• Unclear Behaviour Of A Mysql Container Running With The Devil
• Unclear Behaviour Of A Mysql Container Running With The Bulls
• Unclear Behaviour Of A Mysql Container Running With Scissors

Dockerizing a PHP Application Learn how to leverage Docker’s advantages to easily develop and deploy a PHP application to Heroku, using Semaphore for continuous deployment. Introduction In this tutorial, you will learn what Docker is and how you can use it to create sophisticated working environments. If you already have experience using VMs such as VirtualBox, Vagrant, etc., you'll grasp the concept quickly. To make things more concrete, we will use a which interacts with the API to list popular photos, view, upvote and comment on them. The application is built using, but this shouldn't present an issue in our case.

Let's get started. What is Docker? Most developers use the (W L M)AMP stack as a starting point, but this environment can become overwhelming very quickly. Once you start feeling this pain, you'll start using VirtualBox to keep your host computer clean, and the projects separated.

To make machines portable and easy to share and reproduce, comes into play. Vagrant makes the virtual machines that we can share with our project members distributable, which helps developers reproduce the same configurable environment as the other developers in their team. Is a cutting-edge solution to this problem.

It provides us with containers that have all the virtualization capabilities we need, while also being more lightweight than the traditional virtual machines. Prerequisites Docker can be installed on any platform. You can install it from a binary executable, or by using the official installer. Docker runs natively on Linux platforms.

OSX and Windows users need to access Docker through a VM. The below pictures from the documentation illustrate the difference. Installing Docker Follow one of the installation guides below for your operating system:. Linux. Mac. Windows After installing Docker on our host, we need to run the docker-machine ls to see the list of available VMs. A default VM is created by default.

$ docker images REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE ubuntu 14.04 91e54dfb1179 5 months ago 188.4 MB nimmis/apache-php5 latest bdd370e4f83b 6 months ago 484.4 MB eboraas/apache-php latest 0501b3fdd0c2 6 months ago 367 MB mysql latest a128139aadf2 6 months ago 283.8 MB ubuntu latest d2a0ecffe6fa 7 months ago 188.4 MB eboraas/laravel latest 407e2d00b528 12 months ago 404.5 MB To browse the available images, we can visit and run docker pull to download them to the host machine. Docker Containers An image can be considered a class definition. We define its properties and behavior.

Containers are instances created from this class. We can create multiple instances of the same image. The docker ps command prints the list of containers running on the machine. We don't have any containers at the moment, so let's create a new one.

$ docker-machine ip default 192.168.99.100 $ docker-machine env default export DOCKERTLSVERIFY = '1' export DOCKERHOST = 'tcp://192.168.99.100:2376' export DOCKERCERTPATH = '/Users/admin/.docker/machine/machines/default' export DOCKERMACHINENAME = 'default' # Run this command to configure your shell: # eval '$(docker-machine env default)' The last part is to map the Apache server to run our application instead of the default Apache homepage. This means that we need to keep our application folder synced with the server root folder ( /var/www). We can do that using the -v option. You can read more about container volumes.

Docker run -tid -p 80:80 -name = 'apacheserver' -v /Users/admin/Desktop/www/500pxAPITest:/var/www nimmis/apache-php5 It's always a good idea to take a look at the image description on the and read the instructions about the proper to create containers from the image. Working with Dockerfiles We mentioned earlier that everyone can make a Docker image and share it on the Docker HUB, and that Dockerfiles are the main tool to achieve this. We're going to see how we can configure our own image and make it fit our needs. You can check the for the list of available commands. We've already explained that images are like class definitions. We can expand an existing image and add more functionality to it. # Dockerfile FROM nimmis/apache-php5 MAINTAINER SemaphoreCI COPY 000-default.conf /etc/apache2/sites-available/000-default.conf EXPOSE 80 EXPOSE 443 CMD '/usr/sbin/apache2ctl', '-D', 'FOREGROUND' Useful Commands Although our image is ready, we'll go through some commands that could be useful for many projects.

What if we wanted to install to manage our front-end assets, run build commands, etc.? RUN apt-get update && apt-get install nodejs && apt-get install npm This will install Node.js and the npm manager in our image. We can use the RUN command many times inside the same Dockerfile, because Docker keeps a history for our image creation. Every RUN command is stored as a commit in the versioning history.

Another useful command is ENV. It lets us set an environment variable through the build process, and will also be present when a container is created. Be sure to check the full list of supported commands in. ENV MYSQLROOTPASSWORD=root ENV MYSQLROOTUSER=root Building the Image If you've previously pulled the base image, it will be loaded from your computer instead of being downloaded again. This means that the build process won't take much time. Our folder contains a Dockerfile and a 000-default.conf file. The docker build.

Command will build the Dockerfile inside the current directory. $ docker build. $ docker images REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE sha256:2096d 3 seconds ago 484.3 MB nimmis/apache latest sha256:1f64f 2 weeks ago 348.5 MB ubuntu 14.04 sha256:0109b 6 months ago 188.3 MB nimmis/apache-php5 latest sha256:a344c 6 months ago 484.3 MB eboraas/apache-php latest sha256:b39df 6 months ago 367 MB mysql latest sha256:0460d 7 months ago 283.8 MB ubuntu latest sha256:2a274 7 months ago 188.3 MB eboraas/laravel latest sha256:34e3f 12 months ago 404.5 MB Currently, our image has no name, and it isn't tagged.

The -t option let us specify the image repository and tag. We need to remove the previously built image to avoid polluting our host machine ( docker rmi -f sha256:2096d). $ docker build -t younesrafie/apache-php:v0.1. $ docker images REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE younesrafie/apache-php v0.1 sha256:b447c 24 seconds ago 484.3 MB Our image is now labeled and tagged. The final step is to push it to the Docker HUB.

This step is optional, but it's still useful if we're planning on sharing the image and and helping others with their development environment. After logging into our account, we need to click on the Create Repository link on the dashboard.

Next, we need to run docker login in the terminal and type in our login credentials. After a successful login, we can push our image to the Docker HUB. $ docker push younesrafie/apache-php:v0.1 Docker Compose Using terminals and remembering commands is not very practical for creating application containers and getting started quickly. Docker Compose uses YAML files to configure and run containers. This means that we can ship our application Dockerfile to build the environment and use a docker-compose.yml to run the containers. The first step is to install Docker Composer on our machine.

Follow the instructions in the before proceeding with the following steps. The docker-compose.yml file allows us to configure multiple services inside the same file, and specify after that if the image needs to be built or if we can use a predefined image. $ docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES d3 younesrafie/apache-php:v0.1 '/usr/sbin/apache2ctl' 4 seconds ago Up 3 seconds 0.0.0.0:80-80/tcp, 0.0.0.0:443-443/tcp semaphoredockerserver1 Use the docker-compose stop rm to manage your container.

Using Docker with Heroku Heroku provides a plugin called heroku-docker for building our development environment and deploying it easily. After installing the Heroku toolbelt on our machine, we need to install the Docker plugin. Check the for the instructions. # Create a new application server called `semaphore-docker-demo` heroku create semaphore-docker-demo -buildpack heroku/php # Run the build process heroku docker:release -app semaphore-docker-demo # Add the Heroku remote to Git remotes heroku git:remote -a semaphore-docker-demo # Push the application to our new server git push heroku master Continuous Deployment Using Semaphore has made it easy to continuously deploy applications when ready. After creating an account, you'll be asked to create a new project. We can use our for a test.

After choosing a repository, we need to choose a branch: The next step is to configure our build environment. We can choose the proper PHP version, test commands, multiple parallel tests, etc.: Click on Build With These Settings to run the tests: Now, return to the project dashboard page and click on the set Up Deployment button: Check for a detailed explanation of how to use Docker with Semaphore. There are two deployment methods available on Semaphore: automatic and manual deployment. Automatic deployment means that a deploy will be triggered after every passed build on the selected branch.

In addition, you can also manually deploy any build from any branch at any time. For automatic deployment, you will be asked to select which branch will be automatically deployed after each passed build. Manual deployment requires the manual selection of the builds to deploy. We will choose Automatic deployment.

Note: You can change the deployment strategy at any time in server settings once the setup is complete. You can find your Heroku API key under on the Heroku dashboard. By providing the Heroku API key, we authorize Semaphore to configure and set SSH keys that are needed for deployment. Semaphore will now listen for push events on our repository and, depending on the deployment strategy, automatically trigger the deployment process to the production server.

We'll be notified when the process is done. Conclusion In this tutorial, we learned the basics of using Docker and how to create our own Docker image. We deployed a demo application to Heroku, and we used Semaphore for continuous deployment to the production server. Take a look at the final demo on. If you have any questions or comments, make sure to post them below, and we'll do our best to answer them.

Want to continuously deliver your applications made with Docker? Read next:.

By Seth Proctor, CTO, NuoDB Today, organizations are spending a lot of time trying to simplify, align development with operations, and manage commodity or virtualized resources on-demand. This helps to reduce costs, improve efficiency and evolve solutions in a more agile fashion. Over the past few years, I’ve been having conversation after conversation with architects, developers, and CTOs around Docker and container computing.

As a long-time Sun Microsystems engineer, these are familiar topics, but the recent popularity is still impressive. These conversations are with large companies and small, in virtually every industry, at many different levels.

And of course, as CTO for a database company, the conversation inevitably comes to the role the database can – or should – play within a container-based environment. To address that, you need to ask yourself a few questions:. Do you really need a database, or is there some other kind of data management solution you can rely on?.

Can your (new) architecture support a non-container-based database?. Are you interested in containers for production or for dev-test self-service and efficiency?. What are your production application needs around scale, resilience, and durability? To what extent must those needs be reflected in your database deployment? Answering these questions will help you make sound architectural choices.

Let’s break down each of these questions. So Why a Database Anyway? Operational databases are core to many applications and services. By their nature, they tend to be carefully resourced and deployed, somewhat unchanging and upgraded infrequently. Containers, on the other hand, are lightweight and transient – well-suited for stateless scale-out services. Those services, of course, may need to be layered on stateful services like operational databases. They may also rely on configuration, credentials, shared data or other state that is not typically provided by an operational database.

Because of the lightweight, on-demand nature of containers, they work well in support of microservices architectures where monolithic and tightly coupled components are broken down into their smallest elements. Containers offer agility and flexibility.

This agility is especially well-suited for scaling out front-end components like web servers or caches, or spinning up compute on-demand for AI, analytics or other resource-intensive tasks. Often these classes of application simply don’t need an operational database. On the other hand, if you scale web-server instances to help scale rich application logic provided through a web service, then it’s likely the application is relying on a backing database. Ditto if the compute-intensive operation is doing ingest. The first question you should ask is what kind of data service your application actually relies on. What Are Your Architectural Constraints? Let’s assume that your application needs an operational database.

Unclear Behaviour Of A Mysql Container Running With The Devil

Containers are being adopted at different rates and at different levels of the stack. So the next question you need to consider is – do you want or need to deploy your database in containers from the start?

For instance, you may be in a Platform as a Service (PaaS) environment where everything must run in a container. Or, as an organization, you may have made an architectural choice to deploy only in containers. In these cases you need to deploy your database the same way. Alternatively, you may be in a cloud, like Amazon, where you’re choosing to use a database as a service (DBaaS) independent of the application tier. The choice to deploy your database within or outside of containers brings a number of trade-offs you should consider.

Choosing to deploy your database on bare metal or a long-lived virtual machine is familiar, and in many cases is simpler than running within a container. Databases tend to be long-lived services, which need consistent and controlled disk and network IO access.

Unclear Behaviour Of A Mysql Container Running With The Bulls

Traditionally, resources are provisioned so that the database has clearly defined MTBF expectations. When physical resources are contended, it can have a significant effect on database throughput or predictable latency.

Unclear Behaviour Of A Mysql Container Running With Scissors

It may be easier to let your application containers be flexible while your database tier stays fixed. As both container-based platforms and distributed databases evolve, however, it’s becoming easier to think about running databases within containers while still getting the predictable performance that your system of record needs to provide. The advantage to deploying and running your application and database tiers using the same tools is pretty obvious. As I’ll discuss in the next few sections, there are a number of additional advantages as well.

The key considerations are resource management and predictable behavior. You should ask what latencies, throughput, and sustainable spikes you need to support and prefer databases designed to work on commodity that don’t assume tight coupling with hardware. If this is your first attempt deploying a database within containers, start with a simpler workload and use that to get comfortable with the trade-offs. Let’s assume that you want to deploy your database in a container. Is This for Production or for a Test / Development Environment or Both? If you’re only deploying your database for production, you can move along to the next question.

If it’s for a development or testing environment, containers are a way to make your team more self-sufficient and apply resources more efficiently. Tools like Docker provide a single, repeatable way to deploy and run your database across systems.

Containers also make it simpler to pool and share resources. In that case, you may not actually care if the database state is durable. You may just need a scratch database that can be spun up quickly for testing and then thrown away just as quickly.

Containers are a great tool for this kind of efficiency, where you don’t need production-level persistence and reliability. What Capabilities do You Want Your Production Application to Have That Need to be Reflected in the Database? On the other hand, there are characteristics of and service level agreements (SLAs) for your applications in production. Containers are increasingly a key element for meeting these application needs. The question, then, is how those needs impact database deployment. For example, if elastic scale is a primary goal, but your application is read-mostly, then you might use a caching tier and avoid scaling the database.

If the application is write-intensive, you may need to scale the database itself (through sharding, replication or other techniques) and containers will help. In both cases, in-memory database technologies will be a good fit. Instead of scale, your focus may be service availability, resilience, and failure handling. In that case you might use containers to manage your database tier, replicating data and responding automatically to failure.

Regardless, fully understanding your priorities and requirements will help you determine the right database architecture for your needs. Databases that can scale out while providing a single, logical view of your data will greatly enhance the value of deploying within containers. Putting It All Together All of the considerations outlined above will help you decide if and when you should deploy databases within containers. If you’re making dev-test more effective, or if you can replicate across multiple container-local filesystems, then deploying your database inside containers with no external, durable storage may be the right approach. If you want to keep the database tier operating in a familiar fashion and are willing to sacrifice some of the benefits of container deployment, then you may want your database tier operating independent of your containerized application. If you need a durable database that can scale out with your application and respond to failure efficiently, then you should be looking at memory-centric solutions that fit well with the dynamic nature of container lifecycles. Ultimately, there isn’t one right answer, but understanding how different requirements factor into your decision is critical when deciding how to deploy and operate databases in container-based architectures.

About Seth Proctor Seth has 15+ years of experience in the research, design and implementation of scalable systems. That experience includes work on distributed computing, networks, security, languages, operating systems and databases all of which are integral to. His particular focus is on how to make technology scale and how to make users scale effectively with their systems.

Prior to NuoDB Seth worked at Nokia on their private cloud architecture. Before that he was at Sun Microsystems Laboratories and collaborated with several product groups and universities. His previous work includes contributions to the Java security framework, the Solaris operating system and several open source projects, in addition to the design of new distributed security and resource management systems. Seth developed new ways of looking at distributed transactions, caching, resource management and profiling in his contributions to Project Darkstar.

Darkstar was a key effort at Sun which provided greater insights into how to distribute databases. Seth holds eight patents for his cutting edge work across several technical disciplines. He has several additional patents awaiting approval related to achieving greater database efficiency and end-user agility Please enable Javascript in your browser, before you post the comment! Now Javascript is disabled.