A typical Hadoop job has map and reduce tasks. Hadoop distributes the mapper workload uniformly across Hadoop Distributed File System (HDFS) and across map tasks, while preserving the data locality. In this way, it reduces skew in the mappers.
Hadoop also hashes the map-output keys uniformly across all reducers. This strategy works well when there are many more keys than reducers, and each key represents a very small portion of the workload. However, it is not effective when the mapper output is concentrated into a small number of keys. Hashing these keys results in skew and does not work in applications like sorting, which require range partitioning.
Perfect Balance distributes the load evenly across reducers by first sampling the data, optionally chopping large keys into two or more smaller keys, and using a load-aware partitioning strategy to assign keys to reduce tasks.