Using Docker Compose we are able to quickly set up complex environments consisting of multiple containers, which allows to speed up software evaluation process.
\nThings evolving quickly, tools come and go, last year cutting edge is often legacy now. Dozens of new tools emerges every month\nand approach for being up to date also needs to evolve.
\nContainers on production with all its benefits and challenges is a common thing nowadays, but do not forget that containers could also help us in other use cases. One of them is to improve technologies evaluation process.
\nMany moons ago when new fancy tool was released and looks so promising to give it a try options were limited - build it from sources, use installer or package manager. Giving a try for new promising tool was quite time consuming. Steps required to download, install, configure system and dependencies were usually not most exciting part of evaluating new message broker or database. Now containers or PaaS solutions allow us to shift focus directly from setup to features that particular products offers.
\nUsing Docker one terminal command is enough to download, run and evaluate interesting applications.
\ndocker run postgresAbove example command will pull latest version of PostgreSQL image from Docker Hub and run it locally. This is a quick and straightforward way of running hundreds of interesting applications. Container will run in sandbox environment, so we do not need to think about operation system settings and other specific configuration that may be required with standard installation. Although it is a simpler way than using installers or building from sources, then comparing to common package managers where also one command is usually enough, user experience looks quite similar.
\nThe real benefits of using Docker in local environment we can observe if we would like to build some complex environment that contains multiple applications linked together as usually things are more complicated than just running a database or monitoring tool separately. Challenge often starts when we would like to evaluate a monitoring tool that needs a datasource and entire environment should be relatively easily to set up and manage. That is where Docker Compose comes to help.
As stated in official documentation Compose is a tool that allow us to define and run multi-container applications. Using it in most common scenarios comes down to creating YAML file with required images and configurations.
\nversion: '3'\nservices:\n myService1:\n image: service1\n {configuration:}\n myService2:\n image: service2\n {configuration:}Having docker-compose.yml file ready we are able to start it with one terminal command.
docker-compose upDocker will build or download all necessary images, set up necessary configurations, networks and run defined containers for us.
\nIn below example we would like to see in action Grafana - open source monitoring tool with PostgreSQL as datasource.
\nFirst thing w need to do is to create empty docker-compose.yml file and add PostgreSQL as datasource.
version: '3'\nservices:\n myDatasource:\n image: postgresAbove we have defined one service called myDatasource that will be created using postgres image.
Having our datasource configured we can extend our Compose file with second service - Grafana.
\nversion: '3'\nservices:\n myDatasource:\n image: postgres\n myMonitoring:\n image: grafana/grafana\n ports:\n - 3000:3000Above we have defined second service called myMonitoring that will be created using grafana/grafana image.\nAdditionaly to basic service definition we have also exposed port 3000, so Grafana user interface can be access from outside the container.
At this point we have configured our environment, so by running docker-compose up two containers should be configured and run by Docker.\nThen navigating to localhost:3000 will allow us to access Grafana web interface where datasource could be set up.\nThing that is worth notice is that if we would like to configure our PostgreSQL datasource with default suggested Host - localhost:5432