CMRESHandler.py

Python Elasticsearch Log handler

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

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

Installation

Install using pip:

pip install CMRESHandler

Requirements Python 2

This library requires the following dependencies

elasticsearch
requests
enum

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 dependecy

requests-kerberos

Additional requirements for AWS IAM user authentication (request signing)

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

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}],
                           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 initialisation, providing more data of the execution context :

from cmreslogging.handlers import CMRESHandler
handler = CMRESHandler(hosts=[{'host': 'localhost', 'port': 9200}],
                           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

Initialisation parameters

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 CMRESHandler.AuthType = NO_AUTH, BASIC_AUTH, KERBEROS_AUTH
auth_details: When CMRESHandler.AuthType.BASIC_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'
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 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 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}],
            '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

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. All this can be also done from logstash configuration, but it still requires to provide quite a lot of context to

Contributing back

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

johns1342/python-elasticsearch-logger