David Currie

It’s been on my to-do list for some time to try setting up Prometheus to monitor WebSphere Liberty. There is a JMX Exporter which makes the job pretty simple even if there ended up being more steps than I had originally hoped.

My first pass was to try to configure the exporter as a Java agent but sadly the current Java client attempts to use some com.sun packages that don’t work with an IBM JRE. I started down the path of rebuilding our Liberty image on OpenJDK but, when I discovered that the Java agent actually uses Jetty to expose its HTTP endpoint I decided that I really didn’t want that bolted on to the side of my Liberty process! Ideally I’d get the Java client fixed and then create a Liberty feature to expose the HTTP endpoint but that will have to wait for another day… This time round I decided to configure the exporter as an HTTP server in a side-car container.

The first step was to create a Liberty image with monitoring enabled using the following Dockerfile:

And then build and run the image and extract the JMX URL:

Note that, in addition to the normal HTTP and HTTPS ports, we’ve exposed a port (5556) that the exporter container is going to use.

Next we need to build the JMX exporter JAR file using maven:

And we also need a config file for the exporter that uses the JMX_URL that we extracted from the Liberty image earlier:

The pattern here is subscribing us to all the available MBeans. The following Dockerfile constructs an image with these two artifacts based on the openjdk image from Docker Hub:

Note that we tell the exporter to run on the same port that we exposed from the Liberty container earlier. Now we build and run the image. We use the network from our Liberty container so that the exporter can connect to it on localhost. The curl should retrieve the metrics being exposed by the exporter.

The last step is to run Prometheus. Create a prometheus.yml file to provide the scrape configuration:

We can then run the standard Prometheus image from Docker Hub:

You can then access the Prometheus UI in your browser on port 9090 of the host where your Docker engine is running. If you’re new to Prometheus, try switching to the Graph tab, entering the name of a metric (e.g. WebSphere_JvmStats_ProcessCPU) and then hit Execute. If all is well, you should see something along the following lines:

If the metrics don’t look all that exciting then try applying a bit of load to the server, such as using the siege tool:

The content of the WebSphere Liberty Docker images currently match the runtime install zips that we make available for download from WASdev.net. One consequence of this is that none of them contain the admin center. Adding it is very simple though as the following Dockerfile shows:

This Dockerfile adds a snippet of server XML under the configDropins directory that adds the adminCenter-1.0 feature. It then uses installUtility to install that feature. The admin center requires a user registry to be defined for authentication and here we use the quickStartSecurity stanza to define a wsadmin user. We’ll come back to remoteFileAccess in a moment.

We can then build and run this image as follows:

Once the server has started you should then be able to access /adminCenter on the HTTPS port return by docker port admin 9443 using the credentials defined in the Dockerfile.

If you then click on the Explore icon in the toolbox you’ll find information about any applications that are (or are not) deployed to the server, the server configuration, and server-level metrics. The last of these may be of particular interest when trying to determine suitable resource constraints for a container.

In a single-server, it’s not currently possible to deploy an application via the admin center. For a simple application you could just place it in the dropins directory but, for argument’s sake, let’s say that we need to provide some extra configuration. I’m going to assume that you have ferret-1.2.war in the current directory. We then copy the file in to the container:

In the admin center, we then navigate to Configure > server.xml, click Add child under the Server element, select Application and click Add. Fill in the location as ferret-1.2.war and the context root as ferret then click Save. It is the remoteFileAccess stanza that we added to the server configuration that allows us to edit the server configuration on the fly.

If you return to the applications tab you should see the application deployed and you can now access the ferret application at /ferret!

Obviously modifying the server configuration in a running container is at odds with the idea of an immutable server but it may still be of use at development time or for making non-functional updates e.g. to the trace enabled for a server.

Having written a few posts on using the IBM Containers service in Bluemix I thought I’d cover another option for running Docker on IBM Cloud: using Docker on VMs provisioned from IBM’s SoftLayer IaaS. This is particularly easy with Docker Machine as there is a SoftLayer driver. As the docs state, there are three required values which I prefer to set as the environment variables SOFTLAYER_USER, SOFTLAYER_API_KEY and SOFTLAYER_DOMAIN. The instructions to retrieve/generate an API key for your SoftLayer account are here. Don’t worry if you don’t have a domain name free – it is only used as a suffix on the machine names when they appear in the SoftLayer portal so any valid value will do. With those variables exported, spinning up three VMs with Docker is as simple as:

Provisioning the VMs and installing the latest Docker engine may take some time. Thankfully, initialising swarm mode across the three VMs with a single manager and two worker nodes can then be achieved very quickly:

Now we can target our local client at the swarm and create a service (running the WebSphere Liberty ferret application):

Once service ps reports the task as running, due to the routing mesh, we can call the application via any of the nodes:

Scale up the number of instances and wait for all three to report as running:

With the default spread strategy, you should end up with a container on each node:

Note that the image has a healthcheck defined which uses the default interval of 30 seconds so expect it to take some multiple of 30 seconds for each task to start. Liam’s WASdev article talks more about the healthcheck and also demonstrates how to rollout an update. Here I’m going to look at the reconciliation behaviour. Let’s stop one of the work nodes and then watch the task state again:

You will see the swarm detect that the task is no longer running on the node that has been stopped and is moved to one of the two remaining nodes:

(You’ll see that there is a niggle here in the reporting of the state of the task that is shutdown.)

This article only scratches the surface of the capabilities of both swarm mode and SoftLayer. For the latter, I’d particularly recommend looking at the bare metal capabilities where you can benefit from the raw performance of containers without the overhead of a hypervisor.

For a while now I’ve had a bullet point on a chart that blithely stated that you could add an application on top of our WebSphere Application Server traditional Docker image using wsadmin and, in particular, with the connection type set to NONE i.e. without the server running. Needless to say, when I actually tried to do this shortly before a customer demo it didn’t work! Thankfully, with moral support from a colleague and the excellent command assistance in the admin console, it turns out that my initial assertion was correct and the failure was just down to my rusty scripting skills. Here’s how…

First, the Dockerfile that builds an image containing our ferret sample application, taking the WAR file from Maven Central.

The following script then builds an image from the Dockerfile in the gist above, runs it, waits for the server to start, and then retrieves the ferret webpage.

Looking for solutions to my archive extraction problem brought me to look at the Rocker project from Grammarly. I’d looked at it before but not in any great detail. In a nutshell, it aims to extend the Dockerfile syntax to overcome some of its current limitations. Although not an option for me (because I can’t depend anything beyond the standard Docker toolset) the Rocker solution to my earlier problem would be as simple as follows:

The extra syntax here is the MOUNT command which follows the same syntax as the --volume flag on docker run. As the Grammarly team point out, there are trade-offs here which help to explain why the Docker maintainers are reluctant to add volume mounts to docker build. Here, changes to the contents of the mounted directories do not result in the cache being busted.

Anyway, this post is meant to be about a different problem: building Docker images where the chosen language requires compilation e.g. Java. One approach (that taken by OpenShift’s source-to-image) is to add the source to an image that contains all of the necessary pieces to build, package and run it. As shown in Jamie’s WASdev post, for Liberty that might mean putting Maven and a full JDK in to the image. I’m not a fan off this approach: I prefer to end up with an image that only contains what is needed to run the application.

The following shows how this might look using Rocker:

Here we’re building one of the Micro Profile samples (which uses Maven) and then creating an image with the resulting WAR and the new WebSphere Liberty Micro Profile image. You’ll note that there are two FROM statements in the file. First we build on the maven image to create the WAR file. We then use the Rocker EXPORT command to make the WAR file available to subsequent build steps. The second part of the build then starts with the websphere-liberty:microProfile image, imports the WAR and tags it. Building, running and then calling the application is then as simple as follows:

The other thing of note is that we’ve used the MOUNT command to create a volume for the Maven cache. The volume is tied to this Rockerfile so, if you run rocker build --no-cache . you’ll see that it rebuilds the images from scratch but Maven does not need to download the dependencies again.

The MOUNT is also a great way to overcome the long-running issue of how to provide credentials required to access resources at build time without them ending up in the final image. Other nice features include the ATTACH command which effectively allows you to set a breakpoint in your Rockerfile, and the fact that the Rockerfile is pre-processed as a golang template allowing much more advanced substitutions than with Docker’s build arguments.

The way we build our WebSphere traditional Docker images is as a two-step process. First we install the products using Installation Manager and generate a tar file containing the installed product. Then we suck that tar file in to a clean image so that the resulting image does not contain all the cruft left lying around from the install process. (Not including Installation Manager in the final image also helps to reinforce that these images are intended to be immutable.)

The ADD Dockerfile command is very convenient for copying in a tar file from the context and unpacking it, all in one atomic operation. Unfortunately the ADD command ignores the current user and always unpacks as root. (Aside: Docker recently re-opened the proposal created by IBMer Megan Kostick to address this problem.) You could run a chown following the ADD but this results in all the files being copied in to a new layer (not cool when the contents of your tar file weighs in at 1.5GB!). Our initial starting point was to make sure that all the files already had the right ownership when they are added to the tar file. This involved creating the same user/group in the initial image and relying on getting the same uid/guid in the eventual image, something I wasn’t entirely happy with.

A related problem that we had run in to elsewhere was that the copy and unpack magic of ADD doesn’t extend to zip files, a format in which many of our install binaries are found. Where those binaries are already hosted somewhere, it’s simple enough to use wget or curl to pull the files, unpack, and perform any necessary cleanup as part of a RUN command. The obvious solution to my local tar or zip file was to host the file somehow. I decided to spin up a python container to serve up the files as follows:

That URL can then be consumed in a subsequent build. For example, if I had example.tar.gz in the directory on the host, I could unpack as the user/group foo:bar in my image with the following Dockerfile:

To build the image, we then just need to pass in the URL as a build argument and, when we’re done, we can clean up the python container:

The result of all of this is that we then get the default non-root behavior of tar which is to unpack as the current user.