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flight price history

see how the prices for your favourite flightpaths change over time

how it works:

  1. daily cronjob scheduled with github actions hits /api/flights/ endpoint
  2. serverless function pulls a bunch of data from skyscanner api
  3. process & reformat data
  4. write into a postgres database hosted on railway.app
  5. this triggers a nextjs deploy hook to rebuild the static website & update with new data

i did this as an exercise in backend architecture. i wanted to see how easy you can make a zero-cost fullstack app in 2023 with no running servers (bar pg) #serverlessenjoyer

strech goals (PRs welcome):

  • click on individual days to see the skyscanner /poll/ endpoint results
  • make better graph, the chart.js api is a residentSleeper
  • skyscanner giving me a private API key

todo: [ ] build cronjob

The main problem is that I'm using Skyscanner's public API

Skyscanner lets you retrieve prices in two ways:

  • The /create endpoint, which gets cached, and sometimes outdated, prices for a range of dates.
  • The /poll endpoint, which gets the live price for one date.

For each route, we need the ticket price for 7 days ahead and 30 days ahead. This means for n routes, we need n*2 prices. If we used the /poll endpoint, we'd make n*2 requests, but with the /create endpoint, we only have to make n requests. The catch is that the /create endpoint often provides inaccurate prices. Skyscanner says the cache limit is 4 days.

Here's an example of the discrepancy between prices the two endpoints provide. Prices in USD. Example uses the LGW to BOS route:

dateinjan createprice pollprice difference
3 731 731 0
4 798 798 0
5 761 442 -319
6 761 437 -324
7 761 761 0
8 832 827 -5
9 731 407 -324
10 406 405 -1
11 390 389 -1
12 761 442 -319
13 430 437 7
14 423 437 14
15 477 437 -40
16 408 382 -26
17 383 407 24
18 387 407 20
19 415 414 -1
20 468 437 -31
21 422 437 15
22 431 436 5
23 658 407 -251
24 408 407 -1
25 404 404 0
26 431 437 6
27 690 437 -253
28 438 407 -31
29 438 437 -1
30 568 407 -161
31 403 407 4
  • largest difference: -324
  • average difference: -52.74

here's the original data for the table above because i asked copilot to make it for me and i cba to proofread it.

dateinjan,createprice,pollprice,difference 3,731,731,0 4,798,798,0 5,761,442,-319 6,761,437,-324 7,761,761,0 8,832,827,-5 9,731,407,-324 10,406,405,-1 11,390,389,-1 12,761,442,-319 13,430,437,7 14,423,437,14 15,477,437,-40 16,408,382,-26 17,383,407,24 18,387,407,20 19,415,414,-1 20,468,437,-31 21,422,437,15 22,431,436,5 23,658,407,-251 24,408,407,-1 25,404,404,0 26,431,437,6 27,690,437,-253 28,438,407,-31 29,438,437,-1 30,568,407,-161 31,403,407,4