Why HDFS data Becomes unbalanced

Addition of DataNodes

When new DataNodes are added to a cluster, newly created blocks are written to these DataNodes from time to time. The existing blocks are not moved to them without using the HDFS Balancer.

Behavior of the client application

In some cases, a client application might not write data uniformly across the DataNode machines. A client application might be skewed in writing data, and might always write to some particular machines but not others. HBase is an example of such a client application. In other cases, the client application is not skewed by design, for example, MapReduce or YARN jobs.

The data is skewed so that some of the jobs write significantly more data than others. When a Datanode receives the data directly from the client, it stores a copy to its local storage for preserving data locality. The DataNodes receiving more data generally have higher storage utilization.

Block Allocation in HDFS

HDFS uses a constraint satisfaction algorithm to allocate file blocks. Once the constraints are satisfied, HDFS allocates a block by randomly selecting a storage device from the candidate set uniformly. For large clusters, the blocks are essentially allocated randomly in a uniform distribution, provided that the client applications write data to HDFS uniformly across the DataNode machines. Uniform random allocation might not result in a uniform data distribution because of randomness. This is generally not a problem when the cluster has sufficient space. The problem becomes serious when the cluster is nearly full.