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        "description": "ALCO - Autonomous Log Collector and Observer\n============================================\n\n|PyPI version|\n\nWhat's the problem\n------------------\n\nThere is a widely used stack of technologies for parsing, collecting and\nanalysing logs - `ELK Stack <https://www.elastic.co/products>`__. It has\nvery functional web interface, search cluster and a log transformation\ntool. Very cool, but:\n\n-  It's Java with well-known requirements for memory and CPUs\n-  It's ElasticSearch with it's requirements for disk space\n-  It's Logstash which suddenly stops processing logs in some\n   conditions.\n-  It's Kibana with very cool RICH interface which looses on all counts\n   to ``grep`` and ``less`` in a task of log reading and searching.\n\nIntroducing ALCO\n----------------\n\nALCO is a simple ELK analog which primary aim is to provide a online\nreplacement for ``grep`` and ``less``. Main features are:\n\n-  Django application for incident analysis in distributed systems\n-  schemeless full-text index with filtering and searching\n-  configurable log collection and rotation from RabbitMQ messaging\n   server\n-  not a all-purpose monster\n\nTechnology stack\n----------------\n\nLet's trace log message path from some distributed system to ALCO web\ninterface.\n\n1. Python-based project calls ``logger.debug()`` method with text 'hello\n   world'\n2. At startup time\n   `Logcollect <https://github.com/rutube/logcollect/>`__ library\n   automatically configures python logging (or even\n   `Django <https://github.com/django/django/>`__ and\n   `Celery <https://github.com/celery/celery>`__ one's) to send log\n   messages to RabbitMQ server in JSON format readable both with ELK and\n   ALCO projects.\n3. ALCO log collector binds a queue to RabbitMQ exchange and processes\n   messages in a batch.\n4. It uses Redis to collect unique values for filterable fields and\n   SphinxSearch to store messages in a realtime index.\n5. When a message is inserted to sphinxsearch, it contains indexed\n   ``message`` field, timestamp information and schemeless JSON field\n   named ``js`` with all log record attributes sent by python log.\n6. Django-based web interface provides API and client-side app for\n   searching collected logs online.\n\nRequirements\n------------\n\n-  Python 2.7 or 3.3+\n-  `Logcollect <https://github.com/rutube/logcollect/>`__ for python\n   projects which logs are collected\n-  `RabbitMQ <https://www.rabbitmq.com/>`__ server for distributed log\n   collection\n-  `SphinxSearch <http://sphinxsearch.com/>`__ server 2.3 or later for\n   log storage\n-  `Redis <http://redis.io/>`__ for SphinxSearch docid management and\n   field values storage\n-  `django-sphinxsearch <https://github.com/rutube/django_sphinxsearch>`__\n   as a database backend for ``Django>=1.8`` (will be available from\n   PyPI)\n\nSetup\n-----\n\n1.  You need to configure logcollect in analyzed projects (see\n    `README <https://github.com/rutube/logcollect#tips-for-configuration>`__).\n    If RabbitMQ admin interface shows non-zero message flow in\n    ``logstash`` exchange - \"It works\" :-)\n\n2.  Install alco and it's requirements from PyPi\n\n    .. code:: sh\n\n        pip install alco\n\n3.  Next, create django project, add ``sphinxsearch`` database\n    connection and configure ``settings.py`` to enable alco applications\n\n    .. code:: python\n\n        # For SphinxRouter\n        SPHINX_DATABASE_NAME = 'sphinx'\n\n        DATABASES[SPHINX_DATABASE_NAME] = {\n              'ENGINE': 'sphinxsearch.backend.sphinx',\n              'HOST': '127.0.0.1',\n              'PORT': 9306,\n          }\n        }\n\n        # Auto routing log models to SphinxSearch database\n        DATABASE_ROUTERS = (\n          'sphinxsearch.routers.SphinxRouter',\n        )\n\n        INSTALLED_APPS += [\n        'rest_framework', # for API to work\n        'alco.collector',\n        'alco.grep'\n        ]\n\n        ROOT_URLCONF = 'alco.urls'\n\n4.  Configure ALCO resources in ``settings.py``:\n\n    .. code:: python\n\n        ALCO_SETTINGS = {\n          # log messaging server\n          'RABBITMQ': {\n              'host': '127.0.0.1',\n              'userid': 'guest',\n              'password': 'guest',\n              'virtual_host': '/'\n          },\n\n          # redis server\n          'REDIS': {\n              'host': '127.0.0.1',\n              'db': 0\n          },\n          # url for fetching sphinx.conf dynamically\n          'SPHINX_CONF_URL': 'http://127.0.0.1:8000/collector/sphinx.conf',\n          # name of django.db.connection for SphinxSearch\n          'SPHINX_DATABASE_NAME': 'sphinx',\n          # number of results in log view API\n          'LOG_PAGE_SIZE': 100\n        }\n\n        # override defaults for sphinx.conf template\n        ALCO_SPHINX_CONF = {\n          # local index definition defaults override \n          'index': {\n            'min_word_len': 8\n          },\n          # searchd section defaults override\n          'searchd': {\n            'dist_threads': 8\n          }\n        }\n\n5.  Run ``syncdb`` or better ``migrate`` management command to create\n    database tables.\n\n6.  Run webserver and create a LoggerIndex from `django\n    admin <http://127.0.0.1:8000/admin/collector/loggerindex/>`__.\n\n7.  Created directories for sphinxsearch:\n\n    ::\n\n        /var/log/sphinx/\n        /var/run/sphinx/\n        /data/sphinx/\n\n8.  Next, configure sphinxsearch to use generated config:\n\n    .. code:: sh\n\n\n        searchd -c sphinx_conf.py\n\n    ``sphinx_conf.py`` is a simple script that imports\n    ``alco.sphinx_conf`` module which fetches generated ``sphinx.conf``\n    from alco http api and created directories for SphinxSearch indices:\n\n    .. code:: python\n\n        #!/data/alco/virtualenv/bin/python\n\n        # coding: utf-8\n        import os\n        os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'settings')\n\n        from alco import sphinx_conf\n\n9.  Run log collectors:\n\n    .. code:: sh\n\n        python manage.py start_collectors --no-daemon\n\n    If it shows number of collected messages periodically - then log\n    collecting is set up correctly.\n\n10. Configure system services to start subsystems automatically:\n\n    -  nginx or apache http server\n    -  django uwsgi backend\n    -  alco collectors (``start_collectors`` management command)\n    -  sphinxsearch, redis, default database for Django\n\n11. Open ``http://127.0.0.1:8000/grep/<logger_name>/`` to read and\n    search logs online.\n\nVirtualenv\n----------\n\nWe successfully configured SphinxSearch to use python from\n``virtualenv``, adding some environment variables to start script (i.e.\nFreeBSD rc.d script):\n\n.. code:: sh\n\n\n    sphinxsearch_prestart ()\n    {\n        # nobody user has no HOME\n        export PYTHON_EGG_CACHE=/tmp/.python-eggs\n        # python path for virtualenv interpreter should be redeclared\n        export PYTHONPATH=${venv_path}/lib/python3.4/:${venv_path}/lib/python3.4/site-packages/\n        . \"${virtualenv_path}/bin/activate\" || err 1 \"Virtualenv is not found\"\n        echo \"Virtualenv ${virtualenv_path} activated: `which python`\"\n\n    }\n\nIn this case *shebang* for ``sphinx_conf.py`` must point virtualenv's\npython interpreter.\n\nProduction usage\n----------------\n\nFor now ALCO stack is tested in preproduction environment in our company\nand is actively developed. There are no reasons to say that it's not\nready for production usage.\n\n.. |PyPI version| image:: https://badge.fury.io/py/alco.svg\n   :target: http://badge.fury.io/py/alco",
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