Sharone Zitzman

For orchestrating the creation of the correct number of instances, managing errors and retries, and finally managing the deallocation of resources we use RabbitMQ in conjunction with the Celery Project framework, along with a self-developed workflow engine. Celery

The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings. RabbitMQ

Cloudify uses Elasticsearch both to store runtime information (it’s actually a little known fact that Elasticsearch can serve as a NoSQL database), as well as, in its more classic capacity to index system logs and events, making them available over API calls. Elasticsearch

This functions as our load balancer and traffic cop directing API and incoming requests to the correct component. nginx

This lightweight web framework enables quick REST API development while enabling easy clustering, and the usage of multiple worker processes required to scale the REST API service to meet high volume requirements. Flask