معرفی شرکت ها
django-mongolog-0.9.4
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A simple mongo based log handler for python/django
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | django-mongolog-0.9.4 |
| نام | django-mongolog |
| نسخه کتابخانه | 0.9.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | John Furr |
| ایمیل نویسنده | john.furr@gmail.com |
| آدرس صفحه اصلی | https://github.com/gnulnx/django-mongolog |
| آدرس اینترنتی | https://pypi.org/project/django-mongolog/ |
| مجوز | GPL V3 |
MongoLog
========
.. image:: http://pepy.tech/badge/django-mongolog
:target: http://pepy.tech/count/django-mongolog
MongoLog is a simple Mongo based log handler that can be easly used
with standard python/django logging.
Please visit the `MongoLog Users Group <https://groups.google.com/forum/#!forum/mongolog-users>`_ with any questions/suggestions. Thanks.
.. image:: https://travis-ci.org/gnulnx/django-mongolog.svg?branch=master
:target: https://travis-ci.org/gnulnx/django-mongolog
.. image:: https://coveralls.io/repos/gnulnx/django-mongolog/badge.svg?branch=master&service=github
:target: https://coveralls.io/github/gnulnx/django-mongolog?branch=master
.. image:: https://api.codacy.com/project/badge/grade/d8d4eaa24bbe4ae5afe608210e4b8d28
:target: https://www.codacy.com/app/gnulnx/django-mongolog
Quick start
-----------
1. Add "mongolog" to your INSTALLED_APPS like this
.. code:: python
INSTALLED_APPS = (
...
'mongolog',
)
2. Add the SimpleMongoLogHandler to your LOGGING config.
.. code:: python
LOGGING = {
'version': 1,
'handlers': {
'mongolog': {
'level': 'DEBUG',
'class': 'mongolog.SimpleMongoLogHandler',
# Set the connection string to the mongo instance.
'connection': 'mongodb://localhost:27017',
# define mongo collection the log handler should use. Default is mongolog
# This is useful if you want different handlers to use different collections
'collection': 'mongolog'
},
},
# Define a logger for your handler. We are using the root '' logger in this case
'loggers': {
'': {
'handlers': ['mongolog'],
'level': 'DEBUG',
'propagate': True
},
},
}
3) Start your management shell::
./manage.py shell
4) Create a couple of log entries
.. code:: python
import logging
import pymongo
logger = logging.getLogger(__name__)
One of the cool things about mongolog is that it can log complex data structures
in a way that makes them both human parsable and queryable. So for instance if
we create the following log message:
.. code:: python
# Pro Tip: You can copy and paste all of this
LOG_MSG = {
'test': True,
'test class': 'TestBaseMongoLogHandler',
'Life': {
'Domain': {
'Bacteria': [
{
'name': ValueError, # intentional bad value
'description': 'Just a bad description'
}
],
'Archaea': [],
'Eukaryota': [
{
'name': 'Excavata',
'description': 'Various flagellate protozoa',
},
{
'name': 'Amoebozoa',
'descritpion': 'most lobose amoeboids and slime moulds',
},
{
'name': 'Opisthokonta',
'description': 'animals, fungi, choanoflagellates, etc.',
},
]
}
}
}
Now let's log our message at each of the defined log levels...
.. code:: python
logger.debug(LOG_MSG)
logger.info(LOG_MSG)
logger.warn(LOG_MSG)
logger.error(LOG_MSG)
try:
raise ValueError("Bad Value")
except ValueError as e:
logger.exception(LOG_MSG)
raise
5) Now log into your mongo shell and look at some results
.. code:: python
./mongo
use mongolog
db.mongolog.findOne({'level': "INFO"})
Will produde a mongo document like:
.. code:: python
{
"_id" : ObjectId("5664a22bdd162ca58f0693d2"),
"name" : "__builtin__",
"thread" : NumberLong("140735229362944"),
"level" : "INFO",
"process" : 42383,
"module" : "<console>",
"filename" : "<console>",
"func" : "<module>",
"time" : ISODate("2015-12-06T21:01:31.258Z"),
"msg" : {
"test" : true,
"Life" : {
"Domain" : {
"Eukaryota" : [
{
"name" : "Excavata",
"description" : "Various flagellate protozoa"
},
{
"name" : "Amoebozoa",
"descritpion" : "most lobose amoeboids and slime moulds"
},
{
"name" : "Opisthokonta",
"description" : "animals, fungi, choanoflagellates, etc."
}
],
"Archaea" : [ ],
"Bacteria" : [
{
"name" : "<type 'exceptions.ValueError'>",
"description" : "Just a bad description"
}
]
}
},
"test class" : "TestBaseMongoLogHandler"
},
"path" : "<console>",
"line" : 1
}
Take a look at the "msg" section and you will notice that all of the information from our LOG_MSG
is contained under that key in standard mongo data structures. This means that we can query
based on our log message. For example in your mongo shell try the following queries:
.. code:: javascript
// Find all documents logged with a 'test' key
> db.mongolog.find({'msg.test': {$exists: true}}).count()
5
// Find all documents that have a Eukaryota name in the list of ["Amoebozoa", "Opisthokonta"]
> db.mongolog.find({
'msg.Life.Domain.Eukaryota.name': {
$in: ["Amoebozoa", "Opisthokonta"]
}
}).count()
1
// Same as above but only those documents logged at level INFO
>db.mongolog.find({
'level': 'INFO',
'msg.Life.Domain.Eukaryota.name': {$in: ["Amoebozoa", "Opisthokonta"]},
}).count()
1
// And again at level ERROR.
>db.mongolog.find({
'level': 'INFO',
'msg.Life.Domain.Eukaryota.name': {$in: ["Amoebozoa", "Opisthokonta"]},
}).count()
2
// Notice that now two records are returned. This is because
// logger.exception(...) also logs at level ERROR, but also notice that if when we
// pretty print the records...
>db.mongolog.find({
'level': 'ERROR',
'msg.Life.Domain.Eukaryota.name': {$in: ["Amoebozoa", "Opisthokonta"]},
}).pretty()
// ...that one of the entries has exception info. When running in a real environment
// and not the console the 'trace' section will be populated with the full stack trace.
"exception" : {
"info" : [
"<type 'exceptions.ValueError'>",
"Bad Value",
"<traceback object at 0x106853b90>"
],
"trace" :
null
}
Management Commands (Django Only)
---------------------------------
1) ml_purge
The ml_urge command is used to clean up mongo collections. The command has two basic modes: --purge and --delete. Purge will remove all documents and delete will remove documents older than {n} day's.
To backup and PURGE all documents from the collection defined in mongolog handler
./manage.py ml_purge --purge --backup -logger mongolog
To remove all documents older than 14 days without backing up first
./manage.py ml_purge --delete 14 -logger mongolog
Future Roadmap
---------------
Currently mongolog has pretty solid support for logging arbitrary datastructures. If it finds
an object it doesn't know how to natively serialize it will try to convert it to str().
The next steps are to create a set of most used query operations for probing the log.
Please give a shout out with `feedback <https://groups.google.com/forum/#!forum/mongolog-users>`_ and feature requests.
Thanks
نحوه نصب
نصب پکیج whl django-mongolog-0.9.4:
pip install django-mongolog-0.9.4.whl
نصب پکیج tar.gz django-mongolog-0.9.4:
pip install django-mongolog-0.9.4.tar.gz