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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

Querying the nodes

We have the patient data set along with the encounters with the healthcare system. We will look at a question and how we can represent it in Cypher. To find the total number of patients in the system, type the following into the console:

MATCH (p:Patient) RETURN count(p)

This Cypher query returns the total number of patients in the system. This screenshot shows the response in the browser:

Figure 4.7 – Patient count query

You can see that it is instantaneous. Neo4j maintains the count stores for labels, so when we ask for counts in this way, Neo4j uses the count stores to return the response. The response time is consistent when we have one node or one million nodes of this label.

The Patient node only had one label. We know the Encounter node has multiple labels. Let us write a query to find label distribution. The Cypher query looks as follows:

MATCH (e:Encounter)
RETURN labels(e) as labels, count(e) as counts

This...

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