Using Graph Database with High Performance Networks

Companies today are finding that the size and growth of stored data is becoming overwhelming. As the databases grow, the challenge is to create value by discovering insights and connections in the big databases in as close to real time as possible. In the recently published whitepaper, Achieving Real-Time Business Solutions Using Graph Database Technology and High Performance Networks we describe a combination of high performance networking and graph base and analytics technologies which offers a solution to this need.

 

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Each of the examples in the paper is based on an element of a typical analysis solution. In the first example, involving Vertex Ingest Rate shows the value of using high performance equipment to enhance real-time data availability. Vertex objects represent nodes in a graph, such as Customers, so this test is representative of the most basic operation: loading new customer data into the graph. In the second example, Vertex Query Rate highlights the improvement in the time needed to receive results, such as finding a particular customer record or a group of customers.

 

The third example, Distributed graph navigation processing starts at a Vertex and explores its connections to other Vertices. This is representative of traversing social networks, finding optimal transportation or communications routes and similar probĀ­lems. The final example, Task Ingest Rate shows the performance improvement when loading the data connecting each of the vertices. This is similar to entering orders for products, transit times over a communications path and so on.

 

Each of these elements is an important part of a Big Data analysis solution. Taken together, they show that InfiniteGraph can be made significantly more effective when combined with Mellanox interconnect technology.

 

Resources: Mellanox Web 2.0 Solutions

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