Tag: graphs

Graph Database Crash Course

I am one of the lead architects for Spider, a php-based graph database abstraction library similar to Eloquent or Doctrine. In getting ready for the first truly functional release, I thought I would share some great links to a crash course in Graph Databases as a whole. You can also check out by graphs tag for other posts about graph databases and php.

Some great resources:

The real purpose of this post was to repost this incredible breakdown of the top contenders for graph databases in 2015


PHP Graph Database Tools

I have talked about Graph Databases and PHP. It occurred to me that there are any number of fantastic tools available now. So this is a curated list of several graph databases with bindings in PHP, some comparison between them, and a few other tools that are database agnostic. These NoSQL databases can have some incredible benefits. It is definitely worth checking out.


The GraphDB landscape is still emerging, and a lot of technologies and people are fighting to define it. It’s especially difficult since SQL has dominated since, well, the birth of the internet. Developers know SQL and are comfortable with SQL database structures. Many of the burgeoning NoSQL (not only sql) databases try to mimic SQL in a lot of ways.

But, in many ways, graph databases are just different. They require a different way of thinking. There is no standard, no Structured Query Language that everyone agrees on. At least not yet.

Enter the Tinkerpop group, a collection of very smart engineers who are slowly standardizing graph databases. If you want to use graph data, tinkerpop is your first step, and NOT just for PHP. In fact, all of their tools are language agnostic.

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PHP, Graph Databases, and the future

Graph. Databases. Are. Awesome.

At least in certain use cases.

Who knows whom, and how? Graph data.

By way of a quick intro, a graph database is a type of datastore called NoSQL or “Not Only SQL”. Since the beginning of the interwebs, the dominant form of databases were SQL, with MySQL taking the lead. And MySQL is great. However, when your data is highly related in complex ways, SQL starts to crack. Tack onto that the enormous amounts of data manipulated by today’s applications, and SQL really starts to slow things down both in the actual read/write operations and in the development of code because of ridiculously complicated JOIN statements.

This is where graph databases shine because they treat the relationships between data as first class citizens, not an after thought. Therefore, you data is actually saved as you would expect relational data to be saved: as a property graph.

Now, its not only easier to visualize, but much, much faster to traverse. Graph databases shine in asking questions about your data like:

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