Press "Enter" to skip to content

Category: Graph

Building Graph Tables

Tomaz Kastrun uses a set of e-mails as his SQL Server 2017 graph table data source:

To put the graph database to the test, I took bunch of emails from a particular MVP SQL Server distribution list (content will not be shown and all the names will be anonymized). On my gmail account, I have downloaded some 90MiB of emails in mbox file format. With some python scripting,  only FROM and SUBJECTS were extracted:

writer.writerow(['from','subject'])
for index, message in enumerate(mailbox.mbox(infile)): content = get_content(message) row = [ message['from'].strip('>').split('<')[-1], decode_header(message['subject'])[0][0],"|" ] writer.writerow(row)

This post walks you through loading data, mostly.  But at the end, you can see how easy it is to find who replied to whose e-mails.

Comments closed

Graph Database In SQL Server

Niko Neugebauer has started a new series, this time on graph database functionality in SQL Server 2017:

Graph databases are based on graph theory, and employ nodes, edges, and properties. The graph theory is the study of the graphs that are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of nodes, edges which are connected by edges, arcs, or lines.
A graph can be directed or undirected (uni or bi-directional) that might point the direction of the relationship between the edges.
Graph databases can be compared to the Network Model Databases, that were focusing on solving the same problem the interconnected world.

The most popular graph database in the world currently is NEO4J, which is implemented in Java and is using CQL (Cypher Query Language), a language that has definitely inspired the SQL Graph T-SQL language extension.

Niko notes that this is not a fully mature product yet, but it’s an interesting start.

Comments closed