This document summarizes a presentation about RethinkDB, an open-source database built on JSON documents. It discusses key features of RethinkDB like supporting queries, being distributed, and ease of administration. It also compares RethinkDB to other NoSQL databases. The document provides examples of using the ReQL query language in Python to create and query a database. It demonstrates how to perform operations like counts, filters, and map-reduce queries.

1. Pych(Pythonists Chennai) Meetup
Rethinking DB with Python
- Raghav Prabhu
28-Jun-2014

2. Image Courtesy : RethinDB

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Manipulates purely on JSON
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Supports Query
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Distributed Database
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Sharding & Replication
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Ease of administration
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Failover
RethinkDB

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Developer Oriented
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CouchDB
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MongoDB
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Operations Oriented
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Cassandra
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Riak
RethinkDB Vs Other NoSQL

5. Image Courtesy : RethinDB
Introduction to ReQL

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Embeds in programming languages
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Python
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JavaScript
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Ruby
ReQL in Python
import rethinkdb as r # import the RethinkDB package
conn = r.connect() # connect to the server on localhost and default port
ReQL

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Run some queries
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r.db('meetup').tableCreate('py_conf') #creates DB & a table
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# insert some records
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r.table('py_conf').insert([{ name: 'Member 1', age: 26 },
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{ name: 'Member 2', age: 22 }])
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# count total records
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r.table('py_conf').count()
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# more sophisticated query
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r.table('py_conf').filter(r.row('age').gt(25))
ReQL

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SQL
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SELECT * FROM users WHERE
users.age >= 25
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# imperative algorithm
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results = []
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for user in users:
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if user['age'] >= 25:
results.append(user)
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Nothing but lambda
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In Python
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def fun(args...)
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lambda args...
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r.table('users').filter(lambda user:
user['age'] >= 25)
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# Some more examples
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>>> r.expr("meet").do(lambda
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x: x + "up").run()
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'meetup'
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>>> r.expr(12).do(lambda
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x: x * x).run()
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144
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9. ReQL Web Interface

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Map Reduce
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Group operation (Optional)
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Map operation
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Reduce operation
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Many ReQL commands automatically compile to map/reduce queries
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Our own map/Reduce
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Map-reduce in RethinkDB

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Table – 'posts'
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{
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"id": "7644aaf2­9928­4231­aa68­4e65e31bf219"
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"title": "The line must be drawn here"
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"content": "This far, no further! …"
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"category": "Fiction"
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}
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Map-reduce in RethinkDB
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In Python
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Simple way
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r.table('posts').group
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('category').count().run(conn)
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Map Reduce
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r.table('posts')
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.group('category')
.map(lambda post: 1)
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.reduce(lambda a,b: a+b)
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.run(conn)
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12. Case Study
RethinkDB ­
Muni's prediction
(San Francisco Municipal Transportation Agency)
Blog : http://blog.justonepixel.com/geek/2013/01/22/muni­
prediction/
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13. Questions
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14. Thank you
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