Introduction

A separate database plugin to support Neo4j-specific features and configurations.

Use-case

• Users can choose and install Neo4j source and sink plugins.
• Users should see Neo4j logo on plugin configuration page for better experience.
• Users should get relevant information from the tool tip:
  
  • The tool tip should describe accurately what each field is used for.
• Users should not have to specify any redundant configuration.
• Users should get field level lineage for the source and sink that is being used.
• Reference documentation should be updated to account for the changes.
• The source code for Neo4j database plugin should be placed in repo under data-integrations.org.
• The data pipeline using source and sink plugins should run on both mapreduce and spark engines.

User Storie

• User should be able to install Neo4j specific database source and sink plugins from the Hub.
• Users should have each tool tip accurately describe what each field does.
• Users should get field level lineage information for the Neo4j source and sink.
• Users should be able to setup a pipeline avoiding specifying redundant information.
• Users should get updated reference document for Neo4j source and sink.
• Users should be able to read all the DB types.

Plugin Type

• Batch Source
• Batch Sink 
• Real-time Source
• Real-time Sink
• Action
• Post-Run Action
• Aggregate
• Join
• Spark Model
• Spark Compute

Design Tips

• Reference to the Neo4j driver manual: https://neo4j.com/docs/driver-manual/1.7/
• Reference to Cypher Query Language manual: https://neo4j.com/docs/cypher-manual/current/

Design

Neo4j Overview

Neo4j is a graph database management system with native graph storage and processing. In Neo4j, everything is stored in the form of an edge, node, or attribute. Each node and edge can have any number of attributes. Both nodes and edges can be labelled. Labels can be used to narrow searches.

Cypher Query Language

Cypher is a declarative graph query language that allows for expressive and efficient querying and updating of the graph.
Cypher is inspired by a number of different approaches and builds on established practices for expressive querying. Many of the keywords, such as WHERE and ORDER BY, are inspired by SQL. Pattern matching borrows expression approaches from SPARQL. Some of the list semantics are borrowed from languages such as Haskell and Python. 

Here are a few clauses used to read from the graph:

• MATCH: The graph pattern to match. This is the most common way to get data from the graph.
  
• WHERE: Not a clause in its own right, but rather part of MATCH, OPTIONAL MATCH and WITH. Adds constraints to a pattern, or filters the intermediate result passing through WITH.
  
• RETURN: What to return.
  

Here’s an example of simple Cypher Query:

Source Properties

Source Data Types Mapping

Sink Properties

Source Data Types Mapping

Approach

Create a new maven project in it's own repository.

Pipeline Samples

Please attach one or more sample pipeline(s) and associated data. 

Releases

         Release X.Y.Z

Related Work

Database plugin enhancements