This Python library makes it possible to implement real-time, highly scalable analytics that can answer following questions:

• Has user 123 been online today? This week? This month?
• Has user 123 performed action "X"?
• How many users have been active have this month? This hour?
• How many unique users have performed action "X" this week?
• How many % of users that were active last week are still active?
• How many % of users that were active last month are still active this month?

This library is very easy to use and enables you to create your own reports easily.

Using Redis bitmaps you can store events for millions of users in a very little amount of memory (megabytes). You should be careful about using huge ids (e.g. 2^32 or bigger) as this could require larger amounts of memory.

Now with Cohort charts! Read more here:

• Releasing bitmapist.cohort - or how we saved over $2000/month!

If you want to read more about bitmaps please read following:

• http://blog.getspool.com/2011/11/29/fast-easy-realtime-metrics-using-redis-bitmaps/
• http://redis.io/commands/setbit
• http://en.wikipedia.org/wiki/Bit_array
• http://www.slideshare.net/crashlytics/crashlytics-on-redis-analytics
• http://amix.dk/blog/post/19714 [my blog post]

Requires Redis 2.6+ and newest version of redis-py.

Can be installed very easily via:

$ sudo pip install bitmapist

• PHP port: https://github.com/jeremyFreeAgent/Bitter

Setting things up:

from datetime import datetime, timedelta
from bitmapist import setup_redis, delete_all_events, mark_event,\
                      MonthEvents, WeekEvents, DayEvents, HourEvents,\
                      BitOpAnd, BitOpOr

now = datetime.utcnow()
last_month = datetime.utcnow() - timedelta(days=30)

Mark user 123 as active and has played a song:

mark_event('active', 123)
mark_event('song:played', 123)

Answer if user 123 has been active this month:

assert 123 in MonthEvents('active', now.year, now.month)
assert 123 in MonthEvents('song:played', now.year, now.month)
assert MonthEvents('active', now.year, now.month).has_events_marked() == True

How many users have been active this week?:

print len(WeekEvents('active', now.year, now.isocalendar()[1]))

Perform bit operations. How many users that have been active last month are still active this month?

active_2_months = BitOpAnd(
    MonthEvents('active', last_month.year, last_month.month),
    MonthEvents('active', now.year, now.month)
)
print len(active_2_months)

# Is 123 active for 2 months?
assert 123 in active_2_months

Work with nested bit operations (imagine what you can do with this ;-))!

active_2_months = BitOpAnd(
    BitOpAnd(
        MonthEvents('active', last_month.year, last_month.month),
        MonthEvents('active', now.year, now.month)
    ),
    MonthEvents('active', now.year, now.month)
)
print len(active_2_months)
assert 123 in active_2_months

Copyright: 2012 by Doist Ltd.

Developer: Amir Salihefendic ( http://amix.dk )

License: BSD