The real center of the network or also known as The King of the Network, Closeness Centrality is a measure which represents the relative location of the Vertice to the center of the network, or better to say the average distance to all other Vertices within that network.
This measure results in the high effectiveness of information spreading/flow within the given network, because of the necessary number of Edges to cross to reach to any given connected Vertice.
Read on to see why this is useful and how you can calculate it in SQL Server 2019.
Today, I want to show you how Graph Processing Tables can make your data models flexible and smart. Let’s say we work in a e-commerce company, we have many users and products just like Amazon. We also have many warehouses, same product might be located in multiple warehouses. Whenever we want to ship a product, we want to pick the closest warehouse to buyer. In this way, we should be able save good amount of money for shipping and products will arrive to our customers locations faster.
Click through for the demo.
SHORTEST_PATH() function will allow you to traverse the given graph looking for the shortest path between different Nodes. It will use the Arbitrary Length Pattern to define the traversal path. This function will not return any results any results in SELECT clause because it must be used within MATCH clause only!
To my understanding because one of the mechanisms being used is depth-first search, in situation where multiple shortest path do exist, the function will return the first one only.
Click through for a detailed article on the topic. There are some nice parts to this but also a couple not-so-nice limitations in the current CTP.
The > determines the direction of the query. By changing this arrow into other direction, you can use the same relation table to find who manages employee. For example, let’s rewrite this query and look who manages East Manager
SELECT emp.Name as Emp, emp2.Name as Mgr
FROM Hr as emp, ReportsTo as r, Hr as emp2
WHERE MATCH(emp<-(r)-emp2) and emp.empid = 8
There are a few relational approaches which work well for hierarchies, though they all come with tradeoffs in terms of read complexity, write complexity, and development complexity. Click through for Hasan’s take on the graph-based approach.
We are expanding the graph database capabilities with several new features. In this blog we discuss one of these features that is now available for public preview in SQL Server 2019, Shortest Path, which can be used to find a shortest path between two nodes in a graph. The shortest path function can also be used to compute a transitive closure or for arbitrary length traversals.
With CTP3.1, we are introducing a new function SHORTEST_PATH, which can be used inside MATCH to find a shortest path between any 2 nodes in a graph or to perform arbitrary length traversals. Users can specify a pattern they would like to search for in the graph using a regular expression style syntax.
I’d be interested in seeing how well it performs. But it’s good to see graph functionality fleshed out a bit more.
We’ve been using the work we did for the Kafka sink – Neo4j extension and have made it available via remote connections over our binary bolt protocol. So you can stream your events from Apache Kafka®directly into Neo4j to create, update and enrich your graph structures. Then it is really up to you what you want to with the event data.
The events can come from frontend systems, API notifications, other databases or streaming systems like Apache Spark™ and Apache Flink®.
Read on for details and demos.
In my previous post I shared a SQL Server 2017 graph database of US capitals. Graphs are a computer science core competency and present some interesting challenges for programmers. Most notable of these challenges is finding the shortest path between nodes. Dijkstra’s algorithm is a commonly taught algorithm for finding shortest path. Dijkstra’s is often asked about during entry level developer interviews and it is a great algorithm to implement when learning a new language since it requires utilizing loops, logic, and data structures.
Here’s my implementation of Dijkstra’s algorithm using PowerShell, traversing a graph of US capitals. Rather than manage our own graph nodes and edges, we’ll utilize graph tables and queries in SQL Server. There’s a lot of different ways to implement this in PowerShell, my first cut of this ended up using a hash table so I could perform random access. There’s a give-and-take with custom PowerShell objects, which sacrifice random access for some other benefits.
Click through for the code.
While there’s countless relational databases out there for practice, there’s not much in the way of graph databases. It is my intent to share my graph databases with the world in hopes that it removes the friction associated with your learning.
US Capitals is a popular data set for working with graphs. Nodes identify a state capital. An edge connects a capital in one state with the capital of a neighboring state. Only the lower 48 states are present. While the data is readily available, I was unable to find TSQL scripts to create the graph using SQL Server 2017 graph database. I created those scripts and have made them readily available on GitHub.
I’m interested in the forthcoming post on Dijkstra’s algorithm; I think the last time I saw that was my undergrad days.
Now, in the next step we shall create a derived view, which shall contain the list with all Persons and Businesses, joining them together:CREATE OR ALTER VIEW dbo.Followers AS SELECT PersonId as Id, FullName FROM dbo.Person UNION ALL SELECT BusinessId, BusinessName FROM dbo.Business;
Now, the real new thing is that we can use such derived tables in SQL Server 2019 CTP 2.1 and Azure SQL Database together with the MATCH clause, in the statements such as the one below where we list all the followers of the “Real Stuff” company:SELECT Followers.ID, Followers.FullName FROM Followers, Follows, Company WHERE MATCH(Followers-(Follows)->Company) AND CompanyName = 'Real Stuff'
This query works fine, delivering us the expected results while generating a pretty complex execution plan in the background.
Niko focuses on heterogeneous nodes and edges, as well as derived views.
We will be further expanding the graph database capabilities with several new features. In this blog we will discuss one of those features that is now available for public preview in Azure SQL Database and SQL Server 2019 CTP2.1: use of derived tables and views on graph tables in MATCH queries.
Graph queries on Azure SQL Database now support using view and derived table aliases in the MATCH syntax. To use these aliases in MATCH, the views and derived tables must be created either on a node or edge table which may or may not have some filters on it or a set of node or edge tables combined together using the UNION ALL operator. The ability to use derived table and view aliases in MATCH queries, could be very useful in scenarios where you are looking to query heterogeneous entities or heterogeneous connections between two or more entities in your graph.
It’s good to see the product team expand on what they released in 2017, getting the graph product closer to production-quality.