This page introduces the main GUI’s used throughout HySDS. A brief description of their function and instructions on how to access them are provided.

Tosca

Open

Overview:

Tosca is an interface which allows users to facet on desired data in datasets using a wide array of faceting options. Users can submit jobs, set on-demand and trigger rules.

Why Use it:

View datasets or submit jobs.

Figaro

Open

Overview:

Figaro is an interface which allows users to interact with jobs within HySDS. Jobs can be in one of several states, and can be faceted upon in a similar manner as seen in Tosca using the faceting menu. Users can also create on-demand and trigger rules in Figaro for specific job processing actions.

Why Use it:

Job tracking and management within HySDS.

Kibana

Overview:

Kibana is the analytics layer that sits on top of the ELK stack. It lets you visualize Elasticsearch data and navigate the Elastic stack.

Why Use it:

Reveals dataset and job trends that may not be visible at the Figaro/Tosca level.

How to Access:

Kibana can be accessed via the “metric:Worker” link for Worker analytics or the “metric:Jobs” link for Jobs analytics in the top menu bar inside Figaro.

The Dashboard looks like this:

RabbitMQ

Overview:

RabbitMQ is an open source message broker software. In HySDS, RabbitMQ is used in combination with Celery to queue jobs. Logging into RabbitMQ can give you a broader view on the state of the system. Some examples where RabbitMQ can be useful is in determining why a job has been queued for a long time, determining if a job is in the wrong queue, or receiving real-time status of jobs.

Why Use it:

It's a useful debugging tool when debugging issues with perpetually-queued jobs because it provides a comprehensive view of the queues in the cluster, their state, and the number of messages being processed in those queues.

How to Access:

RabbitMQ can be accessed via the “Queues” link in the top menu bar inside Figaro.

1. After clicking the link, if the page fails to load, append “https://” to the start of the URL address.

2. Once on the RabbitMQ login page, enter your username and password.

3. After logging in, the Celery related queues are located under the “Queues” tab.

4. To display only Celery queues, type ^(?!celery*) in the filter search box; select the “Regex” box.

Celery

Overview:

Celery is a simple, flexible and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system.

Why Use it:

Not commonly used directly by operators nor developers. May be used to debug worker scheduling/distribution issues.

How to Access:

The Celery GUI is accessible in some HySDS adaptations via the “Celery” link in the top menu bar inside Figaro. However, the useful data within Celery is also found in Figaro and Metrics; these are the primary means of access for any Celery-related data. The ARIA adaptation of the Celery Dashboard is shown.

Elasticsearch (es:head)

Overview:

This is the elasticsearch front-end. There are typically two ES clusters: one for datasets and another for jobs. These clusters contain all the dataset and job documents, respectively.

Why Use it:

Search for or perform actions on documents directly, without going through Tosca/Figaro.

How to Access:

Mozart API

Overview:

The Mozart API allows developers to perform job functions programmatically.

Why Use It:

This is typically used by developers to submit jobs programmatically.

How to Access:

Amazon Web Services (AWS)

Overview:

The AWS console is where users can access all of the cloud computing services associated with HySDS, including EC2 compute instances, Auto Scaling Groups, S3 storage buckets, Elastic Block Storage (EBS), Security Group settings, and Amazon Machine Images (AMI).

Why Use it:

Operators can inspect the health and status of worker nodes using the AWS console. They can adjust Auto Scale Group settings as well as shutdown problematic nodes.

How to Access:

The AWS console can be accessed via: www.Goto.jpl.nasa.gov/awsconsole using your JPL credentials. Once you’ve successfully logged in, you’re redirected to the AWS Management Console: