Real Time Graph Computations in Storm, Neo4J, Python - PyCon India 2013

Real-Time stream computation on
graphs using Storm, Neo4j and
Python
Sonal Raj
http://www.sonalraj.com
Presented at Pycon India 2013
Bangalore, India
Copyrights © 2013, Sonal Raj, http://www.sonalraj.com
1

Introduction
2
• With data multiplying each day, storage and
knowledge extraction is a major concern.
• Social Data Analysis, Business Intelligence
• Constraints of Real Time and Fault-Tolerant
Processing

. . In this Talk
3
• A look at storm as a distributed
computation Framework
• Neo4J as a NoSQL graph database
• Some Cool Pictures
• What are we trying to achieve ?

Disclaimer !
4
• This talk presents an overview of Storm and
Neo4J . . Less dirty details 
• I’m going to go pretty fast . . . Please hang on.

5
Part -1
Storm – The Hadoop
of Real Time

Don’t we have Hadoop ?
6

Storm v/s Hadoop
7
STORM
HADOOP
• Distributed
Processing
• Fault Tolerance

Storm v/s Hadoop
8
HADOOP
• Large but Finite Jobs
• Processes a Lot of Data at Once
• High Latency

Storm v/s Hadoop
9
HADOOP
• Large but Finite Jobs
• Processes a Lot of Data at Once
• High Latency
Storm
Infinite Computations called Topologies
Process Infinite Streams of data one-tuple-at-a-time
Low Latency

So, what Storm gives us . .
10
 Real-Time Computations
 Guaranteed data Processing
 Horizontal Scalability and Fault-Tolerance
 No intermediate message Brokers
 Higher Abstraction than Message Passing, so makes
sense !

A little deeper . . Concepts
11
Streams
Tuple Tuple Tuple Tuple Tuple
An unbounded sequence of Tuples

12
Streams
Tuple Tuple Tuple Tuple Tuple
An unbounded sequence of Tuples
So, what kind of
a tuple is this ?

13
Spouts
A source of Streams

14
Spouts
A source of Streams
But, what is the
source FOR the
spouts ?

15
Bolts
Computational units processing input
streams and producing new streams

16
Bolts
Computational units processing input
streams and producing new streams
Just 1 stream ?

17
Topologies
A network of spouts and bolts

Is that it . . . ?
18
Tasks and Parallelism
A spout or bolt can execute
multiple tasks across the
cluster

19
[ ]Mr. Tuple
O Shoot, where
do I go now?

Groupings . . To the rescue of Mr. Tuple !
20
• Shuffle Grouping #pick a random task
• Fields Grouping #mod hashing on a
subset of tuple fields
• All Grouping #sends to all tasks
• Global Grouping #picks task with lowest
task id

A Storm Cluster
21
NIMBUS
ZOOKEEPER
ZOOKEEPER
ZOOKEEPER
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR

A Storm Cluster
22
NIMBUS
ZOOKEEPER
ZOOKEEPER
ZOOKEEPER
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR
If this were Hadoop
Job Tracker
Task Tracker

A Storm Cluster
23
NIMBUS
ZOOKEEPER
ZOOKEEPER
ZOOKEEPER
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR
SUPERVISOR
But it’s NOT Hadoop !
Co-ordinates
Everything

Salient Features . .
24
• Storm > 0.7 supports Transactional Topologies
 Processes small batches of topologies
 If failure during commit, both batch+commit is
retried
• Storm guarantees message Processing using
acknowledgements
• Petrel by AirSage is a python wrapper for
Storm ; you can write and submit topologies in
Python.

25
Part -2
Neo4J – “Get Graphed”

26
This is how
Graph Data was
represented in
RDBMS.

27
ENTER, NOSQL DATABASES

28
Types of NOSQL Databases
Graph
databases
Document
databases
Column-
Family
Key-Value
Stores
Data Complexity
DataSize

29
Why NOSQL Databases
• Easily horizontally scalable
• Dynamic Schemas, Handle Unstructured data really
well.
• Excel in speed and volume
• Trade off in consistency for efficiency (except in
graph databases . . .We’ll see why  )
• Pleasure to code
• Free to use any query language ( even SQL ! )
• Downtime? What Downtime ?

30
The Property Graph Model of Graph Databases
• Core Abstractions
 Nodes
 Relationship between Nodes
 Properties of both
• Traversal Framework
High Performance Queries on connected datasets
• Bindings
REST, Gremlin, etc.

31
Neo4J
• Fully ACID with rollbacks support (unbelievable!)
• Schema-less and Efficient storage of Semi Structured
Data
• Fast deep traversal instead of slow SQL queries that
span many table joins
• Whiteboard Friendly
• Very natural to express graph related problems with
traversals (recommendation engine, shortest path etc..)

32
Neo4J Pythonized !
• Py2Neo is an excellent binding for Neo4J
• Accesses Neo4J using it’s RESTful API
• Still under development . . Features like labels yet to be
included !

33
So,Will Relational databases be Extinct ?
OOPS!

34
Categories of Graphical Data
• Social Networks
• Citations
• Product Co-Purchasing
• Internet peer-to-peer
• Road Network and Map Data
• Web Graphs
Excellent Source of Sample Graphical Data
“ http://snap.Stanford.edu/data/ “

35
Part -3
Get your hands dirty !

36
A demo . .
• Sample Social Network data set
• Data Includes people signing up info,
adding friends, unfriending etc. . . for a
month’s activity
• Neo4J
 Store and Update the social data
• Storm
 Calculate “friendship-index”

37
A demo . .
• “friendship-index”
 n = Through how many people is
person “A” connected to person “B”
 Gives an idea of how close two people
are !
 Useful while searching friends on Social
Networks ( something like friends of friends concept
in facebook’s graph search )

38
The Topology . .
Update
Spout
Update
Bolt
Query
Spout Query
Bolt
Source
Source

39Copyrights © 2013, Sonal Raj, http://www.sonalraj.com
Update Spout

Update Spout
Define what kind of tuples
are emitted

Update Spout
Gets and emits tuple streams

Update Bolt

Update Bolt
Objects for database access
and indexing service

Update Bolt

Query Spout

Query Spout
The tuple to be emitted
can contain multiple
entities.

Query Bolt

Query Bolt
Retrieve caller friend and
requested friend ids

Query Bolt
Retrieve caller friend
and requested friend
ids as per database

Create Topology

Create Topology
Import all spout and
bolt files

Create Topology
Unfortunately,There was no option in
Petrel to turn off console debug, so the
console view is really messy.

Topology.yaml
Configurations to the topology are
specified in this file

55
A little More . .
Update
Spout
Update
Bolt
Query
Spout Query
Bolt
Source
Source

56
Final Thoughts
• A Storm-Neo4j framework is a boon for real-time
graph computations
• Quite flexible in Java, Python bindings and
implementations still have a long way to go.
• If you are an Admin or developer, Analyse your data
and computing requirements before narrowing down
on a framework.

57
…to play with Storm and Neo4J
• My PyCon Talk Repo – slides, code skeletons,
etc.
http://www.sonalraj.com/neo-storm.html
• Storm documentation (official)
http://github.com/nathanmarz/storm
• Storm Book
http://www.amazon.com/Getting-Started-Storm-Jonathan-
Leibiusky/dp/1449324010
• Deployment of storm on AWS
http://github.com/nathanmarz/storm-deploy
• Neo4J Documentation
http://www.neo4j.org

58
Ex-terminated . . .
- That’s it
- Thanks for Listening !
- Questions

Real Time Graph Computations in Storm, Neo4J, Python - PyCon India 2013

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Real Time Graph Computations in Storm, Neo4J, Python - PyCon India 2013