Building Web Applications with Python and Neo4j by Sumit Gupta

By Sumit Gupta

Strengthen intriguing real-world Python-based net purposes with Neo4j utilizing frameworks similar to Flask, Py2neo, and Django
About This Book

boost a collection of universal purposes and options with Neo4j and Python
safe and installation the Neo4j database in production
A step by step consultant on enforcing and deploying interactive Python-based net purposes on graph facts model

Who This e-book Is For

If you're a Python developer and wish to extend your realizing of Python-based net functions over Neo4j graph info versions, this is often the publication for you.
What you'll Learn

comprehend the licensing and deploy of the Neo4j database and paintings with its numerous instruments and utilities
examine the intricacies of Cypher as a graph question language
paintings with Cypher to create and regulate graph information models
combine Python and Neo4j utilizing Py2neo
increase REST-based companies over social community information utilizing Flask and item graph versions over Neo4j
combine Django-based net functions over graph facts types utilizing Neomodel
discover diverse deployment versions and their applicability with current applications

In Detail

Py2neo is a straightforward and pragmatic Python library that gives entry to the preferred graph database Neo4j through its RESTful net carrier interface. This brings with it a seriously refactored middle, a purifier API, larger functionality, and a few new idioms.

You will commence with licensing and fitting Neo4j, studying the basics of Cypher as a graph question language, and exploring Cypher optimizations. you'll find the way to combine with a variety of Python frameworks comparable to Flask and its extensions: Py2neo, Neomodel, and Django. eventually, the deployment features of your Python-based Neo4j purposes in a construction setting is usually coated. via sequentially operating during the steps in each one bankruptcy, you'll quick study and grasp some of the implementation information and integrations of Python and Neo4j, aiding you to boost your use circumstances extra quickly.

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Extra info for Building Web Applications with Python and Neo4j

Example text

If the given criterion exists, it will not do anything; otherwise, it will create a new node. craeted_timestamp = timestamp() The preceding example first matches the criteria provided in the MERGE statement, and then defines two more clauses—ON MATCH and ON CREATE. ON MATCH will be executed in the scenarios where conditions defined with the MERGE statement are satisfied; otherwise, ON CREATE will be executed. However, in any case, either ON MATCH or ON CREATE will be executed. This would also avoid the creation of duplicate elements.

MATCH is similar to SQL FROM, where we need to specify the appearance of a specific pattern that needs to be searched or scanned to determine the dataset (also known as subgraph) on which further operations need to be performed. MATCH can be used in conjunction with any of the create, update, or delete clauses. It is mostly used in conjunction with the WHERE clause, which specifies and imposes conditions, restrictions, or predicates to the MATCH clause. name is NOT NULL RETURN x; In the preceding example, we are searching the occurrence of all nodes that are labeled with MALE and have the two properties, age and name.

Properties are key/value pair wrapped around the curly braces and two properties are separated by a comma. Consider the following example: Match (a:MALE { name: "John", age : 24 }) return a; In the preceding example, we are searching for a node that is annotated with the label MALE and has two properties name and age, with the values as "John" and 24. Using the where clause with patterns Pattern alone cannot satisfy all our needs, and that's the reason the where clause was introduced; it can be used with patterns for further filtering of data.

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Building Web Applications with Python and Neo4j by Sumit Gupta
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