Aniruddha Bhandari aniruddha27

## sql_window_functions_01.sql
/* Sample data */
insert into emp (EMPID, NAME, JOB, SALARY)
values
(201, 'ANIRUDDHA', 'ANALYST', 2100),
(212, 'LAKSHAY', 'DATA ENGINEER', 2700),
(209, 'SIDDHARTH', 'DATA ENGINEER', 3000),
(232, 'ABHIRAJ', 'DATA SCIENTIST', 2500),
(205, 'RAM', 'ANALYST', 2500),
(222, 'PRANAV', 'MANAGER', 4500),
(202, 'SUNIL', 'MANAGER', 4800),

## mongo_stats_12.py
# Query stats
pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':5}}).explain()['executionStats'])

## mongo_stats_11.py
# Query stats
pprint(db.restaurants.find({'cuisine':'American'}).explain()['executionStats'])

## mongo_stats_10.py
pprint(db.restaurants.find().explain())

## mongo_index_17.py
# Multiple token search
db.restaurants.find_one({"$text": {"$search": "Chinese -Restaurant"}})

## mongo_index_16.py
# Multiple token search
db.restaurants.find_one({"$text": {"$search": "Chinese Kitchen"}})

## mongo_index_15.py
# Find restaurants with Kitchen in their name
db.restaurants.find_one({"$text": {"$search": "Kitchen"}})

## mongo_index_14.py
# 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())

## mongo_stats_9.py
# Query stats
pprint(db.restaurants.find({'cuisine':'French'}).explain()['executionStats'])

## mongo_stats_8.py
# Query stats
pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':2}}).explain()['executionStats'])
	/* Sample data */
	insert into emp (EMPID, NAME, JOB, SALARY)
	values
	(201, 'ANIRUDDHA', 'ANALYST', 2100),
	(212, 'LAKSHAY', 'DATA ENGINEER', 2700),
	(209, 'SIDDHARTH', 'DATA ENGINEER', 3000),
	(232, 'ABHIRAJ', 'DATA SCIENTIST', 2500),
	(205, 'RAM', 'ANALYST', 2500),
	(222, 'PRANAV', 'MANAGER', 4500),
	(202, 'SUNIL', 'MANAGER', 4800),
	# Query stats
	pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':5}}).explain()['executionStats'])
	# Query stats
	pprint(db.restaurants.find({'cuisine':'American'}).explain()['executionStats'])
	# Multiple token search
	db.restaurants.find_one({"$text": {"$search": "Chinese -Restaurant"}})
	# Find restaurants with Kitchen in their name
	db.restaurants.find_one({"$text": {"$search": "Kitchen"}})
	# 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())