docs: Readme & example
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example_file.csv
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example_file.csv
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Warszawa;2018-09-19 05:17:32.619;9.97
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Warszawa;2018-09-20 18:44:42.468;39.02
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Warszawa;2018-09-21 11:17:42.318;33.27
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Warszawa;2018-09-22 19:25:07.568;34.85
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Warszawa;2018-09-23 08:56:57.560;-9.44
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Warszawa;2018-09-24 19:27:15.551;39.3
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Warszawa;2018-09-25 15:29:09.120;30.65
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Warszawa;2018-09-26 11:40:28.793;1.62
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Warszawa;2018-09-27 01:07:44.867;28.86
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Warszawa;2018-09-28 02:11:27.008;-5.91
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Warszawa;2018-09-29 05:16:44.020;14.23
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Warszawa;2018-09-30 00:58:33.934;-9.49
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Warszawa;2018-10-01 11:16:21.279;20.04
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Warszawa;2018-10-02 13:59:18.193;33.58
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Warszawa;2018-10-03 01:19:08.881;4.17
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Warszawa;2018-10-04 17:43:51.113;-0.26
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Warszawa;2018-10-05 15:29:43.375;0.99
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Kraków;2018-09-19 09:44:47.464;37.21
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Kraków;2018-09-20 18:54:09.334;29.66
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Kraków;2018-09-21 19:00:25.035;35.92
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Kraków;2018-09-22 19:49:29.018;35.53
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Kraków;2018-09-23 10:53:42.115;25.72
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Kraków;2018-09-24 22:22:50.956;-9.92
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Kraków;2018-09-25 05:22:06.988;3.45
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Kraków;2018-09-26 07:00:00.795;-7.15
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Kraków;2018-09-27 04:47:39.892;23.13
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Kraków;2018-09-28 07:53:19.488;24.19
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Kraków;2018-09-29 08:09:16.292;28.97
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Kraków;2018-09-30 17:20:06.574;0.08
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Kraków;2018-10-01 08:30:57.134;14.15
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Kraków;2018-10-02 17:41:10.286;-0.4
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Kraków;2018-10-03 13:39:39.436;1.51
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Kraków;2018-10-04 02:40:05.295;12.63
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Kraków;2018-10-05 23:46:29.938;28.88
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Gdańsk;2018-09-19 13:00:59.207;20.66
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Gdańsk;2018-09-20 15:58:18.704;-3.84
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Gdańsk;2018-09-21 15:30:09.718;4.11
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Gdańsk;2018-09-22 09:51:24.588;-6.81
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Gdańsk;2018-09-23 17:35:12.540;29.59
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Gdańsk;2018-09-24 00:06:00.180;-1.85
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Gdańsk;2018-09-25 09:30:35.810;32.79
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Gdańsk;2018-09-26 05:16:01.943;34.23
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Gdańsk;2018-09-27 13:31:36.269;31.19
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Gdańsk;2018-09-28 03:53:11.384;-2.86
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Gdańsk;2018-09-29 05:04:28.006;5.84
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Gdańsk;2018-09-30 19:02:58.074;3.28
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Gdańsk;2018-10-01 02:37:38.908;-9.92
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Gdańsk;2018-10-02 20:49:54.839;3.95
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Gdańsk;2018-10-03 15:06:14.164;38.43
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Gdańsk;2018-10-04 23:48:27.043;35.95
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Gdańsk;2018-10-05 12:47:34.018;-6.01
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Wrocław;2018-09-19 23:05:59.759;5.39
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Wrocław;2018-09-20 10:17:54.933;6.5
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Wrocław;2018-09-21 23:49:08.628;33.71
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Wrocław;2018-09-22 23:58:45.590;30.57
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Wrocław;2018-09-23 17:10:16.441;34.3
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Wrocław;2018-09-24 01:29:27.889;5.18
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Wrocław;2018-09-25 03:22:57.184;30.88
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Wrocław;2018-09-26 16:52:18.510;-0.21
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Wrocław;2018-09-27 14:29:03.227;16.53
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Wrocław;2018-09-28 15:30:58.333;4.95
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Wrocław;2018-09-29 01:18:26.120;30.34
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Wrocław;2018-09-30 23:35:01.930;-5.64
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Wrocław;2018-10-01 21:55:21.002;8.77
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Wrocław;2018-10-02 20:32:59.639;37.39
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Wrocław;2018-10-03 22:20:05.774;19.95
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Wrocław;2018-10-04 04:43:11.867;30.93
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Wrocław;2018-10-05 23:57:51.491;25.14
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Poznań;2018-09-19 12:38:35.735;1.48
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Poznań;2018-09-20 23:23:01.413;22.45
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Poznań;2018-09-21 16:14:48.665;-2.89
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Poznań;2018-09-22 03:10:07.834;32.33
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Poznań;2018-09-23 05:12:05.420;22.4
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Poznań;2018-09-24 01:14:54.474;31.5
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Poznań;2018-09-25 13:05:55.252;34.03
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Poznań;2018-09-26 21:44:31.028;32.77
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Poznań;2018-09-27 03:45:33.921;7.74
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Poznań;2018-09-28 10:02:19.002;18.67
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Poznań;2018-09-29 07:24:54.274;7.01
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Poznań;2018-09-30 17:43:32.273;33.37
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Poznań;2018-10-01 23:03:31.646;29.84
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Poznań;2018-10-02 01:19:15.770;35.72
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Poznań;2018-10-03 14:29:13.295;5.74
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Poznań;2018-10-04 21:32:56.124;6.24
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Poznań;2018-10-05 22:32:07.234;-9.52
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Łódź;2018-09-19 21:19:51.033;36.04
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Łódź;2018-09-20 19:01:12.392;4.65
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Łódź;2018-09-21 04:33:02.884;24.24
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Łódź;2018-09-22 03:15:29.245;-9.14
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Łódź;2018-09-23 03:35:21.291;1.79
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Łódź;2018-09-24 11:41:57.203;32.44
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Łódź;2018-09-25 07:35:08.146;6.07
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Łódź;2018-09-26 10:20:21.582;-7.0
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Łódź;2018-09-27 08:56:59.232;18.29
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Łódź;2018-09-28 12:02:35.443;23.4
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Łódź;2018-09-29 02:22:47.190;28.56
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Łódź;2018-09-30 15:00:26.789;35.3
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Łódź;2018-10-01 06:49:50.375;4.36
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Łódź;2018-10-02 11:15:21.681;-4.61
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Łódź;2018-10-03 01:25:56.397;27.49
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Łódź;2018-10-04 20:43:12.573;32.59
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Łódź;2018-10-05 12:57:09.210;25.48
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readme.md
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readme.md
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# Large file reading challenge
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Welcome in the recruitment challenge.
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Write an application that, at the endpoint specified by you, returns the yearly average temperatures for a given city in the format array of objects with the following fields: year, averageTemperature.
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## Assumptions
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- CSV file with data is no less than 3GB in size.
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- The file represents temperature measurements in the format city;yyyy-mm-dd HH:mm:ss.SSS;temp
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- The content of the source file may change during the application's running
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## Example source file
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[example_file.csv](example_file.csv)
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## Example response
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```json
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[
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{
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"year": "2021",
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"averageTemperature": 12.1
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},
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{
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"year": "2022",
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"averageTemperature": 11.1
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},
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{
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"year": "2023",
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"averageTemperature": 14.1
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}
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]
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```
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