Passing array in query via axios in marshmallow-code webargs
Explanation of the problem
The issue at hand is a discrepancy between the expected behavior of a GET request using Axios to a Flask-Smorest endpoint, and the actual behavior that is observed. The request’s querystring is supposed to contain an array, which Axios sends in the following format:
http://127.0.0.1:5000/api/v1/items/?types[]=cd&types[]=dvd
The endpoint has a defined schema for reading the arguments, which is declared as follows:
class FilterSchema(ma.Schema):
...
types = ma.fields.List(ma.fields.String(), missing=[])
However, when the data is received in the endpoint, the types
argument is empty, as seen in the following code block:
@items_blp.route('/')
class ItemCollection(MethodView):
@items_blp.arguments(FilterSchema(unknown=ma.EXCLUDE), location="query")
@listings_blp.response(ItemSchema(many=True))
def get(self, payload):
# payload['types'] is empty...
if payload['types']:
qs = Item.objects.filter(item_type__in=payload['types'])
return qs
The writer is uncertain if this is a missing feature in Flask-Smorest or if they should seek a solution elsewhere, such as Stack Overflow.
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Problem solution for Passing array in query via axios in marshmallow-code webargs
The solution to the issue of passing an array in the query string using axios and flask-smorest involves using the webargs library in conjunction with marshmallow. To implement this solution, the following steps can be taken:
- Install the webargs library:
pip install webargs
- Modify the endpoint definition to include webargs in the parsing of the query string:
from webargs import fields
from webargs.flaskparser import use_args
args = {
'types': fields.List(fields.String(), required=False, missing=[])
}
@items_blp.route('/')
class ItemCollection(MethodView):
@items_blp.arguments(FilterSchema(unknown=ma.EXCLUDE), location="query")
@use_args(args)
@listings_blp.response(ItemSchema(many=True))
def get(self, payload):
if payload['types']:
qs = Item.objects.filter(item_type__in=payload['types'])
return qs
- Change the axios request to correctly format the array in the query string:
axios.get(`http://127.0.0.1:5000/api/v1/items/?types=${types.join('&types=')}`)
By implementing these changes, the payload[‘types’] argument should now correctly contain the values passed in the query string via axios.
Other popular problems with marshmallow-code webargs
Problem: Inconsistent Parameter Names
One of the common issues with marshmallow-webargs is that the parameter names defined in the API endpoint and the parameter names defined in the schema may not match. This can lead to unexpected behavior and difficult-to-debug errors.
Solution:
To resolve this issue, it is important to ensure that the parameter names in the API endpoint match the parameter names defined in the schema. This can be achieved by using the load_from
argument to explicitly specify the parameter name in the API endpoint. Additionally, using the strict
argument can enforce that only parameters defined in the schema are used.
Problem: Nested Objects Validation
Another common issue with marshmallow-webargs is the validation of nested objects. The standard marshmallow validation process does not work as expected for nested objects, which can lead to incorrect validation and unpredictable errors.
Solution:
To resolve this issue, it is important to define a separate schema for each nested object and use the Nested
field in the parent schema to define the relationship between the parent and child objects. This ensures that the correct validation is applied to each object, and the relationships between objects are defined clearly.
Problem: Performance and Scalability Issues
Marshmallow-webargs is built on top of Flask and is designed to work with small- to medium-sized applications. However, in large-scale applications, the performance and scalability of marshmallow-webargs can become an issue.
Solution:
To resolve performance and scalability issues, it is recommended to use a more robust and scalable solution such as FastAPI or Django REST framework. These frameworks provide more advanced features and better performance, making them well-suited for large-scale applications. Additionally, using caching and other performance optimization techniques can help to improve the performance of marshmallow-webargs in large-scale applications.
A brief introduction to marshmallow-code webargs
Marshmallow-webargs is a library for parsing and validating request arguments in Flask-based web applications. It provides a simple and flexible way to handle request arguments, including query parameters, JSON bodies, and form data. The library is built on top of the Marshmallow library, which provides the core functionality for serialization and validation of data.
One of the key benefits of using marshmallow-webargs is the ability to define request argument validation as part of the schema. This schema can be defined using the Marshmallow library, and it provides a clear and concise way to specify the type, format, and constraints of request arguments. The marshmallow-webargs library then uses this schema to parse and validate the request arguments, returning a Python data structure that can be used directly in the application logic. This approach reduces the amount of code required to handle request arguments, making it easier to write and maintain the application.
Most popular use cases for marshmallow-code webargs
- Parsing and Validating Request Arguments: Marshmallow-webargs can be used to parse and validate request arguments in Flask-based web applications. The library provides a simple and flexible way to handle query parameters, JSON bodies, and form data, and it integrates with the Marshmallow library for serialization and validation. This makes it easy to define and enforce the constraints on the request arguments, reducing the risk of incorrect data being processed.
- Reducing Boilerplate Code: Marshmallow-webargs helps to reduce the amount of boilerplate code required to handle request arguments. By integrating with the Marshmallow library, it provides a simple and concise way to define the format and constraints of request arguments, eliminating the need to write custom parsing and validation code. This makes it easier to write and maintain the application, and it reduces the risk of introducing bugs into the code.
- Example Usage:
from flask import Flask
from webargs import FlaskParser
from webargs.flaskparser import use_args
app = Flask(__name__)
parser = FlaskParser()
# Define a schema using marshmallow
class ItemSchema(ma.Schema):
name = ma.fields.Str(required=True)
quantity = ma.fields.Int(required=True)
# Use the schema in a view function
@app.route("/items/", methods=["POST"])
@use_args(ItemSchema(), location="json")
def add_item(args):
# `args` will be a Python data structure
# based on the defined schema
return {"message": "Item added", "data": args}, 201
In this example, the ItemSchema
is defined using the Marshmallow library, and it is used to parse and validate request arguments in a Flask view function. The use_args
decorator is used to specify the schema and the location of the request arguments, and the args
parameter of the view function is automatically populated with the parsed and validated data.
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