CMRESHandler-NtlmAuth 1.0.1

Elasticsearch Log handler for the logging library

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

---

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 ::

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 ::

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_doc_type: A string with the name of the document type that will be used python_log used by default

• 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.

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.