How I was planning a trip to South America with JavaScript, Python and Google Flights abuse



I was planning a trip to South America for a while. As I have flexible dates and want to visit a few places, it was very hard to find proper flights. So I decided to try to automatize everything.

I’ve already done something similar before with Clojure and Chrome, but it was only for a single flight and doesn’t work anymore.

Parsing flights information

Apparently, there’s no open API for getting information about flights. But as Google Flights can show a calendar with prices for dates for two months I decided to use it:

So I’ve generated every possible combination of interesting destinations in South America and flights to and from Amsterdam. Simulated user interaction with changing destination inputs and opening/closing calendar. By the end, I wrote results as JSON in a new tab. The whole code isn’t that interesting and available in the gist. From the high level it looks like:

const getFlightsData = async ([ from , to ]) => { await setDestination ( FROM , from ); await setDestination ( TO , to ); const prices = await getPrices (); return prices . map (([ date , price ]) => ({ date , price , from , to , })); }; const collectData = async () => { let result = []; for ( let flight of getAllPossibleFlights ()) { const flightsData = await getFlightsData ( flight ); result = result . concat ( flightsData ); } return result ; }; const win = window . open ( '' ); collectData (). then ( ( data ) => win . document . write ( JSON . stringify ( data )), ( error ) => console . error ( " Can't get flights " , error ), );

In action:

I’ve run it twice to have separate data for flights with and without stops, and just saved the result to JSON files with content like:

[{ "date" : "2018-07-05" , "price" : 476 , "from" : "Rio de Janeiro" , "to" : "Montevideo" }, { "date" : "2018-07-06" , "price" : 470 , "from" : "Rio de Janeiro" , "to" : "Montevideo" }, { "date" : "2018-07-07" , "price" : 476 , "from" : "Rio de Janeiro" , "to" : "Montevideo" }, ... ]

Although, it mostly works, in some rare cases it looks like Google Flights has some sort of anti-parser and show “random” prices.

Selecting the best trips

In the previous part, I’ve parsed 10110 flights with stop and 6422 non-stop flights, it’s impossible to use brute force algorithm here (I’ve tried). As reading data from JSON isn’t interesting, I’ll skip that part.

At first, I’ve built an index of from destination → day → to destination :

from_id2day_number2to_id2flight = defaultdict ( lambda : defaultdict ( lambda : {})) for flight in flights : from_id2day_number2to_id2flight [ flight . from_id ] \ [ flight . day_number ][ flight . to_id ] = flight

Created a recursive generator that creates all possible trips:

def _generate_trips ( can_visit , can_travel , can_spent , current_id , current_day , trip_flights ): # The last flight is to home city, the end of the trip if trip_flights [ - 1 ]. to_id == home_city_id : yield Trip ( price = sum ( flight . price for flight in trip_flights ), flights = trip_flights ) return # Everything visited or no vacation days left or no money left if not can_visit or can_travel < MIN_STAY or can_spent == 0 : return # The minimal amount of cities visited, can start "thinking" about going home if len ( trip_flights ) >= MIN_VISITED and home_city_id not in can_visit : can_visit . add ( home_city_id ) for to_id in can_visit : can_visit_next = can_visit . difference ({ to_id }) for stay in range ( MIN_STAY , min ( MAX_STAY , can_travel ) + 1 ): current_day_next = current_day + stay flight_next = from_id2day_number2to_id2flight \ . get ( current_id , {}). get ( current_day_next , {}). get ( to_id ) if not flight_next : continue can_spent_next = can_spent - flight_next . price if can_spent_next < 0 : continue yield from _generate_trips ( can_visit_next , can_travel - stay , can_spent_next , to_id , current_day + stay , trip_flights + [ flight_next ])

As the algorithm is easy to parallel, I’ve made it possible to run with Pool.pool.imap_unordered , and pre-sort for future sorting with merge sort:

def _generator_stage ( params ): return sorted ( _generate_trips ( * params ), key = itemgetter ( 0 ))

Then generated initial flights and other trip flights in parallel:

def generate_trips (): generators_params = [( city_ids . difference ({ start_id , home_city_id }), MAX_TRIP , MAX_TRIP_PRICE - from_id2day_number2to_id2flight [ home_city_id ][ start_day ][ start_id ]. price , start_id , start_day , [ from_id2day_number2to_id2flight [ home_city_id ][ start_day ][ start_id ]]) for start_day in range (( MAX_START - MIN_START ). days ) for start_id in from_id2day_number2to_id2flight [ home_city_id ][ start_day ]. keys ()] with Pool ( cpu_count () * 2 ) as pool : for n , stage_result in enumerate ( pool . imap_unordered ( _generator_stage , generators_pa rams )): yield stage_result

And sorted everything with heapq.merge :

trips = [ * merge ( * generate_trips (), key = itemgetter ( 0 ))]

Looks like a solution to a job interview question.

Without optimizations, it was taking more than an hour and consumed almost whole RAM (apparently typing.NamedTuple isn’t memory efficient with multiprocessing at all), but current implementation takes 1 minute 22 seconds on my laptop.

As the last step I’ve saved results in csv (the code isn’t interesting and available in the gist), like:

price,days,cities,start city,start date,end city,end date,details 1373,15,4,La Paz,2018-09-15,Buenos Aires,2018-09-30,Amsterdam -> La Paz 2018-09-15 498 & La Paz -> Santiago 2018-09-18 196 & Santiago -> Montevideo 2018-09-23 99 & Montevideo -> Buenos Aires 2018-09-26 120 & Buenos Aires -> Amsterdam 2018-09-30 460 1373,15,4,La Paz,2018-09-15,Buenos Aires,2018-09-30,Amsterdam -> La Paz 2018-09-15 498 & La Paz -> Santiago 2018-09-18 196 & Santiago -> Montevideo 2018-09-23 99 & Montevideo -> Buenos Aires 2018-09-27 120 & Buenos Aires -> Amsterdam 2018-09-30 460 1373,15,4,La Paz,2018-09-15,Buenos Aires,2018-09-30,Amsterdam -> La Paz 2018-09-15 498 & La Paz -> Santiago 2018-09-20 196 & Santiago -> Montevideo 2018-09-23 99 & Montevideo -> Buenos Aires 2018-09-26 120 & Buenos Aires -> Amsterdam 2018-09-30 460 1373,15,4,La Paz,2018-09-15,Buenos Aires,2018-09-30,Amsterdam -> La Paz 2018-09-15 498 & La Paz -> Santiago 2018-09-20 196 & Santiago -> Montevideo 2018-09-23 99 & Montevideo -> Buenos Aires 2018-09-27 120 & Buenos Aires -> Amsterdam 2018-09-30 460 ...

Gist with sources.