partitioneres

A partitioner is a component or algorithm that divides a set of items into non-overlapping partitions for organization, parallel processing, or storage. The aim is to distribute work evenly, improve data locality, and enable scalable processing while limiting data movement when the system grows or contracts.

In distributed data processing and databases, partitioners determine how data is spread across nodes, partitions, or storage units. Common strategies include hash partitioning (using a hash of a key to assign a record to a partition), range partitioning (based on value ranges), list partitioning (discrete value sets), and round-robin (even distribution when keys are not relevant). Consistent hashing is a variation designed to minimize movement when partitions change.

Software frameworks and systems implement partitioners to manage data flow. Hadoop MapReduce uses a partitioner to map keys to reducers; Spark provides HashPartitioner and RangePartitioner; Apache Kafka uses a partitioner to assign produced messages to topic partitions. The choice of partitioner affects data locality, parallelism, and fault tolerance.

Key considerations include balance (avoiding hot partitions), the total number of partitions, data skew, rebalancing costs, and compatibility with downstream tasks. Partitioners can be static, determined at deployment, or dynamic, adapting to workload.

See also data partitioning, sharding, load balancing, and consistent hashing.