partitioners

A partitioner is a component that assigns items to partitions, enabling parallel processing and distributed storage. It maps a key or item to a partition index in a fixed range, typically 0 to P-1, where P is the number of partitions. The mapping is usually deterministic so that identical keys consistently route to the same partition.

In data processing frameworks, partitioners influence data locality and workload balance. Common types include hash partitioners, which apply a hash function to the key and take the remainder modulo P; range partitioners, which split the key space into contiguous intervals; and custom partitioners tailored to workload characteristics. Many systems require the partitioner to expose a partition function along with the number of partitions to ensure reproducible behavior across tasks and stages.

Graph partitioning is related but different: it seeks to divide a graph into k parts of roughly equal size while minimizing edge cuts between parts. This helps parallelize graph algorithms and can be solved with heuristics or spectral methods in tools such as Metis or Scotch. In distributed data systems, graph partitioning methods may be used as a preprocessing step to improve locality or to balance work across processors.

Implementation considerations include avoiding data skew, selecting an appropriate number of partitions, and balancing partitioning cost with run-time gains. Some environments support dynamic re-partitioning to adapt to changing workloads or data distributions, or allow user-defined partitioning strategies for specific use cases.

Examples include Hadoop’s HashPartitioner and RangePartitioner, and Apache Spark provides a Partitioner interface with corresponding implementations. In databases and distributed stores, sharding acts as a form of partitioning to distribute data across nodes for scalability and fault tolerance.