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Real time data driven applications (and SQL vs NoSQL databases)

E9f5d8d804cef5201511a3c1654df1f1?s=47 lanzani
November 22, 2014

Real time data driven applications (and SQL vs NoSQL databases)

These are the slides of the talk I gave at NoSQL Barcelona (November 2014)

E9f5d8d804cef5201511a3c1654df1f1?s=128

lanzani

November 22, 2014
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  1. GoDataDriven PROUDLY PART OF THE XEBIA GROUP Real time data

    driven applications Giovanni Lanzani Data Whisperer and SQL vs NoSQL databases
  2. Who am I? 2008-2012: PhD Theoretical Physics 2012-2013: KPMG 2013-Now:

    GoDataDriven
  3. Feedback @gglanzani

  4. Real-time, data driven app? •No store and retrieve; •Store, {transform,

    enrich, analyse} and retrieve; •Real-time: retrieve is not a batch process; •App: something your mother could use: SELECT attendees FROM NoSQLMatters WHERE password = '1234';
  5. Get insight about event impact

  6. Get insight about event impact

  7. Get insight about event impact

  8. Get insight about event impact

  9. Challenges 1. Big Data; 2. Privacy; 3. Some real-time analysis;

    4. Real-time retrieval.
  10. Is it Big Data? Everybody talks about it Nobody knows

    how to do it Everyone thinks everyone else is doing it, so everyone claims they’re doing it… Dan Ariely
  11. Is it Big Data? •Raw logs are in the order

    of 40TB; •We use Hadoop for storing, enriching and pre- processing.
  12. 2. Privacy

  13. 3. (Some) real-time analysis

  14. •Harder than it looks; •Large data; •Retrieval is by giving

    date, center location + radius. 4. Real-Time Retrieval
  15. AngularJS python app REST Front-end Back-end JSON Architecture

  16. JS-1

  17. JS-2

  18. date hour id_activity postcode hits delta sbi 2013-01-01 12 1234

    1234AB 35 22 1 2013-01-08 12 1234 1234AB 45 35 1 2013-01-01 11 2345 5555ZB 2 1 2 2013-01-08 11 2345 5555ZB 55 2 2 Data Example
  19. date hour id_activity postcod e hits delta sbi 2013-01-01 12

    1234 1234AB 35 22 1 2013-01-08 12 1234 1234AB 45 35 1 2013-01-01 11 2345 5555ZB 2 1 2 2013-01-08 11 2345 5555ZB 55 2 2 Data Example
  20. helper.py example def get_statistics(data, sbi): sbi_df = data[data.sbi == sbi]

    # select * from data where sbi = sbi hits = sbi_df.hits.sum() # select sum(hits) from … delta_hits = sbi_df.delta.sum() # select sum(delta) from … if delta_hits: percentage = (hits - delta_hits) / delta_hits else: percentage = 0 return {"sbi": sbi, "total": hits, "percentage": percentage}
  21. helper.py example def get_timeline(data, sbi): df_sbi = data.groupby([“date”, “hour", “sbi"]).aggregate(sum)

    # select sum(hits), sum(delta) from data group by date, hour, sbi return df_sbi
  22. Who has my data? •First iteration was a (pre)-POC, less

    data (3GB vs 500GB); •Time constraints; •Oeps: everything is a pandas df!
  23. Advantage of “everything is a df” Pro: •Fast!! •Use what

    you know •NO DBA’s! •We all love CSV’s! Contra: •Doesn’t scale; •Huge startup time; •NO DBA’s! •We all hate CSV’s!
  24. •Set the dataframe index wisely; •Align the data to the

    index: •Beware of modifications of the original dataframe! source_data.sort_index(inplace=True) If you want to go down this path
  25. The reason pandas is faster is because I came up

    with a better algorithm If you want to go down this path
  26. AngularJS python app REST Front-end Back-end Database JSON ? If

    you don’t
  27. A word about (traditional) databases…

  28. Db: programming language dict

  29. Postgres for data driven apps?

  30. Postgres for data driven apps?

  31. Issues?! •With a radius of 10km, in Amsterdam, you get

    10k postcodes. You need to do this in your SQL: •Index on date and postcode, but single queries running more than 20 minutes. SELECT * FROM datapoints WHERE date IN date_array AND postcode IN postcode_array;
  32. PostGIS is a spatial database extender for PostgreSQL. Supports geographic

    objects allowing location queries: SELECT * FROM datapoints WHERE ST_DWithin(lon, lat, 1500) AND dates IN ('2013-02-30', '2013-02-31'); -- every point within 1.5km -- from (lat, lon) on imaginary dates Postgres + Postgis (2.x)
  33. Other db’s?

  34. How we solved it 1. Align data on disk by

    date; 2. Use the temporary table trick: 3. Lose precision: 1234AB→1234 CREATE TEMPORARY TABLE tmp (postcodes STRING NOT NULL PRIMARY KEY); INSERT INTO tmp (postcodes) VALUES postcode_array; SELECT * FROM tmp JOIN datapoints d ON d.postcode = tmp.postcodes WHERE d.dt IN dates_array;
  35. Take home messages 1. Geospatial problems are “hard” and cam

    kill your queries; 2. Not everybody has infinite resources: be smart and KISS! 3. SQL or NoSQL? (Size, schema)
  36. GoDataDriven We’re hiring / Questions? / Thank you! @gglanzani giovannilanzani@godatadriven.com

    Giovanni Lanzani Data Whisperer