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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

Filtering with WHERE and WITH clauses

Now, let us build on the previous query and say we only want to return drugs that have more than 100 patients associated with them. This is the scenario in which the WHERE clause would come into the picture.

The Cypher query looks like this:

MATCH (d:Drug)<-[:HAS_DRUG]-()<-[:HAS_ENCOUNTER]-(p)
WITH DISTINCT d, p
WITH d.description as drug, count(p) as patients
WHERE patients > 100
RETURN drug, patients

To achieve what we want, we changed the query as shown in the highlighted section. We have to use the WITH clause first to collect the data and then apply a filter using the WHERE clause to prevent the data from being returned:

Figure 5.5 – Drugs with more than 100 patients associated with them

The preceding screenshot shows the response. We can see there are only 10 drugs that have more than 100 patients who have a prescription for them.

We can also use SKIP and LIMIT to filter the data being...

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