PYESHandler 1.0.1

Elasticsearch Log handler for the logging library

Python Elasticsearch Log handler

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This library provides an Elasticsearch logging appender compatible with the python standard logging library.

This library is a fork of https://github.com/cmanaha/python-elasticsearch-logger Thanks to Carlos Manzanedo Rueda for starting this library.

The code source is in github at https://github.com/mkhadher/python-elasticsearch-logger

Installation

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Install using pip

pip install PYESHandler

Requirements Python 2

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This library requires the following dependencies

• elasticsearch

• requests

• enum

Requirements Python 3

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This library requires the following dependencies

• elasticsearch

• requests

Additional requirements for Kerberos support

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Additionally, the package support optionally kerberos authentication by adding the following dependecy

• requests-kerberos

Additional requirements for AWS IAM user authentication (request signing)

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Additionally, the package support optionally AWS IAM user authentication by adding the following dependecy

• requests-aws4auth

Using the handler in your program

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To initialise and create the handler, just add the handler to your logger as follow

from pyeslogging.handlers import PYESHandler

handler = PYESHandler(hosts=[{'host': 'localhost', 'port': 9200}],

                           auth_type=PYESHandler.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 initialisation, providing more data of the execution context

from pyeslogging.handlers import PYESHandler

handler = PYESHandler(hosts=[{'host': 'localhost', 'port': 9200}],

                           auth_type=PYESHandler.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

Initialisation parameters

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The constructors takes the following parameters:

• hosts: The list of hosts that elasticsearch clients will connect, multiple hosts are allowed, for example

  [{'host':'host1','port':9200}, {'host':'host2','port':9200}]

• auth_type: The authentication currently support PYESHandler.AuthType = NO_AUTH, BASIC_AUTH, KERBEROS_AUTH

• auth_details: When PYESHandler.AuthType.BASIC_AUTH or “BASIC_AUTH” string 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 a tuple (‘User’,’Password’) or a dictionary {“username”: “my_username”,”password”: “my_fancy_password”}

• aws_secret_key: When PYESHandler.AuthType.AWS_SIGNED_AUTH or “AWS_SIGNED_AUTH” string is used this argument must contain the AWS secret key of the the AWS IAM user

• aws_region: When PYESHandler.AuthType.AWS_SIGNED_AUTH or “AWS_SIGNED_AUTH” string is used this argument must contain the AWS region of the the AWS Elasticsearch servers, for example 'us-east'

• use_ssl: A boolean that defines if the communications should use SSL encrypted communication

• 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

  ElasticECSHandler.IndexNameFrequency.DAILY, ElasticECSHandler.IndexNameFrequency.WEEKLY,

  ElasticECSHandler.IndexNameFrequency.MONTHLY, ElasticECSHandler.IndexNameFrequency.YEARLY and

  ElasticECSHandler.IndexNameFrequency.NEVER. By default the daily rotation is used.

  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

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It is also very easy to integrate the handler to Django 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 pyeslogging.handlers import PYESHandler

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': 'pyeslogging.handlers.PYESHandler',

            'hosts': [{'host': 'localhost', 'port': 9200}],

            'es_index_name': 'my_python_app',

            'es_additional_fields': {'App': 'Test', 'Environment': 'Dev'},

            'auth_type': PYESHandler.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

Read logging JSON Config File

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The below example can be used by a flask app or any python script. This example shows how to configure logging logger for file and elasticsearch logging using logging json config file.

logging.json

{

   "version":1,

   "disable_existing_loggers":true,

   "formatters":{

      "standard":{

         "format":"%(asctime)s - %(levelname)s - %(threadName)s - %(name)s - %(message)s"

      }

   },

   "handlers":{

      "file":{

         "level":"DEBUG",

         "class":"logging.handlers.TimedRotatingFileHandler",

         "formatter":"standard",

         "filename":"./log_file.txt",

         "when":"midnight",

         "backupCount":25

      },

      "elasticsearch":{

         "level":"DEBUG",

         "class":"pyeslogging.handlers.PYESHandler",

         "hosts": [{"host": "my.elastic.domain.com", "port": 9243}],

         "es_index_name":"PYESLogger",

         "auth_type": "BASIC_AUTH",

         "auth_details": {"username": "my_username","password": "my_fancy_password"},

         "use_ssl":true,

         "index_name_frequency": "monthly",

         "verify_ssl": true

      }

   },

   "root":{

      "handlers":[

         "file",

         "elasticsearch"

      ],

      "level":"DEBUG",

      "propagate":false

   }

}

app.py

import logging

import logging.config

from pyeslogging.handlers import PYESHandler

import json



# Define logging.json path

with open("C:\App\logging.json") as read_file:

    loggingConfigDir = json.load(read_file)

logging.config.dictConfig(loggingConfigDir)

logger = logging.getLogger('root')

logger.info("Hello World !")

Building the sources & Testing

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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

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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.

The same functionality can be implemented in many other different ways. For example, consider the integration

using SysLogHandler and

logstash syslog plugin.

Contributing back

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Feel free to use this as is or even better, feel free to fork and send your pull requests over.