![]() Output. Like this: from json import dumpsįor x in range(length): # xrange in Python 2.7įpg = fake_person_generator(length, fake) To save the order of items you should explicitly preserve the index of an each element. Even if order will be saved in the file - it will breaks when another project will parse that file. ![]() You do not need to use OrderedDict: JSON format may not (and will not) save order of items. ![]() I've tried list comprehension, map(), the results were the same as for loop. While trying to fix this issue, I'm still looking a way to squeeze the generation time even more. The problem is when I try to go further, for example, 2 millions data, which I would expect it to finish in ~1200 seconds, the script runs beyond this time and I'm greeted by this exception MemoryError with no explanation on why it occurred, I believe it has something to with PYPY_GC_MAX, but again a 2M file should weight ~440mb. I'm currently able to generate a json file with 1 million data, which is about 220mb, in ~600 seconds. I tried PyPy, and I was blown away by the results. With open('%s.json' % filename, 'w') as output: ('street_address', fake.street_address()), I'm currently using the Faker package in the code below: from json import dumpsĭatabase.append(collections.OrderedDict([ For simplicity, we added a schema that only contains a name property, and provided an array of examples.Īfter that, we just generate the object like we did in the previous section.I need some dummy data in json format, to use in another project. json generator faker json-generator Updated on Python hutorny / cojson Star 6 Code Issues Pull requests C++ pull-type JSON parser/generator for constrained platforms with automated code generation. You can check below a simple example where we are making use of this option (notice that we are setting it to true at the beginning of the code). JSON Generator allowing users to generate fake data based on a template. When set to true, it will return a random value from the examples array, if it exists. The one we are interested on is called useExamplesValue. You can check the full list of options here. Nonetheless, there is a method called option on the jsf object that we can call to set some configurations. In this particular application of generating testing data, the examples array can also be used by the package to retrieve values. Note that these examples are not used for the actual schema validation but may be helpful to document it. The examples keyword allows to specify an array of examples that validate against the schema. We are not going to cover those more advanced use cases on this tutorial but rather a simpler alternative: the examples keyword of the JSON schema. Providing custom data examplesĪs mentioned before, we can use additional data generators to generate more specific data for some fields, if needed. Take in consideration that running the previous code multiple times will give different results, since the data is randomly generated.įigure 1 – Output of the program, showing the generated object with fake data. Categories Develop responsive apps 4X faster with LT Browser. From HTML, XML, and JSON formaters and converters, to robust test data generators we have all. For example, Faker.js allows to generate random people names, which could have been used in this use case. A collection of free online tools, utilities, and libraries that will help developers, testers, designers, in their day to day tasks. This allows to generate more realistic data if needed. However, additional data generators such as Faker.js or Chance.js can be added, as can be seen here. Naturally, since the type of the property in the JSON schema was a string, there was no additional information that allowed to generate more realistic data. Note that, for the name, we obtained a random string that is not a person name. As can be seen, we got an object that conforms with our schema. You should get an output similar to figure 1. I’ll be using Visual Studio Code with the Code Runner extension. To test the previous code, simply run it in a tool of your choice. Const jsf = require('json-schema-faker') Ĭonst schemaAsObject = JSON.parse(schema) Ĭonst obj = jsf.generate(schemaAsObject)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |