{"id":71137,"date":"2017-12-11T19:00:45","date_gmt":"2017-12-11T17:00:45","guid":{"rendered":"http:\/\/www.javacodegeeks.com\/?p=71137"},"modified":"2023-12-11T10:30:18","modified_gmt":"2023-12-11T08:30:18","slug":"docker-java-developers-develop-docker","status":"publish","type":"post","link":"https:\/\/www.javacodegeeks.com\/2017\/12\/docker-java-developers-develop-docker.html","title":{"rendered":"Docker for Java Developers: Develop on Docker"},"content":{"rendered":"

This article is part of our Academy Course titled Docker Tutorial for Java Developers<\/a>.<\/p>\n

In this course, we provide a series of tutorials so that you can develop your own Docker based applications. We cover a wide range of topics, from Docker over command line, to development, testing, deployment and continuous integration. With our straightforward tutorials, you will be able to get your own projects up and running in minimum time. Check it out here<\/a>!<\/em><\/p>\n

\n

Table Of Contents<\/h3>\n
\n
1. Introduction<\/a><\/dt>\n
2. Docker from your IDE<\/a><\/dt>\n
\n
\n
2.1. Eclipse<\/a><\/dt>\n
2.2. IntelliJ IDEA<\/a><\/dt>\n
2.3. Oracle NetBeans<\/a><\/dt>\n<\/dl>\n<\/dd>\n
3. On the Edge: Your IDE in Docker<\/a><\/dt>\n
4. So What About Debugging?<\/a><\/dt>\n
5. Management and Monitoring<\/a><\/dt>\n
\n
\n
5.1. Portainer<\/a><\/dt>\n
5.2. cAdvisor<\/a><\/dt>\n
5.3. ctop<\/a><\/dt>\n
5.4. And a lot more …<\/a><\/dt>\n<\/dl>\n<\/dd>\n
6. Docker Engine Metrics<\/a><\/dt>\n
7. Conclusions<\/a><\/dt>\n
8. What\u2019s next<\/a><\/dt>\n<\/dl>\n<\/div>\n

<\/a>1. Introduction<\/h2>\n

In the last part of the tutorial<\/a> we learned a number of ways to package (and run) Java applications as Docker<\/a> containers. In this part we are going to focus more on how Docker<\/a> impacts our development practices in a quite positive ways.<\/p>\n

Fairly speaking, there are a lot of areas where Docker<\/a> could be very helpful for developers. The emphasis of our discussion will be made on Docker<\/a> integration with IDE<\/a>s, running IDE<\/a>s as Docker<\/a> containers and getting to know a couple of useful tools for managing and monitoring your Docker<\/a> environments.<\/p>\n

<\/a>2. Docker from your IDE<\/h2>\n

As we have seen so far, Docker<\/a>\u2019s command line tools are very powerful and their usage is pretty straightforward. However, most of us, developers, spend major part of the day in our favorite IDE<\/a>. Switching between dozens of windows and terminals in the middle of the debugging session while hunting for a hard to reproduce bug in the application logic could be quite counterproductive.<\/p>\n

Luckily, all three leading Java development IDE<\/a>s, namely Eclipse<\/a>, JetBrains IntelliJ IDEA<\/a> and NetBeans<\/a>, provide integration with Docker<\/a> tooling through convenient UI views or\/and widgets. Let us quickly go over each of these IDE<\/a>s and take a closer look on what one has to offer.<\/p>\n

<\/a>2.1. Eclipse<\/h3>\n

Eclipse Oxygen<\/a>, the latest released version of Eclipse<\/a> IDE<\/a> as of this moment, has comprehensive Docker<\/a> tooling support provided by Linux Tools<\/a> subproject (available as a plugin). Once installed, it contributes four additional views under Docker<\/code> category.<\/p>\n

\"Docker<\/a>
Docker Views in Eclipse<\/figcaption><\/figure><\/p>\n

Each of these views exposes some details about Docker<\/a> containers or images, with Docker Explorer<\/code> being all-in-one view of both.<\/p>\n

\"Docker<\/a>
Docker Explorer View<\/figcaption><\/figure><\/p>\n

If you are interested just in Docker<\/a> containers, there is a dedicated Docker Containers<\/code> view, which uses compact, table-based representation.<\/p>\n

\"Docker<\/a>
Docker Containers View<\/figcaption><\/figure><\/p>\n

Similarly, if you want to list only Docker<\/a> images, the specialized Docker Images<\/code> view is right here to serve your needs.<\/p>\n

\"Docker<\/a>
Docker Images View<\/figcaption><\/figure><\/p>\n

Last but not least, there is one very useful view called Docker Image Hierarchy<\/code>. It allows to see all the layers the image in question has been built from. Here, for example, we could see how the tagged image with name jcg\/spring-boot-webapp:0.0.1-SNAPSHOT<\/code> (one of the samples we were building in the previous part of the tutorial) is layered out.<\/p>\n

\"Docker<\/a>
Docker Image Hierarchy<\/figcaption><\/figure><\/p>\n

But these views are only the part of the whole story. Another part is a set of new Docker<\/a> launchers which you may use to build Docker<\/a>\u00a0 images and run your Java applications (or even complete Docker Compose<\/a> stacks) as Docker<\/a> containers.<\/p>\n

\"Eclipse<\/a>
Eclipse Launchers<\/figcaption><\/figure>
<\/div> <\/div><\/p>\n

In case you would like to learn more about all the views and launchers discussed above, and, in general, about Docker<\/a> tooling available in Eclipse<\/a>, the official project Wiki<\/a> is a great place to get started.<\/p>\n

<\/a>2.2. IntelliJ IDEA<\/h3>\n

The JetBrains IntelliJ IDEA<\/a>, available in latest version 2017.2.6<\/code>, provides the Docker<\/a> tooling support in a form of the official plugin<\/a>. It is available in Ultimate Edition<\/code> as well as in Community Edition<\/code>, which we are going to use in this section.<\/p>\n

Once the plugin is installed, the new Docker<\/code> view becomes available, with all the details about images and containers baked in.<\/p>\n

\"Docker<\/a>
Docker in IntelliJ Idea<\/figcaption><\/figure><\/p>\n

To be fair, having a single, nicely structured view with most of the required pieces available nearby (in the different tabs) is very convenient.<\/p>\n

<\/a>2.3. Oracle NetBeans<\/h3>\n

The NetBeans<\/a> since the latest 8.2<\/code> release has Docker<\/a> tooling included as part of the Base IDE<\/code> plugin (which you very likely would have activated from the start). After the plugin activation, the addition Docker<\/code> tree node becomes available in the Services<\/code> view.<\/p>\n

\"Docker<\/a>
Docker in Netbeans<\/figcaption><\/figure><\/p>\n

A quite nice summary and discussions about desired features regarding Docker<\/a> integration in NetBeans<\/a> has been published on official community Wiki<\/a>. Please check it out if you are curious or interested in this topic.<\/p>\n

<\/a>3. On the Edge: Your IDE in Docker<\/h2>\n

As we have seen, the Docker<\/a> tooling provided by most popular Java development IDE<\/a>s is quite sufficient, one may say even outstanding. But what if we try to push the limits and run our IDE<\/a> completely in Docker<\/a>, literally anywhere? If it sounds a bit crazy, it surely does, but \u2026<\/p>\n

Eclipse Che<\/a>, the next-generation IDE<\/a> from Eclipse<\/a>, is leveraging containerization at full extent and runs completely on Docker<\/a>. Under the hood, it is powered by REST(ful) web APIs<\/a> and provides feature-rich, browser-based IDE<\/a>.<\/p>\n

\"Eclipse<\/a>
Eclipse Che<\/figcaption><\/figure><\/p>\n

It is quite innovative and young project, with a tremendous potential to revolutionize the concepts of IDE<\/a>s as we know them. It would be very interesting to see how things are going to unfold in the future but the technology is already here, available for evaluation.<\/p>\n

<\/a>4. So What About Debugging?<\/h2>\n

Debugging was, is and very likely will be playing the crucial role in finding, understanding and resolving defects or problems in our programs. And, like it or not, no matter how awesome Docker<\/a> is, it won\u2019t make our applications free from bugs. If you are running your Java applications as Docker<\/a> containers, the debugging process changes a little bit, and is explained in great details in this excellent article<\/a>. It is so comprehensive that there no need to repeat it here, just pick the IDE<\/a> you use and go over handsome tutorials prepared for you.<\/p>\n

<\/a>5. Management and Monitoring<\/h2>\n

Although Docker<\/a> command line tools and, consequently, Docker Engine API<\/a>s, let you accomplish any task you may encounter, many of us still prefer to deal with some kind of web or graphical UIs. Obviously, this is a personal preference and in this section we are going to learn about quite a few interesting options in this regards.<\/p>\n

<\/a>5.1. Portainer<\/h3>\n

The first tool we a going to look at is Portainer<\/a>, an open-source lightweight management UI which allows to easily manage different Docker<\/a> environments (like Docker<\/a> hosts or Docker Swarm<\/a> clusters). It comes as no surprise that Portainer<\/a> itself runs as a lightweight Docker<\/a> container. Let us run one:<\/p>\n

$ docker run -d -p 9000:9000 -v \/var\/run\/docker.sock:\/var\/run\/docker.sock portainer\/portainer\n<\/pre>\n
On Windows machine, the command looks a little bit different:<\/p>\n
$ docker run -d -p 9000:9000 portainer\/portainer<\/pre>\n
Once the Portainer<\/a> container is up and running, you will be able to access it through pretty neat web UI available at http:\/\/localhost:9000<\/a> (or, in case you are using non-native Docker<\/a> installation, http:\/\/<docker-machine>:9000<\/code>).<\/p>\n
\"Portainer<\/a>
Portainer Dashboard<\/figcaption><\/figure><\/p>\n
When the setup steps are completed (if necessary), you are going to be navigated to all-in-one view of the Docker<\/a> environment in question, starting from the simple dashboard page. You will definitely recognize all the Docker<\/a> objects (like volumes, networks, containers, images, \u2026) listed on the left so you could select any of those in order to be redirected to a dedicated view.<\/p>\n
The list of supported Docker<\/a> features which Portainer<\/a> exposes through the intuitive web interface is just astonishing. If you favor UI tools over command line ones, Portainer<\/a> has all the chances to serve as the one-stop shop for you.<\/p>\n
<\/a>5.2. cAdvisor<\/h3>\n
Often while we are developing our applications there is a need to get more insights regarding what is going on with our containers, either we are talking about CPU, memory or network utilization. Luckily, there are quite a few options here and one of the very useful tools we are going to talk about is cAdvisor<\/a> (or in full version, container advisor) which analyzes the resource usage and performance characteristics of running containers.<\/p>\n
\n
\u201ccAdvisor<\/em><\/a> provides container users an understanding of the resource usage and performance characteristics of their running containers. It is a running daemon that collects, aggregates, processes, and exports information about running containers. Specifically, for each container it keeps resource isolation parameters, historical resource usage, histograms of complete historical resource usage and network statistics. This data is exported by container and machine-wide.<\/em>\u201d – https:\/\/github.com\/google\/cadvisor<\/a><\/p>\n<\/blockquote>\n
Interestingly, cAdvisor<\/a> goes beyond just Docker<\/a> and should support just about any other container type out of the box. Sadly for Windows users, cAdvisor<\/a> has limited support<\/a> on this platform. The best way to give the cAdvisor<\/a> a try is, for sure, to run it as Docker<\/a> container.<\/p>\n
sudo docker run \\\n  --volume=\/:\/rootfs:ro \\\n  --volume=\/var\/run:\/var\/run:rw \\\n  --volume=\/sys:\/sys:ro \\\n  --volume=\/var\/lib\/docker\/:\/var\/lib\/docker:ro \\\n  --volume=\/dev\/disk\/:\/dev\/disk:ro \\\n  --publish=8080:8080 \\\n  --detach=true \\\n  --name=cadvisor \\\n  google\/cadvisor:latest\n<\/pre>\n
Once the container is up and running, you can access cAdvisor<\/a> web UI at http:\/\/localhost:8080<\/a> (or, in case you are using non-native Docker<\/a> installation, http:\/\/<docker-machine>:8080<\/code>).<\/p>\n
\"cAdvisor\"<\/a>
cAdvisor<\/figcaption><\/figure><\/p>\n
The web UI is quite simple but exceptionally informative. There are a lot of details collected about resources and processes of each running Docker<\/a> container as well as about the Docker<\/a> host machine itself (CPU, memory, network, storage, \u2026). By all means, it is invaluable tool to learn how your containers consume and manage the resources at runtime.
\n[ulp id=’MD25RnPuC2vrVItl’]
\n <\/p>\n
<\/a>5.3. ctop<\/h3>\n
And last, but not least, we are going to wrap up with ctop<\/a> tool which, in a top<\/a>-like interface, provides a concise and condensed overview of real-time metrics for multiple containers.<\/p>\n
\"ctop\"<\/a>
ctop<\/figcaption><\/figure><\/p>\n
Not only ctop<\/a> has a summary view, it also supports the option to select and focus on any individual container as well.<\/p>\n
<\/a>5.4. And a lot more …<\/h3>\n
The Docker<\/a> tooling space is really thriving, with new interesting projects popping up literally every day. We have just covered a couple of well-known, mature and useful tools but there are a lot more to discover, so please watch this space closely if you are interested.<\/p>\n
<\/a>6. Docker Engine Metrics<\/h2>\n
Docker<\/a> engine exposes a lot of interesting metrics in the format which is fully compatible with Prometheus<\/a>, tremendously popular open-source monitoring and alerting solution. Although this feature became generally available quite a while ago, not many people know about it and as such, not many make use of it.<\/p>\n
This feature is still considered experimental so it is not available by default and needs a bit of configuration tweaking. The official documentation<\/a> thoroughly describes all the necessary steps to be done with respect to the operating system you are running Docker<\/a> on. But essentially, the first step is to modify daemon.json<\/code> and add the following properties there.<\/p>\n
{\n  \"metrics-addr\" : \"localhost:9323\",\n  \"experimental\" : true\n}\n<\/pre>\n
In order for this change to take effect, the Docker<\/a> daemon should be restarted. Right after, the metrics should become available at the http:\/\/localhost:9323\/metrics<\/a> endpoint. Next step is to prepare Prometheus<\/a> configuration, prometheus.yml<\/code>, in order to scrape the Docker<\/a> engine metrics.<\/p>\n
scrape_configs:\n  - job_name: 'prometheus'\n    static_configs:\n      - targets: ['localhost:9090']\n  - job_name: 'docker'\n    static_configs:\n      - targets: ['localhost:9323']\n<\/pre>\n
With this minimal configuration in hand we could start the Prometheus<\/a> server (which has reached version 2.0<\/code> as of moment of this writing) as Docker<\/a> container:<\/p>\n
docker run -p 9090:9090 \\\n    -v prometheus.yml:\/etc\/prometheus\/prometheus.yml \\\n    prom\/Prometheus\n<\/pre>\n
Once the container is started, the Prometheus<\/a> web UI should be available at http:\/\/localhost:9090<\/a>. Let us navigate to this URL and check that all targets we have configured are accessible.<\/p>\n
\"Prometheus<\/a>
Prometheus Targets<\/figcaption><\/figure><\/p>\n
Fairly speaking, the Prometheus<\/a> web UI is quite capable to graph different metrics but Grafana<\/a>, another open-source project, is the state of the art platform to query, visualize, alert on and understand the metrics.<\/p>\n
docker run -d -p 3000:3000 \\\n    -e \"GF_SECURITY_ADMIN_PASSWORD=pa$$w0rd\" \\\n    grafana\/grafana\n<\/pre>\n
Grafana<\/a>\u00a0has a huge list of prebuilt dashboards and one of them, the Docker Engine Metrics<\/a> dashboard, is exactly what we are looking for. Here is the sneak peek on how it shows up (beautiful, indeed).<\/p>\n
\"Grafana<\/a>
Grafana Docker Engine Dashboard<\/figcaption><\/figure><\/p>\n
It may not look very meaningful at first glance but you are free to modify it or even build your own, choosing just the Docker<\/a> engine metrics you are interested in.<\/p>\n
<\/a>7. Conclusions<\/h2>\n
In this section of the tutorial we have talked about how developer\u2019s best friends, the IDE<\/a>s, keep abreast of the times by providing extensive Docker<\/a> tooling and support. Beyond just that, we have also mentioned a couple of very useful tools for managing and monitoring your containers and Docker<\/a> engine itself.<\/p>\n
<\/a>8. What\u2019s next<\/h2>\n
In the next section of the tutorial we are going to talk about using Docker<\/a> for testing, particularly in context of doing integration and end-to-end tests of our Java applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
This article is part of our Academy Course titled Docker Tutorial for Java Developers. In this course, we provide a series of tutorials so that you can develop your own Docker based applications. We cover a wide range of topics, from Docker over command line, to development, testing, deployment and continuous integration. With our straightforward …<\/p>\n","protected":false},"author":141,"featured_media":24013,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[936],"class_list":["post-71137","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-devops","tag-docker"],"yoast_head":"\nDocker for Java Developers: Develop on Docker - Java Code Geeks<\/title>\n<meta name=\"description\" content=\"This article is part of our Academy Course titled Docker Tutorial for Java Developers. 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