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Graph Data Processing with Cypher

Graph Data Processing with Cypher

By : Ravindranatha Anthapu
4.7 (10)
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Graph Data Processing with Cypher

Graph Data Processing with Cypher

4.7 (10)
By: Ravindranatha Anthapu

Overview of this book

While it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way. This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You’ll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users. After having worked through this book, you’ll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.
Table of Contents (18 chapters)
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1
Part 1: Cypher Introduction
4
Part 2: Working with Cypher
9
Part 3: Advanced Cypher Concepts

Executing dynamic Cypher

Cypher does not allow dynamic query execution. If you are getting a value in the data that you want to use as a label on the node, or you need to create a relationship with that name, that is not easy to do in Cypher. It is possible to use the FOREACH trick to check for the value and make the code deterministic, but this is not a practicable solution if the number of combinations is more than five, for example. In these scenarios, APOC procedures can help us to either to build a string and execute it or add or create nodes and relationships with dynamic values:

  1. First, we will take a look at how to add a new label to a node using a dynamic value:
    WITH {label: "MyTest", id:10 , name: 'Another'} as data
    MERGE(p:Person {id:data.id})
    SET p.name=data.name
    WITH data, p
    CALL apoc.create.addLabels([p], [data.label])
    YIELD node
    RETURN node
  2. This query creates a Person node with the data and appends the label from the input data using the apoc...

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