Efficiently digesting data in large volumes can prove to be challenging for any database. The challenges are compounded when this influx must be analyzed on the fly, or "tasted", to satisfy the sophisticated palates of modern apps. Luckily, there are several proven remedies you can concoct with Redis to help with potential indigestion. The URLs from the presentation are also available at: https://gist.github.com/itamarhaber/325e515c1715a12ef132

1. Redis & MongoDB
Stop Big Data Indigestion Before It Starts
@itamarhaber

2. @itamarhaber
A Redis Geek and Chief Developer Advocate
at
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[1] http://bit.ly/RedisWatch

3. You probably haven't seen anything
like this before
VolumeMongoDB truly excels when
is comes to volume and
variety of data…
…but data coming in at
extreme velocity poses
a digestive challenge for
for any disk-based database

4. A talk about MongoDB performance
[2] WiredTiger iiBench Results
I'm hardly an
expert, but with
MongoDB v3
storage engines
and future work
this could very
well be a moot
point 

5. Data ingestion at high velocity
Mobile, online and IoT apps
produce more and more data
with every day that passes.
Simply storing the data as it
comes in doesn't cut it anymore – real time
processing is a must in order distill information
from the data as it rushes in.

6. A talk about more performance
By doing LESS
you can do MORE
(with MongoDB)
Put differently, "chew" your
data with Redis to prevent
data ingestion indigestion

7. ● "...an [4] open source, BSD licensed,
advanced key-value cache and store"
● 5+2 data types, 160+ commands, entirely in
RAM, Lua scripts, PubSub...
● Nee circa 2009, by [5] antirez
(a.k.a Salvatore Sanfilippo)
● Sponsored by Pivotal
[3] Redis (REmote Dictionary Server)

8. OSS, humane, pure,
flexible, efficient,
scalable, highly
clusterable,
sexy, fresh,
is actively
ton of uses,
has a client in every
lean & small, supple,
track record, tiny,
and much moar...
...fun & easy, free
inspiring, simple,
innovative, robust,
available, cool,
portable, geeky,
mature, stable,
developed, has a
rich, dependable,
every language,
proven production
vibrant community,
Why use Redis
❤❤1.5M ops / sec
using a single
EC2 instance!
[6] Recorded webinar
Because it is

9. Getting started with Redis
• Try it online at [7] http://try.redis.io/
• Build it from the source
• [8] Download Redis Labs Enterprise Cluster
• Run it in a container
• [9] Connect to it from any language
git clone https://github.com/antirez/redis
cd redis
git checkout 3.0.1
make; make test; make install
docker run -d --name redis -p 6379:6379 redis

10. Use case A: Google Analytics
• A real time analytics platform provider
• Strongly focuses on users' behavior
• Primary data storage is MongoDB
• Activity is collected immediately or in bulks
• Raw data fed to Hadoop for offline crunching
• Real time metrics and initial information from
the stream is obtained with Redis

11. The tidal flow
Sessions events
Real time analysis
Offline analysis

12. Deep dive topic: sessionizing data
• Stream of events
• A session is a document
• Each has 10s-1000s events
• Events from different users
arrive in order but interleaved
• The result: many small updates
to each session's document
• Peak load: 11M ops/sec and growing

13. You say potato, I say potato
Hash data type:
HSET session:1
event:1 data
HSET session:1
event:2 data
...
HINCRBY session:1
seq 1
JSON:
{
session: 1,
events: [
{ id: 1,
data: data },
{ id: 2,
data: data },
...

14. Swallowing in Python
import redis
import pymongo
r = redis.Redis()
session = r.hgetall('session:1')
# {'event:1': 'data', 'event:2': 'data', 'seq': '2'}
...
m = pymongo.MongoClient()
db = m.rta
sessionid = db.sessions.insert_one(session)

15. Keeping track of sessions
• Sessions end after a logout or a timeout
• Logout events are trivial to detect
• Timeouts, e.g. 30 minutes of inactivity, are
trickier to manage considering there could
be 10,000s of active sessions
• This is where Redis' key expiry and
keyspace notifications come in very handy 

16. Once you see it, it can't be unseen
Using Redis as a buffer in front
of MongoDB for write-
intensive, hot Big Data is a
useful pattern that makes it
easy to get information in real
time as well as distribute the
load more efficiently.

17. Use case B: Waze
• An international navigation app/service
• Strongly focuses on public transit
• 10s of millions of users during peak hours
• Primary data storage is MongoDB
• Base data is created in advance
• Real time updates (traffic, vehicles and
passengers) pour into Redis for scheduling
adjustments and notifications

18. Use case C: Tinder
• A dating app/service
• Strongly focuses on spatially-related groups
• Primary data storage is MongoDB
• Data includes user profiles & preferences
• An influx of positional and preferential
("swipes") events is first munched by Redis

19. Use case D: Clash of Clans
• A massive real time game
• Strongly focuses on matched team play
• 1000s of teams with 100s of members
• Primary data storage is MongoDB
• Match progress is sieved through Redis for
real time resources status, leaderboards and
scoring

20. Use case E: Weather.com
• IoT startup
• Focuses on environmental monitoring
• Pilot: real time fire fighting
• Primary data storage is MongoDB
• Sensor data (temperature, humidity, …) is
aggregated in Redis, providing warnings and
alarms in real time

21. Questions from the audience

22. Questions or feedback? Contact me!
Itamar Haber
Chief Developer Advocate
📧 itamar@redislabs.com
@itamarhaber