aniruddha27

# Drop indexes
db.restaurants.drop_indexes()

# Create text index
db.restaurants.create_index([('name', 'text')], 
                            name='restaurant_name')

# List indexes
pprint(db.restaurants.index_information())

aniruddha27

# Query stats
pprint(db.restaurants.find({'cuisine':'French'}).explain()['executionStats'])

aniruddha27

# Query stats
pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':2}}).explain()['executionStats'])

aniruddha27

# Query stats
pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':5}}).explain()['executionStats'])

aniruddha27

# Drop previous indexes
db.restaurants.drop_indexes()

# Create Partial index
db.restaurants.create_index('cuisine',
                            partialFilterExpression={'grades.score':{'$gt':5}},
                            name='cuisine_score')
# List indexes
pprint(db.restaurants.index_information())

aniruddha27

# Create multikey index
db.restaurants.create_index([('grades.grade', pymongo.ASCENDING),
                             ('grades.score', pymongo.ASCENDING)], 
                             name='grade_score', unique=True)
# List indexes
pprint(db.restaurants.index_information())

aniruddha27

# Create unique Compound Index
db.restaurants.create_index([('cuisine',pymongo.ASCENDING),
                             ('borough',pymongo.ASCENDING)],
                            name='cuisine_borough', 
                            unique=True)
pprint(db.neighborhoods.index_information())

aniruddha27

# drop previous indexes
db.neighborhoods.drop_indexes()

# Create unique index
db.neighborhoods.create_index('name', 
                              name='neighborhood', 
                              unique=True)
# Indexes
pprint(db.neighborhoods.index_information())

aniruddha27

# Query 2dsphere index
db.neighborhoods.find({'geometry':
                           {'$geoWithin':
                            {'$geometry':
                             {"type":'Polygon',
                              'coordinates':[[[-73.89483522924053, 40.8262468412122],
                                                [-73.89487088534919, 40.82393727215577],
                                                [-73.89389702292222, 40.8239839983082],
                                                [-73.89483522924053, 40.8262468412122]]]
                             }}}}).count()

aniruddha27

# Create Geospatial index
db.neighborhoods.create_index([('geometry', pymongo.GEOSPHERE)],
                            name='coordinates')
# List indexes
pprint(db.neighborhoods.index_information())