I support the proposal. I am assisting various companies as a system integrator of Neo4j. There is several Japanese telecommunications companies quickly grasping the state of the network topology. In addition, many advertising censure companies associate enormous metadata and use it for marketing using Neo4j and Cypher query. Cypher Query's flexible search and extraction mechanisms benefit from these activities. Also, many manufacturing industries in Japan interest in the graph database model. We believe that support of Cypher Query in Apache Spark can give Japanese graph data users a more convenient path to distributed processing. In Japan, communities that love Neo4j, Cypher Query are already active and frequently communicating. ( https://jp-neo4j-usersgroup.connpass.com/ ) With Cypher Query support from Apache Spark, they will be encouraged and will love Apache Spark. We are convinced that the Apache Spark developer community will expand further.
Regards, -- Mitsutoshi Kiuchi 2019年1月16日(水) 1:53 Xiangrui Meng <men...@gmail.com>: > Hi all, > > I want to re-send the previous SPIP on introducing a DataFrame-based graph > component to collect more feedback. It supports property graphs, Cypher > graph queries, and graph algorithms built on top of the DataFrame API. If > you are a GraphX user or your workload is essentially graph queries, please > help review and check how it fits into your use cases. Your feedback would > be greatly appreciated! > > # Links to SPIP and design sketch: > > * Jira issue for the SPIP: > https://issues.apache.org/jira/browse/SPARK-25994 > * Google Doc: > https://docs.google.com/document/d/1ljqVsAh2wxTZS8XqwDQgRT6i_mania3ffYSYpEgLx9k/edit?usp=sharing > * Jira issue for a first design sketch: > https://issues.apache.org/jira/browse/SPARK-26028 > * Google Doc: > https://docs.google.com/document/d/1Wxzghj0PvpOVu7XD1iA8uonRYhexwn18utdcTxtkxlI/edit?usp=sharing > > # Sample code: > > ~~~ > val graph = ... > > // query > val result = graph.cypher(""" > MATCH (p:Person)-[r:STUDY_AT]->(u:University) > RETURN p.name, r.since, u.name > """) > > // algorithms > val ranks = graph.pageRank.run() > ~~~ > > Best, > Xiangrui >
