![]() Moreover, jsonschema and deepdiff libraries are documented well and very detailed. It is working perfectly with complex responses. The two-steps comparison method is an effective way of ensuring that your APIs are working as expected. bug(str(()) + " Differences : " + str(ddiff)) Raise Exception("cannot continue data comparison with unequal lengths of responses")ĭdiff = DeepDiff(expected_response, response_content, exclude_paths=modification_path) Len(expected_response)) + " length of actual response is " + str(len(response_content))) bug(str(()) + " length of expected response is " + str( If len(expected_response) != len(response_content): # validate lengths upfront to avoid index exceptions in data comparison code bug(str(()) + " Allowed modifications : " + str(modification_path)) bug(str(()) + " Received response : " + str(response_content)) ![]() bug(str(()) + " Expected response : " + str(expected_response)) ![]() Here is an example of the function that allows us to compare separate values in JSON, where expected_responseis our expected response, response_contentis the body of the received response and modification_pathis excluded part from comparison: def full_comparison(expected_response, response_content, modification_path): This library has advanced functionality, please, find more information here: deepdiff It allows you to compare not only zero-level JSONs but also more complex JSON. The next step is dedicated to comparing separate values of the response using deepdiff library. Validate(instance=json_data, schema=schema)Įxcept as err: Here is my example of code to validate JSON Schema (where json_data - received response and schema - our constructed schema): def validateJsonSchema(json_data, schema): For more information: Schema Validation - jsonschema 3.2.0 documentation () Since I use Python Framework (unittest) for API testing, the simplest way to validate an instance under a given schema is to use the validate() function. There are many tools and libraries available to test API responses against a JSON Schema. Imagine that your API's endpoint POST /api/user/login returns the following response: , In other words, JSON Schema is a contract for your JSON document that defines the expected data types and format of each field in the response. Ensuring quality of client submitted data.Provides clear human- and machine-readable documentation.Describes your existing data format(s).I would suggest the following sequencing:įirstly, let's bring a better understanding of what does JSON Schema mean ( JSON Schema | The home of JSON Schema (): JSON Schema is a vocabulary that allows you to annotate and validate JSON documents. All findings are adjusted to the initial framework idea. *Initially I use a Python framework based on unittest library. This problem I faced recently at work encouraged me to consider a two-steps comparison method. The json library has a json.dumps() method that serializes data into JSON format. However, managing large and diverse responses might be quite challenging. Testing and validating JSON APIs is a crucial part of implementing high-quality web and mobile services.
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