Neo4j is a graph database that is natively designed to store and query graph data. It uses a graph-native architecture that optimizes for storing and traversing relationships between nodes. This allows Neo4j to provide faster performance on workloads involving connected data compared to traditional databases.

1. The Graph-Native Advantage
Dr. Jim Webber
Chief Scientist, Neo4j
@jimwebber

2. Overview
• Graph-Native overview
• A friendly little bit of computer science
• Database architecture from 30,000ft
• Why Neo4j is graph native, and why it matters
• Quantitative performance advantages
• Q&A

3. Assumptions
• Graphs are the most
important data structure on
the planet
• Most workloads can be
conveniently modelled as
graphs

5. An Unordered Singly Linked List
27 1657 5674

6. A Write-Centric Database?
27 1657 5674Client

7. Not a Practical Write-Centric Database?
27 1657 5674Client
Client
Client
Every client contends for write lock in naïve implementation

8. CRDTs to the Rescue!
27 1657 5674Client
5674 7689 6Client
1657 5674 66Client
27 1657 5674 7689 6 66

9. Trees
27
1657
27 1657 5674 7689 6 66
5674
7689
6
66

10. Spread contention around the structure
27
1657
27 1657 5674 7689 6 66
5674
7689
6
66
Client
Writes
Client
Writes
Client
Writes
Client
Writes

11. Databases <3 Trees, usually
• Classic B-trees common pattern for on disk-databases
• “Index” in memory, files on leaf nodes on disk
• B+ Trees for linear scans are neat!
• But…

13. So what?

14. Pick the right tool for the job

15. All Databases have a native model
• It could be tables or columns or KV or documents…
• Each database is likely very good for that model
• Evolution driven by its primary workload in its primary market
• Any add-on doesn’t benefit from this
• Unloved
• Opportunistic (e.g. “multi model”)
• Models don’t compose easily
Some vendors have spotted the enormous graph
trend and are simply jumping on the bandwagon

16. Two Non-Native Approaches to Graph
Graph Layer
• Take existing data store
• Bolt-on Graph-like API from
third-party open source
• Declare victory
Graph Operator
• Take existing data store
• Add graph features into the
query language
• Declare victory

17. Non-Native Architectures
Graph Layer Graph Operator
Other DBMS
(e.g. Column Store)
Graph Layer
Graph API
Other DBMS
(e.g. Document Store)
Other QL Graph Operator

18. Non-Native Architectures
Graph Layer Graph Operator
Other DBMS
(e.g. Column Store)
Graph Layer
Graph API
Other DBMS
(e.g. Document Store)
Other QL Graph Operator
No Cypher!

19. Non-Native Architectures
Graph Layer Graph Operator
Other DBMS
(e.g. Column Store)
Graph Layer
Graph API
Other DBMS
(e.g. Document Store)
Other QL Graph Operator
Requires convention
at user level
Denormalization
No Cypher!

20. Non-Native Architectures
Graph Layer Graph Operator
Other DBMS
(e.g. Column Store)
Graph Layer
Graph API
Other DBMS
(e.g. Document Store)
Other QL Graph Operator
Requires convention
at user level
Denormalization
No Cypher!
Does not understand graphs
Cannot prevent dangling relationships/logical corruption/etc

21. Two Non-Native Approaches to Graph
Graph Layer
• Take existing data store
• Bolt-on Graph-like API from third-party
open source
• Declare victory

22. Popular Implementation: Column Store
http://javahungry.blogspot.com/2013/08/hashing-how-hash-map-works-in-java-or.html

23. Two Non-Native Approaches to Graph
Graph Operator
• Take existing data store
• Add graph features into the query
language
• Declare victory

24. Popular Implementation: B-Trees!
http://zhangliyong.github.io/posts/2014/02/19/mongodb-index-internals.html

25. >
Pick the Right Tool for the Job

26. Connectedness and Size of Data Set
ResponseTime
Relational and
Other NoSQL
Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Neo4j
“Minutes to
milliseconds”
Real-Time Query Performance

27. Real-time Package Routing
• Large postal service with over
500k employees
• Neo4j routes 7M+ packages daily
at peak, with peaks of 5,000+
routing operations per second.
• Many hops per transaction.
Real-time promotion recommendations
• Record “Cyber Monday” sales
• About 35M daily transactions non peak
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
Real-time pricing engine
• 300M pricing operations per day
• 10x transaction throughput on half
the hardware compared to Oracle
• Presentation at
http://graphconnect.com/gc2016-sf/
• Replaced Oracle database
• 7-22 hops per transaction
Use Cases

28. • > 90% of the U.S. population
• Graph of (People)-(Devices)-(Cookies)-(Trackers),
IPs, etc.
• >1B transactions per day
• 3 TB graph, 5B+ nodes, 256 GB RAM

29. Neo4j’s Graph Native Stack
Cypher Engine
Cypher HTTP Endpoint Bolt EndpointCustom Rest
APOC
Extensions
Parser
Compiled
Runtime (EE)
Interpreted
Runtime
Native Graph Engine
In-Memory Page Cache
Native Graph Storage
Indexing
ACID
Cost-based Optimizer
Fast Write Buffering
CAPI Adapter Configuration Data Stores Logging
Security
High Availability
Monitoring
Command
Line Interface
Neo4j
Browser
Sync
Custom
Functions
App or Community Driver Language Drivers
29

30. Neo4j’s Graph Native Stack
Native Label index
speed writes
Composite indexes
speed query
performance
Compiled Cypher
Runtime
for common
queries now, all
soon
Query depth
optimization
for DISTINCT
New JavaScript
framework for
better
flexibility
Cost-based
optimizer
default
Cypher Engine
Cypher HTTP Endpoint Bolt EndpointCustom Rest
APOC
Extensions
Parser
Compiled
Runtime (EE)
Interpreted
Runtime
Native Graph Engine
In-Memory Page Cache
Native Graph Storage
Indexing
ACID
Cost-based Optimizer
Fast Write Buffering
CAPI Adapter Configuration Data Stores Logging
Security
High Availability
Monitoring
Command
Line Interface
Neo4j
Browser
Sync
Custom
Functions
App or Community Driver Language Drivers
30

31. Graph Native Approach
• Declarative query language
• Human readability
• Graph expressiveness
• Optimizer and Query Planner for graphs
• Graph metadata
• Runtime metadata
• Aim to work in main memory
• And optimize for L2 where possible
• Maximize IO performance
• Graph traversals by pointer chasing

33. Design Trade-offAvailability Reliability

35. http://scienceprogress.org/wp-content/uploads/2008/04/two_way_591.jpg

36. LOVES
[
{
"NodeId": 0
},
{
"Country": "USA"
},
{
"LOVES": 1,
"Direction": "in"
}
]
[
{
"NodeId": 1
},
{
"Country": "UK"
},
{
"LOVES": 0,
"Direction": "out"
}
]

37. LOVES
[
{
"NodeId": 0
},
{
"Country": "USA"
},
{
"LOVES": 1,
"Direction": "in”
"Value": "100%"
}
]
[
{
"NodeId": 1
},
{
"Country": "UK"
}
]

38. For graphs:
Reliability > Availability

40. Consistency models
Can you read what you write?
https://aphyr.com/posts/313-strong-consistency-models

41. Cluster members slightly“ahead”or“behind”of each other
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
If I query this server I
won’t see the updates
from transaction 11 .
If I query this server,
I’ll see all updates
from all committed
transactions 11
11
11

42. Register
Login
You need
to login in
to
continue
your
purchase!

43. Register
Login
You need
to login in
to
continue
your
purchase!
Username:
Password:
Create Account

44. Register
Login
You need
to login in
to
continue
your
purchase!
Username:
jim_w
Password:
********
Create Account

45. Register
Login
You need
to login in
to
continue
your
purchase!
Username:
Password:
Login

46. Username:
jim_w
Password:
********
Login

47. Purchase
Login
Successf
ul
Try again
No account
found!Username:
jim_w
Password:
********
Login
𝙓

48. Username:
jim_w
Password:
********
A few moments later...
✓
Login

49. Purchase
Login
SuccessfulUsername:
jim_w
Password:
********
Login
A few moments later...
✓

50. Q Why didn’t this work?
A Eventual Consistency

51. Bookmark
• Session token
• String (for portability)
• Opaque to application
• Represents ultimate user’s most recent
view of the graph
• More capabilities to come

52. Let’s Build a System with Causal Consistency

53. 0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
App
Server A Driver
Create
Account

54. 0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
CREATE (:User)
App
Server A Driver
Create
Account

55. 0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
CREATE (:User)
App
Server A Driver
Create
Account

56. 0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
CREATE (:User)
App
Server A Driver
11
Create
Account
1110

57. CREATE (:User)
App
Server A Driver
Create
Account
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
11
1110

58. CREATE (:User)
App
Server A Driver
Create
Account
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
11
1110

59. CREATE (:User)
App
Server A Driver
Create
Account
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
11
1110

60. CREATE (:User)
App
Server A Driver
MATCH (:User)
Login
App
Server B Driver
Create
Account
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9
11
1110

61. 0 1 2 3 4 5 6 7 8 9 10 11
0
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
CREATE (:User)
MATCH (:User)
Login
App
Server A
App
Server B
Driver
Driver
Create
Account
1 2 3 4 5 6 7 8 9 1110

62. 0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
CREATE (:User)
MATCH (:User)
Login
App
Server A
App
Server B
Driver
Driver
11
Create
Account
1 2 3 4 5 6 7 8 9 1110

63. 0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
CREATE (:User)
MATCH (:User)
Login
App
Server A
App
Server B
Driver
Driver
11
Create
Account
1 2 3 4 5 6 7 8 9 1110

64. 0 1 2 3 4 5 6 7 8 9 10 11
0
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
CREATE (:User)
MATCH (:User)
Login
App
Server A
App
Server B
Driver
Driver
11
Obtain
bookmark
Create
Account
1 2 3 4 5 6 7 8 9 1110

65. 0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10 11
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
CREATE (:User)
MATCH (:User)
Login
App
Server A
App
Server B
Driver
Driver
11
Use
bookmark
Create
Account
1 2 3 4 5 6 7 8 9 1110

66. Fault Tolerance must respect the graph
• Neo4j’s approach maintains the necessary ACID semantics over the
network
• No corruption by design
• Compared to corruption under non-fault operation for EC systems
• Replication model maintains high performance pointer chasing
• No over-the-network traversals
• Compared to expensive hash lookups in others
• All future versions of Neo4j will honor this too, even as the fault
tolerance protocols evolve

67. Pushing Neo4j to the Limits
• Asymptotic benchmarking effort
for native graph tech
• “What Neo4j can do when it’s
pushed to its limits?”
• And the results are pretty
amazing

68. Traversals
• Realistic retail dataset from Amazon
• Commodity dual Xeon processor server
• Social recommendation (Java procedure) equivalent to:
MATCH (you)-[:BOUGHT]->(something)<-[:BOUGHT]-(other)-[:BOUGHT]->(reco)
WHERE id(you)={id}
RETURN reco
Threads Hops/second
1 3-4M
10 17-29M
20 34-50M
30 36-60M

69. Trillions!
@profbriancox
Read Scale
• Can comfortably handle 1 trillion
relationships on a single server
• 24x2TB SSDs, 33TB size on disk.
• Compiled Cypher query
• Random reads
• Sustains over 100k user
transactions/sec
• Even with 99.8% page faults
because of modest 512GB RAM
machine

70. Write Scale
• Import highly connected
Friendster dataset
• 1.8 billion relationships
takes around 20 minutes
• That is 1M
writes/second!
Millions and
billions!
@profbriancox

71. >50M TRAVERSALS/SEC
1,000,000 WRITES/SEC
1,000,000,000 RECORDS

72. Comparison on a ~10M node, ~100M relationship graph
Workload Non-native graph DB: 6 machines, each with
48 VCPUs, 256 GB disk and 256 GB of RAM
Count nodes 201s
Count outgoing rels 202s
Count outgoing rels at depth 2 276s
Count outgoing rels at depth 3 511s
Group nodes by property val 212s
Group rels by type 198s
Count depth 2 knows-likes 324s
Page Rank 2571s
Neo4j: single thread
< 1ms
< 1ms
23s
423s*
8s
54s
149s*
27s*

75. Why you should care about Graph-Native Tech
• Performance
• Blazingly fast for graph workloads on commodity hardware
• Safety
• Don’t compromise graph data
• Usability
• Faster time to value
• Ease of evolution
• Better ecosystem tooling

76. Q&A
@jimwebber