Graph Processing with Neo4j Lecture BigData Analytics Julian M. Kunkel julian.kunkel@googlemail.com University of Hamburg / German Climate Computing Center (DKRZ) 2016-12-09 Disclaimer: Big Data software is constantly updated, code samples may be outdated. Overview Cypher Query Language (CQL) Interfaces Architecture Summary Outline 1 Overview 2 Cypher Query Language (CQL) 3 Interfaces 4 Architecture 5 Summary Julian M. Kunkel Lecture BigData Analytics, 2016 2 / 37 Overview Cypher Query Language (CQL) Interfaces Architecture Summary Neo4j [31, 32] Graph database written in Java Supports ACID transaction semantics One server scales to billions of nodes/relationships Performance: Millions of node traversals/s High availability (and performance) through clustering Declarative query language Cypher (instead of SQL) Note: Very loose connection to Hadoop ecosystem Prepare data in, e.g., HBASE for batch import in Neo4j Suboptimal import of Millions of nodes can take days Schema-optional: You can use a schema To gain performance To improve modeling, e.g., via constraints Many interfaces to the graph database Julian M. Kunkel Lecture BigData Analytics, 2016 3 / 37 Overview Cypher Query Language (CQL) Interfaces Architecture Summary Graph Data Model (Slight Changes for Neo4j) Nodes: Entity Edges: Relationship between two nodes They have a direction Property: (key, value) Attributes describe relationships/nodes Key is string, the value has a type Label: Organize nodes into groups Definitions for queries Path: One or more nodes with connecting relationships Traversal: Navigates through a graph to find paths Source: What’s a Graph Database [31] Julian M. Kunkel Lecture BigData Analytics, 2016 4 / 37 Overview Cypher Query Language (CQL) Interfaces Architecture Summary Example Graph Use-Cases Movie and actors data [31] Movies: label, title, released date, tagline People: label, name, born (date, optional) Relationships ACTED_IN

Lire le document entier