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逆引き!Scala x ビッグデータ Slide 1 逆引き!Scala x ビッグデータ Slide 2 逆引き!Scala x ビッグデータ Slide 3 逆引き!Scala x ビッグデータ Slide 4 逆引き!Scala x ビッグデータ Slide 5 逆引き!Scala x ビッグデータ Slide 6 逆引き!Scala x ビッグデータ Slide 7 逆引き!Scala x ビッグデータ Slide 8 逆引き!Scala x ビッグデータ Slide 9 逆引き!Scala x ビッグデータ Slide 10 逆引き!Scala x ビッグデータ Slide 11 逆引き!Scala x ビッグデータ Slide 12 逆引き!Scala x ビッグデータ Slide 13 逆引き!Scala x ビッグデータ Slide 14 逆引き!Scala x ビッグデータ Slide 15 逆引き!Scala x ビッグデータ Slide 16 逆引き!Scala x ビッグデータ Slide 17 逆引き!Scala x ビッグデータ Slide 18 逆引き!Scala x ビッグデータ Slide 19 逆引き!Scala x ビッグデータ Slide 20 逆引き!Scala x ビッグデータ Slide 21 逆引き!Scala x ビッグデータ Slide 22 逆引き!Scala x ビッグデータ Slide 23 逆引き!Scala x ビッグデータ Slide 24 逆引き!Scala x ビッグデータ Slide 25 逆引き!Scala x ビッグデータ Slide 26 逆引き!Scala x ビッグデータ Slide 27 逆引き!Scala x ビッグデータ Slide 28 逆引き!Scala x ビッグデータ Slide 29 逆引き!Scala x ビッグデータ Slide 30 逆引き!Scala x ビッグデータ Slide 31 逆引き!Scala x ビッグデータ Slide 32 逆引き!Scala x ビッグデータ Slide 33 逆引き!Scala x ビッグデータ Slide 34 逆引き!Scala x ビッグデータ Slide 35 逆引き!Scala x ビッグデータ Slide 36 逆引き!Scala x ビッグデータ Slide 37 逆引き!Scala x ビッグデータ Slide 38 逆引き!Scala x ビッグデータ Slide 39 逆引き!Scala x ビッグデータ Slide 40 逆引き!Scala x ビッグデータ Slide 41 逆引き!Scala x ビッグデータ Slide 42 逆引き!Scala x ビッグデータ Slide 43 逆引き!Scala x ビッグデータ Slide 44 逆引き!Scala x ビッグデータ Slide 45
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Scalaとビッグデータで弱小卓球部を強くします

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逆引き!Scala x ビッグデータ

  1. 1. ! Scala x ScalaMatsuri2018
  2. 2. Who are You ? LIVEDSP GitHub: https://github.com/x1- http://x1.inkenkun.com/ 2
  3. 3. Who are You ? GitHub: https://github.com/inkenkun 3
  4. 4. 5
  5. 5. 6
  6. 6. 7
  7. 7. 8
  8. 8. 9
  9. 9. 10
  10. 10. 11
  11. 11. 12
  12. 12. 13
  13. 13. 14
  14. 14. 15
  15. 15. 16
  16. 16. 17 {"time":"2018-02-12T16:32:50.110 Z","player":" ","speed": 120.3,"x":515.294,"y": 102.540,"z":82.540}
  17. 17. 18 fluentd
  18. 18. 19
  19. 19. 20
  20. 20. 21 Apache Pulsar phaistos-networks/TANK NATS Cloud Pub/Sub Amazon Kinesis Apache Kafka Apache Kafka
  21. 21. 22
  22. 22. 23
  23. 23. 24
  24. 24. 25 {"time":"2018-02-12T16:32:50.110Z","player ":" ","speed":120.3,"pos":{"x": 515.294,"y":102.540,"z":82.540}} {"time":"2018-02-12T16:32:50.210Z","player ":" ","speed":119.8,"pos":{"x": 926.442,"y":130.206,"z":148.540}} {"time":"2018-02-12T16:32:50.310Z","player ":" ","speed":119.3,"pos":{"x": 1215.932,"y":157.120,"z":242.540}} {"time":"2018-02-12T16:32:50.410Z","player ":" ","speed":118.5,"pos":{"x": 1505.125,"y":182.712,"z":126.540}} {"time":"2018-02-12T16:32:50.510Z","player ":" ","speed":116.4,"pos":{"x": 1824.087,"y":211.917,"z":11.540}} {“time":"2018-02-12T16:32:50.610Z","player ":" ","speed":116.4,"pos":{"x": 2105.629,"y":246.344,"z":0.540}} :
  25. 25. 26 {"time":"2018-02-12T16:32:50.110Z","player":" ","speed": 120.3,"pos":{"x":515.294,"y":102.540,"z":82.540}} {"time":"2018-02-12T16:32:50.210Z","player":" ","speed": 119.8,"pos":{"x":926.442,"y":130.206,"z":148.540}} {"time":"2018-02-12T16:32:50.310Z","player":" ","speed": 119.3,"pos":{"x":1215.932,"y":157.120,"z":242.540}} {"time":"2018-02-12T16:32:50.410Z","player":" ","speed": 118.5,"pos":{"x":1505.125,"y":182.712,"z":126.540}} {"time":"2018-02-12T16:32:50.510Z","player":" ","speed": 116.4,"pos":{"x":1824.087,"y":211.917,"z":11.540}} {“time":"2018-02-12T16:32:50.610Z","player":" ","speed": 116.4,"pos":{"x":2105.629,"y":246.344,"z":0.540}} : time player speed angle … 2018-02-12T16:32:50.110Z 120.3 210.52 2018-02-12T16:32:50.210Z 119.8 212.30 2018-02-12T16:32:50.310Z 119.3 214.11 : : :
  26. 26. 27
  27. 27. 28
  28. 28. OSS 29
  29. 29. 30 JVM Java Scala ※ Java API Scala Java
  30. 30. 31 Scala
  31. 31. Scala 32 f(x) g(x) h(x)
  32. 32. 33 f(x) f(x) f(x) f(x) f(x) f(x) f(x) f(x) f(x) f(x) f(x) A B Scala
  33. 33. 34 Scala (Stream, map, filter, fold, reduce) Scala Java Scala
  34. 34. 35 e p p e e e p Functional Programming in Scala 14 Scala
  35. 35. 36
  36. 36. 37 13% time player speed angle … 2018-02-12T16:32:50.110Z 120.3 210.52 2018-02-12T16:32:50.210Z 119.8 212.30 2018-02-12T16:32:50.310Z 119.3 214.11 : : : 15% 17% 3% 2% 5% 5% 30% 10%
  37. 37. 38 time player speed angle … 2018-02-12T16:32:50.110Z 120.3 210.52 2018-02-12T16:32:50.210Z 119.8 212.30 2018-02-12T16:32:50.310Z 119.3 214.11 : : :
  38. 38. 39
  39. 39. 40 Google Data Studio Amazon QuickSight Microsoft Power BI Tableau QlikView DOMO Pentaho Superset Metabase Apache Zeppelin JupyterNotebook(Lab) Vegas Lightning Matplotlib VisPy HoloViews Plotly D3.js OSS Python Scala JavaScript
  40. 40. 41
  41. 41. 42
  42. 42. 43 DEMO
  43. 43. Apache Zeppelin: https://zeppelin.apache.org/ 44
  44. 44. 45 Thank you for listening!
  • RyotaTsunoi

    Apr. 3, 2019
  • bokuoh

    Oct. 10, 2018
  • umi_uyura

    Apr. 21, 2018
  • YoshiiroUeno

    Mar. 18, 2018
  • yoskhdia

    Mar. 16, 2018

Scalaとビッグデータで弱小卓球部を強くします

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