Network Topology and Hadoop
What does it mean for two nodes in a local network to be “close” to each other? In the context of high-volume data processing, the limiting factor is the rate at which we can transfer data between nodes—bandwidth is a scarce commodity. The idea is to use the bandwidth between two nodes as a measure of distance.
Rather than measuring bandwidth between nodes, which can be difficult to do in practice (it requires a quiet cluster, and the number of pairs of nodes in a cluster grows as the square of the number of nodes), Hadoop takes a simple approach in which the network is represented as a tree and the distance between two nodes is the sum of their distances to their closest common ancestor. Levels in the tree are not predefined, but it is common to have levels that correspond to the data center, the rack, and the node that a process is running on. The idea is that the bandwidth available for each of the following scenarios becomes progressively less:
- Processes on the same node
- Different nodes on the same rack
- Nodes on different racks in the same data center
- Nodes in different data centers
For example, imagine a node n1 on rack r1 in data center d1. This can be represented as /d1/r1/n1. Using this notation, here are the distances for the four scenarios:
- distance(/d1/r1/n1, /d1/r1/n1) = 0 (processes on the same node)
- distance(/d1/r1/n1, /d1/r1/n2) = 2 (different nodes on the same rack)
- distance(/d1/r1/n1, /d1/r2/n3) = 4 (nodes on different racks in the same data center)
- distance(/d1/r1/n1, /d2/r3/n4) = 6 (nodes in different data centers)
This is illustrated schematically in the below Figure. (Mathematically inclined readers will notice that this is an example of a distance metric.)
Figure: Network distance in Hadoop
Finally, it is important to realize that Hadoop cannot divine your network topology for you. It needs some help, we’ll cover how to configure topology in “Network Topology” on page 299 . By default, though, it assumes that the network is flat—a singlelevel hierarchy—or in other words, that all nodes are on a single rack in a single data center. For small clusters, this may actually be the case, and no further configuration is required.
 Tom White, Hadoop: The Definitive Guide, Third Edition, pp. 69-70, O’Reilly Media, Inc., 2012.