Note: Maintainers needed : Those that committed in the past code to this repo or are presenting new PRs and have experience and interest on helping to maintain repos & python libraries (code quality, testing, integration, etc). If you are intereted on getting our PR's through and helping others to contribute to the library, please get in touch.
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
Install using pip:
pip install CMRESHandler
- This library requires the following dependencies
- elasticsearch
- requests
- enum
- This library requires the following dependencies
- elasticsearch
- requests
- Additionally, the package support optionally kerberos authentication by adding the following dependecy
- requests-kerberos
- Additionally, the package support optionally AWS IAM user authentication by adding the following dependecy
- requests-aws4auth
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 useraws_secret_key: When
CMRESHandler.AuthType.AWS_SIGNED_AUTH
is used this argument must contain the AWS secret key of the the AWS IAM useraws_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 defaultindex_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 defaultes_additional_fields: A dictionary with all the additional fields that you would like to add to the logs
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
To create the package follow the standard python setup.py to compile. To test, just execute the python tests within the test folder
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
The same functionality can be implemented in many other different ways. For example, consider the integration using SysLogHandler and logstash syslog plugin.
Feel free to use this as is or even better, feel free to fork and send your pull requests over.