How do I deploy Elastic Search Engine

Elasticsearch is a powerful open source engine for search and analytics, that makes data easy to store and explore. Being focused on reliability, real-time analytics, and simple management, it is based on high performance and full-featured Java-powered Apache Lucene library, which helps to implement the provided advanced search and analytics capabilities. Herewith, a special RESTful API allows performing any action by means of a simple JSON over HTTP, making this solution truly developer-friendly.

Since Elasticsearch engine is rather popular and highly demanded among our users, this guide describes how it can be hosted in a matter of minutes at any of our partner’s Platforms, leveraging tight integration of Docker containers support at CloudJiffy Cloud.

• deploy Elasticsearch Docker container
• launch it
• update to the newest version

Being based on Docker standard, the Elasticsearch image can be provisioned for production-ready use at CloudJiffy PaaS in a few clicks, just like it’s described below:

1. Log into my CloudJiffy dashboard and click the New environment button at the top pane. In the opened wizard, switch to the Docker tab and click Select Container in the middle of the frame.

2. At the Search tab, type elasticsearch into the corresponding field and press Enter. Choose the same-named image within the appeared list by simply clicking on it.

Then, select the version I’d like to install by means of the drop-down menu at the top (as an example, we’ll choose the 2.0.2 one) and click Next to proceed.

3. Now, I can configure the general parameters of my environment - adjust the number of allocated resources using cloudlet sliders, select the desired region and type environment name.

To submit the creation, click the same-named button when ready.

4. That's all! In a minute, I'll get my ready-to-use Docker-based environment with Elasticsearch already deployed inside.

Now, let’s ensure our Elasticsearch application is accessible and works fine - this can be confirmed though receiving a piece of JSON upon accessing its start page. As it was mentioned above, depending on my preferences I may connect via:

• The external IP address - the common choice, which ensures simple instance handling and faster response time, whilst being a paid option

• Endpoints - the free of charge alternative, provided by CloudJiffy

Connection via Public IP

In case I’ve attached an external IP address to my elasticsearch container, simply click the Open in browser button next to my environment and add the default 9200 port at the end of the address within the opened tab.

This way is standard and, as I can see, is maximally optimized for convenient usage.

Connection via Endpoints

If I’d like to leverage endpoints solution instead of spending the additional costs for external address utilization, I need to take into consideration that Elasticsearch requires the HTTP and transport.tcp services to be accessible from outside. Here, the first service is used for the incoming HTTP requests’ processing and the second one - for communication between nodes.

So, to make my deployed search engine to work properly, I’ll need to manually expose the appropriate ports via CloudJiffy Shared Load-Balancer for the corresponding container.

1. For that, click the Settings icon next to my environment, switch to the Endpoints section and select Add like it is shown in the image below:

2. Within the opened frame, define the required parameters for the first endpoint (i.e. the HTTP service entry point), exposing the 9200 container port:

Click Add to create.

3. Repeat the previous step for the transport service (9300 port). As a result, I should receive two endpoints, similar to the shown below:

For now, I am able to operate with my elasticsearch instance using the links within the Access URL column. To ensure everything is fine, navigate to the HTTP one.

That’s it! Now I can start building my own index of the required document-oriented data.

CloudJiffy provides an easy way to switch between the available Docker image tags, whilst saving all the inherent server configurations and data stored, with the distinctive container redeploy feature. So, in case of the necessity to upgrade my elasticsearch instance to the newly released version, just follow the next simple steps:

1. Click Redeploy container next to the required Docker container (or, if operating with several nodes, select the same button for the corresponding image name to update all of them at once).

2. Within the appeared frame, choose the desired Tag my application should be updated with (e.g. the latest one) and click Redeploy.

3. In a minute or so, I'll get my container updated. To check the exact Elasticsearch version that is currently run, just refer to its start page.

In such a way, I can move to the newest application version right after it is released without the mess of complex adjustments and special configurations.