Automating Docker Logging: ElasticSearch, Logstash, Kibana, and Logspout

You, too, could Logstash.

Say that title five times fast! Seriously though, I wasn’t sure what else to call this article so that people would actually find it.

This article is a spiritual successor to Evan Hazlett’s article on running the ELK stack in Docker and ClusterHQ’s article on doing it with Fig/Docker Compose and Flocker. Huge shout out to them for being the giants whose shoulders I stand on. It is also influenced by the recent Borg paper which came out and mentions some of these tools in discussing having a “standard stack” for doing the sorts of things that we are interested in here and more.

The problem

Hopefully I don’t need to convince you of the value of keeping meticulous logs which are easily searchable. If I do, simply consider what happens when something goes wrong with your application and nobody has a damn clue what happened - at the very least, the pointy haired bosses are upset, for some reason that has to do with giant piles of money or audits or something like that.

To that end, a lot of solutions have cropped up to help, including very lovely technologies such as Logstash. More on that in a second.

Since most of us know that logging is critical, one of the issues which has been driving people batty with the increase of interest in Docker is, of course, how to handle logs in a Dockerized world.

With Docker, one is suddenly forced to think of logging in a different way than they otherwise might have. In a traditional Linux deployment, the model for application or infrastructure logging is usually to log to files, which are frequently but not always located inside of the /var/log directory. Indeed, I have “fond” memories of checking the PHP logs for a project inside of that directory when things would go blammo and our built-in application logging wasn’t telling me anything useful. I’m not really a fan of splitting the logs like that (more moving parts == harder to debug), and it’s probably better practice to have a uniform way to access logs. Indeed, there is an interesting article about this idea (a “Unified Logging Layer”) here by Kiyoto Tamura of Fluentd, which is a tool similar to Logstash.

So what’s different about Docker? Well, suddenly, instead of having all of your logs in files on one uniform place in the host system, they are scattered in a variety of different isolated environments in containers. Uh oh. Sounds like the opposite of what we said we wanted above.

The way that Docker historically has handled logging is through the docker logs command - Docker captures the STDOUT and STDERR of each container process, stores it on disk, and the user can query it with docker logs <container>. This works pretty well for purposes like development where you just want to dump some output to a terminal screen and access it pretty quickly, but starts to become more troublesome when you want to consider using Docker for more complex environments, or you want to look at logs from more traditionally-architected UNIX daemons which expect to run in the background and log to disk inside of containers. In the first case, the issues are mainly around:

  1. discoverability - if containers are meant to be ephemeral, trying to track down the one with the logs that I want and parse them using something like grep doesn’t sound fun, and-
  2. log rotation - some services are particular chatty, or simply meant to live for a long time, so we need a way of cleaning up after a while and making sure that our disk does not fill up with logs we are not using. Docker out of the box does not really support for this as far as I’m aware.

I’m not going to talk about log rotation in this article, since that’s a whole ‘nother can of worms, but I will describe how the stack outlined here is meant to ease the process of dealing with the first problem.

As for keeping tabs on processes which log to disk inside of containers, there are a variety of solutions and hacks to make this work. One of my favorites, not really outlined here but in my opinion quite useful, is to make the directory where the logs are written in the original container a volume, and have some additional containers inherit that volume using --volumes-from. Then they can follow the logs using tail -f /var/log/foo/access.log or something like that. In my opinion this promotes a decent separation of concerns since your container monitoring the log is different from the one writing to it, and additionally (less substantially) you will actually bypass the union filesystem to do so (just like you would do with a database). No reason to track logs (state) in images, really.

What to do about discoverability, then? Well, we will run an ELK stack inside of Docker, and use the logspout tool to automatically route container logs to Logstash. I really feel like this type of approach is the future in a lot of ways - if you’re going to be running containers, stopping them, deleting them and so on, you might as well hook into those native events and make the lifecycle of a container accessible to track and monitor. Then your infrastructure can be reactive instead of needy for human intervention. Likewise for things like load balancing, service discovery, and so on, but that’s for another article entirely.

The approach

Following the lead of Evan and the ClusterHQ folks, we are going to run:

  1. ElasticSearch to index the log data collected and make it more easily queryable
  2. Logstash to act as a remote syslog to collect the logs from the containers
  3. Logspout to actually send the container logs to Logstash
  4. Kibana as a nice front end for interacting with the collected data
  5. Cadvisor, a dashboard for monitoring container resource metrics, for kicks

If that sounds like an intimidating amount of stuff to run, try not to fret too much - we’re going to use Docker Compose to make starting up this stack and using it very straightforward.

So, if you want to follow along at home, you can run the following commands to get started very quickly (you will need the latest versions of Docker and Docker Compose installed):

$ git clone
$ cd elk
$ docker-compose -f docker-compose-quickstart.yml up

This will boot up the application with prebaked images from Docker Hub, and your Kibana front-end will be accessible from port 80 of whatever host DOCKER_HOST points to.

Personally, I like to kick up a DigitalOcean droplet or equivalent using Docker Machine when I’m doing this kind of work because the bandwidth for image pulls tends to be much better than on your friendly neighborhood WiFi connection. If you want to also do so, the commands to create your own server to run this on will be similar to this (and once again, make sure you have the latest version of Machine installed):

$ docker-machine create -d digitalocean \
    --digitalocean-size 4gb \
    --digitalocean-image docker \
To point your Docker client at it, run this in your shell: eval "$(docker-machine env droplet)"
$ eval "$(docker-machine env droplet)"

I generally recommend a decently beefy server like outlined above because these processes tend to be a little memory-hungry. It will still work fine locally, but the pulls may be a little slower unless you’re one of the lucky few with fiber.

If you’re not a huge fan of running untrusted images (or you just want to tinker and modify the build yourself), no problem: the default docker-compose.yml in that repo is all based on build parameters, so you can actually build the images yourself and

$ docker-compose build
$ docker-compose up

When you boot the containers up, you’ll see output like this in your terminal:

You’ll probably see some errors about Logspout failing to connect to syslog, which is totally fine and normal. It’s just because the Logstash container hasn’t started yet. When it starts, the errors will cease.

If you visit port 80 on the host where you’ve booted up the little group of containers, you should be greeted by a Kibana welcome screen:

Click on the little “Logstash dashboard” link (indicated by the arrow in the picture above), or simply go directly to <machineIp>/#/dashboard/file/default.json, and you will be taken to the dashboard for your new Docker logging infrastructure!

Like I keep mentioning, the basic “stack” of containers for this was pretty much ripped straight out of Evan’s article, which is fantastic, but when I went to go implement things for myself there were a few issues I encountered:

  1. Logspout was sending data in a slightly different format than the grok filter for Logstash in Evan’s original article / image expected, so:
  2. There would be lots of grok parse failures in the logs (this just means Logstash tried to match the log message to a pattern it knows and couldn’t). By itself this wouldn’t be too terrible, but:
  3. Because Logstash is a container monitored by Logspout, Logspout would forward all of Logstash’s logs to Logstash, causing it to spin into a frenetic loop and eat up almost all of the CPU on the box (docker stats, a very useful command which will report container resource usage statistics in realtime, was partially how I caught and understood that this was happening).
This can't be good.

So what’s a hacker to do? Hack, of course! I forked Evan’s original Dockerfiles/repos for the images and modified things a bit. For starters, I threw all of the containers into services in a docker-compose.yml file for quick reference (that prevented having to re-type all of the docker run commands over and over again whenever I wanted to re-run the stack). I noticed in the Logspout Github repo’s documentation that you could specify an environment variable on a container to dictate that its logs should not be forwarded by Logspout. So, I enabled it on the Logstash container: an environment variable setting of LOGSPOUT=ignore did the trick.

It should also be noted, for anyone reading now, that the gliderlabs/logspout image now expects the Docker socket to be mounted in at /var/run/docker.sock (the classic location), rather than at /tmp/docker.sock like it was before - this caused me a few headaches before I realized what was going on as I was trying to use the commands from Evan’s article verbatim. So take note!!

Now my stack was no longer thrashing my CPU by getting into that infinite loop. But I still had a challenge: all of those grok parse failures in the logs. The provided example configuration file for Logstash did not jive well with what logspout was emitting. So, in order to get a better grip on what was happening, I did what anyone should do in this situation and read the source code for Logspout.

It wasn’t long before I stumbled across this block of code related to the syslog adapter:

func NewSyslogAdapter(route *router.Route) (router.LogAdapter, error) {
        transport, found := router.AdapterTransports.Lookup(route.AdapterTransport("udp"))
        if !found {
                return nil, errors.New("bad transport: " + route.Adapter)
        conn, err := transport.Dial(route.Address, route.Options)
        if err != nil {
                return nil, err

        format := getopt("SYSLOG_FORMAT", "rfc5424")
        priority := getopt("SYSLOG_PRIORITY", "{{.Priority}}")
        hostname := getopt("SYSLOG_HOSTNAME", "{{.Container.Config.Hostname}}")
        pid := getopt("SYSLOG_PID", "{{.Container.State.Pid}}")
        tag := getopt("SYSLOG_TAG", "{{.ContainerName}}"+route.Options["append_tag"])
        structuredData := getopt("SYSLOG_STRUCTURED_DATA", "")
        if route.Options["structured_data"] != "" {
                structuredData = route.Options["structured_data"]
        data := getopt("SYSLOG_DATA", "{{.Data}}")

        var tmplStr string
        switch format {
        case "rfc5424":
                tmplStr = fmt.Sprintf("<%s>1 {{.Timestamp}} %s %s %s - [%s] %s\n",
                        priority, hostname, tag, pid, structuredData, data)
        case "rfc3164":
                tmplStr = fmt.Sprintf("<%s>{{.Timestamp}} %s %s[%s]: %s\n",
                        priority, hostname, tag, pid, data)
                return nil, errors.New("unsupported syslog format: " + format)
        tmpl, err := template.New("syslog").Parse(tmplStr)
        if err != nil {
                return nil, err
        return &SyslogAdapter{
                route: route,
                conn:  conn,
                tmpl:  tmpl,
        }, nil

Turns out that in my case Logspout forwards logs according to the syslog RFC5424 standard (you can see how it defaults to this in the code above). I spent some time fiddling with the very cool Logstash grok parse test app, but then wondered if there were any existing resources available online which solved this problem already. Some quick Googling lead me to this article, which brilliantly outlined pretty much the exact grok parse filter I needed. I changed around just a few things (for instance, I changed “app” field to “containername”) but I was soon on my way - parsing Logspout logs into useful data.

My final Logstash configuration file looks like this:

input {
  tcp {
    port => 5000
    type => syslog
  udp {
    port => 5000
    type => syslog

filter {
  if [type] == "syslog" {
    grok {
      match => { "message" => "%{SYSLOG5424PRI}%{NONNEGINT:ver} +(?:%{TIMESTAMP_ISO8601:ts}|-) +(?:%{HOSTNAME:containerid}|-) +(?:%{NOTSPACE:containername}|-) +(?:%{NOTSPACE:proc}|-) +(?:%{WORD:msgid}|-) +(?:%{SYSLOG5424SD:sd}|-|) +%{GREEDYDATA:msg}" }
    syslog_pri { }
    date {
    if !("_grokparsefailure" in [tags]) {
      mutate {
    mutate {

output {
  elasticsearch { host => "elasticsearch" }
  stdout { codec => rubydebug }

There’s probably plenty of room for improvement, but I’ll have to get much better at Logstash first ;P

So what?

Now I actually had logs in a meaningful format, which weren’t thrashing my CPU. It’s great! Whenever I run a container on that host, the logs get indexed in ElasticSearch and made available for querying from Kibana automatically! The Logstash filter takes care of parsing the raw syslog messages into more useful labeled information. This includes, as noted above, the logs from the containers running this stack (except for Logstash - not sure how to handle that one, or if I should even worry about it. Perhaps there’s an additional configuration option that would make it only print its own logs to STDOUT instead of all of them). Imagine how useful this kind of automatic container logging would be with something like RancherOS as well, where everything including system services is running inside of Docker containers.

You can toggle which fields are displayed in the logs messages to get a quick view of what’s being logged to your application. This makes it easier to get a feel for what is happening in your containers in real time. Kibana has an insane amount of power and configurability, allowing you to sort, search, and filter by all of the different fields you have.

Logs which are nice and easy to read and query!

Try running a container against the ELK stack host to see the logs appear in Kibana automatically (you probably will need to refresh your browser or click the little “refresh” button in Kibana)

$ docker run -d --name number_spitter debian:jessie bash -c 'for i in {0..2000}; do echo $i; done'

You can see now that most of the messages in the log are from the number_spitter container, which naturally spits out a bunch of numbers in that little bash loop.

Whaaaaat! The Number Spitter container is so chatty!

There is a huge amount of amazing stuff you can do beyond this basic setup as well. Naturally, there is the time series graph which can be used as a visual representation of your containers’ activity over time, allowing you to zero down on “hot spots” and quickly get a feel for what happened when, and why.

What the hell happened here? I don't know, but I can find out.

Displayed: actual incident with the ElasticSearch container.

There is also a huge world of additional things which can and should be done with Logstash in this setup - the configuration file discussed here is only the beginning. Some containers have their own logging format which should be subjected to additional parsing. For instance, you can see that the log messages in the picture above showing the “table format” are for the Kibana container itself and have their own timestamp, information about which IP address accessed what file, the status code of the HTTP response, and so on.

So, that is additional information which could definitely be parsed into a more useful structured format, and it is the kind of thing which will need to be done on a per-app basis. Likewise, you can probably imagine really cool higher-order constructs like messages which bump their priority up if they match a certain pattern like if the application recovers from a panic, hits a code path which cases a null pointer exception, fails to connect to the database, and so on.

Also, if Logspout also forwarded Docker events (I’m confused as to whether or not it supports this, since I seem to recall seeing delete events for some containers show up but nothing else) and/or Docker daemon logs that would be SLICK! Perhaps there is an easier way to do this than hijacking Logspout to do it though.

Additionally, Docker 1.6 has log drivers that might be able to do a similar thing in a slightly different way, so I’m curious to explore how this setup might mutate when taking that into consideration. I don’t understand the Logspout internals well enough yet to know if you could do --log-driver=none and still have Logspout forward the logs, for instance. That would be pretty cool since then you would only have to track the data in ElasticSearch, not in ES and --log-driver=json format.

I’m not really sure if Logstash has support for eventing as well (e.g. send an e-mail or a text message to the on-call person if too many errors come in a limited period of time), but that’s another potential use case (I’d be surprised if something like this wasn’t already possible if not well-supported). Come to think of it, this kind of thing is really screaming for a Slack integration as well - e.g., notify the #sales channel every time we close a no-touch subscription, which we know about because it got logged. But I digress.

Speaking of digression, the demo app also includes an instance of cAdvisor, a very useful tool to monitor the resource usage of your containers. You can access it at port 8080 on the host you’re working with:

Pretty graphs for your containers

Nice. Should I start using this right away?

This isn’t the exact setup that you would neccessarily want to chuck into production immediately without modifying anything, although it definitely looks a lot better than just “docker run, maybe check up on them later manually using docker logs”. Some additional things to consider, in no particular order:

  1. ElasticSearch replicas: persisting the data on multiple nodes for redundancy. Weird stuff will happen, nodes will go down, and ideally your infrastructure should be set up to handle this kind of failure smoothly. Likewise, setting up a logspout instance on each node which forwards the host’s logs to the “master” Logstash (and I’m not really certain of how redundancy should work for this potential point of failure) is something you need to tackle if you have a multi-host setup.
  2. Backing up and rotating the log data stored in ElasticSearch. To that end, I’m sure ClusterHQ (self-identified as the “container data people”) would love to help you with this ;)
  3. Making sure that access to this interface is constrained at the network and user level (the demo app leaves everything wide open, so if you run it on the public Internet, expect everyone to be able to see it and mess with it)
  4. Adding container restart policies and monitoring to ensure health and uptime of the services.
  5. Running the containers as lower-privileged users for better security

So, there are still many things to think about before using this for Very Real Stuff, but I hope I have gotten some of the gears turning in your head about how you might accomplish this for yourself if you are motivated. I feel like running this tooling is within the reach of even a very small startup nowadays, and I’m pretty excited that world-class tools like this are becoming more accessible. Ultimately it will be critical for teams now and in the future to be capable of scaling to many machines per operator (SRE), and this is exactly the type of tooling which makes that goal more approachable.


Go forth and log my friends!! And let me know if you have ideas or suggestions. I love to see follow-up articles on this sort of thing as well, so maybe you can be the next link in the chain.

Until next time, stay sassy Internet!

  • Nathan