In this article, we will go into detail on how to customize Flowable’s engine. Three changes to the engine will be done: (1) Change the database connection by modifying the data source and adding custom data source properties. (2)Use a strong UUID generator. (3)Implement a custom event handler.
In this post, you will learn how configure Flowable’s apps to use your custom Spring Boot REST API as a backend engine. All this, in a dockerized environment.
In this tutorial, we will be implementing a custom service task in Flowable
This is the second part of our “Deploying Flowable in a Docker Container and MySQL” series. In this post, you will learn how to deploy Flowable’s war files in a container running Tomcat, and connecting to another container running use MySQL database.
In this post, you will learn how to adapt Flowable’s war files to use MySQL as a database source. Once this is done, we will deploy them in a dockerized environment.
In this tutorial, we will be integrating Flowable’s BPM engine into our Spring Boot application.
In this article, you will learn how to compile Flowable source code using MySQL as database source, and generate valid Docker images. Once you have the images, we will use Docker Compose for defining and running a multi-container Docker applications.
Flowable BPM turned into a workflow manager with features such as file transfer via FTP and Samba, integration with Amazon Web Service, file transcodification and analysis using FFmpeg and MediaInfo, and more..
In this article, I will explain one possible method to solved some “limitations” encountered during the integration and implementation of Flowable BPM, when executing long-running tasks, by implementing the Signallable Flowable Behavior and a database table as a task queue. Similar behavior can be achieved using Flowable’s send task and receive task instead of Signallable Flowable Behavior. However, this solution is not limited to be used together with Flowable. Due to its design, it can be used in conjunction with other applications that required executing long-running tasks asynchronically.