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Neo4j:
WHAT’S NEW?
M. David Allen
Partner Solution Architect - Neo4j
I Remember...
The Evolution of Databases
1960 1970 1980 1990 2000 2010 2020 2030
Hierarchical
Relational (+ Object Relational and Object Mapping)
Desktop
Object
NoSQL/NOSQL/NewSQL/Specialised
Graph Databases
Cloud Databases
The Evolution of Databases
1960 1970 1980 1990 2000 2010 2020 2030
TRADITIONAL OLTP/RELATIONAL
Store and retrieve data
2-3 hops
in a query
The Evolution of Databases
1960 1970 1980 1990 2000 2010 2020 2030
TRADITIONAL OLTP/RELATIONAL
Store and retrieve data
2-3 hops
in a query
BIG DATA TECHNOLOGY
Aggregate and filter data
1 hop
in a query
Connections in data
The Evolution of Databases
1960 1970 1980 1990 2000 2010 2020 2030
TRADITIONAL OLTP/RELATIONAL
Store and retrieve data
2-3 hops
in a query
BIG DATA TECHNOLOGY
Aggregate and filter data
1 hop
in a query
Millions of
hops
Connections in data
The Evolution of Databases
TRADITIONAL OLTP/RELATIONAL
Store and retrieve data
2-3 hops
in a query
BIG DATA TECHNOLOGY
Aggregate and filter data
1 hop
in a query
Millions of
hops
Real time storage
& retrieval
Long running queries Real-time connected insights
“Our Neo4j solution is literally thousands of times faster
than the prior MySQL solution, with queries that require
10-100 times less code”
Volker Pacher, Senior Developer
What Is Different in Neo4j?
Index-Free Adjacency
What's New in Neo4j  - David Allen, Neo4j
• Physical data and transaction isolation
underneath shared infrastructure
• Multiple databases run in parallel
on a single Neo4j Cluster
(or standalone server)
• Agility through cloud-friendly
management: quickly move databases
from one online cluster to another
Great for SaaS providers, Enterprises,
data scientists and developers
Multi-tenancy with Neo4j 4.0
The foundation:
Causal Cluster
Unbounded Scalability in Neo4j 4.0
The evolution:
Fabric
The Quirkness of [Maybe] Eventual Consistency
Art by Pascal Jousselin - Europe Comics Publisher Dupuis - Creative Commons
Raft-based cluster and ACID database:
- Graph transactions over ACID
Consistency
- Maintains integrity over time
Causal Clustering with Neo4j
Replica Servers
Query, View
Core Servers
Synced Cluster
• Unbounded scalability to Neo4j by adding horizontal scale-out to the power of Neo4j’s existing
replicated scale-up model.
• Two modes of operations:
SHARDING: operate over single large graph
FEDERATION: query across disjoint graphs
Introducing Sharding and Federated Graphs
• Unbounded scalability to Neo4j by adding horizontal scale-out to the power of Neo4j’s existing
replicated scale-up model.
• Executes queries in parallel on multiple databases, combining or aggregating results.
• Chains queries together from multiple databases for sophisticated real-time analyses.
Multi-graph Cypher Queries
SQL
UNWIND corporate.graphIds() AS gid
CALL {
USE corporate.graph( gid )
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
RETURN boss.name AS Boss,
sub.name AS Subordinate,
count(report) AS Total
}
RETURN Boss, Subordinate, Total ORDER BY Total
Cypher in Neo4j 4.0
http://ldbcouncil.org/developer/snb and https://neo4j.com/fosdem20
Neo4j 4.0 Scalability in Action
Sharding the LDBC Social Network Benchmark
• 1-shard for the Persons graph
• N-shards for the Forums graph
http://ldbcouncil.org/developer/snb and https://neo4j.com/fosdem20
Neo4j 4.0 Scalability in Action
Sharding the LDBC Social Network Benchmark
Up to 300x reduced latency
Up to 10x Performance improvement
First results, more to come!
BobJoe
• Designed with new cases in mind - PII, credit
card, patient information, etc.
• Based on Role-based Access Control for
graphs
• Restrictions on what data can be seen by
different users, applied to all database
interactions
• Implicit security view of the data for each user
through schema-based security definitions
• Grant/Deny permissions to traverse, read or
write data based on node labels, relationship
types or database and property names
• Security rules are replicated across the cluster
via roles that are associated with the users
Security and Data Privacy
Baseline_Personnel
_Security_Standard
Security_Check Counter_Terrorism
_Check
Developed_Vetting
Security and Data Privacy in Practice
Data Query Prior to Neo4j 4.0
Reactive Programming with Neo4j 4.0
The Largest Investment in Graph Databases
Built on Neo4j’s robust native foundation, Neo4j 4.0 brings:
Unlimited scalability with sharding & federation
A fully reactive architecture for modern applications
Granular security controls for security & privacy
Deployment flexibility with multi-database
Neo4j 4.0
Grab it now!
https://neo4j.com/download
24
Neo4j Desktop
from graph idea to Neo4j in production
25
Neo4j Desktop
essential tools
- Neo4j Browser for Cypher editing workflows
- Neo4j Bloom for interactive graph exploration
- Neo4j Labs tools: Neo4j ETL, Halin, Query Log Analyzer, Neuler
- Awesome 3rd party applications from Kineviz, yWorks, and others!
26
Neo4j Desktop
connect to Neo4j databases anywhere
- locally managed single-instances for development and experimentation
- on-premise in test and staging environments
- in production deployments in the cloud using Aura
27
Neo4j Desktop
developer workflows in 2020
- integration with local filesystem
- CLI tooling to integrate with build systems
- project templates for bootstrapping application development
- easy prototyping of graph application user interfaces
- first-class GraphQL development
- fantastic integration with Aura
28
Neo4j Desktop
from graph idea to Neo4j in production
- essential tools for working with Neo4j
- locally managed Neo4j Enterprise Edition for Developers
- connect your development to Neo4j anywhere
- BI Connector
- Kafka
Integrations
Neo4j 4.0-all-the-things!
Ready Today:
• Java, .NET, JavaScript, Spring Data Neo4j Drivers ✅
• Neo4j Desktop ✅
• Neo4j Browser ✅
Work in Progress:
• Python (Q2) & Go (Q4) - Coming!
• Graph Data Science Library (Q2) - Coming!
• Bloom Q2 - Coming!
• Aura (Q2) - Coming!
THANK YOU!

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What's New in Neo4j - David Allen, Neo4j

  • 1. Neo4j: WHAT’S NEW? M. David Allen Partner Solution Architect - Neo4j
  • 3. The Evolution of Databases 1960 1970 1980 1990 2000 2010 2020 2030 Hierarchical Relational (+ Object Relational and Object Mapping) Desktop Object NoSQL/NOSQL/NewSQL/Specialised Graph Databases Cloud Databases
  • 4. The Evolution of Databases 1960 1970 1980 1990 2000 2010 2020 2030 TRADITIONAL OLTP/RELATIONAL Store and retrieve data 2-3 hops in a query
  • 5. The Evolution of Databases 1960 1970 1980 1990 2000 2010 2020 2030 TRADITIONAL OLTP/RELATIONAL Store and retrieve data 2-3 hops in a query BIG DATA TECHNOLOGY Aggregate and filter data 1 hop in a query
  • 6. Connections in data The Evolution of Databases 1960 1970 1980 1990 2000 2010 2020 2030 TRADITIONAL OLTP/RELATIONAL Store and retrieve data 2-3 hops in a query BIG DATA TECHNOLOGY Aggregate and filter data 1 hop in a query Millions of hops
  • 7. Connections in data The Evolution of Databases TRADITIONAL OLTP/RELATIONAL Store and retrieve data 2-3 hops in a query BIG DATA TECHNOLOGY Aggregate and filter data 1 hop in a query Millions of hops Real time storage & retrieval Long running queries Real-time connected insights “Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code” Volker Pacher, Senior Developer
  • 8. What Is Different in Neo4j? Index-Free Adjacency
  • 10. • Physical data and transaction isolation underneath shared infrastructure • Multiple databases run in parallel on a single Neo4j Cluster (or standalone server) • Agility through cloud-friendly management: quickly move databases from one online cluster to another Great for SaaS providers, Enterprises, data scientists and developers Multi-tenancy with Neo4j 4.0
  • 11. The foundation: Causal Cluster Unbounded Scalability in Neo4j 4.0 The evolution: Fabric
  • 12. The Quirkness of [Maybe] Eventual Consistency Art by Pascal Jousselin - Europe Comics Publisher Dupuis - Creative Commons
  • 13. Raft-based cluster and ACID database: - Graph transactions over ACID Consistency - Maintains integrity over time Causal Clustering with Neo4j Replica Servers Query, View Core Servers Synced Cluster
  • 14. • Unbounded scalability to Neo4j by adding horizontal scale-out to the power of Neo4j’s existing replicated scale-up model. • Two modes of operations: SHARDING: operate over single large graph FEDERATION: query across disjoint graphs Introducing Sharding and Federated Graphs
  • 15. • Unbounded scalability to Neo4j by adding horizontal scale-out to the power of Neo4j’s existing replicated scale-up model. • Executes queries in parallel on multiple databases, combining or aggregating results. • Chains queries together from multiple databases for sophisticated real-time analyses. Multi-graph Cypher Queries SQL UNWIND corporate.graphIds() AS gid CALL { USE corporate.graph( gid ) MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) RETURN boss.name AS Boss, sub.name AS Subordinate, count(report) AS Total } RETURN Boss, Subordinate, Total ORDER BY Total Cypher in Neo4j 4.0
  • 16. http://ldbcouncil.org/developer/snb and https://neo4j.com/fosdem20 Neo4j 4.0 Scalability in Action Sharding the LDBC Social Network Benchmark • 1-shard for the Persons graph • N-shards for the Forums graph
  • 17. http://ldbcouncil.org/developer/snb and https://neo4j.com/fosdem20 Neo4j 4.0 Scalability in Action Sharding the LDBC Social Network Benchmark Up to 300x reduced latency Up to 10x Performance improvement First results, more to come!
  • 18. BobJoe • Designed with new cases in mind - PII, credit card, patient information, etc. • Based on Role-based Access Control for graphs • Restrictions on what data can be seen by different users, applied to all database interactions • Implicit security view of the data for each user through schema-based security definitions • Grant/Deny permissions to traverse, read or write data based on node labels, relationship types or database and property names • Security rules are replicated across the cluster via roles that are associated with the users Security and Data Privacy Baseline_Personnel _Security_Standard Security_Check Counter_Terrorism _Check Developed_Vetting
  • 19. Security and Data Privacy in Practice
  • 20. Data Query Prior to Neo4j 4.0
  • 22. The Largest Investment in Graph Databases Built on Neo4j’s robust native foundation, Neo4j 4.0 brings: Unlimited scalability with sharding & federation A fully reactive architecture for modern applications Granular security controls for security & privacy Deployment flexibility with multi-database Neo4j 4.0
  • 24. 24 Neo4j Desktop from graph idea to Neo4j in production
  • 25. 25 Neo4j Desktop essential tools - Neo4j Browser for Cypher editing workflows - Neo4j Bloom for interactive graph exploration - Neo4j Labs tools: Neo4j ETL, Halin, Query Log Analyzer, Neuler - Awesome 3rd party applications from Kineviz, yWorks, and others!
  • 26. 26 Neo4j Desktop connect to Neo4j databases anywhere - locally managed single-instances for development and experimentation - on-premise in test and staging environments - in production deployments in the cloud using Aura
  • 27. 27 Neo4j Desktop developer workflows in 2020 - integration with local filesystem - CLI tooling to integrate with build systems - project templates for bootstrapping application development - easy prototyping of graph application user interfaces - first-class GraphQL development - fantastic integration with Aura
  • 28. 28 Neo4j Desktop from graph idea to Neo4j in production - essential tools for working with Neo4j - locally managed Neo4j Enterprise Edition for Developers - connect your development to Neo4j anywhere
  • 29. - BI Connector - Kafka Integrations
  • 30. Neo4j 4.0-all-the-things! Ready Today: • Java, .NET, JavaScript, Spring Data Neo4j Drivers ✅ • Neo4j Desktop ✅ • Neo4j Browser ✅ Work in Progress: • Python (Q2) & Go (Q4) - Coming! • Graph Data Science Library (Q2) - Coming! • Bloom Q2 - Coming! • Aura (Q2) - Coming!