- \n
- Often used in CI\/CD platform that packages and runs the application with its dependencies inside a container<\/li>\n
- Is a standard for Linux Containers<\/li>\n
- A Container<\/em> is a runtime that runs under any Linux kernel and provides a private machine-like space under Linux<\/li>\n<\/ul>\n
1.1 Docker Terminology<\/h3>\n
- \n
- Image<\/strong>: Representation of Docker container i.e. a JAR or WAR file in Java<\/li>\n
- Container<\/strong>: Runtime of Docker i.e. a deployed and running Docker image. For example, an executable Spring Boot jar<\/li>\n
- Engine<\/strong>: The code that manages, creates and runs the Docker containers<\/li>\n
- Hub<\/strong>: A public developers registry to distribute their code<\/li>\n
- Repository<\/strong>: A collection of Docker related images i.e. different versions of the same application<\/li>\n<\/ul>\n
1.2 Docker Command Basics<\/h3>\n
Here\u2019s an example command.<\/p>\n
\n![\"Docker](\"https:\/\/examples.javacodegeeks.com\/wp-content\/uploads\/2020\/05\/Docker-command-syntax-img1.jpg\")
Fig. 1: Basic structure<\/figcaption><\/figure>\n<\/div>\n 1.3 Need for using Docker<\/h3>\n
- \n
- For environment replication, while the code runs locally on the machine<\/li>\n
- For numerous deployment phases i.e. Dev\/Test\/QA<\/li>\n
- For version control and distributing the application\u2019s OS within a team<\/li>\n<\/ul>\n
1.4 Setting up Docker<\/h3>\n
If someone needs to go through the Docker installation, please watch this<\/a> video.<\/p>\n
To start with this tutorial, we are hoping that users at present have the Docker installation completed.<\/p>\n
2. Docker Elasticsearch Integration Tutorial<\/h2>\n
Before going any further let us take a look at the Elasticsearch introduction. Elasticsearch<\/strong> is a distributed and open-source full-text search and analytics engine that is primarily used in the single-page applications (popularly known as SPA\u2019s).
<\/div> <\/div><\/p>\n- \n
- It offers a nice replacement for NoSQL databases like – MongoDB and RavenDB<\/li>\n
- Used denormalization to improve the search performance and store up to petabytes of structured and non-structured data<\/li>\n
- Uses JSON objects as responses<\/li>\n
- Its key concepts revolve around the following \u2013\n
- \n
- Node<\/em>: Refers to a single instance<\/li>\n
- Cluster<\/em>: Basically a collection of one or more nodes<\/li>\n
- Index<\/em>: Collection of different types of documents and their properties. Also supports sharding<\/em> in order to improve performance<\/li>\n
- Document<\/em>: Collection of fields<\/li>\n
- Replicas<\/em>: Offers to create replicas of their indexes and shards<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
Let us go ahead and explore the steps required to start the Elasticsearch integration with Docker.<\/p>\n
2.1 Pull the image<\/h3>\n
This is the basic step where obtaining the Elasticsearch image for Docker is simple as issuing the
docker pull<\/code> command against the registry.<\/p>\nPull the Docker image<\/em><\/span><\/p>\n
# To pull the elastic search image from docker hub.\ndocker pull docker.elastic.co\/elasticsearch\/elasticsearch:7.7.0\n<\/pre>\n
2.2 Starting the single node cluster<\/h3>\n
For the tutorial purpose, we are starting a single-node Elasticsearch cluster by specifying the single node discovery<\/a> to bypass certain bootstrapping checks<\/a>.<\/p>\n
Pull the Docker image<\/em><\/span><\/p>\n
# To start the docker container in foreground. \n# If user wants to run the container in the background they can add the '-d' attribute to the run command.\ndocker run -p 9200:9200 -p 9300:9300 -e \"discovery.type=single-node\" docker.elastic.co\/elasticsearch\/elasticsearch:7.7.0\n<\/pre>\n
Make note \u2013[ulp id=’6PVIvOz3kDbYmNRn’]<\/p>\n
- \n
- We can also start the container via the docker-compose file (which in the present time is considered as an efficient approach)<\/li>\n
- We can make use of the volume attribute<\/em> (
-v<\/code>) as well to attach a permanent disk to the Elasticsearch container. This helps to ensure that if the container is restarted the data is not lost<\/li>\n- Developers can use the
docker logs <container_name><\/code> command to view the container logs<\/li>\n<\/ul>\n2.2.1 Elasticsearch configuration via docker-compose<\/h4>\n
To address the points stated above here is the simple Elasticsearch configuration via the docker-compose file.<\/p>\n
elasticsearch-7.7.0-compose-volume.yaml<\/em><\/span><\/p>\n
version: '3.3'\nservices:\n elasticsearch:\n image: docker.elastic.co\/elasticsearch\/elasticsearch:7.7.0\n container_name: elasticsearch_development_container\n volumes:\n - \/var\/db\/data\/elasticsearch:\/Users\/docker_volumes\/elasticsearch_bkp\n ports:\n - 9200:9200\n - 9300:9300\n environment:\n ES_JAVA_OPTS: '-Xms256m -Xmx256m'\n network.bind_host: 0.0.0.0\n network.host: 0.0.0.0\n discovery.type: single-node\n<\/pre>\n
Run the docker-compose file with the following command \u2013<\/p>\n
docker-compose -f elasticsearch-7.7.0-compose-volume.yaml up -d<\/code><\/p>\n3. Output<\/h2>\n
Once the
docker run<\/code> command is fired and if everyone thing goes well developers can navigate to the following URL \u2013localhost:9200<\/code> and they will get an output as shown in Fig. 2.<\/p>\n - Developers can use the
- Cluster<\/em>: Basically a collection of one or more nodes<\/li>\n
- Node<\/em>: Refers to a single instance<\/li>\n
- Container<\/strong>: Runtime of Docker i.e. a deployed and running Docker image. For example, an executable Spring Boot jar<\/li>\n
- Image<\/strong>: Representation of Docker container i.e. a JAR or WAR file in Java<\/li>\n
![\"Docker](\"https:\/\/examples.javacodegeeks.com\/wp-content\/uploads\/2020\/05\/docker-elastic-search-demo-tutorial-1.jpg\")