| Name | insights-core JSON |
| Version |
3.6.2
JSON |
| download |
| home_page | https://github.com/redhatinsights/insights-core |
| Summary | Insights Core is a data collection and analysis framework |
| upload_time | 2025-07-17 01:50:43 |
| maintainer | None |
| docs_url | None |
| author | Red Hat, Inc. |
| requires_python | >=2.7 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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=============
Insights Core
=============
Insights Core is a data collection and analysis framework that is built
for extensibility and rapid development. Included are a set of reusable
components for gathering data in myriad ways and providing a reliable
object model for commonly useful unstructured and semi-structured data.
.. code-block:: python
>>> from insights import run
>>> from insights.parsers import installed_rpms as rpm
>>> lower = rpm.Rpm("bash-4.4.11-1.fc26")
>>> upper = rpm.Rpm("bash-4.4.22-1.fc26")
>>> results = run(rpm.Installed)
>>> rpms = results[rpm.Installed]
>>> rpms.newest("bash")
0:bash-4.4.12-7.fc26
>>> lower <= rpms.newest("bash") < upper
True
Features
--------
* Over 200 Enterprise Linux data parsers
* Support for Python 2.7+ and 3.3+
* Built in support for local host collection
* Data collection support for several archive formats
Installation
------------
Releases can be installed via pip
.. code-block:: shell
$ pip install insights-core
Documentation
-------------
There are several resources for digging into the details of how to use ``insights-core``:
- A `detailed walk through of the constructing a rule
<https://github.com/RedHatInsights/insights-core/blob/master/docs/notebooks/Diagnostic%20Walkthrough.ipynb>`_
- The `insights-core-tutorials project docs <https://insights-core-tutorials.readthedocs.io/en/latest/>`_
have three tutorials plus instructions on how to setup the tutorial environment
- `Preparing Your Development Environment
<https://insights-core-tutorials.readthedocs.io/en/latest/prep_tutorial_env.html>`_
- `Custom Parser Development
<https://insights-core-tutorials.readthedocs.io/en/latest/customtut_parsers.html>`_
- `Custom Combiner Development
<https://insights-core-tutorials.readthedocs.io/en/latest/combiner_tutorial.html>`_
- `Rule Development
<https://insights-core-tutorials.readthedocs.io/en/latest/rule_tutorial_index.html>`_
- The basic architectural principles of ``insights-core`` can be found in
the `Insights Core
<https://github.com/RedHatInsights/insights-core/blob/master/docs/notebooks/Insights%20Core%20Tutorial.ipynb>`_ tutorial jupyter notebook
- A simple `stand_alone.py
<https://github.com/RedHatInsights/insights-core/blob/master/examples/rules/stand_alone.py>`_
script encapsulates creating all the basic components in a single script
that can be easily executed locally
- Some `quick-start examples
<https://github.com/RedHatInsights/insights-core/blob/master/examples>`_
are provided in the ``examples`` directory. Each subdirectory under examples
includes a ``README.md`` file that provides a description of the contents
and usage information.
To Run the Jupyter Notebooks
++++++++++++++++++++++++++++
If you would like to execute the jupyter notebooks locally, you can
install jupyter:
.. code-block:: bash
pip install jupyter
To start the notebook server:
.. code-block:: bash
jupyter notebook
This should start a web-server and open a tab on your browser. From
there, you can navigate to ``docs/notebooks`` and select a notebook of
interest.
Motivation
----------
Almost everyone who deals with diagnostic files and archives such as
sosreports or JBoss server.log files eventually automates the process of
rummaging around inside them. Usually, the automation is comprised of
fairly simple scripts, but as these scripts get reused and shared, their
complexity grows and a more sophisticated design becomes worthwhile.
A general process one might consider is:
#. Collect some unstructured data (e.g. from a command, an archive, a
directory, directly from a system)
#. Convert the unstructured data into objects with standard APIs.
#. Optionally combine some of the objects to provide a higher level
interface than they provide individually (maybe all the networking
components go together to provide a high level API, or maybe multiple
individual objects provide the same information. Maybe the same
information can be gotten from multiple sources, not all of which are
available at the same time from a given system or archive).
#. Use the data model above at any granularity to write rules that
formalize support knowledge, persisters that build database tables,
metadata components that extract contextual info for other systems,
and more.
Insights Core provides this functionality. It is an extensible framework
for collecting and analyzing data on systems, from archives,
directories, etc. in a standard way.
Insights Core versus Red Hat Insights
-------------------------------------
A common confusion about this project is how it relates to `Red Hat
Insights <https://access.redhat.com/insights/>`_. Red Hat Insights is a
product produced by `Red Hat <https://www.redhat.com>`_ for automated
discovery and remediation of issues in Red Hat products. The
``insights-core`` project is used by Red Hat Insights, but only represents
the data collection and rule analysis infrastructure. This
infrastructure is meant to be reusable by other projects.
So, ``insights-core`` can be used for individuals wanting to perform
analysis locally, or integrated into other diagnostics systems. Parsers
or rules written using ``insights-core`` can be executed in Red Hat
Insights, but, it is not a requirement.
Raw data
{
"_id": null,
"home_page": "https://github.com/redhatinsights/insights-core",
"name": "insights-core",
"maintainer": null,
"docs_url": null,
"requires_python": ">=2.7",
"maintainer_email": null,
"keywords": null,
"author": "Red Hat, Inc.",
"author_email": "\"Red Hat, Inc.\" <insights@redhat.com>",
"download_url": "https://files.pythonhosted.org/packages/c2/ee/d86e7b357f101a904230a963afb59f83219abd2afb63ad2e28f07bb5948a/insights_core-3.6.2.tar.gz",
"platform": null,
"description": "=============\nInsights Core\n=============\n\nInsights Core is a data collection and analysis framework that is built\nfor extensibility and rapid development. Included are a set of reusable\ncomponents for gathering data in myriad ways and providing a reliable\nobject model for commonly useful unstructured and semi-structured data.\n\n.. code-block:: python\n\n >>> from insights import run\n >>> from insights.parsers import installed_rpms as rpm\n >>> lower = rpm.Rpm(\"bash-4.4.11-1.fc26\")\n >>> upper = rpm.Rpm(\"bash-4.4.22-1.fc26\")\n >>> results = run(rpm.Installed)\n >>> rpms = results[rpm.Installed]\n >>> rpms.newest(\"bash\")\n 0:bash-4.4.12-7.fc26\n >>> lower <= rpms.newest(\"bash\") < upper\n True\n\nFeatures\n--------\n\n* Over 200 Enterprise Linux data parsers\n* Support for Python 2.7+ and 3.3+\n* Built in support for local host collection\n* Data collection support for several archive formats\n\nInstallation\n------------\n\nReleases can be installed via pip\n\n.. code-block:: shell\n\n $ pip install insights-core\n\nDocumentation\n-------------\n\nThere are several resources for digging into the details of how to use ``insights-core``:\n\n- A `detailed walk through of the constructing a rule\n <https://github.com/RedHatInsights/insights-core/blob/master/docs/notebooks/Diagnostic%20Walkthrough.ipynb>`_\n- The `insights-core-tutorials project docs <https://insights-core-tutorials.readthedocs.io/en/latest/>`_\n have three tutorials plus instructions on how to setup the tutorial environment\n\n - `Preparing Your Development Environment\n <https://insights-core-tutorials.readthedocs.io/en/latest/prep_tutorial_env.html>`_\n - `Custom Parser Development\n <https://insights-core-tutorials.readthedocs.io/en/latest/customtut_parsers.html>`_\n - `Custom Combiner Development\n <https://insights-core-tutorials.readthedocs.io/en/latest/combiner_tutorial.html>`_\n - `Rule Development\n <https://insights-core-tutorials.readthedocs.io/en/latest/rule_tutorial_index.html>`_\n\n\n- The basic architectural principles of ``insights-core`` can be found in\n the `Insights Core\n <https://github.com/RedHatInsights/insights-core/blob/master/docs/notebooks/Insights%20Core%20Tutorial.ipynb>`_ tutorial jupyter notebook\n- A simple `stand_alone.py\n <https://github.com/RedHatInsights/insights-core/blob/master/examples/rules/stand_alone.py>`_\n script encapsulates creating all the basic components in a single script\n that can be easily executed locally\n- Some `quick-start examples\n <https://github.com/RedHatInsights/insights-core/blob/master/examples>`_\n are provided in the ``examples`` directory. Each subdirectory under examples\n includes a ``README.md`` file that provides a description of the contents\n and usage information.\n\nTo Run the Jupyter Notebooks\n++++++++++++++++++++++++++++\n\nIf you would like to execute the jupyter notebooks locally, you can\ninstall jupyter:\n\n.. code-block:: bash\n\n pip install jupyter\n\nTo start the notebook server:\n\n.. code-block:: bash\n\n jupyter notebook\n\nThis should start a web-server and open a tab on your browser. From\nthere, you can navigate to ``docs/notebooks`` and select a notebook of\ninterest.\n\nMotivation\n----------\n\nAlmost everyone who deals with diagnostic files and archives such as\nsosreports or JBoss server.log files eventually automates the process of\nrummaging around inside them. Usually, the automation is comprised of\nfairly simple scripts, but as these scripts get reused and shared, their\ncomplexity grows and a more sophisticated design becomes worthwhile.\n\nA general process one might consider is:\n\n#. Collect some unstructured data (e.g. from a command, an archive, a\n directory, directly from a system)\n\n#. Convert the unstructured data into objects with standard APIs.\n\n#. Optionally combine some of the objects to provide a higher level\n interface than they provide individually (maybe all the networking\n components go together to provide a high level API, or maybe multiple\n individual objects provide the same information. Maybe the same\n information can be gotten from multiple sources, not all of which are\n available at the same time from a given system or archive).\n\n#. Use the data model above at any granularity to write rules that\n formalize support knowledge, persisters that build database tables,\n metadata components that extract contextual info for other systems,\n and more.\n\nInsights Core provides this functionality. It is an extensible framework\nfor collecting and analyzing data on systems, from archives,\ndirectories, etc. in a standard way.\n\nInsights Core versus Red Hat Insights\n-------------------------------------\n\nA common confusion about this project is how it relates to `Red Hat\nInsights <https://access.redhat.com/insights/>`_. Red Hat Insights is a\nproduct produced by `Red Hat <https://www.redhat.com>`_ for automated\ndiscovery and remediation of issues in Red Hat products. The\n``insights-core`` project is used by Red Hat Insights, but only represents\nthe data collection and rule analysis infrastructure. This\ninfrastructure is meant to be reusable by other projects.\n\nSo, ``insights-core`` can be used for individuals wanting to perform\nanalysis locally, or integrated into other diagnostics systems. Parsers\nor rules written using ``insights-core`` can be executed in Red Hat\nInsights, but, it is not a requirement.\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. 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