Graph in Apache Cassandra. The World’s Most Scalable Graph Database

•

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1. Building a graph database requires modeling the data, choosing a query language, and providing storage. 2. Existing distributed databases like Cassandra can be used for storage due to their scalability and reliability, though a native graph database provides more functionality. 3. Solving complex graph problems requires capabilities beyond basic queries, including search, analytics, and integration with machine learning, which graph databases are designed to support at scale.

Dados e análise

How to build a
scalable graph database
Bryn Cooke
The smart way

In this talk
1. What does it take to build a graph database?
2. Why shouldn’t you do this at home.
3. What do you use this for?

Graph database recipe
1. Model
2. Language
3. Storage

Model
bob
since: 2001
steph
bob:
knows
:steph
age: 30age: 34
knows known
Property Graph RDF

Language
g.V().has('name', 'marko').out('knows').values('name')

The adjacency list
Vertex Adjacent to
A B, D, E
B
C B
D C
E D, F
F
A
B
C
E
D
F

//TODO
• Storage
• Indexing
• Commit log
• Drivers
• Caching
• Schema
• Metrics
• Backup/Restore
• Logging
• Security
• Testing
• Support
• Failover
• QoS
• Paging
• Partitioning
• Sorting
• Compaction
• Repair
• Community
• Bux ﬁxing
• Optimisation

Storage - Cassandra
• Fast
• Distributed
• Scalable
• Reliable
• 11 years of development
• 54 committers (listed on apache)
• 274 contributors (listed on github)

Here's what you could do
C*
C*
C*
C*C*
My Graph
Database
Client
Client
Client
Client

Client
Here's what you could do
C*
C*
C*
C*C*
My Graph
Database

Here's what you should do
C*
C*
C*
C*C*
DS Graph
Client
Client
Client
Client

Deep integration with DataStax Enterprise
DataStax Enterprise
• DataStax Enterprise scalability > Cassandra scalability.
• Analytics integration.
• Search integration.
• Thread optimisation.
• Continuous paging.
• Prefetching.
• First class schema integration.

Today’s Graph Database Market
Graph
Problems > Graph
Databases

Typical customer 360 queries
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
• Find me Jenny.
• Find me all people
with similar names
to 'Jenny'.
• Tell there are
duplicate Jennys.
• Find how Jenny
and John are
connected.
• Find how
inﬂuential Jenny is
in my application.

Find me Jenny
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Simple
How Fast?
• Machine
What?
• CQL
Why?
• Single partition
lookup
• Single iteration

Find me all people with similar names to 'Jenny'
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Medium
How Fast?
• Human Fast
What?
• Search
• Graph
Why?
• Single index
lookup
• Single iteration

Tell there are duplicate Jennys
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Medium
How Fast?
• Oﬄine
What?
• Analytics
• Graph
Why?
• Aggregation
• Multiple Iteration

Find how Jenny and John are connected
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Complex
How Fast?
• Machine
What?
• Graph
Why?
• Multiple partition
lookup
• Multiple iteration

Find how inﬂuential Jenny is in my application
Oﬄine
fast
Human
fast
Machine
fast
Analytics
CQL
Search
Responsetime
Simple Complex
No go zone
DSE
How Complex?
• Complex
How Fast?
• Oﬄine
What?
• Spark Analytics
• Graph via PageRank
Why?
• Full scan
• Unknown iterations

Summary
1. What it takes to create a graph database
a. Model
b. Language
c. Storage
2. How you can leverage an existing storage engine, and why Cassandra is a
great choice.
3. Solving graph problems requires more than just the basics. Search and
Analytics are essential tools, especially graph database.

Don't try this at home
Do not try replicate 100 person years of
dev eﬀort creating your own storage
engine.
Creating a graph database that scales is
tough enough.

Try it now
https://downloads.datastax.com/#labs
Labs

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1. How to build a scalable graph database Bryn Cooke The smart way

2. In this talk 1. What does it take to build a graph database? 2. Why shouldn’t you do this at home. 3. What do you use this for?

3. Graph family tree

4. Graph database recipe 1. Model 2. Language 3. Storage

5. Model bob since: 2001 steph bob: knows :steph age: 30age: 34 knows known Property Graph RDF

6. Language g.V().has('name', 'marko').out('knows').values('name')

7. Storage

8. The adjacency list Vertex Adjacent to A B, D, E B C B D C E D, F F A B C E D F

9. //TODO • Storage • Indexing • Commit log • Drivers • Caching • Schema • Metrics • Backup/Restore • Logging • Security • Testing • Support • Failover • QoS • Paging • Partitioning • Sorting • Compaction • Repair • Community • Bux ﬁxing • Optimisation

10. Storage - Cassandra • Fast • Distributed • Scalable • Reliable • 11 years of development • 54 committers (listed on apache) • 274 contributors (listed on github)

11. The adjacency list (in Cassandra)

12. Here's what you could do C* C* C* C*C* My Graph Database Client Client Client Client

13. Client Here's what you could do C* C* C* C*C* My Graph Database

14. Here's what you should do C* C* C* C*C* DS Graph Client Client Client Client

15. Deep integration with DataStax Enterprise DataStax Enterprise • DataStax Enterprise scalability > Cassandra scalability. • Analytics integration. • Search integration. • Thread optimisation. • Continuous paging. • Prefetching. • First class schema integration.

16. Today’s Graph Database Market Graph Problems > Graph Databases

17. Typical customer 360 queries Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE • Find me Jenny. • Find me all people with similar names to 'Jenny'. • Tell there are duplicate Jennys. • Find how Jenny and John are connected. • Find how inﬂuential Jenny is in my application.

18. Find me Jenny Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE How Complex? • Simple How Fast? • Machine What? • CQL Why? • Single partition lookup • Single iteration

19. Find me all people with similar names to 'Jenny' Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE How Complex? • Medium How Fast? • Human Fast What? • Search • Graph Why? • Single index lookup • Single iteration

20. Tell there are duplicate Jennys Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE How Complex? • Medium How Fast? • Oﬄine What? • Analytics • Graph Why? • Aggregation • Multiple Iteration

21. Find how Jenny and John are connected Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE How Complex? • Complex How Fast? • Machine What? • Graph Why? • Multiple partition lookup • Multiple iteration

22. Find how influential Jenny is in my application Offline fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE How Complex? • Complex How Fast? • Offline What? • Spark Analytics • Graph via PageRank Why? • Full scan • Unknown iterations

23. Typical customer 360 queries Oﬄine fast Human fast Machine fast Analytics CQL Search Responsetime Simple Complex No go zone DSE • Find me Jenny. • Find me all people with similar names to 'Jenny'. • Tell there are duplicate Jennys. • Find how Jenny and John are connected. • Find how inﬂuential Jenny is in my application.

24. Summary 1. What it takes to create a graph database a. Model b. Language c. Storage 2. How you can leverage an existing storage engine, and why Cassandra is a great choice. 3. Solving graph problems requires more than just the basics. Search and Analytics are essential tools, especially graph database.

25. Don't try this at home Do not try replicate 100 person years of dev eﬀort creating your own storage engine. Creating a graph database that scales is tough enough.

26. Try it now https://downloads.datastax.com/#labs Labs

27. Thank You

Graph in Apache Cassandra. The World’s Most Scalable Graph Database

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