README.md

PyScaler

A Django based application that is able to monitor remote hosts via SSH scripts or connecting to JVMs via JMX.

Complete documentation can be found at https://pyscaler.readthedocs.org <https://pyscaler.readthedocs.org>_

Features

• Monitoring PyScaler is able to monitor remote hosts and store the performance data for further analysis and business logic.

• SSH monitoring

  PyScaler can connect to remote hosts via SSH, execute scripts to gather performance data and index its output.

• JMX monitoring

  PyScaler can connect to remote JVMs via JMX and gather performance data via the exposed mbeans.

• Interactive counter graphs

  The performance data can be easily visualized using available graphs. There are different graphs per monitored target and counters that can be zoomed.

• Control

  PySCaler can be execute actions against its managed nodes. These action can be execute manually or triggered automatically when defined performance thresholds are hit.

• Remote command execution

  It can execute command locally on specific nodes or on all the nodes from a cluster.

• Fabric scripts execution

  It can execute Fabric scripts that can be used to execute local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.

• Triggers

  PyScaler can analyze available performance data and automatically trigger defined actions when a performance threshold is reached during a certain amount of time.

• Provisioning

  PyScaler can be used to provision new servers.

• EC2 node deployment

  PyScaler can launch new Amazon EC2 instances with defined parameters.

• Operating system configuration

  PyScaler can provision nodes using Puppet.

• Deployment scaling

  The previously explained features (monitoring, control and provisioning) can be used together to automatically scale a cloud based cluster. PyScaler will monitor the cluster and when a defined performance threshold is reached then the necessary actions to deploy a new cluster node will
  be executed, thus scaling it.

• Task based engine

  PyScaler makes uses of Celery to launch all lengthy or periodic tasks in the backend. Celery can be used to handle retries and distribution of the tasks.

• Restful urls and API

  PyScaler urls are cleanly designed to be understood by human beings. Most available objects are exposed via a restful API and JSON

• Ajax based interface

  The usage of a task based backend along the jquery and json enables the development of a fluid ajax based interface.

• High availability

  Each component of PyScaler is designed to be highly available.