Titan is a scalable graph database build on top of Apache Cassandra that supports both real time and analytic graph queries across distributed clusters. This talk focuses on the recently released Titan 1.0 and the new features it introduces. Titan implements the most recent version of the popular Apache Gremlin graph query language through a custom rewrite engine and query optimizer to efficiently execute deep traversal queries. Graph analytics and global breadth-first execution of Gremlin queries is executed by Apache Spark through the Cassandra-Spark connector. These features are demonstrated on a social use case to highlight how Titan can deliver relationship value with little development effort.

1. Titan 1.0
A journey and lessons learned
Matthias Broecheler
@mbroecheler

2. Titan 1.0.0
titandb.io

3. June 14th
2012

5. Final Lesson
It’s early days for Graph

7. Lesson 1
Don't reinvent basic
database memory
management

9. Lesson 2
Need for Scale-Out

15. g.V().has("user","name","Marko")
.out("reviewed").values("title")

16. Lesson 3
To use graph effectively
you need to “think”
graph

17. June 14th
2012

20. Lesson 4
Use cases:
Highly connected and
heterogenous

21. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")

22. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")
Graph-global
retrieval
Graph-local
walk

23. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")
TitanGraphStep
TitanVertexStep

24. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")

25. Lesson 5
Flexible and adaptive
query optimization

26. g.V().map(outE().count()).mean()

27. g.V().map(outE().count()).mean()

29. g.V().as("user")
.filter(
outE("reviewed").has("helpfulness")
.count().is(gt(10)))
.outE("reviewed")
.has("helpfulness").as("r")
.group().by(select("user"))
.by(select("r").by("helpfulness"))
.iterate()

30. Lesson 6
Graph OLTP and OLAP
are converging

31. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")

32. g.V().hasLabel("product")
.has("title",textContains("Titan"))
.inE("reviewed")
.has("score",5)
.values("summary")
.map{it.substring(10)}

33. g.V().has("ASIN","B000654OV0").match(
__.as("p1").in("hasProduct").as("c"),
__.as("p1").in("reviewed").as("u"),
__.as("u").out("reviewed").as("p2"),
__.as("p2").in("hasProduct").as("c")
).where("p1", neq("p2")).dedup("u")
.select("u").by("userId")

34. Lesson 7
Graph needs declarative
and imperative query
language constructs

35. Final Lesson
It's early days for Graph

36. Let’s discuss!
Questions, comments, feedback?
We are hiring

37. Abstract
Titan is a scalable graph database build on top of Apache Cassandra that
supports both real time and analytic graph queries across distributed clusters.
This talk focuses on the recently released Titan 1.0 and the new features it
introduces. Titan implements the most recent version of the popular Apache
Gremlin graph query language through a custom rewrite engine and query
optimizer to efficiently execute deep traversal queries. Graph analytics and
global breadth-first execution of Gremlin queries is executed by Apache Spark
through the Cassandra-Spark connector. These features are demonstrated on
a social use case to highlight how Titan can deliver relationship value with little
development effort.