Book Image

Graph Data Processing with Cypher

By : Ravindranatha Anthapu
Book Image

Graph Data Processing with Cypher

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)
1
Part 1: Cypher Introduction
4
Part 2: Working with Cypher
9
Part 3: Advanced Cypher Concepts

Summary

In this chapter, we have taken a look at the graph data model and explored the graph using database statistics and schema visualization.

In this chapter, we learned about how to find nodes by leveraging indexes or without using indexes; compared the performance of different queries to understand the importance of using indexes for querying; used PROFILE to understand performance issues in the queries; learned about using the STARTS WITH clause, which leverages an index; worked with point properties to leverage the geospatial capabilities of Graph; traversed the graph efficiently; leveraged count stores; and built complex queries using the WITH clause.

In the next chapter, we will continue our graph exploration by performing more complex queries, which will involve filtering, sorting, and aggregation.