Functions, which will perform some textual and spatial searching on MongoDB

The required task is two write two functions, which will perform some textual and spatial
searching on MongoDB. Details are explained below.
Steps for Assignments: -
1. Install MongoDB 2.6.11.
2. Install pymongo to act as helper interface with MongoDB.
3. Run the tester file to check everything runs and nothing fails.
4. Now, Implement the function provided in Assignment5_Interface.py to perform the
operations as listed below:
a. FindBusinessBasedOnCity(cityToSearch, saveLocation1, collection)
This function searches the ‘collection’ given to find all the business present in the
city provided in ‘cityToSearch’ and save it to ‘saveLocation1’. For each business
you found, you should store name Full address, city, state of business in the
following format.
Each line of the saved file will contain,
Name$FullAddress$City$State. ($ is the separator and must be present)
b. FindBusinessBasedOnLocation(categoriesToSearch, myLocation, maxDistance,
saveLocation2, collection)
This function searches the ‘collection’ given to find name of all the business
present in the ‘maxDistance’ from the given ‘myLocation’ (please use the
distance algorithm given below) and save them to ‘saveLocation2’.
Each line of the output file will contain the name of the business only.
NOTE: - Please Make sure all fields are in Uppercase while writing in file, makes it easy to
check. ☺
Files given to you: -
Assignment5_Interface.py - You should complete this file. You would need to
implement the function present in this file.
• testData.json – This is the json file which is used as a document to put inside MongoDb.
The structure of one record of this file is < Key value pair: -
{
'type': 'business',
'business_id': (encrypted business id),
'name': (business name),
'neighborhoods': [(hood names)],
'full_address': (localized address),
'city': (city),
'state': (state),
'latitude': latitude,
'longitude': longitude,
'stars': (star rating, rounded to half-stars),
'review_count': review count,
2
'categories': [(localized category names)]
'open': True / False (corresponds to permanently closed, not business hours),
}
Example: -
{"city": "Ahwatukee",
"review_count": 3,
"name": "McDonald's",
"neighborhoods": [],
"type": "business",
"business_id": "LNdwp-9Isnd6xmBKUz4K_A",
"full_address": "10823 S 51st St\nAhwatukee, AZ 85044",
"state": "AZ",
"longitude": -111.975004,
"stars": 2.0,
"latitude": 33.348560900000003,
"open": true,
"categories": ["Burgers", "Fast Food", "Restaurants"]}
Note: - The order of key value pair does not matter.
tester.py – DO NOT change this file. This is just to help you run the code for your
implementation.
Distance Algorithm needs to be used:
Given two pair of latitude and longitude as [lat2, lon2] and [lat1, lon1], you can calculate the
distance between them using the formula given below:
DistanceFunction(lat2, lon2, lat1, lon1):
dlon = lon2 - lon1
dlat = lat2 - lat1
a = (sin(dlat/2))^2 + cos(lat1) * cos(lat2) * (sin(dlon/2))^2
c = 2 * atan2( sqrt(a), sqrt(1-a) )
dist = R * c (where R is the radius of the Earth)
return dist
dist is the distance between the given pair of latitude and longitude.
Instructions for Assignment: -
Please follow these instructions closely else Marks will be deducted.
1. Please follow the function signature as provided in the Assignment5_Interfacy.py.
2. Please use the same database name and collection name as provided in the tester to keep
it consistent.
3. Please use the same distance algorithm given above to make it consistent for everyone.
4. Please make sure to run the file before submitting and make sure there is no indentation
error. In case of any compilation error, 0 marks will be given.
5. Do not modify any function signature in Assignment5_Interface.py. In case any
modification is needed, please post the same on discussion board.
6. For any case of doubt in the assignment, PLEASE USE Discussion Boards, Individual
mails would not be entertained.
7. Also, It is an individual’s responsibilities to clarify his/her doubts, so read and use
Discussion Board extensively.
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