Call json.dumps(jsonobject, sortkeys) with sortkeys set to True on. Get laptops with CPU frequency equal to 2200 or 2400 > Product.objects. Use json.dumps() and the equal-to operator to compare JSON objects regardless of order. With btree index - 46 ms, EXPLAIN ANALYSE. Without btree index query time is 352 ms, EXPLAIN ANALYSE. Expected - the original data object that you want to see. Think of them as the same variables that you use in tests. We can sort them, copy-paste them, merge, and compare them. First you need to define two variables: expected & actual. As shown in the image above, the diff mode offers different methods of comparing two JSON objects.
Install pip install -U pip jsoncomparison Usage. If such request is common, we can create btree index on needed JSON keys: CREATE INDEX attrs_screen_speed ON catalogue_simple_product ((attributes -> 'screen'), (attributes -> 'speed')) This package is designed to compare two objects with a JSON-like structure and data types. "catalogue_simple_product"."attributes" -> 'speed' > '2000') "catalogue_simple_product"."attributes" -> 'screen' > '13' ("catalogue_simple_product"."category_id" = 7 Get laptops with CPU greater that 2000 and screen larger that 13 > (category=notebook, attributes_speed_gt=2000,Ĭorresponding SQL: SELECT * FROM "catalogue_simple_product" WHERE Only ~25% of objects are filtered by sizes in this query. (name='Silk tshirt', category=tshirt, price=100, attributes=).count()
One of the approaches to design such data in SQL is Entity–attribute–value model (EAV).īut now we have JSON, so let's try to organise data using this type.Ĭreate simple model for products: from django.db import modelsįrom import JSONFieldĪs we can see, there are several common fields for all products (name, category, price) plus specific to particular product attributes (as JSON field).Ĭreate objects: tshirt = (name='tshirts') Obviously, such goods will have different attributes: t-shirts will have size, color and laptops - screen size, CPU frequency, hard drive and so on. Now imagine that we have an e-commerce site, where we offer products of different types. Reading JSON data from a file is quite an easy task in python as python script. Let's try to work with it and find out, in what situations it can be useful.Ĭurrently django 1.9 alpha is available, final version is scheduled on December 2015.Īlpha can be installed with pip: pip install -pre django Python provides json.load() method to read a file containing the JSON object.
New JSONField will be added in Django 1.9, it can be used with PostgreSQL >= 9.4. The compare () method can also be called as a library from other Python code, in which case item1 and item2 can also be dict or array objects.
There’s a lot more you can do with Deepdiff, refer to the link to know more.Trying JSON in Django and PostgreSQL (and compare with MongoDB) jsoncompare is a simple Python utility for comparing two JSON objects USAGE python jsoncompare.pyWhile doing so, the consumer of the service should not be affected in any way. python compare.py Compare JSON result is: True JSON files a.json and b.json are loaded via loadjson () function and structures passed into comparejsondata. It is not uncommon to migrate services written in one language to another for performance benefits.