Tiago Oliveira TIAGOOOLIVEIRA

Useful Links

##Neo4j GraphGist - Enterprise Architectures: Real-time Graph Updates using Kafka Messaging

Neo4j Use Case: Low Latency Graph Analytics & OLTP - Update 1M Nodes in 90 secs with Kafka and Neo4j Bolt

Introduction

A recent Neo4j whitepaper describes how Monsanto is performing real-time updates on a 600M node Neo4j graph using Kafka to consume data extracted from a large Oracle Exadata instance.

This modern data architecture combines a fast, scalable messaging platform (Kafka) for low latency data provisioning and an enterprise graph database (Neo4j) for high performance, in-memory analytics & OLTP - creating new and powerful real-time graph analytics capabilities for your enterprise applications.

	// this script uses the neo4j-shell-tools

	// create personal nodes
	import-cypher -d ; -i ./IMPORT/INPUT/PersonalNodes.csv -o ./IMPORT/OUTPUT/personalnodeout.csv create (n:url {id:{id}+1000,name:{name},type:{type}}) return n.name as name

	// create indices
	CREATE index on :url(name);
	CREATE index on :url(id);
	CREATE index on :url(type);

	#######
	# GENERAL NOTES
	#######

	* Node labels are case-sensitive. Customer and customer are different!
	* Relationship names are case-sensitive. OWNS and owns are different!
	* You can create nodes/relationships iff they don't exist using UNIQUE: http://docs.neo4j.org/chunked/snapshot/query-create-unique.html.

	#######
	# ADMIN QUERIES

	package prizedraw;

	import com.twilio.Twilio;
	import com.twilio.base.ResourceSet;
	import com.twilio.rest.api.v2010.account.Message;
	import com.twilio.type.PhoneNumber;
	import org.joda.time.DateTime;

	import java.util.Collections;
	import java.util.stream.Collectors;

	//Let's create the top of the tree, the PRODUCT
	create (n1:PRODUCT {id:1});

	//Let's create 100 children of the PRODUCT, COST_GROUPS connected to the top of the tree
	match (n1:PRODUCT {id:1})
	with range(1,100) as RANGE, n1
	foreach (r in RANGE \| create (n2:COST_GROUP {id:r})-[:PART_OF {quantity:round(rand()*100)}]->(n1) );

	//for every COST_GROUP, let's connect 100 children at a 3rd level, the COST_TYPEs
	match (n2:COST_GROUP)