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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.