Jupyter and RethinkDB

Ba70f10866cbab0bc6b9b1d547ef8015?s=47 Ryan Paul
August 26, 2015

Jupyter and RethinkDB

Learn how to use RethinkDB with Jupyter, a rich platform for interactive programming built on top of the powerful IPython REPL. In this talk, I'll demonstrate how to perform ReQL queries in a Jupyter notebook, integrating with matplotlib and other libraries to generate data visualizations.


Ryan Paul

August 26, 2015


  1. RethinkDB and Jupyter Interactive Data Science

  2. Ryan Paul RethinkDB Evangelist @segphault

  3. Introduction What is Jupyter?

  4. In the beginning, there was the REPL

  5. In the beginning, there was the REPL READ EVAL PRINT

  6. Jupyter’s Origin The IPython REPL

  7. Jupyter’s Origin IPython Notebook

  8. • Rich interactive REPL with terminal and desktop frontends •

    Persistent REPL notebook that can evaluate code and save results What is Jupyter?
  9. • Language-agnostic platform abstracted out of IPython • IPython itself

    now provides Jupyter’s Python kernel • Wide range of other programming languages are supported What is Jupyter?
  10. • Interactive literate programming environment that runs in browser •

    Combine code snippets and output with rich text content • Displays embedded content like visualizations Jupyter Notebook
  11. RethinkDB Consume data in Jupyter

  12. What is RethinkDB? • Open source database for building realtime

    web applications • NoSQL database that stores schemaless JSON documents • Distributed database that is easy to scale • High availability database that is resilient against failure
  13. ReQL & Jupyter • ReQL is the RethinkDB query language

    • ReQL integrates with syntax of the underlying language • ReQL is expressive and provides useful tools for data manipulation
  14. Data Explorer

  15. Jupyter Notebook

  16. Jupyter Extensibility Magic functions & APIs

  17. • Add special commands to Jupyter that work on REPL

    and notebook • Typically prefixed with a % sign • Can programmatically transform user input Magic Functions
  18. Magic Functions from IPython.core.magic import register_line_magic @register_line_magic def r(line): import

    rethinkdb as r conn = r.connect() response = eval(line).run(conn) if type(response) == r.net.DefaultCursor: response = list(response) print to_pretty_json(response) conn.close()
  19. • Jupyter provides APIs for displaying rich content • Can

    embed images, HTML, JSON, and other kinds of content • Import from IPython.display Display Functions
  20. Display Functions from IPython.display import Image, display display(Image("http://i.imgur.com/lswhE2n.jpg"))

  21. Data Visualizations Graphing with matplotlib

  22. Using Matplotlib %matplotlib inline from matplotlib import pyplot import rethinkdb

    as r conn = r.connect() quakes = r.table("quake") \ .filter(r.row["time"].month() == r.now().month()) \ .group(r.row["time"].day()).count() \ .ungroup().order_by(r.row["group"]) \ .do([r.row["group"], r.row["reduction"]]).run(conn) conn.close() pyplot.bar(quakes[0], quakes[1]) pyplot.show()
  23. Using Matplotlib %matplotlib inline import mplleaflet from matplotlib import pyplot

    import rethinkdb as r conn = r.connect() near_tokyo = list(r.table("quake").get_intersecting( r.circle([139.69, 35.68], 200, unit="mi"), index="geometry") ["geometry"] \ .map(r.row.to_geojson()["coordinates"]).run(conn)) conn.close() pyplot.plot([p[0] for p in near_tokyo], [p[1] for p in near_tokyo], 'rs') mplleaflet.display()
  24. Additional Resources • RethinkDB website:
 http://rethinkdb.com • Jupyter:
 http://jupyter.org/ •