Pellet helps improve your Django app performance by discovering N+1 queries
Project description
Pellet
Pellet helps improve your Django app performance by discovering N+1
queries.
The Django ORM makes it easy to forget using select_related
and prefetch_related
correctly and can accidentally cause N+1
queries to happen.
Pellet ultimately aims to recreate Bullet for Django.
Installing Pellet
pip install pellet
Enabling Pellet
- Add
pellet.middleware.PelletMiddleware
to your django middleware list. - Configure pellet behaviour by using the
PELLET
variable in your django settings file.
Configuring Pellet
You can configure pellet by setting the PELLET
variable in your django settings file. The default values used by pellet if this variable or any field in the object is not found is as follows:
PELLET = {
# Enable/Disable pellet
# If set to False the pellet
# middleware does nothing
"enabled": False,
# Enable this if you want count and time
# metrics at a query level
"query_level_metrics_enabled": False,
# Settings related to response headers
# set by pellet
"headers": {
# Enables setting response headers
"enabled": False,
# Header to be used for setting total query count
"query_count_header": "X-Pellet-Count",
# Header to be used for setting total query time
"query_time_header": "X-Pellet-Time"
},
# Settings related to pellet debug mode
"debug": {
# Enable debug mode
# Don't enable on prod as it will slow down your app
"enabled": False,
# Query count thresholds which will
# be used by pellet to report metrics
# on the console
"count_threshold": {
# Min number of times a query should happen
# for it to be classified as N+1
# Queries with less count than this will
# not show up in the debug table
"min": 2,
# Max number of times a query should happen
# for it to be classified as a low impact
# performance issue
"low": 5,
# Max number of times a query should happen
# for it to be classified as a warning impact
# performance issue
# Every query happening more times than this
# is classified as a high impact performance issue
"medium": 10
}
},
# Path to a callback function which will be called
# with the request, response and
# pellet metrics object
"callback": None
}
Callback function
The callback function should accept three arguments:
request
-> django request objectresponse
-> django response objectpellet_metrics
-> dict containing metrics collected by pellet
Example functionality:
- collect and send api call level pellet metrics to an external service like datadog
- make integration tests fail for an api if too many queries are happening by raising an exception
- send alert emails, slack messages, etc on too many queries
Steps:
- Create a callback function:
# app/user/callbacks.py
from pellet.utils import get_sanitised_path
def write_datadog_metrics(path, metrics):
# Writes metrics to datadog
pass
def pellet_callback(request, response, pellet_metrics):
# Get id stripped path
# eg: /api/user/1/ -> /api/user/_id_/
sanitised_path = get_sanitised_path(request.path)
write_datadog_metrics(sanitised_path, pellet_metrics)
- Specify the callback function in the pellet config object.
PELLET = {
# ..... rest of pellet config
"callback": "app.user.callbacks.pellet_callback"
}
Project details
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