What's New in Neo4j - David Allen, Neo4j
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This document summarizes the key new features and capabilities in Neo4j 4.0. It discusses how Neo4j 4.0 provides unlimited scalability through sharding and federation. It also introduces a fully reactive architecture and granular security controls for privacy. Finally, it highlights how Neo4j Desktop can help developers work with Neo4j from idea to production.
![The Quirkness of [Maybe] Eventual Consistency
Art by Pascal Jousselin - Europe Comics Publisher Dupuis - Creative Commons](https://image.slidesharecdn.com/whatsnew-inneo4jdavidallen-200310011128/85/What-s-New-in-Neo4j-David-Allen-Neo4j-12-320.jpg)
![• 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](https://image.slidesharecdn.com/whatsnew-inneo4jdavidallen-200310011128/85/What-s-New-in-Neo4j-David-Allen-Neo4j-15-320.jpg)
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
- 21. Reactive Programming with 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!
- 31. THANK YOU!