File:Gridgain affinity.png

English: The diagram illustrates the difference between using data grids without and with GridGain. The left side shows execution flow without GridGain, in which a remote data server is queried for data, the data is then delivered to caller (master) node, which is faster than DB access, but results into unnecessary network traffic. On the right hand side, you can see the value that GridGain brings to the picture. The whole computation logic together with data access logic is brought to data server for local execution. Assuming that serialization of computation logic is much lighter than serializing data, the network traffic in this case is minimal. Also, your computation may access data from both, Node 2 and Node 3. In this case, GridGain will split your computation into logical jobs and route appropriate logical jobs to the corresponding data servers to ensure that all computations still remain local. Now, if one of the data server nodes crashes, your jobs will be automatically failed-over to other nodes, which allows you to fail-over logic together with data (not just data fail-over provided by data grids or distributed caches).