Docker Connector
Seamless Connection to Docker Engine
Overview
The Anypoint Connector for Docker, built using Docker Java API client, is a communication tool that provides seamless
integration with the Docker engine from a mule flow.
The connector exposes Docker operations by executing API calls as per configuration.
It supports HTTP and HTTPS connections and can be used as an inbound or outbound connector from Mule flows.
Technical Specifications
Published on – 9th February, 2018
Version – 1.0.0
Compatible with – Runtime version 3.8.5
Available at Anypoint Exchange
Leveraging the Full Potential of the Docker Container
The real value of the Docker connector is in the way it can be used at design time in conjunction with other functional features available in Mule.
The Docker connector functions within a Mule application. Using the connector, applications can perform several operations that Docker exposes via APIs. When building an application that connects with Docker, for example an application which executes in a docker container, there is no need to custom code (or secure!) a connection. Rather, a connector can just be dropped into the flow and the connection details configured to have the application running in Docker.
Data Sense
DataSense extracts metadata for Docker standard response to automatically determine the data type and format that applications must deliver to, or can expect from, Docker. Mule does the heavy lifting of discovering the type of data that must be sent to or received from Docker.
Transform Message Component
This component’s integrated scripting language called DataWeave can automatically extract response metadata that can be used to visually map and/or transform to a different data format or structure. Essentially, DataWeave lets the user control mappings between data types. For example, if a Docker connector is configured in an application, and then a Transform Message component dropped after the connector, the component uses DataWeave to gather information that DataSense extracted to pre-populate input values for mapping. In other words, DataSense makes sure that DataWeave knows the data format and structure it must work with, so that it doesn’t have to be figured out manually.
