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graphdb: All content tagged as graphdb in NoSQL databases and polyglot persistence

How to Import Large Graphs to Neo4j With Spring Data

In my case, I wanted to create a simple recommendation engine (the domain doesn’t matter so much). To do that, I had to import FAST 20 million nodes of one-to-many, sparse matrix data. This became a bit more complicated (and interesting) task than originally anticipated, so it became a mini-project itself.

Bulk insert is a scenario that every database should have it covered.

Original title and link: How to Import Large Graphs to Neo4j With Spring Data (NoSQL database©myNoSQL)


Distributed Temporal Graph Database Using Datomic

Davy Suvee describes the solution in the Gremlin group and shares the code on GitHub:

Last week I spend some time on implementing the Blueprints interface on top of Datomic. The RDF and SPARQL feel of the Datomic data model and query approach makes it a good target for implementing a property graph. I finished the implementation and all unit tests are passing. Now, what makes it really cool is that it is the only distributed “temporal” graph database that I’m aware of. It allows to perform queries against a version of the graph in the past.

This is the first solution I’m reading about addressing the time dimension in a graph model.

Original title and link: Distributed Temporal Graph Database Using Datomic (NoSQL database©myNoSQL)

Neo4j REST API Tutorial

A detailed language agnostic intro to the Neo4j REST API:

In the above examples we have seen how nodes, relationships, and properties can be created, edited, updated, and deleted from the Neo4j HTTP terminal.

Original title and link: Neo4j REST API Tutorial (NoSQL database©myNoSQL)


Different Graph Visualization Models: Graphs Beyond the Hairball

Networks are usually drawn using a technique called node-link diagrams. While that works well for small graphs (the technical name for networks), it breaks down beyond a few dozen nodes. […] For a while now, people in visualization have talked about the graph without the graph, i.e., graph visualization without the hairballs. Networks are clearly important and challenging data, and it seems a bit myopic to only look at node-link visualization. Node quilts and the PivotGraph represent promising steps into a very different direction.

These are some good answers to how to scale graph visualizations.

Original title and link: Different Graph Visualization Models: Graphs Beyond the Hairball (NoSQL database©myNoSQL)


NoSQL Databases Adoption in Numbers

Source of data is Jaspersoft NoSQL connectors downloads. RedMonk published a graphic and an analysis and Klint Finley followed up with job trends:

NoSQL databases adoption

Couple of things I don’t see mentioned in the RedMonk post:

  1. if and how data has been normalized based on each connector availability

    According to the post data has been collected between Jan.2011-Mar.2012 and I think that not all connectors have been available since the beginning of the period.

  2. if and how marketing pushes for each connectors have been weighed in

    Announcing the Hadoop connector at an event with 2000 attendees or the MongoDB connector at an event with 800 attendeed could definitely influence the results (nb: keep in mind that the largest number is less than 7000, thus 200-500 downloads triggered by such an event have a significant impact)

  3. Redis and VoltDB are mostly OLTP only databases

Original title and link: NoSQL Databases Adoption in Numbers (NoSQL database©myNoSQL)

Sones GraphDB Adds Data Visualization

An interesting addition for the upcoming sones GraphDB 2.1:

With the abil–ity to run queries and use plug-ins to deter–mine how the out–put will look like the Web–Shell is a per–fect place to enhance user expe–ri–ence. Since there are sev–eral out–put plug-ins avail–able with ver–sion 2.0 already (JSON, XML, Text, HTML,…) we thought it would be a great idea to have a sim–ple visu–al–iza–tion imple–mented just by adding a new out–put plug-in to GraphDB.

sones GraphDB data visualization

Original title and link: Sones GraphDB Adds Data Visualization (NoSQL database©myNoSQL)


Sones GraphDB Changes License for Libraries

If you check the quick review of existing graph databases and the NoSQL graph databases matrix you’ll notice that most of these came under either an AGPL license or a commercial one.

The game changed radically when Neo4j became available also under a GPL license. And now, Sones has changed the license of their GraphDB connectors to LGPL.

I’m no lawyer but I think this means you can use Sones GraphDB without having to open source your product even if commercial. And because the way you interact with Sones GraphDB is through its connectors it doesn’t matter anymore what the core graph database license is.

Original title and link: Sones GraphDB Changes License for Libraries (NoSQL database©myNoSQL)

Sones Hires New CEO to Increase Sales and Expand Partner Program

According to TechCrunch Europe, Sones, producers of the GraphDB graph database, has hired a new CEO to focus on increasing sales and expanding their partner programs. This only weeks after Sones has announced a new round of funding.

I think I’ve already said it a couple of times: competition on the graph database segment of the NoSQL market is getting more interesting by the day.

Original title and link: Sones Hires New CEO to Increase Sales and Expand Partner Program (NoSQL databases © myNoSQL)

Sones Receives Investment

This is not news anymore, but Sones, producers of GraphDB, have raised an undisclosed amount of additional funding. I guess things will get a bit hotter in the graph database space where there are already a few quite interesting competitors.

On a related note I was wondering how are graph database producers perceived keeping in mind object databases’ history — touted as the replacement of relational databases, thing that never really happened. And if not somehow this is the reason graph databases are trying to catch the NoSQL train.

Original title and link: Sones Receives Investment (NoSQL databases © myNoSQL)


Graph Theory and Databases

Pere Urbón-Bayes must check slides deck on graph databases and their applicability. I like this graph database products slide most:

  • Neo4j: open source database NoSQL graph
  • Dex: the high performance graph database
  • HyperGraphDB: an IA and semantic web graph database
  • Infogrid: the Internet graph database
  • Sones: SaaS dot Net graph database
  • VertexDB: high performance database server

By the way I’ve heard Pere (@purbon) is currently looking for a job ;-).

Original title and link: Graph Theory and Databases (NoSQL databases © myNoSQL)

NoSQL Frankfurt: A Quick Review of the Conference

Yesterday was the NoSQL Frankfurt conference and today we have the chance to review some of the slide decks presented.

Beyond NoSQL with MarkLogic and The Universal Index

Nuno Job (@dscape) has presented on MarkLogic — an XML server we haven’t talked too much about, its universal index, and a couple of other interesting features.

The GraphDB Landscape and sones

Achim Friedland (@ahzf) has provided a very interesting overview of the graph databases products, the goals and some scenarios for graph databases, a brief comparison of property graphs with other models (relational databases, object-oriented, semantic web/RDF, and many other interesting aspects.

Data Modeling with Cassandra Column Families

Gary Dusbabek (@gdusbabek) has covered data modeling with Cassandra (the topic I’m still finding to be one of the most complicated).

Neo4j Spatial - GIS for the rest of us

Peter Neubauer (@peterneubauer) covered another interesting topic in the data space: geographic information (GIS) in graph databases.

Even if GISers suggested this integration some time ago Neo4j announced recently support for GEO.

Cassandra vs Redis

Tim Lossen (@tlossen) slides compare Cassandra and Redis from the perspective of a Facebook game requirements. All I can say is that the conclusion is definitely interesting, but you’ll have to check the slides by yourselves.

Mastering Massive Data Volumes with Hypertable

Doug Judd — who impressed me with his fantastic Hypertable: The Ultimate Scaling Machine at the Berlin Buzzwords NoSQL conference — gave a talk on Hypertable, its architecture and performance. The presentation also mentioned two Hypertable case studies: Zvents (an analytics platform) and (spam classification)[1]:

More presentations will be added as I’m receiving them.

  1. Just recently I’ve posted about Hadoop being used for spam detection.  ()

Original title and link: NoSQL Frankfurt: A Quick Review of the Conference (NoSQL databases © myNoSQL)

Graph Databases: What Are They and Where do They Fit

InfoQ’s Jonathan Allen talking to Daniel Kirstenpfad, founder and CTO of sones GmbH, creators of sones GraphDB:

Jonathan Allen: Can you explain what a graph databases is and why developers would choose one over a tradition database?

Daniel Kirstenpfad: […] o unlike other database approaches which only implicitly can form a graph structure a graph database explicitly represents a graph. And while other databases need to use indices and relational helpers (like relational tables which are coupled using JOINs) a graph database can traverse from one object to the next objects because those objects are organized to have index free adjacency.

While experimenting with another graph database, neo4j, I’ve found the lack of implicit direct node referenceability quite awkward.

Original title and link: Graph Databases: What Are They and Where do They Fit (NoSQL databases © myNoSQL)