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Working with massive secure graphs in Sqrrl Enterprise

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by

Chris McCubbin

on 29 May 2015

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Transcript of Working with massive secure graphs in Sqrrl Enterprise

Sqrrl Entity Graphs
Example: Enron's Email Graph
Sqrrl Graph Analytics
Working with massive secure graphs in Sqrrl Enterprise
Graphs 101
Advanced Analytics
Anomaly Detection
Luke
Leia
Darth Vader
Graphs are made of entities
(also called nodes or vertices)
Luke
Leia
Darth Vader
...and
relationships
or
edges
The relationships can be pretty much anything.
kissed
daughter
father
There are many variants.
Luke
Leia
Darth Vader
Di(rected)graph
kissed
daughter
father
Luke
Leia
Darth Vader
Multigraph
kissed
daughter
father
Luke
Leia
Darth Vader
Weighted graph
kissed
daughter
father
1.0
2.4
3.3
Graphs have properties
Planar
Hamiltonian Cycle
Graph
Data Model
Relational Model
is-a
is-a
RDBMS
can be stored in
can be stored in
Graph
Database
Flat File
Stone Tablet
can be stored in
can be stored in
can be carved on
Representation is important
Adjacency List
Adjacency Matrix

Beyond?

label:"additional info"
value:"edges may be connected to 'virtual' document uuids"
label:"is related to"
label:"in same prezi as"
Neighbor Search
Breadth First Search
SqrrlQL Integration
Stream Processing
Link Prediction
Pagerank
Triangle Count
6 Degrees
Aggregation
{
"Title": "Sqrrl Node Info",
"Overview": "Sqrrl Nodes are simply sqrrl documents",
"Details": {
"Fields": "made up of a hierarchy of fields like json",
"Types": [
"string",
"number",
"boolean",
"aggregates",
"..."
]
}
}
{
"Title": "Sqrrl Edge Info",
"Edge Components": {
"Direction": "Sqrrl Edges are directed",
"Label": "Sqrrl Edges are labeled with a string",
"Value": [
"Sqrrl Edges have a single unnamed value attached to them",
"values can be any sqrrl type and can implement e.g. weights"
]
},
"Identifier": "edges are unique up to the 4-tuple
(source, destination, label, direction)"
}
R
Spark
Multiple Datasources,
Graph Cell-level security

Ingest Some Twitter into the same dataset
What's going on here?
Enron email
Enron email
Tweet
sqrrlAggregate
Graphs are everywhere
Binary Properties
Triangle count = 4
Average Degree = 4.0
Numeric Properties
Vector Properties
PageRank
Betweeness
Complex Properties
Minimum Spanning Tree
Subgraph Matching
Graphs analytics compute
graph properties
And they can be analyzed and put to good use
Try it out!
Luke
Leia
Darth Vader
Property graph
kissed
daughter
father
1.0
2.4
3.3
Class:jedi
Last Name:Skywalker
Class:Sith
Last Name:Vader
Class:princess
Last Name:Organa
Some analytics are harder than others.
Sublinear
Near-linear
Polynomial
Intractable
Degree of a node
Average Degree
PageRank
Planarity
Spanning Tree
Betweeness
Triangle Count
Hamiltonian Cycle
Subgraph Matching
The sqrrl graph store
is designed to be
secure
scalable
powered by
Chris McCubbin
chris@sqrrl.com
@_SecretStache_
Full transcript