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/SHolzhauer/python-elasticsearch-logger
This is a fork of the original work by cmanaha.
Tested against
Elasticsearch 7.3.1
withPython3.6
WIP
This library requires the following dependencies
- elasticsearch
- requests
- enum
- elasticsearch
- requests
- packaging
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
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
,CMRESHandler.IndexNameFrequency.NONE
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