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.

Transcript

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 LOOP
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/ • Matplotlib:
 http://matplotlib.org/