pyramid_debugtoolbar
provides a useful debug toolbar while you're developing a Pyramid
application.
The toolbar is a blatant rip-off of Michael van Tellingen's flask-debugtoolbar
(which itself was derived from Rob Hudson's django-debugtoolbar
). It also includes a lightly sanded down version of the Werkzeug debugger code by Armin Ronacher and team.
Install using pip, e.g. (within a virtualenv):
$ pip install pyramid_debugtoolbar
Once the pyramid_debugtoolbar
is installed, you must use the config.include
mechanism to include it into your Pyramid project's configuration:
config = Configurator(.....)
config.include('pyramid_debugtoolbar')
Alternately, you may activate the toolbar by changing your application's .ini
file by adding it to the pyramid.includes
list:
pyramid.includes = pyramid_debugtoolbar
Warning
The debug toolbar should never be enabled in a production environment or on a machine with its Pyramid HTTP port exposed directly to the internet; it allows arbitrary code execution from only semi-trusted sources when configured poorly.
Once Pyramid is restarted, the toolbar will be available to inspect requests and responses by the application by visiting the /_debug_toolbar/
URL (note the trailing slash). For example, if your application is available at http://localhost:6543/
then you may visit http://localhost:6543/_debug_toolbar/
to inspect the requests.
For any HTML responses generated by the application, a link to the toolbar for the current page will be available in the upper right corner, provided the response contains a closing </body>
tag.
If an exception is unhandled by the Pyramid application, the toolbar will catch it and render an HTML page with a traceback and an interactive debugger that can be used to dive into the stack and execute arbitrary Python expressions to inspect the state of the system.
A URL leading to a debugging page for each exception raised by your application will additionally be logged to the console.
Settings can be used to control the operation of the toolbar. These settings are typically specified in the Pyramid "app" section of the Pyramid .ini
file.
debugtoolbar.hosts
If the request's
REMOTE_ADDR
is not in this list, the toolbar will not be displayed and the exception handler will not be active. Default value is['127.0.0.1', '::1']
. Note that each of the values in the list can be a hostmask, e.g.,192.168.1.0/24
.This should be a list if setup is done in Python or, if defined in a Paste
.ini
file, a single-line list of IP addresses/hostmasks separated by spaces. For example:debugtoolbar.hosts = 192.168.1.1 192.168.2.0/24
To enable access from any host, use the hostmask
0.0.0.0/0
.
debugtoolbar.enabled
true
if the toolbar is enabled;false
if the toolbar is disabled. Default istrue
. This disables both the exception handler and the toolbar overlay.
debugtoolbar.includes
The debugtoolbar will use Pyramid's default
pyramid.config.Configurator.include
mechanism to extend the toolbar's internal Pyramid application with custom logic. This is a good spot to add custom panels, affect static assets used by the toolbar, or add custom urls.Within the
includeme
the application registry may be accessed asconfig.registry.parent_registry
.
debugtoolbar.intercept_exc
This setting can have one of three values:
display
,debug
orfalse
. Default isdebug
. If this value isdisplay
, the toolbar will display a "pretty" traceback page which allows source viewing and when an exception happens. If this value isdebug
, the "pretty" traceback page will be shown, but it will also contain interactive debugging controls which allow you to evaluate arbitrary Python expressions in the context of a portion of the traceback, which is useful when attempting to track down the cause of the exception. If this value isfalse
, the "pretty" traceback will be disabled and all exceptions will be raised to the caller of the Pyramid application (usually a WSGI server). Default isdebug
. This setting differs fromdebugtoolbar.enabled
: it only enables or disables the exception handler. Note that, for backwards compatibility purposes, the valuetrue
provided to this setting is interpreted asdebug
.
debugtoolbar.show_on_exc_only
Default is
false
. If set totrue
the debugtoolbar will only be injected into the response in case a exception is raised. If the response is processed without exception, the returned html code is not changed by the debugtoolbar at all. This option allows the developer to use the toolbar for debugging purposes without interfering with successful responses.Inspection of requests is still possible by visiting the toolbar manually at
/_debug_toolbar/
.
debugtoolbar.intercept_redirects
true
if the redirection handler is enabled;false
if the handler is disabled. Default isfalse
. This differs fromdebugtoolbar.enabled
: it only enables or disables the redirection handler.
debugtoolbar.panels
A list of panel names. Defaults to a list of all panels known by
pyramid_debugtoolbar
, as documented inpyramid_debugtoolbar_api
. If this is spelled in an.ini
file, it overrides the default list and should be a space- or newline-separated sequence of panel names. This setting should be mainly used to override the default order of panels. For example:debugtoolbar.panels = headers logging performance renderings request_vars sqlalchemy traceback
For compatibility with older versions of the toolbar, the panel name may also be the dotted Python path to the panel class. For example,
pyramid_debugtoolbar.panels.sqla.SQLADebugPanel
.
debugtoolbar.extra_panels
A list of panel names that will be appended to the
debugtoolbar.panels
list. This setting is mostly useful if you have a panel that is not included by default (usingdebugtoolbar.includes
and you do not want to maintain the list of all panels viadebugtoolbar.panels
). This may be a dotted Python path to the panel class.
debugtoolbar.global_panels
A list of panel names. Defaults to a list of all global panels known by
pyramid_debugtoolbar
, as documented inpyramid_debugtoolbar_api
. If this is spelled in an.ini
file, it overrides the default list and should be a space- or newline-separated sequence of panel names. For example:debugtoolbar.panels = introspection routes settings tweens versions
For compatibility with older versions of the toolbar, the panel name may also be the dotted Python path to the panel class. For example,
pyramid_debugtoolbar.panels.settings.SettingsDebugPanel
.
debugtoolbar.extra_global_panels
A list of panel names that will be appended to the
debugtoolbar.global_panels
list. This setting is mostly useful if you have a panel that is not included by default (usingdebugtoolbar.includes
and you do not want to maintain the list of all panels viadebugtoolbar.panels
). This may be a dotted Python path to the panel class.
debugtoolbar.button_style
Any inline css styles you want to apply to the toolbar button. This will override the default style (
top:30px;
) set bytoolbar.css
. If, for example, you want the toolbar button to show up at the bottom off the screen, just setdebugtoolbar.button_style
totop:auto;bottom:30px;
. If your browser supports the zoom property, you can even control the magnification level of the toolbar button, e.g.,zoom:50%;
.
debugtoolbar.exclude_prefixes
The debug toolbar won't be shown and no data will be recorded if the
PATH_INFO
variable starts with any of the prefixes listed in this setting. If configuration is done via an.ini
file, the prefixes should be separated by carriage returns. For example:debugtoolbar.exclude_prefixes = /favicon.ico /settings /static
If configuration is done via Python, the setting should be a list.
By default, the setting is
['/favicon.ico']
.
debugtoolbar.active_panels
A space-separated list of panel names (see
pyramid_debugtoolbar.panels.DebugPanel.name
). This list of panels will have theirpyramid_debugtoolbar.panels.DebugPanel.is_active
state set toTrue
always. For example:debugtoolbar.active_panels = performance
This will set the listed panels to always be active. Instead, in order to enable per-request activation see
activating_panels
.
debugtoolbar.max_request_history
The debug toolbar works by storing the original request and it's associated data in memory, and making this data available to subsequent requests. By default, the toolbar maintains a history of the last 100 requests made to the application. By setting
debugtoolbar.max_request_history
, one can override the default of 100 and set it to a different number.
debugtoolbar.max_visible_requests
The number of requests shown in the sidebar. The default is 10.
When developing custom panels for an application, the following settings may be used to influence how debugtoolbar itself behaves and what information it logs.
debugtoolbar.debug_notfound
Print view-related
NotFound
debug messages tostderr
when this value istrue
.
debugtoolbar.debug_routematch
Print debugging messages related to URL dispatch route matching when this value is
true
.
debugtoolbar.reload_templates
When this value is
true
, templates are automatically reloaded whenever they are modified without restarting the application, so you can see changes to templates take effect immediately during development. This flag is meaningful to Chameleon and Mako templates, as well as most third-party template rendering extensions.
debugtoolbar.reload_resources
Alias for
debugtoolbar.reload_assets
.
debugtoolbar.reload_assets
Don't cache any asset file data when this value is
true
.
debugtoolbar.prevent_http_cache
Prevent the
http_cache
view configuration argument from having any effect globally in this process when this value istrue
. No HTTP caching-related response headers will be set by the Pyramidhttp_cache
view configuration feature when this istrue
.
Since version 1.0.5 pyramid_debugtoolbar
offers custom authorization mechanism to control toolbar feature on per-request basis. Using the config.set_debugtoolbar_request_authorization(callback)
directive, you can specify your own function to control whether toolbar functionality is enabled or not.
Note
Custom authorization is performed after a successful IP address check when the debugtoolbar.hosts
settings option is used.
Note
Custom authorization does not have an effect on the pyramid_debugtoolbar
static route and /_debug_toolbar/static/*
contents will still be accessible.
from pyramid.security import authenticated_userid
from pyramid.settings import aslist
def admin_only_debugtoolbar(request):
"""
Enable toolbar for administrators only.
Returns True when it should be enabled.
"""
admins = aslist(request.registry.settings.get('admins', ''))
userid = authenticated_userid(request)
toolbar_enabled = userid and userid in admins
return toolbar_enabled
config = Configurator(.....)
config.include('pyramid_debugtoolbar')
config.set_debugtoolbar_request_authorization(admin_only_debugtoolbar)
Most panels do not support any extra active features and need not be explicitly activated. However, some panels support an optional ~pyramid_debugtoolbar.panel.DebugPanel.is_active
state in which they will do some extra work. For example, the ~pyramid_debugtoolbar.panels.performance.PerformanceDebugPanel
` will not do profiling of your requests unless it has been activated.
This activation can be controlled on a per-request basis by setting the pdtb_active
cookie to a comma-separated list of panel names. For example:
Cookie: pdtb_active=performance,foo,bar
A panel name is defined by the ~pyramid_debugtoolbar.panels.DebugPanel.name
attribute of each debug panel.
The cookie may also be set via the web interface in the Settings tab but, remember, since it is a cookie it must be set on the exact HTTP client you are using or the panel will not be active for the request.
When you include the toolbar in your application, a floating Pyramid logo will appear on the upper right over your application's HTML:
If you click on the Pyramid logo, a new target window will open with your current request highlighted and all of your configured panels loaded.
These are the default toolbar panels:
Displays versions of all installed Python software as well as the Python version and platform itself.
Displays Pyramid deployment settings, i.e., registry.settings
.
Displays HTTP request and response headers for the current page.
Displays objects attached to the request of the current page and the WSGI environment.
Displays the renderings performed by Pyramid for the current page.
Displays messages logged by the current page.
Displays timing information, and, if enabled, Python profiling information for the current page. When it is red, only timing will be done and no profiling information.
Note
An internal profiler can be enabled through the "performance" checkmark in the "Settings" tab in the navigation bar. When the checkbox is green, the request will be profiled and profiling information will be gathered and displayed on the "Performance" panel output.
Displays the routes currently configured in your application.
Displays the tween chain for your application, and whether they were defined explicitly or implicitly.
Displays SQL queries made by SQLAlchemy by the current page along with timing information.
Provides the ability to re-run the query using the "SELECT" link.
Provides the ability to get more detail about the query using the "EXPLAIN" link.
Displays a rendering of the data available in Pyramid's configuration introspection system (available in Pyramid 1.3+ only).
When an exception is raised and the debugtoolbar.intercept_exc
setting is display
or debug
, Pyramid presents a pretty traceback page. If the setting value is debug
, you will be able to examine locals in each frame in the traceback and execute code in the context of each frame. Read the instructions on the exception page for more information.
When a response is returned to Pyramid that has a redirect status code (301, 302, etc.) and the debugtoolbar.intercept_redirect
setting is true
, Pyramid presents an interim page with a link to the target of the redirect. You can use the toolbar on the redirect source page, then when finished, use the link to continue to the target page.
In some cases it can be desirable to add a custom panel to the toolbar to display some application specific data. There are two steps for adding such a panel to an application: writing the panel, and adding it to your application settings.
Before writing the panel, you should understand how pyramid_debugtoolbar interacts with your application in its two phase process.
pyramid_debugtoolbar wraps every request within a Pyramid "tween" via toolbar_tween_factory
. This tween allows the toolbar to record data during the original request (Phase 1) and injects a link to the toolbar interface into the rendered Pyramid web pages. The data is displayed during a secondary request to the toolbar (Phase 2).
Phase 1 - The original request
When pyramid_debugtoolbar is enabled, it can start tracking data on the original request. This involves calling the following panel methods in-band with the original request:
pyramid_debugtoolbar.panels.DebugPanel.__init__
pyramid_debugtoolbar.panels.DebugPanel.wrap_handler
pyramid_debugtoolbar.panels.DebugPanel.process_beforerender
pyramid_debugtoolbar.panels.DebugPanel.process_response
These methods are used to store and manipulate a self.data
variable on each panel during this original request. Typically self.data
is first generated on the __init__
method. It is important to note that the request
and event variables available to these methods refer to the original request.
Phase 2 - The debugtoolbar request
When the "/_debug_toolbar/{request_id}" is accessed, the history of the original request_id and its associated panels are accessed. Variables such as data
that were generated during the original request are made available for further processing. The data
variable is injected into the template for display.
The following panel methods are called or accessed on the debugtoolbar request:
pyramid_debugtoolbar.panels.DebugPanel.name
pyramid_debugtoolbar.panels.DebugPanel.template
pyramid_debugtoolbar.panels.DebugPanel.user_activate
pyramid_debugtoolbar.panels.DebugPanel.is_active
pyramid_debugtoolbar.panels.DebugPanel.has_content
pyramid_debugtoolbar.panels.DebugPanel.render_content
pyramid_debugtoolbar.panels.DebugPanel.render_vars
pyramid_debugtoolbar.panels.DebugPanel.title
pyramid_debugtoolbar.panels.DebugPanel.nav_title
The panel can be created as part of your application or as a standalone package. The easiest way to write a panel is to subclass from the pyramid_debugtoolbar.panels.DebugPanel
class. Here is the code for a sample panel:
from pyramid_debugtoolbar.panels import DebugPanel
_ = lambda x: x
class SampleDebugPanel(DebugPanel):
"""
Sample debug panel
"""
name = 'Sample'
has_content = True
template = 'myapp.lib.debugtoolbar_custom.panels:templates/sample.dbtmako'
def __init__(self, request):
self.data = { 'request_path' : request.path_info }
def nav_title(self):
return _('Sample')
def title(self):
return _('Sample')
def includeme(config):
config.add_debugtoolbar_panel(SampleDebugPanel)
After inheriting from the DebugPanel
class, you have to define a few methods and attributes on your panel:
name
Attribute. String value. A unique identifier for the name of the panel. This must be defined by a subclass.
has_content
Attribute. Boolean value. Default is
True
This attribute determines if the tab is enabled or not. IfFalse
then the panel's tab will be disabled and.render_content
will not be invoked. Most subclasses will want to set this toTrue
by default. An example of this panel's dynamic utility is the SQLA panel; if no SqlAlchemy statements were executed in the request, this value is set toFalse
and the tab is simply disabled.user_activate
Attribute. Boolean value. If the client is able to activate/de-activate the panel then this should be
True
.is_active
Attribute. Boolean value. This property will be set by the toolbar, indicating the user's decision to activate or deactivate the panel. If
user_activate
isFalse
thenis_active
will always be set toTrue
.template
Attribute. String value. Must be overridden. A mako asset specification. The default implementation of
render_content
in the base class (DebugPanel
) will attempt to rendertemplate
. Iftemplate
is not defined, andrender_content
is not overridden, aNotImplemented
exception will be raised.nav_title
Method. Returns a string. Called to get the title to be used on the toolbar's navigation bar for this panel.
url
Method. Returns a string. Can be overridden to point the panel at any arbitrary URL when the tab is clicked.
title
Method. Returns a string. Called to get the title to be used on the panel's display page.
__init__
Method. This method should defines a
data
attribute, which is used when rendering the template. This is the first (and often most appropriate) opportunity to initializedata
with values that can be derived from the request object itself.render_content
Method. Return a string containing the HTML to be rendered for the panel. By default this will render the template defined by the
template
attribute with a rendering context defined by thedata
attribute combined with thedict
returned fromrender_vars
. Therequest
here is the active request in the toolbar. Not the original request that this panel represents.render_vars
Method. Invoked by the default implementation of
render_content
as an opportunity to enhance the rendering context. This method is expected to return adict
of values to use when rendering the panel's HTML content. This value is usually injected into templates as the rendering context. This is a useful hook for adding any data you need in the templates, which was not already added into the panel's.data. The default SQLA panel is a good example of this functionality in use. Therequest
here is the active request in the toolbar. Not the original request that this panel represents.wrap_handler
Method. This method is a hook available to the panel in order to track the lifecycle of the original request. A handler accepts a request and returns a response; it is essentially the same as a Pyramid
tween
. This can be used to update thedata
dict with values that are wanted for rendering. The main toolbar routine works by wrapping each request in a handler (tween). Before generating a response, the main toolbar routine will call the`wrap_handler` method of each panel. This functionality is often used for decorating the handlers with timing or performance metrics.process_beforerender
Method. Arguments:
self
,event
. This method is a hook available to the panel in order to track the lifecycle of the original request. The debugtoolbar uses a subscriber event (pyramid.events.BeforeRender
) to call theprocess_beforerender
method of each enabled panel. This can be used to update thedata
dict with values that are wanted for rendering or track properties of the rendering events.process_response
Method. Arguments:
self
,response
. This method is a hook available to the panel in order to track the lifecycle of the original request. The main toolbar routine works by wrapping each request in a tween. Theprocess_response
method of each panel is called within the tween, after the original request has generated a response.
When creating a new panel, some of these methods must be subclassed, while others can rely on the base class.
Once you define the panel it should be registered with the toolbar by defining an includeme
function that calls pyramid_debugtoolbar.add_debugtoolbar_panel
and then having the user add the panel to their debugtoolbar.includes
setting in their app.
The source code for the standard debugpanel request_vars.py
is a good starting point for inspiration.
The render_vars
and render_content
methods may use the request.toolbar_panels
dictionary to introspect and work with other panels that captured data for the original request. The dictionary keys are the names of other panels and the values are the panel instances.
Once your panel is ready, you can simply add its package name to the debugtoolbar.includes
setting on your application configuration file:
pyramid.includes =
pyramid_debugtoolbar
debugtoolbar.includes =
samplepanel
It may be the case that your panel needs to instrument the parent application with extra settings or configuration. For example, maybe it wants to wrap the session factory with its own. To make this happen, your includeme
that is included from debugtoolbar.includes
should use pyramid_debugtoolbar.inject_parent_action
to register a callable that can modify the parent application.
from pyramid.interfaces import ISessionFactory
import time
class SessionFactoryWrapper:
def __init__(self, factory):
self.factory = factory
def __call__(self, request):
request.session_created_at = time.time()
return self.factory(request)
def includeme(config):
def action(parent_config):
factory = parent_config.registry.queryUtility(ISessionFactory)
if factory:
wrapped_factory = SessionFactoryWrapper(factory)
parent_config.set_session_factory(wrapped_factory)
config.inject_parent_action(action)
In this example, you may also register a new toolbar panel that cares about request.session_created_at
to determine when the session was created during the request lifecycle.
pyramid_debugtoolbar
automatically loads several Javascript and CSS libraries that you can take advantage of when writing custom panels.
- Bootstrap [http://getbootstrap.com/]
- jQuery [https://jquery.org/]
- jquery.tablesorter [http://mottie.github.io/tablesorter]
If you wish to enable tablesorting, add the CSS class "pDebugSortable" to the opening <table>
tag. For example:
<table class="pDebugSortable table table-striped table-condensed">
The following is a listing of panels and user interface extras for pyramid_debugtoolbar
created by its users. These extras are unofficial and not supported by the Pylons Project. To add your contribution, please submit a pull request to update this documentation.
- Page Up
For tabs that have content which requires lots of scrolling down or to the right, clicking the Page Up icon resets the window to 0,0.
- pyramid_debugtoolbar_ajax
Adds an "AJAX" panel to the
pyramid_debugtoolbar
. This panel contains a button to replay the request in a new window -- allowing you to spawn a debugger window for errors encountered on background ajax requests.- pyramid_debugtoolbar_dogpile
dogpile caching support for pyramid_debugtoolbar.
api.rst changes.rst glossary.rst
Visit https://github.com/Pylons/pyramid_debugtoolbar to download development or tagged versions.
Visit https://github.com/Pylons/pyramid_debugtoolbar/issues to report bugs.
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