sumitasok
7/1/2015 - 12:47 PM
Blog.md
// r.db("arkinventory").table("reportsdata").insert(
{
"order_id": "session-id-4321",
"kart_id": "kart-id-4321",
"entities": [
{
"entity_id": "54d68a99-3ae9-4b2c-9de3-5ee55f57d3a5",
"quantity": 1,
"price": 1000.00,
"intial_amount": 1000.00,
"final_amount": 800.00,
"offer_applied": {
"offer_id": "PERC20",
"discount_amount": 200,
}
},
{
"entity_id": "cbdd6ccd-ae7a-45c9-9627-a3322b2d4cc1",
"quantity": 2,
"price": 1000.00,
"intial_amount": 2000.00,
"final_amount": 1960.00,
"offer_applied": {
"offer_id": "CASH20",
"discount_amount": 20,
}
}
],
"intial_amount": 3000,
"final_amount": 2760,
"attendees": [
{
"ticketId": "ticket-id-4321",
"attendees": [
{
"firstname": "Sumit",
"lastname": "Asok",
"email": "sumitasok@gmail.com",
"mobile": "9895865899"
}
]
},
{
"ticketId": "ticket-id-7723",
"attendees": [
{
"firstname": "Achu",
"lastname": "Kuttan",
"email": "achu.kuttan@gmail.com",
"mobile": "9897668222"
}
]
}
],
"payee": {
"firstname": 'Sumit',
"lastname": 'Asok',
"email": 'sumitasok@gmail.com',
"mobile": '9895865899',
"address_1": 'Baker STreet',
"address_2": 'Inglando',
"city": 'Stathis',
"state": 'Kerala',
"country": 'India',
"pincode": '680664',
"pay_mode": 'CC',
"pay_id": 'mhpayid-oqpweipie'
},
"updated_at": '2013-07-13 16:32:48 -0700'
}
// )conn = r.connect(host="localhost", port=28015, db="arkinventory", auth_key="", timeout=20)
r.table("reportsdata").
concat_map(lambda attendees: [attendees['attendees']]).
concat_map(lambda attendees: attendees['attendees']).sample(4).run(conn)
r.table("reportsdata").
concat_map(lambda attendees: attendees['attendees']).
filter({'ticketId': "ticket-id-4321"}).sample(3).run(conn)
r.table("reportsdata").concat_map(lambda attendees: attendees['attendees']).
concat_map(lambda attendees: attendees['attendees']).
filter(r.row["email"] == "sumitasok@gmail.com").sample(4).run(conn)
# fetch list of attendees who have bouth tickets for this ticketId
r.table("reportsdata").concat_map(lambda attendees: attendees['attendees']).
filter(r.row["ticketId"] == "ticket-id-4321").
concat_map(lambda attendees:attendees['attendees']).sample(4).run(conn)
r.table("reportsdata").filter(r.row["kart_id"] == "kart-id-4321").sample(4).count().run(conn)
r.table("reportsdata").filter(r.row["updated_at"] > "2012-01-01" and r.row["updated_at"] < "2015-01-01").sample(4).run(conn)
r.table("reportsdata").filter(r.row["updated_at"] > "2013-07-12" and r.row["updated_at"] < "2013-08-14").
concat_map(lambda attendees: attendees['attendees']).
filter(r.row["ticketId"] == "ticket-id-4321").
concat_map(lambda attendees:attendees['attendees']).sample(4).run(conn)
r.table("reportsdata").concat_map(lambda report: report["entities"]).
group("entity_id").
reduce(lambda left, right: {
"entity_id":left["entity_id"],"quantity" : left["quantity"]+right["quantity"], "price": left["price"]+right["price"]
}).run(conn)
r.table("reportsdata").concat_map(lambda report: report["entities"]).
group("entity_id").
reduce(lambda left, right: {
"entity_id":left["entity_id"],"quantity" : left["quantity"]+right["quantity"], "price": left["price"]+right["price"], "final_amount": left["final_amount"]+right["final_amount"]
}).run(conn)
# Map
r.table("reportsdata").concat_map(lambda report: report["entities"]).
group("entity_id").
map(lambda entity: {
"entity_id":entity["entity_id"],"quantity" : entity["quantity"], "price": entity["price"], "final_amount": entity["final_amount"], "discount_amount": entity["offer_applied"]["discount_amount"]
}).run(conn)
# Reduce
r.table("reportsdata").concat_map(lambda report: report["entities"]).
group("entity_id").map(lambda entity: {
"entity_id":entity["entity_id"],"quantity" : entity["quantity"], "price": entity["price"], "final_amount": entity["final_amount"], "discount_amount": entity["offer_applied"]["discount_amount"]
}).
reduce(lambda left, right: {
"entity_id":left["entity_id"],"quantity" : left["quantity"]+right["quantity"], "price": left["price"], "final_amount": left["final_amount"]+right["final_amount"], "discount_amount": left["discount_amount"]+right["discount_amount"]
}).run(conn)
r.table("reportsdata").filter(r.row["updated_at"] > "2013-07-12" and r.row["updated_at"] < "2013-08-13").
concat_map(lambda report: report["entities"]).
group("entity_id").
map(lambda entity: {
"entity_id":entity["entity_id"],"quantity" : entity["quantity"], "price": entity["price"], "final_amount": entity["final_amount"], "discount_amount": entity["offer_applied"]["discount_amount"]
}).
reduce(lambda left, right: {
"entity_id":left["entity_id"],"quantity" : left["quantity"]+right["quantity"], "price": left["price"], "final_amount": left["final_amount"]+right["final_amount"], "discount_amount": left["discount_amount"]+right["discount_amount"]
}).run(conn)This post deals with using rethinkdb with python repl
Do your installations
Note: in every python example, I have intended it wrongly for easy reading. Please make it into a single line, before pasting in the Repl. Try typing.
Get the Conn
[]
create your table
[]
Understand the Data. Nested Data. We need to fetch multiple kinds of results from this data.
[]
Insert the Data
[]
Concat Map:
Used to collect arrays of data inside various documents, under a same key. Map, can be used for the same, but that would give a nested array kind result. While Concat Map gives plain array.
Sample command sets the limit of results to be displayed.
[gist file="3-concat_map-single.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
lambda calls each row in the document, as attendees and returns the value of key "attendee" inside each row into the result array.
The Result would be
[gist file="3-concat_map-single_result.js"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
To further collect the list of attendee details alone nested inside the order/report, do one more Concat Map on the resulting data.
[gist file="3-concat_map.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
The Result would be
[gist file="3-concat_map_result.js"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
Filters:
Let us fetch the data with some conditions, so that, only data with a particular ticketId is displayed in result.
[gist file="4-concat_map_filter.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
The result would be like this
[gist file="4-concat_map_filter_result.js"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
To further reduce the list to have more conditions attach a filter to the resulting data. Try doing a count on this to see how many records have same email ID.
[gist file="5-concat_twice-filter.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
Now try fetching Attendees list who has bought a particular ticket
[gist file="6-fetch_by_ticketId.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
Result:
Only Sumit has bought one ticket of ID ticket-id-4321
[gist file="6-fetch_by_ticketId-result.js"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
Now you must be clear about how concat_map works and how to do a simple filter on data.
Do a filter on Date. So any record which was updated between "2012-01-01" and "2015-01-01" will be the result.
[gist file="7-filter_by_date.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
Now attach the same filter before all our other pipelines. So that first time, the filter is run, then on the resulting data concat_map is run, which return an array of tickets type and attendees on which run the filter to find out the particular ticketID you are looking for, and concat_map the attendees
[gist file="7-filter_by_date-concat.py"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]
The result would be, the array of users who has done booking ticket "ticket-id-4321" in the specified time interval.
[gist file="7-filter_by_date-concat_result.json"]https://gist.github.com/sumitasok/086792d3814ad229b3cb[/gist]