# SQLAlchemy Easy Profile
[](https://travis-ci.com/dmvass/sqlalchemy-easy-profile)
[](https://pypi.python.org/pypi/sqlalchemy-easy-profile)
[](https://codecov.io/gh/dmvass/sqlalchemy-easy-profile)
[](https://github.com/dmvass/sqlalchemy-easy-profile/blob/master/LICENSE)
Inspired by [django-querycount](https://github.com/bradmontgomery/django-querycount),
is a library that hooks into SQLAlchemy to collect metrics, streaming statistics into
console output and help you understand where in application you have slow or redundant
queries.
## Installation
Install the package with pip:
```
pip install sqlalchemy-easy-profile
```
## Session profiler
The profiling session hooks into SQLAlchemy and captures query statements, duration information,
and query parameters. You also may have multiple profiling sessions active at the same
time on the same or different Engines. If multiple profiling sessions are active on the
same engine, queries on that engine will be collected by both sessions and reported on
different reporters.
You may begin and commit a profiling session as much as you like. Calling begin on an already
started session or commit on an already committed session will raise an `AssertionError`.
You also can use a contextmanager interface for session profiling or used it like a decorator.
This has the effect of only profiling queries occurred within the decorated function or inside
a manager context.
How to use `begin` and `commit`:
```python
from easy_profile import SessionProfiler
profiler = SessionProfiler()
profiler.begin()
session.query(User).filter(User.name == "Arthur Dent").first()
profiler.commit()
print(profiler.stats)
```
How to use as a context manager interface:
```python
profiler = SessionProfiler()
with profiler:
session.query(User).filter(User.name == "Arthur Dent").first()
print(profiler.stats)
```
How to use profiler as a decorator:
```python
profiler = SessionProfiler()
class UsersResource:
@profiler()
def on_get(self, req, resp, **args, **kwargs):
return session.query(User).all()
```
Keep in mind that profiler decorator interface accepts a special reporter and
If it was not defined by default will be used a base streaming reporter. Decorator
also accept `name` and `name_callback` optional parameters.
## WSGI integration
Easy Profiler provides a specified middleware which can prints the number of database
queries for each HTTP request and can be applied as a WSGI server middleware. So you
can easily integrate Easy Profiler into any WSGI application.
How to integrate with a Flask application:
```python
from flask import Flask
from easy_profile import EasyProfileMiddleware
app = Flask(__name__)
app.wsgi_app = EasyProfileMiddleware(app.wsgi_app)
```
How to integrate with a Falcon application:
```python
import falcon
from easy_profile import EasyProfileMiddleware
api = application = falcon.API()
application = EasyProfileMiddleware(application)
```
## How to customize output
The `StreamReporter` accepts medium-high thresholds, output file destination (stdout by default), a special
flag for disabling color formatting and number of displayed duplicated queries:
```python
from flask import Flask
from easy_profile import EasyProfileMiddleware, StreamReporter
app = Flask(__name__)
app.wsgi_app = EasyProfileMiddleware(app.wsgi_app, reporter=StreamReporter(display_duplicates=100))
```
Any custom reporter can be created as:
```python
from easy_profile.reporters import Reporter
class CustomReporter(Reporter):
def report(self, path, stats):
"""Do something with path and stats.
:param str path: where profiling occurred
:param dict stats: profiling statistics
"""
...
```
## Testing
To run the tests:
```
python setup.py test
```
Or use `tox` for running in all tests environments.
## License
This code is distributed under the terms of the MIT license.
## Changes
A full changelog is maintained in the [CHANGELOG](https://github.com/dmvass/sqlalchemy-easy-profile/blob/master/CHANGELOG.md) file.
## Contributing
**sqlalchemy-easy-profile** is an open source project and contributions are
welcome! Check out the [Issues](https://github.com/dmvass/sqlalchemy-easy-profile/issues)
page to see if your idea for a contribution has already been mentioned, and feel
free to raise an issue or submit a pull request.
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"description": "# SQLAlchemy Easy Profile\n[](https://travis-ci.com/dmvass/sqlalchemy-easy-profile)\n[](https://pypi.python.org/pypi/sqlalchemy-easy-profile)\n[](https://codecov.io/gh/dmvass/sqlalchemy-easy-profile)\n[](https://github.com/dmvass/sqlalchemy-easy-profile/blob/master/LICENSE)\n\nInspired by [django-querycount](https://github.com/bradmontgomery/django-querycount),\nis a library that hooks into SQLAlchemy to collect metrics, streaming statistics into\nconsole output and help you understand where in application you have slow or redundant\nqueries.\n\n\n\n## Installation\nInstall the package with pip:\n```\npip install sqlalchemy-easy-profile\n```\n\n## Session profiler\nThe profiling session hooks into SQLAlchemy and captures query statements, duration information,\nand query parameters. You also may have multiple profiling sessions active at the same\ntime on the same or different Engines. If multiple profiling sessions are active on the\nsame engine, queries on that engine will be collected by both sessions and reported on\ndifferent reporters.\n\nYou may begin and commit a profiling session as much as you like. Calling begin on an already\nstarted session or commit on an already committed session will raise an `AssertionError`.\nYou also can use a contextmanager interface for session profiling or used it like a decorator.\nThis has the effect of only profiling queries occurred within the decorated function or inside\na manager context.\n\nHow to use `begin` and `commit`:\n```python\nfrom easy_profile import SessionProfiler\n\nprofiler = SessionProfiler()\n\nprofiler.begin()\nsession.query(User).filter(User.name == \"Arthur Dent\").first()\nprofiler.commit()\n\nprint(profiler.stats)\n```\n\nHow to use as a context manager interface:\n```python\nprofiler = SessionProfiler()\nwith profiler:\n session.query(User).filter(User.name == \"Arthur Dent\").first()\n\nprint(profiler.stats)\n```\n\nHow to use profiler as a decorator:\n```python\nprofiler = SessionProfiler()\n\nclass UsersResource:\n @profiler()\n def on_get(self, req, resp, **args, **kwargs):\n return session.query(User).all()\n```\n\nKeep in mind that profiler decorator interface accepts a special reporter and\nIf it was not defined by default will be used a base streaming reporter. Decorator\nalso accept `name` and `name_callback` optional parameters.\n\n## WSGI integration\nEasy Profiler provides a specified middleware which can prints the number of database\nqueries for each HTTP request and can be applied as a WSGI server middleware. So you\ncan easily integrate Easy Profiler into any WSGI application.\n\nHow to integrate with a Flask application:\n```python\nfrom flask import Flask\nfrom easy_profile import EasyProfileMiddleware\n\napp = Flask(__name__)\napp.wsgi_app = EasyProfileMiddleware(app.wsgi_app)\n```\n\nHow to integrate with a Falcon application: \n```python\nimport falcon\nfrom easy_profile import EasyProfileMiddleware\n\napi = application = falcon.API()\napplication = EasyProfileMiddleware(application)\n```\n\n## How to customize output\n\nThe `StreamReporter` accepts medium-high thresholds, output file destination (stdout by default), a special\nflag for disabling color formatting and number of displayed duplicated queries:\n\n```python\nfrom flask import Flask\nfrom easy_profile import EasyProfileMiddleware, StreamReporter\n\napp = Flask(__name__)\napp.wsgi_app = EasyProfileMiddleware(app.wsgi_app, reporter=StreamReporter(display_duplicates=100))\n```\n\nAny custom reporter can be created as:\n\n```python\nfrom easy_profile.reporters import Reporter\n\nclass CustomReporter(Reporter):\n\n def report(self, path, stats):\n \"\"\"Do something with path and stats.\n \n :param str path: where profiling occurred\n :param dict stats: profiling statistics\n\n \"\"\"\n ...\n\n```\n\n## Testing\nTo run the tests:\n```\npython setup.py test\n```\n\nOr use `tox` for running in all tests environments.\n\n## License\nThis code is distributed under the terms of the MIT license.\n\n## Changes\nA full changelog is maintained in the [CHANGELOG](https://github.com/dmvass/sqlalchemy-easy-profile/blob/master/CHANGELOG.md) file.\n\n## Contributing \n**sqlalchemy-easy-profile** is an open source project and contributions are\nwelcome! Check out the [Issues](https://github.com/dmvass/sqlalchemy-easy-profile/issues)\npage to see if your idea for a contribution has already been mentioned, and feel\nfree to raise an issue or submit a pull request.\n",
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