targetsby

Targetsby is a data transformation concept used in various data processing frameworks to group a collection of items by one or more target keys. The operation produces a mapping from distinct key values to the subset of items that share that value, enabling targeted analysis, routing, or aggregation.

Definition and behavior: Given a collection and a key selector function, targetsby returns a dictionary or associative array where each key is a distinct result of the key selector and each value is the list (or stream) of elements that map to that key. The order of groups may follow the first occurrence of the key or preserve input order, depending on implementation. Null keys may be allowed or excluded depending on settings.

Syntax and examples: In pseudocode, targetsby(collection, keySelector). Example: targetsby(users, u => u.country) yields a map from country to the list of users in that country. Another example: targetsby(events, e => e.type) groups events by type.

Variants and options: A variant can apply an aggregator to each group, such as counting items, summing a field, or selecting a representative element. Some libraries expose targetsby as a method on a stream: stream.targetsby(keySelector).

Implementation notes: The operation runs in linear time relative to the input size and uses additional space proportional to the number of distinct keys. Performance depends on the efficiency of the key hash function and the data structure used for the groups.

Relation to related concepts: Targetsby is closely related to groupBy, partition, and fold/aggregate patterns. It differs mainly in its explicit mapping output and convenience in routing workflows.

Limitations and considerations: Large datasets may require streaming or chunked processing to avoid excessive memory usage. Key selector functions should be deterministic to ensure stable grouping.