Max Kießling

Graph algorithms play an increasingly important role in real-world applications. The Neo4j Graph Algorithms library contains a set of ~50 graph algorithms covering a lot of different problem domains. In our talk, we’ll present the architecture of the library and demonstrate the different execution phases using a real world example.

Graph algorithms play an increasingly important role in real-world applications. Use-cases that we see in the wild are related to fraud detection, fraud detection, retail recommendation and identifying influencers for marketing campaigns. The Neo4j Graph Algorithms library contains a set of ~50 graph algorithms covering the above-mentioned problem domains.

Running a graph algorithm in Neo4j involves three essential steps: loading the graph from the database in an optimized in-memory format, executing the algorithm, and streaming or writing of results. For the user, these steps are hidden behind single procedure calls, integrated in the Cypher query language.

In our talk, we will explain and demonstrate what happens in the system when a user calls an algorithm procedure. This involves scanning Neo4j store files, constructing our in-memory graph representation and executing an algorithm via our Java Graph API.

Attendees will learn how to setup and use the Neo4j Graph Algorithms Library. Furthermore, they will get a good understanding of how the library works internally and how to tune it for specific use-cases.

Abstract

Graph pattern matching is one of the most interesting and challenging operations in graph analytics. Query languages like openCypher, implemented in systems like Neo4j, SAP HANA Graph and Redis Graph, allow the intuitive definition of graph patterns including structural and semantic predicates.

For now, graph query languages are most prominent in graph database systems such as Neo4j. However, we think that many systems can benefit from having such a language in their toolbox. One of these systems is Apache Spark, which is one of the most popular open source frameworks in the context of distributed processing of large data volumes within complex analytical workloads. To bring the benefits of Cypher from the graph database realm into the world of Big Data, we at Neo4j started developing Cypher for Apache Spark (CAPS). CAPS is primarily focused on graph-powered data integration and graph analytical query workloads within the Spark ecosystem. In addition, CAPS is our testbed for Cypher language extensions as specified in the openCypher project; for example, multiple graphs, graph transformations and construction, and query composition.

In our talk, we want to motivate use-cases for CAPS and give an overview of new querying capabilities which we demonstrate using Apache Spark and Apache Zeppelin. Furthermore, we briefly present the internal architecture highlighting the main differences between Neo4j and CAPS.

Intended audience and goal of the talk

Developers and analysts, trying to express graph queries on distributed graphs

Links to background information on the given talk for the hungry and impatient

• OpenCypher
• Neo4j
• Apache Spark

Links to previous talks, code snippets or repositories

• CAPS

Abstract

Graph pattern matching is one of the most interesting and challenging operations in graph analytics. However, it is primarily supported by graph database systems such as Neo4j but, besides research prototypes, not generally available for distributed (not-only graph) processing frameworks like Apache Flink or Apache Spark.

In our talk, we want to give an overview of our current implementation of Cypher on Apache Flink. Cypher is the Neo4j graph query language and enables the intuitive definition of graph patterns including structural and semantic predicates. As the Neo4j graph data model is not supported out-of-the box by Apache Flink, we leverage Gradoop, a Flink-based graph analytics framework based on Apache Flink that already provides an abstraction of schema-free property graphs.

We will give a brief overview about the technologies used to implement Cypher, explain our query engine and give a demonstration of the available language features. Finally, we will discuss open challenges and missing features hopefully motivating people to contribute.

The project is a cooperation between the University of Leipzig and Neo Technology.

Intended audience and goal of the talk

Developers and analysts, trying to express graph queries on distributed graphs

Links to background information on the given talk for the hungry and impatient

• OpenCypher
• Neo4j
• Apache Flink
• Gradoop

Links to previous talks, code snippets or repositories

• GitHub
• Extended Property Graph Model
• FlinkForward 2015
• FOSDEM 2016