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What's New in Neo4j
- 2. What Makes Neo4j Different? 2 TRADITIONAL DATABASES BIG DATA TECHNOLOGY Store and retrieve data Aggregate and filter data Connections in data Real time storage & retrieval Real-Time Connected Insights Long running queries Aggregation & filtering “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 Up to 3 Max # of hops 1 Million s
- 4. 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” What Makes Neo4j Different : “Minutes to Milliseconds” Real-Time Query Performance
- 6. What Is Different In Neo4j? Cypher Query Language 6 MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, count(report) AS Total Project Impact Less time writing queries • More time understanding the answers • Leaving time to ask the next question Less time debugging queries: • More time writing the next piece of code • Improved quality of overall code base Code that’s easier to read: • Faster ramp-up for new project members • Improved maintainability & troubleshooting
- 8. Neo4j Enterprise Maturity & Robustness 8 Neo4j Security Foundation Multi-Clustering Support for Global Internet Apps Rolling Upgrades Schema Constraints Concurrent/Transactional Write Performance Auto Cache Reheating For Restarts, Restores and Cluster Expansion Neo4j 3.4 now supports rolling upgrades 3.4 3.5 Upgrade older instances while keeping other members stable and without requiring a restart of the environment 3.5
- 10. Neo4j: Enabling the Connected Enterprise Consumers of Connected Data 10 AI & Graph Analytics • Sentiment analysis • Customer segmentation • Machine learning • Cognitive computing • Community detection Transactional Graphs • Fraud detection • Real-time recommendations • Network and IT operations management • Knowledge Graphs • Master Data Management Discovery & Visualization • Fraud detection • Network and IT operations • Product information management • Risk and portfolio analysis Data Scientists Business Users Applications
- 18. 18 Graph Visualization Options for Neo4j Neo4j Bloom Provided by Neo4j Exclusively optimized for Neo4j graphs Deploys easily in Neo4j Desktop Focused on graph exploration thru a code-free UI Near natural language search Currently caters to data analysts and graph SMEs Currently for individual or small team use Viz Toolkits 3rd party e.g. vis.js, d3.js, Keylines Some offer data hooks into Neo4j, others may require custom integration Offer robust APIs for flexible control of the viz output Cater to developers who will create a custom solution, usually with limited interactivity Departmental, enterprise or public use BI Tools 3rd party e.g. Tableau, Qlik Not optimized for graph data, may require a special connector UI for dashboard and report creation with many kinds of viz, in addition to graph viz Cater to business users and data analysts Departmental, cross- department or enterprise use Graph Viz Solutions 3rd party e.g. Linkurious, Tom Sawyer Have to support multiple graph models and sources Feature UI for exploration or APIs for customizing output and embedding/publishing Solutions may cater to business users, analysts or developers Small team, departmental or cross-department use Little technical expertise Most technically involved Exploration focused Publishing / Consumption focused Smaller deployments Larger deployments
- 19. Perspective Visualization Exploration Inspection Editing Search 19 Business view of the graph Departmental views • Hiding PII • Styling GPU Accelerated Visualization High performance physics & rendering Direct graph interactions Select, expand, dismiss, find paths Node + Relationship details Browse from neighbor to neighbor Create, Connect, Update Code-free graph changes Near-natural Language Search Full-text search • Graph patterns • Custom Search Phrases Neo4j Bloom Features
- 20. Confidential - Neo4j, Inc. A Journey through Neo4j 3.X 20 3.0 3.1 3.2 3.3 3.4 3.5 Causal Clustering N/A - Causal Clustering released - Multi-datacenter support - Tiered replicas - Least-connected load balancing - ID re-use - Multi-clustering - Improved large Txn handling Cypher & Performance - User-Defined Procedures - Increased relationship type limits - Native Label index - Node Keys - Composite Indexes - Depth query in DISTINCT function - Compiled runtime - Faster and less memory intensive runtime - Native Numeric schema indexes - Local locks for schema changes - Datetime data types - Spatial data types - 70% faster Cypher reads (average) - Native String schema index - Full-text search - Index-based ORDER BY - Native index for all data types - Improved large Txn handling Security - Native users and roles - LDAP integration - Kerberos authentication plugin - Intra-cluster encryption - Property blacklisting - SNI / Hostname verification - Cluster discovery service encryption Developer - Bolt (binary) Protocol w/ Java, JS, .NET, Python - APOC v.1 - Bolt+routing - Schema viewer within Browser - Faster Neo4j Browser UI - Offline bulk import performance improvements - Neo4j Desktop - Graph Algorithms v.1 - Go Driver - New Graph Algorithms Operations - Official Debian packages - View and manage running queries - Execution guard for long running queries - Detailed query metrics - Official RPM packages - IPv6 Support - Dynamic config settings - Off-heap page cache metadata - Rolling Upgrades - Improved online backup performance (in CC)
- 21. Confidential - Neo4j, Inc. Causal Cluster APPLICATION SERVERS Replica Servers Query, View Core Servers Synced Cluster CORE SERVERS READ REPLICAS Async Replication Writes (transactions) Reads (Graph Queries)
- 22. Confidential - Neo4j, Inc. Causal Cluster - Multi-Data Center
- 24. Where AI and ML fit in 24 Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers & APIs AI
- 25. Confidential - Neo4j, Inc. Differences between ML and Analytics 25 Machine learning: • Determine domain parameters • Historical-based discoveries • Learn and improve without explicit programming
- 26. Confidential - Neo4j, Inc. Graph analytics: • Uses inherent graph structures • Uncover real-world networks through their connections • Forecast complex network behavior and identify action Differences between ML and Analytics
- 27. Confidential - Neo4j, Inc. Today challenges with Machine Learning: • Doesn’t take multiple relationship hops into account • Takes time to iteratively train a model • Computational inefficiency of connecting data Benefits of Mixing Graph Analytics with ML Graphs bring: • Context to machine learning • Feature filtration • Connected feature extraction
- 28. Confidential - Neo4j, Inc. • Support for many languages (Python, .Net, Java, Go, JavaScript, R, etc.) • Different data integration options • Triggers, event-driven architecture • User-defined functions and procedures Working with your Machine Learning algorithms and Neo4j