  Here is what the relevant configuration looks like.

--log-driver=gelf \
--log-opt gelf-address=udp://<logstash-server>:12201 \
--log-opt tag=rancher-server \

Just specify the public address of the Logstash log collector and optionally add tags.  The extra tags make filtering the logs much easier, so I definitely recommend adding at least one.

Here are a few of the Logstash filters for parsing the Rancher logs.  Be aware though, it is currently not possible to log full Java stack traces in Logstash using the gelf input.

if [tag] == "rancher-server" {
    mutate { remove_field => "command" }
    grok {
      match => [ "host", "ip-(?<ipaddr>\d{1,3}-\d{1,3}-\d{1,3}-\d{1,3})" ]

    # Various filters for Rancher server log messages
    grok {
     match => [ "message", "time=\"%{TIMESTAMP_ISO8601}\" level=%{LOGLEVEL:debug_level} msg=\"%{GREEDYDATA:message_body}\"" ]
     match => [ "message", "%{TIMESTAMP_ISO8601} %{WORD:debug_level} (?<context>\[.*\]) %{GREEDYDATA:message_body}" ]
     match => [ "message", "%{DATESTAMP} http: %{WORD:http_type} %{WORD:debug_level}: %{GREEDYDATA:message_body}" ]

There are some issues open for addressing this, but it doesn’t seem like there is much movement on the topic, so if you see a lot of individual messages from stack traces that is the reason.

One option to mitigate the problem of stack traces would be to run a local log collection agent (in a container of course) on the rancher server host, like Filebeat or Fluentd that has the ability to clean up the logs before sending it to something like Logstash, ElasticSearch or some other centralized logging.  This approach has the added benefit of adding encryption to the logs, which GELF does not have (currently).

If you don’t have a centralized logging solution or just don’t care about rancher-server logs shipping to it – the easiest option is to tail the logs locally as I mentioned previously, using the json-file log format.  The only additional configuration I would recommend to the json-file format is to turn on log rotation which can be accomplished with the following configuration.

 --log-driver=json-file \
 --log-opt max-size=100mb \
 --log-opt max-file=2 \

Adding these logging options will ensure that the container logs for rancher-server will never full up the disk on the server.

Bonus: Debug logs

Additional debug logs can be found inside of each rancher-server container.  Since these debug logs are typically not needed in day to day operations, they are sort of an easter egg, tucked away.  To access these debug logs, they are located in /var/lib/cattle/logs/ inside of the rancher-server container.  The easiest way to analyze the logs is to get them off the server and onto a local machine.

Below is a sample of how to do this.

docker exec -it <rancher-server> bash
cd /var/lib/cattle/logs
cp cattle-debug.log /tmp

Then from the host that the container is sitting on you can docker cp the logs out of the container and onto the working directory of the host.

docker cp <rancher-server>:/tmp/cattle-debug.log .

From here you can either analyze the logs in a text editor available on the server, or you can copy the logs over to a local machine.  In the example below, the server uses ssh keys for authentication and I chose to copy the logs from the server into my local /tmp directory.

 scp -i ~/.ssh/<rancher-server-pem> user@rancher-server:/tmp/cattle-debug.log /tmp/cattle-debug.log

With a local copy of the logs you can either examine the logs using your favorite text editor or you can upload them elsewhere for examination.


With all of our Rancher server metrics dumping into Sysdig Cloud along with our logs dumping into Logstash it has made it easier for multiple people to quickly view and analyze what was going on with the Rancher servers.  In HA Rancher environments with more than one rancher-server running, it also makes filtering logs based on the server or IP much easier.  Since we use 2 hosts in our HA setup we can now easily filter the logs for only the server that is acting as the master.

As these container based grow up, they also become much more complicated to troubleshoot.  With better logging and monitoring systems in place it is much easier to tell what is going on at a glance and with the addition of the monitoring solution we can be much more proactive about finding issues earlier and mitigating potential problems much faster.

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Docker for Mac file system performance summary

One of the more controversial topics right now in the Docker community is the issue surrounding file system performance in the Docker for Mac application.

For a very long time users have been forced to use workarounds to speed up performance when dealing with slow read and write times.  For example, this thread has been open on the Docker forums for over a year now, describing the problem and various workarounds users have found during that time.  There have been blog posts describing various optimizations, as well as scripts and tools to alleviate some of the frustration around slow file system performance on Docker for Mac.

There is a great explanation from the Docker team that lays out the details of the file system performance issues and what the crux of the problem is right now.

At the highest level, there are two dimensions to file system performance: throughput (read/write IO) and latency (roundtrip time). In a traditional file system on a modern SSD, applications can generally expect throughput of a few GB/s. With large sequential IO operations, osxfs can achieve throughput of around 250 MB/s which, while not native speed, will not be the bottleneck for most applications which perform acceptably on HDDs.

The article later goes on to highlight the plan to improve performance along with a number of specific items for accomplishing this.

Under development, we have:

  1. A Linux kernel patch to reduce data path latency by 2/7 copies and 2/5 context switches
  2. Increased OS X integration to reduce the latency between the hypervisor and the file system server
  3. A server-side directory read cache to speed up traversal of large directories
  4. User-facing file system tracing capabilities so that you can send us recordings of slow workloads for analysis
  5. A growing performance test suite of real world use cases (more on this below in What you can do)
  6. Experimental support for using Linux’s inode, writeback, and page caches
  7. End-user controls to configure the coherence of subsets of cross-OS bind mounts without exposing all of the underlying complexity

Additionally, with the latest release of the Docker for Mac 17.04-ce-mac7 (April 6 2017) client, a new :cached flag has been introduced for volume mounts to help with read times for lots of files.  There is also work going on to introduce another :delegated flag to help speed up write times.

Initial user testing of the :cached flag has been good, and shown up to a 4x improvement in some cases.  You can follow this issue on Github to get the most up to date information.  There is some really good detail and discussion going on over there (towards the bottom of the issue is where the new flags are discussed).

Overall I think Docker has done a great job of keeping users informed and updated on the various aspects of the problem and has been steadily making progress in addressing the situation.  The container ecosystem is still very young so there will be growing pains along the way and I think the way that Docker has been handling things has been more than reasonable as they have consistently been making progress on addressing the issue and have been transparent in recent months about what’s going on and how they’re working on the problem.

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Defaulting Google search results to the past year

If you spend as much time looking around the internet for answers to obscure questions as I do, you quickly realize that often times, Google will happily present you with results that are many years old and out of date.  This is especially frustrating when you eventually find an answer after spending quite some time searching, only to realize the answer you’re looking for is 5 years old.  For more reading on efficient searching check out this post on the 10 tab rule, which has some useful ideas for better searching in general.


There is a trick that will allow you to customize the Google default results.  The key to mapping the Google search terms is really just the URL.  Google uses search parameters for querying, so you can do some really cool things.  Obviously this is a powerful concept, so a lot of useful searches can be mapped.  This idea can be taken further to map keywords to other searches, like YouTube, Google maps, Stack Overflow, etc, or basically any site that provides a search interface.

I have only tested these key/search mappings for Google search results on Google Chrome, so if you use another browser there might be a similar trick, I just haven’t attempted it.  Open Chrome settings and navigate to the “Search” section.

This will pull up a dialog box with a list of default search engines.  Scroll to the bottom of the list and add the following values to the corresponding fields from the screenshot.

The Keyword is just a “>” symbol, and it can be anything really.  I chose the > because it is quick and easy to get to.  The rest is pretty self explanatory.  After the entry has been added, scroll through the search engines and find the new “Google recent” entry – there is a button that says “Make default” if you hover over the search engine entry.  Click that and then click done.

Now when you do a Google search from your search bar it will default to items from the past year.


You can extend this trick further to map keys in your Google search bar to do other searches.  For example, you can map a key (or word) to search for image results.  In the below example, I am using “I” as the keyword.

After adding the above snippet into the search settings you can navigate to the search bar, type in I (followed by a tab) and the term to search for and it will automatically do an image search.  The key to making the mapping being a tab completion in the search bar is the q=%s part in the URL.

The last bonus search that has worked for me is the “feeling lucky” search.

That keyword (I used a tilde) can once again be anything, but preferably should be fast to get reach to make searching easier.

One final note

Sometimes you actually do want to search for results that are over a year old.  This is true of information that doesn’t really change often.  So if you are having trouble finding a website you think should be at the top of the search, make sure that the default search result is set to any time.  Ideally you would make another key mapping to handle this searching behavior.

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Awesome lists are a newish phenomenon for organizing links and information that have been gaining popularity on Github.  You can read more about awesome lists here, but essentially they are curated lists of helpful links and other various resources that are put together by members of their communities.

There are lots of awesome lists these days on Github, and there are even awesome lists for non-tech categories, including recipes and video games. Oddly enough though through my own searching, I quickly discovered that there was no awesome list for Rancher.

So I decided to give a little bit back to the Rancher community in my own way.  If you aren’t aren’t familiar with Rancher, it is essentially a platform and orchestration engine for container based workloads.  Out of all of the container management and orchestration platforms that have been growing out of the container popularity explosion, using Rancher has definitely the most pleasant experience and easiest to use.  Also, if you are new to Rancher, please go check out the list on Github for more information, that is what it is there for.

Check out the awesome-rancher project here.

The goal for me and for the project is to make awesome-rancher the go to resource for finding useful information.  Therefore, I want awesome-rancher to be as easy to use and as complete as possible so new users have a good jumping off place when they decide to dive into Rancher.

If you are a Rancher community member or even just notice a key resource missing from the list, please either let me know with a comment here, on twitter/facebook, or just open a PR on the project itself (which is probably the easiest).

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Configure a Rancher HAProxy health check

If you are familiar with ELB/ALB you will know that there are slight idiosyncrasies between the two.  For example, ELB allows you to health check a back end server by TCP port.  Basically allowing the user to check if a back end comes up and is listening on a specified port.  ALB is slightly different in its method for health checking.  ALB uses HTTP checks (layer 7) to ensure back end instances are up and listening.

This becomes a problem in Rancher, when you have multiple stacks in a single environment that are fronted by the Rancher HAProxy load balancer.  By default, the HAProxy config does not have a health check endpoint configured, so ALB is never able to know if the back end server is actually up and listening for requests.

A colleague and I  recently discovered a neat trick for solving this problem if you are fronting your environment with an ALB.  The solution to this conundrum is to sprinkle a little bit of custom configuration to the Rancher HAProxy config.

In Rancher, you can modify the live settings without downtime.  Click on the load balancer that sits behind the ALB and navigate to the Custom haproxy.cfg tab.

haproxy config

Modify the HAProxy config by adding the following:

# Use to report haproxy's status
    mode http
    monitor-uri /_ping

Click the “Edit” button to apply these changes and you should be all set.

Next, find the health check configuration for the associated ALB in the AWS console and add a check the the /_ping path on port 80 (or whichever port you are exposing/plan to listen on).  It should look similar to the following example.

Health checks

Below is an example that maps a DNS name to an internal Nginx container that is listening for requests on port 80.

HAPRoxy configuration

The check in ALB ensures that the HAProxy load balancer in Rancher is up and running before allowing traffic to be routed to it.  You can verify that your Rancher load balancer is working if the instances behind your ALB start showing a status of healthy in the AWS console.

NOTE: If you don’t have any apps initially behind the Rancher load balancer (or that are listening on the port specified in the health check) the AWS instances behind ALB will remain unhealthy until you add configuration in Rancher for the stacks to be exposed, as pictured above.

After setting up HAProxy, publicly accessible services in private Rancher environments can easily be managed by updating the HAProxy config.  Just add a dns name and a service to link to and HAProxy is able to figure out how and where to route requests to.  To map other services that aren’t listening on port 80, the process is  very similar.  Use the above as a guideline and simply update the target port to whichever port the app is listening on internally.

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