Michael Hackstein

The complexity and amount of data rises. Modern graph databases are designed to handle the complexity but still not for the amount of data. When hitting a certain size of a graph, many dedicated graph databases reach their limits in vertical or, most common, horizontal scalability. In this talk I'll provide a brief overview about current approaches and their limits towards scalability. Dealing with complex data in a complex system doesn't make things easier... but more fun finding a solution. Join me on my journey to handle billions of edges in a graph database.

The amount and complexity of data rises. The need for a database capable of dealing with those requirements is rising as well. Modern graph databases are designed to handle the complexity but what about the increasing amount of data? When hitting a certain size of a graph many dedicated graph databases reach their limits in vertical or most common horizontal scalability.

In this talk Michael will address the glitches and most important the solutions on the journey to a scalable and distributed graph database. The main topics of this talk are:

• Modern architectures in graph databases
• Scaling a graph: Bottlenecks in read and write performance
• Index free adjacency or hybrid indexes for graphs?
• Dealing with billions of edges: A graphy journey to London with ArangoDB

What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Most applications need a stateful layer which holds the data. There are at least three necessary ingredients which are everything else than trivial to combine and of course even more challenging when heading for an acceptable performance.

Over the past years there has been significant progress in respect in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores.

What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Most applications need a stateful layer which holds the data. There are at least three necessary ingredients which are everything else than trivial to combine and of course even more challenging when heading for an acceptable performance.

Over the past years there has been significant progress in respect in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores.

Topics are:

• Challenges in developing a distributed, resilient data store
• Consensus, distributed transactions, distributed query optimization and execution
• The inner workings of ArangoDB, Cassandra, Cockroach and RethinkDB

The talk will touch complex and difficult computer science, but will at the same time be accessible to and enjoyable by a wide range of developers.

If you are using a graph database you might want to get a visual representation of your data. In this talk I will present a visualization tool build on top of the Open Source Database ArangoDB. This tool allows a user to explore the graph by visually traversing through it. I will also present some challenges of graph visualization and my solutions for them.

In the NoSQL world a type of databases has emerged which is called graph database. These databases allow you to store data in a graph format (like social networks) natively and query it efficiently. But how can we visualize the data? Typically the resulting graph is too large to be displayed as a whole, but a local view on specific vertex is often useful. The user should be able to continue exploring the graph from this starting point. As we are talking about a graph database the user should be able to modify the graph during this process.

In this talk we will see strategies to layout the graph in the process above. Further more we will see limitations of such visualisation and I will present my solutions to these limitations:

• grouping of vertices
• zooming strategies

• layout optimization Technologies presented in this talk:

• d3.js: Library to render the graph and for the layout algorithm

• ArangoDB: The underlying graph database