CMRESHandler-NtlmAuth 1.1.0
Elasticsearch Log handler for the logging library
License: Apache2
Requires Python: >=3.6
Keywords: logging elasticsearch handler log django instrumentation

># Origin project location.

> Please note that this repo is a fork from: https://github.com/cmanaha/python-elasticsearch-logger

>

> Base code is extended by:

> - adding the HttpNtlmAuth as new authentication

> - add support for elasticsearch 8.x version

> - those changes are waiting for review in:

>   - https://github.com/cmanaha/python-elasticsearch-logger/pull/94

>   - https://github.com/cmanaha/python-elasticsearch-logger/pull/93



___



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# Python Elasticsearch Log handler



This library provides an Elasticsearch logging appender compatible with the

python standard `logging <https://docs.python.org/2/library/logging.html>`_ library.



The code source is in github at `https://github.com/cmanaha/python-elasticsearch-logger

<https://github.com/cmanaha/python-elasticsearch-logger>`_





## Installation



Install using pip::



    pip install CMRESHandler-NtlmAuth



## Requirements Python 3



This library requires the following dependencies

 - elasticsearch

 - requests



### Additional requirements for Kerberos support



Additionally, the package support optionally kerberos authentication by adding the following dependency

 - requests-kerberos



### Additional requirements for NTLM support



Additionally, the package support optionally NTLM authentication by adding the following dependency

 - requests-ntlm



### Additional requirements for AWS IAM user authentication (request signing)



Additionally, the package support optionally AWS IAM user authentication by adding the following dependency

 - requests-aws4auth



# Using the handler in  your program



To initialise and create the handler, just add the handler to your logger as follow ::



```python

from cmreslogging.handlers import CMRESHandler



handler = CMRESHandler(hosts=[{'host': 'localhost', 'port': 9200, 'scheme': 'http'}],

                            auth_type=CMRESHandler.AuthType.NO_AUTH,

                            es_index_name="my_python_index")

log = logging.getLogger("PythonTest")

log.setLevel(logging.INFO)

log.addHandler(handler)

```



You can add fields upon initialization, providing more data of the execution context ::



```python

from cmreslogging.handlers import CMRESHandler



handler = CMRESHandler(hosts=[{'host': 'localhost', 'port': 9200, 'scheme': 'http'}],

                        auth_type=CMRESHandler.AuthType.NO_AUTH,

                        es_index_name="my_python_index",

                        es_additional_fields={'App': 'MyAppName', 'Environment': 'Dev'})

log = logging.getLogger("PythonTest")

log.setLevel(logging.INFO)

log.addHandler(handler)

```



This additional fields will be applied to all logging fields and recorded in elasticsearch



To log, use the regular commands from the logging library ::



    log.info("This is an info statement that will be logged into elasticsearch")



Your code can also dump additional extra fields on a per log basis that can be used to instrument

operations. For example, when reading information from a database you could do something like::



    start_time = time.time()

    database_operation()

    db_delta = time.time() - start_time

    log.debug("DB operation took %.3f seconds" % db_delta, extra={'db_execution_time': db_delta})



The code above executes the DB operation, measures the time it took and logs an entry that contains

in the message the time the operation took as string and for convenience, it creates another field

called db_execution_time with a float that can be used to plot the time this operations are taking using

Kibana on top of elasticsearch



# Initialization parameters



The constructors takes the following parameters:

  - hosts:  The list of hosts that elasticsearch clients will connect, multiple hosts are allowed.

   Use ```'scheme'``` to determinate if use SSL (`use_ssl` is deprecated). To use SSL set ```'scheme': 'https'```, or if you don't need SSL Sset ```'scheme': 'http'```.

   for example::



    [{'host':'host1','port':9200, 'scheme': 'https'}, {'host':'host2','port':9200, 'scheme': 'http'}]





 - auth_type: The authentication currently support CMRESHandler.AuthType = NO_AUTH, BASIC_AUTH, KERBEROS_AUTH, NTLM_AUTH

 - auth_details: When ``CMRESHandler.AuthType.BASIC_AUTH`` or ``CMRESHandler.AuthType.NTLM_AUTH`` is used this argument must contain a tuple of string with the user and password that will be used to authenticate against the Elasticsearch servers, for example ('User','Password')

 - aws_access_key: When ``CMRESHandler.AuthType.AWS_SIGNED_AUTH`` is used this argument must contain the AWS key id of the  the AWS IAM user

 - aws_secret_key: When ``CMRESHandler.AuthType.AWS_SIGNED_AUTH`` is used this argument must contain the AWS secret key of the  the AWS IAM user

 - aws_region: When ``CMRESHandler.AuthType.AWS_SIGNED_AUTH`` is used this argument must contain the AWS region of the  the AWS Elasticsearch servers, for example ``'us-east'``

 - verify_ssl: A boolean that defines if the SSL certificates are validated or not

 - buffer_size: An int, Once this size is reached on the internal buffer results are flushed into ES

 - flush_frequency_in_sec: A float representing how often and when the buffer will be flushed

 - es_index_name: A string with the prefix of the elasticsearch index that will be created. Note a date with

   YYYY.MM.dd, ``python_logger`` used by default

 - index_name_frequency: The frequency to use as part of the index naming. Currently supports

   CMRESHandler.IndexNameFrequency.DAILY, CMRESHandler.IndexNameFrequency.WEEKLY,

   CMRESHandler.IndexNameFrequency.MONTHLY, CMRESHandler.IndexNameFrequency.YEARLY by default the daily rotation

   is used

 - es_additional_fields: A dictionary with all the additional fields that you would like to add to the logs



# Django Integration



It is also very easy to integrate the handler to `Django <https://www.djangoproject.com/>`_ And what is even

better, at DEBUG level django logs information such as how long it takes for DB connections to return so

they can be plotted on Kibana, or the SQL statements that Django executed. 



```python

from cmreslogging.handlers import CMRESHandler



LOGGING = {

    'version': 1,

    'disable_existing_loggers': False,

    'handlers': {

        'file': {

            'level': 'DEBUG',

            'class': 'logging.handlers.RotatingFileHandler',

            'filename': './debug.log',

            'maxBytes': 102400,

            'backupCount': 5,

        },

        'elasticsearch': {

            'level': 'DEBUG',

            'class': 'cmreslogging.handlers.CMRESHandler',

            'hosts': [{'host': 'localhost', 'port': 9200, 'scheme': 'http'}],

            'es_index_name': 'my_python_app',

            'es_additional_fields': {'App': 'Test', 'Environment': 'Dev'},

            'auth_type': CMRESHandler.AuthType.NO_AUTH,

            'use_ssl': False,

        },

    },

    'loggers': {

        'django': {

            'handlers': ['file','elasticsearch'],

            'level': 'DEBUG',

            'propagate': True,

        },

    },

}

```



There is more information about how Django logging works in the

`Django documentation <https://docs.djangoproject.com/en/1.9/topics/logging//>`_





# Building the sources & Testing



To create the package follow the standard python setup.py to compile.

To test, just execute the python tests within the test folder



# Why using an appender rather than logstash or beats



In some cases is quite useful to provide all the information available within the LogRecords as it contains

things such as exception information, the method, file, log line where the log was generated.



If you are interested on understanding more about the differences between the agent vs handler

approach, I'd suggest reading `this conversation thread <https://github.com/cmanaha/python-elasticsearch-logger/issues/44/>`_



The same functionality can be implemented in many other different ways. For example, consider the integration

using `SysLogHandler <https://docs.python.org/3/library/logging.handlers.html#sysloghandler>`_ and

`logstash syslog plugin <https://www.elastic.co/guide/en/logstash/current/plugins-inputs-syslog.html>`_.





# Contributing back



Feel free to use this as is or even better, feel free to fork and send your pull requests over.